diff --git a/full-path-realtime/quotes/databricks/.gitignore b/full-path-realtime/quotes/databricks/.gitignore new file mode 100644 index 0000000..45e618e --- /dev/null +++ b/full-path-realtime/quotes/databricks/.gitignore @@ -0,0 +1,6 @@ +.venv-runner/ +.venv-zerobus/ +.local-runs/ + + +*.log diff --git a/full-path-realtime/quotes/databricks/DATABRICKS_BENCHMARK_CONTRACT.md b/full-path-realtime/quotes/databricks/DATABRICKS_BENCHMARK_CONTRACT.md new file mode 100644 index 0000000..8cb0353 --- /dev/null +++ b/full-path-realtime/quotes/databricks/DATABRICKS_BENCHMARK_CONTRACT.md @@ -0,0 +1,316 @@ +# Databricks benchmark contract + +This file is the provider-specific contract for the Databricks implementation +of the quotes full-path real-time analytics benchmark. The repository-level +`../README.md` remains the accepted cross-provider contract and operating +guide. Databricks is not part of the current accepted global result set; a run +is publishable only after it passes every gate below. + +## Workload identity + +- The source is the complete **113,219,565,734-row** StockHouse capture used by + the accepted ClickHouse Cloud T2 run. The producer enumerates the canonical + file list in its recorded order, each file's row groups in ascending index + order, and rows in their physical row-group order. It does not add + `quotes_0.parquet`, omit empty files, loop the capture, or stop at the + 100-billion-row presentation cap. +- Every submitted batch records its canonical file, row-group, and row-range + coordinates. Parallel streams may become durable in a different physical + order, but they must not change the canonical source sequence or membership. +- The source-rate target is **1,000,000 provider-committed events per second**. + Reported EPS is the number of rows for which Zerobus has confirmed durability + divided by the corresponding active ingest interval. Rows read, queued, + submitted, or merely accepted by the client are not committed throughput. +- Ingestion uses direct Zerobus Apache Arrow Flight `DoPut` with Arrow + `RecordBatch` payloads. Staging files, `COPY INTO`, SQL `INSERT`, Auto Loader, + Kafka, and JSON or Protobuf re-encoding are different variants and cannot be + substituted into the primary result. +- The producer decodes Parquet into Arrow and performs only vectorized + schema-safe casts before submission. It must not perform an extra Arrow IPC + serialization for instrumentation. Client `RecordBatch.nbytes` is labeled as + an uncompressed buffer estimate; provider `committed_bytes` from + `system.lakeflow.zerobus_ingest` is authoritative. +- The producer globally paces submissions to the 1M-EPS target while preserving + headroom. Stream count and Arrow batch sizing are tuning parameters, not + throughput evidence. Qualification must preserve interval host CPU and + network telemetry and reject a producer whose p95 utilization exceeds its + frozen thresholds. +- Structured Streaming real-time mode is not part of this architecture. As of + September 2026, its [supported sources and sinks](https://docs.databricks.com/aws/en/structured-streaming/real-time/reference) + exclude Delta, so it cannot directly read the benchmark raw table or use a + native Delta sink. A Kafka or Kinesis source plus a custom `ForeachWriter` + that calls Zerobus could land its output in Delta, but would add a broker, + processing compute, custom sink, and another recovery protocol, changing the + measured workload and cost boundary. + +## Provider mapping + +| Benchmark concept | Databricks implementation | +|---|---| +| Continuous ingest | Zerobus Ingest over direct Apache Arrow Flight | +| Delivery | Provider-documented at-least-once streams with durability offsets | +| Raw physical intent | Unpartitioned Unity Catalog managed Delta table, liquid-clustered by `(sym, t)` | +| Incremental summary | Strictly incremental Delta materialized view grouped by `(sym, day)` | +| MV cadence | `TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE` | +| Dashboard path | Four queries directly against `quotes_daily` | +| Drill-down path | Two queries directly against `quotes` | +| Query serving | Lakehouse//RT through the Statement Execution API | +| Canonical query time | Query-history `total_duration_ms`, excluding result fetch | +| Query telemetry | Statement ID, compilation, execution, queue/other phases, result-fetch, and cache fields | + +## Environment and managed-storage prerequisites + +`create.sql` intentionally creates neither a catalog nor a schema and specifies +no `LOCATION`. Before running it, an administrator must: + +1. create the Unity Catalog catalog and schema represented by `__CATALOG__` and + `__SCHEMA__`; +2. configure an explicit, non-default managed storage location on the catalog; + schemas may inherit that catalog location; +3. grant the benchmark principals the required catalog, schema, table, + materialized-view, pipeline, and query permissions; and +4. enable the required Zerobus, serverless pipeline, and Lakehouse//RT features + in one supported region. + +The namespace model is one dedicated `costbench` catalog, one reusable +`rt_qualification` schema, and one fresh `rt_full_` schema per full +attempt. Qualification cases must run sequentially and must recreate the raw +table and MV before each case. Qualification must never target a full-run +schema. + +The raw target must remain a Unity Catalog **managed Delta table**. An external +table, a table that resolves to Unity Catalog metastore default storage, a +path-backed table, or a DDL with an explicit table `LOCATION` is outside this +contract. A schema inherited from the explicit catalog managed location is +valid. This managed-storage requirement is shared by the Zerobus target and +Lakehouse//RT. + +The raw table has exactly the 12 canonical columns. `UINT8` logical fields use +`SMALLINT`; captured `UINT64` fields use `BIGINT` only after the source preflight +proves that every value fits the signed 64-bit range. The table is unpartitioned +and uses `CLUSTER BY (sym, t)`. Deletion vectors, row tracking, and change data +feed must be enabled. Row tracking and CDF support incremental maintenance; +they are not synthetic benchmark columns. + +The materialized view is Delta, unpartitioned, and uses +`CLUSTER BY (sym, day)`. Its defining query contains only the canonical +`COUNT`, `MIN`, `MAX`, and `SUM` aggregates. `day` is the UTC calendar date +derived arithmetically from Unix epoch milliseconds, independent of a session +time zone. The view must be created with `REFRESH POLICY INCREMENTAL STRICT`; +a refresh that cannot remain incremental must fail rather than silently +perform a full recomputation. + +## At-least-once delivery, recovery, and reconciliation + +Zerobus provides at-least-once, not exactly-once, delivery. Arrow Flight can +split one logical batch into multiple transport messages, so a failed logical +batch can be partially durable. The benchmark does not claim transactionality +for a complete Arrow batch. + +- Track every pending Arrow batch in memory until its durability acknowledgment. + Persist only the compact durable task ranges and aggregate row/batch counters + needed for recovery and exact final reconciliation; do not publish a + per-batch event ledger. +- Advance the durable source checkpoint only after `wait_for_offset()` or + `flush()` confirms the corresponding rows are durable. `close()` must flush + all outstanding work on graceful shutdown. +- Enable SDK recovery for retryable transport failures and retain proactive + ten-minute stream rotation. Persist every slow/failed acknowledgment wait, + including its duration and recovery outcome. After a terminal stream failure, + attempt `close()` and always persist `get_unacked_batches()`, even when + `close()` repeats the terminal error. +- Replaying an unacknowledged batch can duplicate rows. Because the canonical + 12-column raw schema has no added idempotency key, an ambiguous terminal + replay makes a measured run non-publishable. Start again with the fresh-table + DDL unless a separately documented, correctness-proven reconciliation can + establish exact source membership without changing the measured table. +- Inspect the Zerobus durable fallback location. Loading fallback Parquet after + a possibly published copy can also duplicate rows; fallback recovery is + performed outside measured windows and requires the same exact + reconciliation. + +At completion, reconcile the source manifest total, client durability +acknowledgments, and Zerobus provider usage/monitoring records. The required +value is exactly **113,219,565,734** in all applicable provider counters, with +no unexplained unacknowledged or fallback rows. Do not issue `SELECT COUNT(*)` +against the raw table or materialized view. Counter agreement is necessary but +not sufficient: canonical result captures, compact durable source ranges, and +stable source hashes must also exclude omissions offset by duplicates. The full +row-group task manifest is transient recovery state and is excluded from the +published result package after it has driven validation. + +Operational recovery is allowed to finish a characterization run, but it does +not relax publication correctness. Any provider-committed surplus over the +client's source/durability total is reported as replay/duplicate rows and makes +the result non-publishable. + +## Materialized-view and current-result semantics + +`quotes_daily` is a persisted snapshot. A successful refresh computes the +correct result of its defining query as of the source snapshot processed by +that refresh. Between refresh completions, a direct query returns that persisted +snapshot; Databricks does not merge the unmaterialized raw-table delta into the +dashboard answer at query time. + +Therefore: + +- dashboard results are current **as of the last completed MV refresh**, not + necessarily as of query start; +- strict incremental policy controls refresh method, not zero-lag freshness; +- trigger-on-update with a one-minute minimum interval is a cadence control, + not a one-minute completion SLA; and +- dashboard comparisons must use the last completed MV refresh interval, while + raw drill-down comparisons use the recorded producer and Zerobus progress. + +Every refresh records its provider update ID, start and finish times, status, +refresh method, and serverless pipeline usage. The harness never queries the +raw table or materialized-view contents for monitoring. Databricks does not +expose the exact Delta source snapshot consumed by a materialized-view refresh +in the documented event-log schema, so the post-run analysis brackets +raw-to-MV freshness using Zerobus commit times and the successful refresh start +and finish times. Refresh output-row metrics are retained only as refresh-work +telemetry; they are not treated as the total MV row count or source coverage. +Raw visibility is reported separately from Zerobus durability acknowledgment. + +## Query workload and scheduling + +`queries_mv.sql` contains the four accepted dashboard queries in this order: +single-symbol all-time summary, watchlist all-time summary, top historical +movers, and daily market activity. `queries_raw.sql` contains the two accepted +ClickHouse T2 drill-down translations in this order: hourly OHLCV and the +risk/liquidity profile. Symbol predicates, lack of a time predicate, grouping +grain, ordering, limits, population statistics, and output order are part of +the contract. + +- Start all four dashboard statements every fixed-rate **600 seconds**. +- Start both drill-down statements every fixed-rate **3,600 seconds**. +- Schedules are anchored to their runner start time, not to the prior + iteration's finish. A runner never overlaps itself; when an iteration + overruns, its next iteration starts immediately and records the overrun. +- Each statement runs once per iteration and failures retain their array + position as `null`. Dashboard and drill-down runners may overlap one another + as they do in the accepted workload. +- Execute measured reads on Lakehouse//RT using only the Statement Execution + API. A Thrift/JDBC/ODBC path that does not explicitly select Statement + Execution, a conventional SQL warehouse, or notebook wall-clock timing is a + different variant. + +Lakehouse//RT always applies ANSI semantics. The drill-down SQL uses explicit +numeric casts and guarded division. Its skew is `mu3 / mu2^(3/2)` and its +kurtosis is `mu4 / mu2^2`: raw population kurtosis, not sample or excess +kurtosis. `percentile_approx` is not ClickHouse `quantilesTDigest`; correctness +uses a declared tolerance and the algorithmic difference must be disclosed. + +## Timing, cache, and compute sizing + +The canonical elapsed time for each measured statement is +`system.query.history.total_duration_ms / 1000`. Databricks defines this field +as total provider-side statement duration excluding result fetch. Do not use +client wall-clock, time-to-first-row, or +`total_duration_ms + result_fetch_duration_ms`. + +For every statement, retain its Statement Execution API ID and aligned +per-query arrays for at least: + +- canonical total duration; +- `compilation_duration_ms`; +- `execution_duration_ms`; +- `result_fetch_duration_ms`; +- queue and other available phase durations; +- bytes/files/partitions read and rows produced where available; and +- `from_result_cache`, `cache_origin_statement_id`, and + `read_io_cache_percent`. + +Compilation and execution are supporting telemetry; they are not added +together to replace canonical total duration. Query-history collection may lag, +so the collector retries by statement ID and fails closed if canonical +telemetry never appears. + +Disable query-result reuse with `use_cached_result = false` in the measured +Statement Execution session. Verify every measured history record has +`from_result_cache = false` (and no foreign cache-origin statement). A missing +cache field or cache hit invalidates that observation. Provider-managed data/IO +caching is not represented as a result-cache hit and may not be user +disableable; retain `read_io_cache_percent` and disclose that physical cache +state instead of describing the run as wholly cache-free. + +Target approximately **16 CPU per query when the provider exposes a comparable +control**. Lakehouse//RT currently exposes named query sizes rather than a +published CPU count, so choose the nearest available query size using +provider/account-team evidence, freeze it before the measured run, and record +the name, effective resource evidence, autoscaling maximum, auto-stop setting, +and all changes. If no defensible 16-CPU mapping exists, say so and report the +chosen named size; do not invent a CPU equivalence. Autoscaling for concurrency +does not change the per-query-size contract. + +## Cost boundaries + +Use one declared UTC measurement window and preserve provider billing exports. +Report these components separately before applying a documented pricing model: + +- source producer compute, storage reads, and network egress; +- Zerobus ingestion usage; +- managed raw Delta storage and automatic table maintenance; +- serverless materialized-view refresh compute and persisted MV storage; +- Lakehouse//RT warehouse usage, including billed minimum/idle and autoscaled + usage during the window; and +- cloud storage, requests, and any separately billed platform services. + +Attribute measured query cost from the Lakehouse//RT billing SKU without +double-counting shared warehouse uptime between dashboard and drill-down +runners. MV refresh is fresh-data-path cost, not measured-query cost. Setup, +DDL, preflight, correctness queries, system-table collection, failed tuning +runs, and post-run analysis are excluded from the benchmark score but itemized +as excluded costs. Credits, commitments, negotiated pricing, taxes, and the +Beta introductory discount are recorded separately from resource consumption. + +## Validity gates before a publishable run + +1. Use a fresh raw table and MV created from the checked-in rendered DDL; save + the online preflight and rendered SQL. +2. Prove the source manifest has 113,219,565,734 rows and that dispatch follows + canonical file, row-group, and row order. +3. Verify the raw object is a non-default-storage Unity Catalog managed Delta + table with exactly 12 columns, no partition columns, liquid clustering on + `(sym, t)`, and deletion vectors, row tracking, and CDF enabled. +4. Verify the MV is Delta, unpartitioned, liquid-clustered on `(sym, day)`, + trigger-on-update at most once per minute, and `INCREMENTAL STRICT`. + `EXPLAIN CREATE MATERIALIZED VIEW` must report incremental eligibility. + Record the system-managed-job preview state and the MV schedule's + Performance optimized setting separately; the full run must preserve the + performance mode accepted during qualification. +5. Before ingestion, require the Unity Catalog API effective Predictive + Optimization flag to be `ENABLE` for both the raw table and MV. Direct and + inherited enablement are accepted; missing or `DISABLE` evidence fails. +6. Require every refresh in the measured window to succeed incrementally. + Any full refresh, strict-policy failure, or unexplained refresh gap + invalidates the affected window. +7. Reconcile exact source, acknowledged, provider, fallback, and queryable + counts; require no unexplained duplicates, omissions, or terminal recovery. +8. Demonstrate approximately 1M provider-committed EPS over the complete run + using a predeclared tolerance and show the time series, not only its mean. +9. Record raw and MV provider metadata continuously and attach every dashboard + result to the last completed refresh interval. Publish metadata-only + raw-to-MV lower and upper bounds, never refresh age as exact source lag. +10. Require exactly four dashboard and two drill-down statements in canonical + order, fixed-rate cadence evidence, and no self-overlap. +11. Require Lakehouse//RT Statement Execution IDs, canonical total duration, + compilation/execution telemetry, `from_result_cache = false`, and the + frozen query-size evidence for every accepted observation. +12. Validate query results at matched source/MV checkpoints outside timed + iterations, including tolerance checks for floating-point moments and + approximate percentiles. +13. Separate active-ingestion from post-ingestion observations and preserve + raw JSONL, refresh, ingest, query-history, billing, schema, and table-detail + evidence without hand edits. + +## Beta disclosure + +The run must record the feature release stages and documentation date observed +at execution time. As of September 2026, Lakehouse//RT, the Apache Arrow +Zerobus record format, writing with Zerobus into liquid-clustered tables, and +the materialized-view `REFRESH POLICY` clause are Beta. The system-managed-job +preview used to configure MV refresh performance is also Beta. Results must be +labeled as a Databricks Beta-path evaluation; +performance, behavior, pricing, and supported SQL can change before general +availability. diff --git a/full-path-realtime/quotes/databricks/INFRASTRUCTURE_SETUP.md b/full-path-realtime/quotes/databricks/INFRASTRUCTURE_SETUP.md new file mode 100644 index 0000000..b6280db --- /dev/null +++ b/full-path-realtime/quotes/databricks/INFRASTRUCTURE_SETUP.md @@ -0,0 +1,540 @@ +# Databricks AWS infrastructure setup + +This guide records the successful AWS and Unity Catalog setup used to prepare +the Databricks Lakehouse//RT benchmark on September 14, 2026. Deployment-specific identifiers are represented by placeholders so the guide can be +shared safely. + +Creating this infrastructure does not qualify a benchmark run. Lakehouse//RT +must become available, all online preflight checks must pass, and +`qualification_report.json` must contain exact JSON boolean `"qualified": +true` before creating a full-run schema. + +## Deployment record + +- Databricks workspace region: `eu-west-1` +- Databricks workspace host: + `https://` +- Databricks workspace ID: `` +- Zerobus endpoint: + `https://.zerobus..cloud.databricks.com` +- Unmeasured control warehouse: ``, serverless `2X-Small`, ID + ``, one cluster minimum/maximum, ten-minute auto-stop, + Current channel version `2026.32` +- Producer: EC2 `m6i.8xlarge` in `eu-west-1c`, Ubuntu 26.04, 32 vCPU, + 123 GiB RAM +- Producer data volume: 7.8 TiB ext4 mounted at `/data` +- Licensed source: `s3:////` in `us-east-2` +- Local source directory: `/data/quotes` +- Source validation: complete; 232 Parquet files, 865,579 row groups, and + exactly 113,219,565,734 rows +- Unity Catalog managed-storage bucket: `` in + `eu-west-1` +- Automatically created external location: `` +- External-location URL: `s3:///` +- Automatically created storage credential: + `` +- External location is writable, has file events enabled, and was created by + Databricks automatic setup +- Benchmark catalog: `costbench` +- Catalog managed location: `s3:///costbench` +- Reusable qualification schema: `costbench.rt_qualification`; it inherits + the catalog managed location +- Permanent raw qualification target: + `costbench.rt_qualification.quotes`, created as an empty Unity Catalog + managed Delta table +- Canonical `quotes_daily` definition: checked with + `EXPLAIN CREATE MATERIALIZED VIEW`; Databricks reported it eligible for + incremental refresh with no issues +- Materialized view: `costbench.rt_qualification.quotes_daily`, created with + strict incremental refresh and a 60-second update trigger; ownership + transferred to `` +- Shared benchmark identity: ``, Application ID + ``, assigned to the workspace with + Workspace and Databricks SQL access and granted the documented Unity Catalog + privileges on the raw table +- Zerobus OAuth: client-credentials token issuance verified successfully from + the EC2 producer; Databricks returned a one-hour bearer token +- System-table evidence access: granted to `` for the required + query, Zerobus, compute, Predictive Optimization, usage, and price tables +- Standard workspace OAuth and SQL Statement Execution: verified through + ``; the service principal successfully queried + `system.lakeflow.zerobus_stream` +- Workspace preview enabled September 15, 2026: + `System-Managed Job for Materialized Views & Streaming Tables` + +The `` name is only an object label. It does not control or report +the bucket region. + +## 1. Prepare the same-region producer + +Use a dedicated `m6i.8xlarge`-equivalent host in the Databricks workspace +region. Confirm that the attached data disk is empty before formatting it: + +```sh +sudo file -s /dev/nvme1n1 +sudo wipefs -n /dev/nvme1n1 +``` + +Only if the output proves that the intended device is blank, create and mount +the filesystem: + +```sh +sudo mkfs.ext4 -F -L costbench-data /dev/nvme1n1 +sudo mkdir -p /data +UUID="$(sudo blkid -s UUID -o value /dev/nvme1n1)" +printf 'UUID=%s /data ext4 defaults,nofail 0 2\n' "$UUID" | + sudo tee -a /etc/fstab +sudo mount /data +sudo chown ubuntu:ubuntu /data +mkdir -p /data/quotes /data/databricks-runs /data/databricks-qualification +``` + +Install `uv`, Python 3.12, and AWS CLI v2. Install the two Python dependency +sets into separate environments: the SQL kernel requires PyArrow 23.x while +Zerobus 1.8 requires PyArrow earlier than 22. + +The prepared host uses: + +```text +/home/ubuntu/costbench/full-path-realtime/quotes/databricks +├── .venv-runner +└── .venv-zerobus +``` + +The non-secret host environment is `/home/ubuntu/databricks-host.env`. +Credentials must not be written to this file. + +## 2. Stage the licensed source + +The source bucket is in `us-east-2`, but the producer and Databricks workspace +are in `eu-west-1`. Downloading the source is an unmeasured preparation step; +record its cross-region transfer cost separately and do not include it in the +measured ingestion window. + +Enter temporary AWS credentials without putting their values in shell history: + +```sh +tmux new -s source-stage +source /home/ubuntu/databricks-host.env + +read -rsp 'AWS access key ID: ' AWS_ACCESS_KEY_ID; echo +read -rsp 'AWS secret access key: ' AWS_SECRET_ACCESS_KEY; echo +read -rsp 'AWS session token: ' AWS_SESSION_TOKEN; echo +export AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY AWS_SESSION_TOKEN + +export S3_SOURCE_URI='s3:///' +"$DBX_BENCH_DIR/stage_source_from_s3.sh" +``` + +The staging script is resumable, excludes `quotes_0.parquet`, and fails unless +the top-level Parquet files contain exactly 113,219,565,734 rows with the +required columns. If temporary credentials expire, enter a fresh set and run +the same command again. + +After completion, clear the credential variables: + +```sh +unset AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY AWS_SESSION_TOKEN +``` + +The successful staging run wrote +`/data/databricks-qualification/source_inventory.json` with status +`complete`, 232 files, 865,579 row groups, and exactly 113,219,565,734 rows. +The local directory occupies approximately 637 GiB on the producer. + +## 3. Create private same-region managed storage + +Unity Catalog managed storage is separate from the licensed source bucket. +Create a dedicated bucket in the Databricks workspace region. Bucket names are +globally unique; another deployment should include its AWS account ID if the +name below is unavailable. + +The successful deployment used: + +```sh +BUCKET='' + +aws s3api create-bucket \ + --bucket "$BUCKET" \ + --region eu-west-1 \ + --create-bucket-configuration LocationConstraint=eu-west-1 +``` + +Explicitly block every form of public access: + +```sh +aws s3api put-public-access-block \ + --bucket "$BUCKET" \ + --public-access-block-configuration \ + 'BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true' +``` + +Verify the bucket location and public-access controls: + +```sh +aws s3api get-bucket-location --bucket "$BUCKET" +aws s3api get-public-access-block --bucket "$BUCKET" +``` + +Do not make this bucket public. Databricks accesses it through a Unity Catalog +storage credential backed by an AWS IAM role. + +## 4. Create the external location with automatic setup + +The successful setup used Databricks automatic external-location setup rather +than the abandoned manual IAM-role flow: + +1. In Databricks, open **Catalog > Connect > External locations**. +2. Select **Create external location**, then **Set up automatically**. +3. Enter external-location name ``. +4. Enter S3 URL `s3:///`. +5. Leave **Limit to read-only use** disabled. +6. Select **Log into AWS and create external location**. +7. Review the requested resources and select **Initiate external location + creation**. +8. In AWS, select **Allow access** for the temporary IAM delegation request. +9. Return to Databricks and wait for the location to move from **Pending + locations** to **Existing locations**. +10. Open the external location and select **Test connection**. + +Automatic setup uses AWS IAM temporary delegation, not CloudFormation. The +request is visible under **AWS IAM > Temporary delegation requests**, and the +resulting operations are recorded in CloudTrail. Databricks documents that +provisioning can take up to eight minutes. + +The completed setup automatically created this storage credential: + +```text + +``` + +It also enabled file events and configured writable access. No AWS access key +or secret key was copied into Databricks. + +## 5. Create the catalog and qualification schema + +Run the following on a standard Databricks SQL warehouse: + +```sql +CREATE CATALOG costbench +MANAGED LOCATION 's3:///costbench'; + +ALTER CATALOG costbench ENABLE PREDICTIVE OPTIMIZATION; + +CREATE SCHEMA costbench.rt_qualification; +``` + +The schema intentionally inherits the catalog's explicit managed location. +This is not Unity Catalog metastore default storage. Tables created without a +table-level `LOCATION` remain Unity Catalog managed tables under the catalog's +storage boundary. + +Verify every resulting object: + +```sql +DESCRIBE EXTERNAL LOCATION ``; +DESCRIBE CATALOG EXTENDED costbench; +DESCRIBE SCHEMA EXTENDED costbench.rt_qualification; +``` + +The catalog description must report +`s3:///costbench`, and the external location must remain +writable. The online benchmark preflight independently verifies the resolved +raw-table storage location and rejects metastore default storage. + +## 6. Create the raw qualification target + +On September 14, 2026, the empty raw qualification target was created directly +from the Databricks SQL editor. No Python setup tool was used for this step. +Because the schema was empty, no `DROP` statement or `IF NOT EXISTS` clause was +used: + +```sql +CREATE TABLE costbench.rt_qualification.quotes ( + sym STRING, + bx SMALLINT, + bp DOUBLE, + bs BIGINT, + ax SMALLINT, + ap DOUBLE, + `as` BIGINT, + c SMALLINT, + i ARRAY, + t BIGINT, + q BIGINT, + z SMALLINT +) +USING DELTA +CLUSTER BY (sym, t) +TBLPROPERTIES ( + 'delta.enableDeletionVectors' = 'true', + 'delta.enableRowTracking' = 'true', + 'delta.enableChangeDataFeed' = 'true' +); +``` + +The table has no table-level `LOCATION`, so it resolves under the explicit +managed location inherited from the `costbench` catalog. The three Delta +features are enabled before ingestion because Databricks recommends them on +materialized-view source tables; row tracking is required by some incremental +refresh plans. + +Verify the table before registering it as a Zerobus target: + +```sql +DESCRIBE DETAIL costbench.rt_qualification.quotes; +SHOW TBLPROPERTIES costbench.rt_qualification.quotes; +``` + +Do not drop and recreate this name after Zerobus has targeted it. Current +Zerobus documentation states that recreating a target table is unsupported. +Qualification cases must therefore use fresh target objects rather than +resetting this target with `DROP TABLE`. + +## 7. Prove materialized-view incrementalization + +Before creating the materialized view, the canonical DDL was prefixed with +`EXPLAIN` and run in the Databricks SQL editor. The result was: + +```text +== Incremental Update Eligibility == +The Materialized View can be incrementally refreshed. + +== Detailed Incrementalization Info == +No issues detected. +``` + +The physical plan also preserved all required properties: + +- target `costbench.rt_qualification.quotes_daily`; +- `IncrementalStrict` refresh policy; +- 60-second update trigger; +- `CLUSTER BY (sym, day)`; and +- the complete canonical aggregate, including `sum(ap - bp)` over the two + `DOUBLE` source columns. + +This provider-generated result resolves the potential concern about +floating-point `SUM` eligibility for this exact definition. Preserve the full +SQL editor output with the qualification artifacts. + +## 8. Create the materialized view + +After the incrementalization check passed, the same canonical statement was +run without `EXPLAIN`: + +```sql +CREATE MATERIALIZED VIEW costbench.rt_qualification.quotes_daily +USING DELTA +CLUSTER BY (sym, day) +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE +AS +SELECT + sym, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, + count(*) AS n_quotes, + min(bp) AS bp_min, + max(bp) AS bp_max, + min(ap) AS ap_min, + max(ap) AS ap_max, + sum(bs) AS bs_sum, + sum(`as`) AS as_sum, + sum(ap - bp) AS spread_sum +FROM costbench.rt_qualification.quotes +GROUP BY sym, day; +``` + +The resulting namespace contains the empty raw table and its materialized +view: + +```sql +SHOW TABLES IN costbench.rt_qualification; +DESCRIBE TABLE EXTENDED costbench.rt_qualification.quotes_daily; +``` + +Databricks owns the serverless pipeline that refreshes this standalone +materialized view. Lakehouse//RT will serve read queries but will not run the +refresh. + +### Enable scheduled-refresh performance controls + +After the first sustained-ingestion characterization exposed long +`WAITING_FOR_RESOURCES` phases, enable this workspace preview under +**Workspace Settings > Previews**: + +```text +System-Managed Job for Materialized Views & Streaming Tables +``` + +This Beta preview was enabled on September 15, 2026. It exposes the +system-managed-job features described by Databricks. Enabling the preview does +not change an existing schedule by itself. + +For the existing `TRIGGER ON UPDATE` MV, Catalog Explorer continued to show +only `Trigger on update (at most every 1 minute)`, and its **Edit** link opened +documentation rather than a schedule editor. No Performance optimized toggle +was available. Therefore, this deployment must not claim that the setting was +enabled or that the preview provides that control for trigger-on-update MVs. + +Databricks' detailed UI instruction specifically refers to pipelines scheduled +with a `SCHEDULE` clause. A fresh-target experiment may test whether the +control appears while the creating user still owns the MV, before ownership is +transferred to the service principal. If it remains unavailable, testing a +performance-optimized `SCHEDULE`/SQL-job refresh or a continuous Lakeflow +pipeline is a separate architecture variant, not a tuning change to the +current trigger-on-update path. + +Performance mode is scheduler/compute metadata and cannot currently be set +with the materialized-view SQL definition. Standalone serverless MV refresh +does not expose an assignable warehouse or cluster size. + +## 9. Create the Zerobus service principal + +Create a dedicated service principal named `` under +**Settings > Identity and Access > Service principals**. Use these workspace +entitlements: + +- Consumer access: off +- Databricks SQL access: on +- Workspace access: on +- Admin access: off + +Copy its Application ID and generate a client secret. Store the secret outside +the repository and never include it in SQL, shell history, environment files, +or benchmark evidence. + +Grant only the permissions documented for a Zerobus target: + +```sql +GRANT USE CATALOG ON CATALOG costbench +TO ``; + +GRANT USE SCHEMA ON SCHEMA costbench.rt_qualification +TO ``; + +GRANT SELECT, MODIFY +ON TABLE costbench.rt_qualification.quotes +TO ``; + +SHOW GRANTS ON TABLE costbench.rt_qualification.quotes; +``` + +`SELECT` permits target schema discovery and `MODIFY` permits direct +ingestion. This deployment intentionally reuses the principal for control and +query operations. Give it `CAN MONITOR` on the control warehouse and, once +available, the Lakehouse//RT warehouse. `CAN MONITOR` permits both query +execution and warehouse/query monitoring without warehouse administration. + +Make the service principal the owner of +`costbench.rt_qualification.quotes_daily`. The owner identity runs future +refreshes and is required to query the standalone materialized view's event log +directly. Before transferring ownership, retain `MANAGE` and `SELECT` for the +administrator who created the view if continued interactive access is needed. + +The transfer was completed through Catalog Explorer after granting the benchmark administrator the account-level **Use** role on ``. the benchmark administrator retained +explicit `MANAGE` and `SELECT` privileges on the materialized view. + +Grant the shared identity bounded system-table access: + +```sql +GRANT USE CATALOG ON CATALOG system +TO ``; + +GRANT USE SCHEMA ON SCHEMA system.query +TO ``; +GRANT SELECT ON TABLE system.query.history +TO ``; + +GRANT USE SCHEMA ON SCHEMA system.lakeflow +TO ``; +GRANT SELECT ON TABLE system.lakeflow.zerobus_stream +TO ``; +GRANT SELECT ON TABLE system.lakeflow.zerobus_ingest +TO ``; + +GRANT USE SCHEMA ON SCHEMA system.compute +TO ``; +GRANT SELECT ON TABLE system.compute.warehouses +TO ``; +GRANT SELECT ON TABLE system.compute.warehouse_events +TO ``; + +GRANT USE SCHEMA ON SCHEMA system.storage +TO ``; +GRANT SELECT ON TABLE + system.storage.predictive_optimization_operations_history +TO ``; + +GRANT USE SCHEMA ON SCHEMA system.billing +TO ``; +GRANT SELECT ON TABLE system.billing.usage +TO ``; +GRANT SELECT ON TABLE system.billing.list_prices +TO ``; +``` + +The tables under `system.query` and `system.billing` contain account-wide +evidence. Table-level `SELECT` grants deliberately avoid granting access to +every table in those system schemas. This account-wide read access is still +the security trade-off accepted by reusing one principal instead of separating +ingestion and control identities. + +OAuth was verified from the producer without writing any rows. The request +used the workspace-specific Zerobus resource and explicit authorization +details for the three grants above. Databricks returned: + +```json +{ + "token_type": "Bearer", + "expires_in": 3600, + "has_access_token": true +} +``` + +Only this redacted summary is retained. The client secret and bearer token were +unset immediately and are not benchmark artifacts. + +## 10. Remaining Databricks preparation + +Network connectivity was verified from the EC2 producer after recording the +workspace coordinates. The Zerobus hostname resolved successfully, its HTTPS +endpoint returned the expected unauthenticated `401`, and the workspace host +returned its normal login redirect. This proves DNS, routing, and TLS access; +OAuth token issuance still needs to be verified with the client secret. + +These steps can be completed before Lakehouse//RT becomes available: + +The dedicated serverless `2X-Small` control warehouse `` +(``) is created. The shared benchmark principal has +`CAN MONITOR`, which includes query execution and monitoring but not warehouse +administration. It has a fixed one-cluster minimum and maximum, a ten-minute +auto-stop, and Current channel version `2026.32`. + +Remaining: + +The initial grant attempt failed with `PERMISSION_DENIED: User does not have +MANAGE on Catalog 'system'` because workspace-admin privileges do not control +the system catalog. A metastore administrator subsequently granted the bounded +table access above to ``. + +1. If the benchmark administrator was assigned temporarily as Metastore Admin, confirm that the + temporary role has been removed after the explicit grants were applied. +2. Verify the remaining required system tables and preserve only redacted + statement IDs and statuses. + +Standard workspace OAuth and Statement Execution were verified with the shared +principal through ``. Statement +`01f1b10c-83ff-1ae8-962c-b397351c5eb1` completed successfully for a +zero-row access probe against `system.lakeflow.zerobus_stream`; no token or +secret was retained. + +Do not create `rt_full_` yet. Create a fresh full-run schema only after +Lakehouse//RT is available and the complete qualification matrix passes. diff --git a/full-path-realtime/quotes/databricks/INGESTION_ARCHITECTURE.md b/full-path-realtime/quotes/databricks/INGESTION_ARCHITECTURE.md new file mode 100644 index 0000000..7b0a9aa --- /dev/null +++ b/full-path-realtime/quotes/databricks/INGESTION_ARCHITECTURE.md @@ -0,0 +1,314 @@ +# Databricks ingestion architecture decision + +This decision was reviewed on September 15, 2026 for the CostBench +Lakehouse//RT experiment. The benchmark continuously replays the canonical +113,219,565,734-row quotes capture, queries the raw table for drill-downs, and +queries a daily pre-aggregation for dashboard requests while ingestion is +active. + +## Executive decision + +Use this measured path: + +```text +same-region replay producer + -> Zerobus gRPC with Arrow Flight + -> Unity Catalog managed Delta quotes + -> raw drill-down queries on Lakehouse//RT + -> strict-incremental quotes_daily materialized view + -> dashboard queries on Lakehouse//RT +``` + +This is the shortest Databricks-supported path from an external high-volume +producer into a Lakehouse//RT-readable table. It is also the closest match to +the native direct-write paths selected for ClickHouse, Snowflake, and BigQuery. + +The decision is not that Zerobus is always preferable. It is preferable for +this workload because the destination is Delta, the producer already has +ready-to-ingest records, no streaming transformation is required before raw +landing, and seconds-level table materialization meets the analytical +freshness model. + +## Keep the layers separate + +Several Databricks features use “streaming” or “real-time” in their names but +solve different parts of a system: + +- **Zerobus Ingest is an ingestion transport.** It accepts pushed records, + durably buffers them, and materializes them into managed Delta. +- **A streaming table is a specialized managed Delta data object.** It adds + Lakeflow flow ownership, checkpointing, and process-once streaming + semantics. It is not itself a network transport. +- **Structured Streaming is a processing engine.** It reads a source such as + Kafka or files, executes transformations, and writes a sink. +- **Structured Streaming real-time mode is an execution trigger.** It lowers + processing latency for immediate operational decisions. +- **Liquid clustering and Predictive Optimization are table-layout and + maintenance choices.** They are orthogonal to the ingestion transport. +- **Lakehouse//RT is read-serving compute.** It executes selective SQL reads; + it does not ingest, transform, refresh, or maintain tables. + +## Option 1: direct Zerobus to a managed Delta table — selected + +```text +producer -> Zerobus -> managed Delta quotes +``` + +Why it fits: + +- Databricks describes Zerobus as the most direct path when the destination is + the lakehouse: no broker, partition fleet, or ingestion pipeline is needed. +- The official guidance recommends Arrow Flight for columnar or batched + workloads. The replay input is Parquet and the producer naturally emits + Arrow record batches. +- Default documented limits are 100,000 records/second and 100 MB/second per + gRPC stream, 10 GB/second per target table, and unlimited concurrent streams + per workspace. Capacity still has to be qualified against this record shape. +- Databricks documents approximately 150 ms to durability and approximately + five seconds until data is materialized in the table. The benchmark measures + actual provider metadata rather than claiming these published figures. +- Lakehouse//RT directly supports Unity Catalog managed Delta tables and + materialized views. +- Zerobus is serverless and billed under the Jobs Serverless SKU. All ingestion + and background maintenance cost remains inside the Databricks ledger. + +Trade-offs that must remain visible: + +- Delivery is at least once. Durable offsets, recovery attempts, and final + reconciliation must be retained. +- A durability acknowledgement does not prove that a row is queryable yet. +- The Arrow Flight interface is Beta. +- Zerobus does not support recreating a target table. Every qualification case + must use a fresh target object. + +### Zerobus pricing + +Zerobus has volume pricing rather than a provisioned hourly charge. The +official AWS list-price page displayed the following rates on September 15, +2026: + +- Premium: USD 0.050 per GB sent to the ingestion API; +- Enterprise: USD 0.064 per GB sent to the ingestion API. + +Underlying Zerobus compute is included. Databricks translates usage at 0.143 +DBU per GB, so charges can appear on the bill as Jobs Serverless or Automated +Serverless DBUs. There is no separate Zerobus stream, cluster, or idle-hour +charge. + +These costs remain separate and must also be reported: + +- the replay producer; +- cloud network transfer, if any; +- managed Delta storage; +- serverless materialized-view refresh; +- Predictive Optimization serverless compute; and +- Lakehouse//RT read compute. + +The run ledger uses `system.billing.usage` and the price version from +`system.billing.list_prices` that overlaps the measured window. It does not +infer billable volume from compressed Parquet or Delta storage size. + +### Zerobus interface choice + +Zerobus itself provides several client interfaces: + +- **Arrow Flight — selected:** Databricks recommends it for columnar or batched + workloads. It maps directly from the replay's Parquet/Arrow batches and + avoids converting every row to JSON. It is Beta. +- **Protocol Buffers:** recommended for production row-oriented streams. It is + a reasonable alternative for live applications, but converting this + columnar capture into individual protobuf messages adds unnecessary producer + work. +- **JSON over gRPC:** easiest to develop, but adds serialization and parsing + overhead that is not representative of this prepared columnar source. +- **REST:** intended for large fleets of low-frequency devices. Per-request + handshakes and the 10,000 requests/second default quota make it the wrong + interface for one producer targeting roughly one million rows/second. +- **Kafka-compatible API:** useful for existing Kafka producers, but currently + Beta with a 50,000 messages/second default workspace quota. It offers no + benefit to this replay producer. + +## Option 2: Zerobus to a streaming table — not a distinct transport + +```text +producer -> Zerobus -> streaming table +``` + +Databricks states that Zerobus writes to standard managed Delta tables and +streaming tables with the same Zerobus limits and quotas. This changes the +target object, not the wire path. + +A streaming table is useful when the table needs Lakeflow-managed flows, +checkpoints, process-once semantics, or low-latency streaming transformations. +It also has different lifecycle semantics: a backing pipeline owns it, query +definition changes normally affect only newly processed rows, and some table +operations are restricted. + +It does not add a needed capability here: + +- raw quotes require no transformation before landing; +- the raw table must remain a general queryable record of every event; +- exact batch-equivalent aggregation belongs in the materialized view; and +- Zerobus ingestion into streaming tables is itself Beta. + +Lakehouse//RT can read a streaming table, so this route is technically viable. +It should be a separate characterization only if Databricks claims a concrete +advantage for this workload. Current documentation claims the same Zerobus +limits and quotas, not an ingestion advantage. + +## Option 3: Lakeflow or Structured Streaming micro-batch into Delta + +Typical variants are: + +```text +producer -> Kafka/MSK -> Structured Streaming or Lakeflow -> managed Delta +producer -> cloud files -> Auto Loader or Lakeflow -> managed Delta +``` + +This is the recommended family when data already resides in Kafka or cloud +files, or when parsing, validation, deduplication, joins, CDC, or medallion +transformations must happen before the destination table. + +For this benchmark it adds a broker or file-staging hop plus Spark/Lakeflow +compute and checkpoint management. Those components add latency, cost, sizing, +and failure modes without performing a required transformation. Using Auto +Loader would also turn the producer benchmark into a cloud-file-arrival +benchmark. + +Default Structured Streaming micro-batch can write Delta and is appropriate +for analytical ETL measured in seconds or minutes. It is a valid alternative +architecture, but not the shortest native ingest path for an external producer +whose destination is already known. + +## Option 4: Structured Streaming real-time mode — different workload + +The September 2026 Redis article uses: + +```text +Kafka -> Structured Streaming real-time mode + -> stateful per-user scoring + -> custom ForeachWriter + -> Redis + -> application request +``` + +That architecture optimizes immediate operational decisions. Redis, not +Lakehouse//RT, is the serving layer. The article's application does not land +the computed result in Delta and does not issue analytical SQL against a raw +table and materialized view. + +Current Databricks real-time-mode documentation lists Delta as unsupported as +both source and sink. Supported paths center on Kafka-compatible sources and +Kafka or custom sinks. Therefore, using real-time mode for this benchmark +would require another hop after processing to populate the required Delta +table. That would test an operational stream processor plus a custom sink, not +Lakehouse//RT's native ingest-to-query path. + +Real-time mode cannot consume Zerobus as a source. Zerobus is a single-sink +ingestion service, including when a Kafka producer uses its Kafka-compatible +API; it is not a Kafka broker with a consumer interface. + +A valid composition can put real-time mode upstream and implement a custom +`ForeachWriter` that calls the Zerobus SDK: + +```text +Kafka or Kinesis + -> real-time-mode flow + -> custom ForeachWriter using Zerobus + -> managed Delta table or streaming table +``` + +Real-time mode is enabled on the upstream Structured Streaming or Lakeflow +flow, not on the target streaming table. Zerobus is the custom output bridge +that works around real-time mode's lack of a native Delta sink. + +That composition is useful when each source event requires low-latency +processing before landing in Delta. For this pass-through replay, it adds +Kafka or Kinesis, processing compute, another serialization boundary, custom +sink code, and two recovery/delivery protocols without performing a required +transformation. Its millisecond processing latency still terminates at +Zerobus's seconds-level Delta materialization and then at the +materialized-view refresh cycle. It provides no benefit for this analytical +benchmark unless an independent millisecond operational transformation is +also being tested. + +Use real-time mode when an event must trigger an action within milliseconds, +such as fraud blocking, personalization, or online feature publication. Use +the default micro-batch mode for analytical ETL whose latency objective is +seconds or minutes. + +Real-time mode and Lakehouse//RT are unrelated despite their similar names: + +- real-time mode continuously **processes** events; +- Lakehouse//RT quickly **queries** data already materialized in Unity Catalog. + +## Lakehouse//RT and the materialized view + +Lakehouse//RT explicitly supports materialized views. It can issue the +benchmark's `SELECT` statements against +`costbench.rt_qualification.quotes_daily` while its serverless Lakeflow +pipeline refreshes the object. + +Lakehouse//RT reads the last completed materialized-view snapshot. It does not +refresh the view, and it does not reconcile rows that are present in `quotes` +but absent from a lagging `quotes_daily` snapshot. DDL, `REFRESH`, and system +table queries must use the unmeasured control SQL warehouse. MV freshness is +therefore measured separately from query runtime. + +## Option 5: SQL inserts, COPY, or staged files — legacy baseline + +The previous Databricks experiment uploaded Parquet to a Unity Catalog volume +and drove ingestion through a SQL warehouse. This remains possible, but it +adds staging and SQL-warehouse work and is no longer the most direct +Databricks ingestion path. + +It is useful only as a historical baseline. Selecting it for the new run would +test the old loading architecture rather than the current Lakehouse//RT and +Zerobus architecture. + +## Cross-vendor comparability + +The selected path applies the same principle used elsewhere in CostBench: +choose the provider's native production ingestion route into its queryable +analytical storage. + +- ClickHouse uses native asynchronous inserts into the raw MergeTree while its + incremental materialized view is maintained. +- Snowflake uses the Snowpipe Streaming push SDK into its queryable raw object. +- BigQuery uses long-lived Storage Write API streams. +- Redshift uses MSK because Redshift's supported native streaming ingestion + requires Kinesis or MSK; the broker is part of that provider's recommended + path. +- Databricks uses Zerobus because it provides a direct push API into managed + Delta and explicitly removes the broker or ingestion-pipeline hop. + +Fairness does not require forcing every provider through Kafka. It requires +including every mandatory component and its cost for the provider's selected +native path. + +## Meeting answer + +If asked “Why not streaming tables, Structured Streaming, or real-time mode?”: + +> Streaming tables are a Delta target type, Structured Streaming is a +> transformation engine, and real-time mode is a millisecond processing +> trigger. None is a substitute for the producer-to-Delta transport. Our +> workload needs direct high-rate landing into a raw Delta table and exact +> incremental pre-aggregation for analytical reads. Zerobus is Databricks' +> shortest supported producer-to-Delta path, while Lakehouse//RT serves the raw +> table and materialized view. We would choose Lakeflow or real-time mode if we +> needed broker-based transformations or millisecond actions before storage, +> but we do not. + +## Official references + +- [Zerobus Ingest overview](https://docs.databricks.com/aws/en/ingestion/zerobus-overview) +- [Use Zerobus Ingest](https://docs.databricks.com/aws/en/ingestion/zerobus-ingest) +- [Zerobus quotas, latency, guarantees, and table behavior](https://docs.databricks.com/aws/en/ingestion/zerobus-quotas) +- [Streaming tables](https://docs.databricks.com/aws/en/ldp/concepts/streaming-tables) +- [Structured Streaming real-time mode concepts](https://docs.databricks.com/aws/en/structured-streaming/real-time/concepts) +- [Real-time mode source and sink support](https://docs.databricks.com/aws/en/structured-streaming/real-time/reference) +- [Real-time mode in Lakeflow pipelines](https://docs.databricks.com/aws/en/ldp/real-time) +- [Incremental refresh for materialized views](https://docs.databricks.com/aws/en/ldp/incremental-refresh) +- [Lakehouse Real-Time](https://docs.databricks.com/aws/en/compute/sql-warehouse/real-time) +- [Redis and Databricks real-time personalization architecture](https://redis.io/blog/delivering-real-time-personalization-with-databricks-and-redis/) diff --git a/full-path-realtime/quotes/databricks/QUERY_BASELINE.md b/full-path-realtime/quotes/databricks/QUERY_BASELINE.md new file mode 100644 index 0000000..6e0fbd7 --- /dev/null +++ b/full-path-realtime/quotes/databricks/QUERY_BASELINE.md @@ -0,0 +1,197 @@ +# Serverless SQL query baseline + +This baseline runs the canonical read workload on a dedicated Databricks +Serverless SQL warehouse while Lakehouse//RT is unavailable. The later +Lakehouse//RT run must use the same SQL, cadence, ordering, cache policy, +timing source, runner version, and result schema. Only the query warehouse ID, +declared warehouse mode, and provider size change. + +## Cross-provider workload contract + +The accepted ClickHouse, Snowflake, BigQuery, and Redshift implementations all +use the same cadence: + +- dashboard: four statements against the persisted daily aggregate, every 600 + seconds; +- drill-down: two statements against the raw table, every 3,600 seconds. +- canonical post-ingest window: three hours, adding 18 dashboard and three + drill-down schedules after ingestion completes. + +Both schedules are fixed-rate from their initial scheduled start. Statements +within one iteration execute serially in canonical file order. A runner never +overlaps itself; if an iteration exceeds its cadence, the next starts +immediately and records the overrun. Dashboard and drill-down runners may run +concurrently. + +## Create the query warehouse + +Create a dedicated SQL warehouse named `` with: + +- type: Serverless; +- cluster size: `X-Small`; +- minimum clusters: 1; +- maximum clusters: 1; and +- auto-stop: 80 minutes. + +Grant `` **CAN MONITOR**. This permits query execution and +query/warehouse monitoring without warehouse administration. + +Record the warehouse ID, channel/runtime, actual autoscaling values, auto-stop, +creation time, and owner. Do not use `` for measured reads. +Start the warehouse before launching the runners. The same warm-start and +auto-stop policy must be used for Lakehouse//RT. + +### Prepared warehouse + +The September 17, 2026 baseline warehouse is: + +- name: ``; +- ID: ``; +- type: Serverless; +- size: `X-Small`; +- scaling: one cluster minimum and maximum; +- channel: Current, version `2026.36`; +- owner: the benchmark administrator; and +- `` permission: `CAN MONITOR`. + +The 80-minute auto-stop was intentionally retained. It exceeds both the +ten-minute dashboard cadence and the one-hour drill-down cadence. Dashboard +activity should keep the warehouse running during a healthy workload, but the +long timeout protects the hourly-only path if dashboard execution stops. Stop +the warehouse explicitly when the baseline ends; otherwise up to 80 minutes of +post-run idle usage must be excluded and disclosed. Preserve the same policy +for the Lakehouse//RT comparison or report the difference. + +## Identical execution semantics + +Both Serverless SQL and Lakehouse//RT use the Statement Execution kernel path: + +- `databricks-sql-connector[kernel]==4.5.0`; +- `use_kernel=True`; +- `use_cached_result=false`; +- ANSI mode enabled; +- UTC session time zone; +- one Statement Execution ID per statement; +- canonical latency from + `system.query.history.total_duration_ms`, excluding result fetch; +- compilation, execution, queue, result-fetch, IO-cache, bytes/files/rows, and + result-cache evidence retained; +- full result draining and stable result hashing outside canonical provider + timing; and +- producer progress plus the latest completed MV source-row watermark attached + to every iteration. + +The query files contain table-name placeholders, allowing the same SQL to +target immutable qualification objects without editing the query text. + +The first online attempt on September 17 failed before executing SQL because +the runner supplied the connector's Thrift-style custom +`credentials_provider` while `use_kernel=True`. The Statement Execution kernel +explicitly rejects that combination. The runner now passes +`oauth_client_id` and `oauth_client_secret`, allowing the kernel to manage M2M +token lifecycle directly. The failed attempt is retained as invalid setup +evidence and is not a latency observation. + +The second attempt authenticated and executed all six SQL statements, but the +kernel cursor requires `fetchmany(size)` while the initial drain loop called +`fetchmany()` without a size. Provider history was captured, but the runner +correctly nullified canonical results because full result draining and hashing +failed. The drain loop now uses explicit 1,000-row batches. This failed attempt +is also excluded; server timings visible in its diagnostics are not promoted +to benchmark observations. + +The third attempt started at 9,891,162,743 provider-acknowledged raw rows and +successfully produced the first complete partial-baseline iteration. Canonical +provider durations were: + +- dashboard Q1–Q4: 1.038, 0.734, 0.840, and 0.852 seconds; +- drill-down Q1–Q2: 10.074 and 9.906 seconds. + +All statements reported `FINISHED`, `result_from_cache=false`, complete result +hashes, and no errors. Client wall times were retained only as diagnostics and +were not used as latency. This attempt began after ingestion was underway and +remains a partial-window characterization. + +The operator stopped ingestion after 4h08m, so the valid query attempt ended +after approximately 1h24m. It produced nine dashboard and two drill-down +iterations spanning roughly 9.89B–14.69B raw rows. Every observation remained +valid and uncached. + +Provider-duration ranges and medians were: + +- dashboard Q1: 0.743–1.066 seconds, median 0.968; +- dashboard Q2: 0.411–0.734 seconds, median 0.610; +- dashboard Q3: 0.384–0.855 seconds, median 0.723; +- dashboard Q4: 0.448–0.852 seconds, median 0.720; +- drill-down Q1: 10.074 and 18.794 seconds, median 14.434; and +- drill-down Q2: 9.906 and 16.661 seconds, median 13.284. + +The two earlier setup attempts are excluded. This partial baseline validates +the query harness but does not replace a from-time-zero canonical baseline. + +## Launch against an active ingest run + +Set only the new query warehouse ID: + +```sh +export DATABRICKS_QUERY_WAREHOUSE_ID='' +export DATABRICKS_WAREHOUSE_MODE='serverless-baseline' +export DATABRICKS_QUERY_SIZE='X-Small' + +tmux new -As query-baseline +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/run_query_workloads.sh +``` + +The wrapper reads the active ingest context, prompts once for the benchmark +service-principal secret, and launches both runners. It stops them after the +producer stops, allowing an in-flight iteration to complete cleanly. + +If the baseline starts after ingestion has already begun, its context records +the exact starting provider row count and marks the result +`partial_ingest_window=true`. Such a result is useful characterization but is +not a complete six-hour baseline. + +## Lakehouse//RT swap + +For the later run, create a fresh ingest target and use the same wrapper: + +```sh +export DATABRICKS_QUERY_WAREHOUSE_ID='' +export DATABRICKS_WAREHOUSE_MODE='lakehouse-rt' +export DATABRICKS_QUERY_SIZE='' + +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/run_query_workloads.sh +``` + +No query SQL or cadence may change. Generate a new output directory and run +ID; never compare two warehouses using cached results or a shared mutable +target. + +## Cost treatment + +Attribute query-warehouse billing separately from: + +- Zerobus ingestion; +- MV refresh; +- Predictive Optimization; +- storage; +- the excluded `` instrumentation warehouse; and +- the producer. + +The Serverless SQL baseline uses its account-effective SKU and list-price +history. The later Lakehouse//RT result uses the Lakehouse Serverless SKU and +is reported independently. Query cost allocation uses the union of +run-tagged successful query intervals so concurrent dashboard and drill-down +queries do not double-count shared warehouse uptime. + +After billing has settled for at least 24 hours, collect the partial baseline's +query-warehouse cost with: + +```sh +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/collect_query_baseline_settled.sh \ + /data/databricks-qualification/ingest_mv_6h_r2_20260917T104305Z/queries/serverless-baseline__20260917T132826Z +``` + +The existing cost summarizer uses the legacy internal category name +`primary_rt_serving_compute` for the selected query warehouse; the wrapper +relabels it `primary_query_serving_compute` for this Serverless baseline. diff --git a/full-path-realtime/quotes/databricks/QUERY_RESULT_SCHEMA.md b/full-path-realtime/quotes/databricks/QUERY_RESULT_SCHEMA.md new file mode 100644 index 0000000..e599ae1 --- /dev/null +++ b/full-path-realtime/quotes/databricks/QUERY_RESULT_SCHEMA.md @@ -0,0 +1,11 @@ +# Query result schema + +The query-result definitions have moved into the two canonical result +references: + +- [Result field glossary](RESULT_FIELD_GLOSSARY.md) defines every packaged + field plus the excluded full query-audit sidecar. +- [Result collection reference](RESULT_COLLECTION_REFERENCE.md) documents the + SQL, REST, SDK, host, and offline derivation sources. + +This compatibility pointer keeps older links working. diff --git a/full-path-realtime/quotes/databricks/README.md b/full-path-realtime/quotes/databricks/README.md index 1513f33..6293d31 100644 --- a/full-path-realtime/quotes/databricks/README.md +++ b/full-path-realtime/quotes/databricks/README.md @@ -1,234 +1,373 @@ -# Stockhouse — Databricks Ingestion - -End-to-end steps to spin up an EC2 box, download the Stockhouse dataset, ingest it into Databricks, and run benchmark queries. - -## 1. Spin up an EC2 instance - -```bash -# Create key pair -aws ec2 create-key-pair \ - --region eu-west-1 \ - --key-name benchmarking-eu-west-1 \ - --query 'KeyMaterial' \ - --output text > ~/.ssh/benchmarking-eu-west-1.pem -chmod 600 ~/.ssh/benchmarking-eu-west-1.pem - -# Get default VPC and subnet -VPC_ID=$(aws ec2 describe-vpcs --region eu-west-1 \ - --filters "Name=isDefault,Values=true" \ - --query 'Vpcs[0].VpcId' --output text) -SUBNET_ID=$(aws ec2 describe-subnets --region eu-west-1 \ - --filters "Name=vpcId,Values=$VPC_ID" \ - --query 'Subnets[0].SubnetId' --output text) - -# Security group (SSH from your IP only) -MY_IP=$(curl -s https://checkip.amazonaws.com) -SG_ID=$(aws ec2 create-security-group \ - --region eu-west-1 \ - --group-name benchmarking-sg \ - --description "Benchmarking instance" \ - --vpc-id $VPC_ID \ - --query 'GroupId' --output text) -aws ec2 authorize-security-group-ingress \ - --region eu-west-1 \ - --group-id $SG_ID \ - --protocol tcp --port 22 --cidr $MY_IP/32 - -# Launch m5.16xlarge (64 vCPU, 256 GB RAM) with 2 TB gp3 -aws ec2 run-instances \ - --region eu-west-1 \ - --image-id ami-0daff188b5216c5f0 \ - --instance-type m5.16xlarge \ - --key-name benchmarking-eu-west-1 \ - --security-group-ids $SG_ID \ - --subnet-id $SUBNET_ID \ - --associate-public-ip-address \ - --block-device-mappings '[{"DeviceName":"/dev/sda1","Ebs":{"VolumeSize":2000,"VolumeType":"gp3","DeleteOnTermination":true}}]' \ - --tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=benchmarking-databricks}]' +# Databricks full-path real-time benchmark + +This directory contains the current Databricks implementation of the +113,219,565,734-row StockHouse quotes benchmark. The path is: + +1. a same-region producer reads the canonical Parquet capture; +2. Zerobus writes Arrow `RecordBatch` payloads directly into a Unity Catalog + managed Delta table; +3. a strict-incremental Delta materialized view maintains the dashboard + summary; and +4. Lakehouse//RT serves the measured reads through Reyden. Four dashboard + queries read the materialized view and two drill-down queries read the raw + table. + +Zerobus is the write path; Reyden is the Lakehouse//RT read path. This is not +the old SQL `INSERT` implementation. + +### Why this architecture + +[Structured Streaming real-time mode](https://docs.databricks.com/aws/en/structured-streaming/real-time/concepts) +is a separate millisecond-latency stream-processing trigger, not the +Lakehouse//RT serving engine. As documented in September 2026, its +[supported sources and sinks](https://docs.databricks.com/aws/en/structured-streaming/real-time/reference) +exclude Delta. It therefore cannot read the benchmark's raw Delta table or +write a Delta table, streaming table, or materialized view that Lakehouse//RT +could serve directly. + +Bridging real-time mode through Kafka or a custom sink and then ingesting the +result into Delta would add another processing and ingestion hop, changing the +full-path workload and cost boundary. Direct Zerobus into managed Delta, +followed by a Delta materialized view and Lakehouse//RT reads, is the shortest +supported path that preserves the benchmark's queryable raw and pre-aggregated +layers. + +## Status and release-stage disclosure + +No online Databricks benchmark result exists for this implementation. The +same-region producer and explicit Unity Catalog managed storage are prepared, +and source staging is in progress. Lakehouse//RT is not yet available in the +workspace, so qualification and the 113,219,565,734-row run have not been +executed. Repository history, `results/`, old JSONL/Markdown files, and legacy +scripts are not accepted evidence for this path. + +As documented in September 2026, Lakehouse//RT, Zerobus Arrow Flight, Zerobus +writes into liquid-clustered tables, and materialized-view `REFRESH POLICY` are +Beta features. The `System-Managed Job for Materialized Views & Streaming +Tables` preview was enabled while investigating scheduled-refresh performance +controls, but did not expose a toggle for the trigger-on-update MV. Zerobus +Ingest availability in other formats does not make this +complete architecture generally available. Any future result must be labeled +a Databricks Beta-path evaluation and must record the release stages, +workspace previews, and documentation date observed at execution time. +Behavior, SQL support, performance, and pricing can change. + +The authoritative documents are: + +- [INFRASTRUCTURE_SETUP.md](INFRASTRUCTURE_SETUP.md) for the reproducible AWS, + Unity Catalog, producer, and source-staging setup; +- [INGESTION_ARCHITECTURE.md](INGESTION_ARCHITECTURE.md) for the ingestion + alternatives, real-time terminology, and rationale for selecting Zerobus; +- [DATABRICKS_BENCHMARK_CONTRACT.md](DATABRICKS_BENCHMARK_CONTRACT.md) for + workload identity, semantics, evidence, cost, and validity gates; +- [TUNING.md](TUNING.md) for clean-target qualification and frozen settings; +- [SIX_HOUR_INGEST_MV.md](SIX_HOUR_INGEST_MV.md) for the Lakehouse//RT-free + six-hour write, maintenance, freshness, and cost characterization; +- [RECOVERY_40M.md](RECOVERY_40M.md) for the fresh-target journal-scaling and + fail-closed stream-rotation qualification; +- [SIX_HOUR_INGEST_MV_R2.md](SIX_HOUR_INGEST_MV_R2.md) for the fresh six-hour + retry using the settings accepted by the recovery qualification; +- [QUERY_BASELINE.md](QUERY_BASELINE.md) for the Serverless SQL X-Small + baseline and the later warehouse-only Lakehouse//RT swap; +- [REAL_SERVERLESS_BASELINE.md](REAL_SERVERLESS_BASELINE.md) for the canonical + complete-source baseline and three-hour post-ingest read window; +- [REAL_RUN.md](REAL_RUN.md) for the only supported full-run sequence. + +The full run is prohibited unless the qualification report contains exact +JSON boolean `"qualified": true`. + +Use one benchmark catalog with only two schema roles: + +```text +costbench +├── rt_qualification +└── rt_full_ ``` -Get the public IP once running: - -```bash -aws ec2 describe-instances \ - --region eu-west-1 \ - --instance-ids \ - --query 'Reservations[0].Instances[0].PublicIpAddress' \ - --output text -``` - -SSH in: - -```bash -ssh -i ~/.ssh/benchmarking-eu-west-1.pem ubuntu@ -``` - -**Cost:** ~$3.07/hr — terminate when done. - -## 2. Set up Python environment on EC2 - -```bash -sudo apt-get update -q -sudo apt-get install -y python3.12-venv unzip -q -python3 -m venv ~/venv -~/venv/bin/pip install databricks-sdk pyarrow -``` - -Install AWS CLI: - -```bash -curl -s 'https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip' -o awscliv2.zip -unzip -q awscliv2.zip -sudo ./aws/install -``` - -## 3. Download the dataset - -Copy `download_stockhouse.sh` to the instance and run it with your AWS credentials exported: - -```bash -scp -i ~/.ssh/benchmarking-eu-west-1.pem download_stockhouse.sh ubuntu@:~/ - -# On the EC2 instance: -chmod u+x ~/download_stockhouse.sh -export AWS_ACCESS_KEY_ID="..." -export AWS_SECRET_ACCESS_KEY="..." -export AWS_SESSION_TOKEN="..." - -bash ~/download_stockhouse.sh # all files -bash ~/download_stockhouse.sh 10 # first 10 files only -``` - -Data is saved to `~/data/stockhouse/`. Files are named `quotes_YYYY-MM-DD.parquet` (daily files) plus `quotes_0.parquet` (10B row historical file, excluded by the script). - -### Dataset summary - -| File | Rows | Size | -|---|---|---| -| quotes_0.parquet | 10,000,000,000 | 45 GB | -| quotes_YYYY-MM-DD.parquet (10 files) | ~5,030,000,000 | ~7 GB total | - -Note: some daily files may be empty (0 rows) for days with no data. - -## 4. Create Databricks schema and table - -Run `create.sql` in the Databricks SQL editor, or via the API: - -```bash -export DATABRICKS_TOKEN="dapi..." - -# Create schema -curl -s -X POST \ - "https://dbc-37858cc0-7910.cloud.databricks.com/api/2.0/sql/statements" \ - -H "Authorization: Bearer $DATABRICKS_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{"warehouse_id":"47486f539b196632","statement":"CREATE SCHEMA IF NOT EXISTS workspace.benchmarking","wait_timeout":"50s"}' - -# Create table -curl -s -X POST \ - "https://dbc-37858cc0-7910.cloud.databricks.com/api/2.0/sql/statements" \ - -H "Authorization: Bearer $DATABRICKS_TOKEN" \ - -H "Content-Type: application/json" \ - -d '{"warehouse_id":"47486f539b196632","statement":"","wait_timeout":"50s"}' +Qualification cases run sequentially in `rt_qualification`; `create.sql` +recreates empty raw and MV objects before each case. Every full attempt gets a +new `rt_full_` schema, which qualification must never touch. + +## Prerequisites + +- Python 3.10 or newer on a dedicated producer in the same cloud region as the + Databricks workspace. Size it as an `m6i.8xlarge` equivalent: 32 vCPU, + 128 GiB memory, and comparable network capacity. Record the actual machine, + region, storage, and network placement; this producer size is not evidence + for Lakehouse//RT query CPU. +- The complete canonical source capture with a manifest total of exactly + 113,219,565,734 rows. Do not add `quotes_0.parquet`, omit files, loop input, + or cap the full run. +- A supported Databricks region with Zerobus, serverless materialized-view + pipelines, and Lakehouse//RT enabled. Predictive Optimization must be + effectively `ENABLE` for both the raw table and materialized view, whether + configured directly or inherited. Record the system-managed-job preview and + the MV schedule's Performance optimized setting independently. +- A dedicated Unity Catalog `costbench` catalog with an explicit, non-default + managed storage location. `rt_qualification` and each fresh + `rt_full_` schema may inherit that catalog location. The raw target + must remain a managed Delta table. Unity Catalog metastore default storage, + external/path-backed tables, and a table-level `LOCATION` are outside the + contract. +- Two distinct warehouses: + - a Lakehouse//RT warehouse for measured Statement Execution reads; + - a standard control SQL warehouse for DDL, preflight, bounded metadata and + system-table reads, evidence collection, and empty-table protection. + Record both IDs, the Lakehouse//RT HTTP path, frozen query size, autoscaling + limit, auto-stop behavior, and all infrastructure changes. +- A dedicated benchmark service principal with Workspace and Databricks SQL + entitlements. This deployment reuses it for workspace/control and Zerobus + access: standard workspace OAuth authorizes SQL and metadata requests, while + a separate resource-bound OAuth token authorizes Zerobus direct writes. It + requires `USE CATALOG`, `USE SCHEMA`, `SELECT`, and `MODIFY` on the target, + `CAN MONITOR` on measured/control warehouses, MV ownership for direct event + log reads, and bounded system-schema reads. A two-principal deployment is + also valid and has a smaller security blast radius. Never persist secrets in + environment files, reports, commands, or source control. +- Access to `system.query.history`, Unity Catalog metadata, warehouse + configuration/history, materialized-view event logs, Zerobus system tables, + predictive-optimization history, billing usage, and list prices needed by + the bounded collector. + +Create separate virtual environments for the checked-in dependency sets: + +```sh +cd /Users/lio/Clickhouse/CostBench/full-path-realtime/quotes/databricks +python3.12 -m venv .venv-runner +python3.12 -m venv .venv-zerobus +.venv-runner/bin/python -m pip install -r requirements-runner.txt +.venv-zerobus/bin/python -m pip install -r requirements-zerobus.txt + +export DATABRICKS_RUNNER_PYTHON="$PWD/.venv-runner/bin/python" +export DATABRICKS_ZEROBUS_PYTHON="$PWD/.venv-zerobus/bin/python" ``` -## 5. Copy ingestion script to EC2 - -```bash -scp -i ~/.ssh/benchmarking-eu-west-1.pem \ - ingest.py ubuntu@:~/ingest.py +`requirements-runner.txt` pins +`databricks-sql-connector[kernel]==4.5.0` and `databricks-sdk==0.133.0`; +the SDK is required for the connector's OAuth M2M credential provider. +`requirements-zerobus.txt` pins +`databricks-zerobus-ingest-sdk[arrow]==1.8.0`. +Keep these environments separate: the SQL kernel requires PyArrow 23.x while +Zerobus 1.8 requires PyArrow earlier than 22. `preflight.py` checks each +dependency using its role-specific interpreter. + +Copy `databricks-host.env.example` outside the repository, replace its +placeholders, and source it before launching any shell harness. It contains +only non-secret deployment coordinates. Enter the service-principal secret +interactively when prompted; never add it to that file. + +Stage the licensed source from an authorized private S3 prefix using an EC2 +instance role or another non-persisted AWS credential source: + +```sh +export S3_SOURCE_URI='s3:///' +export SOURCE_DIR=/data/quotes +./stage_source_from_s3.sh ``` -## 6. Run ingestion - -SSH in and run inside a `screen` session: - -```bash -screen -S ingest - -export DATABRICKS_HOST="https://dbc-37858cc0-7910.cloud.databricks.com" -export DATABRICKS_HTTP_PATH="/sql/1.0/warehouses/47486f539b196632" -export DATABRICKS_TOKEN="dapi..." - -~/venv/bin/python3 ~/ingest.py \ - --dir ~/data/stockhouse \ - --parallel 32 \ - --row-groups-per-insert 4 2>&1 | tee ingest.log +The staging command resumes existing downloads, does not delete local files, +and fails unless the top-level `quotes_*.parquet` files contain exactly +113,219,565,734 rows with the required columns. Its inventory is not a +substitute for the producer's file hashes and canonical task manifest. + +## Current implementation map + +- `README.md`: architecture, prerequisites, evidence, and operational entry + point. +- `databricks-host.env.example`: placeholder-only non-secret runtime + configuration required by the shell harnesses. +- `INFRASTRUCTURE_SETUP.md`: exact successful AWS and Unity Catalog setup plus + reusable producer and source-staging instructions. +- `INGESTION_ARCHITECTURE.md`: decision record comparing Zerobus, streaming + tables, Structured Streaming, real-time mode, and staged SQL ingestion. +- `DATABRICKS_BENCHMARK_CONTRACT.md`: provider-specific benchmark contract. +- `TUNING.md`: offline planning, online tuning/endurance matrix, and + qualification evaluator. +- `SIX_HOUR_INGEST_MV.md`: bounded six-hour Zerobus/MV characterization and + settled-cost recollection procedure. +- `REAL_RUN.md`: qualification-gated, fresh-target full-run procedure. +- `create.sql`: destructive four-statement raw-table and materialized-view DDL; + catalog and schema are administrator prerequisites. +- `queries_mv.sql`: the four canonical dashboard statements in fixed order. +- `queries_raw.sql`: the two canonical drill-down statements in fixed order. +- `dbx_common.py`: standard-library REST, SQL rendering/splitting, bounded + result retrieval, atomic evidence writes, configuration, and redaction. +- `apply_ddl.py`: standard-library destructive-confirmation wrapper that + renders and applies all of `create.sql` on the control warehouse and records + hashes and statement IDs. +- `preflight.py`: offline contract checks and fail-closed online workspace, + table, effective Predictive Optimization inheritance, storage, warehouse, + query, package, permission, and endpoint checks. +- `stage_source_from_s3.sh`: resumable private-S3 source staging and exact + Parquet metadata validation. +- `run_capacity_1m.sh`: destructive-confirmation wrapper for the preliminary + 75M-row, 1M-EPS capacity characterization. +- `run_ingest_mv_10m.sh`: preliminary 600M-row sustained-ingest trial with + concurrent metadata-only MV freshness monitoring. +- `run_ingest_mv_6h.sh`: duration-bounded six-hour write/maintenance + characterization with compact long-run state, post-ingest catch-up, and + initial evidence capture. +- `run_ingest_mv_40m.sh`: 40-minute validation of long-run journal scaling and + proactive, zero-unacknowledged stream rotation. +- `run_ingest_mv_6h_r2.sh`: fresh six-hour retry frozen to the accepted + recovery settings. +- `run_query_workloads.sh`: warehouse-parameterized dashboard and drill-down + orchestration for Serverless SQL and Lakehouse//RT. +- `RESULT_COLLECTION_REFERENCE.md`: SQL, REST, SDK, host, and offline + derivation provenance for every accepted result artifact. +- `RESULT_FIELD_GLOSSARY.md`: field-by-field glossary for every accepted + JSON/JSONL artifact and the retained full query-audit sidecar. +- `collect_query_baseline_settled.sh`: target/query-window evidence and billing + recollection for the Serverless SQL baseline. +- `run_full_serverless_baseline.sh`: canonical full-source orchestration for + ingest, MV freshness, Serverless reads, hashing, compact evidence, and + initial cost. +- `collect_full_serverless_settled.sh`: settled billing recollection and final + validation for the canonical compact Serverless result package. +- `collect_ingest_mv_6h_settled.sh`: immutable-window billing recollection and + cost summary after the provider's settlement delay. +- `ingest_zerobus.py`: canonical Parquet enumeration, direct Arrow Flight + Zerobus ingestion, provider-durability checkpoints, pacing, recovery state, + and source/ingest evidence. +- `runner_common.py`: Lakehouse//RT connector/session setup, fixed-rate + scheduling, query-history collection, canonical timing, cache rejection, + result hashing, and aligned JSONL records. +- `run_dashboard.py`: four-query materialized-view runner at 600-second + cadence. +- `run_drilldown.py`: two-query raw-table runner at 3,600-second cadence. +- `monitor_freshness.py`: bounded 30–60-second metadata-only observations of + producer progress, Zerobus commits, and materialized-view refresh events. +- `collect_evidence.py`: bounded read-only export of required Databricks system + and billing datasets into transient runtime state. +- `compact_evidence.py`: normalized provider reconciliation, MV refresh, and + clustering-status evidence for the reviewable result package. +- `compact_query_results.py`: schema-v3 reviewable query JSONL generation while + preserving schema-v2 audit records in runtime state. +- `finalize_results.py`: compact source summary plus SHA-256 artifact manifest. +- `costs/summarize_run.py`: deterministic offline cost classification, + subledgers, pricing completeness checks, and overlap-safe query allocation. +- `validate_run.py`: offline acceptance gates for source membership, + durability, throughput, queries, freshness, infrastructure, evidence, and + cost. +- `qualify.py`: deterministic qualification plan generator and offline + evaluator. +- `publish_results.py`: fail-closed accepted-sample filtering, 100B + presentation capping, monotonic ClickHouse matching, cost-summary + construction, and optional global-manifest activation. +- `visualizations/plot_publication.py`: pairwise charts that verify the + publisher's source and artifact hashes before rendering. +- `requirements-runner.txt` and `requirements-zerobus.txt`: exact online + dependency sets. +- `tests/test_offline.py`: standard-library offline tests for SQL, REST, + evidence, scheduling, producer state, cost, validation, qualification, + publication, and DDL application. + +`ingest.py`, `run_queries.py`, `run_dashboard.sh`, `run_drilldown.sh`, +`queries/`, `RUNNERS_SPEC.md`, historical `results/`, and the older shell/JSON +cost files are legacy or historical material. They are not part of this +implementation and cannot supply evidence for a current result. + +## Evidence model + +A run is evidence-ledger based; console output or a screenshot is not enough. + +- The producer writes the source manifest, atomic progress journal, logical + batch ledger, five-second provider-committed metrics, and terminal summary. + Throughput counts only rows whose Zerobus offsets have been acknowledged as + durable. Client byte telemetry uses `RecordBatch.nbytes`, an uncompressed + in-memory estimate that does not serialize the batch a second time. + `system.lakeflow.zerobus_ingest.committed_bytes` remains the authoritative + provider byte count. Interval evidence also records whole-host CPU and + receive/transmit throughput so qualification rejects a saturated producer. +- Dashboard and drill-down runners retain every Statement Execution ID, + fixed-rate schedule timestamps, ordered result hashes and row counts, + canonical timing, detailed query-history phases, queue/wait signals, result + cache fields, IO-cache observations, and the contemporaneous producer and + freshness checkpoints. +- The freshness monitor records bounded provider timestamps and + materialized-view refresh status/method without querying raw or MV contents. + MV row count is intentionally `null`; refresh output rows are not interpreted + as total MV rows or source coverage. +- The collector exports the run-bounded Zerobus summaries, refresh event log, + tagged query history, warehouse events/configuration, predictive operations, + table detail/history and Unity Catalog metadata, billing usage, and list + prices. Required dataset failures make evidence incomplete. +- `validate_run.py` consumes only preserved files and accepts a run only when + every gate passes. Historical artifacts cannot be substituted or merged. + +Raw and materialized-view ages are observational: they are the difference +between a sample time and provider-reported timestamps. They are not exact +per-record ingest lag or materialized-view lag. Post-run analysis uses Zerobus +commit metadata and successful refresh start/finish times to publish +metadata-only lag bounds. Dashboard answers are current as of a source snapshot +taken during the last completed materialized-view refresh; the exact snapshot +instant is not exposed in documented metadata. + +## Canonical query timing and cache semantics + +The only canonical elapsed time is +`system.query.history.total_duration_ms / 1000`, which excludes result fetch. +Client wall time, time to first row, compilation plus execution, and canonical +duration plus fetch are not substitutes. + +Measured sessions set `use_cached_result=false`. Every accepted query must have +`from_result_cache=false`, no foreign cache-origin statement, complete +query-history telemetry, and its Statement Execution ID. A missing cache field +or a result-cache hit rejects the observation. Provider-managed data/IO caching +is separate; preserve `read_io_cache_percent` and disclose the observed +physical cache state rather than calling the run cache-free. + +## Cost boundary and subledgers + +Use one declared UTC window. Preserve resource usage before applying any price: +producer compute/storage reads/network egress; Zerobus ingest; managed raw +Delta storage and maintenance; materialized-view refresh compute and storage; +Lakehouse//RT billed minimum/idle/autoscaled usage; cloud storage/requests; and +separately billed services. + +`costs/summarize_run.py` builds the Databricks-billed portion of the canonical +undiscounted primary ledger and separates: + +- fresh-data-path cost: Zerobus ingestion, materialized-view refresh, and + predictive optimization; +- measured-query serving: Lakehouse//RT usage allocated by the union overlap + of successful tagged query intervals, avoiding double-counting shared + warehouse time. + +Storage, control-warehouse work, idle/minimum intervals that cannot be +attributed, failed work, setup, DDL, preflight, correctness, tuning, and +post-ingest analysis remain itemized excluded categories. Unknown, ambiguous, +unpriced, or multi-currency primary usage fails cost completeness. Credits, +commitments, negotiated prices, promotions, taxes, and any Beta discount are +separate scenarios, never the canonical total. No price is asserted in this +README. + +Producer compute, source-storage reads, network egress, and cloud +storage/request charges are outside Databricks system billing. Preserve and +report them as a separate external subledger over the same UTC boundaries; do +not silently treat their absence as zero or splice them into provider exports. + +Databricks billing usage can lag by as much as 24 hours. Preserve an initial +bounded collection, then repeat the same bounded collection after settlement; +run cost summary and final validation only against the settled collection. + +## Local and online gates + +Run the complete offline suite without credentials: + +```sh +cd /Users/lio/Clickhouse/CostBench/full-path-realtime/quotes/databricks +python3 -m unittest discover -s tests -p 'test_*.py' +python3 -m py_compile \ + apply_ddl.py collect_evidence.py dbx_common.py ingest_zerobus.py \ + monitor_freshness.py preflight.py publish_results.py qualify.py \ + runner_common.py run_dashboard.py run_drilldown.py validate_run.py \ + costs/summarize_run.py visualizations/plot_publication.py +python3 preflight.py --output /absolute/path/to/offline_preflight.json ``` -Detach with `Ctrl-A D`. Reattach with `screen -r ingest`. - -### Key parameters - -| Flag | Default | Description | -|---|---|---| -| `--parallel` | required | Number of worker processes | -| `--row-groups-per-insert` | 1 | Batch N row groups per INSERT — reduces Delta transaction log pressure | -| `--target-rps` | 0 (unlimited) | Max rows/s across all workers | -| `--max-files` | all | Limit to first N files | -| `--live-eps-interval` | 30s | Seconds between live row-count samples | - -### Observed throughput - -- ~32 workers → ~600-900k rows/s on a Small warehouse (3 clusters) -- Bottleneck is Delta Lake transaction log contention under concurrent writes -- Read times drop from ~1.2s (single 45GB file) to ~110ms (daily files) due to reduced file contention - -### Ingestion results (dated files, 10 files) - -| Metric | Value | -|---|---| -| Duration | 89.5 min | -| Rows ingested | 5,029,995,919 | -| Average throughput | ~937k rows/s | -| Errors | 0 | - -## 7. Run benchmark queries - -Copy the query runner and queries directory to EC2: - -```bash -scp -i ~/.ssh/benchmarking-eu-west-1.pem run_queries.py ubuntu@:~/ -scp -i ~/.ssh/benchmarking-eu-west-1.pem queries/*.sql ubuntu@:~/queries/ -``` - -Run on an interval, recording results to CSV: - -```bash -~/venv/bin/python3 ~/run_queries.py \ - --queries-dir ~/queries \ - --interval 60 \ - --output ~/results.csv -``` - -### Queries - -| File | Description | -|---|---| -| `spread_by_symbol.sql` | Avg/min/max bid-ask spread per symbol | -| `top_symbols_by_volume.sql` | Quote count per symbol | -| `bid_ask_imbalance.sql` | Bid vs ask size imbalance per symbol | -| `quote_activity_over_time.sql` | Quote count bucketed by hour | - -### Benchmark results (Small warehouse, 1 cluster) - -Query times during and after ingestion at 6.59B total rows: - -| Row Count | bid_ask_imbalance | quote_activity_over_time | spread_by_symbol | top_symbols_by_volume | -|---|---|---|---|---| -| 1.78B | 3.9s | 3.0s | 4.4s | 2.9s | -| 2.50B | 4.3s | 3.1s | 4.5s | 3.1s | -| 3.24B | 5.4s | 3.2s | 6.0s | 4.8s | -| 4.02B | 6.7s | 3.1s | 7.2s | 4.7s | -| 4.84B | 7.6s | 3.4s | 8.2s | 5.3s | -| 5.69B | 8.7s | 4.6s | 8.9s | 5.8s | -| 6.59B (during ingest) | 9.6s | 5.2s | 9.5s | 6.1s | -| 6.59B (ingest complete) | **0.5s** | **0.5s** | **0.5s** | **0.5s** | - -Queries degrade gradually during ingest as the cache is invalidated by concurrent writes, then drop to ~0.5s once ingest completes and the cache stabilises. - -## 8. Terminate the instance - -```bash -aws ec2 terminate-instances --region eu-west-1 --instance-ids -``` +Offline checks do not prove service availability or produce a benchmark +result. Complete the qualification matrix in [TUNING.md](TUNING.md), require +exact `"qualified": true`, and then follow [REAL_RUN.md](REAL_RUN.md). The +online preflight runs only after the fresh administrator-managed namespace and +checked-in DDL exist; any online error stops the run. diff --git a/full-path-realtime/quotes/databricks/REAL_RUN.md b/full-path-realtime/quotes/databricks/REAL_RUN.md new file mode 100644 index 0000000..4060aed --- /dev/null +++ b/full-path-realtime/quotes/databricks/REAL_RUN.md @@ -0,0 +1,830 @@ +# Canonical Databricks 113,219,565,734-row run + +This is the launch, stop, collection, and validation sequence for one fresh +full run. Do not use it for tuning or partial runs. Do not begin namespace +creation or DDL until the selected qualification report contains exact JSON +boolean `"qualified": true`. + +Read [INFRASTRUCTURE_SETUP.md](INFRASTRUCTURE_SETUP.md), +[DATABRICKS_BENCHMARK_CONTRACT.md](DATABRICKS_BENCHMARK_CONTRACT.md), and +[TUNING.md](TUNING.md) first. The sequence below never uses `--max-files`, +`--max-row-groups`, `--max-rows`, `--allow-partial`, or +`--allow-nonempty-table`. + +## 1. Freeze the non-secret environment + +Run on the same-region, dedicated `m6i.8xlarge`-equivalent producer. Use Python +3.12 and install the checked-in requirements into two environments. The SQL +kernel requires PyArrow 23.x while Zerobus 1.8 requires PyArrow earlier than +22, so combining the requirement files into one environment is invalid. + +```sh +cd /absolute/path/to/CostBench/full-path-realtime/quotes/databricks +python3.12 -m venv .venv-runner +python3.12 -m venv .venv-zerobus +.venv-runner/bin/python -m pip install -r requirements-runner.txt +.venv-zerobus/bin/python -m pip install -r requirements-zerobus.txt +``` + +Create a non-secret environment file. Use a new run ID, a new result root +outside the checked-in historical `results/` directory, the dedicated +`costbench` catalog, and a new full-run schema: + +```sh +umask 077 +cat > /absolute/path/to/databricks-full-run.env <<'EOF' +export PATH="$HOME/.local/bin:$PATH" +export DBX_BENCH_DIR="/absolute/path/to/CostBench/full-path-realtime/quotes/databricks" +export DATABRICKS_RUNNER_PYTHON="$DBX_BENCH_DIR/.venv-runner/bin/python" +export DATABRICKS_ZEROBUS_PYTHON="$DBX_BENCH_DIR/.venv-zerobus/bin/python" +export SOURCE_DIR="/absolute/path/to/canonical-stockhouse-parquet" +export QUALIFICATION_REPORT="/absolute/path/to/qualification/qualification_report.json" + +export DATABRICKS_HOST="https://" +export DATABRICKS_CLOUD="" +export DATABRICKS_REGION="" +export DATABRICKS_WORKSPACE_ID="" +export DATABRICKS_CATALOG="costbench" +export DATABRICKS_SCHEMA="rt_full_" +export DATABRICKS_RAW_TABLE="quotes" +export DATABRICKS_MV_TABLE="quotes_daily" +export DATABRICKS_RT_WAREHOUSE_ID="" +export DATABRICKS_HTTP_PATH="/sql/1.0/warehouses/" +export DATABRICKS_CONTROL_WAREHOUSE_ID="" +export ZEROBUS_ENDPOINT="https://.zerobus.." + +export BENCHMARK_RUN_ID="" +export RUN_ROOT="/absolute/path/to/databricks-runs/${BENCHMARK_RUN_ID}" +export FROZEN_ENV="${RUN_ROOT}/qualification-frozen.env" +export RUNTIME_ENV="${RUN_ROOT}/runtime.env" +export WINDOW_ENV="${RUN_ROOT}/measurement-window.env" +export WINDOW_JSON="${RUN_ROOT}/measurement-window.json" +export DDL_OUTPUT_DIR="${RUN_ROOT}/ddl" +export PREFLIGHT_OUTPUT="${RUN_ROOT}/preflight_report.json" +export INGEST_OUTPUT_DIR="${RUN_ROOT}/ingest" +export INGEST_PROGRESS="${INGEST_OUTPUT_DIR}/ingest_progress.json" +export INGEST_MANIFEST="${INGEST_OUTPUT_DIR}/source_manifest.json" +export INGEST_METRICS="${INGEST_OUTPUT_DIR}/ingest_metrics.jsonl" +export DASHBOARD_OUTPUT="${RUN_ROOT}/dashboard.jsonl" +export DRILLDOWN_OUTPUT="${RUN_ROOT}/drilldown.jsonl" +export FRESHNESS_OUTPUT="${RUN_ROOT}/freshness.jsonl" +export FRESHNESS_PROGRESS="${RUN_ROOT}/freshness_progress.json" +export EVIDENCE_INITIAL_DIR="${RUN_ROOT}/evidence-initial" +export EVIDENCE_SETTLED_DIR="${RUN_ROOT}/evidence-settled" +export COST_SUMMARY="${RUN_ROOT}/cost_summary.json" +export VALIDATION_REPORT="${RUN_ROOT}/validation_report.json" +export FRESHNESS_INTERVAL_SECONDS="30" +export TMUX_SESSION="dbx-${BENCHMARK_RUN_ID}" +EOF + +chmod 600 /absolute/path/to/databricks-full-run.env +. /absolute/path/to/databricks-full-run.env +mkdir -p "$RUN_ROOT" +``` + +If the licensed capture is staged from private S3, complete it before +qualification and preserve its metadata inventory: + +```sh +export S3_SOURCE_URI='s3:///' +export SOURCE_INVENTORY_OUTPUT=/absolute/path/to/source_inventory.json +"$DBX_BENCH_DIR/stage_source_from_s3.sh" +``` + +Stop unless that command reports `status: complete` and +`actual_rows: 113219565734`. The producer independently creates the canonical +file-hash and row-group manifest when the measured run starts. + +Do not put a PAT, OAuth access token, client secret, cloud secret, or source +credential in that file. + +Control and Zerobus authorization use distinct OAuth token requests but may +use the same dedicated benchmark service principal, as in this deployment: + +```sh +unset DATABRICKS_TOKEN +export DATABRICKS_CLIENT_ID='' +export DATABRICKS_CLIENT_SECRET='' +``` + +Those are variable contracts, not values to paste into shell history. In the +actual terminals, fetch from a secret manager or use `read -s` as shown below. +Workspace/control clients request a standard workspace OAuth token. The +Zerobus client requests its separate workspace-specific +`zerobusDirectWriteApi` resource token with explicit Unity Catalog +authorization details. A deployment may instead use separate principals or a +short-lived `DATABRICKS_TOKEN` for control access, but the chosen identity +model must be frozen and disclosed. + +## 2. Prove qualification and extract frozen settings + +This gate also proves that the qualified host, Zerobus endpoint, and +Lakehouse//RT warehouse match this run. It writes only non-secret settings. + +```sh +QUALIFICATION_REPORT="$QUALIFICATION_REPORT" \ +FROZEN_ENV="$FROZEN_ENV" \ +DATABRICKS_HOST="$DATABRICKS_HOST" \ +ZEROBUS_ENDPOINT="$ZEROBUS_ENDPOINT" \ +DATABRICKS_RT_WAREHOUSE_ID="$DATABRICKS_RT_WAREHOUSE_ID" \ +"$DATABRICKS_RUNNER_PYTHON" - <<'PY' +import json +import os +import shlex +from pathlib import Path + +report_path = Path(os.environ["QUALIFICATION_REPORT"]).resolve() +report = json.loads(report_path.read_text(encoding="utf-8")) +if report.get("qualified") is not True: + raise SystemExit(f"STOP: {report_path} does not contain qualified=true") + +frozen = report.get("frozen_settings") +if not isinstance(frozen, dict): + raise SystemExit("STOP: qualified report omits frozen_settings") +producer = frozen.get("producer") +rt = frozen.get("rt_warehouse") +if not isinstance(producer, dict) or not isinstance(rt, dict): + raise SystemExit("STOP: qualified report omits producer or RT settings") + +required_producer = ( + "stream_count", + "batch_size", + "queue_capacity", + "compression", + "host_description", + "network_capacity_gbps", + "maximum_host_cpu_percent", + "maximum_network_utilization_percent", +) +missing = [name for name in required_producer if producer.get(name) in (None, "")] +if missing: + raise SystemExit(f"STOP: frozen producer settings omit {missing}") + +host = os.environ["DATABRICKS_HOST"].rstrip("/") +endpoint = os.environ["ZEROBUS_ENDPOINT"].rstrip("/") +if str(producer.get("host", "")).rstrip("/") != host: + raise SystemExit("STOP: qualified producer host differs from DATABRICKS_HOST") +if str(producer.get("endpoint", "")).rstrip("/") != endpoint: + raise SystemExit("STOP: qualified Zerobus endpoint differs from ZEROBUS_ENDPOINT") +if rt.get("warehouse_id") != os.environ["DATABRICKS_RT_WAREHOUSE_ID"]: + raise SystemExit("STOP: qualified RT warehouse ID differs from this run") +if report.get("selected_query_size") != rt.get("query_size"): + raise SystemExit("STOP: selected and frozen RT query sizes disagree") +if frozen.get("target_eps") != 1_000_000: + raise SystemExit("STOP: qualification did not freeze exactly 1,000,000 EPS") +if frozen.get("dashboard_interval_seconds") != 600: + raise SystemExit("STOP: qualification did not freeze dashboard interval 600") +if frozen.get("drilldown_interval_seconds") != 3600: + raise SystemExit("STOP: qualification did not freeze drill-down interval 3600") + +values = { + "FROZEN_STREAM_COUNT": producer["stream_count"], + "FROZEN_BATCH_SIZE": producer["batch_size"], + "FROZEN_QUEUE_CAPACITY": producer["queue_capacity"], + "FROZEN_COMPRESSION": producer["compression"], + "FROZEN_PRODUCER_HOST_DESCRIPTION": producer["host_description"], + "FROZEN_PRODUCER_NETWORK_CAPACITY_GBPS": producer["network_capacity_gbps"], + "FROZEN_MAX_PRODUCER_CPU_PERCENT": producer["maximum_host_cpu_percent"], + "FROZEN_MAX_PRODUCER_NETWORK_PERCENT": producer[ + "maximum_network_utilization_percent" + ], + "FROZEN_TARGET_EPS": frozen["target_eps"], + "FROZEN_QUERY_SIZE": rt["query_size"], + "FROZEN_RT_MIN_CLUSTERS": rt["min_num_clusters"], + "FROZEN_RT_MAX_CLUSTERS": rt["max_num_clusters"], + "FROZEN_RT_SERVERLESS": rt["enable_serverless_compute"], +} +output = Path(os.environ["FROZEN_ENV"]) +if output.exists(): + raise SystemExit(f"STOP: refusing to overwrite {output}") +output.write_text( + "".join(f"export {key}={shlex.quote(str(value))}\n" for key, value in values.items()), + encoding="utf-8", +) +print(f"qualified=true; frozen settings written to {output}") +PY + +. "$FROZEN_ENV" +``` + +Planning examples such as 16 streams, 50,000-row batches, and `NONE` +compression are placeholders only. They are not frozen for a full run unless +the qualified report selected those exact values. The launch command reads the +actual values from `qualification-frozen.env`. + +## 3. Create the fresh full-run schema as administrator + +This happens outside the measured window. The `costbench` catalog must already +exist from the qualification infrastructure setup with an explicit, +non-default managed location. Verify it and its inherited Predictive +Optimization setting: + +```sql +DESCRIBE CATALOG EXTENDED costbench; +ALTER CATALOG costbench ENABLE PREDICTIVE OPTIMIZATION; +``` + +For every full attempt, create only its new schema. It inherits the explicit +catalog managed location: + +```sql +CREATE SCHEMA costbench.`rt_full_`; +``` + +Do not use `IF NOT EXISTS` for the full-run schema: a collision means the +target is not proven fresh. Record the administrator statement IDs, object +metadata, resolved managed locations, grants, and creation times. Grant the +benchmark principal only the privileges required by the contract. + +`apply_ddl.py` intentionally cannot create catalogs, schemas, external +locations, storage credentials, or managed storage. + +## 4. Apply the checked-in DDL + +`create.sql` drops and recreates the raw table and materialized view. Confirm +the exact new target. `DDL_OUTPUT_DIR` must not already exist; a retry requires +a new output directory and a newly reviewed target. + +Use only workspace/control credentials: + +```sh +. /absolute/path/to/databricks-full-run.env +. "$FROZEN_ENV" +cd "$DBX_BENCH_DIR" + +read -rsp 'Workspace PAT or OAuth access token: ' DATABRICKS_TOKEN; echo +export DATABRICKS_TOKEN +unset DATABRICKS_CLIENT_ID DATABRICKS_CLIENT_SECRET + +"$DATABRICKS_RUNNER_PYTHON" apply_ddl.py \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --confirm-destructive-target "$DATABRICKS_CATALOG.$DATABRICKS_SCHEMA" \ + --output-dir "$DDL_OUTPUT_DIR" + +unset DATABRICKS_TOKEN +``` + +The directory preserves the complete rendered SQL and an atomic report with +input/rendered/statement hashes, statement IDs, timestamps, statuses, and +redacted errors. Stop unless all four statements succeeded: + +```sh +"$DATABRICKS_RUNNER_PYTHON" - "$DDL_OUTPUT_DIR/apply_ddl_report.json" <<'PY' +import json +import sys +from pathlib import Path + +path = Path(sys.argv[1]) +report = json.loads(path.read_text(encoding="utf-8")) +ok = ( + report.get("status") == "succeeded" + and report.get("statement_count") == 4 + and len(report.get("statements", [])) == 4 + and all(item.get("status") == "succeeded" and item.get("statement_id") + for item in report.get("statements", [])) +) +if not ok: + raise SystemExit(f"STOP: DDL report is not complete and successful: {path}") +print("DDL gate passed") +PY +``` + +## 5. Run online preflight and fail closed + +Online preflight uses the shared benchmark principal to check both standard +workspace OAuth and the distinct resource-bound Zerobus path: + +```sh +unset DATABRICKS_TOKEN +read -rp 'Benchmark service-principal client ID: ' DATABRICKS_CLIENT_ID +export DATABRICKS_CLIENT_ID +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET; echo +export DATABRICKS_CLIENT_SECRET + +"$DATABRICKS_RUNNER_PYTHON" preflight.py \ + --online \ + --output "$PREFLIGHT_OUTPUT" \ + --runner-python "$DATABRICKS_RUNNER_PYTHON" \ + --zerobus-python "$DATABRICKS_ZEROBUS_PYTHON" \ + --host "$DATABRICKS_HOST" \ + --cloud "$DATABRICKS_CLOUD" \ + --region "$DATABRICKS_REGION" \ + --workspace-id "$DATABRICKS_WORKSPACE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --raw-table "$DATABRICKS_RAW_TABLE" \ + --mv-table "$DATABRICKS_MV_TABLE" \ + --rt-warehouse "$DATABRICKS_RT_WAREHOUSE_ID" \ + --control-warehouse "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --zerobus-endpoint "$ZEROBUS_ENDPOINT" + +QUALIFICATION_REPORT="$QUALIFICATION_REPORT" \ +PREFLIGHT_OUTPUT="$PREFLIGHT_OUTPUT" \ +"$DATABRICKS_RUNNER_PYTHON" - <<'PY' +import json +import os +from pathlib import Path + +preflight_path = Path(os.environ["PREFLIGHT_OUTPUT"]) +qualification_path = Path(os.environ["QUALIFICATION_REPORT"]) +preflight = json.loads(preflight_path.read_text(encoding="utf-8")) +qualification = json.loads(qualification_path.read_text(encoding="utf-8")) + +if preflight.get("mode") != "online": + raise SystemExit("STOP: preflight report is not online") +summary = preflight.get("summary", {}) +online = preflight.get("online", {}) +if summary.get("error_count") != 0 or summary.get("status") not in ("ok", "warning"): + raise SystemExit(f"STOP: preflight summary failed: {summary}") +if online.get("status") not in ("ok", "warning") or online.get("errors") != []: + raise SystemExit("STOP: online preflight contains errors") + +frozen = qualification["frozen_settings"]["rt_warehouse"] +actual = online["warehouses"]["lakehouse_rt"]["metadata"] +checks = { + "warehouse_id": (actual.get("id"), frozen.get("warehouse_id")), + "query_size": (actual.get("cluster_size"), frozen.get("query_size")), + "min_num_clusters": (actual.get("min_num_clusters"), frozen.get("min_num_clusters")), + "max_num_clusters": (actual.get("max_num_clusters"), frozen.get("max_num_clusters")), + "enable_serverless_compute": ( + actual.get("enable_serverless_compute"), + frozen.get("enable_serverless_compute"), + ), +} +drift = {name: pair for name, pair in checks.items() if pair[0] != pair[1]} +if drift: + raise SystemExit(f"STOP: Lakehouse//RT configuration drift: {drift}") +if online["tables"]["raw_storage_assumption"].get("status") != "ok": + raise SystemExit("STOP: non-default managed storage was not proven") +if online.get("zerobus_service_principal", {}).get("status") != "ok": + raise SystemExit("STOP: Zerobus service-principal check failed") +predictive = online.get("predictive_optimization", {}) +for table in ("raw", "materialized_view"): + effective = predictive.get(table, {}) + if ( + effective.get("status") != "ok" + or effective.get("effective_value") != "ENABLE" + or effective.get("effectively_enabled") is not True + ): + raise SystemExit( + f"STOP: Predictive Optimization is not effectively enabled for {table}" + ) +print("online preflight and frozen-configuration gates passed") +PY + +unset DATABRICKS_TOKEN DATABRICKS_CLIENT_ID DATABRICKS_CLIENT_SECRET +``` + +Warnings must be read and retained. A warning is not permission to ignore an +error. Any error, package mismatch, unsupported SQL, inaccessible system +table, storage failure, endpoint failure, permission failure, or frozen +configuration drift stops the run. + +## 6. Prepare four tmux terminals + +Create one tmux session with four named windows. `remain-on-exit` preserves the +process status and terminal output: + +```sh +tmux new-session -d -s "$TMUX_SESSION" -n ingest +tmux new-window -t "$TMUX_SESSION" -n freshness +tmux new-window -t "$TMUX_SESSION" -n dashboard +tmux new-window -t "$TMUX_SESSION" -n drilldown +tmux set-option -t "$TMUX_SESSION" remain-on-exit on + +for window in ingest freshness dashboard drilldown; do + tmux send-keys -t "$TMUX_SESSION:$window" \ + '. /absolute/path/to/databricks-full-run.env; . "$FROZEN_ENV"; cd "$DBX_BENCH_DIR"' Enter +done +``` + +Attach to each window before launch and load the shared benchmark principal +without placing its secret in shell history: + +```sh +unset DATABRICKS_TOKEN +read -rp 'Benchmark service-principal client ID: ' DATABRICKS_CLIENT_ID +export DATABRICKS_CLIENT_ID +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET; echo +export DATABRICKS_CLIENT_SECRET +``` + +Repeat in `ingest`, `freshness`, `dashboard`, and `drilldown`. Standard +workspace clients request ordinary OAuth tokens; the ingest client requests +the resource-bound Zerobus token. If a deployment uses separate principals, +load only the role-appropriate identity in each window and record that +deviation. + +Detach after all four terminals are authenticated. Confirm that no output path +already exists: + +```sh +for path in \ + "$INGEST_OUTPUT_DIR" "$DASHBOARD_OUTPUT" "$DRILLDOWN_OUTPUT" \ + "$FRESHNESS_OUTPUT" "$FRESHNESS_PROGRESS" \ + "$EVIDENCE_INITIAL_DIR" "$EVIDENCE_SETTLED_DIR" \ + "$COST_SUMMARY" "$VALIDATION_REPORT" "$WINDOW_ENV" "$WINDOW_JSON"; do + if [ -e "$path" ]; then + printf 'STOP: output already exists: %s\n' "$path" >&2 + exit 1 + fi +done +``` + +## 7. Launch in canonical order + +Record one UTC start immediately before launching and source it into all four +terminals: + +```sh +export RUN_STARTED_AT="$( + "$DATABRICKS_RUNNER_PYTHON" -c 'from datetime import datetime, timezone; print(datetime.now(timezone.utc).isoformat(timespec="milliseconds").replace("+00:00", "Z"))' +)" +printf 'export RUN_STARTED_AT=%q\n' "$RUN_STARTED_AT" > "$RUNTIME_ENV" + +for window in ingest freshness dashboard drilldown; do + tmux send-keys -t "$TMUX_SESSION:$window" '. "$RUNTIME_ENV"' Enter +done +``` + +Launch in this exact order: ingest first, then immediately freshness, +dashboard, and drill-down. The latter three run concurrently with ingest. + +```sh +tmux send-keys -t "$TMUX_SESSION:ingest" \ + 'exec "$DATABRICKS_ZEROBUS_PYTHON" ingest_zerobus.py --dir "$SOURCE_DIR" --expected-rows 113219565734 --host "$DATABRICKS_HOST" --endpoint "$ZEROBUS_ENDPOINT" --catalog "$DATABRICKS_CATALOG" --schema "$DATABRICKS_SCHEMA" --table "$DATABRICKS_RAW_TABLE" --count-warehouse "$DATABRICKS_CONTROL_WAREHOUSE_ID" --workers "$FROZEN_STREAM_COUNT" --batch-size "$FROZEN_BATCH_SIZE" --queue-capacity "$FROZEN_QUEUE_CAPACITY" --target-eps "$FROZEN_TARGET_EPS" --compression "$FROZEN_COMPRESSION" --metrics-interval 5 --output-dir "$INGEST_OUTPUT_DIR" --run-id "$BENCHMARK_RUN_ID"' Enter + +tmux send-keys -t "$TMUX_SESSION:freshness" \ + 'exec "$DATABRICKS_RUNNER_PYTHON" monitor_freshness.py --host "$DATABRICKS_HOST" --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" --catalog "$DATABRICKS_CATALOG" --schema "$DATABRICKS_SCHEMA" --raw-table "$DATABRICKS_RAW_TABLE" --mv-table "$DATABRICKS_MV_TABLE" --producer-progress "$INGEST_PROGRESS" --since "$RUN_STARTED_AT" --interval "$FRESHNESS_INTERVAL_SECONDS" --output "$FRESHNESS_OUTPUT" --progress-json "$FRESHNESS_PROGRESS" --run-id "$BENCHMARK_RUN_ID"' Enter + +tmux send-keys -t "$TMUX_SESSION:dashboard" \ + 'exec "$DATABRICKS_RUNNER_PYTHON" run_dashboard.py --interval 600 --output "$DASHBOARD_OUTPUT" --host "$DATABRICKS_HOST" --rt-warehouse-id "$DATABRICKS_RT_WAREHOUSE_ID" --http-path "$DATABRICKS_HTTP_PATH" --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" --catalog "$DATABRICKS_CATALOG" --schema "$DATABRICKS_SCHEMA" --mv-table "$DATABRICKS_MV_TABLE" --producer-progress "$INGEST_PROGRESS" --freshness-monitor "$FRESHNESS_PROGRESS" --system "Databricks" --machine "$FROZEN_QUERY_SIZE" --cluster-size "$FROZEN_RT_MAX_CLUSTERS" --run-id "$BENCHMARK_RUN_ID"' Enter + +tmux send-keys -t "$TMUX_SESSION:drilldown" \ + 'exec "$DATABRICKS_RUNNER_PYTHON" run_drilldown.py --interval 3600 --output "$DRILLDOWN_OUTPUT" --host "$DATABRICKS_HOST" --rt-warehouse-id "$DATABRICKS_RT_WAREHOUSE_ID" --http-path "$DATABRICKS_HTTP_PATH" --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" --catalog "$DATABRICKS_CATALOG" --schema "$DATABRICKS_SCHEMA" --mv-table "$DATABRICKS_MV_TABLE" --producer-progress "$INGEST_PROGRESS" --freshness-monitor "$FRESHNESS_PROGRESS" --system "Databricks" --machine "$FROZEN_QUERY_SIZE" --cluster-size "$FROZEN_RT_MAX_CLUSTERS" --run-id "$BENCHMARK_RUN_ID"' Enter +``` + +Do not resize, restart, repoint, or reconfigure either warehouse while the +window is live. + +## 8. Monitor and abort gates + +Monitor all four windows plus the atomic progress files: + +```sh +tmux attach -t "$TMUX_SESSION" +``` + +Abort the measured run on any of the following: + +- producer `terminal_error`, non-empty `errors`, `ambiguous=true`, + `safe_to_resume=false`, unexplained replay/reconnect, fallback rows, + unacknowledged batches after a terminal stream failure, or nonzero exit; +- source manifest count other than 113,219,565,734 or any source membership, + canonical-order, signed-range, or empty-target failure; +- after the declared startup grace, freshness errors, a + failed/full/non-incremental materialized-view refresh, an unexplained refresh + gap, or a declared raw/MV observational-age gate violation; an initial + missing progress/freshness file while the ingest process creates its first + atomic journal is startup state, not durability evidence; +- active-ingest runner query errors, a missing canonical query-history record, + result-cache evidence other than exact false, a cache origin, + queue/capacity wait, cadence/self-overlap failure, or changed warehouse + metadata; +- service, warehouse, permission, network, storage, or region drift. + +On abort, preserve everything. Stop `dashboard`, then `drilldown`, then +`freshness` with one `Ctrl-C` each; then send one `Ctrl-C` to `ingest` and let +it flush. Never force-kill unless required to protect the host. A clean +checkpoint is not an accepted full result. If recovery is ambiguous, do not +use `--resume`; create another fresh namespace, rerun DDL with a new output +directory, and start a new run ID. + +Raw and materialized-view ages shown by the monitor are observational ages +from sample time to provider timestamps. They are useful gates and context, +not exact per-record latency measurements. + +## 9. Finish only after provider durability + +Do not stop ingest because all source rows were read or queued. Let it finish +naturally only after Zerobus durability acknowledgments cover every row. Then +run this gate: + +```sh +"$DATABRICKS_RUNNER_PYTHON" - "$INGEST_PROGRESS" <<'PY' +import json +import sys +from pathlib import Path + +path = Path(sys.argv[1]) +progress = json.loads(path.read_text(encoding="utf-8")) +streams = progress.get("stream_status", {}) +source = progress.get("source", {}) +task_count = source.get("task_count") +ok = ( + progress.get("finished") is True + and progress.get("running") is False + and source.get("total_rows") == 113_219_565_734 + and progress.get("provider_committed_rows") == 113_219_565_734 + and progress.get("logical_raw_rows") == 113_219_565_734 + and progress.get("submitted_rows") == 113_219_565_734 + and progress.get("baseline_table_rows") == 0 + and progress.get("baseline_table_rows_unknown") is False + and progress.get("pending_batches") == 0 + and progress.get("pending_rows") == 0 + and progress.get("partial_task_rows") == {} + and isinstance(task_count, int) + and task_count > 0 + and progress.get("completed_tasks") == task_count + and progress.get("completed_task_ordinals") == list(range(task_count)) + and progress.get("clean_checkpoint") is True + and progress.get("safe_to_resume") is True + and progress.get("stopped_early") is False + and progress.get("ambiguous") is False + and progress.get("terminal_error") in (None, "") + and progress.get("errors") == [] + and isinstance(streams, dict) + and len(streams) == progress.get("config", {}).get("workers") + and all( + item.get("close_succeeded") is True + and item.get("unacked_inspection_succeeded") is True + and item.get("unacked_batches") == 0 + for item in streams.values() + ) +) +if not ok: + raise SystemExit(f"STOP: producer durability gate failed: {path}") +print("producer durability gate passed") +PY +``` + +Allow one final freshness interval to complete, then stop and wait for each +process in this exact order: + +```sh +sleep "$FRESHNESS_INTERVAL_SECONDS" + +tmux send-keys -t "$TMUX_SESSION:dashboard" C-c +while [ "$(tmux display-message -p -t "$TMUX_SESSION:dashboard" '#{pane_dead}')" != 1 ]; do sleep 1; done + +tmux send-keys -t "$TMUX_SESSION:drilldown" C-c +while [ "$(tmux display-message -p -t "$TMUX_SESSION:drilldown" '#{pane_dead}')" != 1 ]; do sleep 1; done + +tmux send-keys -t "$TMUX_SESSION:freshness" C-c +while [ "$(tmux display-message -p -t "$TMUX_SESSION:freshness" '#{pane_dead}')" != 1 ]; do sleep 1; done +``` + +Now define and preserve the exact UTC evidence/cost window. Its start is the +timestamp captured immediately before launch; its end is after all measured +readers and the monitor have stopped. The producer finish timestamp remains a +separate boundary: + +```sh +export MEASURED_UNTIL="$( + "$DATABRICKS_RUNNER_PYTHON" -c 'from datetime import datetime, timezone; print(datetime.now(timezone.utc).isoformat(timespec="milliseconds").replace("+00:00", "Z"))' +)" + +RUN_STARTED_AT="$RUN_STARTED_AT" \ +MEASURED_UNTIL="$MEASURED_UNTIL" \ +INGEST_PROGRESS="$INGEST_PROGRESS" \ +WINDOW_ENV="$WINDOW_ENV" \ +WINDOW_JSON="$WINDOW_JSON" \ +BENCHMARK_RUN_ID="$BENCHMARK_RUN_ID" \ +"$DATABRICKS_RUNNER_PYTHON" - <<'PY' +import json +import os +import shlex +from pathlib import Path + +progress = json.loads(Path(os.environ["INGEST_PROGRESS"]).read_text(encoding="utf-8")) +finished = progress.get("finished_at") +if not isinstance(finished, str) or not finished: + raise SystemExit("STOP: ingest progress omits finished_at") +window = { + "run_id": os.environ["BENCHMARK_RUN_ID"], + "since": os.environ["RUN_STARTED_AT"], + "until": os.environ["MEASURED_UNTIL"], + "producer_finished_at": finished, +} +window_json = Path(os.environ["WINDOW_JSON"]) +window_env = Path(os.environ["WINDOW_ENV"]) +window_json.write_text(json.dumps(window, indent=2, sort_keys=True) + "\n", encoding="utf-8") +window_env.write_text( + "".join( + f"export {name}={shlex.quote(value)}\n" + for name, value in ( + ("MEASURED_SINCE", window["since"]), + ("MEASURED_UNTIL", window["until"]), + ("PRODUCER_FINISHED_AT", window["producer_finished_at"]), + ) + ), + encoding="utf-8", +) +print(json.dumps(window, indent=2, sort_keys=True)) +PY + +. "$WINDOW_ENV" +``` + +## 10. Collect initial and settled evidence + +Collection is read-only and uses only workspace/control credentials. Ensure +the Zerobus service-principal variables are not present: + +```sh +read -rsp 'Workspace PAT or OAuth access token: ' DATABRICKS_TOKEN; echo +export DATABRICKS_TOKEN +unset DATABRICKS_CLIENT_ID DATABRICKS_CLIENT_SECRET + +"$DATABRICKS_RUNNER_PYTHON" collect_evidence.py \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --rt-warehouse-id "$DATABRICKS_RT_WAREHOUSE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --raw-table "$DATABRICKS_RAW_TABLE" \ + --mv-table "$DATABRICKS_MV_TABLE" \ + --since "$MEASURED_SINCE" \ + --until "$MEASURED_UNTIL" \ + --run-id "$BENCHMARK_RUN_ID" \ + --output-dir "$EVIDENCE_INITIAL_DIR" + +unset DATABRICKS_TOKEN +``` + +Preserve this initial collection even if billing rows are incomplete. +`system.billing.usage` can lag actual usage by up to 24 hours. After billing +has settled, rerun the same bounded collection against the same immutable UTC +window and run ID, using the fresh settled directory: + +```sh +. /absolute/path/to/databricks-full-run.env +. "$WINDOW_ENV" +cd "$DBX_BENCH_DIR" + +read -rsp 'Workspace PAT or OAuth access token: ' DATABRICKS_TOKEN; echo +export DATABRICKS_TOKEN +unset DATABRICKS_CLIENT_ID DATABRICKS_CLIENT_SECRET + +"$DATABRICKS_RUNNER_PYTHON" collect_evidence.py \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --rt-warehouse-id "$DATABRICKS_RT_WAREHOUSE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --raw-table "$DATABRICKS_RAW_TABLE" \ + --mv-table "$DATABRICKS_MV_TABLE" \ + --since "$MEASURED_SINCE" \ + --until "$MEASURED_UNTIL" \ + --run-id "$BENCHMARK_RUN_ID" \ + --output-dir "$EVIDENCE_SETTLED_DIR" + +unset DATABRICKS_TOKEN +``` + +If required billing rows still have not settled, preserve that directory and +repeat later into another new directory. Do not overwrite or splice +collections. Do not run final cost or validation against the initial, +unsettled collection. + +Export the producer/cloud bill for the same exact UTC boundaries into a +separate, immutable external subledger under `RUN_ROOT`. It must identify +producer compute, source-storage reads, network egress, and applicable cloud +storage/request charges. These costs are not present in Databricks system +billing and `summarize_run.py` does not invent or silently zero them. + +## 11. Build cost ledger and validate + +These commands are offline: + +```sh +"$DATABRICKS_RUNNER_PYTHON" costs/summarize_run.py \ + --evidence-dir "$EVIDENCE_SETTLED_DIR" \ + --since "$MEASURED_SINCE" \ + --until "$MEASURED_UNTIL" \ + --rt-warehouse-id "$DATABRICKS_RT_WAREHOUSE_ID" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --producer-finished-at "$PRODUCER_FINISHED_AT" \ + --output "$COST_SUMMARY" + +"$DATABRICKS_RUNNER_PYTHON" validate_run.py \ + --source-manifest "$INGEST_MANIFEST" \ + --ingest-progress "$INGEST_PROGRESS" \ + --ingest-metrics "$INGEST_METRICS" \ + --dashboard "$DASHBOARD_OUTPUT" \ + --drilldown "$DRILLDOWN_OUTPUT" \ + --freshness "$FRESHNESS_OUTPUT" \ + --evidence-summary "$EVIDENCE_SETTLED_DIR/evidence_summary.json" \ + --cost-summary "$COST_SUMMARY" \ + --expected-rows 113219565734 \ + --output "$VALIDATION_REPORT" +``` + +Both commands must exit zero, and acceptance must be exact JSON boolean true: + +```sh +"$DATABRICKS_RUNNER_PYTHON" - "$COST_SUMMARY" "$VALIDATION_REPORT" <<'PY' +import json +import sys +from pathlib import Path + +cost_path, validation_path = map(Path, sys.argv[1:]) +cost = json.loads(cost_path.read_text(encoding="utf-8")) +validation = json.loads(validation_path.read_text(encoding="utf-8")) +if cost.get("complete") is not True: + raise SystemExit(f"STOP: cost ledger is incomplete: {cost_path}") +if validation.get("accepted") is not True: + raise SystemExit(f"STOP: validation accepted is not true: {validation_path}") +print("accepted=true") +PY +``` + +`accepted=true` is necessary, not sufficient for publication by itself. The +contract review must also confirm the separately preserved producer/cloud +subledger and every manual infrastructure/release-stage disclosure. It is not +permission to fabricate, backfill, or merge online results. + +## 12. Publish accepted artifacts + +First build a review copy outside the repository. The publisher fails unless +validation acceptance and cost completeness are exact JSON booleans `true`. +It selects only validator-approved active-ingest observations, caps presented +query and freshness samples at 100 billion rows, matches equal ClickHouse and +Databricks query counts monotonically by observed row progress, and keeps +complete-ingest cost at all 113,219,565,734 rows. + +```sh +export PUBLICATION_REVIEW_OUTPUT="${RUN_ROOT}/publication-review" +export CLICKHOUSE_INGEST_COST="$DBX_BENCH_DIR/../clickhouse-cloud/costs/out_t2/ingest.json" + +"$DATABRICKS_RUNNER_PYTHON" publish_results.py \ + --validation-report "$VALIDATION_REPORT" \ + --cost-summary "$COST_SUMMARY" \ + --dashboard "$DASHBOARD_OUTPUT" \ + --drilldown "$DRILLDOWN_OUTPUT" \ + --freshness "$FRESHNESS_OUTPUT" \ + --clickhouse-ingest-cost "$CLICKHOUSE_INGEST_COST" \ + --output-dir "$PUBLICATION_REVIEW_OUTPUT" + +uv run visualizations/plot_publication.py \ + --publication-manifest "$PUBLICATION_REVIEW_OUTPUT/publication_manifest.json" \ + --output-dir "$PUBLICATION_REVIEW_OUTPUT/charts" +``` + +Review the publication manifest, source hashes, selected indices, matching +report, cost allocations, charts, external producer/cloud subledger, and Beta +disclosure. The review directory is not an accepted global result. + +Only after that review succeeds, publish into a new repository result +directory and activate Databricks in the global manifest in the same command. +The publisher writes the manifest last and never overwrites an existing +publication artifact. + +```sh +export GLOBAL_MANIFEST="$DBX_BENCH_DIR/../global/visualizations/manifest.json" +export FINAL_PUBLICATION_OUTPUT="$DBX_BENCH_DIR/results/$BENCHMARK_RUN_ID/publication" + +"$DATABRICKS_RUNNER_PYTHON" publish_results.py \ + --validation-report "$VALIDATION_REPORT" \ + --cost-summary "$COST_SUMMARY" \ + --dashboard "$DASHBOARD_OUTPUT" \ + --drilldown "$DRILLDOWN_OUTPUT" \ + --freshness "$FRESHNESS_OUTPUT" \ + --clickhouse-ingest-cost "$CLICKHOUSE_INGEST_COST" \ + --output-dir "$FINAL_PUBLICATION_OUTPUT" \ + --global-manifest "$GLOBAL_MANIFEST" \ + --apply-global-manifest +``` + +Do not hand-edit accepted output or add Databricks directly to the global +manifest. Run the commands in `../global/visualizations/_commands.txt` only +after activation; those renderers ignore Databricks until the publisher adds +the accepted provider entry and required labels. + +## 13. Cleanup, only after settlement and acceptance + +Keep the workspace objects and both warehouses available until settled +evidence has been captured and the final report is accepted. Copy the complete +run root to durable, access-controlled storage before cleanup. Preserve source +manifest, producer ledgers, runner/freshness JSONL, rendered DDL and report, +preflight, both evidence collections, UTC window, cost summary, validation, +infrastructure records, and billing exports without hand edits. + +Then stop the producer instance and warehouses according to the recorded +infrastructure procedure. An administrator may drop only the exact fresh run +objects after independently confirming the catalog/schema names and that no +other objects are present: + +```sql +DROP MATERIALIZED VIEW ``.``.`quotes_daily`; +DROP TABLE ``.``.`quotes`; +DROP SCHEMA ``.``; +DROP CATALOG ``; +``` + +Do not aim `create.sql`, cleanup SQL, cloud deletion, or result-directory +commands at historical targets or checked-in historical artifacts. Historical +files remain untouched and are never evidence for this run. diff --git a/full-path-realtime/quotes/databricks/REAL_SERVERLESS_BASELINE.md b/full-path-realtime/quotes/databricks/REAL_SERVERLESS_BASELINE.md new file mode 100644 index 0000000..ddadee6 --- /dev/null +++ b/full-path-realtime/quotes/databricks/REAL_SERVERLESS_BASELINE.md @@ -0,0 +1,193 @@ +# Canonical Serverless SQL baseline + +This run ingests the complete 113,219,565,734-row source while serving the +canonical dashboard and drill-down workloads on Serverless SQL `X-Small`. +Lakehouse//RT is unavailable and is not part of this result. + +The later Lakehouse//RT run must preserve the source, SQL, cadence, cache +policy, timing source, stream settings, table definitions, and three-hour +post-ingest query window. Only the query warehouse ID, mode, and qualified size +change. + +## Frozen runtime + +- Raw target: + `costbench.rt_full_serverless_baseline_r3_20260918.quotes` +- MV target: + `costbench.rt_full_serverless_baseline_r3_20260918.quotes_daily` +- Source rows: exactly 113,219,565,734 +- Target ingest rate: 1,000,000 provider-acknowledged rows/second +- Expected ingest duration at target: about 31h27m +- Zerobus: 16 Arrow Flight streams, 50,000-row batches +- Automatic recovery: enabled; 15-second attempt timeout, 2-second backoff, + four attempts +- Proactive stream rotation: every ten minutes, zero-unacknowledged proof +- Recovery evidence: acknowledgment waits of at least two seconds and every + failed wait are persisted with duration and outcome +- MV monitor: every 60 seconds +- Dashboard: four queries every 600 seconds +- Drill-down: two queries every 3,600 seconds +- Post-ingest query window: exactly three hours +- Query warehouse: ``, + Serverless `X-Small`, ID ``, scaling 1–1 +- Control warehouse: ``, Serverless `2X-Small`, + ID `` +- Canonical query latency: + `system.query.history.total_duration_ms / 1000` +- Result cache: disabled; any hit rejects the observation + +## Cost exposure + +The latest provider evidence averages approximately 73 bytes sent to Zerobus +per row. The complete source is therefore expected to send about 8.27 TB. +Public Zerobus list pricing implies approximately USD 414 at USD 0.050/GB or +USD 529 at USD 0.064/GB. Canonical cost uses settled account billing rather +than this estimate. + +The X-Small Serverless SQL warehouse consumes 6 DBU/hour while running. At the +current Ireland account list price of USD 0.91/DBU, approximately 34.5 hours of +ingestion plus post-ingest queries exposes about USD 188 of query-warehouse +cost. MV refresh, Predictive Optimization, monitor warehouse, EC2, S3, EBS, +and network costs are additional. Obtain explicit spend approval before +launch. + +## Create the fresh namespace + +Run +[`create_full_serverless_baseline_r3_20260918.sql`](create_full_serverless_baseline_r3_20260918.sql) +once on `` as the benchmark administrator. It contains: + +- fresh schema creation with no `DROP` or `IF NOT EXISTS`; +- explicit raw-table DDL rather than copying mutable properties from another + target; +- service-principal grants; +- `EXPLAIN CREATE MATERIALIZED VIEW` before actual MV creation; +- MV ownership transfer to ``, while retaining the benchmark administrator's + explicit `MANAGE` and `SELECT`; and +- all metadata-only verification statements. + +The active canonical target is +`costbench.rt_full_serverless_baseline_r3_20260918`. + +The first target was consumed by an aborted September 17 attempt in which the +query wrapper inherited hard-coded qualification namespace defaults. R2 then +held 1M EPS for 17h17m before two Databricks-side HTTP/2 stream resets stopped +it at 62,222,954,896 durable rows. Settled provider totals exactly matched the +client durability watermark and excluded the two pending batches, but the +query/ingest outage invalidated that run. R3 enables SDK recovery, preserves +unacknowledged batches after terminal closure, and records recovery duration. +Any final replay surplus remains non-publishable. + +## Pre-launch gates + +Use the SQL verification gate in +[SIX_HOUR_INGEST_MV.md](SIX_HOUR_INGEST_MV.md), substituting the full schema. +Require: + +- raw Delta version 0, zero files and zero bytes; +- `CLUSTER BY (sym, t)`; +- deletion vectors, row tracking, and CDF enabled; +- effective Predictive Optimization `ENABLE`; +- MV ownership by ``; +- `CLUSTER BY (sym, day)`; +- `INCREMENTAL STRICT` and one-minute trigger; +- incremental eligibility with no issues; +- both warehouses running with their frozen settings; +- `` has `CAN MONITOR` on both warehouses; +- no prior benchmark process is running; and +- no background maintenance from prior qualification targets overlaps launch. + +Do not run a warm-up data query. Starting state and physical cache telemetry +are recorded; provider-managed IO cache is not user-disableable. + +## Launch + +On the dedicated EC2 producer: + +```sh +tmux new -As serverless-full +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/run_full_serverless_baseline.sh +``` + +Type the exact target and enter the benchmark service-principal secret. One +orchestrator starts: + +1. complete-source Zerobus ingestion; +2. metadata-only freshness monitoring; +3. the four-query dashboard runner; +4. the two-query drill-down runner; +5. three additional hours of post-ingest queries; +6. final MV catch-up; +7. post-window hashing of all 232 source files; +8. provider evidence collection; and +9. an initial cost summary. + +The reviewable run layout matches the structured BigQuery result package: + +```text +serverless_baseline_full_/ +├── run_context.json +├── ingest/ +├── freshness/ +├── mv/ +├── raw/ +├── evidence/ +├── costs/ +├── validation/ +└── charts/ +``` + +`mv/` contains dashboard JSONL and `raw/` contains drill-down JSONL. +`ingest/ingest_metrics.jsonl` is a compact one-minute progress series rather +than a five-second dump of the complete producer state. `freshness/` contains a +one-minute metadata-only MV series. `evidence/` contains only normalized +provider reconciliation, MV refresh, and clustering status. + +Runtime-only recovery and diagnostic files are written to +`/data/databricks-state//`, outside the result package: + +- the row-group task manifest; +- a lifecycle/error-only ingest ledger; +- raw provider exports; and +- process logs. + +The producer still samples resources every five seconds for safety checks, but +persists only one compact sample per minute. The runtime state is retained on +the producer for troubleshooting and settled validation; it is not downloaded +or published with benchmark results. The 4h08m characterization compacted from +355 MB of runtime artifacts to a 1.35 MB review package, establishing the +packaging behavior before the canonical run. + +The orchestrator watches child processes and fails immediately if a query or +freshness process exits during ingestion. It fails closed on stream +interruption, unacknowledged data, cache hits, query errors, full MV refresh, +row mismatch, missing source hashes, or incomplete final MV catch-up. + +## Stop and recovery + +Do not interrupt a healthy run. One `SIGTERM` or `Ctrl-C` requests clean +stream flushing; a second signal makes the result ambiguous and +non-publishable. A manually stopped canonical run is not complete and cannot +be promoted as the baseline. + +Zerobus cannot safely recreate or resume an ambiguous target. Use a new schema +for any retry. + +## Settled cost + +Initial billing can be incomplete for 24 hours. At least 24 hours after the +complete measurement window, run: + +```sh +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/collect_full_serverless_settled.sh \ + /data/databricks-runs/serverless_baseline_full_ +``` + +This recollects the immutable full window with the query warehouse ID, MV +pipeline ID, target Predictive Optimization operations, Zerobus usage, +control/query warehouse usage, account price history, and storage metadata. Raw +exports remain in runtime state; the command writes only settled cost, compact +provider/clustering summaries, final validation, and an updated artifact +manifest into the result package. +Stop `` manually when the orchestrator completes to +avoid its 80-minute post-run idle timeout. diff --git a/full-path-realtime/quotes/databricks/RECOVERY_40M.md b/full-path-realtime/quotes/databricks/RECOVERY_40M.md new file mode 100644 index 0000000..6296730 --- /dev/null +++ b/full-path-realtime/quotes/databricks/RECOVERY_40M.md @@ -0,0 +1,120 @@ +# Forty-minute Zerobus recovery qualification + +This fresh-target run validates two fixes exposed by the September 16 +six-hour characterization: + +1. completed-task evidence is maintained with incremental ordinal ranges + rather than rescanning all prior row groups under one lock; and +2. SDK automatic recovery is disabled. Each logical worker proactively rotates + its stream every ten minutes only after flushing, closing, and proving zero + unacknowledged Arrow batches. + +Any unexpected stream interruption fails closed. Nothing is replayed onto the +measured target. + +## September 17 result + +Run `ingest_mv_40m_20260917T090123Z` passed the recovery qualification: + +- 2,400,607,668 rows were submitted and durably acknowledged by the client; +- `system.lakeflow.zerobus_ingest` reported exactly 2,400,607,668 rows and + 175,385,857,760 committed bytes; +- provider/client row difference was exactly zero; +- 2,401.61-second duration and 999,582 average committed EPS; +- all 478 derived five-second intervals were within 900k–1.1M EPS; +- 10-minute window averages were 1,000,094, 1,000,136, 999,731, and 1,000,252 + EPS, showing no downward trend; +- producer process CPU p95 was 0.371 cores and median transmit throughput was + 76.65 MB/s; +- all 16 logical workers completed three proactive rotations; +- provider metadata showed exactly 64 streams: 16 at startup and 16 at each + ten-minute rotation; +- every rotation and final close had zero unacknowledged batches; +- automatic recovery remained disabled and provider stream errors were zero; +- 34 MV refreshes were incremental and none was a full recompute; +- active MV rows-behind p50/p95/max were 62.1M, 134.6M, and 139.3M; +- final MV source rows exactly matched the provider raw-row total; and +- final MV catch-up completed approximately 248 seconds after the last + provider raw commit. + +The target-scoped evidence collection completed without errors. Source hashes +also completed after the measurement window. Billing had not populated yet, +so cost remains pending the settled recollection. + +This result validates the client journal fix and the fail-closed proactive +rotation strategy over three rotation boundaries. It does not by itself +replace a six-hour endurance run; the same frozen settings must now pass on a +fresh six-hour target. + +## Create fresh targets + +```sql +CREATE TABLE costbench.rt_qualification.quotes_ingest_mv_40m_20260917 +LIKE costbench.rt_qualification.quotes; + +GRANT SELECT, MODIFY +ON TABLE costbench.rt_qualification.quotes_ingest_mv_40m_20260917 +TO ``; + +CREATE MATERIALIZED VIEW +costbench.rt_qualification.quotes_daily_ingest_mv_40m_20260917 +USING DELTA +CLUSTER BY (sym, day) +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE +AS +SELECT + sym, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, + count(*) AS n_quotes, + min(bp) AS bp_min, + max(bp) AS bp_max, + min(ap) AS ap_min, + max(ap) AS ap_max, + sum(bs) AS bs_sum, + sum(`as`) AS as_sum, + sum(ap - bp) AS spread_sum +FROM costbench.rt_qualification.quotes_ingest_mv_40m_20260917 +GROUP BY sym, day; +``` + +Transfer MV ownership to `` and retain the benchmark administrator's `MANAGE` and +`SELECT`. Apply the same metadata-only verification gate documented in +[SIX_HOUR_INGEST_MV.md](SIX_HOUR_INGEST_MV.md), substituting the 40-minute +object names. + +## Run + +```sh +tmux new -As ingest-mv-40m +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/run_ingest_mv_40m.sh +``` + +The run lasts 40 minutes, targets 1M EPS, and monitors MV freshness for ten +additional minutes. + +## Acceptance + +- average committed EPS is 900k–1.1M and does not trend down with task count; +- all logical workers complete at least three proactive stream rotations; +- automatic recovery is disabled; +- every rotation and final close reports zero unacknowledged batches; +- producer submitted/acknowledged rows exactly equal + `system.lakeflow.zerobus_ingest` records and the MV source snapshot; +- no terminal, ambiguous, stream, or freshness errors occur; +- every data-changing MV refresh is incremental; and +- the MV catches up and a subsequent no-op confirms no remaining source + changes. + +Do not proceed to another six-hour run if any row discrepancy remains. Zerobus +does not expose a server-side idempotency key for this canonical 12-column +schema; a persistent mismatch must be escalated as SDK/service evidence rather +than corrected in the measured table. diff --git a/full-path-realtime/quotes/databricks/RESULT_COLLECTION_REFERENCE.md b/full-path-realtime/quotes/databricks/RESULT_COLLECTION_REFERENCE.md new file mode 100644 index 0000000..cfd4df4 --- /dev/null +++ b/full-path-realtime/quotes/databricks/RESULT_COLLECTION_REFERENCE.md @@ -0,0 +1,451 @@ +# Databricks result collection reference + +## Scope and filename notation + +This reference explains how each artifact in the compact result package is +collected. It is intentionally generic: run identifiers, literal timestamps, +and observed result values belong in the artifacts, not in this document. + +- `` is the UTC token assigned by the launcher or settled collector. +- `` is the zero-padded worker number. +- JSONL files contain one JSON object per line. +- Runtime credentials are inputs only. Writers redact secrets before persisting + provider responses. + +## Shared collection rules + +### Direct and derived data + +- **Direct workload SQL** is executed by `databricks-sql-connector` on the query + warehouse. The connector statement ID is the join key to provider history. +- **Direct control and evidence SQL** is submitted through + `POST /api/2.0/sql/statements`, polled through + `GET /api/2.0/sql/statements/`, and, when needed, read through + same-workspace result-chunk links. +- **Direct REST metadata** comes from the endpoint named under the relevant + filename. +- **Zerobus SDK observations** come from stream calls such as `ingest_batch`, + `wait_for_offset`, `flush`, `close`, and `get_unacked_batches`. +- **Local observations** come from the producer journal, source files, + filesystem metadata, monotonic clocks, process statistics, `/proc`, and the + PyArrow memory pool. +- **Offline derivations** read previously collected files only. They do not + query a benchmark table or call a Databricks endpoint. + +Field names that include `provider_committed_rows` in producer files are +historical producer-journal names. In those files the value is a client +durability counter advanced after successful SDK acknowledgement. The +provider-authoritative committed count comes from +`system.lakeflow.zerobus_ingest`. + +### Collection windows + +- `run_context.json` fixes the run's UTC `since` and `until` boundaries. +- Settled evidence uses the half-open interval + `since <= event_time < until`. +- Query-history evidence additionally filters by query warehouse and run tag. +- Predictive Optimization operations use interval overlap: + `start_time < until` and `coalesce(end_time, until) > since`. +- Freshness monitoring is a live cumulative view. Each sample uses + `since <= event_time <= observed_at`; `observed_at` is captured before its + control SQL is executed. +- `DESCRIBE` and Unity Catalog responses are collection-time metadata + snapshots, not time-travel observations. + +### Runtime audit material + +The result directory is a compact publication package, not a full replay +bundle. The collection workflow retains the following outside the package: + +- the source manifest with file, row-group, task, size, and mtime identity; +- the producer transition ledger; +- full query JSONL with result hashes, result row counts, normalized Query + History records, and per-query errors; +- raw provider exports and collector statement/error logs; +- process stdout and stderr logs. + +`compact_query_results.py` preserves the full query bytes in the retained audit +location before rewriting the packaged query JSONL. `validate_run.py` uses the +full audit form when available. Reproducing every offline derivation therefore +requires both the compact package and its retained runtime audit inputs. + +Before publication, deployment-specific workspace/endpoint coordinates, +warehouse identifiers and names, service-principal/user identity, S3/storage +locations, and producer-local absolute paths are replaced with stable +placeholders. This sanitization changes no benchmark metric. The final manifest +records the public sanitization profile and hashes the sanitized bytes. + +## `run_context.json` + +**How data was collected** + +- Producer: `run_full_serverless_baseline.sh`, with final updates by the + settled collection launcher. +- Direct source: launcher arguments and environment configuration, followed by + selected fields read from producer, freshness, reconciliation, and + validation outputs. +- Collection class: orchestration metadata and offline copying; this file does + not issue SQL or REST requests itself. + +The launcher writes the file before measurement and updates it atomically as +the run reaches later states. It records target names, warehouse identifiers, +configured cadences, source-row expectation, ingest controls, collection +window, producer finish, package status, and client/provider reconciliation. +It is the authority for the window reused by settled evidence collection. + +## `mv/dashboard_.jsonl` + +**How data was collected** + +- Producers: `run_dashboard.py` and shared execution code in + `runner_common.py`. +- Direct SQL source: the statements in `queries_mv.sql`, executed against the + materialized view through `databricks-sql-connector`. +- Direct REST source: + `GET /api/2.0/sql/history/queries` with + `filter_by.statement_ids=`, `include_metrics=true`, and one + requested result. +- Local source: monotonic client duration plus snapshots of producer progress, + freshness progress, and frozen query-warehouse metadata. +- Packaged form: `compact_query_results.py` rewrites the full audit JSONL after + validation. + +Each line represents one dashboard iteration. The workload covers the +single-symbol summary, watchlist summary, historical movers, and daily market +activity defined in `queries_mv.sql`. Statements within an iteration execute +sequentially and carry run, workload, query-number, and iteration tags. + +The canonical query duration is: + +```text +duration_ms = + first present Query History field of + duration, total_duration_ms, total_time_ms + +result_seconds = duration_ms / 1000 +``` + +The canonical result remains usable only when connector execution succeeded, +Query History is final with status `FINISHED`, result-cache evidence is exact +`false`, no cache-origin statement exists, and the observation has no other +error. `client_wall_time` is retained separately and is not substituted for +provider duration. + +The compact file keeps published latency arrays, statement IDs, cache flags, +I/O-cache percentage, scheduling metadata, watermarks, and a sanitized +warehouse snapshot. Full result hashes, row counts, and Query History payloads +remain in the runtime audit copy. + +## `raw/drilldown_.jsonl` + +**How data was collected** + +- Producers: `run_drilldown.py` and `runner_common.py`. +- Direct SQL source: the statements in `queries_raw.sql`, executed against the + raw Delta table through `databricks-sql-connector`. +- Direct REST source: the same statement-ID lookup on + `GET /api/2.0/sql/history/queries` used by dashboard collection. +- Local source: monotonic client duration and producer/freshness progress + snapshots. +- Packaged form: `compact_query_results.py` performs the same audit-preserving + compaction as for the dashboard file. + +Each line represents one drill-down iteration. The SQL file defines the +hourly market summary and the risk/liquidity profile. Query tags, canonical +duration selection, final-status checks, and cache rejection follow the +dashboard rules above. + +The runner drains the ordered result, counts rows, and hashes type-aware JSON +framing in the full audit record. Those validation fields are intentionally +removed from the compact package. + +## `freshness/mv_freshness_.jsonl` + +**How data was collected** + +- Producer: `monitor_freshness.py`. +- Direct SQL sources, submitted on the control warehouse: + - `system.lakeflow.zerobus_ingest`, aggregated for the target table; + - `DESCRIBE HISTORY LIMIT 1`; + - `event_log(TABLE())`, bounded to + `update_progress` and `planning_information` events. +- Local source: the current producer progress journal. +- Derived source: per-sample watermarks, maintenance classification, row gap, + and observational ages. + +The monitor appends one compact sample per configured interval. It correlates +the latest completed `update_progress` event with its +`planning_information` event by update ID. `mv_source_rows` is the planning +event's single-source `num_rows` value. + +The row-gap formula is: + +```text +mv_rows_behind = + max(0, provider_committed_rows - mv_source_rows) +``` + +The formula is evaluated only when both operands exist. Provider rows come +from `system.lakeflow.zerobus_ingest`, not from the producer's client counter. + +Raw-commit, producer-progress, and MV-refresh ages are +`max(0, observed_at - source_timestamp)`. They are timestamp-age proxies, not +per-record lag measurements. `mv_rows` remains null because refresh output +rows and aggregate MV rows do not prove raw-to-MV coverage; the monitor does +not query MV contents. + +## `freshness/freshness_progress.json` + +**How data was collected** + +- Producer: `monitor_freshness.py`. +- Direct and derived sources: exactly the same SQL, producer snapshot, and + formulas as `freshness/mv_freshness_.jsonl`. + +After each observation, the monitor atomically replaces this file with the +latest compact sample. It is a current-state snapshot, not an aggregate over +the JSONL history. + +## `ingest/ingest_metrics.jsonl` + +**How data was collected** + +- Producer: `ingest_zerobus.py`. +- Zerobus SDK source: submitted rows and rows made durable after + `wait_for_offset` or `flush`. +- Local host source: `time.monotonic`, `resource.getrusage`, `/proc/stat`, + `/proc/net/dev`, `/proc/self/status`, `/proc/meminfo`, `os.cpu_count`, and + the PyArrow memory pool. +- Derived source: elapsed-time rates, process CPU cores, host CPU utilization, + network rates, stream-state counts, and rate-limiter state. + +The monitor samples frequently for safety but appends compact records at the +configured publication interval and at shutdown. Cumulative average ingest +rate is durable rows divided by elapsed time. Offline validation can derive an +interval rate from adjacent samples: + +```text +interval_rows_per_second = + (durable_rows[i] - durable_rows[i-1]) + / (elapsed_seconds[i] - elapsed_seconds[i-1]) +``` + +Host metrics describe the machine running the producer, not Databricks +server-side compute. Arrow buffer bytes are an in-memory estimate, not encoded +wire bytes. + +## `ingest/ingest_progress.json` + +**How data was collected** + +- Producer: `ingest_zerobus.py`; the measured launcher copies the final atomic + progress journal into the package. +- Zerobus SDK source: stream offsets, acknowledgement checkpoints, flushes, + recovery state, closure state, and unacknowledged-batch inspection. +- Local source: source-task completion, pending work, rate-limiter state, + memory, CPU, network, and error state. + +The journal is updated throughout ingestion and contains compact completed-task +ranges, submitted and client-durable row counters, pending rows/batches, +stream states, transport recovery, completion flags, and clean-checkpoint +status. Whole-task accounting is checked against the durable row counter. + +## `ingest/ingest_summary.json` + +**How data was collected** + +- Producer: `ingest_zerobus.py` after all workers and the metrics monitor stop. +- Direct source: the final producer journal and worker stream states. +- Metadata SQL source: the target-protection result collected before streams + open, using `DESCRIBE DETAIL` and `DESCRIBE HISTORY` for a fresh target. +- Local source: installed SDK/PyArrow versions and evidence paths. + +The summary is a final superset of progress state. It adds dependency versions, +target-protection metadata, source/task totals, and a concise final result +block. Its SDK durability counter must be reconciled later with provider +telemetry; it is not itself the provider system-table count. + +## `ingest/source_hashes_post_run.json` + +**How data was collected** + +- Producer: `hash_source_manifest.py`. +- Direct source: local source files and the retained source manifest. +- Collection class: post-measurement filesystem observation; no Databricks + call is made. + +For every selected file, the script verifies size and nanosecond mtime against +the source manifest, streams SHA-256, and verifies size and mtime again. It +also hashes the serialized source-manifest bytes and records per-file path, +ordinal, size, mtime, and digest plus aggregate file/byte counts. + +## `ingest/streams/worker--arrow.json` + +**How data was collected** + +- Producer: the corresponding `ingest_zerobus.py` worker in its `finally` + path. +- Direct Zerobus SDK source: `close()` and `get_unacked_batches()`. +- Local source: any diagnostic Arrow files persisted from SDK-returned + unacknowledged batches. + +One file is written per worker. It records worker/stream identity, whether +close and inspection succeeded, unacknowledged batch/row counts, persisted +diagnostic artifacts, and inspection errors. The file exists even on a worker +failure so that ambiguous durability is visible. + +## `evidence/provider_reconciliation.json` + +**How data was collected** + +- Packaged producer: `compact_evidence.py`. +- Raw collector: `collect_evidence.py`, with raw exports retained outside the + package. +- Direct SQL source: + - aggregate records, bytes, errors, versions, and times from + `system.lakeflow.zerobus_ingest`; + - optional stream/error evidence from + `system.lakeflow.zerobus_stream` after live schema inspection; + - `DESCRIBE DETAIL ` and + `DESCRIBE TABLE EXTENDED AS JSON`. +- Direct REST source: + `GET /api/2.1/unity-catalog/tables/` contributes target + metadata collection completeness. +- Derived source: `evidence_summary.json`, built offline by + `collect_evidence.py`, then projected by `compact_evidence.py`. + +The compact file carries provider-authoritative committed records/bytes/errors, +commit bounds, optional stream errors, metadata row-count fields when exposed, +and required/optional dataset completeness. No raw-table `COUNT(*)` is used as +a substitute. + +## `evidence/mv_refresh_summary.json` + +**How data was collected** + +- Packaged producer: `compact_evidence.py`. +- Direct SQL source collected by `collect_evidence.py`: + `event_log(TABLE())`, restricted to + `planning_information` and `update_progress` in the settled window. +- Derived source: offline decoding of planning details and aggregation in + `evidence_summary.json`. + +For each planning event, the collector extracts the chosen +`maintenance_type`. `NO_OP` is classified as neutral, complete/full +recomputation as full, and other known techniques as incremental. The compact +file reports type counts, class counts, and the derived full-refresh count. + +## `evidence/clustering_status.json` + +**How data was collected** + +- Packaged producer: `compact_evidence.py`. +- Direct SQL sources collected by `collect_evidence.py`: + - `DESCRIBE DETAIL `; + - `DESCRIBE TABLE EXTENDED AS JSON`; + - `system.storage.predictive_optimization_operations_history`, filtered to + the target tables and overlapping collection window. +- Direct REST source inherited from `validation/preflight.json`: + `GET /api/2.1/unity-catalog/tables/`. +- Derived source: offline normalization of nested table properties and + operation metrics. + +The file keeps table format/location metadata, clustering columns, relevant +Delta features and properties, compression settings, configured/effective +Predictive Optimization state, operation summaries, and the count of +clustering/optimize operations. It does not inspect table contents. + +## `validation/preflight.json` + +**How data was collected** + +- Producer: `preflight.py`. +- Local sources: configuration checks, credential-presence booleans, query/DDL + files, and package discovery/version probes run in the configured Python + interpreters. +- Direct SQL source through the Statement Execution REST API: + - a query-warehouse connectivity/current-namespace probe; + - `EXPLAIN` of the materialized-view creation statement; + - `EXPLAIN FORMATTED` for every workload statement; + - a permission probe against `system.query.history`; + - `DESCRIBE DETAIL`, `DESCRIBE HISTORY`, and + `DESCRIBE TABLE EXTENDED ... AS JSON` target checks. +- Direct REST sources: + - `GET /api/2.0/sql/warehouses/`; + - `GET /api/2.0/sql/config/warehouses`; + - `GET /api/2.0/sql/history/queries` for statement visibility; + - `GET /api/2.1/unity-catalog/tables/`; + - `GET /api/2.1/unity-catalog/metastore_summary`; + - `GET /api/2.1/unity-catalog/catalogs/`; + - `POST /oidc/v1/token` for the scoped Zerobus authorization probe. +- Network source: a TLS connection to the configured Zerobus endpoint. + +The SQL probes compile workload and MV definitions or inspect metadata; they +do not execute benchmark scans. The report records sanitized observations, +warnings, and errors. Tokens, client secrets, and authorization headers are +not written. + +## `validation/query_context.json` + +**How data was collected** + +- Producer: `run_query_workloads.sh`. +- Direct source: launcher configuration, the producer progress file, child + process exit statuses, and line counts from the two query JSONL files. +- Collection class: local orchestration metadata; no SQL or REST call is made + by this writer. + +The file is written before query processes start and updated after they stop. +It records target/warehouse configuration, configured cadences, query-window +scope, start/final producer watermarks, post-ingest scheduling metadata, +iteration counts, and process statuses. + +## `validation/validation_report_settled_.json` + +**How data was collected** + +- Producer: `validate_run.py`, invoked by the settled collection launcher. +- Direct source: none. Validation is an offline derivation and makes no + Databricks request. +- Inputs: retained source manifest and evidence summary, packaged ingest + progress/metrics and freshness history, and preferably the retained full + dashboard and drill-down audit JSONL. It also consumes the settled cost + summary generated from retained billing evidence; that standalone cost file + is intentionally not part of the compact result package. + +The validator checks input availability, source identity, producer completion, +ingest-rate intervals, query execution/timing/cache evidence, scheduled +cadence, monotonic query watermarks, freshness windows, row reconciliation, +provider evidence completeness, MV incrementality, warehouse configuration, +and Predictive Optimization status. + +Query acceptance uses full per-query evidence: connector success, canonical +provider duration, statement ID, final noncached Query History, result row +count/hash, and no observation errors. Freshness acceptance uses only the +configured eligible window and treats raw/MV ages as observational proxies. + +The report stores every gate, its evidence and reasons, accepted/rejected +sample indices, normalized window/configuration, and the overall acceptance +decision. The packaged query files may already be compact, so the retained +audit copies are required to reproduce all query gates. + +## `validation/manifest.json` + +**How data was collected** + +- Producer: `finalize_results.py`. +- Direct source: files under the completed result root plus the retained source + manifest and `ingest/source_hashes_post_run.json`. +- Collection class: offline filesystem derivation; no SQL or REST call is + made. + +The finalizer recursively enumerates packaged files, recording each relative +path, byte size, and SHA-256. It also records compact source identity, aggregate +package size, package status, and the classes of runtime state excluded from +publication. + +For public packages, `sanitization` records the profile, confirms metrics were +not changed, and lists the redacted information classes. + +The manifest excludes itself from enumeration and hashing, so it must be +generated last. This avoids an impossible self-referential digest. diff --git a/full-path-realtime/quotes/databricks/RESULT_FIELD_GLOSSARY.md b/full-path-realtime/quotes/databricks/RESULT_FIELD_GLOSSARY.md new file mode 100644 index 0000000..d0747c5 --- /dev/null +++ b/full-path-realtime/quotes/databricks/RESULT_FIELD_GLOSSARY.md @@ -0,0 +1,498 @@ +# Databricks packaged-result field glossary + +This glossary covers the compact JSON and JSONL files in a packaged result. Sections are keyed by package-relative filename pattern. `` and `` are placeholders. + +## Conventions + +- `a.b` means member `b` inside object `a`; `items[]` means each array element; `{key}` means a dynamic object key. +- `gates[id=name]` means the element of `gates[]` whose `id` is `name`. +- Each non-empty JSONL line is one independent JSON object. +- Timestamps are UTC ISO-8601 strings unless explicitly described as epoch milliseconds or nanoseconds. +- `_sec` and `_seconds` are seconds; `_ms` is milliseconds; `_ns` is nanoseconds. +- `_bytes` is bytes; `_gib` is GiB; `_percent` is percentage points; `_per_second` is a rate per second. +- Row, file, batch, task, operation, iteration, and error counts are integers. Query and host durations are JSON numbers. +- Money amounts and provider usage quantities in validation evidence are decimal strings. +- SHA-256 fields are hexadecimal hashes of the named bytes or canonical payload. +- `null` means unavailable, not applicable, or not yet observed; it never means zero. +- An empty array or object means the collection was present with no entries. An absent optional member was not emitted by that producing branch. + +## Reusable objects + +### `collection_window` + +- `since`, `until`: inclusive start and exclusive end. +- `duration_seconds`: window duration. + +### `money_total` + +Used only by cost-related evidence embedded in the validation report. + +- `currency`, `amount`: singular currency code and total; both may be `null` when the total is not singular. +- `amount_by_currency.{currency}`: exact total for each represented currency. + +### `error_entry` + +- `source` or `dataset`: component or dataset associated with the error. +- `required`: optional required-source flag. +- `error`: redacted error text. + +### `latest_successful_refresh` + +The whole object may be `null`. + +- `finished_at`, `planned_at`: completion time and optional matching planning time. +- `update_id`: optional provider update identifier. +- `output_rows`: provider-reported refresh output rows. +- `maintenance_type`, `maintenance_class`: provider maintenance type and normalized category. +- `source_snapshot.table_name`: source table represented by the refresh. +- `source_snapshot.num_rows`, `source_snapshot.num_files`, `source_snapshot.full_size_bytes`: complete snapshot totals. +- `source_snapshot.changed_rows`, `source_snapshot.changed_files`, `source_snapshot.change_size_bytes`: refresh-change totals. +- `source_snapshot.delta_version`: source Delta version when exposed. + +### `transport_recovery` + +- `event_count`, `completed_count`, `failed_count`: attempt totals. +- `total_duration_sec`, `max_duration_sec`: summed and maximum attempt duration. +- `last_event`: latest event, or `null`. +- `last_event.at`, `last_event.stream`, `last_event.generation`: event time and stream identity. +- `last_event.operation`, `last_event.status`, `last_event.duration_sec`: operation, outcome, and duration. +- `last_event.pending_batches`, `last_event.pending_rows`: data awaiting durability. +- `last_event.error`: redacted failure text, if any. + +### `stream_status` + +Used at `stream_status.{worker}`. + +- `worker`, `stream`, `state`: one-based worker number, stable stream label, and lifecycle state. +- `automatic_recovery`, `manual_stream_rotation_seconds`: recovery switch and proactive rotation interval. +- `generation`, `rotation_count`: current generation and completed proactive rotations. +- `opened_at`, `last_rotation_at`, `closed_at`: lifecycle times. +- `last_rotation_unacked_batches`: unacknowledged batches at the last rotation. +- `close_succeeded`: final SDK close result. +- `inspected_at`, `unacked_inspection_succeeded`: final inspection time and result. +- `unacked_batches`, `unacked_rows`: final unacknowledged totals. +- `artifacts[]`: preserved unacknowledged Arrow batches. +- `artifacts[].index`, `artifacts[].path`, `artifacts[].rows`, `artifacts[].bytes`, `artifacts[].sha256`: artifact ordinal, producer path, row count, size, and byte hash. +- `inspection_error`: optional redacted inspection failure. +- `summary_path`: producer path of the worker summary. + +### `warehouse_snapshot` + +Used at `query_warehouse` in compact query records. + +- `captured_at`, `warehouse_id`, `warehouse_mode`, `http_path`: capture time, serving identity, mode, and connector path. +- `connector_version`, `use_kernel`: SQL connector version and Statement Execution kernel switch. +- `session_configuration.use_cached_result`, `session_configuration.ansi_mode`, `session_configuration.timezone`: session settings. +- `api_metadata.id`, `api_metadata.name`, `api_metadata.size`, `api_metadata.cluster_size`: provider identity and size labels. +- `api_metadata.min_num_clusters`, `api_metadata.max_num_clusters`: configured scaling bounds. +- `api_metadata.num_clusters`, `api_metadata.num_active_sessions`: observed cluster and session counts. +- `api_metadata.auto_stop_mins`, `api_metadata.auto_resume`: idle-stop and resume settings. +- `api_metadata.tags.{tag}`: provider warehouse tags. +- `api_metadata.spot_instance_policy`, `api_metadata.enable_photon`, `api_metadata.enable_serverless_compute`: compute settings. +- `api_metadata.warehouse_type`, `api_metadata.state`, `api_metadata.health.status`: provider type, observed state, and health. + +### `validation_gate` + +- `id`: stable gate identifier. +- `passed`: gate decision. +- `evidence`: gate-specific bounded evidence. +- `reasons[]`: failure reasons. + +## `run_context.json` + +- `schema_version`, `run_id`: schema revision and immutable run identifier. +- `run_root`: producer-side result path. +- `status`, `publishable`: final lifecycle and publication decisions. +- `since`, `until`, `producer_finished_at`: measurement bounds and producer completion time. +- `catalog`, `schema`, `raw_table`, `mv_table`: target namespace and tables. +- `query_warehouse_id`, `control_warehouse_id`: serving and control warehouse identifiers. +- `query_warehouse_mode`, `query_size`: configured serving mode and size. +- `expected_rows`, `target_eps`: required source rows and target rows per second. +- `dashboard_interval_seconds`, `drilldown_interval_seconds`: workload schedule intervals. +- `post_ingest_query_seconds`: configured post-ingest query duration. +- `result_profile`: result packaging profile. +- `ingest_metrics_interval_seconds`, `freshness_interval_seconds`: telemetry persistence intervals. +- `automatic_stream_recovery`: SDK recovery switch. +- `recovery_timeout_ms`, `recovery_backoff_ms`, `recovery_retries`: recovery timing and retry limit. +- `manual_stream_rotation_seconds`: proactive stream-rotation interval. +- `reconciliation.client_durable_rows`, `reconciliation.provider_committed_rows`: producer and settled provider row totals. +- `reconciliation.duplicate_surplus_rows`, `reconciliation.exact`: row surplus and equality result. +- `settled_validation_process_status`: settled validator process status. +- `settled_cost_collected_at`, `settled_cost_file`: collection time and basename of the external settled cost input. +- `settled_validation_file`: basename of the packaged settled validation report. + +## `mv/dashboard_.jsonl` and `raw/drilldown_.jsonl` + +Both patterns use compact query schema version 3. Fields shaped as `[query][trial]` align to query order in the corresponding SQL workload. + +- `schema_version`, `run_id`: compact schema revision and correlated run identifier. +- `runner`, `workload`, `iteration`: runner label, workload label, and one-based iteration. +- `scheduled_start_at`, `start_lag_sec`, `scheduled_interval_sec`: scheduled start, start lag, and schedule interval. +- `iteration_started_at`, `iteration_finished_at`, `iteration_elapsed_sec`: observed bounds and total duration. +- `raw_rows`, `raw_rows_source`: producer logical-row watermark and its source expression. +- `system`, `version`, `machine`, `cluster_size`: platform and compute display labels. +- `comment`, `tags[]`: configuration description and grouping labels. +- `result[query][trial]`: canonical provider-side duration in seconds. +- `compilation_time[query][trial]`, `execution_time[query][trial]`, `queue_time[query][trial]`, `result_fetch_time[query][trial]`: provider phase durations in seconds. +- `client_wall_time[query][trial]`: client end-to-end duration in seconds. +- `statement_ids[query][trial]`: provider statement identifiers. +- `cache_hit[query][trial]`, `read_io_cache_percent[query][trial]`: result-cache indicator and IO-cache percentage. +- `query_errors[query][]`: redacted errors for each aligned query. +- `warehouse_mode`, `control_warehouse_id`: serving mode and control warehouse identifier. +- `query_warehouse`: shared `warehouse_snapshot`. +- `producer_progress_path`: producer journal path. +- `producer_progress_loaded_at`, `producer_progress_file_modified_at`, `producer_progress_updated_at`: read, file-modification, and journal-update times. +- `producer_progress_run_id`, `producer_progress_finished`, `producer_progress_error`: journal run identity, completion flag, and read or validation error. +- `producer_progress_evidence.logical_raw_rows`, `producer_progress_evidence.provider_committed_rows`: logical and durable row watermarks. +- `producer_progress_evidence.baseline_table_rows`, `producer_progress_evidence.baseline_table_rows_unknown`: starting rows and baseline-proof flag. +- `producer_progress_evidence.running`, `producer_progress_evidence.finished`: producer lifecycle flags. +- `freshness_monitor_path`: compact freshness-progress path. +- `freshness_monitor_loaded_at`, `freshness_monitor_file_modified_at`, `freshness_monitor_updated_at`: read, file-modification, and snapshot-update times. +- `mv_refresh_finished_at`, `mv_source_rows`, `mv_rows_behind`: latest refresh time, represented source rows, and unrepresented committed rows. +- `freshness_monitor_error`: freshness snapshot read or validation error. + +Full query-audit sidecars are excluded runtime state, not packaged result artifacts. + +## `freshness/freshness_progress.json` and `freshness/mv_freshness_.jsonl` + +The progress file is the latest snapshot; each JSONL line has the same shape. + +- `schema_version`, `run_id`, `iteration`: schema revision, correlated run identifier, and one-based monitor iteration. +- `updated_at`, `observed_at`: snapshot write time and scheduled sample time. +- `client_durable_rows`, `provider_committed_rows`, `provider_committed_bytes`: producer and provider watermarks. +- `provider_error_count`: cumulative provider ingest errors. +- `latest_commit_version`, `latest_commit_time`: latest provider commit watermark. +- `raw_delta_version`, `raw_delta_timestamp`: latest raw-table Delta history watermark. +- `mv_rows`: reserved materialized-view row-count field. +- `latest_successful_refresh`: shared `latest_successful_refresh` object. +- `mv_source_rows`, `mv_rows_behind`: rows represented by the latest refresh and committed rows not represented. +- `raw_commit_age_sec`, `producer_progress_age_sec`, `mv_refresh_age_sec`: observational ages relative to the sample time. +- `age_semantics`: interpretation of the observational age fields. +- `statement_ids.zerobus_ingest`, `statement_ids.raw_history`, `statement_ids.mv_events`: provider evidence statements. +- `errors[]`: shared `error_entry` objects. + +## `ingest/ingest_metrics.jsonl` + +Each line is one compact producer metrics snapshot. + +- `schema_version`, `run_id`, `observed_at`: schema revision, correlated run identifier, and observation time. +- `elapsed_sec`, `source_rows`: cumulative producer time and selected source rows. +- `provider_committed_rows`, `submitted_rows`, `pending_rows`: durable, submitted, and outstanding rows. +- `completed_tasks`: fully durable source tasks. +- `target_rows_per_sec`, `average_committed_rows_per_sec`, `session_committed_rows_per_sec`: configured, cumulative, and session rates. +- `terminal_error`, `ambiguous`, `error_count`: terminal error, durability-uncertainty flag, and error count. +- `streams.count`, `streams.states.{state}`: total streams and count by lifecycle state. +- `streams.rotation_count_total`, `streams.rotation_count_min`, `streams.rotation_count_max`: stream-rotation aggregates. +- `transport_recovery`: shared `transport_recovery` object. +- `producer.process_rss_bytes`, `producer.process_peak_rss_bytes`, `producer.system_available_bytes`, `producer.arrow_allocated_bytes`: memory gauges. +- `producer.cpu_cores`, `producer.host_cpu_utilization_percent`: process and host CPU gauges. +- `producer.network_receive_bytes_per_second`, `producer.network_transmit_bytes_per_second`: host network rates. +- `rate_limiter.scheduled_rows`, `rate_limiter.next_send_delay_sec`: limiter reservations and current delay. + +## `ingest/ingest_progress.json` + +- `schema_version`, `run_id`: producer-state schema revision and correlated run identifier. +- `started_at`, `session_started_at`, `updated_at`, `finished_at`: first-run start, session start, latest checkpoint, and completion times. +- `running`, `finished`, `stopped_early`: lifecycle flags. +- `clean_checkpoint`, `safe_to_resume`, `ambiguous`: checkpoint, resume-safety, and durability-uncertainty flags. +- `terminal_error`, `errors[]`, `resume_count`, `elapsed_sec`: terminal error, redacted error list, resume count, and cumulative time. +- `baseline_table_rows`, `baseline_table_rows_unknown`: proven starting rows and proof-availability flag. +- `provider_committed_rows`, `logical_raw_rows`: durable producer watermark and baseline-plus-durable rows. +- `submitted_rows`, `submitted_batches`, `durable_batches`, `durable_uncompressed_arrow_buffer_bytes`: submission and durability totals. +- `pending_rows`, `pending_batches`: outstanding submission totals. +- `assigned_tasks`, `completed_tasks`, `completed_task_ranges[]`: task totals and inclusive completed-ordinal ranges. +- `partial_task_rows.{task_ordinal}`: durable rows in each incomplete task. +- `pending_batch_evidence[]`: outstanding submitted batches. +- `pending_batch_evidence[].task_ordinal`, `pending_batch_evidence[].file_ordinal`, `pending_batch_evidence[].file`, `pending_batch_evidence[].row_group`: source coordinate. +- `pending_batch_evidence[].batch_ordinal`, `pending_batch_evidence[].row_offset`, `pending_batch_evidence[].row_count`: batch coordinate and size. +- `pending_batch_evidence[].uncompressed_arrow_buffer_bytes`, `pending_batch_evidence[].logical_offset`, `pending_batch_evidence[].submitted_at`: buffer estimate, acknowledgment offset, and submit time. +- `pending_batch_evidence[].worker`, `pending_batch_evidence[].stream`: submitting worker identity. +- `config.allow_partial`, `config.include_quotes_0`, `config.source_hashes_deferred`: source-selection switches. +- `config.automatic_recovery`, `config.manual_stream_rotation_seconds`: stream recovery settings. +- `config.recovery_backoff_ms`, `config.recovery_timeout_ms`, `config.recovery_retries`, `config.recovery_log_threshold_seconds`: recovery policy. +- `config.batch_size`, `config.checkpoint_batches`, `config.checkpoint_method`, `config.checkpoint_seconds`: batching and checkpoint policy. +- `config.compact_evidence`, `config.compact_metrics`, `config.compact_progress`: compact-output switches. +- `config.evidence_paths.output_dir`, `config.evidence_paths.progress`, `config.evidence_paths.ledger`: output, journal, and event-ledger paths. +- `config.evidence_paths.metrics`, `config.evidence_paths.manifest`, `config.evidence_paths.summary`: metrics, source-manifest, and summary paths. +- `config.expected_rows`, `config.target_rows_per_sec`, `config.workers`: row target, rate target, and worker count. +- `config.ipc_compression`, `config.memory_trim_interval`, `config.min_system_available_gib`: IPC and memory settings. +- `config.max_files`, `config.max_row_groups`, `config.max_rows`: optional source caps. +- `config.pattern`, `config.queue_capacity`: source filename pattern and task-queue capacity. +- `config.sdk_distribution`, `config.sdk_version`: producer SDK identity. +- `source.manifest_sha256`, `source.total_rows`, `source.file_count`, `source.task_count`: source fingerprint and totals. +- `target.catalog`, `target.schema`, `target.table`, `target.full_name`: target identity. +- `target.host`, `target.zerobus_endpoint`: workspace and ingest endpoints. +- `eps.target_rows_per_sec`, `eps.average_provider_committed_rows_per_sec`, `eps.session_provider_committed_rows_per_sec`: configured and observed rates. +- `rate_limiter.scheduled_rows`, `rate_limiter.next_send_delay_sec`, `rate_limiter.no_catch_up`, `rate_limiter.target_rows_per_sec`: limiter state. +- `memory.arrow_allocated_bytes`, `memory.arrow_peak_bytes`, `memory.process_rss_bytes`, `memory.process_peak_rss_bytes`: Arrow and process memory. +- `memory.system_available_bytes`, `memory.system_total_bytes`: host memory. +- `memory.trim_attempts`, `memory.trim_successes`, `memory.trim_unsupported`, `memory.gc_collected_objects`: maintenance counters. +- `memory.trim_reclaimed_rss_bytes`, `memory.last_trim_at`, `memory.last_trim_duration_sec`: observed reclamation and latest trim. +- `host.logical_cpu_count`, `host.sample_interval_seconds`: host CPU count and sampling interval. +- `host.producer_process_cpu_cores`, `host.producer_process_cpu_percent_of_host`, `host.host_cpu_utilization_percent`: process and host CPU gauges. +- `host.network_receive_bytes_per_second`, `host.network_transmit_bytes_per_second`: host network rates. +- `stream_status.{worker}`: shared `stream_status` object. +- `transport_recovery`: shared `transport_recovery` object. + +## `ingest/ingest_summary.json` + +This pattern contains every field defined for `ingest/ingest_progress.json` plus: + +- `manifest_path`, `progress_path`, `evidence_ledger_path`, `metrics_path`: producer source-manifest, journal, event-ledger, and metrics paths. +- `dependencies.{distribution}`: dependency version by package distribution. +- `table_protection.attempted`, `table_protection.available`, `table_protection.method`: starting-state check execution, availability, and proof method. +- `table_protection.allow_nonempty_table`, `table_protection.fresh`: configured exception and derived fresh-target result. +- `table_protection.row_count`, `table_protection.num_files`, `table_protection.size_in_bytes`: starting metadata totals. +- `table_protection.history_versions[]`, `table_protection.statement_ids[]`, `table_protection.warehouse_id`: history and control-query evidence. +- `result.source_files`, `result.source_tasks`, `result.source_rows`: selected source totals. +- `result.provider_committed_rows`, `result.submitted_rows`, `result.completed_tasks`: final producer totals. +- `result.finished`, `result.clean_checkpoint`, `result.safe_to_resume`: final state flags. + +## `ingest/source_hashes_post_run.json` + +- `schema_version`, `status`, `generated_at`: schema revision, report status, and completion time. +- `source_manifest_path`, `source_manifest_sha256`: producer manifest path and exact file hash. +- `file_count`, `total_size_bytes`: hash-record count and summed selected-file size. +- `files[]`: selected source-file hash records. +- `files[].file_ordinal`, `files[].path`: canonical source ordinal and source-root-relative path. +- `files[].size_bytes`, `files[].mtime_ns`, `files[].sha256`: filesystem size, epoch-nanosecond modification time, and content hash. + +## `ingest/streams/worker--arrow.json` + +- `worker`, `stream`: one-based worker number and stable stream label. +- `close_succeeded`, `inspected_at`, `unacked_inspection_succeeded`: final close and inspection evidence. +- `unacked_batches`, `unacked_rows`: final unacknowledged totals. +- `artifacts[]`: preserved unacknowledged Arrow batches using the artifact fields under `stream_status`. +- `inspection_error`: optional redacted inspection failure. + +## `evidence/provider_reconciliation.json` + +- `schema_version`, `run_id`, `collected_at`: schema revision, correlated run identifier, and collection time. +- `collection_window`: shared `collection_window`. +- `provider_committed_records`, `provider_committed_bytes`, `provider_errors`: settled provider totals. +- `zerobus_stream_error_count`: errors in stream evidence. +- `zerobus_ingest_bounds.min_commit_version`, `zerobus_ingest_bounds.max_commit_version`: commit-version bounds. +- `zerobus_ingest_bounds.min_commit_time`, `zerobus_ingest_bounds.max_commit_time`: commit-time bounds. +- `table_num_rows.raw`, `table_num_rows.materialized_view`: provider metadata row counts when exposed. +- `required_dataset_completeness.required[]`, `required_dataset_completeness.succeeded[]`, `required_dataset_completeness.failed[]`: required dataset outcomes. +- `required_dataset_completeness.complete`: required-source completeness. +- `optional_dataset_errors[]`, `complete`: optional failures and overall compact-evidence completeness. + +## `evidence/mv_refresh_summary.json` + +- `schema_version`, `run_id`, `collected_at`: schema revision, correlated run identifier, and collection time. +- `collection_window`: shared `collection_window`. +- `maintenance_type_counts.{maintenance_type}`: refresh count by provider maintenance type. +- `maintenance_class_counts.{class}`: refresh count by normalized class. +- `full_refresh_count`: refreshes classified as full. + +## `evidence/clustering_status.json` + +- `schema_version`, `run_id`, `collected_at`: schema revision, correlated run identifier, and collection time. +- `collection_window`: shared `collection_window`. +- `tables.{role}.name`, `tables.{role}.catalog`, `tables.{role}.schema`: normalized table identity. +- `tables.{role}.type`, `tables.{role}.format`, `tables.{role}.location`: normalized provider metadata. +- `tables.{role}.clustering_columns[]`, `tables.{role}.cluster_by_auto`, `tables.{role}.partition_columns[]`: clustering and partition metadata. +- `tables.{role}.num_files`, `tables.{role}.size_in_bytes`, `tables.{role}.table_features[]`: table totals and features. +- `tables.{role}.properties.{property}`: filtered table-property map. +- `tables.{role}.enable_predictive_optimization`, `tables.{role}.effective_predictive_optimization_flag`, `tables.{role}.predictive_optimization_effectively_enabled`: configured, effective, and normalized values. +- `predictive_optimization.preflight_status`: inherited preflight status. +- `predictive_optimization.summary.operation_count`, `predictive_optimization.summary.operation_counts_by_type.{type}`: operation totals. +- `predictive_optimization.summary.usage_by_unit.{unit}`: usage quantity by provider unit. +- `predictive_optimization.clustering_operation_count`: clustering-class operation count. +- `predictive_optimization.operations[]`: compact provider operations. +- `operations[].table_name`, `operations[].operation_id`, `operations[].operation_type`, `operations[].operation_status`: operation identity and status. +- `operations[].start_time`, `operations[].end_time`, `operations[].usage_unit`, `operations[].usage_quantity`: operation bounds and usage. +- `operations[].operation_metrics.{metric}`: decoded provider metric map. + +## `validation/preflight.json` + +- `generated_at`, `mode`: report time and preflight mode. +- `config.host`, `config.cloud`, `config.region`, `config.workspace_id`: requested workspace. +- `config.catalog`, `config.schema`, `config.raw_table`, `config.mv_table`: requested target. +- `config.rt_warehouse_id`, `config.control_warehouse_id`, `config.zerobus_endpoint`: requested warehouses and ingest endpoint. +- `offline`, `online`: local and workspace check groups. +- `summary.status`, `summary.error_count`, `summary.warning_count`: aggregate status and issue counts. +- `offline.status`, `online.status`, `offline.errors[]`, `online.errors[]`, `offline.warnings[]`, `online.warnings[]`: group results. +- `offline.config_validation`, `online.config_validation`: objects with `errors[]` and `warnings[]`. +- `offline.credentials`, `online.credentials`: credential-presence objects. +- `credentials.pat_configured`, `credentials.oauth_client_id_configured`, `credentials.oauth_client_secret_configured`, `credentials.oauth_client_credentials_complete`: redacted credential indicators. +- `offline.packages.{module}`, `online.packages.{module}`: dependency probes. +- `packages.{module}.available`, `version`, `probe_error`, `distribution`, `required_for`, `online_required`, `environment`, `interpreter`: probe fields. +- `offline.query_contract.expected.{workload}`, `offline.query_contract.actual.{workload}`, `offline.query_contract.files.{workload}`: SQL counts and paths. +- `offline.ddl_contract.file`, `offline.ddl_contract.create_materialized_view_found`: DDL path and CREATE MV detection. +- `online.authentication.mode`, `online.authentication.status`: authentication configuration. +- `online.query_warehouse_mode`: requested serving mode. +- `online.lakehouse_rt_type.status`, `warehouse_api_signal`, `statement_execution_succeeded`, `reason`: serving-type check. +- `online.warehouses.{role}.status`: warehouse lookup status. +- `online.warehouses.{role}.metadata.id`, `name`, `state`, `cluster_size`: warehouse identity and observed state. +- `online.warehouses.{role}.metadata.min_num_clusters`, `max_num_clusters`, `auto_stop_mins`, `enable_serverless_compute`, `warehouse_type`, `creator_name`: warehouse settings. +- `online.warehouses.workspace_configuration.enabled_warehouse_types`, `enable_serverless_compute`: optional workspace settings. +- `online.statement_execution_on_rt.status`, `statement_id`, `rows[]`: serving statement result and provider row maps. +- `online.statement_execution_on_rt.rows[].preflight_ok`, `current_catalog`, `current_schema`: serving probe result and namespace. +- `online.system_table_permission.status`, `table`, `statement_id`, `reason`: system-table permission check. +- `online.query_history_api.status`, `statement_id`, `warehouse_id`, `status_value`, `reason`: Query History check. +- `online.materialized_view_create_compatibility.method`, `warehouse_id`, `status`, `statement_id`, `incrementalizable`: MV compatibility result. +- `online.materialized_view_create_compatibility.rows[]`, `error`: provider EXPLAIN row maps and optional error. +- `online.materialized_view_create_compatibility.rows[].plan`: provider compatibility plan text. +- `online.query_compatibility.method`, `online.query_compatibility.queries[]`: query compilation method and checks. +- `queries[].workload`, `query_number`, `status`, `statement_id`, `error`: per-query compilation fields. +- `online.tables.{role}.status`, `online.tables.{role}.errors[]`: table lookup result. +- `online.tables.{role}.metadata.full_name`, `name`, `catalog_name`, `schema_name`: table identity. +- `online.tables.{role}.metadata.table_type`, `data_source_format`, `storage_location`: table type, format, and location. +- `online.tables.{role}.metadata.created_at`, `updated_at`: epoch-millisecond metadata times. +- `online.predictive_optimization.status`: combined table result. +- `online.predictive_optimization.{role}.status`, `source`, `configured_value`, `effective_value`, `effectively_enabled`: per-table result. +- `online.predictive_optimization.{role}.inherited_from_type`, `inherited_from_name`: inheritance source. +- `online.metastore.metastore_id`, `cloud`, `region`, `storage_root_configured`, `status`: metastore metadata and optional failure status. +- `online.catalog_storage.name`, `storage_root`, `status`: catalog storage metadata and optional failure status. +- `online.tables.raw_storage_assumption.status`, `raw_storage_location_configured`, `catalog_managed_storage_configured`, `metastore_default_storage_configured`: storage-assumption result. +- `online.tables.raw_storage_assumption.uses_catalog_managed_storage`, `uses_metastore_default_storage`: normalized location matches. +- `online.target_contract.status`, `method`, `errors[]`: target contract result. +- `online.target_contract.statements.{check}.statement_id`, `row_count`: control statement evidence. +- `online.target_contract.observed.raw.format`, `num_files`, `size_in_bytes`, `clustering_columns[]`: raw metadata. +- `online.target_contract.observed.raw.history_versions[]`, `history_operations[]`, `source_features.{property}`: raw history and required features. +- `online.target_contract.observed.materialized_view.owner`, `clustering_columns[]`, `predictive_optimization`, `refresh_policy`, `refresh_schedule`: MV contract evidence. +- `online.target_contract.checks.{check}`: normalized contract-check result. +- `online.zerobus_endpoint.url`, `status`: endpoint and probe result. +- `online.zerobus_endpoint.tls.hostname`, `port`, `tls_version`, `latency_ms`: TLS evidence. +- `online.zerobus_service_principal.status`, `scoped_oauth_token_acquired`: scoped-token check. +- `online.zerobus_service_principal.required_privileges[]`: required grants. +- `required_privileges[].type`, `object_type`, `object_full_path`, `privileges[]`: grant target and privileges. + +Status-bearing preflight objects may also include `error`, `errors[]`, `reason`, or `skipped`. + +## `validation/query_context.json` + +- `schema_version`, `status`: schema revision and orchestration status. +- `query_run_id`, `ingest_run_id`: correlated query and ingest identifiers. +- `started_at`, `finished_at`, `post_ingest_started_at`: orchestration and post-ingest bounds. +- `starting_provider_committed_rows`, `final_provider_committed_rows`: producer watermarks at query-window boundaries. +- `query_warehouse_id`, `warehouse_mode`, `query_size`: serving configuration. +- `catalog`, `schema`, `raw_table`, `mv_table`: target identity. +- `dashboard_interval_seconds`, `drilldown_interval_seconds`, `post_ingest_query_seconds`: workload timing. +- `query_window_scope`, `partial_ingest_window`: query scope and partial-window flag. +- `dashboard_iterations`, `drilldown_iterations`: completed iteration counts. +- `dashboard_process_status`, `drilldown_process_status`: child process statuses. + +## `validation/validation_report_settled_.json` + +- `schema_version`, `accepted`: schema revision and conjunction of all gate decisions. +- `window.since`, `window.until`, `window.duration_seconds`, `window.error`: normalized window and optional error. +- `configuration.expected_rows`: required source rows. +- `configuration.eps_bounds.minimum`, `configuration.eps_bounds.maximum`, `configuration.minimum_interval_pass_fraction`: ingest-rate criteria. +- `configuration.maximum_raw_observational_age_seconds`, `configuration.maximum_mv_observational_age_seconds`: freshness-age bounds. +- `configuration.freshness_startup_grace_seconds`, `configuration.cadence_tolerance_seconds`, `configuration.freshness_age_semantics`: timing criteria. +- `gates[]`: ordered shared `validation_gate` objects. +- `query_samples.dashboard`, `query_samples.drilldown`: workload sample reports also carried by their gates. +- `freshness_samples`: freshness sample report also carried by its gate. + +### `gates[id=input_availability]` + +- `evidence.paths.source_manifest`, `ingest_progress`, `ingest_metrics`, `dashboard`, `drilldown`, `freshness`, `evidence_summary`, `cost_summary`: validated input paths. +- `evidence.errors_by_input.{input}[]`: parse or read errors by input. + +### `gates[id=source_manifest]` + +- `evidence.expected_rows`, `evidence.actual_rows`, `evidence.excluded_files[]`: required rows, selected rows, and exclusions. +- `evidence.checks.total_rows_exact`, `quotes_0_flag_false`, `quotes_0_not_selected`, `manifest_sha256_present`, `tasks_present`: source checks. +- `evidence.checks.file_count_consistent`, `task_count_consistent`, `canonical_task_order`, `canonical_file_order`: count and ordering checks. +- `evidence.checks.task_rows_sum_exact`, `file_rows_sum_exact`: row-sum checks. + +### `gates[id=producer_completion]` + +- `evidence.provider_committed_rows`, `evidence.completed_tasks`, `evidence.expected_tasks`, `evidence.stream_count`: final producer totals. +- `evidence.checks.provider_committed_exact`, `logical_raw_exact`, `submitted_exact`: row checks. +- `evidence.checks.finished`, `not_running`, `clean_checkpoint`, `not_ambiguous`: lifecycle checks. +- `evidence.checks.no_terminal_error`, `no_errors`, `no_pending_batches`, `no_pending_rows`, `no_partial_tasks`: unresolved-state checks. +- `evidence.checks.fresh_table_baseline`, `manifest_hash_matches`, `source_total_matches`: source-identity checks. +- `evidence.checks.all_tasks_completed`, `all_streams_clean`: completion checks. + +### `gates[id=ingest_rate]` + +- `evidence.bounds.minimum`, `evidence.bounds.maximum`, `evidence.average_provider_committed_rows_per_sec`, `evidence.average_passed`: bounds and cumulative result. +- `evidence.intervals[]`: per-metrics-record rate decisions. +- `intervals[].index`, `ignored`, `reason`, `eps`, `source`, `passed`: record index, exclusion, rate, derivation, and decision. +- `evidence.interval_pass_count`, `evidence.interval_count`, `evidence.interval_pass_fraction`, `evidence.minimum_pass_fraction`: aggregate interval result. + +### `gates[id=dashboard_queries]` and `gates[id=drilldown_queries]` + +The same object is repeated at `query_samples.{workload}`. + +- `accepted_indices[]`, `accepted_count`, `rejected[]`, `rejected_count`: accepted and rejected record summaries. +- `rejected[].index`, `rejected[].reasons[]`, `active_invalid[].index`, `active_invalid[].reasons[]`: record-level failures. +- `self_overlap_failures[]`: pairs of overlapping record indices. +- `cadence_failures[].previous_index`, `cadence_failures[].index`, `cadence_failures[].scheduled_delta_seconds`: schedule failures. +- `raw_rows_monotonic_failures[]`: pairs of decreasing row-watermark indices. +- `expected_query_count`, `expected_interval_seconds`, `cadence_tolerance_seconds`: workload shape and timing. + +### `gates[id=query_raw_rows_monotonic]` + +- `evidence.sample_count`: combined query sample count. +- `evidence.failures[].previous`, `current`, `previous_raw_rows`, `current_raw_rows`: cross-workload row-watermark decrease. + +### `gates[id=freshness]` + +The same object is repeated at `freshness_samples`. + +- `accepted_indices[]`, `accepted_count`, `rejected[]`, `rejected_count`: accepted and rejected record summaries. +- `rejected[].index`, `rejected[].reasons[]`, `invalid_eligible[].index`, `invalid_eligible[].reasons[]`: record-level failures. +- `startup_grace_seconds`, `active_measurement_until`: measurement bounds. +- `maximum_raw_observational_age_seconds`, `maximum_mv_observational_age_seconds`, `age_interpretation`: age criteria. + +### `gates[id=row_reconciliation]` + +- `evidence.expected_rows`: required row count. +- `evidence.counters.source_manifest_total_rows`, `producer_provider_committed_rows`, `system_zerobus_provider_committed_records`: reconciled row counters. +- `evidence.optional_describe_detail_raw_numRows`: optional table metadata count. +- `evidence.provider_errors`, `evidence.zerobus_stream_error_count`: provider error totals. + +### `gates[id=evidence_completeness]` + +- `evidence.complete`: overall evidence-summary completeness. +- `evidence.required_dataset_completeness.required[]`, `succeeded[]`, `failed[]`, `complete`: required dataset outcomes. +- `evidence.optional_dataset_errors[]`: failed optional datasets. + +### `gates[id=mv_incrementality]` + +- `evidence.maintenance_types[]`, `evidence.maintenance_classes[]`, `evidence.full_refresh_count`: maintenance evidence. + +### `gates[id=warehouse_configuration]` + +- `evidence.expected_warehouse_ids[]`, `evidence.snapshot_warehouse_ids[]`, `evidence.changed_or_unproven_warehouse_ids[]`: warehouse identity and stability evidence. +- `evidence.accepted_runner_config_count`, `evidence.accepted_runner_config_sha256[]`, `evidence.missing_runner_configs[]`: runner snapshot evidence. + +### `gates[id=cost_completeness]` + +This gate summarizes an external cost input; no cost artifact is packaged. + +- `evidence.complete`: external cost-summary completeness. +- `evidence.completeness.sources_complete`, `pricing_complete`, `classification_complete`, `single_primary_currency`, `canonical_primary_total_present`, `complete`: completeness checks. +- `evidence.completeness.ambiguous_price_usage_row_indices[]`, `unpriced_usage_row_indices[]`, `unknown_usage_row_indices[]`, `invalid_quantity_usage_row_indices[]`: exception rows. +- `evidence.canonical_undiscounted_primary_total`, `canonical_undiscounted_fresh_path_total`, `canonical_undiscounted_query_serving_total`, `unknown_total`: shared `money_total` objects. +- `evidence.unpriced_total.list_cost`, `usage_quantity_by_unit.{unit}`, `usage_row_indices[]`: unpriced-usage summary. +- `evidence.beta_scenario`: separately labeled noncanonical pricing scenario. +- `beta_scenario.discount_rate`, `applies_only_to`, `canonical_total_unchanged`: scenario parameters and canonical-preservation flag. +- `beta_scenario.rt_serving_undiscounted`, `rt_serving_discounted`, `scenario_primary_total`, `canonical_undiscounted_primary_total`: scenario `money_total` objects. + +### `gates[id=predictive_optimization]` + +- `evidence.operation_count`, `evidence.failed_operation_count`, `evidence.failed_operation_row_indices[]`: provider operation and failure totals. + +## `validation/manifest.json` + +- `schema_version`, `generated_at`, `status`, `run_id`: schema revision, generation time, package stage, and run identifier. +- `artifact_count`, `result_size_bytes`: listed-file count and summed size. +- `artifacts[]`: package file records; the manifest does not list itself. +- `artifacts[].path`, `artifacts[].size_bytes`, `artifacts[].sha256`: package-relative path, exact size, and byte hash. +- `source.total_rows`, `source.selected_file_count`, `source.selected_row_group_count`: selected source totals. +- `source.quotes_0_parquet_included`, `source.excluded_files[]`: source-file inclusion evidence. +- `source.manifest_sha256`: canonical source-selection fingerprint. +- `source.hash_validation_status`, `source.hash_validation_file_count`: post-run source-hash result. +- `runtime_state.included_in_results`: runtime-state inclusion flag. +- `runtime_state.files_excluded[]`: excluded runtime-state classes, including source manifests, ingest ledgers, full query-audit sidecars, raw provider exports, and process logs. +- `sanitization.profile`: publication sanitization profile. +- `sanitization.metrics_changed`: whether sanitization altered benchmark + measurements; public packages require `false`. +- `sanitization.redacted_classes[]`: classes of deployment-specific metadata + replaced by placeholders. diff --git a/full-path-realtime/quotes/databricks/SIX_HOUR_INGEST_MV.md b/full-path-realtime/quotes/databricks/SIX_HOUR_INGEST_MV.md new file mode 100644 index 0000000..118b91e --- /dev/null +++ b/full-path-realtime/quotes/databricks/SIX_HOUR_INGEST_MV.md @@ -0,0 +1,345 @@ +# Six-hour Zerobus and materialized-view run + +This run characterizes the write and maintenance path while Lakehouse//RT is +unavailable. It is not a publishable full-path result because it has no +Lakehouse//RT dashboard or drill-down reads. + +## September 16 run result + +Run `ingest_mv_6h_20260916T164822Z` completed the six-hour window without a +terminal client or Zerobus error, but **did not pass the 1M-EPS gate**: + +- producer-acknowledged rows: 11,539,474,122; +- provider table/system metadata rows: 11,539,549,122; +- unexplained provider excess: 75,000 rows; +- average provider-acknowledged rate: 534,103 rows/second; +- first 30-minute average: 1,000,083 rows/second; +- final 30-minute average: 309,663 rows/second; +- all 16 client streams closed cleanly with zero pending batches; and +- host CPU remained around 3%, memory remained healthy, and network output + declined with throughput. + +The throughput decline is attributable to the evidence-journal +implementation, not established as a Databricks capacity limit. Every batch +called `_refresh_locked()`, which scanned and sorted the entire growing +completed-task map while holding one lock. Completed row-group tasks grew from +13,744 after 30 minutes to 88,202 at the end, while the producer process +converged on one CPU core and network output fell from 76.7 MB/s to 23.7 MB/s. +The implementation has since been changed to update compact completion ranges +incrementally rather than rescanning completed tasks. + +The 75,000-row provider/client mismatch is independently fatal for +publication. Zerobus opened 413 provider streams over the run despite 16 +logical client workers, consistent with periodic SDK recovery/rotation. +Zerobus is at least once and Arrow recovery can replay data. The mismatch must +be explained or eliminated before another accepted endurance run. + +The maintenance path otherwise behaved correctly: + +- 330 incremental `GROUP_AGGREGATE` refreshes completed during ingestion; +- zero full recomputes occurred; +- median refresh completion cadence was 61.1 seconds and p95 was 118.1 + seconds; +- median `WAITING_FOR_RESOURCES` was 10.4 seconds; +- median incremental `RUNNING` time was 27.8 seconds; +- the final data refresh completed 49.1 seconds after the last raw commit; +- a subsequent `NO_OP` confirmed full MV catch-up; +- active MV rows-behind p50/p95/max were 24.2M, 66.5M, and 145.2M; and +- all 751 monitor observations were error-free. + +Predictive Optimization performed four clustering operations and two ANALYZE +operations. The final raw table retained `CLUSTER BY (sym, t)` and occupied +96,971,982,760 bytes across 2,235 active files. The largest observed +clustering operation processed 164,736,452,264 bytes. + +Initial, not-yet-settled list cost was USD 40.38 for Zerobus, USD 45.29 for MV +refresh, and USD 6.32 for Predictive Optimization: USD 91.98 for the +fresh-data path. The monitor warehouse added USD 18.33 of excluded +instrumentation cost. These values use account price history of USD 0.39/DBU +for Premium Jobs Serverless and USD 0.91/DBU for Premium Serverless SQL in +Ireland. Billing was only partially settled and must be recollected after 24 +hours. + +The initial collector incorrectly attempted `DESCRIBE DETAIL` and `DESCRIBE +HISTORY` on the MV, which Databricks exposes as a view for those commands. +This made the evidence summary incomplete even though the cost inputs were +collected. The settled collector now uses `DESCRIBE TABLE EXTENDED ... AS +JSON` for the MV and no longer requires unsupported MV history. + +## Frozen workload + +- Duration at target rate: 6 hours +- Target rate: 1,000,000 provider-acknowledged rows/second +- Nominal result at target rate: 21,600,000,000 rows +- Source-selection envelope: 22,000,000,000 rows; a wall-clock deadline stops + ingestion at six hours +- Producer: prepared same-region `m6i.8xlarge` +- Zerobus: 16 Arrow Flight streams, 50,000-row batches, no IPC compression +- Durability accounting: wait after every Arrow batch +- Raw target: + `costbench.rt_qualification.quotes_ingest_mv_6h_20260916` +- MV target: + `costbench.rt_qualification.quotes_daily_ingest_mv_6h_20260916` +- Raw layout: liquid clustering inherited from the canonical table, + `CLUSTER BY (sym, t)` +- Maintenance: inherited Predictive Optimization +- MV: strict incremental, `CLUSTER BY (sym, day)`, + `TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE` +- Freshness sampling: every 60 seconds during ingestion and for 15 minutes + after the target duration +- Control warehouse: ``, serverless `2X-Small`, + `` + +The MV remains on the observed trigger-on-update serverless path. The +system-managed-job preview is enabled, but no Performance optimized control is +available for this MV type in the current workspace. Do not claim that mode. + +## Create fresh targets + +Run on ``: + +```sql +CREATE TABLE costbench.rt_qualification.quotes_ingest_mv_6h_20260916 +LIKE costbench.rt_qualification.quotes; + +GRANT SELECT, MODIFY +ON TABLE costbench.rt_qualification.quotes_ingest_mv_6h_20260916 +TO ``; + +CREATE MATERIALIZED VIEW +costbench.rt_qualification.quotes_daily_ingest_mv_6h_20260916 +USING DELTA +CLUSTER BY (sym, day) +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE +AS +SELECT + sym, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, + count(*) AS n_quotes, + min(bp) AS bp_min, + max(bp) AS bp_max, + min(ap) AS ap_min, + max(ap) AS ap_max, + sum(bs) AS bs_sum, + sum(`as`) AS as_sum, + sum(ap - bp) AS spread_sum +FROM costbench.rt_qualification.quotes_ingest_mv_6h_20260916 +GROUP BY sym, day; +``` + +Before ingestion: + +1. transfer the new MV's ownership to ``; +2. retain the benchmark administrator's `MANAGE` and `SELECT` grants; +3. verify the raw table has zero active files and only Delta version 0; +4. verify liquid clustering and the three Delta source features were copied; +5. verify effective Predictive Optimization is enabled; and +6. verify the MV definition is incrementalizable and owned by the service + principal. + +The harness repeats the zero-file/version-0 metadata check and issues no +`SELECT COUNT(*)`. + +### SQL verification gate + +Run these metadata-only statements after creating the objects and transferring +MV ownership. + +Prove that the raw table is pristine: + +```sql +DESCRIBE DETAIL +costbench.rt_qualification.quotes_ingest_mv_6h_20260916; + +DESCRIBE HISTORY +costbench.rt_qualification.quotes_ingest_mv_6h_20260916 +LIMIT 2; +``` + +Require all of the following: + +- `format = delta`; +- `numFiles = 0`; +- `sizeInBytes = 0`; +- `clusteringColumns = ["sym", "t"]`; and +- history returns exactly one row with `version = 0` and operation + `CREATE TABLE`. + +Prove the three source-table features copied: + +```sql +SHOW TBLPROPERTIES +costbench.rt_qualification.quotes_ingest_mv_6h_20260916 +('delta.enableDeletionVectors'); + +SHOW TBLPROPERTIES +costbench.rt_qualification.quotes_ingest_mv_6h_20260916 +('delta.enableRowTracking'); + +SHOW TBLPROPERTIES +costbench.rt_qualification.quotes_ingest_mv_6h_20260916 +('delta.enableChangeDataFeed'); +``` + +Each value must be `true`. + +Prove effective Predictive Optimization, including inheritance: + +```sql +DESCRIBE CATALOG EXTENDED costbench; + +DESCRIBE TABLE EXTENDED +costbench.rt_qualification.quotes_ingest_mv_6h_20260916; + +DESCRIBE TABLE EXTENDED +costbench.rt_qualification.quotes_daily_ingest_mv_6h_20260916; +``` + +The `Predictive Optimization` field must report `ENABLE` for both objects. An +inherited value from `costbench` is valid; `DISABLE`, missing, or unknown is +not. + +Prove the MV owner and grants: + +```sql +SHOW GRANTS ON MATERIALIZED VIEW +costbench.rt_qualification.quotes_daily_ingest_mv_6h_20260916; + +DESCRIBE TABLE EXTENDED +costbench.rt_qualification.quotes_daily_ingest_mv_6h_20260916; +``` + +The `Owner` field must be `` (Application ID +``). the benchmark administrator must retain explicit `MANAGE` +and `SELECT`. + +Finally, rerun the explained canonical definition without creating another +object: + +```sql +EXPLAIN CREATE MATERIALIZED VIEW +costbench.rt_qualification.quotes_daily_ingest_mv_6h_20260916_explain_only +USING DELTA +CLUSTER BY (sym, day) +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE +AS +SELECT + sym, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, + count(*) AS n_quotes, + min(bp) AS bp_min, + max(bp) AS bp_max, + min(ap) AS ap_min, + max(ap) AS ap_max, + sum(bs) AS bs_sum, + sum(`as`) AS as_sum, + sum(ap - bp) AS spread_sum +FROM costbench.rt_qualification.quotes_ingest_mv_6h_20260916 +GROUP BY sym, day; +``` + +Require: + +```text +The Materialized View can be incrementally refreshed. +No issues detected. +``` + +## Launch + +On the EC2 producer: + +```sh +tmux new -As ingest-mv-6h +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/run_ingest_mv_6h.sh +``` + +Type the exact target when prompted, then enter the service-principal secret. +The secret is read without echo, inherited only by the live processes, and +unset on exit. + +Before opening Zerobus streams, the producer enumerates row-group metadata for +a 22B-row source envelope. It does not read and hash the selected files on the +startup path. File identity, size, and modification time are frozen in the +manifest; SHA-256 hashes are computed after ingestion and post-ingest freshness +monitoring finish, outside the measurement window. + +The wrapper runs: + +- six wall-clock hours of Zerobus ingestion, expected to produce approximately + 21.6B rows at the target rate; +- compact range-based recovery state suitable for the long source selection; +- five-second throughput and producer-resource telemetry; +- 60-second metadata-only Zerobus/MV monitoring; +- 15 minutes of post-ingest monitoring to prove MV catch-up; +- immediate target-scoped provider evidence collection; +- post-run source-file hashing; and +- an initial cost summary, which may be incomplete while billing settles. + +The wrapper requires a clean, duration-driven stop, average committed EPS +within ±10% of 1M, no pending or ambiguous batches, error-free freshness +samples, final MV catch-up within the monitor window, exact client/provider +record reconciliation, and no full MV refresh. + +## Pricing and cost boundary + +Public AWS list pricing observed September 16, 2026: + +- Zerobus Premium: USD 0.050 per GB sent to the ingestion API +- Zerobus Enterprise: USD 0.064 per GB sent to the ingestion API +- Zerobus translation: 0.143 DBU per GB; underlying service compute included +- Serverless Lakeflow Pipelines Premium: USD 0.35 per DBU +- Serverless Lakeflow Pipelines Enterprise: USD 0.45 per DBU +- Serverless Jobs used by Predictive Optimization Premium: USD 0.35 per DBU +- Serverless Jobs used by Predictive Optimization Enterprise: USD 0.45 per DBU + +The 600M-row characterization committed 43,841,095,016 bytes, or approximately +73.07 bytes per row. At the same shape, 21.6B rows would send approximately +1.578 TB and cost about USD 78.91 at Premium list price or USD 101.01 at +Enterprise list price. This is an estimate only; canonical cost uses settled +`system.billing.usage` and the overlapping +`system.billing.list_prices` version. + +Report separately: + +- Zerobus volume usage; +- MV serverless pipeline DBUs, matched by the MV pipeline ID; +- Predictive Optimization serverless Jobs DBUs and operation history; +- raw and MV managed-storage size; +- the excluded `` instrumentation cost; +- EC2 producer compute and EBS storage; and +- cloud data transfer and requests. + +The source download from `us-east-2` is setup and remains outside the measured +window. Measured producer-to-Zerobus traffic is same-region. + +## Settled recollection + +Billing usage can take up to 24 hours to settle. At least 24 hours after the +run, execute: + +```sh +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/collect_ingest_mv_6h_settled.sh +``` + +The script reads the immutable window and targets from the latest six-hour run +context, prompts for the secret, writes a new settled evidence directory, and +produces a versioned cost summary. It never overwrites the initial evidence. diff --git a/full-path-realtime/quotes/databricks/SIX_HOUR_INGEST_MV_R2.md b/full-path-realtime/quotes/databricks/SIX_HOUR_INGEST_MV_R2.md new file mode 100644 index 0000000..97a9b31 --- /dev/null +++ b/full-path-realtime/quotes/databricks/SIX_HOUR_INGEST_MV_R2.md @@ -0,0 +1,126 @@ +# Six-hour Zerobus and MV retry + +This retry uses a fresh target and the settings accepted by +`ingest_mv_40m_20260917T090123Z`: + +- compact incrementally maintained task ranges; +- six wall-clock hours at a 1M-EPS target; +- Arrow Flight automatic recovery disabled; +- proactive ten-minute stream rotation; +- flush, close, and zero-unacknowledged proof before each replacement stream; +- fail-closed behavior on any unexpected interruption; +- 60-second metadata-only MV monitoring; +- 15 minutes of post-ingest MV catch-up monitoring; +- deferred post-measurement source hashing; and +- initial plus settled evidence/cost collection. + +## September 17 stopped-run result + +The operator intentionally stopped run +`ingest_mv_6h_r2_20260917T104305Z` after 4h08m to collect results and prepare +the canonical full baseline. The shortened run passed every applicable +ingest, recovery, MV, and evidence gate: + +- client submitted and acknowledged 14,877,188,034 rows; +- provider metadata reported exactly 14,877,188,034 rows and + 1,086,746,940,112 committed bytes; +- average committed throughput was 999,930 EPS; +- 2,956 of 2,957 derived five-second intervals were within ±10%; the sole + exception was the deliberately shortened final interval; +- every completed 30-minute window averaged approximately 1M EPS; +- all 16 workers completed 24 proactive rotations with zero unacknowledged + batches; +- automatic recovery remained disabled; +- provider and client error counts were zero; +- 184 MV refreshes were incremental and zero were full; +- final MV source rows exactly matched the raw provider count; +- final MV catch-up completed about 195 seconds after producer shutdown; +- source hashes completed for 39 selected files; and +- initial target-scoped evidence collection completed without errors. + +Initial billing is provisional. It currently reports USD 28.08 Zerobus, USD +14.50 MV refresh, and USD 1.74 Predictive Optimization: USD 44.31 fresh-path +cost. The excluded monitor warehouse currently reports USD 4.02. Recollect +after at least 24 hours before using any cost result. + +Because the run was stopped before six hours, it is a successful long-run +characterization rather than the originally requested six-hour completion. + +## Create the fresh target + +Run on ``: + +```sql +CREATE TABLE costbench.rt_qualification.quotes_ingest_mv_6h_r2_20260917 +LIKE costbench.rt_qualification.quotes; + +GRANT SELECT, MODIFY +ON TABLE costbench.rt_qualification.quotes_ingest_mv_6h_r2_20260917 +TO ``; + +CREATE MATERIALIZED VIEW +costbench.rt_qualification.quotes_daily_ingest_mv_6h_r2_20260917 +USING DELTA +CLUSTER BY (sym, day) +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE +AS +SELECT + sym, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, + count(*) AS n_quotes, + min(bp) AS bp_min, + max(bp) AS bp_max, + min(ap) AS ap_min, + max(ap) AS ap_max, + sum(bs) AS bs_sum, + sum(`as`) AS as_sum, + sum(ap - bp) AS spread_sum +FROM costbench.rt_qualification.quotes_ingest_mv_6h_r2_20260917 +GROUP BY sym, day; +``` + +Transfer the MV owner to `` and retain the benchmark administrator's `MANAGE` and +`SELECT`. Run the SQL verification gate from +[SIX_HOUR_INGEST_MV.md](SIX_HOUR_INGEST_MV.md), substituting the retry object +names. Do not continue unless the raw target is pristine version 0 with zero +files, the Delta features and clustering copied, Predictive Optimization is +effectively enabled, and the MV is incrementally refreshable. + +## Launch + +```sh +tmux new -As ingest-mv-6h-r2 +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/run_ingest_mv_6h_r2.sh +``` + +Expected nominal output is approximately 21.6B rows. Actual acceptance is +duration-based: average committed throughput must remain within 900k–1.1M EPS +for the six-hour window. + +The run also requires: + +- client submitted/acknowledged rows exactly equal provider records and the + final MV source snapshot; +- all 16 logical streams complete at least 35 clean proactive rotations; +- every rotation and final close has zero unacknowledged batches; +- provider stream errors and client ambiguity remain zero; +- all data-changing MV updates are incremental; +- final MV rows-behind reaches zero; and +- target-scoped evidence collection completes. + +Billing can take up to 24 hours to settle. Recollect using the exact run root +printed at completion: + +```sh +/home/ubuntu/costbench/full-path-realtime/quotes/databricks/collect_ingest_mv_6h_settled.sh \ + /data/databricks-qualification/ingest_mv_6h_r2_ +``` diff --git a/full-path-realtime/quotes/databricks/TUNING.md b/full-path-realtime/quotes/databricks/TUNING.md new file mode 100644 index 0000000..cdca5a5 --- /dev/null +++ b/full-path-realtime/quotes/databricks/TUNING.md @@ -0,0 +1,234 @@ +# Databricks qualification and tuning + +`qualify.py` turns `tune-endurance` into a frozen, executable matrix and +evaluates only evidence written by that matrix. Planning is offline: it does +not read credentials, call Databricks, or claim that a case ran. + +## Current status + +The online qualification stages are **not run**. The producer and explicit +Unity Catalog managed storage are prepared, but Lakehouse//RT is not yet +available in the workspace. No capacity, query-size, endurance, or +qualification result is asserted here, and no warehouse size or throughput +value has been invented. See [INFRASTRUCTURE_SETUP.md](INFRASTRUCTURE_SETUP.md) +for the completed preparation. + +The 113,219,565,734-row run is prohibited until `qualification_report.json` +contains exact `"qualified": true`. + +The September 16 six-hour characterization is documented in +[SIX_HOUR_INGEST_MV.md](SIX_HOUR_INGEST_MV.md). It is not accepted +qualification evidence: client-side journal work reduced average throughput to +534k EPS over time, and provider metadata reported 75,000 more rows than the +producer journal. The MV remained strictly incremental and Predictive +Optimization ran successfully, but both ingest failures must be resolved before +another endurance attempt. + +The September 17 follow-up in [RECOVERY_40M.md](RECOVERY_40M.md) validated both +ingest fixes across three proactive stream-rotation boundaries. It sustained +999,582 average EPS, kept every five-second interval within ±10%, and produced +exact equality between 2,400,607,668 client and provider rows. This permits a +fresh six-hour retry with the same compact journal, automatic recovery +disabled, and ten-minute clean stream rotation; it does not qualify endurance +by itself. + +### Preliminary 1M-EPS characterization + +Two online characterization runs completed on September 15, 2026. They prove +that the prepared producer and Zerobus path can sustain the target, but they +are not substitutes for the frozen qualification matrix. + +- `capacity_1m_20260915T140716Z` committed all 75,000,000 selected rows with no + errors or ambiguous acknowledgements. It averaged 964,224 committed rows/s + over 77.78 seconds. Five-second committed-EPS observations were bursty + because each stream recorded durability only at five-second checkpoints. +- `ingest_mv_10m_20260915T143250Z` changed durability accounting to wait after + every Arrow batch. It committed all 600,000,000 selected rows with no errors, + no pending rows, and 16 cleanly closed streams. It averaged 997,778 committed + rows/s over 601.34 seconds. Of 119 derived five-second intervals, 118 were + within the 900,000–1,100,000 acceptance band. Peak host CPU was 2.42% and + peak transmit throughput was 77.23 MB/s. + +The second run's live freshness monitor could read Zerobus and raw-table +metadata, but its MV event-log requests were denied because the newly created +MV had not been transferred to the benchmark service principal before launch. +After ownership was corrected, a historical metadata-only recovery completed +without errors. It confirmed 600,000,000 provider records, 43,841,095,016 +committed bytes, zero Zerobus errors, raw Delta version 118, and five completed +MV updates. Four updates used incremental `GROUP_AGGREGATE`; the final update +was `NO_OP`. + +Incremental refresh completions occurred at 14:36:18.042, 14:39:02.541, +14:42:03.182, and 14:46:31.702 UTC. The final raw commit was at +14:43:24.999 UTC, so the next completed incremental refresh provides a +186.703-second final-drain upper bound. A subsequent `NO_OP` completed at +14:49:49.769 and confirms no source changes remained; using that later proof +point gives a fully conservative observed bound of 384.770 seconds. Each +planning event reports its source snapshot's exact `num_rows`, providing a +metadata row watermark, but reports `ingestion_source_table_version=null`. +Time lag is therefore reconstructed by aligning that watermark with monotonic +producer acknowledgements; it is not represented as exact per-row event-time +lag. + +Refresh phase timing shows that compute startup, not the aggregate itself, +dominated freshness. The four incremental updates spent approximately 115, +112, 112, and 199 seconds in `WAITING_FOR_RESOURCES`; their `RUNNING` phases +lasted only about 22, 27, 26, and 23 seconds. + +The trial did not preserve an explicit standalone MV performance-mode setting. +Databricks documents standard mode as the default for definition-scheduled +serverless refreshes; it trades slower startup for lower DBU consumption. +Standalone serverless MV refresh does not expose a warehouse or cluster size. + +The workspace preview `System-Managed Job for Materialized Views & Streaming +Tables` was enabled on September 15, 2026 after this run. It did not expose a +Performance optimized toggle for the existing `TRIGGER ON UPDATE` MV: the +Catalog Explorer **Edit** link continued to open documentation. Databricks' +detailed toggle instructions specifically refer to a `SCHEDULE` clause. + +Do not claim Performance optimized for this path. On the next fresh target, +check once while the creating user still owns the MV, before transferring +ownership to the benchmark principal. If the control remains unavailable, the +trigger-on-update result represents standard mode. A performance-optimized +system-managed schedule/SQL job or continuous Lakeflow pipeline must be +qualified and reported as a separate refresh architecture. + +## Create the frozen plan + +Supply non-secret environment-specific values and list query sizes in provider +order, smallest first: + +```sh +"$DATABRICKS_RUNNER_PYTHON" qualify.py plan \ + --output-dir /absolute/path/to/qualification \ + --repo-dir /Users/lio/Clickhouse/CostBench \ + --data-dir /absolute/path/to/parquet \ + --catalog costbench \ + --schema rt_qualification \ + --rt-warehouse-id RT_WAREHOUSE_ID \ + --control-warehouse-id CONTROL_WAREHOUSE_ID \ + --producer-host-description 'dedicated producer host and network placement' \ + --producer-network-capacity-gbps USABLE_LINK_GBPS \ + --host https://WORKSPACE_HOST \ + --endpoint https://ZEROBUS_ENDPOINT \ + --stream-count 16 \ + --batch-size 50000 \ + --queue-capacity 64 \ + --compression NONE \ + --query-size-candidates 2X-Small X-Small Small Medium \ + --python "$DATABRICKS_RUNNER_PYTHON" \ + --producer-python "$DATABRICKS_ZEROBUS_PYTHON" +``` + +The command writes `qualification_plan.json` and executable +`qualification_commands.sh` atomically. Credential values are never included. +The generated plan uses the runner environment for preflight, metadata, and +query processes and the separate Zerobus environment for ingestion. +Online scripts read either `DATABRICKS_TOKEN` or +`DATABRICKS_CLIENT_ID`/`DATABRICKS_CLIENT_SECRET` from the environment. + +Create `costbench.rt_qualification` once. It may inherit the explicit, +non-default managed location configured on the `costbench` catalog. All +qualification cases reuse that schema sequentially. Before every case, review +and apply `create.sql` so the raw table and materialized view are dropped and +recreated empty. Never run two qualification cases concurrently. + +Run one case section at a time, for example +`QUALIFY_CASE=capacity-100k ./qualification_commands.sh`, after setting that +case's printed clean-target confirmation variable. Query-size and endurance +sections also require the explicit provisioning confirmation printed by the +script. `QUALIFY_CASE=evaluate` runs the final evaluator after +`QUALIFICATION_PLAN` and `QUALIFICATION_REPORT` are set. + +Do not change the matrix JSON after planning. Its digest is checked during +evaluation. If an input changes, generate a new output directory and plan. + +## Clean-target workflow + +Run stages in this order and stop after any failure: + +1. **10M correctness.** Provision the declared configuration, reset the + dedicated qualification schema with `create.sql`, run online + preflight, then run the producer, freshness monitor, four-query dashboard, + two-query drilldown, evidence collection, cost summary, and validation. + The producer command uses exact `--max-rows 10000000` with + `--expected-rows 10000000 --allow-partial`. `--max-rows` is required because + a row-group-only cap can overshoot 10M; the producer now truncates only the + final selected row group and records an exact 10M manifest. +2. **Capacity.** Repeat on three new targets at 100k, 500k, and 1M committed + EPS. Each case selects an exact row count for 75 seconds at its target. With + 5-second metrics this permits one warm-up plus at least ten measured + intervals. Never resume a capacity case onto another target. +3. **Query-size sweep.** Reset the same qualification schema and use a distinct + run ID and artifact directory for every size. Stop measured work before + provisioning or resizing. Explicitly provision the next size, record the + size-to-warehouse mapping, run online preflight, then run one uncached + four-query dashboard iteration and one uncached two-query drilldown + iteration during fixed 1M EPS ingestion. Missing size, queue, + waiting-at-capacity, cache, canonical timing, or error evidence rejects that + size. +4. **Endurance.** Provision the smallest passing query size and repeat online + preflight on another fresh target. Keep the accepted 1M producer stream + count, batch size, compression, host/endpoint, query size, and autoscaling + settings unchanged. Run 1M EPS for 1,800–3,600 seconds with dashboard fixed + at 600-second cadence, drilldown fixed at 3,600-second cadence, and the + freshness monitor active. The default is 1,800 seconds. It requires one + accepted active-ingest drilldown; a second sample scheduled exactly at a + 3,600-second producer endpoint would race completed ingestion and is not an + acceptance requirement. + +After all sweep cases, run the evaluator once to produce an interim, +non-qualified report. A successful sweep records `selected_query_size` even +though endurance remains `not_run`; use that exact value as +`SELECTED_QUERY_SIZE` for the endurance section. The evaluator's nonzero exit +at this interim point is expected and must not be treated as final +qualification. + +`create.sql` contains destructive `DROP` statements. Apply it only to the +dedicated `rt_qualification` schema, after the previous case has stopped and +its artifacts have been preserved. Never point it at `rt_full_`, a +historical result target, or a schema used by another workload. Never resize a +warehouse during a live measurement. + +## Record query-size provisioning + +For each `query_size_sweep` case, record the declared candidate and actual +warehouse ID in the infrastructure change record. Preserve the corresponding +`preflight_report.json`. The evaluator requires both preflight metadata and +every runner record to report the exact candidate size. Candidate order in the +plan is the selection order; the first passing candidate is selected. + +If separate warehouse IDs are used, update the environment and generate a new +plan before running. Do not edit the frozen plan or silently repoint a case. + +## Evaluate qualification + +After all commands have completed and settled evidence has been collected: + +```sh +"$DATABRICKS_RUNNER_PYTHON" qualify.py evaluate \ + --plan /absolute/path/to/qualification/qualification_plan.json \ + --output /absolute/path/to/qualification/qualification_report.json +``` + +The evaluator performs no network calls. It distinguishes: + +- `not_run`: one or more required artifacts are absent; +- `failed`: evidence exists but is invalid or fails an acceptance gate; +- `passed`: the case has complete passing evidence; +- `planned`: the state of every case in the unevaluated plan. + +Qualification requires exact 10M validation, clean and in-tolerance capacity +evidence, monotonic capacity, a passing query size with no queue/cache errors, +and accepted endurance evidence. Endurance must have a 30–60 minute measured +window (the default plan uses 1,800 seconds), at least one accepted active- +ingest drilldown, the planned number of pre-endpoint dashboard iterations, +accepted freshness, no errors, and no producer or RT warehouse configuration +drift from the accepted 1M case. Capacity and endurance also require Linux host +CPU and network telemetry. By default, p95 host CPU must remain at or below 90% +and p95 receive/transmit utilization must remain at or below 80% of +`--producer-network-capacity-gbps`. Supply the evidenced usable link capacity, +not an unverified marketing maximum. + +Only a fully passing report includes frozen settings and returns exit status +zero. Missing evidence never passes. diff --git a/full-path-realtime/quotes/databricks/apply_ddl.py b/full-path-realtime/quotes/databricks/apply_ddl.py new file mode 100644 index 0000000..647a0df --- /dev/null +++ b/full-path-realtime/quotes/databricks/apply_ddl.py @@ -0,0 +1,327 @@ +#!/usr/bin/env python3 +"""Render and apply the checked-in Databricks benchmark DDL, fail closed.""" + +from __future__ import annotations + +import argparse +import hashlib +import os +import re +import sys +import tempfile +import urllib.parse +from pathlib import Path +from typing import Any, Callable, Mapping, Sequence + +from dbx_common import ( + DatabricksRestClient, + atomic_write_json, + iso_utc, + normalize_host, + redact_secrets, + render_sql, + split_sql_statements, +) + +SCRIPT_DIR = Path(__file__).resolve().parent +DDL_PATH = SCRIPT_DIR / "create.sql" +RENDERED_SQL_NAME = "create.rendered.sql" +REPORT_NAME = "apply_ddl_report.json" +SAFE_COMPONENT_RE = re.compile(r"^[A-Za-z0-9][A-Za-z0-9_-]*$") +FORBIDDEN_DDL_RE = ( + re.compile( + r"^\s*CREATE\s+(?:OR\s+REPLACE\s+)?(?:CATALOG|SCHEMA|DATABASE)\b", + re.IGNORECASE, + ), + re.compile( + r"^\s*CREATE\s+(?:OR\s+REPLACE\s+)?" + r"(?:EXTERNAL\s+LOCATION|STORAGE\s+CREDENTIAL)\b", + re.IGNORECASE, + ), + re.compile(r"\bMANAGED\s+LOCATION\b", re.IGNORECASE), +) + + +def sha256_bytes(value: bytes) -> str: + return hashlib.sha256(value).hexdigest() + + +def _atomic_write_new_text(path: Path, value: str) -> None: + """Publish one new UTF-8 file atomically without permitting replacement.""" + path.parent.mkdir(parents=True, exist_ok=True) + fd, tmp_name = tempfile.mkstemp(prefix=f".{path.name}.", dir=path.parent) + try: + with os.fdopen(fd, "w", encoding="utf-8", newline="") as handle: + handle.write(value) + handle.flush() + os.fsync(handle.fileno()) + os.link(tmp_name, path) + finally: + try: + os.unlink(tmp_name) + except FileNotFoundError: + pass + + +def _validate_component(label: str, value: str) -> None: + if not SAFE_COMPONENT_RE.fullmatch(value): + raise ValueError( + f"{label} must contain only ASCII letters, digits, '_' or '-', " + "and must start with a letter or digit" + ) + + +def prepare_ddl( + catalog: str, + schema: str, + *, + ddl_path: Path = DDL_PATH, +) -> tuple[bytes, str, list[str]]: + """Load, render, split, and safety-check the complete checked-in DDL.""" + _validate_component("catalog", catalog) + _validate_component("schema", schema) + source = ddl_path.read_bytes() + rendered = render_sql(source.decode("utf-8"), catalog, schema) + statements = split_sql_statements(rendered) + if not statements: + raise ValueError("rendered create.sql contains no SQL statements") + for index, statement in enumerate(statements, start=1): + if any(pattern.search(statement) for pattern in FORBIDDEN_DDL_RE): + raise ValueError( + f"statement {index} attempts catalog, schema, or managed-storage " + "administration; those are prerequisites and are not applied here" + ) + return source, rendered, statements + + +def _statement_record(index: int, statement: str) -> dict[str, Any]: + return { + "index": index, + "sha256": sha256_bytes(statement.encode("utf-8")), + "status": "not_run", + "statement_id": None, + "error": None, + "started_at": None, + "finished_at": None, + } + + +def apply_ddl( + *, + host: str, + catalog: str, + schema: str, + control_warehouse_id: str, + confirm_destructive_target: str, + output_dir: Path, + token: str | None = None, + client_id: str | None = None, + client_secret: str | None = None, + statement_timeout: float = 300.0, + poll_interval: float = 0.5, + network_timeout: float = 30.0, + ddl_path: Path = DDL_PATH, + client_factory: Callable[..., Any] = DatabricksRestClient, +) -> dict[str, Any]: + """Apply every rendered statement and persist an auditable report.""" + target = f"{catalog}.{schema}" + if confirm_destructive_target != target: + raise ValueError( + "--confirm-destructive-target must exactly equal " + f"{target!r}; received {confirm_destructive_target!r}" + ) + normalized_host = normalize_host(host) + parsed_host = urllib.parse.urlparse(normalized_host or "") + if ( + not normalized_host + or parsed_host.scheme != "https" + or not parsed_host.hostname + or parsed_host.username + or parsed_host.password + or parsed_host.query + or parsed_host.fragment + or parsed_host.path not in ("", "/") + ): + raise ValueError("host must be an HTTPS workspace origin without credentials or a path") + if not control_warehouse_id or not SAFE_COMPONENT_RE.fullmatch(control_warehouse_id): + raise ValueError("control warehouse ID is missing or contains unsupported characters") + if bool(client_id) != bool(client_secret): + raise ValueError("workspace OAuth client ID and secret must be supplied together") + if bool(token) == bool(client_id and client_secret): + raise ValueError("configure exactly one workspace PAT or OAuth M2M credential") + if statement_timeout <= 0 or poll_interval <= 0 or network_timeout <= 0: + raise ValueError("timeouts and poll interval must be greater than zero") + + source, rendered, statements = prepare_ddl(catalog, schema, ddl_path=ddl_path) + output_dir = output_dir.expanduser().resolve() + output_dir.mkdir(parents=True, exist_ok=False) + rendered_path = output_dir / RENDERED_SQL_NAME + report_path = output_dir / REPORT_NAME + _atomic_write_new_text(rendered_path, rendered) + + secrets = tuple(value for value in (token, client_secret) if value) + records = [ + _statement_record(index, statement) + for index, statement in enumerate(statements, start=1) + ] + report: dict[str, Any] = { + "schema_version": 1, + "tool": "apply_ddl", + "status": "running", + "started_at": iso_utc(), + "finished_at": None, + "target": target, + "control_warehouse_id": control_warehouse_id, + "authentication_mode": "pat" if token else "oauth-client-credentials", + "input": { + "path": str(ddl_path.resolve()), + "sha256": sha256_bytes(source), + }, + "rendered_sql": { + "path": str(rendered_path), + "sha256": sha256_bytes(rendered.encode("utf-8")), + }, + "statement_count": len(statements), + "statements": records, + "errors": [], + } + atomic_write_json(report_path, redact_secrets(report, secrets)) + + try: + client = client_factory( + normalized_host, + token=token, + client_id=client_id, + client_secret=client_secret, + timeout=network_timeout, + ) + except Exception as exc: + error = redact_secrets(f"{type(exc).__name__}: {exc}", secrets) + report["status"] = "failed" + report["finished_at"] = iso_utc() + report["errors"].append(error) + atomic_write_json(report_path, redact_secrets(report, secrets)) + return redact_secrets(report, secrets) + + for record, statement in zip(records, statements): + record["status"] = "running" + record["started_at"] = iso_utc() + atomic_write_json(report_path, redact_secrets(report, secrets)) + statement_tags: Mapping[str, str] = { + "benchmark": "quotes-full-path", + "operation": "apply-ddl", + "target": target, + "statement_index": str(record["index"]), + "statement_sha256": str(record["sha256"]), + } + try: + response = client.execute_statement( + statement, + control_warehouse_id, + timeout=statement_timeout, + poll_interval=poll_interval, + catalog=catalog, + schema=schema, + query_tags=statement_tags, + ) + statement_id = response.get("statement_id") + if not isinstance(statement_id, str) or not statement_id: + raise RuntimeError("Statement Execution response omitted statement_id") + record["statement_id"] = statement_id + record["status"] = "succeeded" + except Exception as exc: + error = redact_secrets(f"{type(exc).__name__}: {exc}", secrets) + record["status"] = "failed" + record["error"] = error + report["errors"].append( + { + "statement_index": record["index"], + "error": error, + } + ) + report["status"] = "failed" + record["finished_at"] = iso_utc() + report["finished_at"] = iso_utc() + atomic_write_json(report_path, redact_secrets(report, secrets)) + return redact_secrets(report, secrets) + record["finished_at"] = iso_utc() + atomic_write_json(report_path, redact_secrets(report, secrets)) + + report["status"] = "succeeded" + report["finished_at"] = iso_utc() + atomic_write_json(report_path, redact_secrets(report, secrets)) + return redact_secrets(report, secrets) + + +def build_parser(env: Mapping[str, str] | None = None) -> argparse.ArgumentParser: + source = os.environ if env is None else env + parser = argparse.ArgumentParser( + description="Render and destructively apply the checked-in benchmark create.sql" + ) + parser.add_argument("--host", default=source.get("DATABRICKS_HOST")) + parser.add_argument( + "--control-warehouse-id", + default=source.get("DATABRICKS_CONTROL_WAREHOUSE_ID"), + ) + parser.add_argument("--catalog", default=source.get("DATABRICKS_CATALOG")) + parser.add_argument("--schema", default=source.get("DATABRICKS_SCHEMA")) + parser.add_argument("--confirm-destructive-target", required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--statement-timeout", type=float, default=300.0) + parser.add_argument("--poll-interval", type=float, default=0.5) + parser.add_argument("--network-timeout", type=float, default=30.0) + return parser + + +def run(argv: Sequence[str] | None = None) -> int: + parser = build_parser() + args = parser.parse_args(argv) + for name in ("host", "control_warehouse_id", "catalog", "schema"): + if not getattr(args, name): + parser.error( + f"--{name.replace('_', '-')} or its corresponding environment " + "variable is required" + ) + token = os.environ.get("DATABRICKS_TOKEN", "").strip() or None + client_id = os.environ.get("DATABRICKS_CLIENT_ID", "").strip() or None + client_secret = os.environ.get("DATABRICKS_CLIENT_SECRET", "").strip() or None + try: + report = apply_ddl( + host=args.host, + catalog=args.catalog, + schema=args.schema, + control_warehouse_id=args.control_warehouse_id, + confirm_destructive_target=args.confirm_destructive_target, + output_dir=args.output_dir, + token=token, + client_id=client_id, + client_secret=client_secret, + statement_timeout=args.statement_timeout, + poll_interval=args.poll_interval, + network_timeout=args.network_timeout, + ) + except (OSError, UnicodeError, ValueError) as exc: + safe = redact_secrets(str(exc), (token, client_secret)) + parser.error(safe) + print(f"DDL status: {report['status']}") + print(f"Artifacts: {args.output_dir.expanduser().resolve()}") + return 0 if report["status"] == "succeeded" else 1 + + +def main(argv: Sequence[str] | None = None) -> int: + try: + return run(argv) + except KeyboardInterrupt: + return 130 + except Exception as exc: + secrets = ( + os.environ.get("DATABRICKS_TOKEN"), + os.environ.get("DATABRICKS_CLIENT_SECRET"), + ) + safe = redact_secrets(f"{type(exc).__name__}: {exc}", secrets) + print(f"FATAL: {safe}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/full-path-realtime/quotes/databricks/collect_evidence.py b/full-path-realtime/quotes/databricks/collect_evidence.py new file mode 100644 index 0000000..89b80d7 --- /dev/null +++ b/full-path-realtime/quotes/databricks/collect_evidence.py @@ -0,0 +1,1281 @@ +#!/usr/bin/env python3 +"""Collect bounded, read-only Databricks benchmark evidence.""" + +from __future__ import annotations + +import argparse +import json +import math +import os +import sys +import tempfile +import urllib.parse +from collections import Counter +from datetime import datetime, timedelta, timezone +from decimal import Decimal, InvalidOperation +from pathlib import Path +from typing import Any, Iterable, Mapping, Sequence + +from dbx_common import ( + DatabricksRestClient, + atomic_write_json, + bounded_statement_rows, + iso_utc, + normalize_host, + quote_identifier, + redact_secrets, + sql_string, +) +from monitor_freshness import ( + classify_maintenance_type, + maintenance_type_from_detail, + parse_utc_timestamp, +) + +DEFAULT_MAX_WINDOW_HOURS = 72.0 +DEFAULT_ROW_LIMIT = 100_000 +DEFAULT_HISTORY_LIMIT = 10_000 +DEFAULT_MAX_RESULT_BYTES = 256 * 1024 * 1024 +DEFAULT_MAX_RESULT_CHUNKS = 1_000 + +REQUIRED_DATASETS = ( + "zerobus_ingest_summary", + "zerobus_ingest_by_minute_stream", + "mv_event_log", + "query_history", + "warehouse_events", + "warehouse_config_snapshots", + "predictive_optimization_operations", + "raw_table_detail", + "mv_table_detail", + "raw_table_history", + "uc_table_metadata", + "billing_usage", + "billing_list_prices", +) +OPTIONAL_DATASETS = ( + "zerobus_stream_schema", + "zerobus_stream_events", + "zerobus_stream_errors", +) + + +def _env_first(env: Mapping[str, str], *names: str) -> str | None: + for name in names: + value = env.get(name) + if value is not None and value.strip(): + return value.strip() + return None + + +def _positive_int(value: str) -> int: + parsed = int(value) + if parsed <= 0: + raise argparse.ArgumentTypeError("must be a positive integer") + return parsed + + +def validate_window( + since: Any, + until: Any, + *, + max_window_hours: float = DEFAULT_MAX_WINDOW_HOURS, +) -> tuple[datetime, datetime]: + """Return one strictly bounded UTC collection window.""" + if not math.isfinite(max_window_hours) or max_window_hours <= 0: + raise ValueError("maximum window must be a finite positive number of hours") + start = parse_utc_timestamp(since) + end = parse_utc_timestamp(until) + if end <= start: + raise ValueError("--until must be later than --since") + if end - start > timedelta(hours=max_window_hours): + raise ValueError( + "collection window exceeds the maximum " + f"of {max_window_hours:g} hours" + ) + return start, end + + +def _sql_timestamp(value: datetime) -> str: + return f"CAST({sql_string(iso_utc(value))} AS TIMESTAMP)" + + +def _qualified_name(catalog: str, schema: str, table: str) -> str: + return ".".join(quote_identifier(part) for part in (catalog, schema, table)) + + +def build_evidence_queries( + *, + catalog: str, + schema: str, + raw_table: str, + mv_table: str, + rt_warehouse_id: str, + control_warehouse_id: str, + run_id: str, + since: Any, + until: Any, + row_limit: int = DEFAULT_ROW_LIMIT, + history_limit: int = DEFAULT_HISTORY_LIMIT, + max_window_hours: float = DEFAULT_MAX_WINDOW_HOURS, +) -> dict[str, str]: + """Build fixed, target-scoped SQL for all non-dynamic evidence sources.""" + start, end = validate_window( + since, + until, + max_window_hours=max_window_hours, + ) + if row_limit <= 0 or history_limit <= 0: + raise ValueError("row and history limits must be positive") + start_sql = _sql_timestamp(start) + end_sql = _sql_timestamp(end) + target = ".".join((catalog, schema, raw_table)) + raw_name = _qualified_name(catalog, schema, raw_table) + mv_name = _qualified_name(catalog, schema, mv_table) + table_names = ", ".join(sql_string(value) for value in (raw_table, mv_table)) + warehouse_ids = ", ".join( + sql_string(value) for value in (rt_warehouse_id, control_warehouse_id) + ) + billing_products = ( + "UPPER(billing_origin_product) IN " + "('ZEROBUS', 'ZEROBUS_INGEST', 'PREDICTIVE_OPTIMIZATION', " + "'LAKEFLOW', 'LAKEFLOW_PIPELINES', 'PIPELINES', 'DLT') " + "OR UPPER(billing_origin_product) LIKE 'ZEROBUS%' " + "OR UPPER(billing_origin_product) LIKE 'LAKEFLOW%' " + "OR UPPER(billing_origin_product) LIKE '%PIPELINE%'" + ) + mv_pipeline_billing = ( + "(\n" + " UPPER(billing_origin_product) = 'SQL'\n" + " AND usage_metadata.dlt_pipeline_id = (\n" + " SELECT origin.pipeline_id\n" + f" FROM event_log(TABLE({mv_name}))\n" + " WHERE origin.pipeline_id IS NOT NULL\n" + " ORDER BY timestamp DESC\n" + " LIMIT 1\n" + " )\n" + " )" + ) + billing_filter = ( + f"usage_start_time < {end_sql}\n" + f" AND usage_end_time > {start_sql}\n" + " AND (\n" + f" usage_metadata.warehouse_id IN ({warehouse_ids})\n" + f" OR {billing_products}\n" + f" OR {mv_pipeline_billing}\n" + " )" + ) + + return { + "zerobus_ingest_summary": ( + "SELECT\n" + " COALESCE(SUM(committed_records), 0) AS provider_committed_records,\n" + " COALESCE(SUM(committed_bytes), 0) AS provider_committed_bytes,\n" + " COALESCE(SUM(COALESCE(size(errors), 0)), 0) AS provider_errors,\n" + " MIN(commit_version) AS min_commit_version,\n" + " MAX(commit_version) AS max_commit_version,\n" + " MIN(commit_time) AS min_commit_time,\n" + " MAX(commit_time) AS max_commit_time\n" + "FROM system.lakeflow.zerobus_ingest\n" + f"WHERE table_name = {sql_string(target)}\n" + f" AND commit_time >= {start_sql}\n" + f" AND commit_time < {end_sql}" + ), + "zerobus_ingest_by_minute_stream": ( + "SELECT\n" + " date_trunc('minute', commit_time) AS commit_minute,\n" + " stream_id,\n" + " SUM(committed_records) AS committed_records,\n" + " SUM(committed_bytes) AS committed_bytes,\n" + " SUM(COALESCE(size(errors), 0)) AS error_count,\n" + " MIN(commit_version) AS min_commit_version,\n" + " MAX(commit_version) AS max_commit_version,\n" + " MIN(commit_time) AS min_commit_time,\n" + " MAX(commit_time) AS max_commit_time\n" + "FROM system.lakeflow.zerobus_ingest\n" + f"WHERE table_name = {sql_string(target)}\n" + f" AND commit_time >= {start_sql}\n" + f" AND commit_time < {end_sql}\n" + "GROUP BY date_trunc('minute', commit_time), stream_id\n" + "ORDER BY commit_minute, stream_id\n" + f"LIMIT {row_limit}" + ), + "mv_event_log": ( + "SELECT\n" + " id, timestamp, event_type, message, level,\n" + " details AS details_json,\n" + " origin AS origin_json\n" + f"FROM event_log(TABLE({mv_name}))\n" + f"WHERE timestamp >= {start_sql}\n" + f" AND timestamp < {end_sql}\n" + " AND event_type IN ('planning_information', 'update_progress')\n" + "ORDER BY timestamp, id\n" + f"LIMIT {row_limit}" + ), + "query_history": ( + "SELECT\n" + " account_id, workspace_id, statement_id, session_id,\n" + " execution_status, compute.warehouse_id AS warehouse_id,\n" + " executed_by_user_id, executed_by, statement_type, error_message,\n" + " client_application, client_driver, cache_origin_statement_id,\n" + " total_duration_ms, waiting_for_compute_duration_ms,\n" + " waiting_at_capacity_duration_ms, execution_duration_ms,\n" + " compilation_duration_ms, total_task_duration_ms,\n" + " result_fetch_duration_ms, start_time, end_time, update_time,\n" + " read_partitions, pruned_files, read_files, read_rows, produced_rows,\n" + " read_bytes, read_io_cache_percent, from_result_cache,\n" + " spilled_local_bytes, written_bytes, written_rows, written_files,\n" + " shuffle_read_bytes, to_json(query_source) AS query_source_json,\n" + " to_json(query_tags) AS query_tags_json\n" + "FROM system.query.history\n" + f"WHERE compute.warehouse_id = {sql_string(rt_warehouse_id)}\n" + f" AND query_tags['run_id'] = {sql_string(run_id)}\n" + f" AND start_time >= {start_sql}\n" + f" AND start_time < {end_sql}\n" + "ORDER BY start_time, statement_id\n" + f"LIMIT {row_limit}" + ), + "warehouse_events": ( + "SELECT account_id, workspace_id, warehouse_id, event_type,\n" + " cluster_count, event_time\n" + "FROM system.compute.warehouse_events\n" + f"WHERE warehouse_id IN ({warehouse_ids})\n" + f" AND event_time >= {start_sql}\n" + f" AND event_time < {end_sql}\n" + "ORDER BY event_time, warehouse_id, event_type\n" + f"LIMIT {row_limit}" + ), + "warehouse_config_snapshots": ( + "SELECT warehouse_id, workspace_id, warehouse_name, warehouse_type,\n" + " warehouse_channel, warehouse_size, min_clusters, max_clusters,\n" + " auto_stop_minutes, to_json(tags) AS tags_json,\n" + " change_time, delete_time\n" + "FROM system.compute.warehouses\n" + f"WHERE warehouse_id IN ({warehouse_ids})\n" + f" AND change_time < {end_sql}\n" + "QUALIFY ROW_NUMBER() OVER (\n" + " PARTITION BY warehouse_id ORDER BY change_time DESC\n" + ") = 1\n" + "ORDER BY warehouse_id\n" + "LIMIT 2" + ), + "predictive_optimization_operations": ( + "SELECT metastore_name, catalog_name, schema_name, table_name,\n" + " operation_id, operation_type, start_time, end_time,\n" + " operation_status, usage_unit, usage_quantity,\n" + " to_json(operation_metrics) AS operation_metrics_json\n" + "FROM system.storage.predictive_optimization_operations_history\n" + f"WHERE catalog_name = {sql_string(catalog)}\n" + f" AND schema_name = {sql_string(schema)}\n" + f" AND table_name IN ({table_names})\n" + f" AND start_time < {end_sql}\n" + f" AND COALESCE(end_time, {end_sql}) > {start_sql}\n" + "ORDER BY start_time, operation_id\n" + f"LIMIT {row_limit}" + ), + "raw_table_detail": f"DESCRIBE DETAIL {raw_name}", + "mv_table_detail": f"DESCRIBE TABLE EXTENDED {mv_name} AS JSON", + "raw_table_history": ( + f"DESCRIBE HISTORY {raw_name} LIMIT {history_limit}" + ), + "billing_usage": ( + "SELECT account_id, workspace_id, record_id, sku_name, cloud,\n" + " usage_start_time, usage_end_time, usage_date,\n" + " usage_unit, usage_quantity, record_type, ingestion_date,\n" + " billing_origin_product,\n" + " to_json(usage_metadata) AS usage_metadata_json,\n" + " to_json(product_features) AS product_features_json,\n" + " to_json(custom_tags) AS custom_tags_json,\n" + " to_json(identity_metadata) AS identity_metadata_json\n" + "FROM system.billing.usage\n" + f"WHERE {billing_filter}\n" + "ORDER BY usage_start_time, record_id\n" + f"LIMIT {row_limit}" + ), + "billing_list_prices": ( + "WITH observed_usage AS (\n" + " SELECT sku_name, cloud,\n" + " MIN(usage_start_time) AS first_usage_time,\n" + " MAX(usage_end_time) AS last_usage_time\n" + " FROM system.billing.usage\n" + f" WHERE {billing_filter}\n" + " GROUP BY sku_name, cloud\n" + ")\n" + "SELECT p.price_start_time, p.price_end_time, p.account_id,\n" + " p.sku_name, p.cloud, p.currency_code, p.usage_unit,\n" + " to_json(p.pricing) AS pricing_json\n" + "FROM system.billing.list_prices AS p\n" + "INNER JOIN observed_usage AS u\n" + " ON p.sku_name = u.sku_name AND p.cloud = u.cloud\n" + " AND p.price_start_time < u.last_usage_time\n" + " AND (p.price_end_time IS NULL OR p.price_end_time > u.first_usage_time)\n" + "ORDER BY p.sku_name, p.cloud, p.price_start_time\n" + f"LIMIT {row_limit}" + ), + } + + +def build_zerobus_stream_query( + schema_rows: Sequence[Mapping[str, Any]], + *, + target: str, + since: Any, + until: Any, + row_limit: int, + max_window_hours: float = DEFAULT_MAX_WINDOW_HOURS, +) -> str: + """Build a bounded stream query only after inspecting the live schema.""" + start, end = validate_window( + since, + until, + max_window_hours=max_window_hours, + ) + discovered: list[str] = [] + seen: set[str] = set() + for row in schema_rows: + name = _row_value(row, "col_name") + if name in (None, ""): + name = _row_value(row, "column_name") + text = str(name or "").strip() + if not text or text.startswith("#") or text.lower() in seen: + continue + quote_identifier(text) + discovered.append(text) + seen.add(text.lower()) + missing = [name for name in ("table_name", "event_time") if name not in seen] + if missing: + raise ValueError( + "unsupported system.lakeflow.zerobus_stream schema; missing documented " + "filter column(s): " + ", ".join(missing) + ) + if row_limit <= 0: + raise ValueError("row limit must be positive") + selected = ", ".join(quote_identifier(name) for name in discovered) + stream_order = ", `stream_id`" if "stream_id" in seen else "" + return ( + f"SELECT {selected}\n" + "FROM system.lakeflow.zerobus_stream\n" + f"WHERE `table_name` = {sql_string(target)}\n" + f" AND `event_time` >= {_sql_timestamp(start)}\n" + f" AND `event_time` < {_sql_timestamp(end)}\n" + f"ORDER BY `event_time`{stream_order}\n" + f"LIMIT {row_limit}" + ) + + +def _atomic_write_jsonl(path: Path, rows: Iterable[Mapping[str, Any]]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + fd, temporary = tempfile.mkstemp(prefix=f".{path.name}.", dir=path.parent) + try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + for row in rows: + json.dump(row, handle, separators=(",", ":"), ensure_ascii=False) + handle.write("\n") + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, path) + finally: + try: + os.unlink(temporary) + except FileNotFoundError: + pass + + +def _row_value(row: Mapping[str, Any], name: str) -> Any: + wanted = name.lower() + for key, value in row.items(): + if str(key).lower() == wanted: + return value + return None + + +def _exact_int(value: Any) -> int | None: + if value is None or isinstance(value, bool): + return None + try: + return int(value) + except (TypeError, ValueError, OverflowError): + return None + + +def _json_value(value: Any) -> Any: + decoded = value + for _ in range(3): + if not isinstance(decoded, str): + return decoded + text = decoded.strip() + if not text or text[0] not in "[{\"": + return decoded + try: + decoded = json.loads(text) + except (TypeError, ValueError, json.JSONDecodeError): + return decoded + return decoded + + +def _stream_error_count(rows: Sequence[Mapping[str, Any]]) -> int: + total = 0 + for row in rows: + errors = _json_value(_row_value(row, "errors")) + if isinstance(errors, list): + total += len(errors) + elif errors not in (None, "", []): + total += 1 + return total + + +def _stream_error_rows( + rows: Sequence[Mapping[str, Any]], +) -> list[Mapping[str, Any]]: + return [ + row + for row in rows + if _stream_error_count((row,)) > 0 + ] + + +def _decimal(value: Any) -> Decimal | None: + if value is None or isinstance(value, bool): + return None + try: + parsed = Decimal(str(value)) + except (InvalidOperation, TypeError, ValueError): + return None + return parsed if parsed.is_finite() else None + + +def _mv_maintenance_summary(rows: Sequence[Mapping[str, Any]]) -> dict[str, Any]: + maintenance_types: list[str] = [] + for row in rows: + if str(_row_value(row, "event_type") or "").lower() != "planning_information": + continue + detail = _row_value(row, "details_json") + if detail is None: + detail = _row_value(row, "details") + maintenance_type = maintenance_type_from_detail(detail) + if maintenance_type is not None: + maintenance_types.append(maintenance_type) + classes = Counter(classify_maintenance_type(value) for value in maintenance_types) + return { + "maintenance_types": maintenance_types, + "maintenance_type_counts": dict(Counter(maintenance_types)), + "maintenance_class_counts": dict(classes), + "full_refresh_count": classes.get("full", 0), + } + + +def _predictive_summary(rows: Sequence[Mapping[str, Any]]) -> dict[str, Any]: + counts = Counter(str(_row_value(row, "operation_type") or "UNKNOWN") for row in rows) + usage: dict[str, Decimal] = {} + for row in rows: + quantity = _decimal(_row_value(row, "usage_quantity")) + if quantity is None: + continue + unit = str(_row_value(row, "usage_unit") or "UNKNOWN") + usage[unit] = usage.get(unit, Decimal(0)) + quantity + return { + "operation_count": len(rows), + "operation_counts_by_type": dict(counts), + "usage_by_unit": {unit: format(value, "f") for unit, value in usage.items()}, + } + + +def _billing_summary(rows: Sequence[Mapping[str, Any]]) -> dict[str, Any]: + usage: dict[str, Decimal] = {} + skus: set[str] = set() + for row in rows: + sku = _row_value(row, "sku_name") + if sku not in (None, ""): + skus.add(str(sku)) + quantity = _decimal(_row_value(row, "usage_quantity")) + if quantity is None: + continue + unit = str(_row_value(row, "usage_unit") or "UNKNOWN") + usage[unit] = usage.get(unit, Decimal(0)) + quantity + return { + "row_count": len(rows), + "observed_skus": sorted(skus), + "usage_by_unit": {unit: format(value, "f") for unit, value in usage.items()}, + "settlement_warning": ( + "system.billing.usage can lag actual usage by up to 24 hours; " + "rerun collection after settlement before final cost validation." + ), + } + + +def build_summary( + *, + since: Any, + until: Any, + run_id: str, + targets: Mapping[str, Any], + datasets: Mapping[str, Mapping[str, Any]], + rows: Mapping[str, Sequence[Mapping[str, Any]]], + collected_at: Any | None = None, + max_window_hours: float = DEFAULT_MAX_WINDOW_HOURS, +) -> dict[str, Any]: + """Build the deterministic evidence summary from exported rows.""" + start, end = validate_window( + since, + until, + max_window_hours=max_window_hours, + ) + ingest_rows = rows.get("zerobus_ingest_summary", ()) + ingest = ingest_rows[0] if ingest_rows else {} + required_failed = [ + name + for name in REQUIRED_DATASETS + if name not in datasets or datasets[name].get("error") + ] + optional_failed = [ + name + for name in OPTIONAL_DATASETS + if name not in datasets or datasets[name].get("error") + ] + required_succeeded = [ + name for name in REQUIRED_DATASETS if name not in required_failed + ] + table_details = { + "raw": ( + dict(rows["raw_table_detail"][0]) + if rows.get("raw_table_detail") + else None + ), + "materialized_view": ( + dict(rows["mv_table_detail"][0]) + if rows.get("mv_table_detail") + else None + ), + } + return { + "schema_version": 1, + "collected_at": iso_utc( + datetime.now(timezone.utc) + if collected_at is None + else parse_utc_timestamp(collected_at) + ), + "collection_window": { + "since": iso_utc(start), + "until": iso_utc(end), + "duration_seconds": (end - start).total_seconds(), + }, + "run_id": run_id, + "targets": dict(targets), + "datasets": {name: dict(value) for name, value in datasets.items()}, + "provider_committed_records": _exact_int( + _row_value(ingest, "provider_committed_records") + ), + "provider_committed_bytes": _exact_int( + _row_value(ingest, "provider_committed_bytes") + ), + "provider_errors": _exact_int(_row_value(ingest, "provider_errors")), + "zerobus_ingest_bounds": { + name: _row_value(ingest, name) + for name in ( + "min_commit_version", + "max_commit_version", + "min_commit_time", + "max_commit_time", + ) + }, + "zerobus_stream_error_count": _stream_error_count( + rows.get("zerobus_stream_errors", ()) + ), + "mv_maintenance": _mv_maintenance_summary(rows.get("mv_event_log", ())), + "frozen_warehouse_configs": [ + dict(row) for row in rows.get("warehouse_config_snapshots", ()) + ], + "table_details": table_details, + "table_num_rows": { + "raw": _exact_int(_row_value(table_details["raw"] or {}, "numRows")), + "materialized_view": _exact_int( + _row_value(table_details["materialized_view"] or {}, "numRows") + ), + }, + "predictive_optimization": _predictive_summary( + rows.get("predictive_optimization_operations", ()) + ), + "billing": _billing_summary(rows.get("billing_usage", ())), + "required_dataset_completeness": { + "required": list(REQUIRED_DATASETS), + "succeeded": required_succeeded, + "failed": required_failed, + "complete": not required_failed, + }, + "optional_dataset_errors": optional_failed, + "complete": not required_failed and not optional_failed, + } + + +def _assert_read_only(sql: str) -> None: + first = sql.lstrip().split(None, 1)[0].upper() if sql.strip() else "" + if first not in {"SELECT", "WITH", "DESCRIBE"}: + raise ValueError(f"collector refused non-read-only SQL beginning with {first!r}") + + +class EvidenceCollector: + def __init__( + self, + client: DatabricksRestClient, + *, + control_warehouse_id: str, + catalog: str, + schema: str, + run_id: str, + output_dir: Path, + statement_timeout: float, + poll_interval: float, + max_rows: int, + max_result_bytes: int, + max_result_chunks: int, + secrets: Sequence[str], + ): + self.client = client + self.control_warehouse_id = control_warehouse_id + self.catalog = catalog + self.schema = schema + self.run_id = run_id + self.output_dir = output_dir + self.statement_timeout = statement_timeout + self.poll_interval = poll_interval + self.max_rows = max_rows + self.max_result_bytes = max_result_bytes + self.max_result_chunks = max_result_chunks + self.secrets = tuple(secrets) + self.rows: dict[str, list[dict[str, Any]]] = {} + self.datasets: dict[str, dict[str, Any]] = {} + self.statements: list[dict[str, Any]] = [] + self.errors: list[dict[str, Any]] = [] + + def _path(self, name: str) -> Path: + return self.output_dir / f"{name}.jsonl" + + def _record( + self, + name: str, + rows: Sequence[Mapping[str, Any]], + *, + statement_id: str | None, + error: str | None, + required: bool, + metadata: Mapping[str, Any] | None = None, + ) -> None: + safe_rows = redact_secrets(list(rows), self.secrets) + assert isinstance(safe_rows, list) + path = self._path(name) + write_error: str | None = None + try: + _atomic_write_jsonl(path, safe_rows) + except Exception as exc: + write_error = f"{type(exc).__name__}: {exc}" + combined_error = error + if write_error: + combined_error = ( + f"{combined_error}; output write failed: {write_error}" + if combined_error + else f"output write failed: {write_error}" + ) + self.rows[name] = safe_rows + self.datasets[name] = { + "required": required, + "row_count": len(safe_rows), + "path": str(path), + "statement_id": statement_id, + "error": combined_error, + } + if metadata: + self.datasets[name]["result"] = dict(metadata) + if combined_error: + self.errors.append( + {"dataset": name, "required": required, "error": combined_error} + ) + + def sql( + self, + name: str, + sql: str, + *, + required: bool, + max_rows: int | None = None, + ) -> list[dict[str, Any]]: + _assert_read_only(sql) + response: Mapping[str, Any] | None = None + statement_id: str | None = None + metadata: dict[str, Any] | None = None + error: str | None = None + result_rows: list[dict[str, Any]] = [] + try: + response = self.client.execute_statement( + sql, + self.control_warehouse_id, + timeout=self.statement_timeout, + poll_interval=self.poll_interval, + catalog=self.catalog, + schema=self.schema, + query_tags={ + "benchmark": "full-path-realtime", + "run_id": self.run_id, + "collector": "evidence", + "dataset": name, + }, + ) + statement_id_value = response.get("statement_id") + statement_id = ( + str(statement_id_value) + if statement_id_value not in (None, "") + else None + ) + result_rows, metadata = bounded_statement_rows( + self.client, + response, + max_rows=max_rows or self.max_rows, + max_bytes=self.max_result_bytes, + max_chunks=self.max_result_chunks, + ) + except Exception as exc: + error = f"{type(exc).__name__}: {exc}" + statement = { + "dataset": name, + "statement_id": statement_id, + "warehouse_id": self.control_warehouse_id, + "sql": sql, + "row_count": len(result_rows), + "error": error, + } + if metadata: + statement["result"] = metadata + self.statements.append(redact_secrets(statement, self.secrets)) + self._record( + name, + result_rows, + statement_id=statement_id, + error=error, + required=required, + metadata=metadata, + ) + return result_rows + + def rest_table_metadata( + self, + targets: Sequence[tuple[str, str]], + ) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + item_errors: list[str] = [] + for role, full_name in targets: + encoded = urllib.parse.quote(full_name, safe="") + try: + metadata = self.client.request( + "GET", f"/api/2.1/unity-catalog/tables/{encoded}" + ) + rows.append({"role": role, **metadata}) + except Exception as exc: + item_errors.append(f"{role}: {type(exc).__name__}: {exc}") + error = "; ".join(item_errors) or None + self._record( + "uc_table_metadata", + rows, + statement_id=None, + error=error, + required=True, + metadata={"method": "GET", "target_count": len(targets)}, + ) + return rows + + def finish_metadata(self) -> None: + _atomic_write_jsonl( + self.output_dir / "collector_statements.jsonl", self.statements + ) + safe_errors = redact_secrets(self.errors, self.secrets) + assert isinstance(safe_errors, list) + _atomic_write_jsonl(self.output_dir / "collector_errors.jsonl", safe_errors) + + +def _validate_collected_targets( + collector: EvidenceCollector, + *, + rt_warehouse_id: str, + control_warehouse_id: str, +) -> None: + checks: list[tuple[str, str | None]] = [] + if len(collector.rows.get("zerobus_ingest_summary", ())) != 1: + checks.append( + ("zerobus_ingest_summary", "aggregate query did not return exactly one row") + ) + config_ids = { + str(_row_value(row, "warehouse_id")) + for row in collector.rows.get("warehouse_config_snapshots", ()) + } + missing_ids = {rt_warehouse_id, control_warehouse_id} - config_ids + if missing_ids: + checks.append( + ( + "warehouse_config_snapshots", + "missing end-of-window warehouse snapshot(s): " + + ", ".join(sorted(missing_ids)), + ) + ) + if len(collector.rows.get("uc_table_metadata", ())) != 2: + checks.append( + ("uc_table_metadata", "Unity Catalog REST metadata omitted a target table") + ) + for name, description in ( + ("raw_table_detail", "DESCRIBE DETAIL"), + ("mv_table_detail", "DESCRIBE TABLE EXTENDED AS JSON"), + ): + if len(collector.rows.get(name, ())) != 1: + checks.append( + (name, f"{description} did not return exactly one row") + ) + + for name, error in checks: + dataset = collector.datasets.get(name) + if dataset is None or not error: + continue + old = dataset.get("error") + dataset["error"] = f"{old}; {error}" if old else error + collector.errors.append({"dataset": name, "required": True, "error": error}) + + +def collect( + client: DatabricksRestClient, + *, + control_warehouse_id: str, + rt_warehouse_id: str, + catalog: str, + schema: str, + raw_table: str, + mv_table: str, + since: datetime, + until: datetime, + run_id: str, + output_dir: Path, + row_limit: int, + history_limit: int, + statement_timeout: float, + poll_interval: float, + max_result_bytes: int, + max_result_chunks: int, + max_window_hours: float = DEFAULT_MAX_WINDOW_HOURS, + secrets: Sequence[str] = (), +) -> tuple[dict[str, Any], bool]: + """Collect all evidence and return ``(summary, required_sources_complete)``.""" + output_dir.mkdir(parents=True, exist_ok=True) + raw_full_name = ".".join((catalog, schema, raw_table)) + mv_full_name = ".".join((catalog, schema, mv_table)) + targets = { + "catalog": catalog, + "schema": schema, + "raw_table": raw_table, + "materialized_view": mv_table, + "raw_full_name": raw_full_name, + "materialized_view_full_name": mv_full_name, + "rt_warehouse_id": rt_warehouse_id, + "control_warehouse_id": control_warehouse_id, + } + collector = EvidenceCollector( + client, + control_warehouse_id=control_warehouse_id, + catalog=catalog, + schema=schema, + run_id=run_id, + output_dir=output_dir, + statement_timeout=statement_timeout, + poll_interval=poll_interval, + max_rows=row_limit, + max_result_bytes=max_result_bytes, + max_result_chunks=max_result_chunks, + secrets=secrets, + ) + summary: dict[str, Any] | None = None + try: + queries = build_evidence_queries( + catalog=catalog, + schema=schema, + raw_table=raw_table, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + run_id=run_id, + since=since, + until=until, + row_limit=row_limit, + history_limit=history_limit, + max_window_hours=max_window_hours, + ) + for name, sql in queries.items(): + if name in ( + "zerobus_ingest_summary", + "raw_table_detail", + "mv_table_detail", + ): + result_row_limit = 1 + elif name in ("raw_table_history", "mv_table_history"): + result_row_limit = history_limit + elif name == "warehouse_config_snapshots": + result_row_limit = 2 + else: + result_row_limit = row_limit + collector.sql( + name, + sql, + required=True, + max_rows=result_row_limit, + ) + + schema_rows = collector.sql( + "zerobus_stream_schema", + "DESCRIBE TABLE system.lakeflow.zerobus_stream", + required=False, + max_rows=1_000, + ) + if collector.datasets["zerobus_stream_schema"]["error"]: + unsupported_error = ( + "unsupported schema: DESCRIBE TABLE " + "system.lakeflow.zerobus_stream failed" + ) + collector._record( + "zerobus_stream_events", + (), + statement_id=None, + error=unsupported_error, + required=False, + ) + collector._record( + "zerobus_stream_errors", + (), + statement_id=None, + error=unsupported_error, + required=False, + ) + else: + try: + stream_sql = build_zerobus_stream_query( + schema_rows, + target=raw_full_name, + since=since, + until=until, + row_limit=row_limit, + max_window_hours=max_window_hours, + ) + except Exception as exc: + unsupported_error = f"{type(exc).__name__}: {exc}" + collector._record( + "zerobus_stream_events", + (), + statement_id=None, + error=unsupported_error, + required=False, + ) + collector._record( + "zerobus_stream_errors", + (), + statement_id=None, + error=unsupported_error, + required=False, + ) + else: + stream_rows = collector.sql( + "zerobus_stream_events", + stream_sql, + required=False, + ) + stream_dataset = collector.datasets["zerobus_stream_events"] + collector._record( + "zerobus_stream_errors", + _stream_error_rows(stream_rows), + statement_id=stream_dataset.get("statement_id"), + error=stream_dataset.get("error"), + required=False, + metadata={"source_dataset": "zerobus_stream_events"}, + ) + + collector.rest_table_metadata( + (("raw", raw_full_name), ("materialized_view", mv_full_name)) + ) + _validate_collected_targets( + collector, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + ) + except Exception as exc: + collector.errors.append( + { + "dataset": "collector", + "required": True, + "error": f"{type(exc).__name__}: {exc}", + } + ) + for name in REQUIRED_DATASETS: + if name not in collector.datasets: + collector._record( + name, + (), + statement_id=None, + error="collection aborted before this required source was collected", + required=True, + ) + for name in OPTIONAL_DATASETS: + if name not in collector.datasets: + collector._record( + name, + (), + statement_id=None, + error="collection aborted before this optional source was collected", + required=False, + ) + finally: + for name in REQUIRED_DATASETS: + if name not in collector.datasets: + collector._record( + name, + (), + statement_id=None, + error="required source was not collected", + required=True, + ) + for name in OPTIONAL_DATASETS: + if name not in collector.datasets: + collector._record( + name, + (), + statement_id=None, + error="optional source was not collected", + required=False, + ) + try: + collector.finish_metadata() + except Exception as exc: + collector.errors.append( + { + "dataset": "collector_metadata", + "required": True, + "error": f"{type(exc).__name__}: {exc}", + } + ) + summary = build_summary( + since=since, + until=until, + run_id=run_id, + targets=targets, + datasets=collector.datasets, + rows=collector.rows, + max_window_hours=max_window_hours, + ) + summary["errors"] = list(collector.errors) + summary["required_dataset_completeness"]["complete"] = ( + not summary["required_dataset_completeness"]["failed"] + and not any( + error.get("required") + for error in collector.errors + if error.get("dataset") not in collector.datasets + ) + ) + if not summary["required_dataset_completeness"]["complete"]: + summary["complete"] = False + atomic_write_json( + output_dir / "evidence_summary.json", + redact_secrets(summary, secrets), + ) + assert summary is not None + return summary, bool(summary["required_dataset_completeness"]["complete"]) + + +def build_parser(env: Mapping[str, str] | None = None) -> argparse.ArgumentParser: + source = os.environ if env is None else env + parser = argparse.ArgumentParser( + description="Export bounded, read-only Databricks benchmark evidence" + ) + parser.add_argument( + "--host", + default=_env_first( + source, + "DATABRICKS_HOST", + "DATABRICKS_SERVER_HOSTNAME", + "DATABRICKS_WORKSPACE_URL", + ), + ) + parser.add_argument( + "--token", + default=_env_first(source, "DATABRICKS_TOKEN", "DATABRICKS_ACCESS_TOKEN"), + help=argparse.SUPPRESS, + ) + parser.add_argument("--client-id", default=_env_first(source, "DATABRICKS_CLIENT_ID")) + parser.add_argument( + "--client-secret", + default=_env_first(source, "DATABRICKS_CLIENT_SECRET"), + help=argparse.SUPPRESS, + ) + parser.add_argument( + "--control-warehouse-id", + "--control-warehouse", + dest="control_warehouse_id", + default=_env_first( + source, + "DATABRICKS_CONTROL_WAREHOUSE_ID", + "DBX_CONTROL_WAREHOUSE_ID", + "CONTROL_WAREHOUSE_ID", + ), + ) + parser.add_argument( + "--rt-warehouse-id", + "--rt-warehouse", + dest="rt_warehouse_id", + default=_env_first( + source, + "DATABRICKS_RT_WAREHOUSE_ID", + "DBX_RT_WAREHOUSE_ID", + "RT_WAREHOUSE_ID", + ), + ) + parser.add_argument( + "--catalog", + default=_env_first(source, "DATABRICKS_CATALOG", "DBX_CATALOG") or "workspace", + ) + parser.add_argument( + "--schema", + default=_env_first(source, "DATABRICKS_SCHEMA", "DBX_SCHEMA") + or "benchmarking", + ) + parser.add_argument( + "--raw-table", + default=_env_first(source, "DATABRICKS_RAW_TABLE", "DBX_RAW_TABLE") or "quotes", + ) + parser.add_argument( + "--mv-table", + default=_env_first(source, "DATABRICKS_MV_TABLE", "DBX_MV_TABLE") + or "quotes_daily", + ) + parser.add_argument("--since", required=True, help="required UTC ISO-8601 start") + parser.add_argument("--until", required=True, help="required UTC ISO-8601 end") + parser.add_argument("--max-window-hours", type=float, default=DEFAULT_MAX_WINDOW_HOURS) + parser.add_argument( + "--run-id", + required=not bool(_env_first(source, "BENCHMARK_RUN_ID", "DATABRICKS_RUN_ID")), + default=_env_first(source, "BENCHMARK_RUN_ID", "DATABRICKS_RUN_ID"), + ) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--row-limit", type=_positive_int, default=DEFAULT_ROW_LIMIT) + parser.add_argument( + "--history-limit", type=_positive_int, default=DEFAULT_HISTORY_LIMIT + ) + parser.add_argument( + "--max-result-bytes", + type=_positive_int, + default=DEFAULT_MAX_RESULT_BYTES, + ) + parser.add_argument( + "--max-result-chunks", + type=_positive_int, + default=DEFAULT_MAX_RESULT_CHUNKS, + ) + parser.add_argument("--statement-timeout", type=float, default=120.0) + parser.add_argument("--statement-poll-interval", type=float, default=0.5) + parser.add_argument("--network-timeout", type=float, default=30.0) + return parser + + +def finalize_args( + parser: argparse.ArgumentParser, + args: argparse.Namespace, +) -> argparse.Namespace: + if ( + args.statement_timeout <= 0 + or args.statement_poll_interval <= 0 + or args.network_timeout <= 0 + ): + parser.error("timeouts and poll interval must be greater than zero") + try: + args.since, args.until = validate_window( + args.since, + args.until, + max_window_hours=args.max_window_hours, + ) + except (TypeError, ValueError, OverflowError, OSError) as exc: + parser.error(f"invalid bounded UTC collection window: {exc}") + args.host = normalize_host(args.host) + if not args.host: + parser.error("--host or DATABRICKS_HOST is required") + parsed_host = urllib.parse.urlparse(args.host) + if ( + parsed_host.scheme != "https" + or not parsed_host.hostname + or parsed_host.username + or parsed_host.password + or parsed_host.query + or parsed_host.fragment + or parsed_host.path not in ("", "/") + ): + parser.error("--host must be an HTTPS workspace origin without a path") + if not args.control_warehouse_id or not args.rt_warehouse_id: + parser.error("--control-warehouse-id and --rt-warehouse-id are required") + if bool(args.client_id) != bool(args.client_secret): + parser.error("--client-id and --client-secret must be supplied together") + if bool(args.token) == bool(args.client_id and args.client_secret): + parser.error("configure exactly one of PAT or OAuth M2M credentials") + for name in ("catalog", "schema", "raw_table", "mv_table"): + try: + quote_identifier(getattr(args, name)) + except ValueError as exc: + parser.error(f"--{name.replace('_', '-')} is invalid: {exc}") + if not str(args.run_id).strip(): + parser.error("--run-id must be non-empty") + args.output_dir = args.output_dir.expanduser().resolve() + return args + + +def run(argv: Sequence[str] | None = None) -> int: + parser = build_parser() + args = finalize_args(parser, parser.parse_args(argv)) + client = DatabricksRestClient( + args.host, + token=args.token, + client_id=args.client_id, + client_secret=args.client_secret, + timeout=args.network_timeout, + ) + secrets = tuple( + value for value in (args.token, args.client_secret) if isinstance(value, str) + ) + summary, required_complete = collect( + client, + control_warehouse_id=args.control_warehouse_id, + rt_warehouse_id=args.rt_warehouse_id, + catalog=args.catalog, + schema=args.schema, + raw_table=args.raw_table, + mv_table=args.mv_table, + since=args.since, + until=args.until, + run_id=args.run_id, + output_dir=args.output_dir, + row_limit=args.row_limit, + history_limit=args.history_limit, + statement_timeout=args.statement_timeout, + poll_interval=args.statement_poll_interval, + max_result_bytes=args.max_result_bytes, + max_result_chunks=args.max_result_chunks, + max_window_hours=args.max_window_hours, + secrets=secrets, + ) + for error in summary.get("errors", []): + print( + f"{error.get('dataset')}: {error.get('error')}", + file=sys.stderr, + ) + print(f"Evidence written to {args.output_dir}") + return 0 if required_complete else 1 + + +def main(argv: Sequence[str] | None = None) -> int: + try: + return run(argv) + except KeyboardInterrupt: + print("Interrupted.", file=sys.stderr) + return 130 + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/full-path-realtime/quotes/databricks/collect_full_serverless_settled.sh b/full-path-realtime/quotes/databricks/collect_full_serverless_settled.sh new file mode 100755 index 0000000..9fde1c7 --- /dev/null +++ b/full-path-realtime/quotes/databricks/collect_full_serverless_settled.sh @@ -0,0 +1,204 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Recollect settled billing for one completed canonical compact-result run. + +umask 077 + +DBX_BENCH_DIR="/home/ubuntu/costbench/full-path-realtime/quotes/databricks" +: "${DATABRICKS_HOST:?Set the Databricks workspace HTTPS origin}" +: "${DATABRICKS_CLIENT_ID:?Set the benchmark service-principal application ID}" +RUN_ROOT="${1:?Usage: collect_full_serverless_settled.sh RUN_ROOT}" +RUN_CONTEXT="$RUN_ROOT/run_context.json" +if [[ ! -s "$RUN_CONTEXT" ]]; then + printf 'Missing run context: %s\n' "$RUN_CONTEXT" >&2 + exit 1 +fi +jq -e ' + ( + .status == "measurement_complete" + or .status == "complete" + or .status == "complete_with_duplicates" + ) + and .run_id + and .since + and .until + and .producer_finished_at +' "$RUN_CONTEXT" >/dev/null + +RUN_ID="$(jq -er '.run_id' "$RUN_CONTEXT")" +RUN_STATE="/data/databricks-state/${RUN_ID}" +SOURCE_MANIFEST="$RUN_STATE/source_manifest.json" +SINCE_UTC="$(jq -er '.since' "$RUN_CONTEXT")" +UNTIL_UTC="$(jq -er '.until' "$RUN_CONTEXT")" +PRODUCER_FINISHED_AT="$(jq -er '.producer_finished_at' "$RUN_CONTEXT")" +CATALOG="$(jq -er '.catalog' "$RUN_CONTEXT")" +SCHEMA="$(jq -er '.schema' "$RUN_CONTEXT")" +RAW_TABLE="$(jq -er '.raw_table' "$RUN_CONTEXT")" +MV_TABLE="$(jq -er '.mv_table' "$RUN_CONTEXT")" +QUERY_WAREHOUSE_ID="$(jq -er '.query_warehouse_id' "$RUN_CONTEXT")" +CONTROL_WAREHOUSE_ID="$(jq -er '.control_warehouse_id' "$RUN_CONTEXT")" +STAMP="$(date -u +%Y%m%dT%H%M%SZ)" +EVIDENCE_DIR="$RUN_STATE/evidence-settled-$STAMP" +COST_OUTPUT="$RUN_ROOT/costs/cost_summary_settled_$STAMP.json" +VALIDATION_OUTPUT="$RUN_ROOT/validation/validation_report_settled_$STAMP.json" + +shopt -s nullglob +dashboards=("$RUN_ROOT"/mv/dashboard_*.jsonl) +drilldowns=("$RUN_ROOT"/raw/drilldown_*.jsonl) +freshness=("$RUN_ROOT"/freshness/mv_freshness_*.jsonl) +shopt -u nullglob +if [[ "${#dashboards[@]}" -ne 1 || "${#drilldowns[@]}" -ne 1 || \ + "${#freshness[@]}" -ne 1 ]]; then + echo "Expected exactly one dashboard, drill-down, and freshness JSONL." >&2 + exit 1 +fi +QUERY_AUDIT_DIR="$RUN_STATE/query-evidence" +DASHBOARD_AUDIT="$QUERY_AUDIT_DIR/$(basename "${dashboards[0]}")" +DRILLDOWN_AUDIT="$QUERY_AUDIT_DIR/$(basename "${drilldowns[0]}")" +DASHBOARD_VALIDATION="${dashboards[0]}" +DRILLDOWN_VALIDATION="${drilldowns[0]}" +if [[ -s "$DASHBOARD_AUDIT" ]]; then + DASHBOARD_VALIDATION="$DASHBOARD_AUDIT" +fi +if [[ -s "$DRILLDOWN_AUDIT" ]]; then + DRILLDOWN_VALIDATION="$DRILLDOWN_AUDIT" +fi +if [[ ! -s "$SOURCE_MANIFEST" ]]; then + printf 'Missing retained runtime source manifest: %s\n' "$SOURCE_MANIFEST" >&2 + exit 1 +fi + +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET +echo +export DATABRICKS_CLIENT_SECRET +export DATABRICKS_CLIENT_ID +unset DATABRICKS_TOKEN +trap 'unset DATABRICKS_CLIENT_SECRET' EXIT + +RUNNER_PYTHON="$DBX_BENCH_DIR/.venv-runner/bin/python" +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/collect_evidence.py" \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$CONTROL_WAREHOUSE_ID" \ + --rt-warehouse-id "$QUERY_WAREHOUSE_ID" \ + --catalog "$CATALOG" \ + --schema "$SCHEMA" \ + --raw-table "$RAW_TABLE" \ + --mv-table "$MV_TABLE" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --max-window-hours 36 \ + --history-limit 30000 \ + --statement-timeout 600 \ + --run-id "$RUN_ID" \ + --output-dir "$EVIDENCE_DIR" + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/compact_evidence.py" \ + --evidence-dir "$EVIDENCE_DIR" \ + --preflight "$RUN_ROOT/validation/preflight.json" \ + --output-dir "$RUN_ROOT/evidence" + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/costs/summarize_run.py" \ + --evidence-dir "$EVIDENCE_DIR" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --producer-finished-at "$PRODUCER_FINISHED_AT" \ + --rt-warehouse-id "$QUERY_WAREHOUSE_ID" \ + --control-warehouse-id "$CONTROL_WAREHOUSE_ID" \ + --output "$COST_OUTPUT" + +CLIENT_DURABLE_ROWS="$( + jq -er '.provider_committed_rows' "$RUN_ROOT/ingest/ingest_summary.json" +)" +PROVIDER_COMMITTED_ROWS="$( + jq -er '.provider_committed_records' "$EVIDENCE_DIR/evidence_summary.json" +)" +if (( PROVIDER_COMMITTED_ROWS < CLIENT_DURABLE_ROWS )); then + echo "Settled provider rows are below the client durability watermark." >&2 + exit 1 +fi +DUPLICATE_ROWS=$((PROVIDER_COMMITTED_ROWS - CLIENT_DURABLE_ROWS)) + +set +e +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/validate_run.py" \ + --source-manifest "$SOURCE_MANIFEST" \ + --ingest-progress "$RUN_ROOT/ingest/ingest_progress.json" \ + --ingest-metrics "$RUN_ROOT/ingest/ingest_metrics.jsonl" \ + --dashboard "$DASHBOARD_VALIDATION" \ + --drilldown "$DRILLDOWN_VALIDATION" \ + --freshness "${freshness[0]}" \ + --evidence-summary "$EVIDENCE_DIR/evidence_summary.json" \ + --cost-summary "$COST_OUTPUT" \ + --output "$VALIDATION_OUTPUT" +VALIDATION_STATUS=$? +set -e + +if ! jq -e --argjson duplicate_rows "$DUPLICATE_ROWS" ' + all( + .gates[]; + .passed == true + or ($duplicate_rows > 0 and .id == "row_reconciliation") + ) +' "$VALIDATION_OUTPUT" >/dev/null; then + echo "Settled validation has failures beyond the accepted duplicate surplus." >&2 + if [[ "$VALIDATION_STATUS" -ne 0 ]]; then + exit "$VALIDATION_STATUS" + fi + exit 1 +fi + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/compact_query_results.py" \ + --input "$DASHBOARD_VALIDATION" \ + --output "${dashboards[0]}" \ + --audit-output "$DASHBOARD_AUDIT" \ + >/dev/null +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/compact_query_results.py" \ + --input "$DRILLDOWN_VALIDATION" \ + --output "${drilldowns[0]}" \ + --audit-output "$DRILLDOWN_AUDIT" \ + >/dev/null + +SETTLED_AT="$(date -u +%Y-%m-%dT%H:%M:%S.000Z)" +jq \ + --arg settled_at "$SETTLED_AT" \ + --arg settled_cost "$(basename "$COST_OUTPUT")" \ + --arg settled_validation "$(basename "$VALIDATION_OUTPUT")" \ + --argjson validation_status "$VALIDATION_STATUS" \ + --argjson client_durable_rows "$CLIENT_DURABLE_ROWS" \ + --argjson provider_committed_rows "$PROVIDER_COMMITTED_ROWS" \ + --argjson duplicate_rows "$DUPLICATE_ROWS" \ + '.status = ( + if $duplicate_rows == 0 + then "complete" + else "complete_with_duplicates" + end + ) + | .publishable = ($duplicate_rows == 0 and $validation_status == 0) + | .reconciliation = { + client_durable_rows: $client_durable_rows, + provider_committed_rows: $provider_committed_rows, + duplicate_surplus_rows: $duplicate_rows, + exact: ($duplicate_rows == 0) + } + | .settled_validation_process_status = $validation_status + | .settled_cost_collected_at = $settled_at + | .settled_cost_file = $settled_cost + | .settled_validation_file = $settled_validation' \ + "$RUN_CONTEXT" > "$RUN_CONTEXT.tmp" +mv "$RUN_CONTEXT.tmp" "$RUN_CONTEXT" +LATEST_CONTEXT="/data/databricks-runs/latest_serverless_baseline.json" +if [[ "$(jq -r '.run_id' "$LATEST_CONTEXT" 2>/dev/null || true)" == "$RUN_ID" ]]; then + cp "$RUN_CONTEXT" "$LATEST_CONTEXT" +fi + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/finalize_results.py" \ + --run-root "$RUN_ROOT" \ + --run-id "$RUN_ID" \ + --source-manifest "$SOURCE_MANIFEST" \ + --source-hashes "$RUN_ROOT/ingest/source_hashes_post_run.json" \ + --status settled \ + >/dev/null + +printf 'Settled cost: %s\n' "$COST_OUTPUT" +printf 'Settled validation: %s\n' "$VALIDATION_OUTPUT" +printf 'Compact run root: %s\n' "$RUN_ROOT" diff --git a/full-path-realtime/quotes/databricks/collect_ingest_mv_6h_settled.sh b/full-path-realtime/quotes/databricks/collect_ingest_mv_6h_settled.sh new file mode 100644 index 0000000..a94bb7f --- /dev/null +++ b/full-path-realtime/quotes/databricks/collect_ingest_mv_6h_settled.sh @@ -0,0 +1,93 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Recollect settled Databricks billing and provider evidence for a completed +# ingest/MV or full baseline run. Run at least 24 hours after its window. + +umask 077 + +DBX_BENCH_DIR="/home/ubuntu/costbench/full-path-realtime/quotes/databricks" +: "${DATABRICKS_HOST:?Set the Databricks workspace HTTPS origin}" +: "${DATABRICKS_CLIENT_ID:?Set the benchmark service-principal application ID}" +: "${DATABRICKS_CONTROL_WAREHOUSE_ID:?Set the control warehouse ID}" +LATEST="/data/databricks-qualification/latest_ingest_mv_6h_run.json" +RUN_ROOT="${1:-}" +if [[ -z "$RUN_ROOT" ]]; then + RUN_ROOT="$(jq -er '.run_root' "$LATEST")" +fi +CONTEXT="$RUN_ROOT/run_context.json" +if [[ ! -s "$CONTEXT" ]]; then + printf 'Missing run context: %s\n' "$CONTEXT" >&2 + exit 1 +fi +jq -e '.status == "complete" and .until and .producer_finished_at' \ + "$CONTEXT" >/dev/null + +RUN_ID="$(jq -er '.run_id' "$CONTEXT")" +SINCE_UTC="$(jq -er '.since' "$CONTEXT")" +UNTIL_UTC="$(jq -er '.until' "$CONTEXT")" +PRODUCER_FINISHED_AT="$(jq -er '.producer_finished_at' "$CONTEXT")" +RAW_TABLE="$(jq -er '.raw_table' "$CONTEXT")" +MV_TABLE="$(jq -er '.mv_table' "$CONTEXT")" +CATALOG="$(jq -r '.catalog // "costbench"' "$CONTEXT")" +SCHEMA="$(jq -r '.schema // "rt_qualification"' "$CONTEXT")" +CONTROL_WAREHOUSE_ID="$( + jq -r '.control_warehouse_id // empty' "$CONTEXT" +)" +: "${CONTROL_WAREHOUSE_ID:=$DATABRICKS_CONTROL_WAREHOUSE_ID}" +QUERY_WAREHOUSE_ID="$( + jq -r '.query_warehouse_id // .control_warehouse_id // empty' \ + "$CONTEXT" +)" +: "${QUERY_WAREHOUSE_ID:=$CONTROL_WAREHOUSE_ID}" +STAMP="$(date -u +%Y%m%dT%H%M%SZ)" +EVIDENCE_DIR="$RUN_ROOT/evidence/settled-$STAMP" +COST_OUTPUT="$RUN_ROOT/costs/cost_summary_settled-$STAMP.json" +mkdir -p "$(dirname "$EVIDENCE_DIR")" "$(dirname "$COST_OUTPUT")" + +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET +echo +export DATABRICKS_CLIENT_SECRET +export DATABRICKS_CLIENT_ID +unset DATABRICKS_TOKEN +trap 'unset DATABRICKS_CLIENT_SECRET' EXIT + +"$DBX_BENCH_DIR/.venv-runner/bin/python" \ + "$DBX_BENCH_DIR/collect_evidence.py" \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$CONTROL_WAREHOUSE_ID" \ + --rt-warehouse-id "$QUERY_WAREHOUSE_ID" \ + --catalog "$CATALOG" \ + --schema "$SCHEMA" \ + --raw-table "$RAW_TABLE" \ + --mv-table "$MV_TABLE" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --max-window-hours 72 \ + --history-limit 100000 \ + --run-id "$RUN_ID" \ + --output-dir "$EVIDENCE_DIR" + +"$DBX_BENCH_DIR/.venv-runner/bin/python" \ + "$DBX_BENCH_DIR/costs/summarize_run.py" \ + --evidence-dir "$EVIDENCE_DIR" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --producer-finished-at "$PRODUCER_FINISHED_AT" \ + --rt-warehouse-id "$QUERY_WAREHOUSE_ID" \ + --control-warehouse-id "$CONTROL_WAREHOUSE_ID" \ + --output "$COST_OUTPUT" + +jq '{ + complete, + measurement_window, + primary_category_totals, + canonical_undiscounted_fresh_path_total, + excluded_category_totals, + unknown_total, + completeness, + errors +}' "$COST_OUTPUT" + +printf 'Settled evidence: %s\n' "$EVIDENCE_DIR" +printf 'Settled cost: %s\n' "$COST_OUTPUT" diff --git a/full-path-realtime/quotes/databricks/collect_query_baseline_settled.sh b/full-path-realtime/quotes/databricks/collect_query_baseline_settled.sh new file mode 100644 index 0000000..6690e4e --- /dev/null +++ b/full-path-realtime/quotes/databricks/collect_query_baseline_settled.sh @@ -0,0 +1,81 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Recollect provider and billing evidence for one completed Serverless SQL +# baseline query window. Run at least 24 hours after the final query. + +umask 077 + +DBX_BENCH_DIR="/home/ubuntu/costbench/full-path-realtime/quotes/databricks" +: "${DATABRICKS_HOST:?Set the Databricks workspace HTTPS origin}" +: "${DATABRICKS_CLIENT_ID:?Set the benchmark service-principal application ID}" +: "${DATABRICKS_CONTROL_WAREHOUSE_ID:?Set the control warehouse ID}" +QUERY_DIR="${1:?Usage: collect_query_baseline_settled.sh QUERY_OUTPUT_DIR}" +CONTEXT="$QUERY_DIR/query_context.json" +if [[ ! -s "$CONTEXT" ]]; then + printf 'Missing query context: %s\n' "$CONTEXT" >&2 + exit 1 +fi +jq -e '.status == "complete" and .finished_at and .query_warehouse_id' \ + "$CONTEXT" >/dev/null + +INGEST_RUN_ID="$(jq -er '.ingest_run_id' "$CONTEXT")" +QUERY_RUN_ID="$(jq -er '.query_run_id' "$CONTEXT")" +SINCE_UTC="$(jq -er '.started_at' "$CONTEXT")" +UNTIL_UTC="$(jq -er '.finished_at' "$CONTEXT")" +QUERY_WAREHOUSE_ID="$(jq -er '.query_warehouse_id' "$CONTEXT")" +RAW_TABLE="$(jq -er '.raw_table' "$CONTEXT")" +MV_TABLE="$(jq -er '.mv_table' "$CONTEXT")" +INGEST_CONTEXT="/data/databricks-qualification/${INGEST_RUN_ID}/run_context.json" +PRODUCER_FINISHED_AT="$(jq -er '.producer_finished_at' "$INGEST_CONTEXT")" +STAMP="$(date -u +%Y%m%dT%H%M%SZ)" +EVIDENCE_DIR="$QUERY_DIR/evidence-settled-$STAMP" +COST_OUTPUT="$QUERY_DIR/cost_summary_settled-$STAMP.json" + +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET +echo +export DATABRICKS_CLIENT_SECRET +export DATABRICKS_CLIENT_ID +unset DATABRICKS_TOKEN +trap 'unset DATABRICKS_CLIENT_SECRET' EXIT + +"$DBX_BENCH_DIR/.venv-runner/bin/python" \ + "$DBX_BENCH_DIR/collect_evidence.py" \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --rt-warehouse-id "$QUERY_WAREHOUSE_ID" \ + --catalog costbench \ + --schema rt_qualification \ + --raw-table "$RAW_TABLE" \ + --mv-table "$MV_TABLE" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --max-window-hours 8 \ + --run-id "$QUERY_RUN_ID" \ + --output-dir "$EVIDENCE_DIR" + +"$DBX_BENCH_DIR/.venv-runner/bin/python" \ + "$DBX_BENCH_DIR/costs/summarize_run.py" \ + --evidence-dir "$EVIDENCE_DIR" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --producer-finished-at "$PRODUCER_FINISHED_AT" \ + --rt-warehouse-id "$QUERY_WAREHOUSE_ID" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --output "$COST_OUTPUT" + +jq '{ + complete, + measurement_window, + primary_query_serving_compute: + .primary_category_totals.primary_rt_serving_compute, + fresh_path_during_query_window: + .canonical_undiscounted_fresh_path_total, + excluded_category_totals, + unknown_total, + completeness, + errors +}' "$COST_OUTPUT" + +printf 'Query evidence: %s\n' "$EVIDENCE_DIR" +printf 'Query cost: %s\n' "$COST_OUTPUT" diff --git a/full-path-realtime/quotes/databricks/compact_evidence.py b/full-path-realtime/quotes/databricks/compact_evidence.py new file mode 100644 index 0000000..1277dc8 --- /dev/null +++ b/full-path-realtime/quotes/databricks/compact_evidence.py @@ -0,0 +1,290 @@ +#!/usr/bin/env python3 +"""Normalize provider exports into small, publishable benchmark evidence.""" + +from __future__ import annotations + +import argparse +import json +import sys +from pathlib import Path +from typing import Any, Mapping, Sequence + +from dbx_common import atomic_write_json + + +def _read_json(path: Path) -> dict[str, Any]: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise ValueError(f"{path} is not a JSON object") + return value + + +def _read_jsonl(path: Path) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + with path.open(encoding="utf-8") as handle: + for line_number, line in enumerate(handle, 1): + if not line.strip(): + continue + value = json.loads(line) + if not isinstance(value, dict): + raise ValueError(f"{path}:{line_number} is not a JSON object") + rows.append(value) + return rows + + +def _decode(value: Any) -> Any: + decoded = value + for _ in range(3): + if not isinstance(decoded, str): + break + text = decoded.strip() + if not text or text[0] not in "[{": + break + try: + decoded = json.loads(text) + except (TypeError, ValueError, json.JSONDecodeError): + break + return decoded + + +def _first(mapping: Mapping[str, Any], *names: str) -> Any: + lowered = {str(key).lower(): value for key, value in mapping.items()} + for name in names: + if name.lower() in lowered: + return lowered[name.lower()] + return None + + +def _normalize_clustering_columns(value: Any) -> Any: + decoded = _decode(value) + if not isinstance(decoded, list): + return decoded + normalized = [] + for item in decoded: + if isinstance(item, list) and len(item) == 1: + normalized.append(item[0]) + else: + normalized.append(item) + return normalized + + +def _relevant_properties(value: Any) -> Any: + decoded = _decode(value) + if not isinstance(decoded, Mapping): + return decoded + exact = { + "delta.enablechangedatafeed", + "delta.enabledeletionvectors", + "delta.enablerowtracking", + "delta.parquet.compression.codec", + } + return { + str(key): item + for key, item in decoded.items() + if str(key).lower() in exact + or "clustering" in str(key).lower() + or "predictive" in str(key).lower() + } + + +def _table_status(value: Any) -> dict[str, Any] | None: + decoded = _decode(value) + if not isinstance(decoded, Mapping): + return None + metadata = _decode(_first(decoded, "json_metadata")) + if isinstance(metadata, Mapping): + decoded = metadata + properties = _relevant_properties( + _first(decoded, "properties", "table_properties") + ) + features = _decode(_first(decoded, "tableFeatures", "table_features")) + clustering = _normalize_clustering_columns( + _first( + decoded, + "clusteringColumns", + "clustering_columns", + "clusterBy", + "cluster_by", + ) + ) + if clustering is None and isinstance(properties, Mapping): + clustering = _normalize_clustering_columns( + _first(properties, "clusteringColumns", "clustering_columns") + ) + return { + "name": _first(decoded, "name", "table_name"), + "catalog": _first(decoded, "catalog_name", "catalog"), + "schema": _first(decoded, "schema_name", "schema"), + "type": _first(decoded, "type", "table_type"), + "format": _first(decoded, "format", "data_source_format"), + "location": _first(decoded, "location", "storage_location"), + "clustering_columns": clustering, + "cluster_by_auto": _first(decoded, "clusterByAuto", "cluster_by_auto"), + "partition_columns": _decode( + _first(decoded, "partitionColumns", "partition_columns") + ), + "num_files": _first(decoded, "numFiles", "num_files"), + "size_in_bytes": _first(decoded, "sizeInBytes", "size_in_bytes"), + "table_features": features, + "properties": properties, + "enable_predictive_optimization": _first( + decoded, "enable_predictive_optimization" + ), + "effective_predictive_optimization_flag": _decode( + _first(decoded, "effective_predictive_optimization_flag") + ), + } + + +def build_compact_evidence( + summary: Mapping[str, Any], + predictive_rows: Sequence[Mapping[str, Any]], + preflight: Mapping[str, Any] | None = None, +) -> dict[str, dict[str, Any]]: + operations = [] + for row in predictive_rows: + operations.append( + { + "table_name": row.get("table_name"), + "operation_id": row.get("operation_id"), + "operation_type": row.get("operation_type"), + "start_time": row.get("start_time"), + "end_time": row.get("end_time"), + "operation_status": row.get("operation_status"), + "usage_unit": row.get("usage_unit"), + "usage_quantity": row.get("usage_quantity"), + "operation_metrics": _decode(row.get("operation_metrics_json")), + } + ) + clustering_operations = [ + row + for row in operations + if str(row.get("operation_type") or "").upper() + in {"CLUSTERING", "OPTIMIZE"} + ] + table_details = summary.get("table_details", {}) + if not isinstance(table_details, Mapping): + table_details = {} + predictive = summary.get("predictive_optimization", {}) + if not isinstance(predictive, Mapping): + predictive = {} + maintenance = summary.get("mv_maintenance", {}) + if not isinstance(maintenance, Mapping): + maintenance = {} + maintenance_counts = maintenance.get("maintenance_type_counts", {}) + if not isinstance(maintenance_counts, Mapping): + maintenance_counts = {} + + common = { + "schema_version": 1, + "run_id": summary.get("run_id"), + "collected_at": summary.get("collected_at"), + "collection_window": summary.get("collection_window"), + } + provider = { + **common, + "provider_committed_records": summary.get("provider_committed_records"), + "provider_committed_bytes": summary.get("provider_committed_bytes"), + "provider_errors": summary.get("provider_errors"), + "zerobus_stream_error_count": summary.get("zerobus_stream_error_count"), + "zerobus_ingest_bounds": summary.get("zerobus_ingest_bounds"), + "table_num_rows": summary.get("table_num_rows"), + "required_dataset_completeness": summary.get( + "required_dataset_completeness" + ), + "optional_dataset_errors": summary.get("optional_dataset_errors"), + "complete": summary.get("complete"), + } + table_statuses = { + "raw": _table_status(table_details.get("raw")), + "materialized_view": _table_status(table_details.get("materialized_view")), + } + preflight_predictive: Mapping[str, Any] = {} + if isinstance(preflight, Mapping): + online = preflight.get("online") + if isinstance(online, Mapping): + candidate = online.get("predictive_optimization") + if isinstance(candidate, Mapping): + preflight_predictive = candidate + for role in ("raw", "materialized_view"): + status = table_statuses.get(role) + inherited = preflight_predictive.get(role) + if isinstance(status, dict) and isinstance(inherited, Mapping): + status["enable_predictive_optimization"] = inherited.get( + "configured_value" + ) + status["effective_predictive_optimization_flag"] = inherited.get( + "effective_value" + ) + status["predictive_optimization_effectively_enabled"] = inherited.get( + "effectively_enabled" + ) + + clustering = { + **common, + "tables": table_statuses, + "predictive_optimization": { + "preflight_status": preflight_predictive.get("status"), + "summary": dict(predictive), + "clustering_operation_count": len(clustering_operations), + "operations": operations, + }, + } + refresh = { + **common, + "maintenance_type_counts": dict(maintenance_counts), + "maintenance_class_counts": maintenance.get( + "maintenance_class_counts" + ), + "full_refresh_count": maintenance.get("full_refresh_count"), + } + return { + "provider_reconciliation": provider, + "clustering_status": clustering, + "mv_refresh_summary": refresh, + } + + +def run(argv: Sequence[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--evidence-dir", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--preflight", type=Path) + args = parser.parse_args(argv) + evidence_dir = args.evidence_dir.expanduser().resolve() + output_dir = args.output_dir.expanduser().resolve() + summary = _read_json(evidence_dir / "evidence_summary.json") + predictive_rows = _read_jsonl( + evidence_dir / "predictive_optimization_operations.jsonl" + ) + preflight = ( + _read_json(args.preflight.expanduser().resolve()) + if args.preflight is not None + else None + ) + outputs = build_compact_evidence(summary, predictive_rows, preflight) + output_dir.mkdir(parents=True, exist_ok=True) + for name, value in outputs.items(): + atomic_write_json(output_dir / f"{name}.json", value) + print( + json.dumps( + { + "output_dir": str(output_dir), + "files": sorted(f"{name}.json" for name in outputs), + }, + sort_keys=True, + ) + ) + return 0 + + +def main() -> int: + try: + return run() + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/full-path-realtime/quotes/databricks/compact_query_results.py b/full-path-realtime/quotes/databricks/compact_query_results.py new file mode 100644 index 0000000..fd2838b --- /dev/null +++ b/full-path-realtime/quotes/databricks/compact_query_results.py @@ -0,0 +1,148 @@ +#!/usr/bin/env python3 +"""Create reviewable query JSONL while preserving full audit evidence.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import sys +import tempfile +from pathlib import Path +from typing import Any, Mapping, Sequence + + +REMOVED_TOP_LEVEL_FIELDS = { + "mv_rows", + "mv_rows_source", + "result_row_count", + "result_hash", + "query_evidence", + "rt_warehouse", +} +REMOVED_WAREHOUSE_API_FIELDS = { + "creator_name", + "creator_id", + "channel", + "jdbc_url", + "odbc_params", +} + + +def sha256_bytes(value: bytes) -> str: + return hashlib.sha256(value).hexdigest() + + +def compact_record(record: Mapping[str, Any]) -> dict[str, Any]: + compact = { + key: value + for key, value in record.items() + if key not in REMOVED_TOP_LEVEL_FIELDS + } + compact["schema_version"] = 3 + warehouse = compact.get("query_warehouse") + if isinstance(warehouse, Mapping): + warehouse = dict(warehouse) + metadata = warehouse.get("api_metadata") + if isinstance(metadata, Mapping): + warehouse["api_metadata"] = { + key: value + for key, value in metadata.items() + if key not in REMOVED_WAREHOUSE_API_FIELDS + } + compact["query_warehouse"] = warehouse + return compact + + +def _atomic_write(path: Path, content: bytes) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + fd, temporary = tempfile.mkstemp(prefix=f".{path.name}.", dir=path.parent) + try: + with os.fdopen(fd, "wb") as handle: + handle.write(content) + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, path) + finally: + try: + os.unlink(temporary) + except FileNotFoundError: + pass + + +def compact_file( + source: Path, + output: Path, + *, + audit_output: Path | None = None, +) -> dict[str, Any]: + raw = source.read_bytes() + records = [ + json.loads(line) + for line in raw.decode("utf-8").splitlines() + if line.strip() + ] + if not records: + raise ValueError(f"{source} contains no records") + if any(not isinstance(record, Mapping) for record in records): + raise ValueError(f"{source} contains a non-object JSONL record") + if audit_output is not None: + if audit_output.exists(): + if audit_output.read_bytes() != raw: + raise FileExistsError( + f"audit output exists with different content: {audit_output}" + ) + else: + _atomic_write(audit_output, raw) + compact_records = [compact_record(record) for record in records] + content = "".join( + json.dumps(record, separators=(",", ":"), ensure_ascii=False) + "\n" + for record in compact_records + ).encode("utf-8") + _atomic_write(output, content) + return { + "source": str(source), + "output": str(output), + "audit_output": str(audit_output) if audit_output else None, + "record_count": len(records), + "source_size_bytes": len(raw), + "output_size_bytes": len(content), + "source_sha256": sha256_bytes(raw), + "output_sha256": sha256_bytes(content), + "removed_top_level_fields": sorted(REMOVED_TOP_LEVEL_FIELDS), + "removed_warehouse_api_fields": sorted( + REMOVED_WAREHOUSE_API_FIELDS + ), + } + + +def run(argv: Sequence[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--input", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--audit-output", type=Path) + args = parser.parse_args(argv) + report = compact_file( + args.input.expanduser().resolve(), + args.output.expanduser().resolve(), + audit_output=( + args.audit_output.expanduser().resolve() + if args.audit_output is not None + else None + ), + ) + print(json.dumps(report, sort_keys=True)) + return 0 + + +def main() -> int: + try: + return run() + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/full-path-realtime/quotes/databricks/costs/summarize_run.py b/full-path-realtime/quotes/databricks/costs/summarize_run.py new file mode 100644 index 0000000..d3fba2b --- /dev/null +++ b/full-path-realtime/quotes/databricks/costs/summarize_run.py @@ -0,0 +1,1101 @@ +#!/usr/bin/env python3 +"""Build a deterministic, offline cost ledger from Databricks evidence exports.""" + +from __future__ import annotations + +import argparse +import json +import math +import os +import sys +import tempfile +from datetime import datetime, timedelta, timezone +from decimal import Decimal, InvalidOperation +from pathlib import Path +from typing import Any, Iterable, Mapping, Sequence + +PRIMARY_CATEGORIES = ( + "primary_zerobus_ingest", + "primary_mv_refresh", + "primary_predictive_optimization", + "primary_rt_serving_compute", +) +SECONDARY_CATEGORIES = ( + "secondary_post_ingest_query_serving", +) +EXCLUDED_CATEGORIES = ( + "excluded_storage", + "excluded_control", + "excluded_idle_or_minimum", + "excluded_failed", + "excluded_setup_or_post_ingest", +) +ALL_CATEGORIES = ( + PRIMARY_CATEGORIES + + SECONDARY_CATEGORIES + + EXCLUDED_CATEGORIES + + ("unknown",) +) +REQUIRED_FILES = ( + "billing_usage.jsonl", + "billing_list_prices.jsonl", + "query_history.jsonl", + "mv_event_log.jsonl", + "predictive_optimization_operations.jsonl", +) +SUCCESS_STATUSES = {"FINISHED", "SUCCEEDED", "SUCCESS", "COMPLETED"} +FAILURE_WORDS = {"FAILED", "FAILURE", "ERROR", "CANCELED", "CANCELLED"} +ZERO = Decimal("0") + + +def parse_utc_timestamp(value: Any) -> datetime: + """Parse an exact UTC timestamp and reject local or non-UTC values.""" + if isinstance(value, datetime): + parsed = value + elif isinstance(value, (int, float)) and not isinstance(value, bool): + number = float(value) + if not math.isfinite(number): + raise ValueError("timestamp must be finite") + if abs(number) >= 100_000_000_000: + number /= 1000.0 + parsed = datetime.fromtimestamp(number, tz=timezone.utc) + elif isinstance(value, str): + text = value.strip() + if not text: + raise ValueError("timestamp must be non-empty") + if text.endswith(("Z", "z")): + text = text[:-1] + "+00:00" + parsed = datetime.fromisoformat(text) + else: + raise ValueError("timestamp must be an ISO-8601 string or datetime") + if parsed.tzinfo is None or parsed.utcoffset() is None: + raise ValueError("timestamp must include the UTC timezone") + if parsed.utcoffset() != timedelta(0): + raise ValueError("timestamp must be UTC") + return parsed.astimezone(timezone.utc) + + +def iso_utc(value: datetime) -> str: + return ( + value.astimezone(timezone.utc) + .isoformat(timespec="milliseconds") + .replace("+00:00", "Z") + ) + + +def decimal_value(value: Any) -> Decimal | None: + if value is None or isinstance(value, bool): + return None + try: + result = Decimal(str(value)) + except (InvalidOperation, TypeError, ValueError): + return None + return result if result.is_finite() else None + + +def decimal_text(value: Decimal) -> str: + text = format(value, "f") + if "." in text: + text = text.rstrip("0").rstrip(".") + return text if text not in ("", "-0") else "0" + + +def decode_json(value: Any) -> Any: + decoded = value + for _ in range(3): + if not isinstance(decoded, str): + return decoded + text = decoded.strip() + if not text or text[0] not in '[{"': + return decoded + try: + decoded = json.loads(text) + except (TypeError, ValueError, json.JSONDecodeError): + return decoded + return decoded + + +def _normalized_key(value: Any) -> str: + return "".join(character for character in str(value).lower() if character.isalnum()) + + +def _row_value(row: Mapping[str, Any], name: str) -> Any: + wanted = _normalized_key(name) + for key, value in row.items(): + if _normalized_key(key) == wanted: + return value + return None + + +def _safe_timestamp(value: Any) -> datetime | None: + try: + return parse_utc_timestamp(value) + except (TypeError, ValueError, OverflowError, OSError): + return None + + +def read_jsonl(path: Path) -> tuple[list[dict[str, Any]], list[str]]: + rows: list[dict[str, Any]] = [] + errors: list[str] = [] + try: + lines = path.read_text(encoding="utf-8").splitlines() + except OSError as exc: + return [], [f"{path.name}: {type(exc).__name__}: {exc}"] + for line_number, line in enumerate(lines, start=1): + if not line.strip(): + continue + try: + value = json.loads(line) + if not isinstance(value, Mapping): + raise ValueError("top-level value is not an object") + rows.append(dict(value)) + except (TypeError, ValueError, json.JSONDecodeError) as exc: + errors.append(f"{path.name}:{line_number}: {type(exc).__name__}: {exc}") + return rows, errors + + +def atomic_write_json(path: Path, value: Mapping[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + fd, temporary = tempfile.mkstemp(prefix=f".{path.name}.", dir=path.parent) + try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + json.dump(value, handle, indent=2, sort_keys=True, ensure_ascii=False) + handle.write("\n") + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, path) + finally: + try: + os.unlink(temporary) + except FileNotFoundError: + pass + + +def _price_scalar(value: Any, source: str) -> list[tuple[Decimal, str]]: + decoded = decode_json(value) + direct = decimal_value(decoded) + if direct is not None and direct >= 0: + return [(direct, source)] + if not isinstance(decoded, Mapping): + return [] + lowered = {_normalized_key(key): (key, item) for key, item in decoded.items()} + for key in ("default", "listprice", "unitprice", "price", "value"): + if key in lowered: + original, item = lowered[key] + result = decimal_value(item) + if result is not None and result >= 0: + return [(result, f"{source}.{original}")] + scalar_values = [ + (decimal_value(item), f"{source}.{key}") + for key, item in decoded.items() + if _normalized_key(key) + not in {"promotional", "promotion", "contract", "negotiated", "discount"} + ] + return [ + (value, item_source) + for value, item_source in scalar_values + if value is not None and value >= 0 + ] + + +def extract_canonical_list_price(pricing_json: Any) -> dict[str, Any]: + """Extract only effective-list/default pricing, never promotional/contract pricing.""" + decoded = decode_json(pricing_json) + if not isinstance(decoded, Mapping): + return { + "price": None, + "source": None, + "error": "pricing_json is not a JSON object", + } + by_key = {_normalized_key(key): (key, value) for key, value in decoded.items()} + selected: list[tuple[Decimal, str]] = [] + selected_field = None + for normalized in ("effectivelist", "default"): + if normalized not in by_key: + continue + original, value = by_key[normalized] + selected = _price_scalar(value, f"pricing.{original}") + selected_field = original + if selected: + break + if not selected: + return { + "price": None, + "source": None, + "error": ( + "pricing_json has no canonical effective-list/default scalar; " + "promotional and contract prices are intentionally ignored" + ), + } + distinct = sorted({price for price, _source in selected}) + if len(distinct) != 1: + return { + "price": None, + "source": f"pricing.{selected_field}", + "error": "canonical pricing field contains ambiguous scalar values", + } + sources = sorted(source for price, source in selected if price == distinct[0]) + return {"price": distinct[0], "source": sources[0], "error": None} + + +def match_list_price( + usage: Mapping[str, Any], + prices: Sequence[Mapping[str, Any]], +) -> dict[str, Any]: + """Match a usage interval to exactly one key- and interval-compatible price row.""" + usage_start = _safe_timestamp(_row_value(usage, "usage_start_time")) + usage_end = _safe_timestamp(_row_value(usage, "usage_end_time")) + if usage_start is None or usage_end is None or usage_end <= usage_start: + return { + "status": "unpriced", + "price_row_index": None, + "price": None, + "currency": None, + "source": None, + "reason": "usage row has an invalid or empty billing interval", + } + keys = ( + str(_row_value(usage, "sku_name") or "").casefold(), + str(_row_value(usage, "cloud") or "").casefold(), + str(_row_value(usage, "usage_unit") or "").casefold(), + ) + if any(not key for key in keys): + return { + "status": "unpriced", + "price_row_index": None, + "price": None, + "currency": None, + "source": None, + "reason": "usage row omits sku_name, cloud, or usage_unit", + } + candidates: list[tuple[int, Mapping[str, Any]]] = [] + for index, price_row in enumerate(prices): + price_keys = ( + str(_row_value(price_row, "sku_name") or "").casefold(), + str(_row_value(price_row, "cloud") or "").casefold(), + str(_row_value(price_row, "usage_unit") or "").casefold(), + ) + if price_keys != keys: + continue + price_start = _safe_timestamp(_row_value(price_row, "price_start_time")) + price_end_value = _row_value(price_row, "price_end_time") + price_end = ( + None if price_end_value in (None, "") else _safe_timestamp(price_end_value) + ) + if price_start is None or ( + price_end_value not in (None, "") and price_end is None + ): + continue + if price_start <= usage_start and (price_end is None or usage_end <= price_end): + candidates.append((index, price_row)) + if len(candidates) != 1: + status = "ambiguous" if len(candidates) > 1 else "unpriced" + return { + "status": status, + "price_row_index": None, + "price": None, + "currency": None, + "source": None, + "reason": ( + f"{len(candidates)} effective price rows matched sku_name, cloud, " + "usage_unit, and the complete usage interval" + ), + "candidate_price_row_indices": [index for index, _row in candidates], + } + index, price_row = candidates[0] + extracted = extract_canonical_list_price(_row_value(price_row, "pricing_json")) + currency = str(_row_value(price_row, "currency_code") or "").strip() + if extracted["price"] is None: + return { + "status": "unpriced", + "price_row_index": index, + "price": None, + "currency": currency or None, + "source": extracted["source"], + "reason": extracted["error"], + } + if not currency: + return { + "status": "unpriced", + "price_row_index": index, + "price": None, + "currency": None, + "source": extracted["source"], + "reason": "matched list-price row omits currency_code", + } + return { + "status": "matched", + "price_row_index": index, + "price": extracted["price"], + "currency": currency, + "source": extracted["source"], + "reason": "exact effective-interval list-price match", + "price_start_time": _row_value(price_row, "price_start_time"), + "price_end_time": _row_value(price_row, "price_end_time"), + } + + +def merge_intervals( + intervals: Iterable[tuple[datetime, datetime]], +) -> list[tuple[datetime, datetime]]: + ordered = sorted((start, end) for start, end in intervals if end > start) + merged: list[list[datetime]] = [] + for start, end in ordered: + if not merged or start > merged[-1][1]: + merged.append([start, end]) + elif end > merged[-1][1]: + merged[-1][1] = end + return [(start, end) for start, end in merged] + + +def _duration_microseconds(start: datetime, end: datetime) -> int: + return max(0, round((end - start).total_seconds() * 1_000_000)) + + +def union_overlap_microseconds( + start: datetime, + end: datetime, + intervals: Iterable[tuple[datetime, datetime]], +) -> int: + clipped = ( + (max(start, item_start), min(end, item_end)) + for item_start, item_end in intervals + ) + return sum( + _duration_microseconds(item_start, item_end) + for item_start, item_end in merge_intervals(clipped) + ) + + +def union_overlap_seconds( + start: Any, + end: Any, + intervals: Iterable[tuple[Any, Any]], +) -> float: + parsed_start = parse_utc_timestamp(start) + parsed_end = parse_utc_timestamp(end) + parsed_intervals = [ + (parse_utc_timestamp(item_start), parse_utc_timestamp(item_end)) + for item_start, item_end in intervals + ] + return union_overlap_microseconds(parsed_start, parsed_end, parsed_intervals) / 1e6 + + +def _recursive_identifiers(value: Any, key_names: set[str]) -> set[str]: + decoded = decode_json(value) + found: set[str] = set() + if isinstance(decoded, Mapping): + for key, item in decoded.items(): + if _normalized_key(key) in key_names and item not in (None, ""): + if isinstance(item, (str, int)) and not isinstance(item, bool): + found.add(str(item)) + found.update(_recursive_identifiers(item, key_names)) + elif isinstance(decoded, list): + for item in decoded: + found.update(_recursive_identifiers(item, key_names)) + return found + + +def _run_tag_values(value: Any) -> set[str]: + decoded = decode_json(value) + found: set[str] = set() + if isinstance(decoded, Mapping): + for key, item in decoded.items(): + if _normalized_key(key) == "runid" and item not in (None, ""): + found.add(str(item)) + if ( + _normalized_key(decoded.get("key", "")) == "runid" + and decoded.get("value") not in (None, "") + ): + found.add(str(decoded["value"])) + for item in decoded.values(): + found.update(_run_tag_values(item)) + elif isinstance(decoded, list): + for item in decoded: + found.update(_run_tag_values(item)) + return found + + +def derive_mv_pipeline_ids(event_rows: Sequence[Mapping[str, Any]]) -> list[str]: + names = {"pipelineid", "pipelineuuid", "dltpipelineid", "flowid"} + found: set[str] = set() + for row in event_rows: + for field in ("origin_json", "origin", "details_json", "details"): + found.update(_recursive_identifiers(_row_value(row, field), names)) + return sorted(found) + + +def _operation_ids(rows: Sequence[Mapping[str, Any]]) -> set[str]: + result: set[str] = set() + for row in rows: + for field in ("operation_id", "id"): + value = _row_value(row, field) + if value not in (None, ""): + result.add(str(value)) + return result + + +def _has_failure_marker(value: Any) -> bool: + decoded = decode_json(value) + if isinstance(decoded, Mapping): + for key, item in decoded.items(): + normalized = _normalized_key(key) + if normalized in {"status", "state", "result", "outcome"}: + if str(item).strip().upper() in FAILURE_WORDS: + return True + if normalized in {"failed", "haserror"} and item is True: + return True + if _has_failure_marker(item): + return True + elif isinstance(decoded, list): + return any(_has_failure_marker(item) for item in decoded) + return False + + +def classify_usage( + usage: Mapping[str, Any], + *, + rt_warehouse_id: str, + control_warehouse_id: str, + mv_pipeline_ids: Iterable[str] = (), + predictive_operation_ids: Iterable[str] = (), +) -> tuple[str, str]: + """Classify one billing row before temporal serving/post-ingest allocation.""" + metadata = decode_json(_row_value(usage, "usage_metadata_json")) + features = decode_json(_row_value(usage, "product_features_json")) + warehouse_ids = _recursive_identifiers(metadata, {"warehouseid"}) + pipeline_ids = _recursive_identifiers( + metadata, {"pipelineid", "pipelineuuid", "dltpipelineid", "flowid"} + ) + operation_ids = _recursive_identifiers(metadata, {"operationid"}) + zerobus_request_types = _recursive_identifiers( + features, {"zerobusrequesttype"} + ) + product = str(_row_value(usage, "billing_origin_product") or "").upper() + sku = str(_row_value(usage, "sku_name") or "").upper() + identity = f"{product} {sku}" + if _has_failure_marker(metadata) or _has_failure_marker(features): + return "excluded_failed", "usage metadata/product features identify failed work" + if "STORAGE" in identity: + return "excluded_storage", "billing origin/SKU identifies storage" + if control_warehouse_id in warehouse_ids: + return "excluded_control", "usage_metadata warehouse_id matches control warehouse" + if rt_warehouse_id in warehouse_ids: + return ( + "primary_rt_serving_compute", + "usage_metadata warehouse_id matches Lakehouse//RT warehouse", + ) + if ( + "PREDICTIVE" in identity + or "OPTIMIZATION" in identity + or operation_ids.intersection(str(value) for value in predictive_operation_ids) + ): + return ( + "primary_predictive_optimization", + "billing origin/SKU or operation_id identifies predictive optimization", + ) + if "ZEROBUS" in identity or zerobus_request_types: + return ( + "primary_zerobus_ingest", + "billing identity or product features identify Zerobus ingest", + ) + target_pipelines = {str(value) for value in mv_pipeline_ids} + if pipeline_ids.intersection(target_pipelines): + return ( + "primary_mv_refresh", + "usage_metadata pipeline ID matches target MV event-log pipeline ID", + ) + if any(token in identity for token in ("PIPELINE", "LAKEFLOW", "DLT")): + if not target_pipelines: + return ( + "unknown", + "pipeline usage found but target MV event log exposed no pipeline ID", + ) + return "unknown", "pipeline usage does not match a target MV pipeline ID" + return "unknown", "no target warehouse, pipeline, operation, or product identity matched" + + +def _successful_query_intervals( + rows: Sequence[Mapping[str, Any]], + start: datetime, + end: datetime, + rt_warehouse_id: str, +) -> tuple[list[tuple[datetime, datetime]], list[str]]: + intervals: list[tuple[datetime, datetime]] = [] + errors: list[str] = [] + for index, row in enumerate(rows): + tags = decode_json(_row_value(row, "query_tags_json")) + run_tags = _run_tag_values(tags) + if not run_tags: + continue + warehouse_id = _row_value(row, "warehouse_id") + if warehouse_id not in (None, "") and str(warehouse_id) != rt_warehouse_id: + continue + status = str( + _row_value(row, "execution_status") + or _row_value(row, "status") + or "" + ).upper() + if status not in SUCCESS_STATUSES or _row_value(row, "error_message") not in ( + None, + "", + ): + continue + query_start = _safe_timestamp(_row_value(row, "start_time")) + query_end = _safe_timestamp(_row_value(row, "end_time")) + if query_start is None or query_end is None or query_end <= query_start: + errors.append(f"query_history row {index} has an invalid successful interval") + continue + clipped_start, clipped_end = max(start, query_start), min(end, query_end) + if clipped_end > clipped_start: + intervals.append((clipped_start, clipped_end)) + return merge_intervals(intervals), errors + + +def _total_shape(amounts: Mapping[str, Decimal]) -> dict[str, Any]: + by_currency = { + currency: decimal_text(value) + for currency, value in sorted(amounts.items()) + } + if len(by_currency) == 1: + currency, amount = next(iter(by_currency.items())) + else: + currency, amount = None, None + return { + "currency": currency, + "amount": amount, + "amount_by_currency": by_currency, + } + + +def _add_total( + totals: dict[str, dict[str, Decimal]], + category: str, + currency: str, + value: Decimal, +) -> None: + totals.setdefault(category, {}) + totals[category][currency] = totals[category].get(currency, ZERO) + value + + +def _line( + *, + usage_index: int, + usage: Mapping[str, Any], + price_match: Mapping[str, Any], + category: str, + reason: str, + source_quantity: Decimal, + quantity: Decimal, + allocation_numerator_us: int, + allocation_denominator_us: int, + measured_start: datetime, + measured_end: datetime, +) -> dict[str, Any]: + price = price_match.get("price") + list_cost = quantity * price if isinstance(price, Decimal) else None + record_type = str(_row_value(usage, "record_type") or "") + return { + "usage_row_index": usage_index, + "usage_record_id": _row_value(usage, "record_id"), + "price_row_index": price_match.get("price_row_index"), + "price_match_status": price_match.get("status"), + "price_match_reason": price_match.get("reason"), + "price_candidate_row_indices": price_match.get( + "candidate_price_row_indices", [] + ), + "sku_name": _row_value(usage, "sku_name"), + "cloud": _row_value(usage, "cloud"), + "usage_unit": _row_value(usage, "usage_unit"), + "record_type": record_type, + "signed_quantity_source": decimal_text(source_quantity), + "quantity": decimal_text(quantity), + "category": category, + "classification_reason": reason, + "measured_interval": { + "start": iso_utc(measured_start), + "end": iso_utc(measured_end), + }, + "source_usage_interval": { + "start": _row_value(usage, "usage_start_time"), + "end": _row_value(usage, "usage_end_time"), + }, + "allocation": { + "numerator_microseconds": allocation_numerator_us, + "denominator_microseconds": allocation_denominator_us, + "fraction": decimal_text( + Decimal(allocation_numerator_us) + / Decimal(allocation_denominator_us) + ), + }, + "canonical_list_unit_price": ( + decimal_text(price) if isinstance(price, Decimal) else None + ), + "canonical_price_source": price_match.get("source"), + "price_effective_interval": { + "start": price_match.get("price_start_time"), + "end": price_match.get("price_end_time"), + }, + "currency": price_match.get("currency"), + "list_cost": decimal_text(list_cost) if list_cost is not None else None, + "signed_quantity_policy": ( + "The exported signed usage_quantity is retained for ORIGINAL, " + "RETRACTION, and RESTATEMENT records; record_type never changes its sign." + ), + } + + +def build_cost_summary( + *, + billing_usage: Sequence[Mapping[str, Any]], + billing_list_prices: Sequence[Mapping[str, Any]], + query_history: Sequence[Mapping[str, Any]], + mv_event_log: Sequence[Mapping[str, Any]], + predictive_operations: Sequence[Mapping[str, Any]], + measured_since: Any, + measured_until: Any, + rt_warehouse_id: str, + control_warehouse_id: str, + producer_finished_at: Any | None = None, + input_errors: Sequence[str] = (), + input_paths: Mapping[str, Any] | None = None, +) -> dict[str, Any]: + start = parse_utc_timestamp(measured_since) + end = parse_utc_timestamp(measured_until) + if end <= start: + raise ValueError("measured_until must be later than measured_since") + finish = ( + end + if producer_finished_at in (None, "") + else parse_utc_timestamp(producer_finished_at) + ) + active_end = min(end, max(start, finish)) + query_intervals, query_errors = _successful_query_intervals( + query_history, start, active_end, rt_warehouse_id + ) + post_query_intervals, post_query_errors = _successful_query_intervals( + query_history, active_end, end, rt_warehouse_id + ) + mv_pipeline_ids = derive_mv_pipeline_ids(mv_event_log) + predictive_ids = _operation_ids(predictive_operations) + predictive_failures = [ + index + for index, row in enumerate(predictive_operations) + if str(_row_value(row, "operation_status") or "").upper() in FAILURE_WORDS + or _has_failure_marker(row) + ] + totals: dict[str, dict[str, Decimal]] = {} + lines: list[dict[str, Any]] = [] + processing_errors = ( + list(input_errors) + query_errors + post_query_errors + ) + if not billing_usage: + processing_errors.append("billing_usage.jsonl contains no usage rows") + if billing_usage and not billing_list_prices: + processing_errors.append( + "billing_list_prices.jsonl contains no rows for observed usage" + ) + ambiguous_rows: list[int] = [] + unpriced_rows: list[int] = [] + unknown_rows: list[int] = [] + out_of_window_rows: list[int] = [] + invalid_quantity_rows: list[int] = [] + record_type_quantities: dict[str, Decimal] = {} + + for usage_index, usage in enumerate(billing_usage): + source_quantity = decimal_value(_row_value(usage, "usage_quantity")) + if source_quantity is None: + invalid_quantity_rows.append(usage_index) + processing_errors.append( + f"billing_usage row {usage_index} has invalid usage_quantity" + ) + continue + record_type = str(_row_value(usage, "record_type") or "UNKNOWN").upper() + record_type_quantities[record_type] = ( + record_type_quantities.get(record_type, ZERO) + source_quantity + ) + usage_start = _safe_timestamp(_row_value(usage, "usage_start_time")) + usage_end = _safe_timestamp(_row_value(usage, "usage_end_time")) + if usage_start is None or usage_end is None or usage_end <= usage_start: + processing_errors.append( + f"billing_usage row {usage_index} has invalid usage interval" + ) + unpriced_rows.append(usage_index) + continue + measured_start, measured_end = max(start, usage_start), min(end, usage_end) + if measured_end <= measured_start: + out_of_window_rows.append(usage_index) + continue + duration_us = _duration_microseconds(usage_start, usage_end) + if duration_us <= 0: + processing_errors.append( + f"billing_usage row {usage_index} has zero duration" + ) + unpriced_rows.append(usage_index) + continue + price_match = match_list_price(usage, billing_list_prices) + if price_match["status"] == "ambiguous": + ambiguous_rows.append(usage_index) + elif price_match["status"] != "matched": + unpriced_rows.append(usage_index) + base_category, base_reason = classify_usage( + usage, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + mv_pipeline_ids=mv_pipeline_ids, + predictive_operation_ids=predictive_ids, + ) + allocations: list[tuple[str, str, int, datetime, datetime]] = [] + active_start = measured_start + active_segment_end = min(measured_end, active_end) + post_start = max(measured_start, active_end) + if active_segment_end > active_start: + active_us = _duration_microseconds(active_start, active_segment_end) + if base_category == "primary_rt_serving_compute": + serving_us = union_overlap_microseconds( + active_start, active_segment_end, query_intervals + ) + if serving_us: + allocations.append( + ( + "primary_rt_serving_compute", + base_reason + + "; allocated by union overlap with successful run-tagged " + "query intervals", + serving_us, + active_start, + active_segment_end, + ) + ) + if active_us - serving_us: + allocations.append( + ( + "excluded_idle_or_minimum", + "RT warehouse billed interval not overlapped by the union of " + "successful run-tagged query intervals", + active_us - serving_us, + active_start, + active_segment_end, + ) + ) + else: + allocations.append( + ( + base_category, + base_reason, + active_us, + active_start, + active_segment_end, + ) + ) + if measured_end > post_start: + post_us = _duration_microseconds(post_start, measured_end) + if base_category == "primary_rt_serving_compute": + serving_us = union_overlap_microseconds( + post_start, measured_end, post_query_intervals + ) + if serving_us: + allocations.append( + ( + "secondary_post_ingest_query_serving", + base_reason + + "; post-ingest allocation by union overlap with " + "successful run-tagged query intervals", + serving_us, + post_start, + measured_end, + ) + ) + if post_us - serving_us: + allocations.append( + ( + "excluded_idle_or_minimum", + "post-ingest query warehouse billing not overlapped " + "by successful run-tagged queries", + post_us - serving_us, + post_start, + measured_end, + ) + ) + else: + allocations.append( + ( + "excluded_setup_or_post_ingest", + "billing interval portion starts after producer finish", + post_us, + post_start, + measured_end, + ) + ) + for category, reason, allocated_us, allocation_start, allocation_end in allocations: + quantity = source_quantity * Decimal(allocated_us) / Decimal(duration_us) + item = _line( + usage_index=usage_index, + usage=usage, + price_match=price_match, + category=category, + reason=reason, + source_quantity=source_quantity, + quantity=quantity, + allocation_numerator_us=allocated_us, + allocation_denominator_us=duration_us, + measured_start=allocation_start, + measured_end=allocation_end, + ) + lines.append(item) + if category == "unknown": + unknown_rows.append(usage_index) + if item["list_cost"] is not None: + _add_total( + totals, + category, + str(item["currency"] or ""), + Decimal(item["list_cost"]), + ) + + category_totals = { + category: _total_shape(totals.get(category, {})) + for category in ALL_CATEGORIES + } + primary_amounts: dict[str, Decimal] = {} + fresh_path_amounts: dict[str, Decimal] = {} + excluded_amounts: dict[str, Decimal] = {} + for category in PRIMARY_CATEGORIES: + for currency, amount in totals.get(category, {}).items(): + primary_amounts[currency] = primary_amounts.get(currency, ZERO) + amount + if category != "primary_rt_serving_compute": + fresh_path_amounts[currency] = ( + fresh_path_amounts.get(currency, ZERO) + amount + ) + for category in EXCLUDED_CATEGORIES: + for currency, amount in totals.get(category, {}).items(): + excluded_amounts[currency] = excluded_amounts.get(currency, ZERO) + amount + canonical = _total_shape(primary_amounts) + canonical_fresh_path = _total_shape(fresh_path_amounts) + excluded = _total_shape(excluded_amounts) + secondary = { + category: category_totals[category] + for category in SECONDARY_CATEGORIES + } + unknown = _total_shape(totals.get("unknown", {})) + rt_amounts = totals.get("primary_rt_serving_compute", {}) + discounted_rt = { + currency: amount * Decimal("0.70") + for currency, amount in rt_amounts.items() + } + scenario_amounts = dict(primary_amounts) + for currency, amount in rt_amounts.items(): + scenario_amounts[currency] = ( + scenario_amounts.get(currency, ZERO) - amount + discounted_rt[currency] + ) + pricing_complete = not ambiguous_rows and not unpriced_rows + classification_complete = not unknown_rows + sources_complete = not input_errors + currencies_complete = len(primary_amounts) <= 1 + canonical_total_present = len(primary_amounts) == 1 + complete = ( + sources_complete + and pricing_complete + and classification_complete + and currencies_complete + and canonical_total_present + and not processing_errors + and not invalid_quantity_rows + ) + unpriced_quantities: dict[str, Decimal] = {} + for index in sorted(set(ambiguous_rows + unpriced_rows)): + if 0 <= index < len(billing_usage): + row = billing_usage[index] + quantity = decimal_value(_row_value(row, "usage_quantity")) + if quantity is not None: + unit = str(_row_value(row, "usage_unit") or "UNKNOWN") + unpriced_quantities[unit] = unpriced_quantities.get(unit, ZERO) + quantity + return { + "schema_version": 1, + "measurement_window": { + "since": iso_utc(start), + "until": iso_utc(end), + "duration_seconds": (end - start).total_seconds(), + "producer_finished_at": ( + None if producer_finished_at in (None, "") else iso_utc(finish) + ), + }, + "inputs": dict(input_paths or {}), + "warehouse_ids": { + "rt": rt_warehouse_id, + "control": control_warehouse_id, + }, + "matched_lines": lines, + "record_type_signed_quantities": { + key: decimal_text(value) + for key, value in sorted(record_type_quantities.items()) + }, + "primary_category_totals": { + category: category_totals[category] for category in PRIMARY_CATEGORIES + }, + "excluded_category_totals": { + category: category_totals[category] for category in EXCLUDED_CATEGORIES + }, + "secondary_category_totals": secondary, + "unknown_total": unknown, + "excluded_total": excluded, + "unpriced_total": { + "list_cost": None, + "usage_quantity_by_unit": { + unit: decimal_text(value) + for unit, value in sorted(unpriced_quantities.items()) + }, + "usage_row_indices": sorted(set(ambiguous_rows + unpriced_rows)), + }, + "canonical_undiscounted_primary_total": canonical, + "canonical_undiscounted_primary_total_amount": canonical["amount"], + "canonical_undiscounted_primary_total_currency": canonical["currency"], + "canonical_undiscounted_fresh_path_total": canonical_fresh_path, + "canonical_undiscounted_query_serving_total": _total_shape(rt_amounts), + "lakehouse_rt_beta_30_percent_off_scenario": { + "discount_rate": "0.30", + "applies_only_to": "primary_rt_serving_compute", + "rt_serving_undiscounted": _total_shape(rt_amounts), + "rt_serving_discounted": _total_shape(discounted_rt), + "scenario_primary_total": _total_shape(scenario_amounts), + "canonical_undiscounted_primary_total": canonical, + "canonical_total_unchanged": True, + }, + "query_serving_allocation": { + "method": "union_overlap", + "successful_query_union_intervals": [ + {"start": iso_utc(item_start), "end": iso_utc(item_end)} + for item_start, item_end in query_intervals + ], + "successful_post_ingest_query_union_intervals": [ + {"start": iso_utc(item_start), "end": iso_utc(item_end)} + for item_start, item_end in post_query_intervals + ], + "estimate": True, + "disclosure": ( + "Estimated from the union overlap of successful run-tagged query " + "intervals and RT warehouse billing intervals. Databricks billing " + "usage has no statement_id join, so this is not exact statement-level " + "allocation. Unioning intervals prevents concurrent queries from " + "double-counting shared compute." + ), + }, + "mv_pipeline_ids": mv_pipeline_ids, + "predictive_optimization": { + "operation_count": len(predictive_operations), + "failed_operation_count": len(predictive_failures), + "failed_operation_row_indices": predictive_failures, + }, + "completeness": { + "sources_complete": sources_complete, + "pricing_complete": pricing_complete, + "classification_complete": classification_complete, + "single_primary_currency": currencies_complete, + "canonical_primary_total_present": canonical_total_present, + "ambiguous_price_usage_row_indices": sorted(set(ambiguous_rows)), + "unpriced_usage_row_indices": sorted(set(unpriced_rows)), + "unknown_usage_row_indices": sorted(set(unknown_rows)), + "invalid_quantity_usage_row_indices": sorted(set(invalid_quantity_rows)), + "complete": complete, + }, + "complete": complete, + "errors": processing_errors, + "assumptions": [ + "Canonical primary cost uses only undiscounted effective-list/default " + "prices exported by system.billing.list_prices.", + "The fresh-data-path subledger includes Zerobus ingestion, MV refresh, " + "and predictive optimization; RT serving compute is reported separately " + "as measured-query cost so downstream scores do not double-count it.", + "Promotional, contract, negotiated, commitment, credit, tax, and Beta " + "prices are excluded from the canonical total.", + "Usage quantities are prorated only when a billing interval crosses the " + "declared measurement window, producer finish, or RT query-union boundary.", + "ORIGINAL, RETRACTION, and RESTATEMENT quantities retain the sign exported " + "by Databricks; no sign is inferred from record_type.", + "The Lakehouse//RT Beta scenario is separate and discounts only RT serving " + "list cost by 30%; it never changes the canonical undiscounted total.", + ], + } + + +def summarize( + *, + evidence_dir: Path, + measured_since: Any, + measured_until: Any, + rt_warehouse_id: str, + control_warehouse_id: str, + producer_finished_at: Any | None = None, +) -> dict[str, Any]: + rows: dict[str, list[dict[str, Any]]] = {} + errors: list[str] = [] + paths: dict[str, str] = {} + for filename in REQUIRED_FILES: + path = evidence_dir / filename + paths[filename.removesuffix(".jsonl")] = str(path) + rows[filename], read_errors = read_jsonl(path) + errors.extend(read_errors) + return build_cost_summary( + billing_usage=rows["billing_usage.jsonl"], + billing_list_prices=rows["billing_list_prices.jsonl"], + query_history=rows["query_history.jsonl"], + mv_event_log=rows["mv_event_log.jsonl"], + predictive_operations=rows["predictive_optimization_operations.jsonl"], + measured_since=measured_since, + measured_until=measured_until, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + producer_finished_at=producer_finished_at, + input_errors=errors, + input_paths=paths, + ) + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="Build an offline Databricks benchmark cost evidence ledger" + ) + parser.add_argument("--evidence-dir", type=Path, required=True) + parser.add_argument("--since", "--measured-since", dest="since", required=True) + parser.add_argument("--until", "--measured-until", dest="until", required=True) + parser.add_argument("--rt-warehouse-id", required=True) + parser.add_argument("--control-warehouse-id", required=True) + parser.add_argument( + "--producer-finished-at", + "--producer-finish-timestamp", + dest="producer_finished_at", + ) + parser.add_argument("--output", type=Path, required=True) + return parser + + +def run(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + output = args.output.expanduser().resolve() + if output.exists() and output.is_dir(): + output = output / "cost_summary.json" + summary = summarize( + evidence_dir=args.evidence_dir.expanduser().resolve(), + measured_since=args.since, + measured_until=args.until, + rt_warehouse_id=args.rt_warehouse_id, + control_warehouse_id=args.control_warehouse_id, + producer_finished_at=args.producer_finished_at, + ) + atomic_write_json(output, summary) + print(f"Cost summary written to {output}") + return 0 if summary["complete"] else 1 + + +def main(argv: Sequence[str] | None = None) -> int: + try: + return run(argv) + except KeyboardInterrupt: + return 130 + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/full-path-realtime/quotes/databricks/create.sql b/full-path-realtime/quotes/databricks/create.sql index c0cfd13..de27333 100644 --- a/full-path-realtime/quotes/databricks/create.sql +++ b/full-path-realtime/quotes/databricks/create.sql @@ -1,7 +1,9 @@ -DROP TABLE IF EXISTS workspace.benchmarking.quotes; -DROP MATERIALIZED VIEW IF EXISTS workspace.benchmarking.quotes_daily; +-- The catalog and schema, including their explicit non-default managed storage +-- location, are administrator prerequisites. Render the placeholders before use. +DROP MATERIALIZED VIEW IF EXISTS __CATALOG__.__SCHEMA__.quotes_daily; +DROP TABLE IF EXISTS __CATALOG__.__SCHEMA__.quotes; -CREATE TABLE workspace.benchmarking.quotes ( +CREATE TABLE __CATALOG__.__SCHEMA__.quotes ( sym STRING, bx SMALLINT, bp DOUBLE, @@ -18,16 +20,27 @@ CREATE TABLE workspace.benchmarking.quotes ( USING DELTA CLUSTER BY (sym, t) TBLPROPERTIES ( - delta.enableRowTracking = true, - delta.enableChangeDataFeed = true + 'delta.enableDeletionVectors' = 'true', + 'delta.enableRowTracking' = 'true', + 'delta.enableChangeDataFeed' = 'true' ); -CREATE OR REPLACE MATERIALIZED VIEW workspace.benchmarking.quotes_daily +CREATE MATERIALIZED VIEW __CATALOG__.__SCHEMA__.quotes_daily +USING DELTA CLUSTER BY (sym, day) -TRIGGER ON UPDATE +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE AS SELECT sym, - to_date(from_unixtime(t / 1000)) AS day, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, count(*) AS n_quotes, min(bp) AS bp_min, max(bp) AS bp_max, @@ -36,5 +49,5 @@ AS SELECT sum(bs) AS bs_sum, sum(`as`) AS as_sum, sum(ap - bp) AS spread_sum -FROM workspace.benchmarking.quotes +FROM __CATALOG__.__SCHEMA__.quotes GROUP BY sym, day; \ No newline at end of file diff --git a/full-path-realtime/quotes/databricks/create_full_serverless_baseline_20260917.sql b/full-path-realtime/quotes/databricks/create_full_serverless_baseline_20260917.sql new file mode 100644 index 0000000..27c7a77 --- /dev/null +++ b/full-path-realtime/quotes/databricks/create_full_serverless_baseline_20260917.sql @@ -0,0 +1,145 @@ +-- Canonical full-source Serverless SQL baseline R2 target. +-- +-- Replace both __...__ principal placeholders before execution. Run once on +-- the control warehouse as the benchmark administrator. This file +-- intentionally contains no DROP or IF NOT EXISTS statements. Stop if any +-- statement fails. +-- R2 uses a fresh schema because the first target was consumed by an aborted +-- run whose query wrapper incorrectly used the qualification schema. + +CREATE SCHEMA costbench.rt_full_serverless_baseline_r2_20260917; + +GRANT USE CATALOG ON CATALOG costbench +TO `__SERVICE_PRINCIPAL_APPLICATION_ID__`; + +GRANT USE SCHEMA +ON SCHEMA costbench.rt_full_serverless_baseline_r2_20260917 +TO `__SERVICE_PRINCIPAL_APPLICATION_ID__`; + +CREATE TABLE costbench.rt_full_serverless_baseline_r2_20260917.quotes ( + sym STRING, + bx SMALLINT, + bp DOUBLE, + bs BIGINT, + ax SMALLINT, + ap DOUBLE, + `as` BIGINT, + c SMALLINT, + i ARRAY, + t BIGINT, + q BIGINT, + z SMALLINT +) +USING DELTA +CLUSTER BY (sym, t) +TBLPROPERTIES ( + 'delta.enableDeletionVectors' = 'true', + 'delta.enableRowTracking' = 'true', + 'delta.enableChangeDataFeed' = 'true' +); + +GRANT SELECT, MODIFY +ON TABLE costbench.rt_full_serverless_baseline_r2_20260917.quotes +TO `__SERVICE_PRINCIPAL_APPLICATION_ID__`; + +-- This statement creates nothing. Require: +-- The Materialized View can be incrementally refreshed. +-- No issues detected. +EXPLAIN CREATE MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r2_20260917.quotes_daily_explain_only +USING DELTA +CLUSTER BY (sym, day) +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE +AS +SELECT + sym, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, + count(*) AS n_quotes, + min(bp) AS bp_min, + max(bp) AS bp_max, + min(ap) AS ap_min, + max(ap) AS ap_max, + sum(bs) AS bs_sum, + sum(`as`) AS as_sum, + sum(ap - bp) AS spread_sum +FROM costbench.rt_full_serverless_baseline_r2_20260917.quotes +GROUP BY sym, day; + +CREATE MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r2_20260917.quotes_daily +USING DELTA +CLUSTER BY (sym, day) +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE +AS +SELECT + sym, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, + count(*) AS n_quotes, + min(bp) AS bp_min, + max(bp) AS bp_max, + min(ap) AS ap_min, + max(ap) AS ap_max, + sum(bs) AS bs_sum, + sum(`as`) AS as_sum, + sum(ap - bp) AS spread_sum +FROM costbench.rt_full_serverless_baseline_r2_20260917.quotes +GROUP BY sym, day; + +-- Retain the creator's access before transferring ownership. +GRANT SELECT, MANAGE +ON MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r2_20260917.quotes_daily +TO `__BENCHMARK_ADMIN_PRINCIPAL__`; + +ALTER MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r2_20260917.quotes_daily +SET OWNER TO `__SERVICE_PRINCIPAL_APPLICATION_ID__`; + +-- Metadata-only verification. Do not query either table's contents. +DESCRIBE SCHEMA EXTENDED +costbench.rt_full_serverless_baseline_r2_20260917; + +DESCRIBE DETAIL +costbench.rt_full_serverless_baseline_r2_20260917.quotes; + +DESCRIBE HISTORY +costbench.rt_full_serverless_baseline_r2_20260917.quotes +LIMIT 2; + +SHOW TBLPROPERTIES +costbench.rt_full_serverless_baseline_r2_20260917.quotes; + +DESCRIBE TABLE EXTENDED +costbench.rt_full_serverless_baseline_r2_20260917.quotes +AS JSON; + +DESCRIBE TABLE EXTENDED +costbench.rt_full_serverless_baseline_r2_20260917.quotes_daily +AS JSON; + +DESCRIBE TABLE EXTENDED +costbench.rt_full_serverless_baseline_r2_20260917.quotes_daily; + +SHOW GRANTS ON TABLE +costbench.rt_full_serverless_baseline_r2_20260917.quotes; + +SHOW GRANTS ON MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r2_20260917.quotes_daily; diff --git a/full-path-realtime/quotes/databricks/create_full_serverless_baseline_r3_20260918.sql b/full-path-realtime/quotes/databricks/create_full_serverless_baseline_r3_20260918.sql new file mode 100644 index 0000000..e8b36f6 --- /dev/null +++ b/full-path-realtime/quotes/databricks/create_full_serverless_baseline_r3_20260918.sql @@ -0,0 +1,145 @@ +-- Canonical full-source Serverless SQL baseline R3 target. +-- +-- Replace both __...__ principal placeholders before execution. Run once on +-- the control warehouse as the benchmark administrator. This file +-- intentionally contains no DROP or IF NOT EXISTS statements. Stop if any +-- statement fails. +-- R3 uses a fresh schema because R2 stopped on a remote HTTP/2 stream reset. +-- SDK recovery is enabled for this target; replay surplus is measured explicitly. + +CREATE SCHEMA costbench.rt_full_serverless_baseline_r3_20260918; + +GRANT USE CATALOG ON CATALOG costbench +TO `__SERVICE_PRINCIPAL_APPLICATION_ID__`; + +GRANT USE SCHEMA +ON SCHEMA costbench.rt_full_serverless_baseline_r3_20260918 +TO `__SERVICE_PRINCIPAL_APPLICATION_ID__`; + +CREATE TABLE costbench.rt_full_serverless_baseline_r3_20260918.quotes ( + sym STRING, + bx SMALLINT, + bp DOUBLE, + bs BIGINT, + ax SMALLINT, + ap DOUBLE, + `as` BIGINT, + c SMALLINT, + i ARRAY, + t BIGINT, + q BIGINT, + z SMALLINT +) +USING DELTA +CLUSTER BY (sym, t) +TBLPROPERTIES ( + 'delta.enableDeletionVectors' = 'true', + 'delta.enableRowTracking' = 'true', + 'delta.enableChangeDataFeed' = 'true' +); + +GRANT SELECT, MODIFY +ON TABLE costbench.rt_full_serverless_baseline_r3_20260918.quotes +TO `__SERVICE_PRINCIPAL_APPLICATION_ID__`; + +-- This statement creates nothing. Require: +-- The Materialized View can be incrementally refreshed. +-- No issues detected. +EXPLAIN CREATE MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r3_20260918.quotes_daily_explain_only +USING DELTA +CLUSTER BY (sym, day) +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE +AS +SELECT + sym, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, + count(*) AS n_quotes, + min(bp) AS bp_min, + max(bp) AS bp_max, + min(ap) AS ap_min, + max(ap) AS ap_max, + sum(bs) AS bs_sum, + sum(`as`) AS as_sum, + sum(ap - bp) AS spread_sum +FROM costbench.rt_full_serverless_baseline_r3_20260918.quotes +GROUP BY sym, day; + +CREATE MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r3_20260918.quotes_daily +USING DELTA +CLUSTER BY (sym, day) +REFRESH POLICY INCREMENTAL STRICT +TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE +AS +SELECT + sym, + date_add( + DATE '1970-01-01', + CAST( + floor( + CAST(t AS DECIMAL(20, 0)) + / CAST(86400000 AS DECIMAL(20, 0)) + ) AS INT + ) + ) AS day, + count(*) AS n_quotes, + min(bp) AS bp_min, + max(bp) AS bp_max, + min(ap) AS ap_min, + max(ap) AS ap_max, + sum(bs) AS bs_sum, + sum(`as`) AS as_sum, + sum(ap - bp) AS spread_sum +FROM costbench.rt_full_serverless_baseline_r3_20260918.quotes +GROUP BY sym, day; + +-- Retain the creator's access before transferring ownership. +GRANT SELECT, MANAGE +ON MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r3_20260918.quotes_daily +TO `__BENCHMARK_ADMIN_PRINCIPAL__`; + +ALTER MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r3_20260918.quotes_daily +SET OWNER TO `__SERVICE_PRINCIPAL_APPLICATION_ID__`; + +-- Metadata-only verification. Do not query either table's contents. +DESCRIBE SCHEMA EXTENDED +costbench.rt_full_serverless_baseline_r3_20260918; + +DESCRIBE DETAIL +costbench.rt_full_serverless_baseline_r3_20260918.quotes; + +DESCRIBE HISTORY +costbench.rt_full_serverless_baseline_r3_20260918.quotes +LIMIT 2; + +SHOW TBLPROPERTIES +costbench.rt_full_serverless_baseline_r3_20260918.quotes; + +DESCRIBE TABLE EXTENDED +costbench.rt_full_serverless_baseline_r3_20260918.quotes +AS JSON; + +DESCRIBE TABLE EXTENDED +costbench.rt_full_serverless_baseline_r3_20260918.quotes_daily +AS JSON; + +DESCRIBE TABLE EXTENDED +costbench.rt_full_serverless_baseline_r3_20260918.quotes_daily; + +SHOW GRANTS ON TABLE +costbench.rt_full_serverless_baseline_r3_20260918.quotes; + +SHOW GRANTS ON MATERIALIZED VIEW +costbench.rt_full_serverless_baseline_r3_20260918.quotes_daily; diff --git a/full-path-realtime/quotes/databricks/databricks-host.env.example b/full-path-realtime/quotes/databricks/databricks-host.env.example new file mode 100644 index 0000000..9e22884 --- /dev/null +++ b/full-path-realtime/quotes/databricks/databricks-host.env.example @@ -0,0 +1,18 @@ +# Non-secret deployment coordinates. Copy outside the repository, replace every +# placeholder, and source it before running benchmark scripts. + +export DATABRICKS_HOST='https://' +export DATABRICKS_WORKSPACE_ID='' +export DATABRICKS_REGION='eu-west-1' +export DATABRICKS_CLIENT_ID='' +export DATABRICKS_CONTROL_WAREHOUSE_ID='' +export DATABRICKS_QUERY_WAREHOUSE_ID='' + +# Optional: scripts derive this from workspace ID and region when omitted. +export ZEROBUS_ENDPOINT='https://.zerobus..cloud.databricks.com' + +# Source staging only. +export S3_SOURCE_URI='s3:///' + +# Never add DATABRICKS_CLIENT_SECRET, DATABRICKS_TOKEN, AWS access keys, or +# session tokens to this file. diff --git a/full-path-realtime/quotes/databricks/dbx_common.py b/full-path-realtime/quotes/databricks/dbx_common.py new file mode 100755 index 0000000..f3cb866 --- /dev/null +++ b/full-path-realtime/quotes/databricks/dbx_common.py @@ -0,0 +1,1118 @@ +#!/usr/bin/env python3 +"""Standard-library helpers shared by the Databricks benchmark tools.""" + +from __future__ import annotations + +import base64 +import json +import os +import re +import socket +import ssl +import tempfile +import time +import urllib.error +import urllib.parse +import urllib.request +from dataclasses import asdict, dataclass +from datetime import date, datetime, timezone +from pathlib import Path +from typing import Any, Iterable, Mapping, Sequence + +SQL_PLACEHOLDERS = ( + "__CATALOG__", + "__SCHEMA__", + "__RAW_TABLE__", + "__MV_TABLE__", +) +TERMINAL_STATEMENT_STATES = {"SUCCEEDED", "FAILED", "CANCELED", "CLOSED"} +SECRET_KEY_RE = re.compile( + r"(?:authorization|client[_-]?secret|password|private[_-]?key|access[_-]?token|^token$)", + re.IGNORECASE, +) +BEARER_RE = re.compile(r"(?i)\b(Bearer|Basic)\s+[A-Za-z0-9._~+/=-]+") +PAT_RE = re.compile(r"\bdapi[A-Za-z0-9_-]{8,}\b") +SAFE_COMPONENT_RE = re.compile(r"^[A-Za-z0-9][A-Za-z0-9_-]*$") +WORKSPACE_ID_RE = re.compile(r"^[0-9]+$") +WAREHOUSE_ID_RE = re.compile(r"^[A-Za-z0-9_-]+$") + +ENV_ALIASES: dict[str, tuple[str, ...]] = { + "host": ( + "DATABRICKS_HOST", + "DATABRICKS_SERVER_HOSTNAME", + "DATABRICKS_WORKSPACE_URL", + "WORKSPACE_URL", + ), + "cloud": ("DATABRICKS_CLOUD", "DBX_CLOUD", "CLOUD"), + "region": ("DATABRICKS_REGION", "DBX_REGION", "REGION"), + "workspace_id": ("DATABRICKS_WORKSPACE_ID", "WORKSPACE_ID"), + "catalog": ("DATABRICKS_CATALOG", "DBX_CATALOG", "CATALOG"), + "schema": ("DATABRICKS_SCHEMA", "DBX_SCHEMA", "SCHEMA"), + "raw_table": ("DATABRICKS_RAW_TABLE", "DBX_RAW_TABLE", "RAW_TABLE"), + "mv_table": ("DATABRICKS_MV_TABLE", "DBX_MV_TABLE", "MV_TABLE"), + "rt_warehouse_id": ( + "DATABRICKS_RT_WAREHOUSE_ID", + "DBX_RT_WAREHOUSE_ID", + "RT_WAREHOUSE_ID", + ), + "control_warehouse_id": ( + "DATABRICKS_CONTROL_WAREHOUSE_ID", + "DBX_CONTROL_WAREHOUSE_ID", + "CONTROL_WAREHOUSE_ID", + ), + "zerobus_endpoint": ( + "ZEROBUS_ENDPOINT", + "ZEROBUS_SERVER_ENDPOINT", + "DATABRICKS_ZEROBUS_ENDPOINT", + ), +} + + +def utc_now() -> datetime: + return datetime.now(timezone.utc) + + +def iso_utc(value: datetime | None = None) -> str: + value = utc_now() if value is None else value + if value.tzinfo is None: + value = value.replace(tzinfo=timezone.utc) + return value.astimezone(timezone.utc).isoformat(timespec="milliseconds").replace("+00:00", "Z") + + +def json_default(value: Any) -> Any: + if isinstance(value, datetime): + return iso_utc(value) + if isinstance(value, date): + return value.isoformat() + if isinstance(value, bytes): + return value.hex() + if isinstance(value, Path): + return str(value) + if hasattr(value, "items"): + return dict(value) + return str(value) + + +def _atomic_write(path: Path, data: bytes) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + fd, tmp_name = tempfile.mkstemp(prefix=f".{path.name}.", dir=path.parent) + try: + with os.fdopen(fd, "wb") as handle: + handle.write(data) + handle.flush() + os.fsync(handle.fileno()) + os.replace(tmp_name, path) + finally: + try: + os.unlink(tmp_name) + except FileNotFoundError: + pass + + +def atomic_write_json(path: Path, payload: Any) -> None: + """Replace *path* atomically with one stable, newline-terminated JSON value.""" + rendered = json.dumps(payload, default=json_default, indent=2, sort_keys=True) + "\n" + _atomic_write(path, rendered.encode("utf-8")) + + +def atomic_append_jsonl(path: Path, payload: Any) -> None: + """Append one JSONL record with one O_APPEND write and an fsync.""" + path.parent.mkdir(parents=True, exist_ok=True) + rendered = ( + json.dumps(payload, default=json_default, separators=(",", ":"), sort_keys=False) + "\n" + ).encode("utf-8") + fd = os.open(path, os.O_APPEND | os.O_CREAT | os.O_WRONLY, 0o644) + try: + written = os.write(fd, rendered) + if written != len(rendered): + raise OSError(f"short JSONL write: wrote {written} of {len(rendered)} bytes") + os.fsync(fd) + finally: + os.close(fd) + + +append_jsonl = atomic_append_jsonl + + +def redact_secrets(value: Any, secrets: Iterable[str | None] = ()) -> Any: + """Return a report-safe copy of nested data or a redacted string.""" + known = tuple(secret for secret in secrets if secret) + if isinstance(value, Mapping): + return { + str(key): ( + "***REDACTED***" + if SECRET_KEY_RE.search(str(key)) + else redact_secrets(item, known) + ) + for key, item in value.items() + } + if isinstance(value, (list, tuple, set)): + return [redact_secrets(item, known) for item in value] + if not isinstance(value, str): + return value + redacted = value + for secret in sorted(known, key=len, reverse=True): + redacted = redacted.replace(secret, "***REDACTED***") + redacted = BEARER_RE.sub(lambda match: f"{match.group(1)} ***REDACTED***", redacted) + return PAT_RE.sub("***REDACTED***", redacted) + + +def quote_identifier(value: str) -> str: + if not value or "\x00" in value: + raise ValueError("SQL identifier must be non-empty and contain no NUL") + return f"`{value.replace('`', '``')}`" + + +def sql_string(value: str) -> str: + return "'" + value.replace("'", "''") + "'" + + +def render_sql( + sql: str, + catalog: str, + schema: str, + *, + raw_table: str = "quotes", + mv_table: str = "quotes_daily", +) -> str: + rendered = sql.replace("__CATALOG__", quote_identifier(catalog)) + rendered = rendered.replace("__SCHEMA__", quote_identifier(schema)) + rendered = rendered.replace("__RAW_TABLE__", quote_identifier(raw_table)) + rendered = rendered.replace("__MV_TABLE__", quote_identifier(mv_table)) + unresolved = [token for token in SQL_PLACEHOLDERS if token in rendered] + if unresolved: + raise ValueError(f"unresolved SQL placeholders: {', '.join(unresolved)}") + return rendered + + +def split_sql_statements(sql: str) -> list[str]: + """Split SQL outside strings, identifiers, and line/block comments. + + Ordinary comments are removed. Databricks optimizer hints (``/*+ ... */``) + are retained because removing them can change query behavior. + """ + + statements: list[str] = [] + current: list[str] = [] + quote: str | None = None + block_depth = 0 + keep_block = False + i = 0 + + while i < len(sql): + char = sql[i] + nxt = sql[i + 1] if i + 1 < len(sql) else "" + + if block_depth: + if char == "/" and nxt == "*": + block_depth += 1 + if keep_block: + current.extend((char, nxt)) + i += 2 + continue + if char == "*" and nxt == "/": + block_depth -= 1 + if keep_block: + current.extend((char, nxt)) + i += 2 + if block_depth == 0: + was_kept = keep_block + keep_block = False + if not was_kept: + current.append(" ") + continue + if keep_block: + current.append(char) + elif char in "\r\n": + current.append(char) + i += 1 + continue + + if quote is None and char == "-" and nxt == "-": + i += 2 + while i < len(sql) and sql[i] not in "\r\n": + i += 1 + current.append("\n") + continue + + if quote is None and char == "/" and nxt == "*": + keep_block = i + 2 < len(sql) and sql[i + 2] == "+" + block_depth = 1 + if keep_block: + current.extend((char, nxt)) + else: + current.append(" ") + i += 2 + continue + + if quote is None and char in ("'", '"', "`"): + quote = char + current.append(char) + i += 1 + continue + + if quote is not None: + current.append(char) + if char == quote: + if nxt == quote: + current.append(nxt) + i += 2 + continue + quote = None + elif char == "\\" and nxt: + current.append(nxt) + i += 2 + continue + i += 1 + continue + + if char == ";": + statement = "".join(current).strip() + if statement: + statements.append(statement) + current = [] + else: + current.append(char) + i += 1 + + if quote is not None: + raise ValueError(f"unterminated SQL quote {quote!r}") + if block_depth: + raise ValueError("unterminated SQL block comment") + statement = "".join(current).strip() + if statement: + statements.append(statement) + return statements + + +def load_queries( + path: Path, + catalog: str, + schema: str, + *, + raw_table: str = "quotes", + mv_table: str = "quotes_daily", +) -> list[str]: + return split_sql_statements( + render_sql( + path.read_text(encoding="utf-8"), + catalog, + schema, + raw_table=raw_table, + mv_table=mv_table, + ) + ) + + +def _first_env(env: Mapping[str, str], names: Sequence[str]) -> str | None: + for name in names: + value = env.get(name) + if value is not None and value.strip(): + return value.strip() + return None + + +def _warehouse_from_http_path(env: Mapping[str, str]) -> str | None: + path = env.get("DATABRICKS_HTTP_PATH", "").strip().rstrip("/") + marker = "/sql/1.0/warehouses/" + if marker not in path: + return None + return path.rsplit("/", 1)[-1] or None + + +def normalize_host(value: str | None) -> str | None: + if value is None or not value.strip(): + return None + host = value.strip() + if "://" not in host: + host = f"https://{host}" + return host.rstrip("/") + + +def infer_cloud(host: str | None) -> str | None: + hostname = (urllib.parse.urlparse(host).hostname or "").lower() if host else "" + if hostname.endswith(".azuredatabricks.net"): + return "azure" + if hostname.endswith(".gcp.databricks.com"): + return "gcp" + if hostname.endswith(".cloud.databricks.com"): + return "aws" + return None + + +def normalize_cloud(value: str | None) -> str | None: + if value is None or not value.strip(): + return None + aliases = { + "aws": "aws", + "amazon": "aws", + "azure": "azure", + "az": "azure", + "gcp": "gcp", + "google": "gcp", + "google-cloud": "gcp", + } + return aliases.get(value.strip().lower(), value.strip().lower()) + + +@dataclass(frozen=True) +class EnvironmentConfig: + host: str | None + cloud: str | None + region: str | None + workspace_id: str | None + catalog: str + schema: str + raw_table: str + mv_table: str + rt_warehouse_id: str | None + control_warehouse_id: str | None + zerobus_endpoint: str | None + + @classmethod + def from_env( + cls, + env: Mapping[str, str] | None = None, + **overrides: str | None, + ) -> "EnvironmentConfig": + source = os.environ if env is None else env + values = {name: _first_env(source, aliases) for name, aliases in ENV_ALIASES.items()} + for name, value in overrides.items(): + if name not in values: + raise TypeError(f"unknown configuration override: {name}") + if value is not None: + values[name] = value.strip() if isinstance(value, str) else value + + values["host"] = normalize_host(values["host"]) + values["cloud"] = normalize_cloud(values["cloud"]) or infer_cloud(values["host"]) + values["catalog"] = values["catalog"] or "workspace" + values["schema"] = values["schema"] or "benchmarking" + values["raw_table"] = values["raw_table"] or "quotes" + values["mv_table"] = values["mv_table"] or "quotes_daily" + values["zerobus_endpoint"] = normalize_host(values["zerobus_endpoint"]) + if not values["rt_warehouse_id"]: + values["rt_warehouse_id"] = _warehouse_from_http_path(source) + if ( + not values["zerobus_endpoint"] + and values["workspace_id"] + and values["region"] + and values["cloud"] + ): + try: + values["zerobus_endpoint"] = construct_zerobus_endpoint( + values["workspace_id"], values["region"], values["cloud"] + ) + except ValueError: + pass + return cls(**values) # type: ignore[arg-type] + + def as_report_dict(self) -> dict[str, Any]: + return asdict(self) + + @property + def raw_full_name(self) -> str: + return f"{self.catalog}.{self.schema}.{self.raw_table}" + + @property + def mv_full_name(self) -> str: + return f"{self.catalog}.{self.schema}.{self.mv_table}" + + +def construct_zerobus_endpoint(workspace_id: str, region: str, cloud: str) -> str: + cloud = normalize_cloud(cloud) + if not WORKSPACE_ID_RE.fullmatch(workspace_id): + raise ValueError("workspace ID must contain decimal digits only") + if not SAFE_COMPONENT_RE.fullmatch(region): + raise ValueError("region contains unsupported characters") + suffixes = { + "aws": "cloud.databricks.com", + "azure": "azuredatabricks.net", + "gcp": "gcp.databricks.com", + } + try: + suffix = suffixes[cloud] + except KeyError as exc: + raise ValueError(f"unsupported cloud for Zerobus endpoint: {cloud!r}") from exc + return f"https://{workspace_id}.zerobus.{region}.{suffix}" + + +def zerobus_endpoint_errors( + endpoint: str, + *, + workspace_id: str | None = None, + region: str | None = None, + cloud: str | None = None, +) -> list[str]: + errors: list[str] = [] + parsed = urllib.parse.urlparse(endpoint) + if parsed.scheme != "https": + errors.append("Zerobus endpoint must use https") + if not parsed.hostname: + errors.append("Zerobus endpoint must include a hostname") + if parsed.username or parsed.password or parsed.query or parsed.fragment: + errors.append("Zerobus endpoint must not contain credentials, query, or fragment") + if parsed.path not in ("", "/"): + errors.append("Zerobus endpoint must not contain a path") + try: + port = parsed.port + except ValueError: + errors.append("Zerobus endpoint contains an invalid port") + else: + if port not in (None, 443): + errors.append("Zerobus endpoint port must be 443 when specified") + if workspace_id and region and cloud: + try: + expected = urllib.parse.urlparse( + construct_zerobus_endpoint(workspace_id, region, cloud) + ).hostname + if parsed.hostname and parsed.hostname.lower() != expected: + errors.append(f"Zerobus endpoint hostname does not match {cloud} configuration") + except ValueError as exc: + errors.append(str(exc)) + return errors + + +def validate_config(config: EnvironmentConfig, *, online: bool = False) -> dict[str, list[str]]: + errors: list[str] = [] + warnings: list[str] = [] + parsed = urllib.parse.urlparse(config.host) if config.host else None + + if config.host: + if parsed is None or parsed.scheme != "https" or not parsed.hostname: + errors.append("Databricks host must be an https origin") + elif ( + parsed.username + or parsed.password + or parsed.query + or parsed.fragment + or parsed.path not in ("", "/") + ): + errors.append("Databricks host must not contain credentials, query, fragment, or path") + elif online: + errors.append("Databricks host is required online") + + if config.cloud and config.cloud not in {"aws", "azure", "gcp"}: + errors.append("cloud must be one of: aws, azure, gcp") + elif online and not config.cloud: + errors.append("cloud is required online") + if config.cloud and config.host: + inferred = infer_cloud(config.host) + if inferred and inferred != config.cloud: + errors.append("cloud does not match the Databricks host suffix") + + if config.workspace_id and not WORKSPACE_ID_RE.fullmatch(config.workspace_id): + errors.append("workspace ID must contain decimal digits only") + elif online and not config.workspace_id: + errors.append("workspace ID is required online") + if config.region and not SAFE_COMPONENT_RE.fullmatch(config.region): + errors.append("region contains unsupported characters") + elif online and not config.region: + errors.append("region is required online") + + for label, value in ( + ("catalog", config.catalog), + ("schema", config.schema), + ("raw table", config.raw_table), + ("MV table", config.mv_table), + ): + if not SAFE_COMPONENT_RE.fullmatch(value): + errors.append(f"{label} must contain only ASCII letters, digits, '_' or '-'") + if not re.fullmatch(r"[A-Za-z0-9_]+", config.raw_table): + errors.append("raw table must contain only ASCII letters, digits, and '_' for Zerobus") + + for label, value in ( + ("Lakehouse//RT warehouse ID", config.rt_warehouse_id), + ("control warehouse ID", config.control_warehouse_id), + ): + if value and not WAREHOUSE_ID_RE.fullmatch(value): + errors.append(f"{label} contains unsupported characters") + elif online and not value: + errors.append(f"{label} is required online") + + if config.zerobus_endpoint: + errors.extend( + zerobus_endpoint_errors( + config.zerobus_endpoint, + workspace_id=config.workspace_id, + region=config.region, + cloud=config.cloud, + ) + ) + elif online: + errors.append( + "Zerobus endpoint is required online (or supply workspace ID, region, and cloud)" + ) + elif not (config.workspace_id and config.region and config.cloud): + warnings.append( + "Zerobus endpoint cannot be constructed until workspace ID, region, and cloud are set" + ) + return {"errors": errors, "warnings": warnings} + + +class DatabricksApiError(RuntimeError): + def __init__( + self, + status: int | None, + method: str, + path: str, + detail: Any, + *, + secrets: Iterable[str | None] = (), + ): + self.status = status + self.method = method + self.path = path + self.detail = redact_secrets(detail, secrets) + prefix = f"Databricks API {method} {path}" + if status is not None: + prefix += f" returned HTTP {status}" + super().__init__(f"{prefix}: {self.detail}") + + +class DatabricksRestClient: + """Small Databricks REST client supporting PAT and OAuth M2M.""" + + def __init__( + self, + host: str, + *, + token: str | None = None, + client_id: str | None = None, + client_secret: str | None = None, + timeout: float = 30.0, + opener: Any = None, + ): + normalized = normalize_host(host) + if normalized is None: + raise ValueError("Databricks host is required") + if bool(client_id) != bool(client_secret): + raise ValueError("OAuth client ID and client secret must be supplied together") + if not token and not (client_id and client_secret): + raise ValueError("DATABRICKS_TOKEN or OAuth client credentials are required") + self.host = normalized + self._pat = token + self._client_id = client_id + self._client_secret = client_secret + self.timeout = timeout + self._opener = opener or urllib.request.urlopen + self._oauth_access_token: str | None = None + self._oauth_expires_at = 0.0 + + @property + def auth_mode(self) -> str: + return "pat" if self._pat else "oauth-client-credentials" + + def _known_secrets(self) -> tuple[str | None, ...]: + return (self._pat, self._client_secret, self._oauth_access_token) + + def _open_json(self, request: urllib.request.Request) -> dict[str, Any]: + try: + with self._opener(request, timeout=self.timeout) as response: + raw = response.read() + except urllib.error.HTTPError as exc: + raw = exc.read() + try: + detail: Any = json.loads(raw.decode("utf-8", errors="replace")) + except json.JSONDecodeError: + detail = raw.decode("utf-8", errors="replace")[:4096] + path = urllib.parse.urlsplit(request.full_url).path + raise DatabricksApiError( + exc.code, + request.method, + path, + detail, + secrets=self._known_secrets(), + ) from None + except (urllib.error.URLError, TimeoutError, OSError) as exc: + path = urllib.parse.urlsplit(request.full_url).path + raise DatabricksApiError( + None, + request.method, + path, + str(exc), + secrets=self._known_secrets(), + ) from None + if not raw: + return {} + try: + decoded = json.loads(raw) + except json.JSONDecodeError as exc: + path = urllib.parse.urlsplit(request.full_url).path + raise DatabricksApiError( + None, + request.method, + path, + "non-JSON response", + secrets=self._known_secrets(), + ) from exc + if not isinstance(decoded, dict): + raise DatabricksApiError( + None, + request.method, + urllib.parse.urlsplit(request.full_url).path, + "non-object JSON response", + secrets=self._known_secrets(), + ) + return decoded + + def oauth_token( + self, + *, + resource: str | None = None, + authorization_details: Sequence[Mapping[str, Any]] | None = None, + cache: bool = False, + ) -> str: + if not self._client_id or not self._client_secret: + raise ValueError("OAuth client credentials are required") + if cache and self._oauth_access_token and time.monotonic() < self._oauth_expires_at: + return self._oauth_access_token + form: dict[str, str] = {"grant_type": "client_credentials", "scope": "all-apis"} + if resource: + form["resource"] = resource + if authorization_details: + form["authorization_details"] = json.dumps( + authorization_details, separators=(",", ":") + ) + basic = base64.b64encode( + f"{self._client_id}:{self._client_secret}".encode("utf-8") + ).decode("ascii") + request = urllib.request.Request( + f"{self.host}/oidc/v1/token", + data=urllib.parse.urlencode(form).encode("ascii"), + headers={ + "Accept": "application/json", + "Authorization": f"Basic {basic}", + "Content-Type": "application/x-www-form-urlencoded", + }, + method="POST", + ) + response = self._open_json(request) + access_token = response.get("access_token") + if not isinstance(access_token, str) or not access_token: + raise DatabricksApiError( + None, + "POST", + "/oidc/v1/token", + "response omitted access_token", + secrets=self._known_secrets(), + ) + if cache and not resource and not authorization_details: + self._oauth_access_token = access_token + expires_in = max(0, int(response.get("expires_in", 3600)) - 60) + self._oauth_expires_at = time.monotonic() + expires_in + return access_token + + def _authorization(self) -> str: + token = self._pat or self.oauth_token(cache=True) + return f"Bearer {token}" + + def request( + self, + method: str, + path: str, + *, + payload: Mapping[str, Any] | None = None, + query: Mapping[str, Any] | Sequence[tuple[str, Any]] | None = None, + ) -> dict[str, Any]: + if not path.startswith("/"): + raise ValueError("REST path must start with '/'") + url = f"{self.host}{path}" + if query: + url += "?" + urllib.parse.urlencode(query, doseq=True) + data = None + headers = {"Accept": "application/json", "Authorization": self._authorization()} + if payload is not None: + data = json.dumps(payload, separators=(",", ":")).encode("utf-8") + headers["Content-Type"] = "application/json" + request = urllib.request.Request(url, data=data, headers=headers, method=method.upper()) + return self._open_json(request) + + def get_warehouse(self, warehouse_id: str) -> dict[str, Any]: + safe_id = urllib.parse.quote(warehouse_id, safe="") + return self.request("GET", f"/api/2.0/sql/warehouses/{safe_id}") + + def execute_statement( + self, + statement: str, + warehouse_id: str, + *, + timeout: float = 120.0, + poll_interval: float = 0.5, + catalog: str | None = None, + schema: str | None = None, + query_tags: Mapping[str, str] | None = None, + ) -> dict[str, Any]: + payload: dict[str, Any] = { + "statement": statement, + "warehouse_id": warehouse_id, + "wait_timeout": "0s", + "disposition": "INLINE", + "format": "JSON_ARRAY", + } + if catalog: + payload["catalog"] = catalog + if schema: + payload["schema"] = schema + if query_tags: + payload["query_tags"] = [ + {"key": str(key), "value": str(value)} + for key, value in query_tags.items() + ] + response = self.request("POST", "/api/2.0/sql/statements", payload=payload) + statement_id = response.get("statement_id") + if not isinstance(statement_id, str) or not statement_id: + raise DatabricksApiError( + None, + "POST", + "/api/2.0/sql/statements", + "response omitted statement_id", + secrets=self._known_secrets(), + ) + deadline = time.monotonic() + timeout + while True: + state = str(response.get("status", {}).get("state", "")).upper() + if state in TERMINAL_STATEMENT_STATES: + break + if time.monotonic() >= deadline: + raise TimeoutError( + f"statement {statement_id} did not finish within {timeout:g} seconds" + ) + time.sleep(max(0.05, poll_interval)) + response = self.request( + "GET", + f"/api/2.0/sql/statements/{urllib.parse.quote(statement_id, safe='')}", + ) + if state != "SUCCEEDED": + detail = response.get("status", {}).get("error") or f"terminal state {state}" + raise DatabricksApiError( + None, + "GET", + f"/api/2.0/sql/statements/{statement_id}", + detail, + secrets=self._known_secrets(), + ) + return response + + def query_history_by_statement_id(self, statement_id: str) -> dict[str, Any] | None: + response = self.request( + "GET", + "/api/2.0/sql/history/queries", + query=[ + ("filter_by.statement_ids", statement_id), + ("include_metrics", "true"), + ("max_results", "1"), + ], + ) + records = response.get("res") + if records is None: + records = response.get("queries") + if not isinstance(records, list): + return None + return next( + ( + item + for item in records + if isinstance(item, dict) + and ( + item.get("statement_id") == statement_id + or item.get("query_id") == statement_id + ) + ), + None, + ) + + +def _statement_columns(response: Mapping[str, Any]) -> tuple[list[str], list[dict[str, Any]]]: + raw_columns = response.get("manifest", {}).get("schema", {}).get("columns", []) + columns = ( + [dict(column) for column in raw_columns if isinstance(column, Mapping)] + if isinstance(raw_columns, list) + else [] + ) + names = [ + str(column.get("name", f"column_{index}")) + for index, column in enumerate(columns) + ] + return names, columns + + +def _rows_from_data( + data: Any, + names: Sequence[str], + *, + strict_schema: bool = False, +) -> list[dict[str, Any]]: + if not isinstance(data, list): + raise ValueError("Statement result data_array is not an array") + rows: list[dict[str, Any]] = [] + for raw_row in data: + if not isinstance(raw_row, list): + raise ValueError("Statement result contains a non-array row") + if strict_schema and len(raw_row) != len(names): + raise RuntimeError( + "Statement result row does not match the manifest schema " + f"({len(raw_row)} values for {len(names)} columns)" + ) + rows.append( + { + names[index] if index < len(names) else f"column_{index}": value + for index, value in enumerate(raw_row) + } + ) + return rows + + +def statement_rows(response: Mapping[str, Any]) -> list[dict[str, Any]]: + names, _columns = _statement_columns(response) + result = response.get("result") + data = result.get("data_array", []) if isinstance(result, Mapping) else [] + if not isinstance(data, list): + return [] + rows: list[dict[str, Any]] = [] + for raw_row in data: + if isinstance(raw_row, list): + rows.extend(_rows_from_data([raw_row], names)) + return rows + + +def _bounded_positive_int(name: str, value: int) -> int: + if isinstance(value, bool) or not isinstance(value, int) or value <= 0: + raise ValueError(f"{name} must be a positive integer") + return value + + +def _workspace_chunk_path( + host: str, + link: Any, + *, + statement_id: str | None = None, +) -> str: + if not isinstance(link, str) or not link: + raise ValueError("Statement result next_chunk_internal_link is not a URL") + parsed = urllib.parse.urlsplit(link) + workspace = urllib.parse.urlsplit(host) + if parsed.fragment or parsed.username or parsed.password: + raise ValueError("Statement result chunk link contains unsupported URL components") + if parsed.scheme or parsed.netloc: + if ( + parsed.scheme.lower() != workspace.scheme.lower() + or parsed.netloc.lower() != workspace.netloc.lower() + ): + raise ValueError("Statement result chunk link is outside the workspace origin") + elif not parsed.path.startswith("/"): + raise ValueError("Statement result chunk link must be a workspace-origin path") + prefix = "/api/2.0/sql/statements/" + if not parsed.path.startswith(prefix): + raise ValueError("Statement result chunk link is not a Statement API path") + remainder = parsed.path[len(prefix) :] + linked_statement_id, separator, linked_resource = remainder.partition("/") + resource_parts = linked_resource.split("/") + if ( + separator != "/" + or len(resource_parts) != 3 + or resource_parts[:2] != ["result", "chunks"] + or not resource_parts[2] + or resource_parts[2] in {".", ".."} + ): + raise ValueError("Statement result chunk link is not a result-chunk path") + if ( + statement_id is not None + and urllib.parse.unquote(linked_statement_id) != statement_id + ): + raise ValueError("Statement result chunk link refers to another statement") + return parsed.path + (f"?{parsed.query}" if parsed.query else "") + + +def bounded_statement_rows( + client: DatabricksRestClient, + response: Mapping[str, Any], + *, + max_rows: int, + max_bytes: int, + max_chunks: int, +) -> tuple[list[dict[str, Any]], dict[str, Any]]: + """Retrieve every inline Statement result chunk within strict local limits. + + Databricks can return a successful INLINE result over multiple workspace-local + chunks. This helper follows only same-workspace Statement API links and fails + instead of returning a partial or provider-truncated result. + """ + + row_limit = _bounded_positive_int("max_rows", max_rows) + byte_limit = _bounded_positive_int("max_bytes", max_bytes) + chunk_limit = _bounded_positive_int("max_chunks", max_chunks) + names, columns = _statement_columns(response) + manifest = response.get("manifest", {}) + if not isinstance(manifest, Mapping): + raise ValueError("Statement result manifest is not an object") + expected_rows = manifest.get("total_row_count") + if expected_rows is not None: + try: + expected_rows = int(expected_rows) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError("Statement manifest total_row_count is invalid") from exc + if expected_rows < 0: + raise ValueError("Statement manifest total_row_count is negative") + if expected_rows > row_limit: + raise RuntimeError( + f"Statement result exceeds max_rows ({expected_rows} > {row_limit})" + ) + + expected_chunks = manifest.get("total_chunk_count") + if expected_chunks is not None: + try: + expected_chunks = int(expected_chunks) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError("Statement manifest total_chunk_count is invalid") from exc + if expected_chunks < 0: + raise ValueError("Statement manifest total_chunk_count is negative") + if expected_chunks > chunk_limit: + raise RuntimeError( + f"Statement result exceeds max_chunks ({expected_chunks} > {chunk_limit})" + ) + + raw_result = response.get("result") + if raw_result is None: + result: Mapping[str, Any] | None = None + elif isinstance(raw_result, Mapping): + result = raw_result + else: + raise ValueError("Statement result is not an object") + if manifest.get("truncated") is True or ( + result is not None and result.get("truncated") is True + ): + raise RuntimeError("Statement result was truncated by the provider") + if ( + expected_chunks == 0 + and result is not None + and result.get("data_array", []) == [] + and not result.get("next_chunk_internal_link") + ): + result = None + + all_rows: list[dict[str, Any]] = [] + encoded_bytes = 0 + chunk_count = 0 + seen_links: set[str] = set() + chunk = result + expected_offset = 0 + expected_chunk_index = 0 + + while chunk is not None: + chunk_count += 1 + if chunk_count > chunk_limit: + raise RuntimeError(f"Statement result exceeds max_chunks ({chunk_limit})") + if chunk.get("truncated") is True: + raise RuntimeError("Statement result chunk was truncated by the provider") + data = chunk.get("data_array", []) + if not isinstance(data, list): + raise ValueError("Statement result data_array is not an array") + declared_row_count = chunk.get("row_count") + if declared_row_count is not None: + try: + parsed_row_count = int(declared_row_count) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError("Statement result chunk row_count is invalid") from exc + if parsed_row_count != len(data): + raise RuntimeError( + "Statement result chunk is incomplete " + f"({len(data)} rows returned, chunk declared {parsed_row_count})" + ) + chunk_index = chunk.get("chunk_index") + if chunk_index is not None: + try: + parsed_chunk_index = int(chunk_index) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError("Statement result chunk_index is invalid") from exc + if parsed_chunk_index != expected_chunk_index: + raise RuntimeError( + "Statement result chunks are incomplete or out of order " + f"(chunk {parsed_chunk_index}, expected {expected_chunk_index})" + ) + try: + encoded_bytes += len( + json.dumps(data, separators=(",", ":"), ensure_ascii=False).encode("utf-8") + ) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError("Statement result rows are not JSON serializable") from exc + if encoded_bytes > byte_limit: + raise RuntimeError( + f"Statement result exceeds max_bytes ({encoded_bytes} > {byte_limit})" + ) + + row_offset = chunk.get("row_offset") + if row_offset is not None: + try: + parsed_offset = int(row_offset) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError("Statement result chunk row_offset is invalid") from exc + if parsed_offset != expected_offset: + raise RuntimeError( + "Statement result chunks are incomplete or out of order " + f"(offset {parsed_offset}, expected {expected_offset})" + ) + all_rows.extend(_rows_from_data(data, names, strict_schema=True)) + expected_offset = len(all_rows) + if len(all_rows) > row_limit: + raise RuntimeError( + f"Statement result exceeds max_rows ({len(all_rows)} > {row_limit})" + ) + + link = chunk.get("next_chunk_internal_link") + if not link: + break + response_statement_id = response.get("statement_id") + path = _workspace_chunk_path( + client.host, + link, + statement_id=( + str(response_statement_id) + if response_statement_id not in (None, "") + else None + ), + ) + if path in seen_links: + raise RuntimeError("Statement result chunk link cycle detected") + seen_links.add(path) + if chunk_count >= chunk_limit: + raise RuntimeError(f"Statement result exceeds max_chunks ({chunk_limit})") + fetched = client.request("GET", path) + nested = fetched.get("result") + chunk = nested if isinstance(nested, Mapping) else fetched + expected_chunk_index += 1 + + if expected_rows is not None and len(all_rows) != expected_rows: + raise RuntimeError( + "Statement result is incomplete " + f"({len(all_rows)} rows retrieved, manifest declared {expected_rows})" + ) + if expected_chunks is not None and chunk_count != expected_chunks: + raise RuntimeError( + "Statement result is incomplete " + f"({chunk_count} chunks retrieved, manifest declared {expected_chunks})" + ) + return all_rows, { + "statement_id": response.get("statement_id"), + "row_count": len(all_rows), + "chunk_count": chunk_count, + "encoded_data_bytes": encoded_bytes, + "schema_columns": columns, + } + + +def probe_tls_endpoint(endpoint: str, timeout: float = 5.0) -> dict[str, Any]: + parsed = urllib.parse.urlparse(endpoint) + if parsed.scheme != "https" or not parsed.hostname: + raise ValueError("endpoint must be an https URL with a hostname") + port = parsed.port or 443 + started = time.monotonic() + context = ssl.create_default_context() + with socket.create_connection((parsed.hostname, port), timeout=timeout) as raw: + with context.wrap_socket(raw, server_hostname=parsed.hostname) as tls: + version = tls.version() + return { + "hostname": parsed.hostname, + "port": port, + "tls_version": version, + "latency_ms": round((time.monotonic() - started) * 1000, 1), + } diff --git a/full-path-realtime/quotes/databricks/finalize_results.py b/full-path-realtime/quotes/databricks/finalize_results.py new file mode 100644 index 0000000..552f2e4 --- /dev/null +++ b/full-path-realtime/quotes/databricks/finalize_results.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python3 +"""Create the compact source and artifact manifest for one completed run.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import sys +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Sequence + +from dbx_common import atomic_write_json + + +def _read_json(path: Path) -> dict[str, Any]: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise ValueError(f"{path} is not a JSON object") + return value + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _utc_now() -> str: + return ( + datetime.now(timezone.utc) + .isoformat(timespec="milliseconds") + .replace("+00:00", "Z") + ) + + +def run(argv: Sequence[str] | None = None) -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--run-root", type=Path, required=True) + parser.add_argument("--run-id", required=True) + parser.add_argument("--source-manifest", type=Path, required=True) + parser.add_argument("--source-hashes", type=Path, required=True) + parser.add_argument( + "--status", + default="initial_cost_unsettled", + choices=("initial_cost_unsettled", "settled", "accepted"), + ) + args = parser.parse_args(argv) + run_root = args.run_root.expanduser().resolve() + source_manifest = _read_json(args.source_manifest.expanduser().resolve()) + source_hashes = _read_json(args.source_hashes.expanduser().resolve()) + manifest_path = run_root / "validation" / "manifest.json" + existing_sanitization = None + if manifest_path.exists(): + existing_manifest = _read_json(manifest_path) + candidate = existing_manifest.get("sanitization") + if isinstance(candidate, dict): + existing_sanitization = candidate + artifacts = [] + for path in sorted(item for item in run_root.rglob("*") if item.is_file()): + if path == manifest_path: + continue + artifacts.append( + { + "path": path.relative_to(run_root).as_posix(), + "size_bytes": path.stat().st_size, + "sha256": _sha256(path), + } + ) + manifest = { + "schema_version": 1, + "generated_at": _utc_now(), + "status": args.status, + "run_id": args.run_id, + "source": { + "total_rows": source_manifest.get("total_rows"), + "selected_file_count": source_manifest.get("selected_file_count"), + "selected_row_group_count": source_manifest.get( + "selected_row_group_count" + ), + "quotes_0_parquet_included": source_manifest.get( + "quotes_0_parquet_included" + ), + "excluded_files": source_manifest.get("excluded_files"), + "manifest_sha256": source_manifest.get("manifest_sha256"), + "hash_validation_status": source_hashes.get("status"), + "hash_validation_file_count": source_hashes.get("file_count"), + }, + "runtime_state": { + "included_in_results": False, + "files_excluded": [ + "source_manifest.json", + "ingest_events.jsonl", + "full query audit JSONL", + "raw provider exports", + "process logs", + ], + }, + "artifact_count": len(artifacts), + "result_size_bytes": sum(item["size_bytes"] for item in artifacts), + "artifacts": artifacts, + } + if existing_sanitization is not None: + manifest["sanitization"] = existing_sanitization + atomic_write_json(manifest_path, manifest) + print(json.dumps(manifest, sort_keys=True)) + return 0 + + +def main() -> int: + try: + return run() + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/full-path-realtime/quotes/databricks/hash_source_manifest.py b/full-path-realtime/quotes/databricks/hash_source_manifest.py new file mode 100644 index 0000000..64ba4c0 --- /dev/null +++ b/full-path-realtime/quotes/databricks/hash_source_manifest.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python3 +"""Hash deferred source files after a measured ingest window.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import os +import tempfile +from datetime import datetime, timezone +from pathlib import Path +from typing import Any + + +def sha256_file(path: Path, chunk_size: int = 8 * 1024 * 1024) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for chunk in iter(lambda: handle.read(chunk_size), b""): + digest.update(chunk) + return digest.hexdigest() + + +def atomic_json(path: Path, value: dict[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary = tempfile.mkstemp(prefix=f".{path.name}.", dir=path.parent) + try: + with os.fdopen(descriptor, "w", encoding="utf-8") as handle: + json.dump(value, handle, indent=2, sort_keys=True) + handle.write("\n") + handle.flush() + os.fsync(handle.fileno()) + os.chmod(temporary, 0o600) + os.replace(temporary, path) + finally: + try: + os.unlink(temporary) + except FileNotFoundError: + pass + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + args = parser.parse_args() + + manifest_path = args.manifest.expanduser().resolve() + output = args.output.expanduser().resolve() + manifest = json.loads(manifest_path.read_text(encoding="utf-8")) + root = Path(manifest["root"]).expanduser().resolve() + reports: list[dict[str, Any]] = [] + for index, source in enumerate(manifest["files"], start=1): + path = (root / source["path"]).resolve() + if root not in path.parents: + raise RuntimeError(f"source path escapes manifest root: {path}") + before = path.stat() + if ( + before.st_size != int(source["source_size_bytes"]) + or before.st_mtime_ns != int(source["source_mtime_ns"]) + ): + raise RuntimeError(f"source identity changed before hashing: {path}") + print( + f"SOURCE HASH {index}/{len(manifest['files'])} {source['path']}", + flush=True, + ) + digest = sha256_file(path) + after = path.stat() + if ( + after.st_size != before.st_size + or after.st_mtime_ns != before.st_mtime_ns + ): + raise RuntimeError(f"source changed while hashing: {path}") + reports.append( + { + "file_ordinal": source["file_ordinal"], + "path": source["path"], + "size_bytes": after.st_size, + "mtime_ns": after.st_mtime_ns, + "sha256": digest, + } + ) + + atomic_json( + output, + { + "schema_version": 1, + "status": "complete", + "generated_at": datetime.now(timezone.utc) + .isoformat(timespec="milliseconds") + .replace("+00:00", "Z"), + "source_manifest_path": str(manifest_path), + "source_manifest_sha256": sha256_file(manifest_path), + "file_count": len(reports), + "total_size_bytes": sum(item["size_bytes"] for item in reports), + "files": reports, + }, + ) + print(f"Source hashes written to {output}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/full-path-realtime/quotes/databricks/ingest_zerobus.py b/full-path-realtime/quotes/databricks/ingest_zerobus.py new file mode 100644 index 0000000..1c0feed --- /dev/null +++ b/full-path-realtime/quotes/databricks/ingest_zerobus.py @@ -0,0 +1,2664 @@ +#!/usr/bin/env python3 +"""Stream Parquet row groups to Databricks with Zerobus Arrow Flight. + +The module intentionally imports neither pyarrow nor Zerobus at import time so +its source-selection, recovery, accounting, and rate-limit logic can be tested +with the Python standard library alone. +""" + +from __future__ import annotations + +import argparse +import ctypes +import gc +import hashlib +import importlib.metadata +import inspect +import json +import os +import queue +import resource +import signal +import sys +import threading +import time +import traceback +import urllib.parse +import uuid +from collections import Counter +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Iterable, Mapping, Sequence + +from dbx_common import ( + DatabricksRestClient, + atomic_append_jsonl, + atomic_write_json, + iso_utc, + quote_identifier, + redact_secrets, + sql_string, + statement_rows, + zerobus_endpoint_errors, +) + +EXPECTED_FULL_ROWS = 113_219_565_734 +REQUIRED_SDK_DISTRIBUTION = "databricks-zerobus-ingest-sdk" +REQUIRED_SDK_VERSION = "1.8.0" +TARGET_COLUMNS = ( + "sym", + "bx", + "bp", + "bs", + "ax", + "ap", + "as", + "c", + "i", + "t", + "q", + "z", +) +SIGNED_LIMITS = { + 8: (-(2**7), 2**7 - 1), + 16: (-(2**15), 2**15 - 1), + 32: (-(2**31), 2**31 - 1), + 64: (-(2**63), 2**63 - 1), +} + + +@dataclass(frozen=True) +class Task: + ordinal: int + file_ordinal: int + file_path: Path + relative_path: str + row_group: int + rows: int + + def evidence(self) -> dict[str, Any]: + return { + "canonical_ordinal": self.ordinal, + "task_ordinal": self.ordinal, + "file_ordinal": self.file_ordinal, + "file": self.relative_path, + "row_group": self.row_group, + "row_count": self.rows, + "task_rows": self.rows, + } + + +@dataclass(frozen=True) +class BatchCoordinate: + task_ordinal: int + file_ordinal: int + file: str + row_group: int + batch_ordinal: int + row_offset: int + row_count: int + + @classmethod + def from_task( + cls, + task: Task, + *, + batch_ordinal: int, + row_offset: int, + row_count: int, + ) -> "BatchCoordinate": + if batch_ordinal < 0 or row_offset < 0 or row_count <= 0: + raise ValueError("batch ordinal/offset must be non-negative and rows positive") + if row_offset + row_count > task.rows: + raise ValueError("batch coordinate extends beyond its row group") + return cls( + task_ordinal=task.ordinal, + file_ordinal=task.file_ordinal, + file=task.relative_path, + row_group=task.row_group, + batch_ordinal=batch_ordinal, + row_offset=row_offset, + row_count=row_count, + ) + + def as_dict(self) -> dict[str, Any]: + return { + "task_ordinal": self.task_ordinal, + "file_ordinal": self.file_ordinal, + "file": self.file, + "row_group": self.row_group, + "batch_ordinal": self.batch_ordinal, + "row_offset": self.row_offset, + "row_count": self.row_count, + } + + +@dataclass +class PendingBatch: + coordinate: BatchCoordinate + rows: int + uncompressed_arrow_buffer_bytes: int + logical_offset: Any + report_offset: int | str + submitted_at: str + worker: int + stream: str + + @property + def key(self) -> str: + c = self.coordinate + return f"{self.stream}:{c.task_ordinal}:{c.batch_ordinal}" + + def evidence(self) -> dict[str, Any]: + return { + **self.coordinate.as_dict(), + "row_count": self.rows, + "uncompressed_arrow_buffer_bytes": self.uncompressed_arrow_buffer_bytes, + "logical_offset": self.report_offset, + "submitted_at": self.submitted_at, + "worker": self.worker, + "stream": self.stream, + } + + +@dataclass(frozen=True) +class RuntimeModules: + pa: Any + pc: Any + pq: Any + sdk_class: Any + options_class: Any + compression_enum: Any + sdk_version: str + pyarrow_version: str + + +@dataclass +class MemoryTelemetry: + trim_attempts: int = 0 + trim_successes: int = 0 + trim_unsupported: int = 0 + gc_collected_objects: int = 0 + trim_reclaimed_rss_bytes: int = 0 + last_trim_duration_sec: float | None = None + last_trim_at: str | None = None + + def as_dict(self) -> dict[str, Any]: + return { + "trim_attempts": self.trim_attempts, + "trim_successes": self.trim_successes, + "trim_unsupported": self.trim_unsupported, + "gc_collected_objects": self.gc_collected_objects, + "trim_reclaimed_rss_bytes": self.trim_reclaimed_rss_bytes, + "last_trim_duration_sec": self.last_trim_duration_sec, + "last_trim_at": self.last_trim_at, + } + + +class RateLimiter: + """A global row-rate limiter that never repays idle time with a burst.""" + + def __init__(self, target_rows_per_sec: float, *, clock: Any = time.monotonic): + if target_rows_per_sec < 0: + raise ValueError("target_rows_per_sec must be non-negative") + self.target_rows_per_sec = float(target_rows_per_sec) + self._clock = clock + self._next_send_at = clock() + self._scheduled_rows = 0 + self._lock = threading.Lock() + + def reserve(self, rows: int) -> float: + """Reserve a send start and return the required delay in seconds.""" + if rows <= 0: + raise ValueError("rows must be positive") + if self.target_rows_per_sec == 0: + with self._lock: + self._scheduled_rows += rows + return 0.0 + with self._lock: + now = self._clock() + send_at = max(now, self._next_send_at) + self._next_send_at = send_at + rows / self.target_rows_per_sec + self._scheduled_rows += rows + return max(0.0, send_at - now) + + def wait(self, rows: int, abort_event: threading.Event | None = None) -> bool: + delay = self.reserve(rows) + if delay <= 0: + return abort_event is None or not abort_event.is_set() + if abort_event is None: + time.sleep(delay) + return True + return not abort_event.wait(delay) + + def snapshot(self) -> dict[str, Any]: + with self._lock: + return { + "target_rows_per_sec": self.target_rows_per_sec, + "scheduled_rows": self._scheduled_rows, + "next_send_delay_sec": max(0.0, self._next_send_at - self._clock()), + "no_catch_up": True, + } + + +def target_schema(pa_module: Any | None = None) -> Any: + """Build the nullable Arrow schema that exactly corresponds to create.sql.""" + pa = pa_module or _import_pyarrow() + return pa.schema( + [ + pa.field("sym", pa.large_utf8(), nullable=True), + pa.field("bx", pa.int16(), nullable=True), + pa.field("bp", pa.float64(), nullable=True), + pa.field("bs", pa.int64(), nullable=True), + pa.field("ax", pa.int16(), nullable=True), + pa.field("ap", pa.float64(), nullable=True), + pa.field("as", pa.int64(), nullable=True), + pa.field("c", pa.int16(), nullable=True), + pa.field("i", pa.list_(pa.int16()), nullable=True), + pa.field("t", pa.int64(), nullable=True), + pa.field("q", pa.int64(), nullable=True), + pa.field("z", pa.int16(), nullable=True), + ] + ) + + +def _import_pyarrow() -> Any: + try: + import pyarrow as pa + except ImportError as exc: + raise RuntimeError( + "pyarrow is required; install requirements-zerobus.txt" + ) from exc + return pa + + +def _is_list_type(pa: Any, value: Any) -> bool: + checks = [pa.types.is_list, pa.types.is_large_list] + if hasattr(pa.types, "is_fixed_size_list"): + checks.append(pa.types.is_fixed_size_list) + return any(check(value) for check in checks) + + +def _prove_unsigned_fits( + source: Any, + target_type: Any, + *, + column_name: str, + pa: Any, + pc: Any, +) -> None: + """Explicitly prove unsigned scalar/list values fit a signed target.""" + source_type = source.type + if _is_list_type(pa, source_type) and _is_list_type(pa, target_type): + flattened = pc.list_flatten(source) + _prove_unsigned_fits( + flattened, + target_type.value_type, + column_name=f"{column_name}[]", + pa=pa, + pc=pc, + ) + return + if not ( + pa.types.is_unsigned_integer(source_type) + and pa.types.is_signed_integer(target_type) + ): + return + bit_width = int(target_type.bit_width) + if bit_width not in SIGNED_LIMITS: + raise ValueError(f"unsupported signed target width for {column_name}: {bit_width}") + maximum = pc.max(source, skip_nulls=True) + maximum_value = maximum.as_py() if maximum is not None else None + if maximum_value is not None and maximum_value > SIGNED_LIMITS[bit_width][1]: + raise OverflowError( + f"unsigned values in {column_name} do not fit {target_type}: " + f"maximum {maximum_value}" + ) + + +def normalize_batch( + batch: Any, + *, + pa_module: Any | None = None, + pc_module: Any | None = None, +) -> Any: + """Select the target columns and apply only safe, columnar Arrow casts.""" + pa = pa_module or _import_pyarrow() + if pc_module is None: + import pyarrow.compute as pc + else: + pc = pc_module + schema = target_schema(pa) + missing = [name for name in TARGET_COLUMNS if name not in batch.schema.names] + if missing: + raise ValueError(f"Parquet schema is missing required columns: {missing}") + arrays = [] + for field in schema: + source = batch.column(batch.schema.get_field_index(field.name)) + _prove_unsigned_fits( + source, + field.type, + column_name=field.name, + pa=pa, + pc=pc, + ) + arrays.append(pc.cast(source, field.type, safe=True)) + return pa.RecordBatch.from_arrays(arrays, schema=schema) + + +def map_ipc_compression(name: str, enum_type: Any) -> Any: + normalized = name.strip().upper().replace("-", "_") + if normalized == "LZ4": + normalized = "LZ4_FRAME" + if normalized not in {"NONE", "LZ4_FRAME", "ZSTD"}: + raise ValueError("IPC compression must be one of: none, LZ4_FRAME, ZSTD") + try: + return getattr(enum_type, normalized) + except AttributeError as exc: + raise RuntimeError( + f"installed Zerobus SDK omits IPCCompression.{normalized}" + ) from exc + + +def discover_source_files( + directory: Path, + pattern: str, + *, + include_quotes_0: bool = False, + max_files: int | None = None, +) -> tuple[list[Path], list[str]]: + if not directory.is_dir(): + raise RuntimeError(f"not a directory: {directory}") + matched = sorted( + (path for path in directory.glob(pattern) if path.is_file()), + key=lambda path: path.relative_to(directory).as_posix(), + ) + excluded = [ + path.relative_to(directory).as_posix() + for path in matched + if path.name == "quotes_0.parquet" and not include_quotes_0 + ] + selected = [ + path + for path in matched + if include_quotes_0 or path.name != "quotes_0.parquet" + ] + if max_files is not None: + selected = selected[:max_files] + if not selected: + raise RuntimeError( + f"no source files matched {directory / pattern} after exclusions" + ) + return selected, excluded + + +def sha256_file(path: Path, chunk_size: int = 8 * 1024 * 1024) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + while True: + chunk = handle.read(chunk_size) + if not chunk: + break + digest.update(chunk) + return digest.hexdigest() + + +def enumerate_tasks_and_manifest( + directory: Path, + pattern: str, + pq: Any, + *, + include_quotes_0: bool, + max_files: int | None, + max_row_groups: int | None, + max_rows: int | None = None, + hash_files: bool = True, +) -> tuple[list[Task], dict[str, Any]]: + files, excluded = discover_source_files( + directory, + pattern, + include_quotes_0=include_quotes_0, + max_files=max_files, + ) + tasks: list[Task] = [] + file_reports: list[dict[str, Any]] = [] + exhausted = False + selected_total = 0 + for file_ordinal, file_path in enumerate(files): + stat_before = file_path.stat() + parquet = pq.ParquetFile(file_path) + metadata = parquet.metadata + selected_row_groups: list[int] = [] + for row_group in range(metadata.num_row_groups): + if max_row_groups is not None and len(tasks) >= max_row_groups: + exhausted = True + break + source_rows = int(metadata.row_group(row_group).num_rows) + remaining = ( + None + if max_rows is None + else max_rows - selected_total + ) + if remaining is not None and remaining <= 0: + exhausted = True + break + rows = source_rows if remaining is None else min(source_rows, remaining) + relative = file_path.relative_to(directory).as_posix() + tasks.append( + Task( + ordinal=len(tasks), + file_ordinal=file_ordinal, + file_path=file_path.resolve(), + relative_path=relative, + row_group=row_group, + rows=rows, + ) + ) + selected_row_groups.append(rows) + selected_total += rows + if rows < source_rows: + exhausted = True + break + if selected_row_groups: + print( + "SOURCE MANIFEST " + f"{'hashing' if hash_files else 'recording'} " + f"file={file_path.relative_to(directory).as_posix()} " + f"selected_rows={sum(selected_row_groups):,}", + flush=True, + ) + digest = sha256_file(file_path) if hash_files else None + stat_after = file_path.stat() + if ( + stat_before.st_size != stat_after.st_size + or stat_before.st_mtime_ns != stat_after.st_mtime_ns + ): + raise RuntimeError( + f"source file changed while manifesting: {file_path}" + ) + file_reports.append( + { + "file_ordinal": file_ordinal, + "path": file_path.relative_to(directory).as_posix(), + "source_size_bytes": stat_after.st_size, + "source_mtime_ns": stat_after.st_mtime_ns, + "sha256": digest, + "sha256_status": "complete" if hash_files else "deferred", + "parquet_row_groups": int(metadata.num_row_groups), + "selected_row_group_rows": selected_row_groups, + "selected_rows": sum(selected_row_groups), + } + ) + if exhausted: + break + if not tasks: + raise RuntimeError("source selection produced no Parquet row groups") + manifest_core = { + "schema_version": 1, + "root": str(directory), + "pattern": pattern, + "quotes_0_parquet_included": include_quotes_0, + "excluded_files": excluded, + "max_files": max_files, + "max_row_groups": max_row_groups, + "max_rows": max_rows, + "files": file_reports, + "tasks": [task.evidence() for task in tasks], + "selected_file_count": len(file_reports), + "selected_row_group_count": len(tasks), + "total_rows": sum(task.rows for task in tasks), + } + fingerprint = hashlib.sha256( + json.dumps(manifest_core, separators=(",", ":"), sort_keys=True).encode("utf-8") + ).hexdigest() + return tasks, {**manifest_core, "manifest_sha256": fingerprint} + + +def validate_expected_rows( + actual_rows: int, + expected_rows: int = EXPECTED_FULL_ROWS, + *, + allow_partial: bool, +) -> None: + if actual_rows <= 0: + raise RuntimeError("selected source has no rows") + if actual_rows != expected_rows and not allow_partial: + raise RuntimeError( + f"source has {actual_rows:,} rows, expected {expected_rows:,}; " + "use --allow-partial only for an intentional qualification run" + ) + + +def ordinal_ranges(ordinals: Iterable[int]) -> list[list[int]]: + values = sorted(set(int(value) for value in ordinals)) + if not values: + return [] + ranges: list[list[int]] = [] + start = previous = values[0] + for value in values[1:]: + if value == previous + 1: + previous = value + continue + ranges.append([start, previous]) + start = previous = value + ranges.append([start, previous]) + return ranges + + +def expand_ordinal_ranges(value: Any) -> set[int]: + if not isinstance(value, list): + raise ValueError("completed task ranges must be a list") + result: set[int] = set() + for item in value: + if ( + not isinstance(item, list) + or len(item) != 2 + or isinstance(item[0], bool) + or isinstance(item[1], bool) + ): + raise ValueError("completed task range must be [start, end]") + start, end = int(item[0]), int(item[1]) + if start < 0 or end < start: + raise ValueError("completed task range is invalid") + result.update(range(start, end + 1)) + return result + + +def add_ordinal_range(ranges: list[list[int]], ordinal: int) -> list[list[int]]: + """Insert one completed ordinal into sorted disjoint inclusive ranges.""" + if ordinal < 0: + raise ValueError("completed task ordinal must be nonnegative") + merged: list[list[int]] = [] + start = end = ordinal + inserted = False + for current_start, current_end in ranges: + if current_end + 1 < start: + merged.append([current_start, current_end]) + elif end + 1 < current_start: + if not inserted: + merged.append([start, end]) + inserted = True + merged.append([current_start, current_end]) + else: + start = min(start, current_start) + end = max(end, current_end) + if not inserted: + merged.append([start, end]) + return merged + + +def _report_offset(offset: Any) -> int | str: + if isinstance(offset, int) and not isinstance(offset, bool): + return offset + return str(offset) + + +def frozen_config(args: argparse.Namespace) -> dict[str, Any]: + """Configuration that must remain identical across a safe resume.""" + return { + "workers": args.workers, + "batch_size": args.batch_size, + "queue_capacity": args.queue_capacity, + "target_rows_per_sec": args.target_eps, + "ipc_compression": args.compression, + "checkpoint_batches": args.checkpoint_batches, + "checkpoint_seconds": args.checkpoint_seconds, + "checkpoint_method": args.checkpoint_method, + "automatic_recovery": not args.disable_automatic_recovery, + "recovery_timeout_ms": args.recovery_timeout_ms, + "recovery_backoff_ms": args.recovery_backoff_ms, + "recovery_retries": args.recovery_retries, + "recovery_log_threshold_seconds": args.recovery_log_threshold_seconds, + "manual_stream_rotation_seconds": args.manual_stream_rotation_seconds, + "include_quotes_0": args.include_quotes_0, + "pattern": args.pattern, + "max_files": args.max_files, + "max_row_groups": args.max_row_groups, + "max_rows": args.max_rows, + "expected_rows": args.expected_rows, + "allow_partial": args.allow_partial, + "source_hashes_deferred": args.defer_source_hashes, + "compact_progress": args.compact_progress, + "compact_evidence": args.compact_evidence, + "compact_metrics": args.compact_metrics, + "memory_trim_interval": args.memory_trim_interval, + "min_system_available_gib": args.min_system_available_gib, + "sdk_distribution": REQUIRED_SDK_DISTRIBUTION, + "sdk_version": REQUIRED_SDK_VERSION, + } + + +def target_identity(args: argparse.Namespace) -> dict[str, str]: + return { + "catalog": args.catalog, + "schema": args.schema, + "table": args.table, + "full_name": f"{args.catalog}.{args.schema}.{args.table}", + "host": args.host, + "zerobus_endpoint": args.endpoint, + } + + +def compact_metrics_payload(state: Mapping[str, Any]) -> dict[str, Any]: + """Return the small, stable ingest-progress record used for publication.""" + statuses = [ + value + for value in state.get("stream_status", {}).values() + if isinstance(value, Mapping) + ] + rotation_counts: list[int] = [] + for value in statuses: + candidate = value.get("rotation_count", 0) + if isinstance(candidate, bool): + continue + try: + rotation_counts.append(int(candidate)) + except (TypeError, ValueError, OverflowError): + continue + state_counts = Counter( + str(value.get("state") or "unknown") for value in statuses + ) + memory = state.get("memory", {}) + host = state.get("host", {}) + limiter = state.get("rate_limiter", {}) + source = state.get("source", {}) + eps = state.get("eps", {}) + return { + "schema_version": 1, + "run_id": state.get("run_id"), + "observed_at": state.get("updated_at"), + "elapsed_sec": state.get("elapsed_sec"), + "source_rows": source.get("total_rows"), + "provider_committed_rows": state.get("provider_committed_rows"), + "submitted_rows": state.get("submitted_rows"), + "pending_rows": state.get("pending_rows"), + "completed_tasks": state.get("completed_tasks"), + "target_rows_per_sec": eps.get("target_rows_per_sec"), + "average_committed_rows_per_sec": eps.get( + "average_provider_committed_rows_per_sec" + ), + "session_committed_rows_per_sec": eps.get( + "session_provider_committed_rows_per_sec" + ), + "terminal_error": state.get("terminal_error"), + "ambiguous": state.get("ambiguous"), + "error_count": len(state.get("errors", [])), + "streams": { + "count": len(statuses), + "states": dict(state_counts), + "rotation_count_total": sum(rotation_counts), + "rotation_count_min": min(rotation_counts) if rotation_counts else 0, + "rotation_count_max": max(rotation_counts) if rotation_counts else 0, + }, + "transport_recovery": state.get("transport_recovery", {}), + "producer": { + "process_rss_bytes": memory.get("process_rss_bytes"), + "process_peak_rss_bytes": memory.get("process_peak_rss_bytes"), + "system_available_bytes": memory.get("system_available_bytes"), + "arrow_allocated_bytes": memory.get("arrow_allocated_bytes"), + "cpu_cores": host.get("producer_process_cpu_cores"), + "host_cpu_utilization_percent": host.get( + "host_cpu_utilization_percent" + ), + "network_receive_bytes_per_second": host.get( + "network_receive_bytes_per_second" + ), + "network_transmit_bytes_per_second": host.get( + "network_transmit_bytes_per_second" + ), + }, + "rate_limiter": { + "scheduled_rows": limiter.get("scheduled_rows"), + "next_send_delay_sec": limiter.get("next_send_delay_sec"), + }, + } + + +def validate_resume_journal( + journal: Mapping[str, Any], + *, + manifest: Mapping[str, Any], + target: Mapping[str, Any], + config: Mapping[str, Any], + tasks: Sequence[Task], +) -> set[int]: + reason = ( + "resume is not provably safe; use a fresh target table and start a new run" + ) + if journal.get("schema_version") != 1: + raise RuntimeError(f"{reason}: unsupported progress schema") + if journal.get("running"): + raise RuntimeError(f"{reason}: prior journal still says running") + if not journal.get("clean_checkpoint") or not journal.get("safe_to_resume"): + raise RuntimeError(f"{reason}: prior run did not end at a clean checkpoint") + if journal.get("terminal_error") or journal.get("ambiguous"): + raise RuntimeError(f"{reason}: prior run has terminal or ambiguous evidence") + if int(journal.get("pending_batches", -1)) != 0: + raise RuntimeError(f"{reason}: prior journal has pending batches") + source = journal.get("source", {}) + if source.get("manifest_sha256") != manifest.get("manifest_sha256"): + raise RuntimeError(f"{reason}: source manifest changed") + if int(source.get("total_rows", -1)) != int(manifest.get("total_rows", -2)): + raise RuntimeError(f"{reason}: source row count changed") + if journal.get("target") != dict(target): + raise RuntimeError(f"{reason}: target identity changed") + if journal.get("config") != dict(config): + raise RuntimeError(f"{reason}: frozen producer configuration changed") + completed_values = journal.get("completed_task_ordinals") + try: + completed = ( + {int(value) for value in completed_values} + if isinstance(completed_values, list) + else expand_ordinal_ranges(journal.get("completed_task_ranges", [])) + ) + except (TypeError, ValueError) as exc: + raise RuntimeError(f"{reason}: completed task evidence is invalid") from exc + valid = {task.ordinal for task in tasks} + if not completed.issubset(valid): + raise RuntimeError(f"{reason}: completed task list references unknown tasks") + committed_from_tasks = sum(task.rows for task in tasks if task.ordinal in completed) + if committed_from_tasks != int(journal.get("provider_committed_rows", -1)): + raise RuntimeError( + f"{reason}: committed rows do not equal completed whole tasks" + ) + if int(journal.get("logical_raw_rows", -1)) < committed_from_tasks: + raise RuntimeError(f"{reason}: logical raw-row count is inconsistent") + return completed + + +class ProgressJournal: + """Serialize evidence-ledger and progress-journal state transitions.""" + + def __init__( + self, + *, + progress_path: Path, + ledger_path: Path, + metrics_path: Path, + run_id: str, + manifest: Mapping[str, Any], + target: Mapping[str, Any], + config: Mapping[str, Any], + tasks: Sequence[Task], + baseline_table_rows: int, + baseline_unknown: bool, + secrets: Sequence[str], + previous: Mapping[str, Any] | None = None, + ): + self.progress_path = progress_path + self.ledger_path = ledger_path + self.metrics_path = metrics_path + self.run_id = run_id + self.manifest = dict(manifest) + self.target = dict(target) + self.config = dict(config) + self.tasks = {task.ordinal: task for task in tasks} + self._secrets = tuple(value for value in secrets if value) + self._lock = threading.Lock() + self._started_monotonic = time.monotonic() + self._previous_elapsed = float(previous.get("elapsed_sec", 0.0)) if previous else 0.0 + self._session_start_rows = ( + int(previous.get("provider_committed_rows", 0)) if previous else 0 + ) + if previous: + completed_values = previous.get("completed_task_ordinals") + completed = ( + {int(value) for value in completed_values} + if isinstance(completed_values, list) + else expand_ordinal_ranges( + previous.get("completed_task_ranges", []) + ) + ) + else: + completed = set() + committed = ( + int(previous.get("provider_committed_rows", 0)) if previous else 0 + ) + submitted = int(previous.get("submitted_rows", committed)) if previous else 0 + submitted_batches = ( + int(previous.get("submitted_batches", 0)) if previous else 0 + ) + durable_batches = int(previous.get("durable_batches", 0)) if previous else 0 + self._completed_tasks = set(completed) + self._completed_task_ranges = ordinal_ranges(completed) + completed_evidence = { + "completed_task_ranges": [list(item) for item in self._completed_task_ranges], + "completed_tasks": len(completed), + } + if not config.get("compact_progress"): + completed_evidence["completed_task_ordinals"] = sorted(completed) + self.state: dict[str, Any] = { + "schema_version": 1, + "run_id": run_id, + "started_at": previous.get("started_at", iso_utc()) if previous else iso_utc(), + "session_started_at": iso_utc(), + "updated_at": iso_utc(), + "running": True, + "finished": False, + "clean_checkpoint": False, + "safe_to_resume": False, + "ambiguous": False, + "terminal_error": None, + "errors": [], + "resume_count": ( + int(previous.get("resume_count", 0)) + 1 if previous else 0 + ), + "target": self.target, + "source": { + "manifest_sha256": manifest["manifest_sha256"], + "total_rows": manifest["total_rows"], + "file_count": manifest["selected_file_count"], + "task_count": manifest["selected_row_group_count"], + }, + "config": self.config, + "provider_committed_rows": committed, + "logical_raw_rows": baseline_table_rows + committed, + "baseline_table_rows": baseline_table_rows, + "baseline_table_rows_unknown": baseline_unknown, + "submitted_rows": submitted, + "submitted_batches": submitted_batches, + "durable_batches": durable_batches, + "durable_uncompressed_arrow_buffer_bytes": int( + previous.get("durable_uncompressed_arrow_buffer_bytes", 0) + ) + if previous + else 0, + "pending_rows": 0, + "pending_batches": 0, + "pending_batch_evidence": [], + **completed_evidence, + "partial_task_rows": {}, + "assigned_tasks": int(previous.get("assigned_tasks", 0)) if previous else 0, + "stream_status": {}, + "transport_recovery": ( + dict(previous.get("transport_recovery", {})) + if previous + else { + "event_count": 0, + "completed_count": 0, + "failed_count": 0, + "total_duration_sec": 0.0, + "max_duration_sec": 0.0, + "last_event": None, + } + ), + "eps": {}, + "memory": {}, + "host": {}, + } + self._pending: dict[str, PendingBatch] = {} + self._task_durable_rows: dict[int, int] = {} + with self._lock: + self._write_locked() + self._append_ledger_locked( + { + "event": "run_resumed" if previous else "run_started", + "at": iso_utc(), + "source_manifest_sha256": manifest["manifest_sha256"], + "provider_committed_rows": committed, + } + ) + + def _elapsed_locked(self) -> float: + return self._previous_elapsed + max( + 0.0, time.monotonic() - self._started_monotonic + ) + + def _refresh_locked(self) -> None: + partial = { + str(ordinal): rows + for ordinal, rows in sorted(self._task_durable_rows.items()) + if rows > 0 + } + pending_rows = sum(item.rows for item in self._pending.values()) + elapsed = self._elapsed_locked() + committed = int(self.state["provider_committed_rows"]) + completed_evidence = { + "completed_task_ranges": [ + list(item) for item in self._completed_task_ranges + ], + "completed_tasks": len(self._completed_tasks), + } + if not self.config.get("compact_progress"): + completed_evidence["completed_task_ordinals"] = sorted( + self._completed_tasks + ) + self.state.update( + { + "updated_at": iso_utc(), + "elapsed_sec": elapsed, + "logical_raw_rows": int(self.state["baseline_table_rows"]) + committed, + "pending_rows": pending_rows, + "pending_batches": len(self._pending), + "pending_batch_evidence": [ + item.evidence() + for item in sorted( + self._pending.values(), + key=lambda pending: ( + pending.coordinate.task_ordinal, + pending.coordinate.batch_ordinal, + ), + ) + ], + **completed_evidence, + "partial_task_rows": partial, + "eps": { + "target_rows_per_sec": self.config["target_rows_per_sec"], + "average_provider_committed_rows_per_sec": ( + committed / elapsed if elapsed else 0.0 + ), + "session_provider_committed_rows_per_sec": ( + (committed - self._session_start_rows) + / max(0.000001, time.monotonic() - self._started_monotonic) + ), + }, + } + ) + + def _write_locked(self) -> None: + self._refresh_locked() + atomic_write_json( + self.progress_path, + redact_secrets(self.state, self._secrets), + ) + + def _append_ledger_locked(self, event: Mapping[str, Any]) -> None: + if self.config.get("compact_evidence") and event.get("event") not in { + "run_started", + "run_resumed", + "transport_wait", + "terminal_error", + "run_finished", + }: + return + atomic_append_jsonl( + self.ledger_path, + redact_secrets({"run_id": self.run_id, **dict(event)}, self._secrets), + ) + + def record_assignment(self, task: Task, worker: int, stream: str) -> None: + with self._lock: + self.state["assigned_tasks"] += 1 + self._append_ledger_locked( + { + "event": "task_assigned", + "at": iso_utc(), + **task.evidence(), + "worker": worker, + "stream": stream, + } + ) + self._write_locked() + + def record_submitted(self, pending: PendingBatch) -> None: + with self._lock: + if pending.key in self._pending: + raise RuntimeError(f"duplicate pending batch coordinate: {pending.key}") + self._append_ledger_locked( + {"event": "batch_submitted", **pending.evidence()} + ) + self._pending[pending.key] = pending + self.state["submitted_rows"] += pending.rows + self.state["submitted_batches"] += 1 + self._write_locked() + + def record_durable(self, pending_batches: Sequence[PendingBatch]) -> None: + if not pending_batches: + return + durable_at = iso_utc() + with self._lock: + projected_task_rows = dict(self._task_durable_rows) + for pending in pending_batches: + if pending.key not in self._pending: + raise RuntimeError( + f"durability attempted for unknown pending batch: {pending.key}" + ) + ordinal = pending.coordinate.task_ordinal + if ordinal in self._completed_tasks: + raise RuntimeError( + f"durability attempted for completed task: {ordinal}" + ) + new_rows = projected_task_rows.get(ordinal, 0) + pending.rows + if new_rows > self.tasks[ordinal].rows: + raise RuntimeError( + f"durable rows exceed task {ordinal} source row count" + ) + projected_task_rows[ordinal] = new_rows + for pending in pending_batches: + self._append_ledger_locked( + { + "event": "batch_durable", + **pending.evidence(), + "durable_at": durable_at, + } + ) + for pending in pending_batches: + self._pending.pop(pending.key) + self.state["provider_committed_rows"] += pending.rows + self.state["durable_uncompressed_arrow_buffer_bytes"] += ( + pending.uncompressed_arrow_buffer_bytes + ) + self.state["durable_batches"] += 1 + for ordinal, rows in list(projected_task_rows.items()): + if rows == self.tasks[ordinal].rows: + self._completed_tasks.add(ordinal) + self._completed_task_ranges = add_ordinal_range( + self._completed_task_ranges, ordinal + ) + projected_task_rows.pop(ordinal) + self._task_durable_rows = projected_task_rows + self._write_locked() + + def stream_update(self, stream_label: str, **values: Any) -> None: + with self._lock: + current = dict(self.state["stream_status"].get(stream_label, {})) + current.update(values) + self.state["stream_status"][stream_label] = current + self._write_locked() + + def record_stream_rotation( + self, + stream_label: str, + *, + generation: int, + unacked_batches: int, + ) -> None: + with self._lock: + current = dict(self.state["stream_status"].get(stream_label, {})) + current.update( + { + "generation": generation, + "rotation_count": generation - 1, + "last_rotation_at": iso_utc(), + "last_rotation_unacked_batches": unacked_batches, + } + ) + self.state["stream_status"][stream_label] = current + self._append_ledger_locked( + { + "event": "stream_rotated", + "at": current["last_rotation_at"], + "stream": stream_label, + "generation": generation, + "unacked_batches": unacked_batches, + } + ) + self._write_locked() + + def record_transport_wait( + self, + stream_label: str, + *, + generation: int, + operation: str, + duration_sec: float, + status: str, + pending_batches: int, + pending_rows: int, + error: str | None = None, + ) -> None: + if status not in {"completed", "failed"}: + raise ValueError(f"unsupported transport wait status: {status}") + safe_error = ( + str(redact_secrets(error, self._secrets)) + if error not in (None, "") + else None + ) + event = { + "event": "transport_wait", + "at": iso_utc(), + "stream": stream_label, + "generation": generation, + "operation": operation, + "duration_sec": duration_sec, + "status": status, + "pending_batches": pending_batches, + "pending_rows": pending_rows, + "error": safe_error, + } + with self._lock: + recovery = dict(self.state.get("transport_recovery", {})) + recovery["event_count"] = int(recovery.get("event_count", 0)) + 1 + counter = f"{status}_count" + recovery[counter] = int(recovery.get(counter, 0)) + 1 + recovery["total_duration_sec"] = float( + recovery.get("total_duration_sec", 0.0) + ) + duration_sec + recovery["max_duration_sec"] = max( + float(recovery.get("max_duration_sec", 0.0)), + duration_sec, + ) + recovery["last_event"] = { + key: value for key, value in event.items() if key != "event" + } + self.state["transport_recovery"] = recovery + self._append_ledger_locked(event) + self._write_locked() + + def terminal_error(self, message: str, *, ambiguous: bool = True) -> None: + safe = str(redact_secrets(message, self._secrets)) + with self._lock: + if self.state["terminal_error"] is None: + self.state["terminal_error"] = safe + if len(self.state["errors"]) < 50: + self.state["errors"].append(safe) + self.state["ambiguous"] = bool(self.state["ambiguous"] or ambiguous) + self._append_ledger_locked( + { + "event": "terminal_error", + "at": iso_utc(), + "error": safe, + "ambiguous": ambiguous, + } + ) + self._write_locked() + + def metrics( + self, + *, + memory: Mapping[str, Any], + limiter: Mapping[str, Any], + host: Mapping[str, Any] | None = None, + record: bool = True, + ) -> dict[str, Any]: + with self._lock: + self.state["memory"] = dict(memory) + self.state["rate_limiter"] = dict(limiter) + if host is not None: + self.state["host"] = dict(host) + self._write_locked() + payload = json.loads(json.dumps(self.state)) + if record: + output = ( + compact_metrics_payload(payload) + if self.config.get("compact_metrics") + else payload + ) + atomic_append_jsonl(self.metrics_path, output) + return payload + + def finish(self, *, stopped_early: bool) -> dict[str, Any]: + with self._lock: + stream_statuses = list(self.state["stream_status"].values()) + streams_clean = ( + len(stream_statuses) == int(self.config["workers"]) + and all( + status.get("close_succeeded") + and status.get("unacked_inspection_succeeded") + and int(status.get("unacked_batches", -1)) == 0 + for status in stream_statuses + ) + ) + no_partial_tasks = not self._task_durable_rows + clean = ( + self.state["terminal_error"] is None + and not self.state["ambiguous"] + and not self._pending + and no_partial_tasks + and streams_clean + ) + resume_safe = clean and not self.state["baseline_table_rows_unknown"] + all_tasks = len(self._completed_tasks) == len(self.tasks) + expected_rows = int(self.manifest["total_rows"]) + finished = ( + clean + and all_tasks + and int(self.state["provider_committed_rows"]) == expected_rows + ) + self.state.update( + { + "running": False, + "finished": finished, + "stopped_early": stopped_early or not finished, + "clean_checkpoint": clean, + "safe_to_resume": resume_safe, + "finished_at": iso_utc(), + } + ) + self._append_ledger_locked( + { + "event": "run_finished", + "at": self.state["finished_at"], + "finished": finished, + "clean_checkpoint": clean, + "safe_to_resume": resume_safe, + "provider_committed_rows": self.state[ + "provider_committed_rows" + ], + } + ) + self._write_locked() + return json.loads(json.dumps(self.state)) + + def snapshot(self) -> dict[str, Any]: + with self._lock: + self._refresh_locked() + return json.loads(json.dumps(self.state)) + + +def load_runtime() -> RuntimeModules: + if sys.version_info < (3, 10): + raise RuntimeError("ingest_zerobus.py requires Python 3.10 or newer") + try: + sdk_version = importlib.metadata.version(REQUIRED_SDK_DISTRIBUTION) + except importlib.metadata.PackageNotFoundError as exc: + raise RuntimeError( + f"{REQUIRED_SDK_DISTRIBUTION}[arrow]=={REQUIRED_SDK_VERSION} is required" + ) from exc + if sdk_version != REQUIRED_SDK_VERSION: + raise RuntimeError( + f"{REQUIRED_SDK_DISTRIBUTION} {sdk_version} is installed; " + f"exactly {REQUIRED_SDK_VERSION} is required" + ) + try: + import pyarrow as pa + import pyarrow.compute as pc + import pyarrow.parquet as pq + from zerobus.sdk.shared.arrow import ( + ArrowStreamConfigurationOptions, + IPCCompression, + ) + from zerobus.sdk.sync import ZerobusSdk + except ImportError as exc: + raise RuntimeError( + "Arrow dependencies are incomplete; install requirements-zerobus.txt" + ) from exc + create_signature = inspect.signature(ZerobusSdk.create_arrow_stream) + expected_parameters = [ + "self", + "table_name", + "schema", + "client_id", + "client_secret", + "options", + ] + if list(create_signature.parameters) != expected_parameters: + raise RuntimeError( + "ZerobusSdk.create_arrow_stream API does not match the pinned v1.8.0 contract" + ) + for method in ( + "ingest_batch", + "wait_for_offset", + "flush", + "close", + "get_unacked_batches", + ): + stream_type = getattr( + sys.modules.get("zerobus.sdk.sync"), + "ZerobusArrowStream", + None, + ) + if stream_type is None or not callable(getattr(stream_type, method, None)): + raise RuntimeError(f"Zerobus Arrow stream API omits {method}()") + for name in ("NONE", "LZ4_FRAME", "ZSTD"): + map_ipc_compression(name, IPCCompression) + try: + pyarrow_version = importlib.metadata.version("pyarrow") + except importlib.metadata.PackageNotFoundError: + pyarrow_version = str(getattr(pa, "__version__", "unknown")) + return RuntimeModules( + pa=pa, + pc=pc, + pq=pq, + sdk_class=ZerobusSdk, + options_class=ArrowStreamConfigurationOptions, + compression_enum=IPCCompression, + sdk_version=sdk_version, + pyarrow_version=pyarrow_version, + ) + + +def validate_cli(args: argparse.Namespace, parser: argparse.ArgumentParser) -> None: + positive = { + "--workers": args.workers, + "--batch-size": args.batch_size, + "--queue-capacity": args.queue_capacity, + "--checkpoint-batches": args.checkpoint_batches, + "--metrics-interval": args.metrics_interval, + "--metrics-output-interval": args.metrics_output_interval, + "--statement-timeout": args.statement_timeout, + } + for name, value in positive.items(): + if value <= 0: + parser.error(f"{name} must be greater than zero") + if args.target_eps < 0: + parser.error("--target-eps must be non-negative") + if args.checkpoint_seconds < 0 or args.memory_trim_interval < 0: + parser.error( + "--checkpoint-seconds and --memory-trim-interval must be non-negative" + ) + if args.manual_stream_rotation_seconds < 0: + parser.error("--manual-stream-rotation-seconds must be non-negative") + if args.recovery_timeout_ms <= 0: + parser.error("--recovery-timeout-ms must be greater than zero") + if args.recovery_backoff_ms < 0 or args.recovery_retries < 0: + parser.error( + "--recovery-backoff-ms and --recovery-retries must be non-negative" + ) + if args.recovery_log_threshold_seconds < 0: + parser.error("--recovery-log-threshold-seconds must be non-negative") + if args.min_system_available_gib < 0: + parser.error("--min-system-available-gib must be non-negative") + if args.expected_rows <= 0: + parser.error("--expected-rows must be greater than zero") + if args.max_files is not None and args.max_files <= 0: + parser.error("--max-files must be greater than zero") + if args.max_row_groups is not None and args.max_row_groups <= 0: + parser.error("--max-row-groups must be greater than zero") + if args.max_rows is not None and args.max_rows <= 0: + parser.error("--max-rows must be greater than zero") + if not args.client_id or not args.client_secret: + parser.error( + "DATABRICKS_CLIENT_ID and DATABRICKS_CLIENT_SECRET are required" + ) + if not args.host: + parser.error("--host or DATABRICKS_HOST is required") + if not args.endpoint: + parser.error("--endpoint or ZEROBUS_ENDPOINT is required") + host = urllib.parse.urlparse(args.host) + if ( + host.scheme != "https" + or not host.hostname + or host.username + or host.password + or host.query + or host.fragment + or host.path not in ("", "/") + ): + parser.error("--host must be a credential-free HTTPS origin") + endpoint_errors = zerobus_endpoint_errors(args.endpoint) + if endpoint_errors: + parser.error("; ".join(endpoint_errors)) + for label, value in ( + ("catalog", args.catalog), + ("schema", args.schema), + ("table", args.table), + ): + if not value or any(character in value for character in ".\x00"): + parser.error(f"--{label} must be one non-empty identifier component") + + +def parse_args(argv: Sequence[str] | None = None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Ingest canonical Parquet row groups with Zerobus Arrow Flight" + ) + parser.add_argument("--dir", type=Path, required=True) + parser.add_argument("--pattern", default="*.parquet") + parser.add_argument("--include-quotes-0", action="store_true") + parser.add_argument("--max-files", type=int) + parser.add_argument("--max-row-groups", type=int) + parser.add_argument( + "--max-rows", + type=int, + help="cap selected source rows exactly, truncating only the final row group", + ) + parser.add_argument("--expected-rows", type=int, default=EXPECTED_FULL_ROWS) + parser.add_argument("--allow-partial", action="store_true") + parser.add_argument( + "--defer-source-hashes", + action="store_true", + help="record source file identity now and calculate hashes after measurement", + ) + parser.add_argument( + "--host", + default=os.environ.get("DATABRICKS_HOST") + or os.environ.get("DATABRICKS_WORKSPACE_URL"), + ) + parser.add_argument( + "--endpoint", + default=os.environ.get("ZEROBUS_ENDPOINT") + or os.environ.get("ZEROBUS_SERVER_ENDPOINT") + or os.environ.get("DATABRICKS_ZEROBUS_ENDPOINT"), + ) + parser.add_argument("--catalog", default=os.environ.get("DATABRICKS_CATALOG", "workspace")) + parser.add_argument("--schema", default=os.environ.get("DATABRICKS_SCHEMA", "benchmarking")) + parser.add_argument("--table", default=os.environ.get("DATABRICKS_RAW_TABLE", "quotes")) + parser.add_argument( + "--count-warehouse", + default=os.environ.get("DATABRICKS_CONTROL_WAREHOUSE_ID") + or os.environ.get("DATABRICKS_RT_WAREHOUSE_ID"), + ) + parser.add_argument("--workers", type=int, default=16) + parser.add_argument("--batch-size", type=int, default=50_000) + parser.add_argument("--queue-capacity", type=int, default=64) + parser.add_argument("--target-eps", type=float, default=1_000_000.0) + parser.add_argument( + "--compression", + type=lambda value: value.strip().upper().replace("-", "_"), + choices=("NONE", "LZ4_FRAME", "ZSTD"), + default="NONE", + ) + parser.add_argument("--checkpoint-batches", type=int, default=32) + parser.add_argument("--checkpoint-seconds", type=float, default=5.0) + parser.add_argument( + "--checkpoint-method", choices=("wait", "flush"), default="wait" + ) + parser.add_argument("--disable-automatic-recovery", action="store_true") + parser.add_argument("--recovery-timeout-ms", type=int, default=15_000) + parser.add_argument("--recovery-backoff-ms", type=int, default=2_000) + parser.add_argument("--recovery-retries", type=int, default=4) + parser.add_argument( + "--recovery-log-threshold-seconds", + type=float, + default=2.0, + help=( + "persist successful acknowledgment waits at or above this duration; " + "failed waits are always persisted" + ), + ) + parser.add_argument( + "--manual-stream-rotation-seconds", + type=float, + default=0.0, + help="proactively flush, close, inspect, and replace each stream", + ) + parser.add_argument("--metrics-interval", type=float, default=5.0) + parser.add_argument( + "--metrics-output-interval", + type=float, + help=( + "seconds between persisted metrics records; monitoring and safety " + "checks continue at --metrics-interval" + ), + ) + parser.add_argument( + "--compact-metrics", + action="store_true", + help="persist only benchmark ingest-progress fields", + ) + parser.add_argument( + "--compact-evidence", + action="store_true", + help="persist only run lifecycle and terminal-error ledger events", + ) + parser.add_argument("--memory-trim-interval", type=float, default=60.0) + parser.add_argument("--min-system-available-gib", type=float, default=0.0) + parser.add_argument( + "--compact-progress", + action="store_true", + help="store completed task ranges instead of a growing ordinal list", + ) + parser.add_argument("--statement-timeout", type=float, default=300.0) + parser.add_argument("--allow-nonempty-table", action="store_true") + parser.add_argument("--quiet-worker-logs", action="store_true") + parser.add_argument("--output-dir", type=Path, default=Path("results/ingest_zerobus")) + parser.add_argument("--manifest-output", type=Path) + parser.add_argument("--progress-file", type=Path) + parser.add_argument("--ledger-file", type=Path) + parser.add_argument("--metrics-file", type=Path) + parser.add_argument("--summary-file", type=Path) + parser.add_argument("--run-id") + parser.add_argument("--resume", action="store_true") + args = parser.parse_args(argv) + args.client_id = os.environ.get("DATABRICKS_CLIENT_ID", "").strip() + args.client_secret = os.environ.get("DATABRICKS_CLIENT_SECRET", "").strip() + args.token = os.environ.get("DATABRICKS_TOKEN", "").strip() + args.host = args.host.rstrip("/") if args.host else args.host + args.endpoint = args.endpoint.rstrip("/") if args.endpoint else args.endpoint + if args.metrics_output_interval is None: + args.metrics_output_interval = args.metrics_interval + validate_cli(args, parser) + return args + + +def _extract_single_value(response: Mapping[str, Any], column: str) -> Any: + rows = statement_rows(response) + if len(rows) != 1: + raise RuntimeError(f"{column} metadata returned an unexpected result shape") + normalized = {str(key).lower(): value for key, value in rows[0].items()} + if column.lower() not in normalized: + raise RuntimeError(f"{column} metadata column is missing") + return normalized[column.lower()] + + +def protect_target_table( + args: argparse.Namespace, + *, + resume_journal: Mapping[str, Any] | None, +) -> dict[str, Any]: + """Prove a fresh target from metadata without scanning the data table.""" + report: dict[str, Any] = { + "attempted": False, + "available": False, + "allow_nonempty_table": args.allow_nonempty_table, + "warehouse_id": args.count_warehouse, + } + credentials_available = bool(args.token) or bool( + args.client_id and args.client_secret + ) + if credentials_available and args.count_warehouse: + report["attempted"] = True + try: + client = DatabricksRestClient( + args.host, + token=args.token or None, + client_id=args.client_id or None, + client_secret=args.client_secret or None, + timeout=min(args.statement_timeout, 60.0), + ) + target = ".".join( + quote_identifier(value) + for value in (args.catalog, args.schema, args.table) + ) + if resume_journal is not None: + response = client.execute_statement( + "SELECT COALESCE(SUM(committed_records), 0) AS row_count\n" + "FROM system.lakeflow.zerobus_ingest\n" + f"WHERE table_name = {sql_string('.'.join((args.catalog, args.schema, args.table)))}", + args.count_warehouse, + timeout=args.statement_timeout, + ) + report.update( + { + "available": True, + "method": "system.lakeflow.zerobus_ingest", + "row_count": int(_extract_single_value(response, "row_count")), + "statement_ids": [response.get("statement_id")], + } + ) + else: + detail_response = client.execute_statement( + f"DESCRIBE DETAIL {target}", + args.count_warehouse, + timeout=args.statement_timeout, + catalog=args.catalog, + schema=args.schema, + ) + history_response = client.execute_statement( + f"DESCRIBE HISTORY {target} LIMIT 2", + args.count_warehouse, + timeout=args.statement_timeout, + catalog=args.catalog, + schema=args.schema, + ) + detail_rows = statement_rows(detail_response) + history_rows = statement_rows(history_response) + if len(detail_rows) != 1: + raise RuntimeError( + "DESCRIBE DETAIL returned an unexpected result shape" + ) + detail = { + str(key).lower(): value for key, value in detail_rows[0].items() + } + num_files = int(detail["numfiles"]) + size_bytes = int(detail["sizeinbytes"]) + versions = [ + int( + { + str(key).lower(): value for key, value in row.items() + }["version"] + ) + for row in history_rows + ] + fresh = ( + num_files == 0 + and size_bytes == 0 + and versions == [0] + ) + report.update( + { + "available": True, + "method": "delta_metadata", + "fresh": fresh, + "num_files": num_files, + "size_in_bytes": size_bytes, + "history_versions": versions, + "row_count": 0 if fresh else None, + "statement_ids": [ + detail_response.get("statement_id"), + history_response.get("statement_id"), + ], + } + ) + except Exception as exc: + report["error"] = f"{type(exc).__name__}: {exc}" + else: + report["unavailable_reason"] = ( + "workspace OAuth credentials and --count-warehouse are required for " + "the metadata-only Statement API protection check" + ) + if resume_journal is not None: + if report["available"]: + expected = int(resume_journal["logical_raw_rows"]) + if report["row_count"] != expected: + raise RuntimeError( + f"resume target has {report['row_count']:,} rows, but the clean " + f"journal proves {expected:,}; use a fresh target table" + ) + elif not args.allow_nonempty_table: + detail = report.get("error") or report.get("unavailable_reason") + raise RuntimeError( + "could not verify the resume target from Zerobus metadata; " + "configure workspace OAuth and a control warehouse, or explicitly " + "pass --allow-nonempty-table " + f"({detail})" + ) + return redact_secrets(report, (args.token, args.client_secret)) + if report["available"]: + if report.get("fresh") is not True and not args.allow_nonempty_table: + raise RuntimeError( + f"{args.catalog}.{args.schema}.{args.table} is not a pristine " + "version-0 Delta table with zero active files; use a fresh target" + ) + elif not args.allow_nonempty_table: + detail = report.get("error") or report.get("unavailable_reason") + raise RuntimeError( + "could not prove the target table is pristine from Delta metadata; " + "configure workspace OAuth and a control warehouse, or explicitly " + f"pass --allow-nonempty-table ({detail})" + ) + return redact_secrets(report, (args.token, args.client_secret)) + + +def _proc_value_bytes(path: str, key: str) -> int | None: + try: + with open(path, encoding="utf-8") as handle: + for line in handle: + if line.startswith(f"{key}:"): + parts = line.split() + if len(parts) >= 2: + multiplier = ( + 1024 + if len(parts) < 3 or parts[2].lower() == "kb" + else 1 + ) + return int(parts[1]) * multiplier + except (FileNotFoundError, OSError, ValueError): + pass + return None + + +def memory_snapshot(pa: Any) -> dict[str, int | None]: + process_rss = _proc_value_bytes("/proc/self/status", "VmRSS") + process_peak_rss = _proc_value_bytes("/proc/self/status", "VmHWM") + if process_peak_rss is None: + peak = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + process_peak_rss = int(peak if sys.platform == "darwin" else peak * 1024) + pool = pa.default_memory_pool() + return { + "process_rss_bytes": process_rss, + "process_peak_rss_bytes": process_peak_rss, + "system_available_bytes": _proc_value_bytes( + "/proc/meminfo", "MemAvailable" + ), + "system_total_bytes": _proc_value_bytes("/proc/meminfo", "MemTotal"), + "arrow_allocated_bytes": int(pool.bytes_allocated()), + "arrow_peak_bytes": int(pool.max_memory()), + } + + +def _proc_cpu_ticks(path: str = "/proc/stat") -> tuple[int, int] | None: + """Return Linux host (total, idle) CPU ticks.""" + try: + with open(path, encoding="utf-8") as handle: + parts = handle.readline().split() + if not parts or parts[0] != "cpu": + return None + values = [int(value) for value in parts[1:]] + if len(values) < 4: + return None + idle = values[3] + (values[4] if len(values) > 4 else 0) + return sum(values), idle + except (FileNotFoundError, OSError, ValueError): + return None + + +def _proc_network_bytes(path: str = "/proc/net/dev") -> tuple[int, int] | None: + """Return Linux host receive/transmit bytes, excluding loopback.""" + received = transmitted = 0 + interfaces = 0 + try: + with open(path, encoding="utf-8") as handle: + for line in handle: + if ":" not in line: + continue + name, values = line.split(":", 1) + if name.strip() == "lo": + continue + fields = values.split() + if len(fields) < 9: + continue + received += int(fields[0]) + transmitted += int(fields[8]) + interfaces += 1 + except (FileNotFoundError, OSError, ValueError): + return None + return (received, transmitted) if interfaces else None + + +class HostResourceSampler: + """Produce interval CPU and network evidence without third-party agents.""" + + def __init__(self) -> None: + self._wall = time.monotonic() + usage = resource.getrusage(resource.RUSAGE_SELF) + self._process_cpu = float(usage.ru_utime + usage.ru_stime) + self._host_cpu = _proc_cpu_ticks() + self._network = _proc_network_bytes() + + def sample(self) -> dict[str, int | float | None]: + wall = time.monotonic() + usage = resource.getrusage(resource.RUSAGE_SELF) + process_cpu = float(usage.ru_utime + usage.ru_stime) + host_cpu = _proc_cpu_ticks() + network = _proc_network_bytes() + elapsed = max(0.000001, wall - self._wall) + logical_cpus = os.cpu_count() + process_cores = max(0.0, process_cpu - self._process_cpu) / elapsed + + host_cpu_percent = None + if self._host_cpu is not None and host_cpu is not None: + total_delta = host_cpu[0] - self._host_cpu[0] + idle_delta = host_cpu[1] - self._host_cpu[1] + if total_delta > 0: + host_cpu_percent = 100.0 * max( + 0.0, min(1.0, 1.0 - idle_delta / total_delta) + ) + + receive_rate = transmit_rate = None + if self._network is not None and network is not None: + receive_rate = max(0, network[0] - self._network[0]) / elapsed + transmit_rate = max(0, network[1] - self._network[1]) / elapsed + + self._wall = wall + self._process_cpu = process_cpu + self._host_cpu = host_cpu + self._network = network + return { + "sample_interval_seconds": elapsed, + "logical_cpu_count": logical_cpus, + "producer_process_cpu_cores": process_cores, + "producer_process_cpu_percent_of_host": ( + 100.0 * process_cores / logical_cpus if logical_cpus else None + ), + "host_cpu_utilization_percent": host_cpu_percent, + "network_receive_bytes_per_second": receive_rate, + "network_transmit_bytes_per_second": transmit_rate, + } + + +def trim_process_memory(pa: Any, telemetry: MemoryTelemetry) -> dict[str, Any]: + started = time.monotonic() + before = memory_snapshot(pa) + collected = gc.collect() + supported = False + success = False + if sys.platform.startswith("linux"): + try: + malloc_trim = ctypes.CDLL("libc.so.6").malloc_trim + malloc_trim.argtypes = [ctypes.c_size_t] + malloc_trim.restype = ctypes.c_int + supported = True + success = bool(malloc_trim(0)) + except (AttributeError, OSError): + pass + after = memory_snapshot(pa) + before_rss = before["process_rss_bytes"] + after_rss = after["process_rss_bytes"] + reclaimed = ( + max(0, before_rss - after_rss) + if before_rss is not None and after_rss is not None + else 0 + ) + telemetry.trim_attempts += 1 + telemetry.trim_successes += int(success) + telemetry.trim_unsupported += int(not supported) + telemetry.gc_collected_objects += collected + telemetry.trim_reclaimed_rss_bytes += reclaimed + telemetry.last_trim_duration_sec = time.monotonic() - started + telemetry.last_trim_at = iso_utc() + return {**after, **telemetry.as_dict()} + + +def persist_unacked_batches( + batches: Iterable[Any], + *, + output_dir: Path, + stream_label: str, + pa: Any, +) -> dict[str, Any]: + artifact_dir = output_dir / "unacked" / stream_label + artifacts: list[dict[str, Any]] = [] + for index, batch in enumerate(batches): + artifact_dir.mkdir(parents=True, exist_ok=True) + path = artifact_dir / f"batch_{index:06d}.arrow" + sink = pa.OSFile(str(path), "wb") + try: + with pa.ipc.new_stream(sink, batch.schema) as writer: + writer.write_batch(batch) + finally: + sink.close() + with path.open("rb") as handle: + os.fsync(handle.fileno()) + artifacts.append( + { + "index": index, + "path": str(path), + "rows": int(batch.num_rows), + "bytes": path.stat().st_size, + "sha256": sha256_file(path), + } + ) + return { + "inspected_at": iso_utc(), + "unacked_batches": len(artifacts), + "unacked_rows": sum(item["rows"] for item in artifacts), + "artifacts": artifacts, + } + + +def checkpoint_pending( + stream: Any, + pending: list[PendingBatch], + journal: ProgressJournal, + method: str, + *, + stream_label: str, + generation: int, + log_threshold_seconds: float, +) -> None: + if not pending: + return + started = time.monotonic() + pending_rows = sum(item.rows for item in pending) + try: + if method == "wait": + stream.wait_for_offset(pending[-1].logical_offset) + elif method == "flush": + stream.flush() + else: + raise ValueError(f"unknown checkpoint method: {method}") + except Exception as exc: + duration_sec = time.monotonic() - started + journal.record_transport_wait( + stream_label, + generation=generation, + operation=method, + duration_sec=duration_sec, + status="failed", + pending_batches=len(pending), + pending_rows=pending_rows, + error=f"{type(exc).__name__}: {exc}", + ) + print( + "ZEROBUS TRANSPORT FAILURE " + f"stream={stream_label} generation={generation} " + f"operation={method} " + f"duration_sec={duration_sec:.3f} " + f"pending_batches={len(pending)} pending_rows={pending_rows:,} " + f"error={type(exc).__name__}: {exc}", + file=sys.stderr, + flush=True, + ) + raise + duration_sec = time.monotonic() - started + if duration_sec >= log_threshold_seconds: + journal.record_transport_wait( + stream_label, + generation=generation, + operation=method, + duration_sec=duration_sec, + status="completed", + pending_batches=len(pending), + pending_rows=pending_rows, + ) + print( + "ZEROBUS TRANSPORT RECOVERED " + f"stream={stream_label} generation={generation} " + f"operation={method} " + f"duration_sec={duration_sec:.3f} " + f"pending_batches={len(pending)} pending_rows={pending_rows:,}", + flush=True, + ) + durable = list(pending) + journal.record_durable(durable) + pending.clear() + + +def worker( + worker_id: int, + task_queue: queue.Queue[Task], + producer_done: threading.Event, + stop_requested: threading.Event, + abort_event: threading.Event, + runtime: RuntimeModules, + schema: Any, + journal: ProgressJournal, + limiter: RateLimiter, + args: argparse.Namespace, +) -> None: + stream_label = f"worker-{worker_id:02d}-arrow" + stream: Any | None = None + pending: list[PendingBatch] = [] + close_succeeded = False + last_checkpoint = time.monotonic() + generation = 1 + rotation_count = 0 + rotation_deadline: float | None = None + current_file: Path | None = None + parquet: Any | None = None + journal.stream_update( + stream_label, + worker=worker_id, + stream=stream_label, + state="opening", + close_succeeded=False, + unacked_inspection_succeeded=False, + ) + try: + sdk = runtime.sdk_class( + args.endpoint, + args.host, + application_name="costbench-databricks/1.0", + ) + compression = map_ipc_compression( + args.compression, runtime.compression_enum + ) + + def open_stream() -> Any: + options = runtime.options_class( + ipc_compression=compression, + recovery=not args.disable_automatic_recovery, + recovery_timeout_ms=args.recovery_timeout_ms, + recovery_backoff_ms=args.recovery_backoff_ms, + recovery_retries=args.recovery_retries, + ) + return sdk.create_arrow_stream( + f"{args.catalog}.{args.schema}.{args.table}", + schema, + args.client_id, + args.client_secret, + options=options, + ) + + stream = open_stream() + if args.manual_stream_rotation_seconds > 0: + denominator = max(1, args.workers - 1) + stagger = ( + args.manual_stream_rotation_seconds + * 0.05 + * (worker_id - 1) + / denominator + ) + rotation_deadline = ( + time.monotonic() + + args.manual_stream_rotation_seconds + + stagger + ) + journal.stream_update( + stream_label, + state="open", + opened_at=iso_utc(), + generation=generation, + rotation_count=rotation_count, + automatic_recovery=not args.disable_automatic_recovery, + manual_stream_rotation_seconds=args.manual_stream_rotation_seconds, + ) + while not abort_event.is_set(): + if stop_requested.is_set(): + break + try: + task = task_queue.get(timeout=0.2) + except queue.Empty: + if producer_done.is_set(): + break + continue + journal.record_assignment(task, worker_id, stream_label) + task_started = time.monotonic() + task_rows = 0 + task_bytes = 0 + try: + if task.file_path != current_file: + parquet = runtime.pq.ParquetFile(task.file_path) + current_file = task.file_path + if parquet is None: + raise AssertionError("Parquet reader was not initialized") + for batch_ordinal, source_batch in enumerate( + parquet.iter_batches( + batch_size=args.batch_size, + row_groups=[task.row_group], + columns=list(TARGET_COLUMNS), + ) + ): + if abort_event.is_set(): + raise RuntimeError("aborted after another worker failed") + if ( + rotation_deadline is not None + and time.monotonic() >= rotation_deadline + ): + checkpoint_pending( + stream, + pending, + journal, + args.checkpoint_method, + stream_label=stream_label, + generation=generation, + log_threshold_seconds=( + args.recovery_log_threshold_seconds + ), + ) + stream.flush() + stream.close() + rotation_label = ( + f"{stream_label}-generation-{generation:04d}" + ) + rotation_summary = persist_unacked_batches( + stream.get_unacked_batches(), + output_dir=args.output_dir, + stream_label=rotation_label, + pa=runtime.pa, + ) + if rotation_summary["unacked_batches"]: + raise RuntimeError( + f"{stream_label} proactive rotation closed with " + f"{rotation_summary['unacked_batches']} " + "unacknowledged Arrow batches" + ) + stream = None + generation += 1 + rotation_count += 1 + journal.record_stream_rotation( + stream_label, + generation=generation, + unacked_batches=0, + ) + stream = open_stream() + journal.stream_update( + stream_label, + state="open", + opened_at=iso_utc(), + generation=generation, + rotation_count=rotation_count, + ) + last_checkpoint = time.monotonic() + rotation_deadline = ( + time.monotonic() + + args.manual_stream_rotation_seconds + ) + remaining = task.rows - task_rows + if remaining <= 0: + break + if int(source_batch.num_rows) > remaining: + source_batch = source_batch.slice(0, remaining) + normalized = normalize_batch( + source_batch, + pa_module=runtime.pa, + pc_module=runtime.pc, + ) + rows = int(normalized.num_rows) + coordinate = BatchCoordinate.from_task( + task, + batch_ordinal=batch_ordinal, + row_offset=task_rows, + row_count=rows, + ) + if not limiter.wait(rows, abort_event): + raise RuntimeError("aborted while waiting for the rate limiter") + # This is a cheap in-memory size estimate, not encoded wire + # bytes. Serializing here would duplicate the SDK's Arrow + # IPC encoding on the hot path. Provider-authoritative + # committed bytes are collected from the Zerobus system + # table after the run. + buffer_bytes = int(normalized.nbytes) + ingest_started = time.monotonic() + try: + offset = stream.ingest_batch(normalized) + except Exception as exc: + duration_sec = time.monotonic() - ingest_started + journal.record_transport_wait( + stream_label, + generation=generation, + operation="ingest_batch", + duration_sec=duration_sec, + status="failed", + pending_batches=len(pending), + pending_rows=sum(item.rows for item in pending), + error=f"{type(exc).__name__}: {exc}", + ) + print( + "ZEROBUS TRANSPORT FAILURE " + f"stream={stream_label} generation={generation} " + "operation=ingest_batch " + f"duration_sec={duration_sec:.3f} " + f"error={type(exc).__name__}: {exc}", + file=sys.stderr, + flush=True, + ) + raise + ingest_duration_sec = time.monotonic() - ingest_started + if ( + ingest_duration_sec + >= args.recovery_log_threshold_seconds + ): + journal.record_transport_wait( + stream_label, + generation=generation, + operation="ingest_batch", + duration_sec=ingest_duration_sec, + status="completed", + pending_batches=len(pending), + pending_rows=sum(item.rows for item in pending), + ) + print( + "ZEROBUS TRANSPORT RECOVERED " + f"stream={stream_label} generation={generation} " + "operation=ingest_batch " + f"duration_sec={ingest_duration_sec:.3f}", + flush=True, + ) + submitted_at = iso_utc() + evidence = PendingBatch( + coordinate=coordinate, + rows=rows, + uncompressed_arrow_buffer_bytes=buffer_bytes, + logical_offset=offset, + report_offset=_report_offset(offset), + submitted_at=submitted_at, + worker=worker_id, + stream=stream_label, + ) + pending.append(evidence) + journal.record_submitted(evidence) + task_rows += rows + task_bytes += buffer_bytes + checkpoint_due = len(pending) >= args.checkpoint_batches + time_due = ( + args.checkpoint_seconds > 0 + and time.monotonic() - last_checkpoint + >= args.checkpoint_seconds + ) + if checkpoint_due or time_due: + checkpoint_pending( + stream, + pending, + journal, + args.checkpoint_method, + stream_label=stream_label, + generation=generation, + log_threshold_seconds=( + args.recovery_log_threshold_seconds + ), + ) + last_checkpoint = time.monotonic() + if task_rows != task.rows: + raise RuntimeError( + f"row-group count mismatch for task {task.ordinal}: " + f"metadata={task.rows}, iterated={task_rows}" + ) + elapsed = max(0.000001, time.monotonic() - task_started) + if not args.quiet_worker_logs: + print( + f"[worker {worker_id}] task={task.ordinal} " + f"file={task.relative_path} rg={task.row_group} " + f"rows={task_rows:,} " + f"uncompressed_arrow_buffer_bytes={task_bytes:,} " + f"elapsed={elapsed:.3f}s rate={task_rows / elapsed:,.0f} rows/s", + flush=True, + ) + finally: + task_queue.task_done() + if stream is not None and pending: + stream.flush() + journal.record_durable(list(pending)) + pending.clear() + if stream is not None: + stream.flush() + except Exception as exc: + message = f"{stream_label}: {type(exc).__name__}: {exc}" + journal.terminal_error(message, ambiguous=bool(pending or stream is not None)) + print( + redact_secrets( + f"ERROR: {message}\n{traceback.format_exc()}", + (args.client_secret, args.token), + ), + file=sys.stderr, + flush=True, + ) + stop_requested.set() + abort_event.set() + finally: + if stream is not None and pending: + try: + stream.flush() + journal.record_durable(list(pending)) + pending.clear() + except Exception as exc: + journal.terminal_error( + f"{stream_label} final flush failed: {type(exc).__name__}: {exc}", + ambiguous=True, + ) + if stream is not None: + try: + stream.close() + close_succeeded = True + except Exception as exc: + journal.terminal_error( + f"{stream_label} close failed: {type(exc).__name__}: {exc}", + ambiguous=True, + ) + summary: dict[str, Any] = { + "stream": stream_label, + "worker": worker_id, + "close_succeeded": close_succeeded, + "unacked_inspection_succeeded": False, + "unacked_batches": -1, + } + if stream is None: + summary["inspection_error"] = "stream was never created" + else: + try: + unacked = stream.get_unacked_batches() + summary.update( + persist_unacked_batches( + unacked, + output_dir=args.output_dir, + stream_label=stream_label, + pa=runtime.pa, + ) + ) + summary["unacked_inspection_succeeded"] = True + if summary["unacked_batches"]: + journal.terminal_error( + f"{stream_label} closed with " + f"{summary['unacked_batches']} unacknowledged Arrow batches; " + "artifacts were preserved and will not be replayed", + ambiguous=True, + ) + except Exception as exc: + summary["inspection_error"] = ( + f"{type(exc).__name__}: {exc}" + ) + journal.terminal_error( + f"{stream_label} could not inspect unacknowledged batches " + f"after close: {type(exc).__name__}: {exc}", + ambiguous=True, + ) + summary_path = args.output_dir / "streams" / f"{stream_label}.json" + atomic_write_json( + summary_path, + redact_secrets(summary, (args.client_secret, args.token)), + ) + journal.stream_update( + stream_label, + state="closed" if close_succeeded else "failed", + closed_at=iso_utc(), + summary_path=str(summary_path), + **summary, + ) + + +def metrics_monitor( + finished: threading.Event, + stop_requested: threading.Event, + abort_event: threading.Event, + runtime: RuntimeModules, + journal: ProgressJournal, + limiter: RateLimiter, + args: argparse.Namespace, +) -> None: + telemetry = MemoryTelemetry() + host_sampler = HostResourceSampler() + last_trim = time.monotonic() + last_rows = journal.snapshot()["provider_committed_rows"] + last_time = time.monotonic() + last_recorded = float("-inf") + while not finished.wait(args.metrics_interval): + now = time.monotonic() + if ( + args.memory_trim_interval > 0 + and now - last_trim >= args.memory_trim_interval + ): + memory = trim_process_memory(runtime.pa, telemetry) + last_trim = time.monotonic() + else: + memory = {**memory_snapshot(runtime.pa), **telemetry.as_dict()} + record_metrics = ( + now - last_recorded >= args.metrics_output_interval + ) + payload = journal.metrics( + memory=memory, + limiter=limiter.snapshot(), + host=host_sampler.sample(), + record=record_metrics, + ) + if record_metrics: + last_recorded = now + now = time.monotonic() + delta_rows = payload["provider_committed_rows"] - last_rows + interval_eps = delta_rows / max(0.000001, now - last_time) + total = payload["source"]["total_rows"] + percentage = ( + 100.0 * payload["provider_committed_rows"] / total if total else 100.0 + ) + print( + "ZEROBUS STATUS " + f"committed={payload['provider_committed_rows']:,}/{total:,} " + f"({percentage:.4f}%) submitted={payload['submitted_rows']:,} " + f"pending={payload['pending_rows']:,} " + f"tasks={payload['completed_tasks']:,}/{payload['source']['task_count']:,} " + f"interval_eps={interval_eps:,.0f} " + f"average_eps={payload['eps']['average_provider_committed_rows_per_sec']:,.0f}", + flush=True, + ) + available = memory["system_available_bytes"] + threshold = int(args.min_system_available_gib * 1024**3) + if ( + threshold + and available is not None + and available < threshold + and not stop_requested.is_set() + ): + journal.terminal_error( + f"system MemAvailable fell to {available / 1024**3:.2f} GiB, " + f"below --min-system-available-gib={args.min_system_available_gib:g}", + ambiguous=False, + ) + stop_requested.set() + abort_event.set() + last_rows = payload["provider_committed_rows"] + last_time = now + journal.metrics( + memory={**memory_snapshot(runtime.pa), **telemetry.as_dict()}, + limiter=limiter.snapshot(), + host=host_sampler.sample(), + record=True, + ) + + +def _load_previous(path: Path) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + except FileNotFoundError as exc: + raise RuntimeError(f"--resume progress journal does not exist: {path}") from exc + except (OSError, json.JSONDecodeError) as exc: + raise RuntimeError(f"could not read resume journal {path}: {exc}") from exc + if not isinstance(value, dict): + raise RuntimeError(f"resume journal is not a JSON object: {path}") + return value + + +def main(argv: Sequence[str] | None = None) -> int: + args = parse_args(argv) + args.dir = args.dir.expanduser().resolve() + args.output_dir = args.output_dir.expanduser().resolve() + args.output_dir.mkdir(parents=True, exist_ok=True) + progress_path = ( + args.progress_file or args.output_dir / "ingest_progress.json" + ).expanduser().resolve() + ledger_path = ( + args.ledger_file or args.output_dir / "ingest_evidence.jsonl" + ).expanduser().resolve() + metrics_path = ( + args.metrics_file or args.output_dir / "ingest_metrics.jsonl" + ).expanduser().resolve() + manifest_path = ( + args.manifest_output or args.output_dir / "source_manifest.json" + ).expanduser().resolve() + summary_path = ( + args.summary_file or args.output_dir / "ingest_summary.json" + ).expanduser().resolve() + if not args.resume and any( + path.exists() + for path in (progress_path, ledger_path, metrics_path, summary_path) + ): + raise RuntimeError( + "output evidence already exists; choose a fresh --output-dir or use " + "--resume only with a clean checkpoint" + ) + + runtime = load_runtime() + schema = target_schema(runtime.pa) + print( + "SOURCE MANIFEST enumerating row groups and " + + ( + "deferring selected-file hashes until after measurement" + if args.defer_source_hashes + else "hashing selected files before measurement" + ), + flush=True, + ) + tasks, manifest = enumerate_tasks_and_manifest( + args.dir, + args.pattern, + runtime.pq, + include_quotes_0=args.include_quotes_0, + max_files=args.max_files, + max_row_groups=args.max_row_groups, + max_rows=args.max_rows, + hash_files=not args.defer_source_hashes, + ) + validate_expected_rows( + int(manifest["total_rows"]), + args.expected_rows, + allow_partial=args.allow_partial, + ) + print( + "SOURCE MANIFEST complete " + f"files={manifest['selected_file_count']:,} " + f"row_groups={manifest['selected_row_group_count']:,} " + f"rows={manifest['total_rows']:,}", + flush=True, + ) + atomic_write_json(manifest_path, manifest) + config = frozen_config(args) + config["evidence_paths"] = { + "output_dir": str(args.output_dir), + "progress": str(progress_path), + "ledger": str(ledger_path), + "metrics": str(metrics_path), + "manifest": str(manifest_path), + "summary": str(summary_path), + } + target = target_identity(args) + previous: dict[str, Any] | None = None + completed: set[int] = set() + if args.resume: + previous = _load_previous(progress_path) + completed = validate_resume_journal( + previous, + manifest=manifest, + target=target, + config=config, + tasks=tasks, + ) + if args.run_id and args.run_id != previous.get("run_id"): + raise RuntimeError( + "--run-id does not match the resume journal" + ) + try: + with ledger_path.open(encoding="utf-8") as handle: + first_event = json.loads(handle.readline()) + except (FileNotFoundError, OSError, json.JSONDecodeError) as exc: + raise RuntimeError( + "resume evidence ledger is missing or invalid; use a fresh target table" + ) from exc + if first_event.get("run_id") != previous.get("run_id"): + raise RuntimeError( + "resume evidence ledger belongs to another run; use a fresh target table" + ) + table_check = protect_target_table(args, resume_journal=previous) + if previous is not None: + baseline_rows = int(previous["baseline_table_rows"]) + baseline_unknown = bool(previous.get("baseline_table_rows_unknown")) + run_id = str(previous["run_id"]) + else: + observed = table_check.get("row_count") + baseline_rows = int(observed) if observed is not None else 0 + baseline_unknown = observed is None + run_id = args.run_id or ( + f"dbx-zb-{time.strftime('%Y%m%dT%H%M%SZ', time.gmtime())}-" + f"{uuid.uuid4().hex[:8]}" + ) + + journal = ProgressJournal( + progress_path=progress_path, + ledger_path=ledger_path, + metrics_path=metrics_path, + run_id=run_id, + manifest=manifest, + target=target, + config=config, + tasks=tasks, + baseline_table_rows=baseline_rows, + baseline_unknown=baseline_unknown, + secrets=(args.client_secret, args.token), + previous=previous, + ) + stop_requested = threading.Event() + abort_event = threading.Event() + producer_done = threading.Event() + finished = threading.Event() + + signal_count = 0 + + def request_stop(signum: int, _frame: Any) -> None: + nonlocal signal_count + signal_count += 1 + stop_requested.set() + print( + f"Signal {signum} received; stopping at whole row-group boundaries " + "and flushing streams...", + file=sys.stderr, + flush=True, + ) + if signal_count > 1: + abort_event.set() + + signal.signal(signal.SIGINT, request_stop) + signal.signal(signal.SIGTERM, request_stop) + limiter = RateLimiter(args.target_eps) + task_queue: queue.Queue[Task] = queue.Queue(maxsize=args.queue_capacity) + workers = [ + threading.Thread( + target=worker, + name=f"zerobus-arrow-{index}", + args=( + index, + task_queue, + producer_done, + stop_requested, + abort_event, + runtime, + schema, + journal, + limiter, + args, + ), + ) + for index in range(1, args.workers + 1) + ] + monitor = threading.Thread( + target=metrics_monitor, + name="zerobus-metrics", + args=( + finished, + stop_requested, + abort_event, + runtime, + journal, + limiter, + args, + ), + daemon=True, + ) + print(f"Run ID: {run_id}") + print(f"Target: {target['full_name']}") + print(f"Source files/tasks: {manifest['selected_file_count']:,}/{len(tasks):,}") + print(f"Source rows: {manifest['total_rows']:,}") + print(f"Workers/streams: {args.workers}") + print(f"Global target EPS: {args.target_eps:,.0f}") + print(f"IPC compression: {args.compression}") + print(f"Progress journal: {progress_path}") + print(f"Evidence ledger: {ledger_path}") + print( + f"Resume: {'yes' if previous else 'no'} " + f"({len(completed):,} tasks already committed)" + ) + for thread in workers: + thread.start() + monitor.start() + try: + for task in tasks: + if task.ordinal in completed: + continue + while not stop_requested.is_set() and not abort_event.is_set(): + try: + task_queue.put(task, timeout=0.2) + break + except queue.Full: + continue + if stop_requested.is_set() or abort_event.is_set(): + break + finally: + producer_done.set() + for thread in workers: + thread.join() + finished.set() + monitor.join(timeout=max(2.0, args.metrics_interval + 1.0)) + + if signal_count > 1: + journal.terminal_error( + "second stop signal forced an unclean abort", ambiguous=True + ) + final = journal.finish(stopped_early=stop_requested.is_set()) + summary = { + **final, + "manifest_path": str(manifest_path), + "progress_path": str(progress_path), + "evidence_ledger_path": str(ledger_path), + "metrics_path": str(metrics_path), + "table_protection": table_check, + "dependencies": { + REQUIRED_SDK_DISTRIBUTION: runtime.sdk_version, + "pyarrow": runtime.pyarrow_version, + }, + "result": { + "source_files": manifest["selected_file_count"], + "source_tasks": manifest["selected_row_group_count"], + "source_rows": manifest["total_rows"], + "provider_committed_rows": final["provider_committed_rows"], + "submitted_rows": final["submitted_rows"], + "completed_tasks": final["completed_tasks"], + "finished": final["finished"], + "clean_checkpoint": final["clean_checkpoint"], + "safe_to_resume": final["safe_to_resume"], + }, + } + atomic_write_json( + summary_path, + redact_secrets(summary, (args.client_secret, args.token)), + ) + print("\n================ ZEROBUS SUMMARY ================") + print( + f"Provider committed: {final['provider_committed_rows']:,}/" + f"{manifest['total_rows']:,}" + ) + print(f"Submitted: {final['submitted_rows']:,}") + print(f"Completed tasks: {final['completed_tasks']:,}/{len(tasks):,}") + print(f"Finished: {final['finished']}") + print(f"Clean checkpoint: {final['clean_checkpoint']}") + print(f"Safe to resume: {final['safe_to_resume']}") + print( + "Average EPS: " + f"{final['eps']['average_provider_committed_rows_per_sec']:,.0f}" + ) + print(f"Summary: {summary_path}") + print("==================================================") + if final["finished"]: + return 0 + return 130 if final["clean_checkpoint"] and stop_requested.is_set() else 1 + + +if __name__ == "__main__": + try: + sys.exit(main()) + except Exception as exc: + secrets = ( + os.environ.get("DATABRICKS_CLIENT_SECRET"), + os.environ.get("DATABRICKS_TOKEN"), + ) + print( + f"FATAL: {redact_secrets(f'{type(exc).__name__}: {exc}', secrets)}", + file=sys.stderr, + ) + sys.exit(1) diff --git a/full-path-realtime/quotes/databricks/investigate_full_r2_zerobus_failure.sql b/full-path-realtime/quotes/databricks/investigate_full_r2_zerobus_failure.sql new file mode 100644 index 0000000..cce49cb --- /dev/null +++ b/full-path-realtime/quotes/databricks/investigate_full_r2_zerobus_failure.sql @@ -0,0 +1,72 @@ +-- Read-only provider evidence for the failed canonical R2 run. +-- Run on the control warehouse after system tables have settled. + +-- 1. Exact provider totals for the complete failed target lifetime. +SELECT + sum(committed_records) AS provider_committed_records, + sum(committed_bytes) AS provider_committed_bytes, + sum(COALESCE(size(errors), 0)) AS provider_error_count, + min(commit_version) AS min_commit_version, + max(commit_version) AS max_commit_version, + min(commit_time) AS first_commit_time, + max(commit_time) AS last_commit_time +FROM system.lakeflow.zerobus_ingest +WHERE table_name = + 'costbench.rt_full_serverless_baseline_r2_20260917.quotes' + AND commit_time >= CAST('2026-09-17T18:27:26Z' AS TIMESTAMP) + AND commit_time < CAST('2026-09-18T12:00:00Z' AS TIMESTAMP); + +-- 2. Per-stream commits around the failure. +SELECT + date_trunc('minute', commit_time) AS commit_minute, + stream_id, + sum(committed_records) AS committed_records, + sum(committed_bytes) AS committed_bytes, + sum(COALESCE(size(errors), 0)) AS error_count, + min(commit_version) AS min_commit_version, + max(commit_version) AS max_commit_version, + min(commit_time) AS first_commit_time, + max(commit_time) AS last_commit_time +FROM system.lakeflow.zerobus_ingest +WHERE table_name = + 'costbench.rt_full_serverless_baseline_r2_20260917.quotes' + AND commit_time >= CAST('2026-09-18T11:35:00Z' AS TIMESTAMP) + AND commit_time < CAST('2026-09-18T12:00:00Z' AS TIMESTAMP) +GROUP BY commit_minute, stream_id +ORDER BY commit_minute, stream_id; + +-- 3. Provider stream lifecycle and error payloads around the failure. +SELECT + stream_id, + event_time, + opened_time, + closed_time, + producer_id, + protocol, + data_format, + errors +FROM system.lakeflow.zerobus_stream +WHERE table_name = + 'costbench.rt_full_serverless_baseline_r2_20260917.quotes' + AND event_time >= CAST('2026-09-18T11:35:00Z' AS TIMESTAMP) + AND event_time < CAST('2026-09-18T12:00:00Z' AS TIMESTAMP) +ORDER BY event_time, stream_id; + +-- 4. Any provider-recorded stream errors across the entire run. +SELECT + stream_id, + event_time, + opened_time, + closed_time, + errors +FROM system.lakeflow.zerobus_stream +WHERE table_name = + 'costbench.rt_full_serverless_baseline_r2_20260917.quotes' + AND event_time >= CAST('2026-09-17T18:27:26Z' AS TIMESTAMP) + AND event_time < CAST('2026-09-18T12:00:00Z' AS TIMESTAMP) + AND COALESCE(size(errors), 0) > 0 +ORDER BY event_time, stream_id; + +-- 5. Metadata only; this does not scan the raw table. +DESCRIBE DETAIL +costbench.rt_full_serverless_baseline_r2_20260917.quotes; diff --git a/full-path-realtime/quotes/databricks/monitor_freshness.py b/full-path-realtime/quotes/databricks/monitor_freshness.py new file mode 100644 index 0000000..5f148f6 --- /dev/null +++ b/full-path-realtime/quotes/databricks/monitor_freshness.py @@ -0,0 +1,967 @@ +#!/usr/bin/env python3 +"""Continuously record bounded metadata-only freshness evidence. + +The monitor never queries the raw table or materialized-view contents. Raw +progress comes from Zerobus system telemetry and materialized-view progress +comes from the provider event log. +""" + +from __future__ import annotations + +import argparse +import json +import math +import os +import signal +import sys +import threading +import time +import urllib.parse +from datetime import datetime, timedelta, timezone +from pathlib import Path +from typing import Any, Mapping, Sequence + +from dbx_common import ( + DatabricksRestClient, + atomic_append_jsonl, + atomic_write_json, + iso_utc, + normalize_host, + quote_identifier, + redact_secrets, + sql_string, + statement_rows, + utc_now, +) + +DEFAULT_INTERVAL_SECONDS = 30.0 +EVENT_LIMIT = 100 +EVENT_LIMIT_PER_TYPE = EVENT_LIMIT // 2 +AGE_SEMANTICS = ( + "These are observational ages from the sample time to provider timestamps; " + "they are not exact per-record ingestion or materialized-view lag." +) + + +def _env_first(env: Mapping[str, str], *names: str) -> str | None: + for name in names: + value = env.get(name) + if value is not None and value.strip(): + return value.strip() + return None + + +def _path_default(value: str | None) -> Path | None: + return Path(value).expanduser() if value else None + + +def parse_utc_timestamp(value: Any) -> datetime: + """Parse an aware UTC timestamp and reject local or non-UTC input.""" + if isinstance(value, datetime): + parsed = value + elif isinstance(value, (int, float)) and not isinstance(value, bool): + number = float(value) + if not math.isfinite(number): + raise ValueError("timestamp must be finite") + if abs(number) >= 100_000_000_000: + number /= 1000.0 + parsed = datetime.fromtimestamp(number, tz=timezone.utc) + elif isinstance(value, str): + text = value.strip() + if not text: + raise ValueError("timestamp must be non-empty") + if text.endswith(("Z", "z")): + text = text[:-1] + "+00:00" + parsed = datetime.fromisoformat(text) + else: + raise ValueError("timestamp must be an ISO-8601 string or datetime") + if parsed.tzinfo is None or parsed.utcoffset() is None: + raise ValueError("timestamp must include the UTC timezone") + if parsed.utcoffset() != timedelta(0): + raise ValueError("timestamp must be UTC") + return parsed.astimezone(timezone.utc) + + +def timestamp_age_sec(observed_at: Any, event_at: Any) -> float | None: + """Return a non-negative observational timestamp age in seconds.""" + if event_at in (None, ""): + return None + try: + observed = parse_utc_timestamp(observed_at) + event = parse_utc_timestamp(event_at) + except (TypeError, ValueError, OverflowError, OSError): + return None + return max(0.0, (observed - event).total_seconds()) + + +def _sql_timestamp(value: Any) -> str: + return f"CAST({sql_string(iso_utc(parse_utc_timestamp(value)))} AS TIMESTAMP)" + + +def _qualified_name(catalog: str, schema: str, table: str) -> str: + return ".".join(quote_identifier(part) for part in (catalog, schema, table)) + + +def build_control_queries( + catalog: str, + schema: str, + raw_table: str, + mv_table: str, + since: Any, + until: Any, +) -> dict[str, str]: + """Build target-filtered monitor SQL for one required, bounded UTC window.""" + start = parse_utc_timestamp(since) + end = parse_utc_timestamp(until) + if end < start: + raise ValueError("monitor query window ends before it starts") + start_sql = _sql_timestamp(start) + end_sql = _sql_timestamp(end) + raw_name = _qualified_name(catalog, schema, raw_table) + mv_name = _qualified_name(catalog, schema, mv_table) + target_name = ".".join((catalog, schema, raw_table)) + event_types = ", ".join( + sql_string(value) for value in ("update_progress", "planning_information") + ) + return { + "zerobus_ingest": ( + "SELECT\n" + " COALESCE(SUM(committed_records), 0) AS cumulative_committed_records,\n" + " COALESCE(SUM(committed_bytes), 0) AS cumulative_committed_bytes,\n" + " COALESCE(SUM(COALESCE(size(errors), 0)), 0) AS cumulative_error_count,\n" + " max_by(commit_version, commit_time) AS latest_commit_version,\n" + " MAX(commit_time) AS latest_commit_time\n" + "FROM system.lakeflow.zerobus_ingest\n" + f"WHERE table_name = {sql_string(target_name)}\n" + f" AND commit_time >= {start_sql}\n" + f" AND commit_time <= {end_sql}" + ), + "raw_history": f"DESCRIBE HISTORY {raw_name} LIMIT 1", + "mv_events": ( + "SELECT id, timestamp, event_type, message, level, origin, details\n" + f"FROM event_log(TABLE({mv_name}))\n" + f"WHERE timestamp >= {start_sql}\n" + f" AND timestamp <= {end_sql}\n" + f" AND event_type IN ({event_types})\n" + "QUALIFY row_number() OVER (\n" + " PARTITION BY event_type ORDER BY timestamp DESC\n" + f") <= {EVENT_LIMIT_PER_TYPE}\n" + "ORDER BY timestamp DESC\n" + f"LIMIT {EVENT_LIMIT}" + ), + } + + +def _row_value(row: Mapping[str, Any], name: str) -> Any: + wanted = name.lower() + for key, value in row.items(): + if str(key).lower() == wanted: + return value + return None + + +def _nonnegative_int(value: Any) -> int | None: + if value is None or isinstance(value, bool): + return None + try: + number = int(value) + except (TypeError, ValueError, OverflowError): + return None + return number if number >= 0 else None + + +def _decode_json(value: Any) -> Any: + decoded = value + for _ in range(3): + if not isinstance(decoded, str): + break + text = decoded.strip() + if not text or text[0] not in "[{\"": + break + try: + decoded = json.loads(text) + except (TypeError, ValueError, json.JSONDecodeError): + break + return decoded + + +def _decode_event_row(row: Mapping[str, Any]) -> dict[str, Any]: + decoded = dict(row) + for name in ("details", "origin"): + for key in tuple(decoded): + if str(key).lower() == name: + decoded[key] = _decode_json(decoded[key]) + return decoded + + +def _mapping_path(value: Any, *path: str) -> Any: + current = _decode_json(value) + for component in path: + if not isinstance(current, Mapping): + return None + current = _decode_json(current.get(component)) + return current + + +def _recursive_value(value: Any, names: set[str]) -> Any: + decoded = _decode_json(value) + if isinstance(decoded, Mapping): + for key, item in decoded.items(): + if str(key).lower() in names and item is not None: + return _decode_json(item) + for item in decoded.values(): + found = _recursive_value(item, names) + if found is not None: + return found + elif isinstance(decoded, list): + for item in decoded: + found = _recursive_value(item, names) + if found is not None: + return found + return None + + +def classify_maintenance_type(value: Any) -> str: + """Classify a provider maintenance enum without treating NO_OP as incremental.""" + if value is None: + return "unknown" + normalized = str(value).strip().upper().replace("-", "_").replace(" ", "_") + if not normalized: + return "unknown" + if "NO_OP" in normalized or normalized == "NOOP": + return "neutral" + if "COMPLETE_RECOMPUTE" in normalized or "FULL" in normalized: + return "full" + return "incremental" + + +def maintenance_type_from_detail(detail: Any) -> str | None: + """Extract the chosen maintenance type from a planning-information detail.""" + decoded = _decode_json(detail) + planning = _mapping_path(decoded, "planning_information") + if planning is None: + planning = decoded + techniques = _mapping_path(planning, "technique_information") + if isinstance(techniques, Mapping): + techniques = [techniques] + if isinstance(techniques, list): + mappings = [item for item in techniques if isinstance(item, Mapping)] + chosen = [ + item + for item in mappings + if item.get("is_chosen") is True + or str(item.get("is_chosen", "")).strip().lower() == "true" + ] + for item in chosen or mappings: + maintenance_type = item.get("maintenance_type") + if maintenance_type not in (None, ""): + return str(maintenance_type) + direct = _recursive_value(planning, {"maintenance_type"}) + return None if direct in (None, "") else str(direct) + + +def _event_update_id(row: Mapping[str, Any]) -> str | None: + value = _row_value(row, "update_id") + if value in (None, ""): + value = _mapping_path(_row_value(row, "origin"), "update_id") + if value in (None, ""): + value = _recursive_value(_row_value(row, "details"), {"update_id"}) + return None if value in (None, "") else str(value) + + +def _event_timestamp(row: Mapping[str, Any]) -> str | None: + value = _row_value(row, "timestamp") + if value in (None, ""): + return None + try: + return iso_utc(parse_utc_timestamp(value)) + except (TypeError, ValueError, OverflowError, OSError): + return str(value) + + +def _update_state(row: Mapping[str, Any]) -> str | None: + detail = _row_value(row, "details") + value = _mapping_path(detail, "update_progress", "state") + if value in (None, ""): + value = _mapping_path(detail, "state") + return None if value in (None, "") else str(value).upper() + + +def _output_rows(row: Mapping[str, Any]) -> int | None: + value = _recursive_value( + _row_value(row, "details"), + {"num_output_rows", "output_rows", "output_row_count"}, + ) + return _nonnegative_int(value) + + +def _source_snapshot(planning_row: Mapping[str, Any] | None) -> dict[str, Any] | None: + if planning_row is None: + return None + details = _decode_json(_row_value(planning_row, "details")) + planning = _mapping_path(details, "planning_information") + sources = ( + _mapping_path(planning, "source_table_information") + if planning is not None + else None + ) + if isinstance(sources, Mapping): + sources = [sources] + if not isinstance(sources, list) or len(sources) != 1: + return None + source = sources[0] + if not isinstance(source, Mapping): + return None + origin = _decode_json(_row_value(planning_row, "origin")) + return { + "table_name": source.get("table_name"), + "num_rows": _nonnegative_int(source.get("num_rows")), + "num_files": _nonnegative_int(source.get("num_files")), + "full_size_bytes": _nonnegative_int(source.get("full_size")), + "changed_rows": _nonnegative_int(source.get("num_changed_rows")), + "changed_files": _nonnegative_int(source.get("num_changed_files")), + "change_size_bytes": _nonnegative_int(source.get("change_size")), + "delta_version": _nonnegative_int( + _mapping_path(origin, "ingestion_source_table_version") + ), + } + + +def _event_sort_key(row: Mapping[str, Any]) -> float: + try: + return parse_utc_timestamp(_row_value(row, "timestamp")).timestamp() + except (TypeError, ValueError, OverflowError, OSError): + return float("-inf") + + +def latest_successful_refresh( + event_rows: Sequence[Mapping[str, Any]], +) -> dict[str, Any] | None: + """Correlate the latest completed update with its chosen planning technique.""" + rows = [_decode_event_row(row) for row in event_rows] + rows.sort(key=_event_sort_key, reverse=True) + completed = next( + ( + row + for row in rows + if str(_row_value(row, "event_type")).lower() == "update_progress" + and _update_state(row) == "COMPLETED" + ), + None, + ) + if completed is None: + return None + + update_id = _event_update_id(completed) + finished_at = _event_timestamp(completed) + planning_rows = [ + row + for row in rows + if str(_row_value(row, "event_type")).lower() == "planning_information" + ] + planning = next( + ( + row + for row in planning_rows + if update_id is not None and _event_update_id(row) == update_id + ), + None, + ) + if planning is None and update_id is None and planning_rows: + planning = planning_rows[0] + maintenance_type = ( + maintenance_type_from_detail(_row_value(planning, "details")) + if planning is not None + else None + ) + result: dict[str, Any] = { + "finished_at": finished_at, + "output_rows": _output_rows(completed), + "maintenance_type": maintenance_type, + "maintenance_class": classify_maintenance_type(maintenance_type), + } + if planning is not None: + result["planned_at"] = _event_timestamp(planning) + snapshot = _source_snapshot(planning) + if snapshot is not None: + result["source_snapshot"] = snapshot + if update_id is not None: + result["update_id"] = update_id + return result + + +def read_producer_progress(path: Path) -> dict[str, Any]: + """Read only the producer counters needed by the freshness observation.""" + result: dict[str, Any] = { + "path": str(path), + "provider_committed_rows": None, + "logical_raw_rows": None, + "updated_at": None, + "error": None, + } + try: + loaded = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(loaded, Mapping): + raise ValueError("top-level producer progress value is not an object") + result["provider_committed_rows"] = _nonnegative_int( + loaded.get("provider_committed_rows") + ) + result["logical_raw_rows"] = _nonnegative_int(loaded.get("logical_raw_rows")) + result["updated_at"] = loaded.get("updated_at") + missing = [ + name + for name in ("provider_committed_rows", "logical_raw_rows", "updated_at") + if result[name] is None + ] + if missing: + result["error"] = "producer progress omitted or invalid: " + ", ".join(missing) + except (OSError, TypeError, ValueError, json.JSONDecodeError) as exc: + result["error"] = f"{type(exc).__name__}: {exc}" + return result + + +def _execute_query( + client: DatabricksRestClient, + warehouse_id: str, + sql: str, + *, + timeout: float, + poll_interval: float, + catalog: str, + schema: str, + query_tags: Mapping[str, str], +) -> dict[str, Any]: + try: + response = client.execute_statement( + sql, + warehouse_id, + timeout=timeout, + poll_interval=poll_interval, + catalog=catalog, + schema=schema, + query_tags=query_tags, + ) + rows = statement_rows(response) + return { + "statement_id": response.get("statement_id"), + "row_count": len(rows), + "rows": rows, + "error": None, + } + except Exception as exc: + return { + "statement_id": None, + "row_count": 0, + "rows": [], + "error": f"{type(exc).__name__}: {exc}", + } + + +def _zerobus_summary(rows: Sequence[Mapping[str, Any]]) -> dict[str, Any]: + row = rows[0] if rows else {} + return { + "cumulative_committed_records": _nonnegative_int( + _row_value(row, "cumulative_committed_records") + ), + "cumulative_committed_bytes": _nonnegative_int( + _row_value(row, "cumulative_committed_bytes") + ), + "cumulative_error_count": _nonnegative_int( + _row_value(row, "cumulative_error_count") + ), + "latest_commit_version": _nonnegative_int( + _row_value(row, "latest_commit_version") + ), + "latest_commit_time": _row_value(row, "latest_commit_time"), + } + + +def collect_observation( + client: DatabricksRestClient, + *, + control_warehouse_id: str, + catalog: str, + schema: str, + raw_table: str, + mv_table: str, + producer_progress_path: Path, + since: datetime, + run_id: str, + iteration: int, + timeout: float, + poll_interval: float, + observed_at: datetime | None = None, +) -> dict[str, Any]: + iteration_started_at = iso_utc() + producer = read_producer_progress(producer_progress_path) + observation_time = utc_now() if observed_at is None else parse_utc_timestamp(observed_at) + queries = build_control_queries( + catalog, + schema, + raw_table, + mv_table, + since, + observation_time, + ) + results: dict[str, dict[str, Any]] = {} + for name, sql in queries.items(): + results[name] = _execute_query( + client, + control_warehouse_id, + sql, + timeout=timeout, + poll_interval=poll_interval, + catalog=catalog, + schema=schema, + query_tags={ + "benchmark": "full-path-realtime", + "run_id": run_id, + "collector": "freshness", + "query": name, + "iteration": str(iteration), + }, + ) + + zerobus = _zerobus_summary(results["zerobus_ingest"]["rows"]) + raw_history_rows = results["raw_history"]["rows"] + events = [ + _decode_event_row(row) for row in results["mv_events"]["rows"] + ] + latest_update = next( + ( + row + for row in events + if str(_row_value(row, "event_type")).lower() == "update_progress" + ), + None, + ) + latest_planning = next( + ( + row + for row in events + if str(_row_value(row, "event_type")).lower() == "planning_information" + ), + None, + ) + refresh = latest_successful_refresh(events) + errors = [ + {"source": name, "error": result["error"]} + for name, result in results.items() + if result["error"] + ] + if producer["error"]: + errors.insert(0, {"source": "producer_progress", "error": producer["error"]}) + + observed = iso_utc(observation_time) + refresh_finished_at = refresh.get("finished_at") if refresh else None + source_snapshot = refresh.get("source_snapshot") if refresh else None + mv_source_rows = ( + _nonnegative_int(source_snapshot.get("num_rows")) + if isinstance(source_snapshot, Mapping) + else None + ) + raw_committed_rows = _nonnegative_int( + zerobus.get("cumulative_committed_records") + ) + mv_rows_behind = ( + max(0, raw_committed_rows - mv_source_rows) + if raw_committed_rows is not None and mv_source_rows is not None + else None + ) + return { + "schema_version": 1, + "run_id": run_id, + "iteration": iteration, + "iteration_started_at": iteration_started_at, + "observed_at": observed, + "iteration_finished_at": iso_utc(), + "since": iso_utc(since), + "control_warehouse_id": control_warehouse_id, + "target": { + "catalog": catalog, + "schema": schema, + "raw_table": raw_table, + "materialized_view": mv_table, + "raw_full_name": ".".join((catalog, schema, raw_table)), + "materialized_view_full_name": ".".join((catalog, schema, mv_table)), + }, + "producer_progress": producer, + "zerobus_ingest": zerobus, + "raw_history": raw_history_rows[0] if raw_history_rows else None, + # Deliberately unavailable: neither refresh output-row metrics nor the + # aggregate MV row count measures raw-to-MV coverage. Querying the MV + # would also perturb the data path under test. + "mv_rows": None, + "event_log": { + "latest_update_progress": latest_update, + "latest_planning_information": latest_planning, + "bounded_rows": events, + "limit": EVENT_LIMIT, + }, + "latest_successful_refresh": refresh, + "mv_source_rows": mv_source_rows, + "mv_rows_behind": mv_rows_behind, + "raw_commit_age_sec": timestamp_age_sec( + observation_time, zerobus["latest_commit_time"] + ), + "producer_progress_age_sec": timestamp_age_sec( + observation_time, producer["updated_at"] + ), + "mv_refresh_age_sec": timestamp_age_sec( + observation_time, refresh_finished_at + ), + "age_semantics": AGE_SEMANTICS, + "query_evidence": { + name: { + "statement_id": result["statement_id"], + "row_count": result["row_count"], + "error": result["error"], + } + for name, result in results.items() + }, + "errors": errors, + } + + +def progress_payload(observation: Mapping[str, Any]) -> dict[str, Any]: + """Return the compact runner-facing and publishable observation.""" + producer = observation.get("producer_progress", {}) + zerobus = observation.get("zerobus_ingest", {}) + raw_history = observation.get("raw_history") or {} + query_evidence = observation.get("query_evidence", {}) + return { + "schema_version": observation.get("schema_version", 1), + "run_id": observation.get("run_id"), + "iteration": observation.get("iteration"), + "updated_at": observation.get("iteration_finished_at") + or observation.get("observed_at"), + "observed_at": observation.get("observed_at"), + "client_durable_rows": ( + producer.get("provider_committed_rows") + if isinstance(producer, Mapping) + else None + ), + "provider_committed_rows": ( + zerobus.get("cumulative_committed_records") + if isinstance(zerobus, Mapping) + else None + ), + "provider_committed_bytes": ( + zerobus.get("cumulative_committed_bytes") + if isinstance(zerobus, Mapping) + else None + ), + "provider_error_count": ( + zerobus.get("cumulative_error_count") + if isinstance(zerobus, Mapping) + else None + ), + "latest_commit_version": ( + zerobus.get("latest_commit_version") + if isinstance(zerobus, Mapping) + else None + ), + "latest_commit_time": ( + zerobus.get("latest_commit_time") + if isinstance(zerobus, Mapping) + else None + ), + "raw_delta_version": ( + _row_value(raw_history, "version") + if isinstance(raw_history, Mapping) + else None + ), + "raw_delta_timestamp": ( + _row_value(raw_history, "timestamp") + if isinstance(raw_history, Mapping) + else None + ), + "mv_rows": None, + "latest_successful_refresh": observation.get("latest_successful_refresh"), + "mv_source_rows": observation.get("mv_source_rows"), + "mv_rows_behind": observation.get("mv_rows_behind"), + "raw_commit_age_sec": observation.get("raw_commit_age_sec"), + "producer_progress_age_sec": observation.get("producer_progress_age_sec"), + "mv_refresh_age_sec": observation.get("mv_refresh_age_sec"), + "age_semantics": observation.get("age_semantics", AGE_SEMANTICS), + "statement_ids": { + name: value.get("statement_id") + for name, value in query_evidence.items() + if isinstance(value, Mapping) + } + if isinstance(query_evidence, Mapping) + else {}, + "errors": observation.get("errors", []), + } + + +def build_parser( + env: Mapping[str, str] | None = None, +) -> argparse.ArgumentParser: + source = os.environ if env is None else env + parser = argparse.ArgumentParser( + description="Record bounded Zerobus and materialized-view freshness evidence" + ) + parser.add_argument( + "--host", + default=_env_first( + source, + "DATABRICKS_HOST", + "DATABRICKS_SERVER_HOSTNAME", + "DATABRICKS_WORKSPACE_URL", + ), + ) + parser.add_argument( + "--token", + default=_env_first(source, "DATABRICKS_TOKEN", "DATABRICKS_ACCESS_TOKEN"), + help=argparse.SUPPRESS, + ) + parser.add_argument( + "--client-id", default=_env_first(source, "DATABRICKS_CLIENT_ID") + ) + parser.add_argument( + "--client-secret", + default=_env_first(source, "DATABRICKS_CLIENT_SECRET"), + help=argparse.SUPPRESS, + ) + parser.add_argument( + "--control-warehouse-id", + "--control-warehouse", + dest="control_warehouse_id", + default=_env_first( + source, + "DATABRICKS_CONTROL_WAREHOUSE_ID", + "DBX_CONTROL_WAREHOUSE_ID", + "CONTROL_WAREHOUSE_ID", + ), + ) + parser.add_argument( + "--catalog", + default=_env_first(source, "DATABRICKS_CATALOG", "DBX_CATALOG") or "workspace", + ) + parser.add_argument( + "--schema", + default=_env_first(source, "DATABRICKS_SCHEMA", "DBX_SCHEMA") or "benchmarking", + ) + parser.add_argument( + "--raw-table", + default=_env_first(source, "DATABRICKS_RAW_TABLE", "DBX_RAW_TABLE") or "quotes", + ) + parser.add_argument( + "--mv-table", + default=_env_first(source, "DATABRICKS_MV_TABLE", "DBX_MV_TABLE") + or "quotes_daily", + ) + parser.add_argument( + "--producer-progress", + type=Path, + default=_path_default( + _env_first( + source, + "DATABRICKS_PRODUCER_PROGRESS_JSON", + "DATABRICKS_PRODUCER_PROGRESS", + "PRODUCER_PROGRESS_JSON", + ) + ), + ) + parser.add_argument( + "--since", + default=_env_first( + source, + "DATABRICKS_RUN_STARTED_AT", + "BENCHMARK_RUN_STARTED_AT", + ), + help="required UTC ISO-8601 run start", + ) + parser.add_argument("--interval", type=float, default=DEFAULT_INTERVAL_SECONDS) + parser.add_argument( + "--iterations", type=int, default=0, help="0 runs until signaled" + ) + parser.add_argument( + "--output", + "--output-jsonl", + dest="output", + type=Path, + default=_path_default( + _env_first(source, "DATABRICKS_FRESHNESS_OUTPUT", "FRESHNESS_OUTPUT") + ), + ) + parser.add_argument( + "--progress-json", + "--progress", + dest="progress_json", + type=Path, + default=_path_default( + _env_first( + source, + "DATABRICKS_FRESHNESS_MONITOR_JSON", + "DATABRICKS_FRESHNESS_PROGRESS", + ) + ), + ) + parser.add_argument( + "--compact-event-log", + action="store_true", + help=( + "retain only latest event summaries per sample; use the bounded " + "collector for complete event-log export" + ), + ) + parser.add_argument( + "--compact-output", + action="store_true", + help="persist only benchmark freshness and ingest-progress fields", + ) + parser.add_argument( + "--run-id", + default=_env_first(source, "BENCHMARK_RUN_ID", "DATABRICKS_RUN_ID"), + ) + parser.add_argument("--statement-timeout", type=float, default=120.0) + parser.add_argument("--statement-poll-interval", type=float, default=0.5) + parser.add_argument("--network-timeout", type=float, default=30.0) + return parser + + +def finalize_args( + parser: argparse.ArgumentParser, + args: argparse.Namespace, +) -> argparse.Namespace: + if args.interval <= 0: + parser.error("--interval must be greater than zero") + if args.iterations < 0: + parser.error("--iterations must be zero or greater") + if args.statement_timeout <= 0 or args.statement_poll_interval <= 0: + parser.error("statement timeout and poll interval must be greater than zero") + if args.network_timeout <= 0: + parser.error("--network-timeout must be greater than zero") + args.host = normalize_host(args.host) + if not args.host: + parser.error("--host or DATABRICKS_HOST is required") + parsed_host = urllib.parse.urlparse(args.host) + if ( + parsed_host.scheme != "https" + or not parsed_host.hostname + or parsed_host.username + or parsed_host.password + or parsed_host.query + or parsed_host.fragment + or parsed_host.path not in ("", "/") + ): + parser.error("--host must be an HTTPS workspace origin without credentials or a path") + if not args.control_warehouse_id: + parser.error("--control-warehouse-id is required") + if not args.producer_progress: + parser.error("--producer-progress is required") + if not args.output: + parser.error("--output is required") + if not args.progress_json: + parser.error("--progress-json is required") + if not args.run_id: + parser.error("--run-id is required") + if not args.since: + parser.error("--since is required") + try: + args.since = parse_utc_timestamp(args.since) + except (TypeError, ValueError, OverflowError, OSError) as exc: + parser.error(f"--since must be an aware UTC ISO-8601 timestamp: {exc}") + if bool(args.client_id) != bool(args.client_secret): + parser.error("--client-id and --client-secret must be supplied together") + if bool(args.token) == bool(args.client_id and args.client_secret): + parser.error("configure exactly one of PAT or OAuth M2M credentials") + for name in ("catalog", "schema", "raw_table", "mv_table"): + try: + quote_identifier(getattr(args, name)) + except ValueError as exc: + parser.error(f"--{name.replace('_', '-')} is invalid: {exc}") + args.producer_progress = args.producer_progress.expanduser().resolve() + args.output = args.output.expanduser().resolve() + args.progress_json = args.progress_json.expanduser().resolve() + return args + + +def run(argv: Sequence[str] | None = None) -> int: + parser = build_parser() + args = finalize_args(parser, parser.parse_args(argv)) + client = DatabricksRestClient( + args.host, + token=args.token, + client_id=args.client_id, + client_secret=args.client_secret, + timeout=args.network_timeout, + ) + secrets = tuple( + value for value in (args.token, args.client_secret) if isinstance(value, str) + ) + stop = threading.Event() + signal_count = 0 + + def request_stop(_signum: int, _frame: Any) -> None: + nonlocal signal_count + signal_count += 1 + stop.set() + if signal_count >= 2: + raise KeyboardInterrupt + + signal.signal(signal.SIGINT, request_stop) + signal.signal(signal.SIGTERM, request_stop) + + print( + f"Freshness run {args.run_id}; fixed-rate interval {args.interval:g}s; " + f"writing {args.output}", + file=sys.stderr, + ) + anchor = time.monotonic() + iteration = 0 + while not stop.is_set() and (args.iterations == 0 or iteration < args.iterations): + delay = max(0.0, anchor + args.interval * iteration - time.monotonic()) + if delay and stop.wait(delay): + break + iteration += 1 + observation = collect_observation( + client, + control_warehouse_id=args.control_warehouse_id, + catalog=args.catalog, + schema=args.schema, + raw_table=args.raw_table, + mv_table=args.mv_table, + producer_progress_path=args.producer_progress, + since=args.since, + run_id=args.run_id, + iteration=iteration, + timeout=args.statement_timeout, + poll_interval=args.statement_poll_interval, + ) + safe = redact_secrets(observation, secrets) + if args.compact_event_log: + event_log = safe.get("event_log") + if isinstance(event_log, dict): + event_log["bounded_rows"] = [] + event_log["compacted"] = True + compact = progress_payload(safe) + atomic_append_jsonl(args.output, compact if args.compact_output else safe) + atomic_write_json(args.progress_json, compact) + print( + f"[{observation['observed_at']}] iteration={iteration} " + f"producer_rows={observation['producer_progress']['provider_committed_rows']} " + f"zerobus_rows={observation['zerobus_ingest']['cumulative_committed_records']} " + f"errors={len(observation['errors'])}", + file=sys.stderr, + ) + + print(f"Stopped after {iteration} iteration(s).", file=sys.stderr) + return 0 + + +def main(argv: Sequence[str] | None = None) -> int: + try: + return run(argv) + except KeyboardInterrupt: + print("Interrupted.", file=sys.stderr) + return 130 + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/full-path-realtime/quotes/databricks/preflight.py b/full-path-realtime/quotes/databricks/preflight.py new file mode 100755 index 0000000..0c49d8a --- /dev/null +++ b/full-path-realtime/quotes/databricks/preflight.py @@ -0,0 +1,1271 @@ +#!/usr/bin/env python3 +"""Fail-closed environment preflight for the Databricks Lakehouse//RT benchmark.""" + +from __future__ import annotations + +import argparse +import json +import os +import re +import subprocess +import sys +import time +import urllib.parse +from pathlib import Path +from typing import Any, Mapping + +from dbx_common import ( + DatabricksRestClient, + EnvironmentConfig, + atomic_write_json, + iso_utc, + load_queries, + probe_tls_endpoint, + quote_identifier, + redact_secrets, + render_sql, + split_sql_statements, + sql_string, + statement_rows, + validate_config, +) + +SCRIPT_DIR = Path(__file__).resolve().parent +EXPECTED_QUERY_COUNTS = {"dashboard": 4, "drilldown": 2} +PACKAGE_CONTRACT = { + "databricks.sql": { + "distribution": "databricks-sql-connector", + "required_for": "Lakehouse//RT Statement Execution API runners", + "online_required": True, + "environment": "runner", + }, + "databricks.sdk": { + "distribution": "databricks-sdk", + "required_for": "OAuth M2M authentication for SQL connector runners", + "online_required": True, + "environment": "runner", + }, + "pyarrow": { + "distribution": "pyarrow", + "required_for": "Parquet ingestion", + "online_required": True, + "environment": "zerobus", + }, + "zerobus.sdk": { + "distribution": "databricks-zerobus-ingest-sdk", + "required_for": "Zerobus ingestion", + "online_required": True, + "environment": "zerobus", + }, +} + + +def parse_args(argv: list[str] | None = None) -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=( + "Validate the local Databricks benchmark contract and optionally run " + "read-only workspace checks" + ) + ) + parser.add_argument("--online", action="store_true") + parser.add_argument("--output", type=Path) + parser.add_argument("--host") + parser.add_argument("--cloud", choices=("aws", "azure", "gcp")) + parser.add_argument("--region") + parser.add_argument("--workspace-id") + parser.add_argument("--catalog") + parser.add_argument("--schema") + parser.add_argument("--raw-table") + parser.add_argument("--mv-table") + parser.add_argument("--rt-warehouse") + parser.add_argument( + "--query-warehouse-mode", + choices=("lakehouse-rt", "serverless-baseline"), + default="lakehouse-rt", + ) + parser.add_argument("--control-warehouse") + parser.add_argument("--zerobus-endpoint") + parser.add_argument("--timeout", type=float, default=120.0) + parser.add_argument("--poll-interval", type=float, default=0.5) + parser.add_argument("--network-timeout", type=float, default=30.0) + parser.add_argument( + "--runner-python", + default=os.environ.get("DATABRICKS_RUNNER_PYTHON") or sys.executable, + help="Python interpreter containing requirements-runner.txt", + ) + parser.add_argument( + "--zerobus-python", + default=os.environ.get("DATABRICKS_ZEROBUS_PYTHON") or sys.executable, + help="Python interpreter containing requirements-zerobus.txt", + ) + args = parser.parse_args(argv) + if args.timeout <= 0 or args.poll_interval <= 0 or args.network_timeout <= 0: + parser.error("timeouts and poll interval must be greater than zero") + return args + + +def config_from_args( + args: argparse.Namespace, + env: Mapping[str, str] | None = None, +) -> EnvironmentConfig: + return EnvironmentConfig.from_env( + env, + host=args.host, + cloud=args.cloud, + region=args.region, + workspace_id=args.workspace_id, + catalog=args.catalog, + schema=args.schema, + raw_table=args.raw_table, + mv_table=args.mv_table, + rt_warehouse_id=args.rt_warehouse, + control_warehouse_id=args.control_warehouse, + zerobus_endpoint=args.zerobus_endpoint, + ) + + +def _external_package_probe( + python: str, + module: str, + distribution: str, +) -> dict[str, Any]: + probe = ( + "import importlib.metadata, importlib.util, json, sys\n" + "try:\n" + " available = importlib.util.find_spec(sys.argv[1]) is not None\n" + "except (ImportError, ModuleNotFoundError, AttributeError, ValueError):\n" + " available = False\n" + "version = None\n" + "if available:\n" + " try:\n" + " version = importlib.metadata.version(sys.argv[2])\n" + " except importlib.metadata.PackageNotFoundError:\n" + " available = False\n" + "print(json.dumps({'available': available, 'version': version}))\n" + ) + try: + completed = subprocess.run( + [python, "-c", probe, module, distribution], + check=False, + capture_output=True, + text=True, + timeout=10, + ) + except (OSError, subprocess.SubprocessError) as exc: + return { + "available": False, + "version": None, + "probe_error": f"{type(exc).__name__}: {exc}", + } + if completed.returncode != 0: + error = completed.stderr.strip() or f"exit status {completed.returncode}" + return { + "available": False, + "version": None, + "probe_error": error[:500], + } + try: + result = json.loads(completed.stdout) + except (json.JSONDecodeError, TypeError) as exc: + return { + "available": False, + "version": None, + "probe_error": f"invalid probe output: {type(exc).__name__}: {exc}", + } + return { + "available": result.get("available") is True, + "version": result.get("version"), + "probe_error": None, + } + + +def package_checks( + *, + runner_python: str | None = None, + zerobus_python: str | None = None, +) -> dict[str, dict[str, Any]]: + interpreters = { + "runner": runner_python or sys.executable, + "zerobus": zerobus_python or sys.executable, + } + checks: dict[str, dict[str, Any]] = {} + for module, details in PACKAGE_CONTRACT.items(): + environment = str(details["environment"]) + python = interpreters[environment] + probe = _external_package_probe( + python, + module, + str(details["distribution"]), + ) + checks[module] = { + **details, + "interpreter": python, + **probe, + } + return checks + + +def load_create_materialized_view_statement( + path: Path, + catalog: str, + schema: str, +) -> str: + """Render DDL and return its one complete CREATE MATERIALIZED VIEW statement.""" + rendered = render_sql(path.read_text(encoding="utf-8"), catalog, schema) + statements = split_sql_statements(rendered) + matches = [ + statement + for statement in statements + if re.match( + r"^CREATE\s+(?:OR\s+REPLACE\s+)?MATERIALIZED\s+VIEW\b", + statement, + flags=re.IGNORECASE, + ) + ] + if len(matches) != 1: + raise ValueError( + "rendered create.sql must contain exactly one " + f"CREATE MATERIALIZED VIEW statement; found {len(matches)}" + ) + return matches[0] + + +def _credential_shape(env: Mapping[str, str]) -> dict[str, Any]: + token = bool(env.get("DATABRICKS_TOKEN", "").strip()) + client_id = bool(env.get("DATABRICKS_CLIENT_ID", "").strip()) + client_secret = bool(env.get("DATABRICKS_CLIENT_SECRET", "").strip()) + return { + "pat_configured": token, + "oauth_client_id_configured": client_id, + "oauth_client_secret_configured": client_secret, + "oauth_client_credentials_complete": client_id and client_secret, + } + + +def offline_checks( + config: EnvironmentConfig, + env: Mapping[str, str] | None = None, + *, + script_dir: Path = SCRIPT_DIR, + runner_python: str | None = None, + zerobus_python: str | None = None, +) -> dict[str, Any]: + source = os.environ if env is None else env + validation = validate_config(config, online=False) + errors = list(validation["errors"]) + warnings = list(validation["warnings"]) + credentials = _credential_shape(source) + if credentials["oauth_client_id_configured"] != credentials[ + "oauth_client_secret_configured" + ]: + errors.append( + "DATABRICKS_CLIENT_ID and DATABRICKS_CLIENT_SECRET must be set together" + ) + if not credentials["pat_configured"] and not credentials[ + "oauth_client_credentials_complete" + ]: + warnings.append( + "no Databricks PAT or complete OAuth client credentials are configured" + ) + if not credentials["oauth_client_credentials_complete"]: + warnings.append( + "Zerobus requires DATABRICKS_CLIENT_ID and DATABRICKS_CLIENT_SECRET" + ) + + actual_counts: dict[str, int | None] = {"dashboard": None, "drilldown": None} + queries: dict[str, list[str]] = {} + for workload, filename in ( + ("dashboard", "queries_mv.sql"), + ("drilldown", "queries_raw.sql"), + ): + try: + loaded = load_queries( + script_dir / filename, + config.catalog, + config.schema, + raw_table=config.raw_table, + mv_table=config.mv_table, + ) + queries[workload] = loaded + actual_counts[workload] = len(loaded) + except (OSError, ValueError) as exc: + errors.append(f"could not load {filename}: {type(exc).__name__}: {exc}") + + for workload, expected in EXPECTED_QUERY_COUNTS.items(): + actual = actual_counts[workload] + if actual is not None and actual != expected: + errors.append( + f"expected {expected} {workload} SQL statements, found {actual}" + ) + + create_sql_path = script_dir / "create.sql" + create_materialized_view: str | None = None + try: + create_materialized_view = load_create_materialized_view_statement( + create_sql_path, + config.catalog, + config.schema, + ) + except (OSError, ValueError) as exc: + errors.append( + "could not extract CREATE MATERIALIZED VIEW from create.sql: " + f"{type(exc).__name__}: {exc}" + ) + + packages = package_checks( + runner_python=runner_python, + zerobus_python=zerobus_python, + ) + for module, details in packages.items(): + if not details["available"]: + warnings.append( + f"{details['distribution']} is unavailable; needed for " + f"{details['required_for']}" + ) + + return { + "status": "error" if errors else ("warning" if warnings else "ok"), + "config_validation": validation, + "credentials": credentials, + "query_contract": { + "expected": dict(EXPECTED_QUERY_COUNTS), + "actual": actual_counts, + "files": { + "dashboard": str(script_dir / "queries_mv.sql"), + "drilldown": str(script_dir / "queries_raw.sql"), + }, + }, + "ddl_contract": { + "file": str(create_sql_path), + "create_materialized_view_found": create_materialized_view is not None, + }, + "packages": packages, + "errors": errors, + "warnings": warnings, + "_queries": queries, + "_create_materialized_view": create_materialized_view, + } + + +def _safe_warehouse(metadata: Mapping[str, Any]) -> dict[str, Any]: + allowed = ( + "id", + "name", + "state", + "cluster_size", + "min_num_clusters", + "max_num_clusters", + "auto_stop_mins", + "enable_serverless_compute", + "warehouse_type", + "creator_name", + ) + return {key: metadata.get(key) for key in allowed if key in metadata} + + +def _uri_is_at_or_below(location: str, root: str) -> bool: + child = urllib.parse.urlparse(location.rstrip("/")) + parent = urllib.parse.urlparse(root.rstrip("/")) + if child.scheme.lower() != parent.scheme.lower() or child.netloc.lower() != parent.netloc.lower(): + return False + parent_path = parent.path.rstrip("/") + child_path = child.path.rstrip("/") + return child_path == parent_path or child_path.startswith(parent_path + "/") + + +def _table_summary(metadata: Mapping[str, Any]) -> dict[str, Any]: + return { + key: metadata.get(key) + for key in ( + "full_name", + "name", + "catalog_name", + "schema_name", + "table_type", + "data_source_format", + "storage_location", + "created_at", + "updated_at", + ) + if key in metadata + } + + +def predictive_optimization_check( + full_name: str, + metadata: Mapping[str, Any] | None, + errors: list[str], +) -> dict[str, Any]: + """Require the Unity Catalog API's effective table flag to be ENABLE.""" + metadata = metadata if isinstance(metadata, Mapping) else {} + effective = metadata.get("effective_predictive_optimization_flag") + effective = effective if isinstance(effective, Mapping) else {} + configured = str(metadata.get("enable_predictive_optimization") or "").upper() + effective_value = str(effective.get("value") or "").upper() + enabled = effective_value == "ENABLE" + result = { + "status": "ok" if enabled else "error", + "source": "Unity Catalog Tables API", + "configured_value": configured or None, + "effective_value": effective_value or None, + "effectively_enabled": enabled, + "inherited_from_type": effective.get("inherited_from_type"), + "inherited_from_name": effective.get("inherited_from_name"), + } + if not enabled: + errors.append( + f"{full_name} effective Predictive Optimization flag must be ENABLE; " + f"found {effective_value or 'missing'}" + ) + return result + + +def _decode_json(value: Any) -> Any: + decoded = value + for _ in range(3): + if not isinstance(decoded, str): + break + text = decoded.strip() + if not text or text[0] not in "[{": + break + try: + decoded = json.loads(text) + except (TypeError, ValueError, json.JSONDecodeError): + break + return decoded + + +def _row_value(row: Mapping[str, Any], name: str) -> Any: + wanted = name.lower() + for key, value in row.items(): + if str(key).lower() == wanted: + return value + return None + + +def _clustering_columns(value: Any) -> list[str] | None: + decoded = _decode_json(value) + if not isinstance(decoded, list): + return None + result: list[str] = [] + for item in decoded: + if isinstance(item, list) and len(item) == 1: + item = item[0] + if not isinstance(item, str): + return None + result.append(item) + return result + + +def target_contract_check( + client: DatabricksRestClient, + config: EnvironmentConfig, + *, + raw_metadata: Mapping[str, Any] | None, + expected_mv_owner: str, + timeout: float, + poll_interval: float, + errors: list[str], +) -> dict[str, Any]: + """Prove the fresh raw/MV contract without scanning either data object.""" + statements: dict[str, Any] = {} + observed: dict[str, Any] = {} + checks: dict[str, bool] = {} + local_errors: list[str] = [] + + def execute(name: str, sql: str) -> list[dict[str, Any]]: + response = _statement( + client, + config, + config.control_warehouse_id or "", + sql, + timeout=timeout, + poll_interval=poll_interval, + ) + rows = statement_rows(response) + statements[name] = { + "statement_id": response.get("statement_id"), + "row_count": len(rows), + } + return rows + + try: + detail_rows = execute( + "raw_detail", f"DESCRIBE DETAIL {quote_identifier(config.catalog)}." + f"{quote_identifier(config.schema)}.{quote_identifier(config.raw_table)}" + ) + history_rows = execute( + "raw_history", + f"DESCRIBE HISTORY {quote_identifier(config.catalog)}." + f"{quote_identifier(config.schema)}.{quote_identifier(config.raw_table)} " + "LIMIT 2", + ) + mv_rows = execute( + "mv_metadata", + f"DESCRIBE TABLE EXTENDED {quote_identifier(config.catalog)}." + f"{quote_identifier(config.schema)}.{quote_identifier(config.mv_table)} " + "AS JSON", + ) + if len(detail_rows) != 1 or len(mv_rows) != 1: + raise RuntimeError("target metadata returned an unexpected result shape") + + raw_detail = detail_rows[0] + raw_properties = ( + raw_metadata.get("properties", {}) + if isinstance(raw_metadata, Mapping) + else {} + ) + raw_properties = ( + raw_properties if isinstance(raw_properties, Mapping) else {} + ) + mv_document = _decode_json(_row_value(mv_rows[0], "json_metadata")) + if not isinstance(mv_document, Mapping): + raise RuntimeError("MV JSON metadata is missing or invalid") + mv_properties = mv_document.get("table_properties", {}) + mv_properties = ( + mv_properties if isinstance(mv_properties, Mapping) else {} + ) + refresh = mv_document.get("refresh_information", {}) + refresh = refresh if isinstance(refresh, Mapping) else {} + raw_clustering = _clustering_columns( + raw_properties.get("clusteringColumns") + ) + mv_clustering = _clustering_columns( + mv_properties.get("clusteringColumns") + ) + history_versions = [ + int(_row_value(row, "version")) for row in history_rows + ] + history_operations = [ + str(_row_value(row, "operation") or "").upper() + for row in history_rows + ] + schedule = str(refresh.get("refresh_schedule") or "").upper() + observed = { + "raw": { + "format": _row_value(raw_detail, "format"), + "num_files": _row_value(raw_detail, "numFiles"), + "size_in_bytes": _row_value(raw_detail, "sizeInBytes"), + "clustering_columns": raw_clustering, + "history_versions": history_versions, + "history_operations": history_operations, + "source_features": { + key: raw_properties.get(key) + for key in ( + "delta.enableDeletionVectors", + "delta.enableRowTracking", + "delta.enableChangeDataFeed", + ) + }, + }, + "materialized_view": { + "owner": mv_document.get("owner"), + "clustering_columns": mv_clustering, + "predictive_optimization": mv_document.get( + "predictive_optimization" + ), + "refresh_policy": refresh.get("refresh_policy"), + "refresh_schedule": refresh.get("refresh_schedule"), + }, + } + checks = { + "raw_format_delta": str(_row_value(raw_detail, "format")).lower() + == "delta", + "raw_zero_active_files": int(_row_value(raw_detail, "numFiles")) == 0, + "raw_zero_active_bytes": int(_row_value(raw_detail, "sizeInBytes")) == 0, + "raw_only_version_zero": history_versions == [0], + "raw_created_once": history_operations == ["CREATE TABLE"], + "raw_clustering": raw_clustering == ["sym", "t"], + "raw_deletion_vectors": str( + raw_properties.get("delta.enableDeletionVectors") + ).lower() + == "true", + "raw_row_tracking": str( + raw_properties.get("delta.enableRowTracking") + ).lower() + == "true", + "raw_change_data_feed": str( + raw_properties.get("delta.enableChangeDataFeed") + ).lower() + == "true", + "mv_owner": mv_document.get("owner") == expected_mv_owner, + "mv_clustering": mv_clustering == ["sym", "day"], + "mv_predictive_optimization": str( + mv_document.get("predictive_optimization") + ).upper() + == "ENABLE", + "mv_incremental_strict": str( + refresh.get("refresh_policy") + ).upper() + == "INCREMENTALSTRICT", + "mv_one_minute_update_trigger": ( + "TRIGGER ON UPDATE" in schedule + and ( + "INTERVAL 60 SECOND" in schedule + or "INTERVAL 1 MINUTE" in schedule + ) + ), + } + local_errors.extend(name for name, passed in checks.items() if not passed) + except Exception as exc: + local_errors.append(f"{type(exc).__name__}: {exc}") + + if local_errors: + errors.extend( + f"target contract check failed: {message}" for message in local_errors + ) + return { + "status": "error" if local_errors else "ok", + "method": "Unity Catalog metadata plus DESCRIBE metadata statements", + "statements": statements, + "observed": observed, + "checks": checks, + "errors": local_errors, + } + + +def _statement( + client: DatabricksRestClient, + config: EnvironmentConfig, + warehouse_id: str, + sql: str, + *, + timeout: float, + poll_interval: float, +) -> dict[str, Any]: + return client.execute_statement( + sql, + warehouse_id, + timeout=timeout, + poll_interval=poll_interval, + catalog=config.catalog, + schema=config.schema, + ) + + +def _check_warehouse( + client: DatabricksRestClient, + warehouse_id: str, + label: str, + errors: list[str], +) -> dict[str, Any]: + try: + metadata = client.get_warehouse(warehouse_id) + except Exception as exc: + errors.append(f"could not inspect {label} warehouse: {type(exc).__name__}: {exc}") + return {"status": "error"} + returned_id = metadata.get("id") + if returned_id != warehouse_id: + errors.append( + f"{label} warehouse lookup returned ID {returned_id!r}, expected {warehouse_id!r}" + ) + return { + "status": "ok" if returned_id == warehouse_id else "error", + "metadata": _safe_warehouse(metadata), + } + + +def _check_table( + client: DatabricksRestClient, + full_name: str, + expected_type: str, + errors: list[str], +) -> tuple[dict[str, Any], Mapping[str, Any] | None]: + encoded = urllib.parse.quote(full_name, safe="") + try: + metadata = client.request("GET", f"/api/2.1/unity-catalog/tables/{encoded}") + except Exception as exc: + errors.append(f"could not inspect {full_name}: {type(exc).__name__}: {exc}") + return {"status": "error"}, None + summary = _table_summary(metadata) + table_type = str(metadata.get("table_type", "")).upper() + data_format = str(metadata.get("data_source_format", "")).upper() + table_errors: list[str] = [] + if table_type != expected_type: + table_errors.append( + f"{full_name} must have table_type={expected_type}, found {table_type or 'unknown'}" + ) + if expected_type == "MATERIALIZED_VIEW": + format_valid = data_format in {"", "DELTA"} + else: + format_valid = data_format == "DELTA" + if not format_valid: + table_errors.append( + f"{full_name} must use Delta, found {data_format or 'unknown'}" + ) + errors.extend(table_errors) + return { + "status": "error" if table_errors else "ok", + "metadata": summary, + "errors": table_errors, + }, metadata + + +def online_checks( + config: EnvironmentConfig, + queries: Mapping[str, list[str]], + env: Mapping[str, str] | None = None, + *, + create_materialized_view_statement: str | None = None, + runner_python: str | None = None, + zerobus_python: str | None = None, + timeout: float = 120.0, + poll_interval: float = 0.5, + network_timeout: float = 5.0, + query_warehouse_mode: str = "lakehouse-rt", +) -> dict[str, Any]: + source = os.environ if env is None else env + errors: list[str] = [] + warnings: list[str] = [] + validation = validate_config(config, online=True) + errors.extend(validation["errors"]) + blocking_errors = list(validation["errors"]) + warnings.extend(validation["warnings"]) + credentials = _credential_shape(source) + token = source.get("DATABRICKS_TOKEN", "").strip() or None + client_id = source.get("DATABRICKS_CLIENT_ID", "").strip() or None + client_secret = source.get("DATABRICKS_CLIENT_SECRET", "").strip() or None + if bool(client_id) != bool(client_secret): + message = "DATABRICKS_CLIENT_ID and DATABRICKS_CLIENT_SECRET must be set together" + errors.append(message) + blocking_errors.append(message) + if not token and not (client_id and client_secret): + message = "DATABRICKS_TOKEN or OAuth client credentials are required online" + errors.append(message) + blocking_errors.append(message) + if not (client_id and client_secret): + errors.append( + "Zerobus requires service-principal DATABRICKS_CLIENT_ID and " + "DATABRICKS_CLIENT_SECRET" + ) + packages = package_checks( + runner_python=runner_python, + zerobus_python=zerobus_python, + ) + for details in packages.values(): + if details["online_required"] and not details["available"]: + errors.append( + f"{details['distribution']} is required for benchmark execution but unavailable" + ) + + report: dict[str, Any] = { + "status": "pending", + "config_validation": validation, + "credentials": credentials, + "packages": packages, + "errors": errors, + "warnings": warnings, + } + if blocking_errors: + report["status"] = "error" + report["skipped"] = "online calls skipped because required configuration is invalid" + return report + + assert config.host + assert config.rt_warehouse_id + assert config.control_warehouse_id + assert config.zerobus_endpoint + client = DatabricksRestClient( + config.host, + token=token, + client_id=client_id, + client_secret=client_secret, + timeout=network_timeout, + ) + report["authentication"] = {"status": "configured", "mode": client.auth_mode} + + serving_label = ( + "Lakehouse//RT" + if query_warehouse_mode == "lakehouse-rt" + else "Serverless SQL baseline" + ) + warehouses = { + "lakehouse_rt": _check_warehouse( + client, config.rt_warehouse_id, serving_label, errors + ), + "control": _check_warehouse( + client, config.control_warehouse_id, "control", errors + ), + } + report["warehouses"] = warehouses + try: + workspace_warehouse_config = client.request( + "GET", "/api/2.0/sql/config/warehouses" + ) + warehouses["workspace_configuration"] = { + "enabled_warehouse_types": workspace_warehouse_config.get( + "enabled_warehouse_types" + ), + "enable_serverless_compute": workspace_warehouse_config.get( + "enable_serverless_compute" + ), + } + except Exception as exc: + warnings.append( + "workspace warehouse configuration was unavailable: " + f"{type(exc).__name__}: {exc}" + ) + + rt_select: dict[str, Any] + try: + response = _statement( + client, + config, + config.rt_warehouse_id, + "SELECT 1 AS preflight_ok, current_catalog() AS current_catalog, " + "current_schema() AS current_schema", + timeout=timeout, + poll_interval=poll_interval, + ) + rt_select = { + "status": "ok", + "statement_id": response.get("statement_id"), + "rows": statement_rows(response), + } + except Exception as exc: + rt_select = {"status": "error"} + errors.append( + f"Statement Execution API SELECT failed on {serving_label} warehouse: " + f"{type(exc).__name__}: {exc}" + ) + report["statement_execution_on_rt"] = rt_select + + rt_metadata = warehouses.get("lakehouse_rt", {}).get("metadata", {}) + type_signal = rt_metadata.get("warehouse_type") + if query_warehouse_mode == "lakehouse-rt": + report["lakehouse_rt_type"] = { + "status": "inconclusive", + "warehouse_api_signal": type_signal, + "statement_execution_succeeded": rt_select.get("status") == "ok", + "reason": ( + "The public Warehouses API does not expose a reliable REAL_TIME enum. " + "A successful Statement Execution API query and warehouse " + "configuration are evidence, but do not conclusively identify the " + "warehouse type." + ), + } + warnings.append( + "Lakehouse//RT warehouse type is inconclusive because the public " + "Warehouses API does not expose a reliable REAL_TIME discriminator" + ) + else: + report["lakehouse_rt_type"] = { + "status": "not_applicable", + "warehouse_api_signal": type_signal, + "statement_execution_succeeded": rt_select.get("status") == "ok", + "reason": "This run intentionally uses the Serverless SQL baseline.", + } + report["query_warehouse_mode"] = query_warehouse_mode + + mv_create_compatibility: dict[str, Any] = { + "method": "EXPLAIN CREATE MATERIALIZED VIEW", + "warehouse_id": config.control_warehouse_id, + } + if create_materialized_view_statement is None: + mv_create_compatibility["status"] = "error" + errors.append( + "CREATE MATERIALIZED VIEW statement was unavailable for EXPLAIN" + ) + else: + try: + response = _statement( + client, + config, + config.control_warehouse_id, + "EXPLAIN " + create_materialized_view_statement, + timeout=timeout, + poll_interval=poll_interval, + ) + explain_rows = statement_rows(response) + explain_text = json.dumps(explain_rows, ensure_ascii=False).lower() + incrementalizable = ( + "can be incrementally refreshed" in explain_text + and "no issues detected" in explain_text + ) + mv_create_compatibility.update( + { + "status": "ok" if incrementalizable else "error", + "statement_id": response.get("statement_id"), + "rows": explain_rows, + "incrementalizable": incrementalizable, + } + ) + if not incrementalizable: + errors.append( + "EXPLAIN CREATE MATERIALIZED VIEW did not prove incremental " + "refresh eligibility with no issues" + ) + except Exception as exc: + mv_create_compatibility["status"] = "error" + mv_create_compatibility["error"] = f"{type(exc).__name__}: {exc}" + errors.append( + "CREATE MATERIALIZED VIEW is not control-warehouse SQL-compatible: " + f"{type(exc).__name__}: {exc}" + ) + report["materialized_view_create_compatibility"] = mv_create_compatibility + + compatibility: list[dict[str, Any]] = [] + for workload in ("dashboard", "drilldown"): + for number, query in enumerate(queries.get(workload, []), start=1): + item: dict[str, Any] = {"workload": workload, "query_number": number} + try: + response = _statement( + client, + config, + config.rt_warehouse_id, + f"EXPLAIN FORMATTED\n{query}", + timeout=timeout, + poll_interval=poll_interval, + ) + item.update( + {"status": "ok", "statement_id": response.get("statement_id")} + ) + except Exception as exc: + item["status"] = "error" + item["error"] = f"{type(exc).__name__}: {exc}" + errors.append( + f"{workload} query {number} is not {serving_label} SQL-compatible: " + f"{type(exc).__name__}: {exc}" + ) + compatibility.append(item) + report["query_compatibility"] = { + "method": "EXPLAIN FORMATTED (compiles without running benchmark scans)", + "queries": compatibility, + } + + statement_id = rt_select.get("statement_id") + if statement_id: + response = None + system_permission_error: Exception | None = None + for attempt in range(3): + try: + response = _statement( + client, + config, + config.control_warehouse_id, + "SELECT statement_id FROM system.query.history " + f"WHERE statement_id = {sql_string(str(statement_id))} LIMIT 1", + timeout=timeout, + poll_interval=poll_interval, + ) + system_permission_error = None + break + except Exception as exc: + system_permission_error = exc + if attempt < 2: + time.sleep(2**attempt) + if response is not None: + report["system_table_permission"] = { + "status": "ok", + "table": "system.query.history", + "statement_id": response.get("statement_id"), + } + else: + report["system_table_permission"] = {"status": "error"} + errors.append( + "cannot read system.query.history on the control warehouse: " + f"{type(system_permission_error).__name__}: " + f"{system_permission_error}" + ) + + history = None + history_error: Exception | None = None + for _ in range(5): + try: + history = client.query_history_by_statement_id(str(statement_id)) + history_error = None + except Exception as exc: + history_error = exc + break + if history is not None: + break + time.sleep(0.5) + if history is None: + report["query_history_api"] = {"status": "error"} + if history_error: + errors.append( + "query-history lookup failed: " + f"{type(history_error).__name__}: {history_error}" + ) + else: + errors.append( + "query-history API did not return the Lakehouse//RT preflight statement" + ) + else: + report["query_history_api"] = { + "status": "ok", + "statement_id": history.get("statement_id"), + "warehouse_id": history.get("warehouse_id") + or history.get("endpoint_id"), + "status_value": history.get("status"), + } + else: + report["system_table_permission"] = { + "status": "skipped", + "reason": "Lakehouse//RT statement did not produce an ID", + } + report["query_history_api"] = { + "status": "skipped", + "reason": "Lakehouse//RT statement did not produce an ID", + } + + raw_report, raw_metadata = _check_table( + client, config.raw_full_name, "MANAGED", errors + ) + mv_report, mv_metadata = _check_table( + client, config.mv_full_name, "MATERIALIZED_VIEW", errors + ) + report["tables"] = {"raw": raw_report, "materialized_view": mv_report} + raw_predictive = predictive_optimization_check( + config.raw_full_name, + raw_metadata, + errors, + ) + mv_predictive = predictive_optimization_check( + config.mv_full_name, + mv_metadata, + errors, + ) + report["predictive_optimization"] = { + "status": ( + "ok" + if raw_predictive["effectively_enabled"] + and mv_predictive["effectively_enabled"] + else "error" + ), + "raw": raw_predictive, + "materialized_view": mv_predictive, + } + report["target_contract"] = target_contract_check( + client, + config, + raw_metadata=raw_metadata, + expected_mv_owner=client_id or "", + timeout=timeout, + poll_interval=poll_interval, + errors=errors, + ) + + metastore_summary: Mapping[str, Any] | None = None + catalog_metadata: Mapping[str, Any] | None = None + try: + metastore_summary = client.request( + "GET", "/api/2.1/unity-catalog/metastore_summary" + ) + report["metastore"] = { + "metastore_id": metastore_summary.get("metastore_id"), + "cloud": metastore_summary.get("cloud"), + "region": metastore_summary.get("region"), + "storage_root_configured": bool(metastore_summary.get("storage_root")), + } + except Exception as exc: + errors.append( + "could not inspect metastore default storage: " + f"{type(exc).__name__}: {exc}" + ) + report["metastore"] = {"status": "error"} + + try: + encoded_catalog = urllib.parse.quote(config.catalog, safe="") + catalog_metadata = client.request( + "GET", f"/api/2.1/unity-catalog/catalogs/{encoded_catalog}" + ) + report["catalog_storage"] = { + "name": catalog_metadata.get("name"), + "storage_root": catalog_metadata.get("storage_root"), + } + except Exception as exc: + errors.append( + "could not inspect catalog managed storage: " + f"{type(exc).__name__}: {exc}" + ) + report["catalog_storage"] = {"status": "error"} + + raw_location = raw_metadata.get("storage_location") if raw_metadata else None + metastore_root = ( + metastore_summary.get("storage_root") if metastore_summary else None + ) + catalog_root = ( + catalog_metadata.get("storage_root") if catalog_metadata else None + ) + storage_check: dict[str, Any] = { + "status": "error", + "raw_storage_location_configured": bool(raw_location), + "catalog_managed_storage_configured": bool(catalog_root), + "metastore_default_storage_configured": bool(metastore_root), + } + if not raw_location: + errors.append("raw table metadata omitted storage_location") + elif catalog_root: + uses_catalog = _uri_is_at_or_below( + str(raw_location), str(catalog_root) + ) + uses_default = bool(metastore_root) and _uri_is_at_or_below( + str(raw_location), str(metastore_root) + ) + storage_check["uses_catalog_managed_storage"] = uses_catalog + storage_check["uses_metastore_default_storage"] = uses_default + storage_check["status"] = ( + "ok" if uses_catalog and not uses_default else "error" + ) + if not uses_catalog: + errors.append( + "raw table storage is not under the catalog managed storage root" + ) + if uses_default: + errors.append( + "raw table storage is under the metastore default storage root; " + "Zerobus requires non-default managed storage" + ) + elif not metastore_root: + errors.append( + "catalog and metastore metadata omitted storage roots; managed " + "storage cannot be verified" + ) + else: + uses_default = _uri_is_at_or_below( + str(raw_location), str(metastore_root) + ) + storage_check["uses_metastore_default_storage"] = uses_default + storage_check["status"] = "error" if uses_default else "ok" + if uses_default: + errors.append( + "raw table storage is under the metastore default storage root; " + "Zerobus requires non-default managed storage" + ) + report["tables"]["raw_storage_assumption"] = storage_check + + endpoint_report: dict[str, Any] = {"url": config.zerobus_endpoint} + try: + endpoint_report.update( + { + "status": "ok", + "tls": probe_tls_endpoint( + config.zerobus_endpoint, timeout=network_timeout + ), + } + ) + except Exception as exc: + endpoint_report["status"] = "error" + errors.append( + f"Zerobus endpoint is not reachable over TLS: {type(exc).__name__}: {exc}" + ) + report["zerobus_endpoint"] = endpoint_report + + if client_id and client_secret and config.workspace_id: + authorization_details = [ + { + "type": "unity_catalog_privileges", + "privileges": ["USE CATALOG"], + "object_type": "CATALOG", + "object_full_path": config.catalog, + }, + { + "type": "unity_catalog_privileges", + "privileges": ["USE SCHEMA"], + "object_type": "SCHEMA", + "object_full_path": f"{config.catalog}.{config.schema}", + }, + { + "type": "unity_catalog_privileges", + "privileges": ["SELECT", "MODIFY"], + "object_type": "TABLE", + "object_full_path": config.raw_full_name, + }, + ] + try: + client.oauth_token( + resource=( + f"api://databricks/workspaces/{config.workspace_id}" + "/zerobusDirectWriteApi" + ), + authorization_details=authorization_details, + ) + report["zerobus_service_principal"] = { + "status": "ok", + "scoped_oauth_token_acquired": True, + "required_privileges": authorization_details, + } + except Exception as exc: + report["zerobus_service_principal"] = {"status": "error"} + errors.append( + "could not acquire a scoped Zerobus service-principal token: " + f"{type(exc).__name__}: {exc}" + ) + + report["status"] = "error" if errors else ("warning" if warnings else "ok") + return report + + +def _without_private_fields(report: dict[str, Any]) -> dict[str, Any]: + offline = report.get("offline") + if isinstance(offline, dict): + offline.pop("_queries", None) + offline.pop("_create_materialized_view", None) + return report + + +def main(argv: list[str] | None = None) -> int: + args = parse_args(argv) + config = config_from_args(args) + offline = offline_checks( + config, + runner_python=args.runner_python, + zerobus_python=args.zerobus_python, + ) + queries = offline.get("_queries", {}) + create_materialized_view = offline.get("_create_materialized_view") + report: dict[str, Any] = { + "generated_at": iso_utc(), + "mode": "online" if args.online else "offline", + "config": config.as_report_dict(), + "offline": offline, + } + if args.online: + try: + report["online"] = online_checks( + config, + queries, + create_materialized_view_statement=create_materialized_view, + runner_python=args.runner_python, + zerobus_python=args.zerobus_python, + timeout=args.timeout, + poll_interval=args.poll_interval, + network_timeout=args.network_timeout, + query_warehouse_mode=args.query_warehouse_mode, + ) + except Exception as exc: + report["online"] = { + "status": "error", + "errors": [ + f"unexpected online preflight failure: {type(exc).__name__}: {exc}" + ], + "warnings": [], + } + + all_errors = list(offline["errors"]) + all_warnings = list(offline["warnings"]) + if args.online: + all_errors.extend(report["online"]["errors"]) + all_warnings.extend(report["online"]["warnings"]) + report["summary"] = { + "status": "error" if all_errors else ("warning" if all_warnings else "ok"), + "error_count": len(all_errors), + "warning_count": len(all_warnings), + } + safe_report = redact_secrets( + _without_private_fields(report), + ( + os.environ.get("DATABRICKS_TOKEN"), + os.environ.get("DATABRICKS_CLIENT_SECRET"), + ), + ) + rendered = json.dumps(safe_report, indent=2, sort_keys=True) + print(rendered) + if args.output: + atomic_write_json(args.output, safe_report) + return 1 if all_errors else 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/full-path-realtime/quotes/databricks/publish_results.py b/full-path-realtime/quotes/databricks/publish_results.py new file mode 100644 index 0000000..f576ebc --- /dev/null +++ b/full-path-realtime/quotes/databricks/publish_results.py @@ -0,0 +1,1434 @@ +#!/usr/bin/env python3 +"""Publish a validated Databricks benchmark run without inventing observations.""" + +from __future__ import annotations + +import argparse +import copy +import hashlib +import json +import math +import os +import sys +import tempfile +from datetime import datetime, timezone +from decimal import Decimal, InvalidOperation +from pathlib import Path +from typing import Any, Iterable, Mapping, Sequence + +EXPECTED_ROWS = 113_219_565_734 +PRESENTATION_ROW_CAP = 100_000_000_000 +PROVIDER_LABEL = "Databricks" +PROVIDER_COLOR = "#FF3621" +SOURCE_OUTPUT_NAMES = { + "validation_report": "source_inputs/validation_report.json", + "cost_summary": "source_inputs/cost_summary.json", + "databricks_dashboard": "source_inputs/databricks_dashboard.jsonl", + "databricks_drilldown": "source_inputs/databricks_drilldown.jsonl", + "databricks_freshness": "source_inputs/databricks_freshness.jsonl", + "clickhouse_dashboard": "source_inputs/clickhouse_dashboard.jsonl", + "clickhouse_drilldown": "source_inputs/clickhouse_drilldown.jsonl", + "clickhouse_ingest_cost": "source_inputs/clickhouse_ingest_cost.json", +} +OUTPUT_NAMES = ( + "accepted_dashboard.jsonl", + "accepted_drilldown.jsonl", + "accepted_freshness.jsonl", + "matched_clickhouse_dashboard.jsonl", + "matched_clickhouse_drilldown.jsonl", + "ingest_fresh_path_cost_clickhouse_vs_databricks_summary.json", + "full_path_cost_performance_clickhouse_vs_databricks_summary.json", + "publication_manifest.json", + *SOURCE_OUTPUT_NAMES.values(), +) + +SCRIPT_DIR = Path(__file__).resolve().parent +GIT_ROOT = SCRIPT_DIR.parents[2] +BENCHMARK_ROOT = SCRIPT_DIR.parents[1] +DEFAULT_GLOBAL_MANIFEST = ( + BENCHMARK_ROOT / "quotes" / "global" / "visualizations" / "manifest.json" +) + + +def _decimal(value: Any, context: str) -> Decimal: + if value is None or isinstance(value, bool): + raise ValueError(f"{context} is missing or not numeric") + try: + result = Decimal(str(value)) + except (InvalidOperation, TypeError, ValueError) as exc: + raise ValueError(f"{context} is not numeric") from exc + if not result.is_finite(): + raise ValueError(f"{context} is not finite") + return result + + +def _decimal_text(value: Decimal) -> str: + text = format(value, "f") + if "." in text: + text = text.rstrip("0").rstrip(".") + return text if text not in ("", "-0") else "0" + + +def _number(value: Any, context: str, *, nonnegative: bool = True) -> float: + if value is None or isinstance(value, bool): + raise ValueError(f"{context} is missing or not numeric") + try: + result = float(value) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError(f"{context} is not numeric") from exc + if not math.isfinite(result) or (nonnegative and result < 0): + raise ValueError(f"{context} is invalid") + return result + + +def _integer(value: Any, context: str, *, nonnegative: bool = True) -> int: + if isinstance(value, bool): + raise ValueError(f"{context} is not an integer") + try: + result = int(value) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError(f"{context} is not an integer") from exc + if str(result) != str(value).strip() and not isinstance(value, int): + raise ValueError(f"{context} is not an exact integer") + if nonnegative and result < 0: + raise ValueError(f"{context} is negative") + return result + + +def _timestamp(value: Any, context: str) -> datetime: + if not isinstance(value, str) or not value.strip(): + raise ValueError(f"{context} is not an ISO-8601 timestamp") + text = value.strip() + if text.endswith(("Z", "z")): + text = text[:-1] + "+00:00" + try: + result = datetime.fromisoformat(text) + except ValueError as exc: + raise ValueError(f"{context} is not an ISO-8601 timestamp") from exc + if result.tzinfo is None or result.utcoffset() is None: + raise ValueError(f"{context} has no timezone") + return result.astimezone(timezone.utc) + + +def _parse_json(content: bytes, path: Path, context: str) -> dict[str, Any]: + try: + value = json.loads(content.decode("utf-8")) + except (UnicodeDecodeError, json.JSONDecodeError, TypeError, ValueError) as exc: + raise ValueError(f"cannot parse {context} at {path}: {exc}") from exc + if not isinstance(value, dict): + raise ValueError(f"{context} at {path} is not a JSON object") + return value + + +def _read_json(path: Path, context: str) -> dict[str, Any]: + try: + content = path.read_bytes() + except OSError as exc: + raise ValueError(f"cannot read {context} at {path}: {exc}") from exc + return _parse_json(content, path, context) + + +def _parse_jsonl( + content: bytes, + path: Path, + context: str, +) -> list[dict[str, Any]]: + rows: list[dict[str, Any]] = [] + try: + lines = content.decode("utf-8").splitlines() + except UnicodeDecodeError as exc: + raise ValueError(f"cannot decode {context} at {path}: {exc}") from exc + for line_number, line in enumerate(lines, 1): + if not line.strip(): + continue + try: + value = json.loads(line) + except (json.JSONDecodeError, TypeError, ValueError) as exc: + raise ValueError(f"invalid JSON at {path}:{line_number}: {exc}") from exc + if not isinstance(value, dict): + raise ValueError(f"{path}:{line_number} is not a JSON object") + if "_publisher_source_line" in value: + raise ValueError( + f"{path}:{line_number} contains reserved publisher metadata" + ) + value["_publisher_source_line"] = line_number + rows.append(value) + return rows + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _json_bytes(value: Mapping[str, Any]) -> bytes: + return ( + json.dumps(value, indent=2, sort_keys=True, ensure_ascii=False) + "\n" + ).encode("utf-8") + + +def _jsonl_bytes(rows: Sequence[Mapping[str, Any]]) -> bytes: + return "".join( + json.dumps(row, separators=(",", ":"), sort_keys=True, ensure_ascii=False) + + "\n" + for row in rows + ).encode("utf-8") + + +def _atomic_write(path: Path, content: bytes) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + descriptor, temporary = tempfile.mkstemp(prefix=f".{path.name}.", dir=path.parent) + try: + with os.fdopen(descriptor, "wb") as handle: + handle.write(content) + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, path) + finally: + try: + os.unlink(temporary) + except FileNotFoundError: + pass + + +def _portable_path(path: Path) -> str: + resolved = path.expanduser().resolve() + try: + return resolved.relative_to(BENCHMARK_ROOT).as_posix() + except ValueError: + return str(resolved) + + +def _manifest_relative_path(path: Path) -> str: + resolved = path.expanduser().resolve() + try: + return resolved.relative_to(BENCHMARK_ROOT).as_posix() + except ValueError as exc: + raise ValueError( + f"global-manifest activation source is outside {BENCHMARK_ROOT}: {resolved}" + ) from exc + + +def _declared_path_matches(declared: str, actual: Path, report_path: Path) -> bool: + candidate = Path(declared).expanduser() + candidates = [candidate] if candidate.is_absolute() else [ + report_path.parent / candidate, + BENCHMARK_ROOT / candidate, + GIT_ROOT / candidate, + ] + return any(item.resolve() == actual.resolve() for item in candidates) + + +def _validation_input_paths(report: Mapping[str, Any]) -> Mapping[str, Any]: + for gate in report.get("gates", []): + if isinstance(gate, Mapping) and gate.get("id") == "input_availability": + evidence = gate.get("evidence") + if isinstance(evidence, Mapping): + paths = evidence.get("paths") + if isinstance(paths, Mapping): + return paths + return {} + + +def _require_declared_input( + declared: Mapping[str, Any], + key: str, + actual: Path, + report_path: Path, +) -> None: + value = declared.get(key) + if value in (None, ""): + return + if not isinstance(value, str) or not _declared_path_matches( + value, actual, report_path + ): + raise ValueError( + f"{key} path does not match validation report input: " + f"declared={value!r}, supplied={actual}" + ) + + +def _accepted_indices( + report: Mapping[str, Any], + section: str, + row_count: int, +) -> list[int]: + parent_name = "freshness_samples" if section == "freshness" else "query_samples" + parent = report.get(parent_name) + if not isinstance(parent, Mapping): + raise ValueError(f"validation report omits {parent_name}") + value = parent if section == "freshness" else parent.get(section) + if not isinstance(value, Mapping): + raise ValueError(f"validation report omits accepted {section} samples") + indices = value.get("accepted_indices") + if not isinstance(indices, list) or not indices: + raise ValueError(f"validation report has no accepted {section} indices") + result: list[int] = [] + for position, item in enumerate(indices): + index = _integer(item, f"{section} accepted index {position}") + if index >= row_count: + raise ValueError(f"{section} accepted index {index} is out of range") + result.append(index) + if len(set(result)) != len(result) or result != sorted(result): + raise ValueError(f"{section} accepted indices are duplicated or unordered") + accepted_count = value.get("accepted_count") + if accepted_count is not None and _integer( + accepted_count, f"{section} accepted_count" + ) != len(result): + raise ValueError(f"{section} accepted_count disagrees with accepted_indices") + return result + + +def _valid_hash(value: Any) -> bool: + return ( + isinstance(value, str) + and len(value) == 64 + and all(character in "0123456789abcdefABCDEF" for character in value) + ) + + +def _validate_databricks_query_record( + record: Mapping[str, Any], + expected_queries: int, + *, + context: str, +) -> int: + raw_rows = _integer(record.get("raw_rows"), f"{context}.raw_rows") + if record.get("producer_progress_finished") is not False: + raise ValueError(f"{context} is not an active-ingest observation") + if raw_rows >= EXPECTED_ROWS: + raise ValueError(f"{context} started at or after complete ingestion") + results = record.get("result") + evidence = record.get("query_evidence") + errors = record.get("query_errors") + if not isinstance(results, list) or len(results) != expected_queries: + raise ValueError(f"{context} does not contain {expected_queries} results") + if not isinstance(evidence, list) or len(evidence) != expected_queries: + raise ValueError( + f"{context} does not contain {expected_queries} query evidence records" + ) + if not isinstance(errors, list) or len(errors) != expected_queries: + raise ValueError(f"{context} query_errors is not aligned") + for index, (result, observation, query_errors) in enumerate( + zip(results, evidence, errors, strict=True), 1 + ): + query = f"{context}.query[{index}]" + if not isinstance(result, list) or len(result) != 1: + raise ValueError(f"{query} result is not a one-element trial") + latency = _number(result[0], f"{query}.result") + if not isinstance(observation, Mapping): + raise ValueError(f"{query} evidence is not an object") + canonical = _number( + observation.get("canonical_duration_sec"), + f"{query}.canonical_duration_sec", + ) + if not math.isclose(latency, canonical, rel_tol=0, abs_tol=1e-12): + raise ValueError(f"{query} canonical duration disagrees with result") + if not isinstance(observation.get("statement_id"), str) or not observation.get( + "statement_id" + ): + raise ValueError(f"{query} statement ID is missing") + metrics = observation.get("metrics") + if not isinstance(metrics, Mapping): + raise ValueError(f"{query} metrics are missing") + if metrics.get("result_from_cache") is not False: + raise ValueError(f"{query} cache evidence is not exact false") + if metrics.get("cache_origin_statement_id") not in (None, ""): + raise ValueError(f"{query} has a cache-origin statement") + if observation.get("execution_succeeded") is not True: + raise ValueError(f"{query} execution did not succeed") + _integer(observation.get("result_row_count"), f"{query}.result_row_count") + if not _valid_hash(observation.get("result_hash_sha256")): + raise ValueError(f"{query} result hash is missing or invalid") + if observation.get("errors") != [] or query_errors != []: + raise ValueError(f"{query} contains errors") + return raw_rows + + +def _source_rows( + rows: Sequence[Mapping[str, Any]], + indices: Sequence[int], + expected_queries: int, + *, + workload: str, + cap: int, +) -> list[dict[str, Any]]: + selected: list[dict[str, Any]] = [] + previous = -1 + for index in indices: + record = rows[index] + raw_rows = _validate_databricks_query_record( + record, expected_queries, context=f"{workload}[{index}]" + ) + if raw_rows < previous: + raise ValueError(f"accepted {workload} raw_rows is not monotonic") + previous = raw_rows + if raw_rows > cap: + continue + item = copy.deepcopy(record) + source_line = item.pop("_publisher_source_line") + item["publication"] = { + "status": "accepted", + "validation_accepted": True, + "source_index": index, + "source_line": source_line, + "presentation_row_cap": cap, + } + selected.append(item) + if not selected: + raise ValueError(f"no accepted {workload} observation is at or below {cap:,}") + return selected + + +def _clickhouse_rows( + rows: Sequence[Mapping[str, Any]], + expected_queries: int, + *, + workload: str, + cap: int, +) -> list[dict[str, Any]]: + selected: list[dict[str, Any]] = [] + previous = -1 + for index, record in enumerate(rows): + raw_rows = _integer( + record.get("raw_rows"), f"ClickHouse {workload}[{index}].raw_rows" + ) + if raw_rows < previous: + raise ValueError(f"ClickHouse {workload} raw_rows is not monotonic") + previous = raw_rows + if raw_rows > cap: + continue + results = record.get("result") + if not isinstance(results, list) or len(results) != expected_queries: + raise ValueError( + f"ClickHouse {workload}[{index}] does not have " + f"{expected_queries} results" + ) + for query_index, value in enumerate(results, 1): + if not isinstance(value, list) or len(value) != 1: + raise ValueError( + f"ClickHouse {workload}[{index}] q{query_index} " + "is not a one-element trial" + ) + _number( + value[0], + f"ClickHouse {workload}[{index}].result[{query_index}]", + ) + item = copy.deepcopy(record) + item["_publication_source_index"] = index + item["_publication_source_line"] = item.pop("_publisher_source_line") + selected.append(item) + if not selected: + raise ValueError(f"ClickHouse {workload} has no records under the row cap") + return selected + + +def monotonic_nearest_matches( + left_rows: Sequence[Mapping[str, Any]], + right_rows: Sequence[Mapping[str, Any]], + *, + row_key: str = "raw_rows", +) -> list[dict[str, int]]: + """Match every left row to an ordered right subsequence with minimum row delta.""" + left = [_integer(row.get(row_key), f"left[{index}].{row_key}") for index, row in enumerate(left_rows)] + right = [_integer(row.get(row_key), f"right[{index}].{row_key}") for index, row in enumerate(right_rows)] + if not left or not right: + raise ValueError("both sides of monotonic matching must be nonempty") + if len(left) > len(right): + raise ValueError( + "ClickHouse has fewer eligible observations than Databricks for matching" + ) + if left != sorted(left) or right != sorted(right): + raise ValueError("matching inputs must be monotonic by observed raw rows") + + count_left, count_right = len(left), len(right) + infinity = sum(abs(a - b) for a in left for b in right) + 1 + costs = [[infinity] * (count_right + 1) for _ in range(count_left + 1)] + took = [[False] * (count_right + 1) for _ in range(count_left + 1)] + for j in range(count_right + 1): + costs[0][j] = 0 + for i in range(1, count_left + 1): + for j in range(1, count_right + 1): + skip = costs[i][j - 1] + take = costs[i - 1][j - 1] + abs(left[i - 1] - right[j - 1]) + # Keeping an equally good earlier subsequence gives a stable, + # lexicographically earliest tie-break. + if take < skip: + costs[i][j] = take + took[i][j] = True + else: + costs[i][j] = skip + if costs[count_left][count_right] >= infinity: + raise ValueError("no complete monotonic match exists") + pairs: list[tuple[int, int]] = [] + i, j = count_left, count_right + while i: + if j <= 0: + raise ValueError("no complete monotonic match exists") + if took[i][j]: + pairs.append((i - 1, j - 1)) + i -= 1 + j -= 1 + else: + j -= 1 + pairs.reverse() + return [ + { + "left_index": left_index, + "right_index": right_index, + "left_raw_rows": left[left_index], + "right_raw_rows": right[right_index], + "row_delta": right[right_index] - left[left_index], + "absolute_row_delta": abs(right[right_index] - left[left_index]), + } + for left_index, right_index in pairs + ] + + +def _match_workload( + databricks: list[dict[str, Any]], + clickhouse: list[dict[str, Any]], + *, + workload: str, +) -> tuple[list[dict[str, Any]], list[dict[str, Any]], list[dict[str, Any]]]: + matches = monotonic_nearest_matches(databricks, clickhouse) + matched_clickhouse: list[dict[str, Any]] = [] + report: list[dict[str, Any]] = [] + for match in matches: + db = databricks[match["left_index"]] + ch = clickhouse[match["right_index"]] + db_source = db["publication"] + ch_index = int(ch.pop("_publication_source_index")) + ch_line = int(ch.pop("_publication_source_line")) + details = { + "method": "minimum-total-absolute-row-delta ordered subsequence DP", + "workload": workload, + "databricks_source_index": db_source["source_index"], + "databricks_source_line": db_source["source_line"], + "clickhouse_source_index": ch_index, + "clickhouse_source_line": ch_line, + "databricks_raw_rows": match["left_raw_rows"], + "clickhouse_raw_rows": match["right_raw_rows"], + "row_delta_clickhouse_minus_databricks": match["row_delta"], + "absolute_row_delta": match["absolute_row_delta"], + } + db["publication"]["match"] = details + ch["publication"] = { + "status": "accepted", + "matched_to_databricks": True, + "match": details, + } + matched_clickhouse.append(ch) + report.append(details) + return databricks, matched_clickhouse, report + + +def _freshness_rows( + rows: Sequence[Mapping[str, Any]], + indices: Sequence[int], + *, + cap: int, +) -> list[dict[str, Any]]: + selected: list[dict[str, Any]] = [] + previous = -1 + for index in indices: + record = rows[index] + producer = record.get("producer_progress") + if not isinstance(producer, Mapping): + raise ValueError(f"freshness[{index}] omits producer_progress") + raw_rows = _integer( + producer.get("logical_raw_rows"), + f"freshness[{index}].producer_progress.logical_raw_rows", + ) + if raw_rows < previous: + raise ValueError("accepted freshness logical_raw_rows is not monotonic") + previous = raw_rows + if raw_rows > cap: + continue + lag = _number( + record.get("mv_refresh_age_sec"), + f"freshness[{index}].mv_refresh_age_sec", + ) + if record.get("errors") != []: + raise ValueError(f"freshness[{index}] contains errors") + observed = record.get("observed_at") + _timestamp(observed, f"freshness[{index}].observed_at") + refresh = record.get("latest_successful_refresh") + refresh_watermark = "" + if isinstance(refresh, Mapping): + refresh_watermark = str( + refresh.get("finished_at") + or refresh.get("source_timestamp") + or refresh.get("source_version") + or "" + ) + item = copy.deepcopy(record) + source_line = item.pop("_publisher_source_line") + item.update( + { + "provider": "databricks", + "system": PROVIDER_LABEL, + "raw_rows": raw_rows, + "raw_rows_source": "producer_progress.logical_raw_rows", + "base_table_rows": raw_rows, + "lag_seconds": lag, + "refresh_watermark": refresh_watermark, + "publication": { + "status": "accepted", + "validation_accepted": True, + "source_index": index, + "source_line": source_line, + "presentation_row_cap": cap, + }, + } + ) + selected.append(item) + if not selected: + raise ValueError(f"no accepted freshness observation is at or below {cap:,}") + return selected + + +def _merge_intervals( + intervals: Iterable[tuple[datetime, datetime]], +) -> list[tuple[datetime, datetime]]: + ordered = sorted((start, end) for start, end in intervals if end > start) + merged: list[list[datetime]] = [] + for start, end in ordered: + if not merged or start > merged[-1][1]: + merged.append([start, end]) + elif end > merged[-1][1]: + merged[-1][1] = end + return [(start, end) for start, end in merged] + + +def _intersect_intervals( + left: Sequence[tuple[datetime, datetime]], + right: Sequence[tuple[datetime, datetime]], +) -> list[tuple[datetime, datetime]]: + left_merged, right_merged = _merge_intervals(left), _merge_intervals(right) + result: list[tuple[datetime, datetime]] = [] + i = j = 0 + while i < len(left_merged) and j < len(right_merged): + start = max(left_merged[i][0], right_merged[j][0]) + end = min(left_merged[i][1], right_merged[j][1]) + if end > start: + result.append((start, end)) + if left_merged[i][1] <= right_merged[j][1]: + i += 1 + else: + j += 1 + return _merge_intervals(result) + + +def _microseconds(start: datetime, end: datetime) -> int: + return max(0, round((end - start).total_seconds() * 1_000_000)) + + +def _overlap_microseconds( + start: datetime, + end: datetime, + intervals: Sequence[tuple[datetime, datetime]], +) -> int: + return sum( + _microseconds(max(start, item_start), min(end, item_end)) + for item_start, item_end in _merge_intervals(intervals) + if min(end, item_end) > max(start, item_start) + ) + + +def _record_intervals( + dashboard: Sequence[Mapping[str, Any]], + drilldown: Sequence[Mapping[str, Any]], +) -> list[tuple[datetime, datetime]]: + intervals: list[tuple[datetime, datetime]] = [] + for workload, rows in (("dashboard", dashboard), ("drilldown", drilldown)): + for index, row in enumerate(rows): + start = _timestamp( + row.get("iteration_started_at"), + f"{workload}[{index}].iteration_started_at", + ) + end = _timestamp( + row.get("iteration_finished_at"), + f"{workload}[{index}].iteration_finished_at", + ) + if end <= start: + raise ValueError(f"{workload}[{index}] has an empty interval") + intervals.append((start, end)) + return _merge_intervals(intervals) + + +def allocate_matched_query_cost( + matched_lines: Sequence[Mapping[str, Any]], + matched_intervals: Sequence[tuple[datetime, datetime]], + *, + successful_query_intervals: Sequence[tuple[datetime, datetime]] | None = None, +) -> tuple[Decimal, list[dict[str, Any]]]: + """Prorate RT billing lines against an interval union exactly once.""" + matched_union = _merge_intervals(matched_intervals) + if not matched_union: + raise ValueError("matched query interval union is empty") + query_union = ( + _merge_intervals(successful_query_intervals) + if successful_query_intervals is not None + else None + ) + total = Decimal("0") + allocations: list[dict[str, Any]] = [] + for line_index, line in enumerate(matched_lines): + if line.get("category") != "primary_rt_serving_compute": + continue + interval = line.get("measured_interval") + if not isinstance(interval, Mapping): + raise ValueError(f"matched_lines[{line_index}] omits measured_interval") + start = _timestamp( + interval.get("start"), f"matched_lines[{line_index}].measured_interval.start" + ) + end = _timestamp( + interval.get("end"), f"matched_lines[{line_index}].measured_interval.end" + ) + if end <= start: + raise ValueError(f"matched_lines[{line_index}] has an empty interval") + amount = _decimal(line.get("list_cost"), f"matched_lines[{line_index}].list_cost") + if query_union is None: + allocated_us = _microseconds(start, end) + matched_us = _overlap_microseconds(start, end, matched_union) + else: + allocated_us = _overlap_microseconds(start, end, query_union) + matched_query_union = _intersect_intervals(query_union, matched_union) + matched_us = _overlap_microseconds(start, end, matched_query_union) + allocation = line.get("allocation") + if not isinstance(allocation, Mapping): + raise ValueError(f"matched_lines[{line_index}] omits allocation") + declared_us = _integer( + allocation.get("numerator_microseconds"), + f"matched_lines[{line_index}].allocation.numerator_microseconds", + ) + if declared_us != allocated_us: + raise ValueError( + f"matched_lines[{line_index}] allocation does not reconcile " + "to successful query interval union" + ) + if allocated_us <= 0: + raise ValueError(f"matched_lines[{line_index}] has no allocated duration") + fraction = Decimal(matched_us) / Decimal(allocated_us) + matched_amount = amount * fraction + total += matched_amount + allocations.append( + { + "matched_line_index": line_index, + "source_usage_row_index": line.get("usage_row_index"), + "currency": line.get("currency"), + "source_list_cost": str(line.get("list_cost")), + "allocated_microseconds": allocated_us, + "matched_union_overlap_microseconds": matched_us, + "matched_fraction": _decimal_text(fraction), + "matched_list_cost": _decimal_text(matched_amount), + } + ) + if not allocations: + raise ValueError("cost summary has no primary_rt_serving_compute matched lines") + return total, allocations + + +def _canonical_costs(cost: Mapping[str, Any]) -> tuple[str, Decimal, Decimal, Decimal]: + if cost.get("complete") is not True: + raise ValueError("cost summary complete is not exact true") + completeness = cost.get("completeness") + if not isinstance(completeness, Mapping) or completeness.get("complete") is not True: + raise ValueError("cost summary completeness.complete is not exact true") + primary = cost.get("canonical_undiscounted_primary_total") + fresh = cost.get("canonical_undiscounted_fresh_path_total") + serving = cost.get("canonical_undiscounted_query_serving_total") + if not all(isinstance(item, Mapping) for item in (primary, fresh, serving)): + raise ValueError("canonical primary/fresh-path/query-serving subledgers are missing") + primary_amount = _decimal(primary.get("amount"), "canonical primary total") + fresh_amount = _decimal(fresh.get("amount"), "canonical fresh-path total") + serving_amount = _decimal(serving.get("amount"), "canonical query-serving total") + currency = primary.get("currency") + if not isinstance(currency, str) or not currency: + raise ValueError("canonical primary total has no single currency") + if fresh.get("currency") != currency or serving.get("currency") != currency: + raise ValueError("canonical subledger currencies do not agree") + if min(primary_amount, fresh_amount, serving_amount) < 0: + raise ValueError("canonical subledger amounts must be nonnegative") + if fresh_amount + serving_amount != primary_amount: + raise ValueError("fresh-path and query-serving subledgers do not reconcile") + lines = cost.get("matched_lines") + if not isinstance(lines, list): + raise ValueError("cost summary matched_lines is missing") + primary_lines = [ + (index, line) + for index, line in enumerate(lines) + if isinstance(line, Mapping) + and line.get("category") == "primary_rt_serving_compute" + ] + if not primary_lines: + raise ValueError("query-serving subledger has no primary RT matched lines") + for index, line in primary_lines: + if line.get("currency") != currency: + raise ValueError(f"matched_lines[{index}] currency does not reconcile") + line_total = sum( + ( + _decimal(line.get("list_cost"), f"matched_lines[{index}].list_cost") + for index, line in primary_lines + ), + Decimal("0"), + ) + if line_total != serving_amount: + raise ValueError("primary RT matched lines do not reconcile to query-serving total") + return currency, primary_amount, fresh_amount, serving_amount + + +def _successful_query_intervals( + cost: Mapping[str, Any], +) -> list[tuple[datetime, datetime]]: + allocation = cost.get("query_serving_allocation") + if not isinstance(allocation, Mapping) or allocation.get("method") != "union_overlap": + raise ValueError("cost summary does not declare union-overlap query allocation") + values = allocation.get("successful_query_union_intervals") + if not isinstance(values, list) or not values: + raise ValueError("cost summary omits successful query union intervals") + intervals: list[tuple[datetime, datetime]] = [] + for index, item in enumerate(values): + if not isinstance(item, Mapping): + raise ValueError(f"query union interval {index} is not an object") + start = _timestamp(item.get("start"), f"query union interval {index}.start") + end = _timestamp(item.get("end"), f"query union interval {index}.end") + if end <= start: + raise ValueError(f"query union interval {index} is empty") + intervals.append((start, end)) + return _merge_intervals(intervals) + + +def _clickhouse_cost( + value: Mapping[str, Any], + reader_mem_gib: float, +) -> tuple[Decimal, Decimal, Decimal]: + if "clickhouse" not in str(value.get("system") or "").casefold(): + raise ValueError("ClickHouse ingest cost source has the wrong system") + costs = value.get("costs") + if not isinstance(costs, list): + raise ValueError("ClickHouse ingest cost source omits costs") + entries = [ + item + for item in costs + if isinstance(item, Mapping) + and str(item.get("tier") or "").casefold() == "enterprise" + ] + if len(entries) != 1: + raise ValueError("ClickHouse ingest cost must contain one Enterprise entry") + entry = entries[0] + fresh = _decimal( + entry.get("total_compute_cost_usd"), + "ClickHouse Enterprise complete-ingest cost", + ) + rate = _decimal( + entry.get("compute_price_per_8gib_hour"), + "ClickHouse Enterprise per-8-GiB-hour rate", + ) + memory = _decimal(reader_mem_gib, "ClickHouse reader memory GiB") + if fresh < 0 or rate <= 0 or memory <= 0: + raise ValueError("ClickHouse cost/rate/memory is invalid") + return fresh, rate, memory + + +def _runtime(rows: Sequence[Mapping[str, Any]], context: str) -> Decimal: + total = Decimal("0") + for row_index, row in enumerate(rows): + results = row.get("result") + if not isinstance(results, list): + raise ValueError(f"{context}[{row_index}] omits result") + for query_index, value in enumerate(results): + if not isinstance(value, list) or len(value) != 1: + raise ValueError( + f"{context}[{row_index}].result[{query_index}] is malformed" + ) + latency = _decimal( + value[0], f"{context}[{row_index}].result[{query_index}]" + ) + if latency < 0: + raise ValueError(f"{context} contains negative runtime") + total += latency + if total <= 0: + raise ValueError(f"{context} accumulated runtime is not positive") + return total + + +def _build_summaries( + *, + cost: Mapping[str, Any], + clickhouse_ingest: Mapping[str, Any], + databricks_dashboard: Sequence[Mapping[str, Any]], + databricks_drilldown: Sequence[Mapping[str, Any]], + clickhouse_dashboard: Sequence[Mapping[str, Any]], + clickhouse_drilldown: Sequence[Mapping[str, Any]], + matched_query_cost: Decimal, + matched_allocations: Sequence[Mapping[str, Any]], + reader_mem_gib: float, + sources: Mapping[str, Mapping[str, Any]], +) -> tuple[dict[str, Any], dict[str, Any]]: + currency, primary, db_fresh, full_db_query = _canonical_costs(cost) + if currency.upper() != "USD": + raise ValueError(f"global pairwise summaries require USD, found {currency!r}") + ch_fresh, ch_rate, ch_memory = _clickhouse_cost( + clickhouse_ingest, reader_mem_gib + ) + db_runtime = _runtime( + [*databricks_dashboard, *databricks_drilldown], "Databricks matched queries" + ) + ch_runtime = _runtime( + [*clickhouse_dashboard, *clickhouse_drilldown], "ClickHouse matched queries" + ) + ch_query = ch_runtime / Decimal("3600") * ch_memory / Decimal("8") * ch_rate + beta_matched_query = matched_query_cost * Decimal("0.70") + beta_primary = db_fresh + beta_matched_query + beta = cost.get("lakehouse_rt_beta_30_percent_off_scenario") + if ( + not isinstance(beta, Mapping) + or beta.get("discount_rate") != "0.30" + or beta.get("applies_only_to") != "primary_rt_serving_compute" + or beta.get("canonical_total_unchanged") is not True + or beta.get("canonical_undiscounted_primary_total") + != cost.get("canonical_undiscounted_primary_total") + ): + raise ValueError("cost summary omits the separate Lakehouse//RT Beta scenario") + + common_sources = copy.deepcopy(dict(sorted(sources.items()))) + fresh_summary = { + "schema_version": 1, + "status": "accepted", + "accepted": True, + "chart": "complete_ingest_fresh_data_path_cost_clickhouse_vs_databricks", + "scope": "complete ingestion; storage and read-query costs excluded", + "expected_full_rows": EXPECTED_ROWS, + "presentation_row_cap": PRESENTATION_ROW_CAP, + "costs": { + "clickhouse": { + "bundled_write_service_usd": float(ch_fresh), + "total_usd": float(ch_fresh), + }, + "databricks": { + "canonical_undiscounted_fresh_path_usd": float(db_fresh), + "total_usd": float(db_fresh), + "primary_category_totals": cost.get("primary_category_totals"), + }, + }, + "beta_scenario": { + "separate_from_canonical": True, + "discount_rate": "0.30", + "applies_only_to": "primary_rt_serving_compute", + "complete_ingest_fresh_path_usd_unchanged": float(db_fresh), + }, + "disclosure": ( + "The Databricks fresh-data-path cost charges the validated complete " + f"{EXPECTED_ROWS:,}-row ingest. The 100B cap applies only to chart " + "presentation observations." + ), + "sources": common_sources, + } + + rows = [ + { + "label": "ClickHouse", + "fresh_cost": float(ch_fresh), + "query_cost": float(ch_query), + "runtime_sec": float(ch_runtime), + }, + { + "label": PROVIDER_LABEL, + "fresh_cost": float(db_fresh), + "query_cost": float(matched_query_cost), + "runtime_sec": float(db_runtime), + }, + ] + for row in rows: + row["total_cost"] = row["fresh_cost"] + row["query_cost"] + row["score"] = row["total_cost"] * row["runtime_sec"] + baseline = rows[0]["score"] + if baseline <= 0: + raise ValueError("ClickHouse pairwise score is not positive") + for row in rows: + row["relative_to_clickhouse"] = row["score"] / baseline + performance_summary = { + "schema_version": 1, + "status": "accepted", + "accepted": True, + "chart": "full_path_cost_performance", + "formula": ( + "(fresh_data_path_cost_usd + matched_query_cost_usd) * " + "accumulated_query_runtime_sec" + ), + "lower_is_better": True, + "expected_full_rows": EXPECTED_ROWS, + "presentation_row_cap": PRESENTATION_ROW_CAP, + "rows": rows, + "databricks_query_cost_allocation": { + "method": ( + "prorated overlap of primary_rt_serving_compute billing allocations " + "with the union of matched Databricks iteration intervals" + ), + "estimate": True, + "concurrent_query_durations_double_counted": False, + "canonical_complete_window_query_serving_usd": float(full_db_query), + "matched_query_serving_usd": float(matched_query_cost), + "matched_lines": list(matched_allocations), + "disclosure": ( + "Estimated allocation: billing usage has no exact statement ID join. " + "Interval unions prevent dashboard/drilldown concurrency from being " + "charged more than once." + ), + }, + "clickhouse_query_cost": { + "method": ( + "matched accumulated runtime × reader GiB / 8 × Enterprise " + "per-8-GiB-hour rate" + ), + "reader_memory_gib": float(ch_memory), + "enterprise_price_per_8gib_hour_usd": float(ch_rate), + "matched_query_cost_usd": float(ch_query), + "source_sha256": common_sources["clickhouse_ingest_cost"]["sha256"], + }, + "lakehouse_rt_beta_30_percent_off_scenario": { + "separate_from_canonical": True, + "discount_rate": "0.30", + "applies_only_to": "matched primary_rt_serving_compute", + "matched_query_serving_usd": float(beta_matched_query), + "full_path_total_usd": float(beta_primary), + "canonical_rows_unchanged": True, + }, + "complete_ingest_cost_disclosure": ( + f"Both canonical fresh-path costs charge the supplied complete-ingest " + f"sources; Databricks charges all {EXPECTED_ROWS:,} validated rows. " + "Only query and freshness presentation records are capped at 100B rows." + ), + "sources": common_sources, + } + if primary != db_fresh + full_db_query: + raise ValueError("canonical primary cost changed while summaries were built") + return fresh_summary, performance_summary + + +def _validate_manifest_update( + manifest_path: Path, + output_dir: Path, +) -> dict[str, Any]: + try: + manifest_path.resolve().relative_to(GIT_ROOT) + except ValueError as exc: + raise ValueError("global manifest is outside the repository") from exc + manifest = _read_json(manifest_path, "global manifest") + providers = manifest.get("providers") + required = manifest.get("required_labels") + if not isinstance(providers, dict) or not isinstance(required, dict): + raise ValueError("global manifest omits providers or required_labels") + if "databricks" in providers: + raise ValueError("global manifest already contains providers.databricks") + for chart, labels in required.items(): + if not isinstance(labels, list): + raise ValueError(f"required_labels.{chart} is not a list") + if any( + isinstance(label, str) and label.casefold() == PROVIDER_LABEL.casefold() + for label in labels + ): + raise ValueError(f"required_labels.{chart} already contains Databricks") + if not isinstance(manifest.get("fresh_path_cost"), dict) or not isinstance( + manifest.get("cost_performance"), dict + ): + raise ValueError("global manifest omits cost source sections") + for section in ("fresh_path_cost", "cost_performance"): + if "databricks_pairwise_summary" in manifest[section]: + raise ValueError( + f"global manifest {section}.databricks_pairwise_summary conflicts" + ) + + updated = copy.deepcopy(manifest) + provider = { + "enabled": True, + "label": PROVIDER_LABEL, + "color": PROVIDER_COLOR, + "aggregate": _manifest_relative_path(output_dir / "accepted_dashboard.jsonl"), + "aggregate_queries": _manifest_relative_path(SCRIPT_DIR / "queries_mv.sql"), + "drilldown": _manifest_relative_path(output_dir / "accepted_drilldown.jsonl"), + "drilldown_queries": _manifest_relative_path(SCRIPT_DIR / "queries_raw.sql"), + "freshness": _manifest_relative_path(output_dir / "accepted_freshness.jsonl"), + "validation": _manifest_relative_path( + output_dir / SOURCE_OUTPUT_NAMES["validation_report"] + ), + "publication": _manifest_relative_path(output_dir / "publication_manifest.json"), + } + updated["providers"]["databricks"] = provider + chart_keys = ( + "aggregate_query_latency", + "drilldown_query_latency", + "mv_lag", + "fresh_path_cost", + "full_path_cost_performance", + "full_path_cost_vs_query_runtime", + ) + for chart in chart_keys: + labels = updated["required_labels"].get(chart) + if not isinstance(labels, list): + raise ValueError(f"global manifest omits required_labels.{chart}") + labels.append(PROVIDER_LABEL) + updated["fresh_path_cost"]["databricks_pairwise_summary"] = ( + _manifest_relative_path( + output_dir + / "ingest_fresh_path_cost_clickhouse_vs_databricks_summary.json" + ) + ) + updated["cost_performance"]["databricks_pairwise_summary"] = ( + _manifest_relative_path( + output_dir + / "full_path_cost_performance_clickhouse_vs_databricks_summary.json" + ) + ) + return updated + + +def _default_clickhouse_paths(manifest_path: Path) -> tuple[Path, Path]: + manifest = _read_json(manifest_path, "global manifest") + clickhouse = manifest.get("providers", {}).get("clickhouse") + if not isinstance(clickhouse, Mapping): + raise ValueError("global manifest omits providers.clickhouse") + try: + return ( + (BENCHMARK_ROOT / str(clickhouse["aggregate"])).resolve(), + (BENCHMARK_ROOT / str(clickhouse["drilldown"])).resolve(), + ) + except KeyError as exc: + raise ValueError("global manifest omits ClickHouse query sources") from exc + + +def publish( + *, + validation_report_path: Path, + cost_summary_path: Path, + dashboard_path: Path, + drilldown_path: Path, + freshness_path: Path, + clickhouse_dashboard_path: Path, + clickhouse_drilldown_path: Path, + clickhouse_ingest_cost_path: Path, + output_dir: Path, + clickhouse_reader_mem_gib: float = 64, + global_manifest_path: Path | None = None, + apply_global_manifest: bool = False, +) -> dict[str, Any]: + paths = { + "validation_report": validation_report_path.expanduser().resolve(), + "cost_summary": cost_summary_path.expanduser().resolve(), + "databricks_dashboard": dashboard_path.expanduser().resolve(), + "databricks_drilldown": drilldown_path.expanduser().resolve(), + "databricks_freshness": freshness_path.expanduser().resolve(), + "clickhouse_dashboard": clickhouse_dashboard_path.expanduser().resolve(), + "clickhouse_drilldown": clickhouse_drilldown_path.expanduser().resolve(), + "clickhouse_ingest_cost": clickhouse_ingest_cost_path.expanduser().resolve(), + } + destination = output_dir.expanduser().resolve() + if destination.exists() and not destination.is_dir(): + raise FileExistsError(f"publisher output directory is a file: {destination}") + targets = {name: destination / name for name in OUTPUT_NAMES} + existing = [path for path in targets.values() if path.exists()] + if existing: + raise FileExistsError( + "publisher never overwrites target output: " + + ", ".join(str(path) for path in existing) + ) + input_contents: dict[str, bytes] = {} + for name, path in paths.items(): + try: + input_contents[name] = path.read_bytes() + except OSError as exc: + raise ValueError(f"cannot snapshot {name} at {path}: {exc}") from exc + source_descriptors = { + name: { + "path": _portable_path(destination / SOURCE_OUTPUT_NAMES[name]), + "sha256": hashlib.sha256(content).hexdigest(), + } + for name, content in sorted(input_contents.items()) + } + if apply_global_manifest and global_manifest_path is None: + raise ValueError("--apply-global-manifest requires --global-manifest") + manifest_path = ( + global_manifest_path.expanduser().resolve() + if global_manifest_path is not None + else None + ) + + validation = _parse_json( + input_contents["validation_report"], + paths["validation_report"], + "validation report", + ) + cost = _parse_json( + input_contents["cost_summary"], + paths["cost_summary"], + "cost summary", + ) + if validation.get("accepted") is not True: + raise ValueError("validation report accepted is not exact true") + configuration = validation.get("configuration") + if not isinstance(configuration, Mapping) or _integer( + configuration.get("expected_rows"), "validation expected_rows" + ) != EXPECTED_ROWS: + raise ValueError(f"validation expected_rows must be exactly {EXPECTED_ROWS}") + _canonical_costs(cost) + declared = _validation_input_paths(validation) + for key, path_key in ( + ("dashboard", "databricks_dashboard"), + ("drilldown", "databricks_drilldown"), + ("freshness", "databricks_freshness"), + ("cost_summary", "cost_summary"), + ): + _require_declared_input( + declared, key, paths[path_key], paths["validation_report"] + ) + + dashboard = _parse_jsonl( + input_contents["databricks_dashboard"], + paths["databricks_dashboard"], + "Databricks dashboard", + ) + drilldown = _parse_jsonl( + input_contents["databricks_drilldown"], + paths["databricks_drilldown"], + "Databricks drilldown", + ) + freshness = _parse_jsonl( + input_contents["databricks_freshness"], + paths["databricks_freshness"], + "Databricks freshness", + ) + dashboard_indices = _accepted_indices(validation, "dashboard", len(dashboard)) + drilldown_indices = _accepted_indices(validation, "drilldown", len(drilldown)) + freshness_indices = _accepted_indices(validation, "freshness", len(freshness)) + accepted_dashboard = _source_rows( + dashboard, + dashboard_indices, + 4, + workload="dashboard", + cap=PRESENTATION_ROW_CAP, + ) + accepted_drilldown = _source_rows( + drilldown, + drilldown_indices, + 2, + workload="drilldown", + cap=PRESENTATION_ROW_CAP, + ) + accepted_freshness = _freshness_rows( + freshness, freshness_indices, cap=PRESENTATION_ROW_CAP + ) + + clickhouse_dashboard = _clickhouse_rows( + _parse_jsonl( + input_contents["clickhouse_dashboard"], + paths["clickhouse_dashboard"], + "ClickHouse dashboard", + ), + 4, + workload="dashboard", + cap=PRESENTATION_ROW_CAP, + ) + clickhouse_drilldown = _clickhouse_rows( + _parse_jsonl( + input_contents["clickhouse_drilldown"], + paths["clickhouse_drilldown"], + "ClickHouse drilldown", + ), + 2, + workload="drilldown", + cap=PRESENTATION_ROW_CAP, + ) + accepted_dashboard, matched_ch_dashboard, dashboard_matches = _match_workload( + accepted_dashboard, clickhouse_dashboard, workload="dashboard" + ) + accepted_drilldown, matched_ch_drilldown, drilldown_matches = _match_workload( + accepted_drilldown, clickhouse_drilldown, workload="drilldown" + ) + + matched_intervals = _record_intervals(accepted_dashboard, accepted_drilldown) + query_union = _successful_query_intervals(cost) + lines = cost.get("matched_lines") + if not isinstance(lines, list): + raise ValueError("cost summary matched_lines is missing") + matched_query_cost, matched_allocations = allocate_matched_query_cost( + lines, + matched_intervals, + successful_query_intervals=query_union, + ) + clickhouse_ingest = _parse_json( + input_contents["clickhouse_ingest_cost"], + paths["clickhouse_ingest_cost"], + "ClickHouse complete-ingest cost", + ) + fresh_summary, performance_summary = _build_summaries( + cost=cost, + clickhouse_ingest=clickhouse_ingest, + databricks_dashboard=accepted_dashboard, + databricks_drilldown=accepted_drilldown, + clickhouse_dashboard=matched_ch_dashboard, + clickhouse_drilldown=matched_ch_drilldown, + matched_query_cost=matched_query_cost, + matched_allocations=matched_allocations, + reader_mem_gib=clickhouse_reader_mem_gib, + sources=source_descriptors, + ) + + updated_manifest = None + if apply_global_manifest: + if manifest_path is None: + raise ValueError("--apply-global-manifest requires --global-manifest") + updated_manifest = _validate_manifest_update( + manifest_path, destination + ) + elif manifest_path is not None: + try: + manifest_path.relative_to(GIT_ROOT) + except ValueError as exc: + raise ValueError("global manifest is outside the repository") from exc + + contents: dict[str, bytes] = { + "accepted_dashboard.jsonl": _jsonl_bytes(accepted_dashboard), + "accepted_drilldown.jsonl": _jsonl_bytes(accepted_drilldown), + "accepted_freshness.jsonl": _jsonl_bytes(accepted_freshness), + "matched_clickhouse_dashboard.jsonl": _jsonl_bytes(matched_ch_dashboard), + "matched_clickhouse_drilldown.jsonl": _jsonl_bytes(matched_ch_drilldown), + "ingest_fresh_path_cost_clickhouse_vs_databricks_summary.json": _json_bytes( + fresh_summary + ), + "full_path_cost_performance_clickhouse_vs_databricks_summary.json": _json_bytes( + performance_summary + ), + **{ + SOURCE_OUTPUT_NAMES[name]: content + for name, content in input_contents.items() + }, + } + artifact_counts = { + "accepted_dashboard.jsonl": len(accepted_dashboard), + "accepted_drilldown.jsonl": len(accepted_drilldown), + "accepted_freshness.jsonl": len(accepted_freshness), + "matched_clickhouse_dashboard.jsonl": len(matched_ch_dashboard), + "matched_clickhouse_drilldown.jsonl": len(matched_ch_drilldown), + } + publication = { + "schema_version": 1, + "status": "accepted", + "accepted": True, + "provider": "databricks", + "label": PROVIDER_LABEL, + "color": PROVIDER_COLOR, + "expected_full_rows": EXPECTED_ROWS, + "presentation_row_cap": PRESENTATION_ROW_CAP, + "sources": source_descriptors, + "artifacts": { + name: { + "path": name, + "sha256": hashlib.sha256(content).hexdigest(), + **( + {"records": artifact_counts[name]} + if name in artifact_counts + else {} + ), + } + for name, content in sorted(contents.items()) + }, + "matching": { + "method": "deterministic order-preserving minimum-total-absolute-row-delta DP", + "iteration_numbers_used_for_join": False, + "dashboard": dashboard_matches, + "drilldown": drilldown_matches, + }, + "cost": { + "matched_databricks_query_serving_usd": float(matched_query_cost), + "allocation_is_estimate": True, + "query_duration_concurrency_double_counted": False, + "fresh_path_uses_complete_ingest": True, + "beta_scenario_is_separate": True, + }, + "disclosure": ( + f"The complete validated Databricks ingest contains {EXPECTED_ROWS:,} " + f"rows and remains fully charged. Query and freshness presentation " + f"records are capped at {PRESENTATION_ROW_CAP:,} raw rows." + ), + "global_manifest": { + "path": _portable_path(manifest_path) if manifest_path else None, + "applied": apply_global_manifest, + }, + } + publication_content = _json_bytes(publication) + + for name, content in contents.items(): + _atomic_write(targets[name], content) + _atomic_write(targets["publication_manifest.json"], publication_content) + if updated_manifest is not None: + if manifest_path is None: + raise ValueError("global manifest path disappeared before activation") + _atomic_write(manifest_path, _json_bytes(updated_manifest)) + return publication + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--validation-report", type=Path, required=True) + parser.add_argument("--cost-summary", type=Path, required=True) + parser.add_argument("--dashboard", "--databricks-dashboard", dest="dashboard", type=Path, required=True) + parser.add_argument("--drilldown", "--databricks-drilldown", dest="drilldown", type=Path, required=True) + parser.add_argument("--freshness", "--databricks-freshness", dest="freshness", type=Path, required=True) + parser.add_argument("--clickhouse-dashboard", type=Path) + parser.add_argument("--clickhouse-drilldown", type=Path) + parser.add_argument("--clickhouse-ingest-cost", type=Path, required=True) + parser.add_argument("--clickhouse-reader-mem-gib", type=float, default=64) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--global-manifest", type=Path) + parser.add_argument("--apply-global-manifest", action="store_true") + return parser + + +def run(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + manifest_for_defaults = ( + args.global_manifest.expanduser().resolve() + if args.global_manifest is not None + else DEFAULT_GLOBAL_MANIFEST + ) + default_dashboard, default_drilldown = _default_clickhouse_paths( + manifest_for_defaults + ) + publication = publish( + validation_report_path=args.validation_report, + cost_summary_path=args.cost_summary, + dashboard_path=args.dashboard, + drilldown_path=args.drilldown, + freshness_path=args.freshness, + clickhouse_dashboard_path=args.clickhouse_dashboard or default_dashboard, + clickhouse_drilldown_path=args.clickhouse_drilldown or default_drilldown, + clickhouse_ingest_cost_path=args.clickhouse_ingest_cost, + output_dir=args.output_dir, + clickhouse_reader_mem_gib=args.clickhouse_reader_mem_gib, + global_manifest_path=args.global_manifest, + apply_global_manifest=args.apply_global_manifest, + ) + print( + "Published accepted Databricks result: " + f"{len(publication['artifacts'])} hashed artifacts" + ) + return 0 + + +def main(argv: Sequence[str] | None = None) -> int: + try: + return run(argv) + except KeyboardInterrupt: + return 130 + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/full-path-realtime/quotes/databricks/qualify.py b/full-path-realtime/quotes/databricks/qualify.py new file mode 100755 index 0000000..176a69e --- /dev/null +++ b/full-path-realtime/quotes/databricks/qualify.py @@ -0,0 +1,2609 @@ +#!/usr/bin/env python3 +"""Plan and evaluate the Databricks tune-endurance qualification matrix. + +The planner is intentionally offline. It records commands and evidence paths, +but it never reads credentials or claims that an online command ran. The +evaluator is also offline and fails closed when evidence is absent or +incomplete. +""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +import re +import shlex +import sys +import tempfile +import urllib.parse +from pathlib import Path +from typing import Any, Mapping, Sequence + +SCHEMA_VERSION = 1 +TASK = "tune-endurance" +CORRECTNESS_ROWS = 10_000_000 +CAPACITY_TARGETS = (100_000, 500_000, 1_000_000) +CAPACITY_DURATION_SECONDS = 75 +METRICS_INTERVAL_SECONDS = 5 +MIN_CAPACITY_INTERVALS = 10 +DEFAULT_TOLERANCE = 0.10 +DEFAULT_MAX_PRODUCER_CPU_PERCENT = 90.0 +DEFAULT_MAX_PRODUCER_NETWORK_PERCENT = 80.0 +DEFAULT_ENDURANCE_SECONDS = 1_800 +MIN_ENDURANCE_SECONDS = 1_800 +MAX_ENDURANCE_SECONDS = 3_600 +REQUIRED_CASE_IDS = ( + "correctness-10m", + "capacity-100k", + "capacity-500k", + "capacity-1m", + "endurance-1m", +) +PAT_VALUE = re.compile(r"\bdapi[A-Za-z0-9_-]{8,}\b") +AUTH_VALUE = re.compile(r"(?i)\b(?:Bearer|Basic)\s+[A-Za-z0-9._~+/=-]{8,}") + + +def _atomic_write(path: Path, data: bytes, *, mode: int | None = None) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + fd, temporary = tempfile.mkstemp(prefix=f".{path.name}.", dir=path.parent) + try: + with os.fdopen(fd, "wb") as handle: + handle.write(data) + handle.flush() + os.fsync(handle.fileno()) + if mode is not None: + os.chmod(temporary, mode) + os.replace(temporary, path) + finally: + try: + os.unlink(temporary) + except FileNotFoundError: + pass + + +def atomic_write_json(path: Path, value: Mapping[str, Any]) -> None: + rendered = json.dumps( + value, + indent=2, + sort_keys=True, + ensure_ascii=False, + allow_nan=False, + ) + _atomic_write(path, (rendered + "\n").encode("utf-8")) + + +def _json_digest(value: Any) -> str: + encoded = json.dumps( + value, + sort_keys=True, + separators=(",", ":"), + ensure_ascii=False, + allow_nan=False, + ).encode("utf-8") + return hashlib.sha256(encoded).hexdigest() + + +def _reject_embedded_credentials(value: Any) -> None: + rendered = json.dumps(value, ensure_ascii=False) + if PAT_VALUE.search(rendered) or AUTH_VALUE.search(rendered): + raise ValueError( + "qualification plans must reference credential environment variables, " + "not embed credential values" + ) + + +def _safe_identifier(value: str, label: str, *, allow_hyphen: bool = True) -> str: + pattern = r"[A-Za-z0-9_-]+" if allow_hyphen else r"[A-Za-z0-9_]+" + if not re.fullmatch(pattern, value): + allowed = "letters, digits, '_' and '-'" if allow_hyphen else "letters, digits, and '_'" + raise ValueError(f"{label} must contain only ASCII {allowed}") + return value + + +def _https_origin(value: str, label: str) -> str: + parsed = urllib.parse.urlparse(value) + if ( + parsed.scheme != "https" + or not parsed.hostname + or parsed.username + or parsed.password + or parsed.query + or parsed.fragment + or parsed.path not in ("", "/") + ): + raise ValueError(f"{label} must be a credential-free HTTPS origin") + return value.rstrip("/") + + +def _positive_int(value: Any, label: str) -> int: + if isinstance(value, bool): + raise ValueError(f"{label} must be a positive integer") + try: + parsed = int(value) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError(f"{label} must be a positive integer") from exc + if parsed <= 0: + raise ValueError(f"{label} must be a positive integer") + return parsed + + +def _finite_positive(value: Any, label: str) -> float: + try: + parsed = float(value) + except (TypeError, ValueError, OverflowError) as exc: + raise ValueError(f"{label} must be finite and positive") from exc + if not math.isfinite(parsed) or parsed <= 0: + raise ValueError(f"{label} must be finite and positive") + return parsed + + +def _slug(value: str) -> str: + result = re.sub(r"[^A-Za-z0-9]+", "_", value).strip("_").lower() + if not result: + raise ValueError(f"query size has no usable identifier characters: {value!r}") + return result + + +def _artifact_paths( + case_dir: Path, + *, + full: bool, + query_workloads: bool = False, +) -> dict[str, str]: + ingest = case_dir / "ingest" + result = { + "preflight": str(case_dir / "preflight_report.json"), + "source_manifest": str(ingest / "source_manifest.json"), + "ingest_progress": str(ingest / "ingest_progress.json"), + "ingest_metrics": str(ingest / "ingest_metrics.jsonl"), + "ingest_summary": str(ingest / "ingest_summary.json"), + } + if full or query_workloads: + result.update( + { + "dashboard": str(case_dir / "dashboard.jsonl"), + "drilldown": str(case_dir / "drilldown.jsonl"), + "freshness": str(case_dir / "freshness.jsonl"), + "freshness_progress": str(case_dir / "freshness_progress.json"), + } + ) + if full: + result.update( + { + "evidence_dir": str(case_dir / "evidence"), + "evidence_summary": str(case_dir / "evidence" / "evidence_summary.json"), + "cost_summary": str(case_dir / "cost_summary.json"), + "validation_report": str(case_dir / "validation_report.json"), + } + ) + return result + + +def _command(step: str, argv: Sequence[str], **extra: Any) -> dict[str, Any]: + return {"step": step, "argv": list(argv), **extra} + + +def _preflight_command( + python: str, + zerobus_python: str, + script_dir: Path, + artifacts: Mapping[str, str], + *, + host: str, + endpoint: str, + catalog: str, + schema: str, + raw_table: str, + mv_table: str, + rt_warehouse_id: str, + control_warehouse_id: str, +) -> dict[str, Any]: + return _command( + "online-preflight", + ( + python, + str(script_dir / "preflight.py"), + "--online", + "--output", + artifacts["preflight"], + "--runner-python", + python, + "--zerobus-python", + zerobus_python, + "--host", + host, + "--catalog", + catalog, + "--schema", + schema, + "--raw-table", + raw_table, + "--mv-table", + mv_table, + "--rt-warehouse", + rt_warehouse_id, + "--control-warehouse", + control_warehouse_id, + "--zerobus-endpoint", + endpoint, + ), + credential_environment=[ + "DATABRICKS_TOKEN", + "DATABRICKS_CLIENT_ID", + "DATABRICKS_CLIENT_SECRET", + ], + ) + + +def _ingest_command( + python: str, + script_dir: Path, + artifacts: Mapping[str, str], + *, + data_dir: Path, + host: str, + endpoint: str, + catalog: str, + schema: str, + raw_table: str, + control_warehouse_id: str, + run_id: str, + expected_rows: int, + target_eps: int, + producer: Mapping[str, Any], +) -> dict[str, Any]: + return _command( + "zerobus-ingest", + ( + python, + str(script_dir / "ingest_zerobus.py"), + "--dir", + str(data_dir), + "--host", + host, + "--endpoint", + endpoint, + "--catalog", + catalog, + "--schema", + schema, + "--table", + raw_table, + "--count-warehouse", + control_warehouse_id, + "--workers", + str(producer["stream_count"]), + "--batch-size", + str(producer["batch_size"]), + "--queue-capacity", + str(producer["queue_capacity"]), + "--target-eps", + str(target_eps), + "--compression", + str(producer["compression"]), + "--metrics-interval", + str(producer["metrics_interval_seconds"]), + "--max-rows", + str(expected_rows), + "--expected-rows", + str(expected_rows), + "--allow-partial", + "--run-id", + run_id, + "--output-dir", + str(Path(artifacts["ingest_progress"]).parent), + ), + credential_environment=[ + "DATABRICKS_TOKEN", + "DATABRICKS_CLIENT_ID", + "DATABRICKS_CLIENT_SECRET", + ], + concurrency_group="measured-run", + ) + + +def _runner_command( + python: str, + script_dir: Path, + workload: str, + artifacts: Mapping[str, str], + *, + host: str, + catalog: str, + schema: str, + mv_table: str, + rt_warehouse_id: str, + control_warehouse_id: str, + run_id: str, + interval: int, + iterations: int, + query_size: str, +) -> dict[str, Any]: + argv = [ + python, + str(script_dir / f"run_{workload}.py"), + "--host", + host, + "--rt-warehouse-id", + rt_warehouse_id, + "--control-warehouse-id", + control_warehouse_id, + "--catalog", + catalog, + "--schema", + schema, + "--mv-table", + mv_table, + "--producer-progress", + artifacts["ingest_progress"], + "--run-id", + run_id, + "--interval", + str(interval), + "--iterations", + str(iterations), + "--machine", + query_size, + "--output", + artifacts[workload], + ] + freshness_progress = artifacts.get("freshness_progress") + if freshness_progress: + argv.extend(("--freshness-monitor", freshness_progress)) + return _command( + f"{workload}-runner", + argv, + credential_environment=[ + "DATABRICKS_TOKEN", + "DATABRICKS_CLIENT_ID", + "DATABRICKS_CLIENT_SECRET", + ], + concurrency_group="measured-run", + ) + + +def _freshness_command( + python: str, + script_dir: Path, + artifacts: Mapping[str, str], + *, + host: str, + catalog: str, + schema: str, + raw_table: str, + mv_table: str, + control_warehouse_id: str, + run_id: str, + iterations: int, +) -> dict[str, Any]: + return _command( + "freshness-monitor", + ( + python, + str(script_dir / "monitor_freshness.py"), + "--host", + host, + "--control-warehouse-id", + control_warehouse_id, + "--catalog", + catalog, + "--schema", + schema, + "--raw-table", + raw_table, + "--mv-table", + mv_table, + "--producer-progress", + artifacts["ingest_progress"], + "--since", + "${CASE_SINCE_UTC:?set CASE_SINCE_UTC}", + "--interval", + "30", + "--iterations", + str(iterations), + "--run-id", + run_id, + "--output", + artifacts["freshness"], + "--progress-json", + artifacts["freshness_progress"], + ), + credential_environment=[ + "DATABRICKS_TOKEN", + "DATABRICKS_CLIENT_ID", + "DATABRICKS_CLIENT_SECRET", + ], + concurrency_group="measured-run", + ) + + +def _post_run_commands( + python: str, + script_dir: Path, + artifacts: Mapping[str, str], + *, + host: str, + catalog: str, + schema: str, + raw_table: str, + mv_table: str, + rt_warehouse_id: str, + control_warehouse_id: str, + run_id: str, + expected_rows: int, + eps_min: float, + eps_max: float, +) -> list[dict[str, Any]]: + evidence = _command( + "collect-evidence", + ( + python, + str(script_dir / "collect_evidence.py"), + "--host", + host, + "--control-warehouse-id", + control_warehouse_id, + "--rt-warehouse-id", + rt_warehouse_id, + "--catalog", + catalog, + "--schema", + schema, + "--raw-table", + raw_table, + "--mv-table", + mv_table, + "--since", + "${CASE_SINCE_UTC:?set CASE_SINCE_UTC}", + "--until", + "${CASE_UNTIL_UTC:?set CASE_UNTIL_UTC}", + "--run-id", + run_id, + "--output-dir", + artifacts["evidence_dir"], + ), + credential_environment=[ + "DATABRICKS_TOKEN", + "DATABRICKS_CLIENT_ID", + "DATABRICKS_CLIENT_SECRET", + ], + ) + cost = _command( + "summarize-cost", + ( + python, + str(script_dir / "costs" / "summarize_run.py"), + "--evidence-dir", + artifacts["evidence_dir"], + "--since", + "${CASE_SINCE_UTC:?set CASE_SINCE_UTC}", + "--until", + "${CASE_UNTIL_UTC:?set CASE_UNTIL_UTC}", + "--rt-warehouse-id", + rt_warehouse_id, + "--control-warehouse-id", + control_warehouse_id, + "--output", + artifacts["cost_summary"], + ), + ) + validate = _command( + "validate-run", + ( + python, + str(script_dir / "validate_run.py"), + "--source-manifest", + artifacts["source_manifest"], + "--ingest-progress", + artifacts["ingest_progress"], + "--ingest-metrics", + artifacts["ingest_metrics"], + "--dashboard", + artifacts["dashboard"], + "--drilldown", + artifacts["drilldown"], + "--freshness", + artifacts["freshness"], + "--evidence-summary", + artifacts["evidence_summary"], + "--cost-summary", + artifacts["cost_summary"], + "--expected-rows", + str(expected_rows), + "--eps-min", + format(eps_min, ".12g"), + "--eps-max", + format(eps_max, ".12g"), + "--output", + artifacts["validation_report"], + ), + ) + return [evidence, cost, validate] + + +def _case_common( + case_id: str, + *, + run_id: str, + schema: str, + raw_table: str, + mv_table: str, + artifacts: Mapping[str, str], + commands: Sequence[Mapping[str, Any]], + acceptance: Mapping[str, Any], +) -> dict[str, Any]: + return { + "id": case_id, + "state": "planned", + "run_id": run_id, + "target": { + "schema": schema, + "raw_table": raw_table, + "materialized_view": mv_table, + }, + "fresh_target_required": True, + "destructive_ddl_warning": ( + "create.sql drops the target materialized view and raw table. Apply it " + "before this case to reset the dedicated qualification schema. Never " + "run qualification cases concurrently." + ), + "preconditions": [ + "Provision or select the declared warehouse configuration while no measured run is live.", + "Apply create.sql to reset the dedicated qualification schema after reviewing its DROP statements.", + "Set CASE_SINCE_UTC and CASE_UNTIL_UTC for commands that reference the bounded run window.", + "Keep credentials only in DATABRICKS_TOKEN or DATABRICKS_CLIENT_ID/DATABRICKS_CLIENT_SECRET.", + ], + "artifacts": dict(artifacts), + "commands": [dict(command) for command in commands], + "acceptance": dict(acceptance), + } + + +def build_plan( + *, + output_dir: Path, + repo_dir: Path, + data_dir: Path, + catalog: str, + schema: str, + raw_table: str, + mv_table: str, + rt_warehouse_id: str, + control_warehouse_id: str, + producer_host_description: str, + producer_network_capacity_gbps: float, + host: str, + endpoint: str, + stream_count: int, + batch_size: int, + queue_capacity: int, + compression: str, + query_size_candidates: Sequence[str], + capacity_tolerance: float = DEFAULT_TOLERANCE, + capacity_duration_seconds: int = CAPACITY_DURATION_SECONDS, + metrics_interval_seconds: int = METRICS_INTERVAL_SECONDS, + max_producer_cpu_percent: float = DEFAULT_MAX_PRODUCER_CPU_PERCENT, + max_producer_network_percent: float = DEFAULT_MAX_PRODUCER_NETWORK_PERCENT, + endurance_duration_seconds: int = DEFAULT_ENDURANCE_SECONDS, + run_prefix: str = "tune_endurance", + python: str = "python3", + producer_python: str | None = None, +) -> dict[str, Any]: + """Return one deterministic, secret-free qualification plan.""" + output_dir = output_dir.expanduser().resolve() + repo_dir = repo_dir.expanduser().resolve() + data_dir = data_dir.expanduser().resolve() + script_dir = repo_dir / "full-path-realtime" / "quotes" / "databricks" + for path, label in ((repo_dir, "repo_dir"), (data_dir, "data_dir")): + if not path.is_absolute(): + raise ValueError(f"{label} must resolve to an absolute path") + catalog = _safe_identifier(catalog, "catalog") + schema = _safe_identifier(schema, "schema") + raw_table = _safe_identifier(raw_table, "raw table", allow_hyphen=False) + mv_table = _safe_identifier(mv_table, "materialized-view table") + run_prefix = _safe_identifier(run_prefix, "run prefix") + if not rt_warehouse_id or not control_warehouse_id: + raise ValueError("RT and control warehouse IDs are required") + if rt_warehouse_id == control_warehouse_id: + raise ValueError("RT and control warehouse IDs must differ") + if not producer_host_description.strip(): + raise ValueError("producer host description is required") + producer_network_capacity_gbps = _finite_positive( + producer_network_capacity_gbps, "producer network capacity Gbps" + ) + max_producer_cpu_percent = _finite_positive( + max_producer_cpu_percent, "maximum producer CPU percent" + ) + max_producer_network_percent = _finite_positive( + max_producer_network_percent, "maximum producer network percent" + ) + if max_producer_cpu_percent > 100 or max_producer_network_percent > 100: + raise ValueError("producer CPU and network thresholds cannot exceed 100 percent") + host = _https_origin(host, "host") + endpoint = _https_origin(endpoint, "endpoint") + stream_count = _positive_int(stream_count, "stream count") + batch_size = _positive_int(batch_size, "batch size") + queue_capacity = _positive_int(queue_capacity, "queue capacity") + metrics_interval_seconds = _positive_int( + metrics_interval_seconds, "metrics interval" + ) + capacity_duration_seconds = _positive_int( + capacity_duration_seconds, "capacity duration" + ) + tolerance = _finite_positive(capacity_tolerance, "capacity tolerance") + if tolerance >= 1: + raise ValueError("capacity tolerance must be less than 1") + duration = _positive_int(endurance_duration_seconds, "endurance duration") + if not MIN_ENDURANCE_SECONDS <= duration <= MAX_ENDURANCE_SECONDS: + raise ValueError("endurance duration must be between 1800 and 3600 seconds") + compression = compression.upper().replace("-", "_") + if compression not in {"NONE", "LZ4_FRAME", "ZSTD"}: + raise ValueError("compression must be NONE, LZ4_FRAME, or ZSTD") + candidates = [ + part.strip() + for value in query_size_candidates + for part in str(value).split(",") + if part.strip() + ] + if not candidates or any(not value for value in candidates): + raise ValueError("at least one non-empty query-size candidate is required") + if len(set(candidates)) != len(candidates): + raise ValueError("query-size candidates must be unique") + if capacity_duration_seconds / metrics_interval_seconds < MIN_CAPACITY_INTERVALS + 1: + raise ValueError( + "capacity duration must allow one warm-up plus at least 10 metric intervals" + ) + producer_python = producer_python or python + + producer = { + "host_description": producer_host_description.strip(), + "stream_count": stream_count, + "batch_size": batch_size, + "queue_capacity": queue_capacity, + "compression": compression, + "metrics_interval_seconds": metrics_interval_seconds, + "network_capacity_gbps": producer_network_capacity_gbps, + "maximum_host_cpu_percent": max_producer_cpu_percent, + "maximum_network_utilization_percent": max_producer_network_percent, + } + matrix_dir = output_dir / "cases" + cases: list[dict[str, Any]] = [] + + correctness_id = "correctness-10m" + correctness_schema = schema + correctness_dir = matrix_dir / correctness_id + correctness_artifacts = _artifact_paths(correctness_dir, full=True) + correctness_run = f"{run_prefix}_correctness_10m" + lower = CAPACITY_TARGETS[0] * (1 - tolerance) + upper = CAPACITY_TARGETS[0] * (1 + tolerance) + correctness_commands = [ + _preflight_command( + python, + producer_python, + script_dir, + correctness_artifacts, + host=host, + endpoint=endpoint, + catalog=catalog, + schema=correctness_schema, + raw_table=raw_table, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + ), + _ingest_command( + producer_python, + script_dir, + correctness_artifacts, + data_dir=data_dir, + host=host, + endpoint=endpoint, + catalog=catalog, + schema=correctness_schema, + raw_table=raw_table, + control_warehouse_id=control_warehouse_id, + run_id=correctness_run, + expected_rows=CORRECTNESS_ROWS, + target_eps=CAPACITY_TARGETS[0], + producer=producer, + ), + _freshness_command( + python, + script_dir, + correctness_artifacts, + host=host, + catalog=catalog, + schema=correctness_schema, + raw_table=raw_table, + mv_table=mv_table, + control_warehouse_id=control_warehouse_id, + run_id=correctness_run, + iterations=4, + ), + _runner_command( + python, + script_dir, + "dashboard", + correctness_artifacts, + host=host, + catalog=catalog, + schema=correctness_schema, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + run_id=correctness_run, + interval=600, + iterations=1, + query_size=candidates[0], + ), + _runner_command( + python, + script_dir, + "drilldown", + correctness_artifacts, + host=host, + catalog=catalog, + schema=correctness_schema, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + run_id=correctness_run, + interval=3600, + iterations=1, + query_size=candidates[0], + ), + *_post_run_commands( + python, + script_dir, + correctness_artifacts, + host=host, + catalog=catalog, + schema=correctness_schema, + raw_table=raw_table, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + run_id=correctness_run, + expected_rows=CORRECTNESS_ROWS, + eps_min=lower, + eps_max=upper, + ), + ] + cases.append( + _case_common( + correctness_id, + run_id=correctness_run, + schema=correctness_schema, + raw_table=raw_table, + mv_table=mv_table, + artifacts=correctness_artifacts, + commands=correctness_commands, + acceptance={ + "validation_accepted": True, + "expected_rows": CORRECTNESS_ROWS, + "exact_source_cap_required": "--max-rows 10000000", + "target_eps": CAPACITY_TARGETS[0], + }, + ) + ) + + for target, label in zip(CAPACITY_TARGETS, ("100k", "500k", "1m")): + case_id = f"capacity-{label}" + case_schema = schema + case_dir = matrix_dir / case_id + artifacts = _artifact_paths(case_dir, full=False) + run_id = f"{run_prefix}_capacity_{label}" + expected_rows = target * capacity_duration_seconds + commands = [ + _preflight_command( + python, + producer_python, + script_dir, + artifacts, + host=host, + endpoint=endpoint, + catalog=catalog, + schema=case_schema, + raw_table=raw_table, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + ), + _ingest_command( + producer_python, + script_dir, + artifacts, + data_dir=data_dir, + host=host, + endpoint=endpoint, + catalog=catalog, + schema=case_schema, + raw_table=raw_table, + control_warehouse_id=control_warehouse_id, + run_id=run_id, + expected_rows=expected_rows, + target_eps=target, + producer=producer, + ), + ] + cases.append( + _case_common( + case_id, + run_id=run_id, + schema=case_schema, + raw_table=raw_table, + mv_table=mv_table, + artifacts=artifacts, + commands=commands, + acceptance={ + "target_committed_eps": target, + "expected_rows": expected_rows, + "duration_seconds": capacity_duration_seconds, + "tolerance_fraction": tolerance, + "warmup_metric_intervals": 1, + "minimum_in_tolerance_non_warmup_intervals": MIN_CAPACITY_INTERVALS, + "maximum_host_cpu_percent": max_producer_cpu_percent, + "network_capacity_gbps": producer_network_capacity_gbps, + "maximum_network_utilization_percent": ( + max_producer_network_percent + ), + "clean_finished_exact_progress": True, + "no_errors": True, + }, + ) + ) + + query_cases: list[dict[str, Any]] = [] + for ordinal, query_size in enumerate(candidates, start=1): + case_id = f"query-size-{ordinal:02d}-{_slug(query_size)}" + case_schema = schema + case_dir = matrix_dir / case_id + artifacts = _artifact_paths(case_dir, full=False, query_workloads=True) + run_id = f"{run_prefix}_qsize_{ordinal:02d}" + query_rows = CAPACITY_TARGETS[-1] * 120 + commands = [ + _command( + "provision-query-size", + ( + "operator-provision-query-size", + "--warehouse-id", + rt_warehouse_id, + "--query-size", + query_size, + ), + operator_action=True, + live_resize_prohibited=True, + instruction=( + "Stop all measured work, explicitly provision this size, and " + "record the provider-returned size before preflight." + ), + ), + _preflight_command( + python, + producer_python, + script_dir, + artifacts, + host=host, + endpoint=endpoint, + catalog=catalog, + schema=case_schema, + raw_table=raw_table, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + ), + _ingest_command( + producer_python, + script_dir, + artifacts, + data_dir=data_dir, + host=host, + endpoint=endpoint, + catalog=catalog, + schema=case_schema, + raw_table=raw_table, + control_warehouse_id=control_warehouse_id, + run_id=run_id, + expected_rows=query_rows, + target_eps=CAPACITY_TARGETS[-1], + producer=producer, + ), + _freshness_command( + python, + script_dir, + artifacts, + host=host, + catalog=catalog, + schema=case_schema, + raw_table=raw_table, + mv_table=mv_table, + control_warehouse_id=control_warehouse_id, + run_id=run_id, + iterations=5, + ), + _runner_command( + python, + script_dir, + "dashboard", + artifacts, + host=host, + catalog=catalog, + schema=case_schema, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + run_id=run_id, + interval=600, + iterations=1, + query_size=query_size, + ), + _runner_command( + python, + script_dir, + "drilldown", + artifacts, + host=host, + catalog=catalog, + schema=case_schema, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + run_id=run_id, + interval=3600, + iterations=1, + query_size=query_size, + ), + ] + query_cases.append( + _case_common( + case_id, + run_id=run_id, + schema=case_schema, + raw_table=raw_table, + mv_table=mv_table, + artifacts=artifacts, + commands=commands, + acceptance={ + "candidate_ordinal": ordinal, + "query_size": query_size, + "rt_warehouse_id": rt_warehouse_id, + "target_committed_eps": CAPACITY_TARGETS[-1], + "tolerance_fraction": tolerance, + "expected_rows": query_rows, + "exact_size_proven_by_preflight_and_runner": True, + "dashboard_query_count": 4, + "drilldown_query_count": 2, + "waiting_at_capacity_ms": 0, + "waiting_for_compute_ms": 0, + "queue_time_ms": 0, + "result_from_cache": False, + "no_errors": True, + }, + ) + ) + + endurance_id = "endurance-1m" + endurance_schema = schema + endurance_dir = matrix_dir / endurance_id + endurance_artifacts = _artifact_paths(endurance_dir, full=True) + endurance_run = f"{run_prefix}_endurance_1m" + endurance_rows = CAPACITY_TARGETS[-1] * duration + dashboard_iterations = duration // 600 + 1 + drilldown_iterations = duration // 3600 + 1 + freshness_iterations = duration // 30 + 1 + endurance_commands = [ + _command( + "provision-selected-query-size", + ( + "operator-provision-query-size", + "--warehouse-id", + rt_warehouse_id, + "--query-size", + "${SELECTED_QUERY_SIZE:?set SELECTED_QUERY_SIZE}", + ), + operator_action=True, + live_resize_prohibited=True, + instruction=( + "After the sweep evaluator selects a size, stop all measured work " + "and explicitly provision that size before endurance preflight." + ), + ), + _preflight_command( + python, + producer_python, + script_dir, + endurance_artifacts, + host=host, + endpoint=endpoint, + catalog=catalog, + schema=endurance_schema, + raw_table=raw_table, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + ), + _ingest_command( + producer_python, + script_dir, + endurance_artifacts, + data_dir=data_dir, + host=host, + endpoint=endpoint, + catalog=catalog, + schema=endurance_schema, + raw_table=raw_table, + control_warehouse_id=control_warehouse_id, + run_id=endurance_run, + expected_rows=endurance_rows, + target_eps=CAPACITY_TARGETS[-1], + producer=producer, + ), + _freshness_command( + python, + script_dir, + endurance_artifacts, + host=host, + catalog=catalog, + schema=endurance_schema, + raw_table=raw_table, + mv_table=mv_table, + control_warehouse_id=control_warehouse_id, + run_id=endurance_run, + iterations=freshness_iterations, + ), + _runner_command( + python, + script_dir, + "dashboard", + endurance_artifacts, + host=host, + catalog=catalog, + schema=endurance_schema, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + run_id=endurance_run, + interval=600, + iterations=dashboard_iterations, + query_size="${SELECTED_QUERY_SIZE:?set SELECTED_QUERY_SIZE}", + ), + _runner_command( + python, + script_dir, + "drilldown", + endurance_artifacts, + host=host, + catalog=catalog, + schema=endurance_schema, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + run_id=endurance_run, + interval=3600, + iterations=drilldown_iterations, + query_size="${SELECTED_QUERY_SIZE:?set SELECTED_QUERY_SIZE}", + ), + *_post_run_commands( + python, + script_dir, + endurance_artifacts, + host=host, + catalog=catalog, + schema=endurance_schema, + raw_table=raw_table, + mv_table=mv_table, + rt_warehouse_id=rt_warehouse_id, + control_warehouse_id=control_warehouse_id, + run_id=endurance_run, + expected_rows=endurance_rows, + eps_min=CAPACITY_TARGETS[-1] * (1 - tolerance), + eps_max=CAPACITY_TARGETS[-1] * (1 + tolerance), + ), + ] + cases.append( + _case_common( + endurance_id, + run_id=endurance_run, + schema=endurance_schema, + raw_table=raw_table, + mv_table=mv_table, + artifacts=endurance_artifacts, + commands=endurance_commands, + acceptance={ + "target_committed_eps": CAPACITY_TARGETS[-1], + "expected_rows": endurance_rows, + "minimum_duration_seconds": duration, + "maximum_duration_seconds": MAX_ENDURANCE_SECONDS, + "dashboard_interval_seconds": 600, + "drilldown_interval_seconds": 3600, + "minimum_accepted_dashboard_iterations": max(3, duration // 600), + "minimum_accepted_drilldown_iterations": 1, + "freshness_accepted": True, + "resource_warmup_metric_intervals": 1, + "maximum_host_cpu_percent": max_producer_cpu_percent, + "network_capacity_gbps": producer_network_capacity_gbps, + "maximum_network_utilization_percent": ( + max_producer_network_percent + ), + "no_errors": True, + "configuration_identical_to_accepted_capacity_1m": True, + }, + ) + ) + + matrix = { + "correctness": [cases[0]], + "capacity": cases[1:4], + "query_size_sweep": query_cases, + "endurance": [cases[4]], + } + plan = { + "schema_version": SCHEMA_VERSION, + "task": TASK, + "state": "planned", + "status_note": "Commands are planned and have not been run by this tool.", + "inputs": { + "repo_dir": str(repo_dir), + "data_dir": str(data_dir), + "catalog": catalog, + "base_schema": schema, + "raw_table": raw_table, + "materialized_view": mv_table, + "rt_warehouse_id": rt_warehouse_id, + "control_warehouse_id": control_warehouse_id, + "workspace_host": host, + "zerobus_endpoint": endpoint, + "interpreters": { + "runner": python, + "zerobus_producer": producer_python, + }, + "producer": producer, + "query_size_candidates_smallest_to_largest": candidates, + }, + "credential_environment_references": [ + "DATABRICKS_TOKEN", + "DATABRICKS_CLIENT_ID", + "DATABRICKS_CLIENT_SECRET", + ], + "execution_rules": { + "stage_order": [ + "correctness", + "capacity", + "query_size_sweep", + "endurance", + ], + "stop_on_failure": True, + "shared_dedicated_qualification_schema": True, + "fresh_tables_per_case": True, + "cases_must_run_sequentially": True, + "resize_during_live_measurement_prohibited": True, + "113b_run_requires_qualified_report": True, + }, + "matrix": matrix, + "command_file": str(output_dir / "qualification_commands.sh"), + } + plan["matrix_sha256"] = _json_digest(matrix) + _reject_embedded_credentials(plan) + return plan + + +def _shell_token(value: str) -> str: + if value.startswith("${") and value.endswith("}"): + return value + return shlex.quote(value) + + +def render_command_file(plan: Mapping[str, Any]) -> str: + lines = [ + "#!/bin/sh", + "set -eu", + "# Generated qualification commands. No credential values are embedded.", + "# Export either DATABRICKS_TOKEN or OAuth client credential variables.", + "# Run one case at a time and reset the qualification tables with create.sql.", + ': "${QUALIFY_CASE:?Set QUALIFY_CASE to one case ID or evaluate}"', + "QUALIFY_MATCHED=0", + "", + ] + matrix = plan["matrix"] + for stage in ("correctness", "capacity", "query_size_sweep", "endurance"): + lines.append(f"# ===== {stage} =====") + for case in matrix[stage]: + confirmation = re.sub(r"[^A-Za-z0-9]", "_", case["id"]).upper() + acceptance = case["acceptance"] + duration = acceptance.get("duration_seconds") + if duration is None: + duration = acceptance.get("minimum_duration_seconds") + if duration is None: + expected = _number(acceptance.get("expected_rows")) + target = _number( + acceptance.get("target_eps") + or acceptance.get("target_committed_eps") + ) + duration = int(expected / target) if expected and target else 120 + lines.extend( + ( + "", + f"# Case {case['id']} (planned; fresh tables required)", + f"# {case['destructive_ddl_warning']}", + f"if [ \"$QUALIFY_CASE\" = {_shell_token(case['id'])} ]; then", + "QUALIFY_MATCHED=1", + "# Apply create.sql to reset the qualification schema before continuing.", + ( + f': "${{QUALIFY_PREPARED_{confirmation}:?Set ' + f'QUALIFY_PREPARED_{confirmation}=yes only after reset DDL}}"' + ), + ( + "CASE_SINCE_UTC=$(python3 -c " + + shlex.quote( + "from datetime import datetime,timezone; " + "print(datetime.now(timezone.utc).isoformat(" + "timespec='milliseconds').replace('+00:00','Z'))" + ) + + ")" + ), + ( + "CASE_UNTIL_UTC=$(python3 -c " + + shlex.quote( + "import sys; from datetime import datetime,timedelta; " + "start=datetime.fromisoformat(sys.argv[1].replace('Z','+00:00')); " + "print((start+timedelta(seconds=int(sys.argv[2])))." + "isoformat(timespec='milliseconds').replace('+00:00','Z'))" + ) + + f' "$CASE_SINCE_UTC" {int(duration)})' + ), + "export CASE_SINCE_UTC CASE_UNTIL_UTC", + ) + ) + if stage == "endurance": + lines.append( + ': "${SELECTED_QUERY_SIZE:?Run the sweep evaluator and set ' + 'SELECTED_QUERY_SIZE}"' + ) + concurrent: list[Mapping[str, Any]] = [] + + def flush_concurrent() -> None: + if not concurrent: + return + lines.append("QUALIFY_PIDS=") + for index, grouped in enumerate(concurrent): + command_line = " ".join( + _shell_token(value) for value in grouped["argv"] + ) + lines.append(f"{command_line} &") + lines.append('QUALIFY_PID=$!') + lines.append('QUALIFY_PIDS="$QUALIFY_PIDS $QUALIFY_PID"') + if index == 0 and grouped["step"] == "zerobus-ingest": + progress = _shell_token(case["artifacts"]["ingest_progress"]) + lines.extend( + ( + "QUALIFY_WAITED=0", + f"while [ ! -s {progress} ]; do", + ' kill -0 "$QUALIFY_PID" 2>/dev/null || ' + 'wait "$QUALIFY_PID"', + ' [ "$QUALIFY_WAITED" -lt 300 ] || ' + '{ echo "producer progress did not appear" >&2; exit 1; }', + " sleep 1", + " QUALIFY_WAITED=$((QUALIFY_WAITED + 1))", + "done", + ) + ) + lines.extend( + ( + "QUALIFY_STATUS=0", + "for QUALIFY_PID in $QUALIFY_PIDS; do", + ' wait "$QUALIFY_PID" || QUALIFY_STATUS=$?', + "done", + '[ "$QUALIFY_STATUS" -eq 0 ] || exit "$QUALIFY_STATUS"', + ) + ) + concurrent.clear() + + for command in case["commands"]: + lines.append(f"# Step: {command['step']}") + if command.get("operator_action"): + flush_concurrent() + lines.append("# OPERATOR ACTION: " + command["instruction"]) + lines.append("# " + " ".join(_shell_token(v) for v in command["argv"])) + provision = f"QUALIFY_PROVISIONED_{confirmation}" + lines.append( + f': "${{{provision}:?Set {provision}=yes after explicit provisioning}}"' + ) + continue + if command.get("concurrency_group"): + concurrent.append(command) + else: + flush_concurrent() + lines.append(" ".join(_shell_token(v) for v in command["argv"])) + flush_concurrent() + lines.append("fi") + lines.append("") + lines.extend( + ( + "# Evaluate only after all referenced artifacts exist:", + 'if [ "$QUALIFY_CASE" = evaluate ]; then', + "QUALIFY_MATCHED=1", + " ".join( + ( + _shell_token(sys.executable or "python3"), + _shell_token(str(Path(__file__).resolve())), + "evaluate", + "--plan", + _shell_token("${QUALIFICATION_PLAN:?set QUALIFICATION_PLAN}"), + "--output", + _shell_token("${QUALIFICATION_REPORT:?set QUALIFICATION_REPORT}"), + ) + ), + "fi", + '[ "$QUALIFY_MATCHED" -eq 1 ] || ' + '{ echo "unknown QUALIFY_CASE: $QUALIFY_CASE" >&2; exit 2; }', + "", + ) + ) + return "\n".join(lines) + + +def write_plan(plan: Mapping[str, Any], output_dir: Path) -> tuple[Path, Path]: + output_dir = output_dir.expanduser().resolve() + plan_path = output_dir / "qualification_plan.json" + command_path = Path(str(plan["command_file"])) + atomic_write_json(plan_path, plan) + _atomic_write(command_path, render_command_file(plan).encode("utf-8"), mode=0o755) + return plan_path, command_path + + +def _read_json(path: Path, label: str) -> tuple[dict[str, Any] | None, str | None]: + if not path.is_file(): + return None, f"{label} is missing: {path}" + try: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, Mapping): + raise ValueError("top-level JSON value is not an object") + return dict(value), None + except (OSError, TypeError, ValueError, json.JSONDecodeError) as exc: + return None, f"{label} is invalid: {type(exc).__name__}: {exc}" + + +def _read_jsonl( + path: Path, label: str +) -> tuple[list[dict[str, Any]] | None, str | None]: + if not path.is_file(): + return None, f"{label} is missing: {path}" + rows: list[dict[str, Any]] = [] + try: + for number, line in enumerate( + path.read_text(encoding="utf-8").splitlines(), start=1 + ): + if not line.strip(): + continue + value = json.loads(line) + if not isinstance(value, Mapping): + raise ValueError(f"line {number} is not an object") + rows.append(dict(value)) + except (OSError, TypeError, ValueError, json.JSONDecodeError) as exc: + return None, f"{label} is invalid: {type(exc).__name__}: {exc}" + return rows, None + + +def _nested(value: Any, *path: str) -> Any: + current = value + for component in path: + if not isinstance(current, Mapping) or component not in current: + return None + current = current[component] + return current + + +def _number(value: Any) -> float | None: + if value is None or isinstance(value, bool): + return None + try: + result = float(value) + except (TypeError, ValueError, OverflowError): + return None + return result if math.isfinite(result) else None + + +def _integer(value: Any) -> int | None: + number = _number(value) + if number is None or number < 0 or int(number) != number: + return None + return int(number) + + +def _case_result(case: Mapping[str, Any], state: str, reasons: Sequence[str], **evidence: Any) -> dict[str, Any]: + return { + "id": case.get("id"), + "state": state, + "reasons": list(reasons), + "evidence": evidence, + } + + +def _evaluate_correctness(case: Mapping[str, Any]) -> dict[str, Any]: + path = Path(case["artifacts"]["validation_report"]) + report, error = _read_json(path, "correctness validation report") + if error and not path.is_file(): + return _case_result(case, "not_run", [error]) + if error or report is None: + return _case_result(case, "failed", [str(error)]) + expected = _integer(_nested(report, "configuration", "expected_rows")) + reasons = [] + if report.get("accepted") is not True: + reasons.append("validation report accepted is not exact true") + if expected != CORRECTNESS_ROWS: + reasons.append("validation report expected_rows is not exactly 10000000") + return _case_result( + case, + "failed" if reasons else "passed", + reasons, + path=str(path), + accepted=report.get("accepted"), + expected_rows=expected, + ) + + +def _progress_reasons(progress: Mapping[str, Any], expected_rows: int) -> list[str]: + workers = _integer(_nested(progress, "config", "workers")) + task_count = _integer(_nested(progress, "source", "task_count")) + completed_ranges = progress.get("completed_task_ranges") + compact_complete = ( + task_count is not None + and task_count > 0 + and completed_ranges == [[0, task_count - 1]] + ) + stream_status = progress.get("stream_status") + stream_status = stream_status if isinstance(stream_status, Mapping) else {} + clean_streams = [ + isinstance(status, Mapping) + and status.get("close_succeeded") is True + and status.get("unacked_inspection_succeeded") is True + and _integer(status.get("unacked_batches")) == 0 + for status in stream_status.values() + ] + checks = { + "finished is not exact true": progress.get("finished") is True, + "running is not exact false": progress.get("running") is False, + "clean_checkpoint is not exact true": progress.get("clean_checkpoint") is True, + "safe_to_resume is not exact true": progress.get("safe_to_resume") is True, + "stopped_early is not exact false": progress.get("stopped_early") is False, + "ambiguous is not exact false": progress.get("ambiguous") is False, + "terminal_error is present": progress.get("terminal_error") in (None, ""), + "errors is not an empty array": progress.get("errors") == [], + "pending_batches is not zero": _integer(progress.get("pending_batches")) == 0, + "pending_rows is not zero": _integer(progress.get("pending_rows")) == 0, + "partial_task_rows is not empty": progress.get("partial_task_rows") == {}, + "baseline table was not proven empty": ( + _integer(progress.get("baseline_table_rows")) == 0 + and progress.get("baseline_table_rows_unknown") is False + ), + "provider committed row count is not exact": ( + _integer(progress.get("provider_committed_rows")) == expected_rows + ), + "logical raw row count is not exact": ( + _integer(progress.get("logical_raw_rows")) == expected_rows + ), + "submitted row count is not exact": ( + _integer(progress.get("submitted_rows")) == expected_rows + ), + "source row count is not exact": ( + _integer(_nested(progress, "source", "total_rows")) == expected_rows + ), + "source tasks are not all completed": ( + task_count is not None + and task_count > 0 + and _integer(progress.get("completed_tasks")) == task_count + and ( + progress.get("completed_task_ordinals") == list(range(task_count)) + or compact_complete + ) + ), + "producer streams are not all clean": ( + workers is not None + and workers > 0 + and len(stream_status) == workers + and all(clean_streams) + ), + } + return [reason for reason, passed in checks.items() if not passed] + + +def _capacity_intervals( + rows: Sequence[Mapping[str, Any]], + *, + minimum: float, + maximum: float, + warmup: int, +) -> tuple[list[dict[str, Any]], list[str]]: + evidence: list[dict[str, Any]] = [] + reasons: list[str] = [] + previous_rows: int | None = None + previous_elapsed: float | None = None + eligible_ordinal = 0 + for index, row in enumerate(rows): + if row.get("errors") not in (None, []): + reasons.append(f"metrics row {index} contains errors") + if row.get("terminal_error") not in (None, ""): + reasons.append(f"metrics row {index} contains a terminal error") + committed = _integer(row.get("provider_committed_rows")) + elapsed = _number(row.get("elapsed_sec")) + explicit = _number( + _nested(row, "eps", "interval_provider_committed_rows_per_sec") + ) + if explicit is None: + explicit = _number(row.get("interval_eps")) + interval_eps = explicit + source = "explicit" + if interval_eps is None and committed is not None and elapsed is not None: + if previous_rows is not None and previous_elapsed is not None: + delta_rows = committed - previous_rows + delta_time = elapsed - previous_elapsed + if delta_rows < 0 or delta_time <= 0: + reasons.append(f"metrics row {index} counters are not monotonic") + else: + interval_eps = delta_rows / delta_time + source = "derived" + marked_warmup = any( + row.get(name) is True for name in ("warmup", "warm_up", "is_warmup") + ) + ignored = marked_warmup or eligible_ordinal < warmup + eligible_ordinal += 1 + passed = ( + not ignored + and interval_eps is not None + and minimum <= interval_eps <= maximum + ) + evidence.append( + { + "index": index, + "ignored_as_warmup": ignored, + "eps": interval_eps, + "source": source, + "within_tolerance": passed, + } + ) + if committed is not None and elapsed is not None: + previous_rows, previous_elapsed = committed, elapsed + return evidence, reasons + + +def _producer_resource_headroom( + rows: Sequence[Mapping[str, Any]], + *, + warmup: int, + maximum_host_cpu_percent: float, + network_capacity_gbps: float, + maximum_network_utilization_percent: float, +) -> tuple[dict[str, Any], list[str]]: + """Evaluate producer host saturation from interval telemetry.""" + measured = rows[warmup:] + cpu_values: list[float] = [] + network_values: list[float] = [] + link_bytes_per_second = network_capacity_gbps * 1_000_000_000 / 8 + reasons: list[str] = [] + if not measured: + reasons.append("producer resource telemetry has no post-warmup intervals") + for index, row in enumerate(measured, start=warmup): + cpu = _number(_nested(row, "host", "host_cpu_utilization_percent")) + receive = _number(_nested(row, "host", "network_receive_bytes_per_second")) + transmit = _number( + _nested(row, "host", "network_transmit_bytes_per_second") + ) + if cpu is None or not 0 <= cpu <= 100: + reasons.append(f"metrics row {index} has invalid host CPU utilization") + else: + cpu_values.append(cpu) + if ( + receive is None + or transmit is None + or receive < 0 + or transmit < 0 + ): + reasons.append(f"metrics row {index} has invalid host network throughput") + else: + network_values.append( + 100.0 * max(receive, transmit) / link_bytes_per_second + ) + + def percentile_95(values: Sequence[float]) -> float | None: + if not values: + return None + ordered = sorted(values) + return ordered[max(0, math.ceil(len(ordered) * 0.95) - 1)] + + cpu_p95 = percentile_95(cpu_values) + network_p95 = percentile_95(network_values) + if cpu_p95 is not None and cpu_p95 > maximum_host_cpu_percent: + reasons.append( + f"producer host CPU p95 {cpu_p95:.2f}% exceeds " + f"{maximum_host_cpu_percent:.2f}%" + ) + if ( + network_p95 is not None + and network_p95 > maximum_network_utilization_percent + ): + reasons.append( + f"producer network p95 {network_p95:.2f}% exceeds " + f"{maximum_network_utilization_percent:.2f}% of declared capacity" + ) + return { + "measured_intervals": len(measured), + "host_cpu_p95_percent": cpu_p95, + "maximum_host_cpu_percent": maximum_host_cpu_percent, + "network_p95_utilization_percent": network_p95, + "maximum_network_utilization_percent": maximum_network_utilization_percent, + "declared_network_capacity_gbps": network_capacity_gbps, + }, reasons + + +def _evaluate_capacity( + case: Mapping[str, Any], + expected_producer: Mapping[str, Any], +) -> dict[str, Any]: + progress_path = Path(case["artifacts"]["ingest_progress"]) + metrics_path = Path(case["artifacts"]["ingest_metrics"]) + progress, progress_error = _read_json(progress_path, "capacity progress") + metrics, metrics_error = _read_jsonl(metrics_path, "capacity metrics") + missing = [ + error + for path, error in ( + (progress_path, progress_error), + (metrics_path, metrics_error), + ) + if error and not path.is_file() + ] + if missing: + return _case_result(case, "not_run", missing) + if progress_error or metrics_error or progress is None or metrics is None: + return _case_result( + case, + "failed", + [value for value in (progress_error, metrics_error) if value], + ) + acceptance = case["acceptance"] + expected_rows = int(acceptance["expected_rows"]) + target = float(acceptance["target_committed_eps"]) + tolerance = float(acceptance["tolerance_fraction"]) + minimum, maximum = target * (1 - tolerance), target * (1 + tolerance) + reasons = _progress_reasons(progress, expected_rows) + if _number(_nested(progress, "config", "target_rows_per_sec")) != target: + reasons.append("producer configured target EPS does not match the capacity case") + producer, producer_reasons = _producer_signature(progress) + reasons.extend(producer_reasons) + if producer != dict(expected_producer): + reasons.append("producer configuration differs from the frozen plan inputs") + average = _number(_nested(progress, "eps", "average_provider_committed_rows_per_sec")) + if average is None or not minimum <= average <= maximum: + reasons.append("average provider-committed EPS is missing or outside tolerance") + intervals, interval_reasons = _capacity_intervals( + metrics, + minimum=minimum, + maximum=maximum, + warmup=int(acceptance["warmup_metric_intervals"]), + ) + reasons.extend(interval_reasons) + headroom, headroom_reasons = _producer_resource_headroom( + metrics, + warmup=int(acceptance["warmup_metric_intervals"]), + maximum_host_cpu_percent=float(acceptance["maximum_host_cpu_percent"]), + network_capacity_gbps=float(acceptance["network_capacity_gbps"]), + maximum_network_utilization_percent=float( + acceptance["maximum_network_utilization_percent"] + ), + ) + reasons.extend(headroom_reasons) + passing = sum(item["within_tolerance"] for item in intervals) + minimum_count = int(acceptance["minimum_in_tolerance_non_warmup_intervals"]) + if passing < minimum_count: + reasons.append( + f"only {passing} in-tolerance non-warmup intervals; require {minimum_count}" + ) + return _case_result( + case, + "failed" if reasons else "passed", + reasons, + progress_path=str(progress_path), + metrics_path=str(metrics_path), + target_eps=target, + average_eps=average, + bounds={"minimum": minimum, "maximum": maximum}, + passing_non_warmup_intervals=passing, + intervals=intervals, + producer_resource_headroom=headroom, + producer_signature=producer, + ) + + +def _warehouse_metadata(preflight: Mapping[str, Any]) -> Mapping[str, Any]: + value = _nested(preflight, "online", "warehouses", "lakehouse_rt", "metadata") + return value if isinstance(value, Mapping) else {} + + +def _warehouse_size(value: Mapping[str, Any]) -> Any: + for name in ("cluster_size", "warehouse_size", "size"): + if value.get(name) not in (None, ""): + return value.get(name) + return None + + +def _preflight_reasons( + preflight: Mapping[str, Any], + expected_size: str | None = None, + expected_warehouse_id: str | None = None, +) -> list[str]: + reasons: list[str] = [] + if _nested(preflight, "summary", "status") not in ("ok", "warning"): + reasons.append("preflight summary status is missing or error") + if _nested(preflight, "online", "status") not in ("ok", "warning"): + reasons.append("online preflight status is missing or error") + if _nested(preflight, "online", "errors") != []: + reasons.append("online preflight errors is missing or non-empty") + for table in ("raw", "materialized_view"): + predictive = _nested( + preflight, + "online", + "predictive_optimization", + table, + ) + if ( + not isinstance(predictive, Mapping) + or predictive.get("status") != "ok" + or predictive.get("effective_value") != "ENABLE" + or predictive.get("effectively_enabled") is not True + ): + reasons.append( + f"preflight does not prove Predictive Optimization enabled for {table}" + ) + metadata = _warehouse_metadata(preflight) + if not metadata: + reasons.append("preflight omits RT warehouse metadata") + if expected_size is not None and _warehouse_size(metadata) != expected_size: + reasons.append("preflight does not prove the exact query size") + if expected_warehouse_id is not None: + configured_id = _nested(preflight, "config", "rt_warehouse_id") + if configured_id != expected_warehouse_id: + reasons.append("preflight config does not prove the RT warehouse ID") + if metadata.get("id") != expected_warehouse_id: + reasons.append("preflight metadata does not prove the RT warehouse ID") + return reasons + + +def _record_warehouse_size(record: Mapping[str, Any]) -> Any: + warehouse = record.get("rt_warehouse") + if not isinstance(warehouse, Mapping): + return None + metadata = warehouse.get("api_metadata") + if isinstance(metadata, Mapping): + found = _warehouse_size(metadata) + if found is not None: + return found + return _warehouse_size(warehouse) + + +def _query_record_reasons( + record: Mapping[str, Any], + *, + workload: str, + query_count: int, + query_size: str, + rt_warehouse_id: str, +) -> list[str]: + reasons: list[str] = [] + if record.get("workload", record.get("runner")) != workload: + reasons.append(f"record is not labeled {workload}") + if _record_warehouse_size(record) != query_size: + reasons.append("runner record does not prove the exact query size") + if _nested(record, "rt_warehouse", "warehouse_id") != rt_warehouse_id: + reasons.append("runner record does not prove the RT warehouse ID") + if record.get("producer_progress_error") not in (None, ""): + reasons.append("runner could not read producer progress") + observations = record.get("query_evidence") + if not isinstance(observations, list) or len(observations) != query_count: + reasons.append(f"query_evidence is not exactly {query_count} entries") + return reasons + aligned_results = record.get("result") + if ( + not isinstance(aligned_results, list) + or len(aligned_results) != query_count + or any(not isinstance(value, list) or len(value) != 1 for value in aligned_results) + ): + reasons.append(f"aligned canonical result is not exactly {query_count} entries") + aligned_results = [] + aligned_cache = record.get("cache_hit") + if aligned_cache != [[False] for _ in range(query_count)]: + reasons.append("aligned cache_hit is not exact false for every canonical query") + query_errors = record.get("query_errors") + if query_errors != [[] for _ in range(query_count)]: + reasons.append("aligned query_errors is not empty for every canonical query") + for offset, observation in enumerate(observations): + prefix = f"query {offset + 1}" + if not isinstance(observation, Mapping): + reasons.append(f"{prefix} evidence is not an object") + continue + metrics = observation.get("metrics") + if not isinstance(metrics, Mapping): + reasons.append(f"{prefix} metrics are missing") + continue + if observation.get("execution_succeeded") is not True: + reasons.append(f"{prefix} execution did not succeed") + canonical = _number(observation.get("canonical_duration_sec")) + if canonical is None: + reasons.append(f"{prefix} canonical duration is missing") + if ( + aligned_results + and _number(aligned_results[offset][0]) != canonical + ): + reasons.append(f"{prefix} aligned canonical result disagrees") + if observation.get("errors") != []: + reasons.append(f"{prefix} contains errors") + if metrics.get("result_from_cache") is not False: + reasons.append(f"{prefix} result cache evidence is not exact false") + if metrics.get("cache_origin_statement_id") not in (None, ""): + reasons.append(f"{prefix} reports a cache origin") + if _number(metrics.get("waiting_at_capacity_ms")) != 0: + reasons.append(f"{prefix} waiting-at-capacity is nonzero or unproven") + if _number(metrics.get("waiting_for_compute_ms")) != 0: + reasons.append(f"{prefix} waiting-for-compute is nonzero or unproven") + if _number(metrics.get("queue_time_ms")) != 0: + reasons.append(f"{prefix} queue time is nonzero or unproven") + status = str(metrics.get("status", "")).upper() + if status and status != "FINISHED": + reasons.append(f"{prefix} query-history status is not FINISHED") + return reasons + + +def _evaluate_query_size( + case: Mapping[str, Any], + expected_producer: Mapping[str, Any], +) -> dict[str, Any]: + artifacts = case["artifacts"] + paths = { + name: Path(artifacts[name]) + for name in ("preflight", "ingest_progress", "dashboard", "drilldown") + } + preflight, preflight_error = _read_json(paths["preflight"], "query-size preflight") + progress, progress_error = _read_json( + paths["ingest_progress"], "query-size producer progress" + ) + dashboard, dashboard_error = _read_jsonl(paths["dashboard"], "dashboard report") + drilldown, drilldown_error = _read_jsonl(paths["drilldown"], "drilldown report") + errors = { + "preflight": preflight_error, + "ingest_progress": progress_error, + "dashboard": dashboard_error, + "drilldown": drilldown_error, + } + missing = [ + error + for name, error in errors.items() + if error and not paths[name].is_file() + ] + if missing: + return _case_result(case, "not_run", missing) + invalid = [error for error in errors.values() if error] + if ( + invalid + or preflight is None + or progress is None + or dashboard is None + or drilldown is None + ): + return _case_result(case, "failed", invalid) + acceptance = case["acceptance"] + size = str(acceptance["query_size"]) + rt_warehouse_id = str(acceptance["rt_warehouse_id"]) + reasons = _preflight_reasons(preflight, size, rt_warehouse_id) + reasons.extend(_progress_reasons(progress, int(acceptance["expected_rows"]))) + target = float(acceptance["target_committed_eps"]) + tolerance = float(acceptance["tolerance_fraction"]) + if _number(_nested(progress, "config", "target_rows_per_sec")) != target: + reasons.append("query-size producer target EPS does not match the fixed plan") + average = _number(_nested(progress, "eps", "average_provider_committed_rows_per_sec")) + if ( + average is None + or not target * (1 - tolerance) <= average <= target * (1 + tolerance) + ): + reasons.append("query-size producer average EPS is outside tolerance") + producer, producer_reasons = _producer_signature(progress) + reasons.extend(producer_reasons) + if producer != dict(expected_producer): + reasons.append("query-size producer configuration differs from the frozen plan") + if not dashboard: + reasons.append("dashboard report contains no iterations") + if not drilldown: + reasons.append("drilldown report contains no iterations") + for workload, rows, count in ( + ("dashboard", dashboard, int(acceptance["dashboard_query_count"])), + ("drilldown", drilldown, int(acceptance["drilldown_query_count"])), + ): + for index, record in enumerate(rows): + reasons.extend( + f"{workload} iteration {index + 1}: {reason}" + for reason in _query_record_reasons( + record, + workload=workload, + query_count=count, + query_size=size, + rt_warehouse_id=rt_warehouse_id, + ) + ) + return _case_result( + case, + "failed" if reasons else "passed", + reasons, + query_size=size, + candidate_ordinal=acceptance["candidate_ordinal"], + dashboard_iterations=len(dashboard), + drilldown_iterations=len(drilldown), + producer_signature=producer, + ) + + +def _producer_signature(progress: Mapping[str, Any]) -> tuple[dict[str, Any], list[str]]: + config = progress.get("config") + target = progress.get("target") + config = config if isinstance(config, Mapping) else {} + target = target if isinstance(target, Mapping) else {} + signature = { + "stream_count": config.get("workers"), + "batch_size": config.get("batch_size"), + "queue_capacity": config.get("queue_capacity"), + "compression": config.get("ipc_compression", config.get("compression")), + "host": target.get("host"), + "endpoint": target.get("zerobus_endpoint"), + } + missing = [name for name, value in signature.items() if value in (None, "")] + return signature, [f"producer signature omits {name}" for name in missing] + + +def _rt_signature(preflight: Mapping[str, Any]) -> tuple[dict[str, Any], list[str]]: + metadata = _warehouse_metadata(preflight) + signature = { + "warehouse_id": metadata.get("id"), + "query_size": _warehouse_size(metadata), + "min_num_clusters": metadata.get("min_num_clusters"), + "max_num_clusters": metadata.get("max_num_clusters"), + "enable_serverless_compute": metadata.get("enable_serverless_compute"), + } + missing = [name for name, value in signature.items() if value is None] + return signature, [f"RT warehouse signature omits {name}" for name in missing] + + +def _freshness_gate(validation: Mapping[str, Any]) -> Mapping[str, Any] | None: + gates = validation.get("gates") + if not isinstance(gates, list): + return None + return next( + ( + gate + for gate in gates + if isinstance(gate, Mapping) and gate.get("id") == "freshness" + ), + None, + ) + + +def _evaluate_endurance( + case: Mapping[str, Any], + capacity_1m: Mapping[str, Any], + selected_query_size: str | None, + expected_producer: Mapping[str, Any], +) -> tuple[dict[str, Any], dict[str, Any] | None]: + artifacts = case["artifacts"] + paths = { + "validation": Path(artifacts["validation_report"]), + "progress": Path(artifacts["ingest_progress"]), + "metrics": Path(artifacts["ingest_metrics"]), + "preflight": Path(artifacts["preflight"]), + "capacity_progress": Path(capacity_1m["artifacts"]["ingest_progress"]), + "capacity_preflight": Path(capacity_1m["artifacts"]["preflight"]), + } + loaded: dict[str, dict[str, Any] | None] = {} + errors: dict[str, str | None] = {} + for name, path in paths.items(): + if name == "metrics": + value, errors[name] = _read_jsonl(path, name) + loaded[name] = value # type: ignore[assignment] + else: + loaded[name], errors[name] = _read_json(path, name) + missing = [ + error + for name, error in errors.items() + if error and not paths[name].is_file() + ] + if missing: + return _case_result(case, "not_run", missing), None + invalid = [error for error in errors.values() if error] + if invalid or any(value is None for value in loaded.values()): + return _case_result(case, "failed", invalid), None + validation = loaded["validation"] or {} + progress = loaded["progress"] or {} + metrics = loaded["metrics"] or [] + preflight = loaded["preflight"] or {} + capacity_progress = loaded["capacity_progress"] or {} + capacity_preflight = loaded["capacity_preflight"] or {} + acceptance = case["acceptance"] + reasons: list[str] = [] + if validation.get("accepted") is not True: + reasons.append("endurance validation report accepted is not exact true") + duration = _number(_nested(validation, "window", "duration_seconds")) + minimum_duration = float(acceptance["minimum_duration_seconds"]) + maximum_duration = float(acceptance["maximum_duration_seconds"]) + if duration is None or not minimum_duration <= duration <= maximum_duration: + reasons.append("endurance runtime is outside the required 30-60 minute window") + dashboard_count = _integer( + _nested(validation, "query_samples", "dashboard", "accepted_count") + ) + drilldown_count = _integer( + _nested(validation, "query_samples", "drilldown", "accepted_count") + ) + if dashboard_count is None or dashboard_count < int( + acceptance["minimum_accepted_dashboard_iterations"] + ): + reasons.append( + "fewer than the planned dashboard iterations were accepted" + ) + if drilldown_count is None or drilldown_count < int( + acceptance["minimum_accepted_drilldown_iterations"] + ): + reasons.append( + "fewer than the planned drilldown iterations were accepted" + ) + freshness = _freshness_gate(validation) + freshness_count = _integer( + _nested(validation, "freshness_samples", "accepted_count") + ) + if ( + freshness is None + or freshness.get("passed") is not True + or freshness_count is None + or freshness_count < 1 + ): + reasons.append("freshness evidence was not accepted") + expected_rows = int(acceptance["expected_rows"]) + reasons.extend(_progress_reasons(progress, expected_rows)) + headroom, headroom_reasons = _producer_resource_headroom( + metrics, # type: ignore[arg-type] + warmup=int(acceptance["resource_warmup_metric_intervals"]), + maximum_host_cpu_percent=float(acceptance["maximum_host_cpu_percent"]), + network_capacity_gbps=float(acceptance["network_capacity_gbps"]), + maximum_network_utilization_percent=float( + acceptance["maximum_network_utilization_percent"] + ), + ) + reasons.extend(headroom_reasons) + if _number(_nested(progress, "config", "target_rows_per_sec")) != float( + acceptance["target_committed_eps"] + ): + reasons.append("endurance producer target EPS does not match the plan") + reasons.extend(_preflight_reasons(preflight, selected_query_size)) + reasons.extend(_preflight_reasons(capacity_preflight, selected_query_size)) + producer_1m, producer_1m_reasons = _producer_signature(capacity_progress) + producer_endurance, producer_endurance_reasons = _producer_signature(progress) + reasons.extend(producer_1m_reasons) + reasons.extend(producer_endurance_reasons) + if producer_1m != producer_endurance: + reasons.append("producer stream/batch/compression/host configuration drifted") + if producer_endurance != dict(expected_producer): + reasons.append("endurance producer configuration differs from the frozen plan") + rt_1m, rt_1m_reasons = _rt_signature(capacity_preflight) + rt_endurance, rt_endurance_reasons = _rt_signature(preflight) + reasons.extend(rt_1m_reasons) + reasons.extend(rt_endurance_reasons) + if rt_1m != rt_endurance: + reasons.append("RT warehouse query-size/autoscaling configuration drifted") + if selected_query_size is None: + reasons.append("no query size was selected by a passing sweep case") + elif rt_endurance.get("query_size") != selected_query_size: + reasons.append("endurance RT query size is not the selected query size") + frozen = { + "producer": producer_endurance, + "rt_warehouse": rt_endurance, + "target_eps": acceptance["target_committed_eps"], + "dashboard_interval_seconds": acceptance["dashboard_interval_seconds"], + "drilldown_interval_seconds": acceptance["drilldown_interval_seconds"], + } + return ( + _case_result( + case, + "failed" if reasons else "passed", + reasons, + runtime_seconds=duration, + accepted_dashboard_iterations=dashboard_count, + accepted_drilldown_iterations=drilldown_count, + accepted_freshness_samples=freshness_count, + producer_resource_headroom=headroom, + producer_signature=producer_endurance, + rt_warehouse_signature=rt_endurance, + ), + frozen if not reasons else None, + ) + + +def _validate_plan(plan: Mapping[str, Any]) -> list[str]: + reasons: list[str] = [] + if plan.get("schema_version") != SCHEMA_VERSION: + reasons.append("unsupported qualification plan schema_version") + if plan.get("task") != TASK: + reasons.append("plan task is not tune-endurance") + matrix = plan.get("matrix") + if not isinstance(matrix, Mapping): + reasons.append("plan matrix is missing") + return reasons + stage_cases = { + stage: value if isinstance(value, list) else [] + for stage in ("correctness", "capacity", "query_size_sweep", "endurance") + for value in (matrix.get(stage),) + } + for stage, values in stage_cases.items(): + if not isinstance(matrix.get(stage), list): + reasons.append(f"plan matrix stage {stage} is not an array") + if plan.get("matrix_sha256") != _json_digest(matrix): + reasons.append("frozen matrix digest does not match") + actual_ids = { + case.get("id") + for stage in ("correctness", "capacity", "endurance") + for case in stage_cases[stage] + if isinstance(case, Mapping) + } + missing_ids = set(REQUIRED_CASE_IDS) - actual_ids + if missing_ids: + reasons.append("required cases are missing: " + ", ".join(sorted(missing_ids))) + correctness = stage_cases["correctness"] + if ( + not isinstance(correctness, list) + or len(correctness) != 1 + or _integer(_nested(correctness[0], "acceptance", "expected_rows")) + != CORRECTNESS_ROWS + or _integer(_nested(correctness[0], "acceptance", "target_eps")) + != CAPACITY_TARGETS[0] + ): + reasons.append("correctness matrix is not exact 10M at 100k EPS") + capacity = stage_cases["capacity"] + capacity_targets = ( + [ + _integer(_nested(case, "acceptance", "target_committed_eps")) + for case in capacity + ] + if isinstance(capacity, list) + else [] + ) + if capacity_targets != list(CAPACITY_TARGETS): + reasons.append("capacity matrix is not ordered 100k, 500k, 1M") + elif any( + _integer(_nested(case, "acceptance", "minimum_in_tolerance_non_warmup_intervals")) + is None + or int(case["acceptance"]["minimum_in_tolerance_non_warmup_intervals"]) + < MIN_CAPACITY_INTERVALS + for case in capacity + ): + reasons.append("capacity matrix permits fewer than 10 measured intervals") + query_cases = stage_cases["query_size_sweep"] + if not query_cases: + reasons.append("query-size sweep is empty") + else: + ordinals = [ + _integer(_nested(case, "acceptance", "candidate_ordinal")) + for case in query_cases + ] + if ordinals != list(range(1, len(query_cases) + 1)): + reasons.append("query-size candidates are not frozen smallest-to-largest") + actual_sizes = [ + _nested(case, "acceptance", "query_size") for case in query_cases + ] + expected_sizes = _nested( + plan, "inputs", "query_size_candidates_smallest_to_largest" + ) + if actual_sizes != expected_sizes: + reasons.append("query-size cases do not match the ordered input candidates") + if any( + _integer(_nested(case, "acceptance", "target_committed_eps")) + != CAPACITY_TARGETS[-1] + for case in query_cases + ): + reasons.append("query-size sweep does not use one fixed 1M EPS producer") + endurance = stage_cases["endurance"] + minimum_endurance = ( + _number(_nested(endurance[0], "acceptance", "minimum_duration_seconds")) + if isinstance(endurance, list) and len(endurance) == 1 + else None + ) + maximum_endurance = ( + _number(_nested(endurance[0], "acceptance", "maximum_duration_seconds")) + if isinstance(endurance, list) and len(endurance) == 1 + else None + ) + if ( + not isinstance(endurance, list) + or len(endurance) != 1 + or _integer(_nested(endurance[0], "acceptance", "target_committed_eps")) + != CAPACITY_TARGETS[-1] + or minimum_endurance is None + or minimum_endurance < DEFAULT_ENDURANCE_SECONDS + or maximum_endurance is None + or maximum_endurance > MAX_ENDURANCE_SECONDS + or _integer(_nested(endurance[0], "acceptance", "dashboard_interval_seconds")) + != 600 + or _integer(_nested(endurance[0], "acceptance", "drilldown_interval_seconds")) + != 3600 + ): + reasons.append("endurance matrix is not exact 1M EPS with 600s/3600s schedules") + cases = [ + case + for stage in ("correctness", "capacity", "query_size_sweep", "endurance") + for case in stage_cases[stage] + if isinstance(case, Mapping) + ] + schemas = [_nested(case, "target", "schema") for case in cases] + run_ids = [case.get("run_id") for case in cases] + qualification_schema = _nested(plan, "inputs", "base_schema") + identities_valid = all( + isinstance(value, str) and bool(value) for value in schemas + run_ids + ) + if ( + any(case.get("fresh_target_required") is not True for case in cases) + or not identities_valid + or any(value != qualification_schema for value in schemas) + or len(set(str(value) for value in run_ids)) != len(run_ids) + or _nested( + plan, + "execution_rules", + "shared_dedicated_qualification_schema", + ) + is not True + or _nested(plan, "execution_rules", "fresh_tables_per_case") is not True + or _nested(plan, "execution_rules", "cases_must_run_sequentially") + is not True + ): + reasons.append( + "cases do not prove one shared qualification schema, fresh sequential " + "table resets, and distinct run IDs" + ) + return reasons + + +def evaluate_plan(plan: Mapping[str, Any]) -> dict[str, Any]: + """Evaluate all evidence named by *plan* without using the network.""" + plan_reasons = _validate_plan(plan) + if plan_reasons: + return { + "schema_version": SCHEMA_VERSION, + "task": TASK, + "state": "failed", + "qualified": False, + "reasons": plan_reasons, + "stages": {}, + } + matrix = plan["matrix"] + producer_inputs = _nested(plan, "inputs", "producer") + producer_inputs = producer_inputs if isinstance(producer_inputs, Mapping) else {} + expected_producer = { + "stream_count": producer_inputs.get("stream_count"), + "batch_size": producer_inputs.get("batch_size"), + "queue_capacity": producer_inputs.get("queue_capacity"), + "compression": producer_inputs.get("compression"), + "host": _nested(plan, "inputs", "workspace_host"), + "endpoint": _nested(plan, "inputs", "zerobus_endpoint"), + } + correctness_case = matrix["correctness"][0] + correctness = _evaluate_correctness(correctness_case) + + capacity_results = [ + _evaluate_capacity(case, expected_producer) for case in matrix["capacity"] + ] + capacity_reasons: list[str] = [] + if all(item["state"] == "passed" for item in capacity_results): + averages = [item["evidence"]["average_eps"] for item in capacity_results] + if any(current < previous for previous, current in zip(averages, averages[1:])): + capacity_reasons.append( + "capacity average provider-committed EPS is not monotonic" + ) + capacity_state = ( + "not_run" + if any(item["state"] == "not_run" for item in capacity_results) + else "failed" + if capacity_reasons or any(item["state"] == "failed" for item in capacity_results) + else "passed" + ) + capacity = { + "state": capacity_state, + "reasons": capacity_reasons, + "cases": capacity_results, + } + + query_results = [ + _evaluate_query_size(case, expected_producer) + for case in matrix["query_size_sweep"] + ] + passing_query_results = [ + item for item in query_results if item["state"] == "passed" + ] + selected_query_size = ( + min( + passing_query_results, + key=lambda item: item["evidence"]["candidate_ordinal"], + )["evidence"]["query_size"] + if passing_query_results + else None + ) + query_state = ( + "passed" + if passing_query_results + else "failed" + if any(item["state"] == "failed" for item in query_results) + else "not_run" + ) + query_stage = { + "state": query_state, + "reasons": [] if passing_query_results else ["no query-size candidate passed"], + "cases": query_results, + } + + capacity_1m = next( + case for case in matrix["capacity"] if case["id"] == "capacity-1m" + ) + endurance, frozen = _evaluate_endurance( + matrix["endurance"][0], + capacity_1m, + selected_query_size, + expected_producer, + ) + stages = { + "correctness": correctness, + "capacity": capacity, + "query_size_sweep": query_stage, + "endurance": endurance, + } + qualified = all( + stage["state"] == "passed" + for stage in stages.values() + ) + state = ( + "passed" + if qualified + else "failed" + if any(stage["state"] == "failed" for stage in stages.values()) + else "not_run" + ) + report: dict[str, Any] = { + "schema_version": SCHEMA_VERSION, + "task": TASK, + "plan_matrix_sha256": plan["matrix_sha256"], + "state": state, + "qualified": qualified, + "stages": stages, + "reasons": ( + [] + if qualified + else [ + f"{name} is {stage['state']}" + for name, stage in stages.items() + if stage["state"] != "passed" + ] + ), + } + if selected_query_size is not None: + report["selected_query_size"] = selected_query_size + if qualified and frozen is not None: + frozen["producer"]["host_description"] = producer_inputs.get( + "host_description" + ) + for name in ( + "network_capacity_gbps", + "maximum_host_cpu_percent", + "maximum_network_utilization_percent", + ): + frozen["producer"][name] = producer_inputs.get(name) + report["frozen_settings"] = frozen + return report + + +def _env_default(name: str, default: str | None = None) -> str | None: + value = os.environ.get(name) + return value.strip() if value and value.strip() else default + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="Plan or evaluate tune-endurance qualification" + ) + commands = parser.add_subparsers(dest="command", required=True) + plan = commands.add_parser("plan", help="write a frozen offline qualification plan") + plan.add_argument("--output-dir", type=Path, required=True) + plan.add_argument( + "--repo-dir", + type=Path, + default=Path(__file__).resolve().parents[3], + ) + plan.add_argument( + "--data-dir", + type=Path, + default=Path(value) if (value := _env_default("QUOTES_DATA_DIR")) else None, + required=_env_default("QUOTES_DATA_DIR") is None, + ) + plan.add_argument("--catalog", default=_env_default("DATABRICKS_CATALOG", "workspace")) + plan.add_argument("--schema", default=_env_default("DATABRICKS_SCHEMA", "benchmarking")) + plan.add_argument("--raw-table", default=_env_default("DATABRICKS_RAW_TABLE", "quotes")) + plan.add_argument("--mv-table", default=_env_default("DATABRICKS_MV_TABLE", "quotes_daily")) + plan.add_argument( + "--rt-warehouse-id", + default=_env_default("DATABRICKS_RT_WAREHOUSE_ID"), + required=_env_default("DATABRICKS_RT_WAREHOUSE_ID") is None, + ) + plan.add_argument( + "--control-warehouse-id", + default=_env_default("DATABRICKS_CONTROL_WAREHOUSE_ID"), + required=_env_default("DATABRICKS_CONTROL_WAREHOUSE_ID") is None, + ) + plan.add_argument( + "--producer-host-description", + "--producer-host", + dest="producer_host_description", + default=_env_default("QUALIFICATION_PRODUCER_HOST"), + required=_env_default("QUALIFICATION_PRODUCER_HOST") is None, + ) + plan.add_argument( + "--producer-network-capacity-gbps", + type=float, + default=( + float(value) + if (value := _env_default("QUALIFICATION_PRODUCER_NETWORK_GBPS")) + else None + ), + required=_env_default("QUALIFICATION_PRODUCER_NETWORK_GBPS") is None, + help="declared usable producer link capacity for saturation checks", + ) + plan.add_argument( + "--host", + default=_env_default("DATABRICKS_HOST"), + required=_env_default("DATABRICKS_HOST") is None, + ) + plan.add_argument( + "--endpoint", + "--zerobus-endpoint", + dest="endpoint", + default=_env_default("ZEROBUS_ENDPOINT"), + required=_env_default("ZEROBUS_ENDPOINT") is None, + ) + plan.add_argument("--stream-count", "--streams", type=int, default=16) + plan.add_argument("--batch-size", type=int, default=50_000) + plan.add_argument("--queue-capacity", type=int, default=64) + plan.add_argument( + "--compression", + choices=("NONE", "LZ4_FRAME", "ZSTD"), + default="NONE", + ) + plan.add_argument( + "--query-size-candidates", + "--query-sizes", + dest="query_size_candidates", + nargs="+", + default=( + _env_default("DATABRICKS_QUERY_SIZE_CANDIDATES", "").split(",") + if _env_default("DATABRICKS_QUERY_SIZE_CANDIDATES") + else None + ), + required=_env_default("DATABRICKS_QUERY_SIZE_CANDIDATES") is None, + metavar="SIZE", + help="ordered smallest to largest", + ) + plan.add_argument("--capacity-tolerance", type=float, default=DEFAULT_TOLERANCE) + plan.add_argument( + "--max-producer-cpu-percent", + type=float, + default=DEFAULT_MAX_PRODUCER_CPU_PERCENT, + ) + plan.add_argument( + "--max-producer-network-percent", + type=float, + default=DEFAULT_MAX_PRODUCER_NETWORK_PERCENT, + ) + plan.add_argument( + "--capacity-duration-seconds", + type=int, + default=CAPACITY_DURATION_SECONDS, + ) + plan.add_argument( + "--metrics-interval-seconds", + type=int, + default=METRICS_INTERVAL_SECONDS, + ) + plan.add_argument( + "--endurance-duration-seconds", + type=int, + default=DEFAULT_ENDURANCE_SECONDS, + ) + plan.add_argument("--run-prefix", default="tune_endurance") + plan.add_argument( + "--python", + default=_env_default("DATABRICKS_RUNNER_PYTHON", "python3"), + help="Python interpreter containing requirements-runner.txt", + ) + plan.add_argument( + "--producer-python", + default=_env_default("DATABRICKS_ZEROBUS_PYTHON"), + help="Python interpreter containing requirements-zerobus.txt", + ) + + evaluate = commands.add_parser( + "evaluate", help="evaluate only artifacts referenced by a plan" + ) + evaluate.add_argument("--plan", type=Path, required=True) + evaluate.add_argument("--output", type=Path, required=True) + return parser + + +def run(argv: Sequence[str] | None = None) -> int: + args = build_parser().parse_args(argv) + if args.command == "plan": + plan = build_plan( + output_dir=args.output_dir, + repo_dir=args.repo_dir, + data_dir=args.data_dir, + catalog=args.catalog, + schema=args.schema, + raw_table=args.raw_table, + mv_table=args.mv_table, + rt_warehouse_id=args.rt_warehouse_id, + control_warehouse_id=args.control_warehouse_id, + producer_host_description=args.producer_host_description, + producer_network_capacity_gbps=args.producer_network_capacity_gbps, + host=args.host, + endpoint=args.endpoint, + stream_count=args.stream_count, + batch_size=args.batch_size, + queue_capacity=args.queue_capacity, + compression=args.compression, + query_size_candidates=args.query_size_candidates, + capacity_tolerance=args.capacity_tolerance, + capacity_duration_seconds=args.capacity_duration_seconds, + metrics_interval_seconds=args.metrics_interval_seconds, + max_producer_cpu_percent=args.max_producer_cpu_percent, + max_producer_network_percent=args.max_producer_network_percent, + endurance_duration_seconds=args.endurance_duration_seconds, + run_prefix=args.run_prefix, + python=args.python, + producer_python=args.producer_python, + ) + plan_path, command_path = write_plan(plan, args.output_dir) + print(f"Qualification plan written to {plan_path}") + print(f"Generated command file written to {command_path}") + print("State: planned (no online stages were run)") + return 0 + + plan_path = args.plan.expanduser().resolve() + plan, error = _read_json(plan_path, "qualification plan") + if error or plan is None: + report = { + "schema_version": SCHEMA_VERSION, + "task": TASK, + "state": "failed", + "qualified": False, + "reasons": [error or "qualification plan is unavailable"], + "stages": {}, + } + else: + report = evaluate_plan(plan) + output = args.output.expanduser().resolve() + if output.exists() and output.is_dir(): + output = output / "qualification_report.json" + atomic_write_json(output, report) + print(f"Qualification report written to {output}") + print(f"State: {report['state']}; qualified={str(report['qualified']).lower()}") + return 0 if report["qualified"] else 1 + + +def main(argv: Sequence[str] | None = None) -> int: + try: + return run(argv) + except KeyboardInterrupt: + return 130 + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/full-path-realtime/quotes/databricks/queries_mv.sql b/full-path-realtime/quotes/databricks/queries_mv.sql index 97850c7..fe611c3 100644 --- a/full-path-realtime/quotes/databricks/queries_mv.sql +++ b/full-path-realtime/quotes/databricks/queries_mv.sql @@ -20,7 +20,7 @@ SELECT sum(spread_sum) / sum(n_quotes) AS avg_spread, sum(bs_sum) AS total_bid_volume, sum(as_sum) AS total_ask_volume -FROM workspace.benchmarking.quotes_daily +FROM __CATALOG__.__SCHEMA__.__MV_TABLE__ WHERE sym = 'AAPL'; -- ----------------------------------------------------------------------------- @@ -35,7 +35,7 @@ SELECT min(bp_min) AS lowest_bid, max(bp_max) AS highest_bid, sum(spread_sum) / sum(n_quotes) AS avg_spread -FROM workspace.benchmarking.quotes_daily +FROM __CATALOG__.__SCHEMA__.__MV_TABLE__ WHERE sym IN ('AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA', 'META', 'NVDA', 'NFLX') GROUP BY sym ORDER BY total_quotes DESC; @@ -48,7 +48,7 @@ ORDER BY total_quotes DESC; SELECT sym, (max(bp_max) - min(bp_min)) / min(bp_min) * 100 AS pct_range -FROM workspace.benchmarking.quotes_daily +FROM __CATALOG__.__SCHEMA__.__MV_TABLE__ GROUP BY sym ORDER BY abs(pct_range) DESC LIMIT 20; @@ -62,6 +62,6 @@ SELECT sum(n_quotes) AS total_quotes, sum(bs_sum) + sum(as_sum) AS total_volume, sum(spread_sum) / sum(n_quotes) AS avg_spread -FROM workspace.benchmarking.quotes_daily +FROM __CATALOG__.__SCHEMA__.__MV_TABLE__ GROUP BY day ORDER BY day; diff --git a/full-path-realtime/quotes/databricks/queries_raw.sql b/full-path-realtime/quotes/databricks/queries_raw.sql index 8276116..c186236 100644 --- a/full-path-realtime/quotes/databricks/queries_raw.sql +++ b/full-path-realtime/quotes/databricks/queries_raw.sql @@ -1,24 +1,98 @@ --- ============================================================================= --- DRILLDOWN — one query, run ~hourly against the raw quotes table. --- Filter by sym only (no time filter) so query cost scales with the per-sym --- data volume, exercising the (sym, t) sort key on the base table. --- ============================================================================= +-- Raw-table drill-down queries: same order and logical work as ClickHouse T2. +-- Lakehouse//RT runs in ANSI mode, so arithmetic uses explicit widening and +-- guarded division. Both queries filter only by symbol, never by time. +-- 1. Hourly OHLCV bars. SELECT - count(*) AS total_ticks, - from_unixtime(min(t) / 1000) AS first_tick, - from_unixtime(max(t) / 1000) AS last_tick, + date_trunc('HOUR', timestamp_millis(t)) AS hour, + min_by(bp, t) AS open, + max(bp) AS high, + min(bp) AS low, + max_by(bp, t) AS close, + sum(CAST(bs AS DECIMAL(38, 0))) AS volume, + try_divide( + sum(CAST(bp AS DOUBLE) * CAST(bs AS DOUBLE)), + sum(CAST(bs AS DOUBLE)) + ) AS vwap, + stddev_pop(bp) AS volatility, + avg(CAST(ap AS DOUBLE) - CAST(bp AS DOUBLE)) AS avg_spread, + count(*) AS ticks +FROM __CATALOG__.__SCHEMA__.__RAW_TABLE__ +WHERE sym = 'AAPL' +GROUP BY hour +ORDER BY hour; - min(bp) AS lowest_bid, - max(bp) AS highest_bid, - min(ap) AS lowest_ask, - max(ap) AS highest_ask, - - avg(ap - bp) AS avg_spread, - sum(bs) AS total_bid_volume, - sum(`as`) AS total_ask_volume, - - approx_count_distinct(bx) AS unique_bid_exchanges, - approx_count_distinct(ax) AS unique_ask_exchanges -FROM workspace.benchmarking.quotes -WHERE sym = 'AAPL'; +-- 2. Risk and liquidity profile (B7). +-- Databricks skewness/kurtosis do not express ClickHouse's population/raw +-- contract directly, so derive population central moments from raw moments. +WITH raw_metrics AS +( + SELECT + ( + CAST(bp AS DOUBLE) + CAST(ap AS DOUBLE) + ) / CAST(2 AS DOUBLE) AS mid, + CAST(ap AS DOUBLE) - CAST(bp AS DOUBLE) AS spread, + CAST(bs AS DOUBLE) + CAST(`as` AS DOUBLE) AS depth, + try_divide( + CAST(bs AS DOUBLE) - CAST(`as` AS DOUBLE), + CAST(bs AS DOUBLE) + CAST(`as` AS DOUBLE) + ) AS book_imbalance + FROM __CATALOG__.__SCHEMA__.__RAW_TABLE__ + WHERE sym = 'AAPL' +), +aggregates AS +( + SELECT + count(*) AS n, + avg(mid) AS avg_mid, + stddev_pop(mid) AS mid_volatility, + avg(spread) AS avg_spread, + stddev_pop(spread) AS spread_volatility, + max(spread) AS max_spread, + corr(spread, depth) AS corr_spread_depth, + avg(book_imbalance) AS avg_book_imbalance, + percentile_approx( + spread, + array(CAST(0.95 AS DOUBLE), CAST(0.99 AS DOUBLE)) + ) AS spread_p95_p99, + avg(spread) AS raw_m1, + avg(power(spread, 2)) AS raw_m2, + avg(power(spread, 3)) AS raw_m3, + avg(power(spread, 4)) AS raw_m4 + FROM raw_metrics +), +moments AS +( + SELECT + *, + raw_m2 - power(raw_m1, 2) AS mu2, + raw_m3 + - CAST(3 AS DOUBLE) * raw_m1 * raw_m2 + + CAST(2 AS DOUBLE) * power(raw_m1, 3) AS mu3, + raw_m4 + - CAST(4 AS DOUBLE) * raw_m1 * raw_m3 + + CAST(6 AS DOUBLE) * power(raw_m1, 2) * raw_m2 + - CAST(3 AS DOUBLE) * power(raw_m1, 4) AS mu4 + FROM aggregates +) +SELECT + n AS ticks, + avg_mid, + mid_volatility, + avg_spread, + spread_volatility, + CASE + WHEN mu2 > CAST(0 AS DOUBLE) + THEN try_divide(mu3, power(mu2, CAST(1.5 AS DOUBLE))) + ELSE NULL + END AS spread_skew, + CASE + WHEN mu2 > CAST(0 AS DOUBLE) + THEN try_divide(mu4, power(mu2, CAST(2 AS DOUBLE))) + ELSE NULL + END AS spread_kurtosis, + max_spread, + corr_spread_depth, + avg_book_imbalance, + spread_p95_p99 +FROM moments; diff --git a/full-path-realtime/quotes/databricks/recover_ingest_mv_10m_freshness.sh b/full-path-realtime/quotes/databricks/recover_ingest_mv_10m_freshness.sh new file mode 100644 index 0000000..6de8043 --- /dev/null +++ b/full-path-realtime/quotes/databricks/recover_ingest_mv_10m_freshness.sh @@ -0,0 +1,60 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Recover metadata-only MV refresh evidence for the completed ten-minute trial +# after correcting materialized-view ownership. + +umask 077 + +DBX_BENCH_DIR="/home/ubuntu/costbench/full-path-realtime/quotes/databricks" +RUN_ROOT="/data/databricks-qualification/ingest_mv_10m_20260915T143250Z" +OUTPUT_DIR="$RUN_ROOT/freshness-recovered" +RUN_ID="ingest_mv_10m_20260915T143250Z" +: "${DATABRICKS_HOST:?Set the Databricks workspace HTTPS origin}" +: "${DATABRICKS_CLIENT_ID:?Set the benchmark service-principal application ID}" +: "${DATABRICKS_CONTROL_WAREHOUSE_ID:?Set the control warehouse ID}" + +if [[ -e "$OUTPUT_DIR" ]]; then + printf 'Refusing existing recovery directory: %s\n' "$OUTPUT_DIR" >&2 + exit 1 +fi +if [[ ! -s "$RUN_ROOT/ingest/ingest_progress.json" ]]; then + echo "Completed ingest progress is missing." >&2 + exit 1 +fi + +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET +echo +export DATABRICKS_CLIENT_SECRET +export DATABRICKS_CLIENT_ID +unset DATABRICKS_TOKEN +trap 'unset DATABRICKS_CLIENT_SECRET' EXIT + +mkdir -p "$OUTPUT_DIR" + +"$DBX_BENCH_DIR/.venv-runner/bin/python" \ + "$DBX_BENCH_DIR/monitor_freshness.py" \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --catalog costbench \ + --schema rt_qualification \ + --raw-table quotes_ingest_mv_10m_20260915 \ + --mv-table quotes_daily_ingest_mv_10m_20260915 \ + --producer-progress "$RUN_ROOT/ingest/ingest_progress.json" \ + --since 2026-09-15T14:32:50.000Z \ + --interval 1 \ + --iterations 1 \ + --output "$OUTPUT_DIR/freshness.jsonl" \ + --progress-json "$OUTPUT_DIR/freshness_progress.json" \ + --run-id "$RUN_ID" + +tail -n 1 "$OUTPUT_DIR/freshness.jsonl" | jq '{ + run_id, + observed_at, + zerobus_ingest, + latest_successful_refresh, + raw_commit_age_sec, + mv_refresh_age_sec, + event_log_rows: .event_log.bounded_rows, + errors +}' diff --git a/full-path-realtime/quotes/databricks/requirements-runner.txt b/full-path-realtime/quotes/databricks/requirements-runner.txt new file mode 100644 index 0000000..a0195e0 --- /dev/null +++ b/full-path-realtime/quotes/databricks/requirements-runner.txt @@ -0,0 +1,2 @@ +databricks-sql-connector[kernel]==4.5.0 +databricks-sdk==0.133.0 diff --git a/full-path-realtime/quotes/databricks/requirements-zerobus.txt b/full-path-realtime/quotes/databricks/requirements-zerobus.txt new file mode 100644 index 0000000..a838930 --- /dev/null +++ b/full-path-realtime/quotes/databricks/requirements-zerobus.txt @@ -0,0 +1 @@ +databricks-zerobus-ingest-sdk[arrow]==1.8.0 diff --git a/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/evidence/clustering_status.json b/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/evidence/clustering_status.json new file mode 100644 index 0000000..2fac812 --- /dev/null +++ b/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/evidence/clustering_status.json @@ -0,0 +1,313 @@ +{ + "collected_at": "2026-09-21T09:33:06.725Z", + "collection_window": { + "duration_seconds": 124129.353, + "since": "2026-09-18T17:04:53.000Z", + "until": "2026-09-20T03:33:42.353Z" + }, + "predictive_optimization": { + "clustering_operation_count": 12, + "operations": [ + { + "end_time": "2026-09-18T18:08:27.023Z", + "operation_id": "b9608877-833b-4669-b147-81c4856ea541", + "operation_metrics": { + "amount_of_scanned_bytes": "2250815", + "number_of_scanned_files": "1", + "staleness_percentage_reduced": "100" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "ANALYZE", + "start_time": "2026-09-18T18:08:26.240Z", + "table_name": "quotes_daily", + "usage_quantity": "0.003850426071963021", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-18T18:09:54.953Z", + "operation_id": "c056a74d-866a-436f-939d-75c789129106", + "operation_metrics": { + "amount_of_scanned_bytes": "23351937778", + "number_of_scanned_files": "734", + "staleness_percentage_reduced": "100" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "ANALYZE", + "start_time": "2026-09-18T18:08:28.910Z", + "table_name": "quotes", + "usage_quantity": "0.423119042796825300", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-18T18:25:50.593Z", + "operation_id": "8b9c95d7-87ed-4891-a2d8-6f49d8185595", + "operation_metrics": { + "amount_of_clustered_data_bytes": "26994492817", + "amount_of_data_removed_bytes": "29149309984", + "number_of_clustered_files": "477", + "number_of_removed_files": "915" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-18T18:12:31.256Z", + "table_name": "quotes", + "usage_quantity": "4.402446913333334000", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-18T23:02:42.961Z", + "operation_id": "68fbc7b0-90af-42d8-af0d-b922341856fd", + "operation_metrics": { + "amount_of_clustered_data_bytes": "317207281404", + "amount_of_data_removed_bytes": "269238046522", + "number_of_clustered_files": "4274", + "number_of_removed_files": "4439" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-18T22:39:28.812Z", + "table_name": "quotes", + "usage_quantity": "24.988888520000000000", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T03:39:52.295Z", + "operation_id": "cf1147d0-e3c5-4a50-bdc3-088d7b003579", + "operation_metrics": { + "amount_of_clustered_data_bytes": "22798576", + "amount_of_data_removed_bytes": "25380240", + "number_of_clustered_files": "1", + "number_of_removed_files": "4" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T03:39:35.086Z", + "table_name": "quotes_daily", + "usage_quantity": "0.315890972198273800", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T03:57:05.276Z", + "operation_id": "3351e7d7-2684-41b2-84af-756454189a41", + "operation_metrics": { + "amount_of_clustered_data_bytes": "145497616557", + "amount_of_data_removed_bytes": "133966273581", + "number_of_clustered_files": "2000", + "number_of_removed_files": "3596" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T03:39:56.603Z", + "table_name": "quotes", + "usage_quantity": "18.882475102801727000", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T08:11:33.405Z", + "operation_id": "91733356-5a24-40d7-b0d0-476c76504012", + "operation_metrics": { + "amount_of_clustered_data_bytes": "32014286", + "amount_of_data_removed_bytes": "34004475", + "number_of_clustered_files": "1", + "number_of_removed_files": "3" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T08:11:17.606Z", + "table_name": "quotes_daily", + "usage_quantity": "0.329653282424341660", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T08:37:22.640Z", + "operation_id": "a48ac109-cc32-487d-94e5-a9ffb3e8806f", + "operation_metrics": { + "amount_of_clustered_data_bytes": "267422169692", + "amount_of_data_removed_bytes": "276499979984", + "number_of_clustered_files": "4681", + "number_of_removed_files": "5328" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T08:11:37.860Z", + "table_name": "quotes", + "usage_quantity": "32.232533554242330000", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T13:12:12.036Z", + "operation_id": "3d2997cf-f3cd-480a-994b-d60dde6ac068", + "operation_metrics": { + "amount_of_clustered_data_bytes": "41943376", + "amount_of_data_removed_bytes": "44078922", + "number_of_clustered_files": "1", + "number_of_removed_files": "3" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T13:11:57.346Z", + "table_name": "quotes_daily", + "usage_quantity": "0.300876639921395130", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T13:31:24.061Z", + "operation_id": "097c744a-ecdd-4bf4-98e5-5b2c6d758fb9", + "operation_metrics": { + "amount_of_clustered_data_bytes": "157791579022", + "amount_of_data_removed_bytes": "176794584575", + "number_of_clustered_files": "3054", + "number_of_removed_files": "4299" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T13:12:16.482Z", + "table_name": "quotes", + "usage_quantity": "23.504405280078604000", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T18:26:58.023Z", + "operation_id": "190d843c-ff32-4a59-bc66-242326ad4834", + "operation_metrics": { + "amount_of_clustered_data_bytes": "125249697405", + "amount_of_data_removed_bytes": "142872069851", + "number_of_clustered_files": "2481", + "number_of_removed_files": "3916" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T18:09:30.144Z", + "table_name": "quotes", + "usage_quantity": "26.533541081666666000", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T19:12:34.289Z", + "operation_id": "6d6d14b4-4124-42fa-8b94-9560fd7219bd", + "operation_metrics": { + "amount_of_clustered_data_bytes": "12854402", + "amount_of_data_removed_bytes": "14226315", + "number_of_clustered_files": "1", + "number_of_removed_files": "3" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T19:12:20.196Z", + "table_name": "quotes_daily", + "usage_quantity": "0.147797944999999960", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T22:57:24.884Z", + "operation_id": "e78e6798-303c-4da7-86bd-a9287fac6851", + "operation_metrics": { + "amount_of_clustered_data_bytes": "108366869957", + "amount_of_data_removed_bytes": "126296032157", + "number_of_clustered_files": "2231", + "number_of_removed_files": "3482" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T22:39:32.372Z", + "table_name": "quotes", + "usage_quantity": "19.642641795000000000", + "usage_unit": "ESTIMATED_DBU" + }, + { + "end_time": "2026-09-19T23:40:05.709Z", + "operation_id": "c59d63ba-e231-4c72-b3e7-0c3e7c4183c0", + "operation_metrics": { + "amount_of_clustered_data_bytes": "22789394", + "amount_of_data_removed_bytes": "24818725", + "number_of_clustered_files": "1", + "number_of_removed_files": "3" + }, + "operation_status": "SUCCESSFUL", + "operation_type": "CLUSTERING", + "start_time": "2026-09-19T23:39:50.823Z", + "table_name": "quotes_daily", + "usage_quantity": "0.153180498333333400", + "usage_unit": "ESTIMATED_DBU" + } + ], + "preflight_status": "ok", + "summary": { + "operation_count": 14, + "operation_counts_by_type": { + "ANALYZE": 2, + "CLUSTERING": 12 + }, + "usage_by_unit": { + "ESTIMATED_DBU": "151.861301053868793271" + } + } + }, + "run_id": "serverless_baseline_full_20260918T170453Z", + "schema_version": 1, + "tables": { + "materialized_view": { + "catalog": "costbench", + "cluster_by_auto": null, + "clustering_columns": [ + "sym", + "day" + ], + "effective_predictive_optimization_flag": "ENABLE", + "enable_predictive_optimization": "ENABLE", + "format": null, + "location": "s3:///", + "name": "quotes_daily", + "num_files": null, + "partition_columns": null, + "predictive_optimization_effectively_enabled": true, + "properties": { + "clusteringColumns": "[[\"sym\"],[\"day\"]]", + "delta.enableChangeDataFeed": "true", + "delta.enableDeletionVectors": "true", + "delta.enableRowTracking": "true", + "delta.feature.clustering": "supported", + "delta.parquet.compression.codec": "zstd" + }, + "schema": "rt_full_serverless_baseline_r3_20260918", + "size_in_bytes": null, + "table_features": null, + "type": "MANAGED" + }, + "raw": { + "catalog": null, + "cluster_by_auto": "false", + "clustering_columns": [ + "sym", + "t" + ], + "effective_predictive_optimization_flag": "ENABLE", + "enable_predictive_optimization": "INHERIT", + "format": "delta", + "location": "s3:///", + "name": "costbench.rt_full_serverless_baseline_r3_20260918.quotes", + "num_files": "14348", + "partition_columns": [], + "predictive_optimization_effectively_enabled": true, + "properties": { + "delta.enableChangeDataFeed": "true", + "delta.enableDeletionVectors": "true", + "delta.enableRowTracking": "true", + "delta.parquet.compression.codec": "zstd" + }, + "schema": null, + "size_in_bytes": "707457705990", + "table_features": [ + "appendOnly", + "changeDataFeed", + "clustering", + "deletionVectors", + "domainMetadata", + "invariants", + "rowTracking", + "v2Checkpoint" + ], + "type": null + } + } +} diff --git a/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/evidence/mv_refresh_summary.json b/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/evidence/mv_refresh_summary.json new file mode 100644 index 0000000..cf029ea --- /dev/null +++ b/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/evidence/mv_refresh_summary.json @@ -0,0 +1,17 @@ +{ + "collected_at": "2026-09-21T09:33:06.725Z", + "collection_window": { + "duration_seconds": 124129.353, + "since": "2026-09-18T17:04:53.000Z", + "until": "2026-09-20T03:33:42.353Z" + }, + "full_refresh_count": 0, + "maintenance_class_counts": { + "incremental": 1028 + }, + "maintenance_type_counts": { + "MAINTENANCE_TYPE_GROUP_AGGREGATE": 1028 + }, + "run_id": "serverless_baseline_full_20260918T170453Z", + "schema_version": 1 +} diff --git a/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/evidence/provider_reconciliation.json b/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/evidence/provider_reconciliation.json new file mode 100644 index 0000000..4393dc1 --- /dev/null +++ b/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/evidence/provider_reconciliation.json @@ -0,0 +1,60 @@ +{ + "collected_at": "2026-09-21T09:33:06.725Z", + "collection_window": { + "duration_seconds": 124129.353, + "since": "2026-09-18T17:04:53.000Z", + "until": "2026-09-20T03:33:42.353Z" + }, + "complete": true, + "optional_dataset_errors": [], + "provider_committed_bytes": 8270494036080, + "provider_committed_records": 113219565734, + "provider_errors": 0, + "required_dataset_completeness": { + "complete": true, + "failed": [], + "required": [ + "zerobus_ingest_summary", + "zerobus_ingest_by_minute_stream", + "mv_event_log", + "query_history", + "warehouse_events", + "warehouse_config_snapshots", + "predictive_optimization_operations", + "raw_table_detail", + "mv_table_detail", + "raw_table_history", + "uc_table_metadata", + "billing_usage", + "billing_list_prices" + ], + "succeeded": [ + "zerobus_ingest_summary", + "zerobus_ingest_by_minute_stream", + "mv_event_log", + "query_history", + "warehouse_events", + "warehouse_config_snapshots", + "predictive_optimization_operations", + "raw_table_detail", + "mv_table_detail", + "raw_table_history", + "uc_table_metadata", + "billing_usage", + "billing_list_prices" + ] + }, + "run_id": "serverless_baseline_full_20260918T170453Z", + "schema_version": 1, + "table_num_rows": { + "materialized_view": null, + "raw": null + }, + "zerobus_ingest_bounds": { + "max_commit_time": "2026-09-20T00:33:47.215Z", + "max_commit_version": "21510", + "min_commit_time": "2026-09-18T17:06:45.177Z", + "min_commit_version": "1" + }, + "zerobus_stream_error_count": 0 +} diff --git a/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/freshness/freshness_progress.json b/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/freshness/freshness_progress.json new file mode 100644 index 0000000..75388bf --- /dev/null +++ b/full-path-realtime/quotes/databricks/results/serverless_baseline_full_20260918T170453Z/freshness/freshness_progress.json @@ -0,0 +1,46 @@ +{ + "age_semantics": "These are observational ages from the sample time to provider timestamps; they are not exact per-record ingestion or materialized-view lag.", + "client_durable_rows": 113219565734, + "errors": [], + "iteration": 2068, + "latest_commit_time": "2026-09-20T00:33:47.215Z", + "latest_commit_version": 21510, + "latest_successful_refresh": { + "finished_at": "2026-09-20T00:36:23.999Z", + "maintenance_class": "incremental", + "maintenance_type": "MAINTENANCE_TYPE_GROUP_AGGREGATE", + "output_rows": null, + "planned_at": 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detected.\n\n== Physical Plan ==\nExecute CreateMaterializedViewCommand\n +- CreateMaterializedViewCommand `costbench`.`rt_full_serverless_baseline_r3_20260918`.`quotes_daily`, [spark.internal.materialized_view.refresh_policy=IncrementalStrict], SELECT\n sym,\n date_add(\n DATE '1970-01-01',\n CAST(\n floor(\n CAST(t AS DECIMAL(20, 0))\n / CAST(86400000 AS DECIMAL(20, 0))\n ) AS INT\n )\n ) AS day,\n count(*) AS n_quotes,\n min(bp) AS bp_min,\n max(bp) AS bp_max,\n min(ap) AS ap_min,\n max(ap) AS ap_max,\n sum(bs) AS bs_sum,\n sum(`as`) AS as_sum,\n sum(ap - bp) AS spread_sum\nFROM `costbench`.`rt_full_serverless_baseline_r3_20260918`.quotes\nGROUP BY sym, day, 'Aggregate [sym#14274, 'day], [sym#14274, date_add(1970-01-01, cast(FLOOR((cast(t#14283L as decimal(20,0)) / cast(86400000 as decimal(20,0)))) as int)) AS day#14247, count(1) AS n_quotes#14248L, min(bp#14276) AS bp_min#14249, max(bp#14276) AS bp_max#14250, min(ap#14279) AS ap_min#14251, max(ap#14279) AS ap_max#14252, sum(bs#14277L) AS bs_sum#14253L, sum(as#14280L) AS as_sum#14254L, sum((ap#14279 - bp#14276)) AS spread_sum#14255], false, false, PersistedView, true, false, [cluster_by(sym, day)], false, Trigger(Some(60)), CREATE MATERIALIZED VIEW `costbench`.`rt_full_serverless_baseline_r3_20260918`.quotes_daily\nUSING DELTA\nCLUSTER BY (sym, day)\nREFRESH POLICY INCREMENTAL STRICT\nTRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE\nAS SELECT\n sym,\n date_add(\n DATE '1970-01-01',\n CAST(\n floor(\n CAST(t AS DECIMAL(20, 0))\n / CAST(86400000 AS DECIMAL(20, 0))\n ) AS INT\n )\n ) AS day,\n count(*) AS n_quotes,\n min(bp) AS bp_min,\n max(bp) AS bp_max,\n min(ap) AS ap_min,\n max(ap) AS ap_max,\n sum(bs) AS bs_sum,\n sum(`as`) AS as_sum,\n sum(ap - bp) AS spread_sum\nFROM `costbench`.`rt_full_serverless_baseline_r3_20260918`.quotes\nGROUP BY sym, day\n\n\n== Photon Explanation ==\nCommands are not directly photonized. They must be run to determine if they can be Photon-ized. 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}, + { + "index": 1956, + "reasons": [ + "sample is outside the post-grace measurement window" + ] + }, + { + "index": 1957, + "reasons": [ + "sample is outside the post-grace measurement window" + ] + }, + { + "index": 1958, + "reasons": [ + "sample is outside the post-grace measurement window" + ] + }, + { + "index": 1959, + "reasons": [ + "sample is outside the post-grace measurement window" + ] + }, + { + "index": 1960, + "reasons": [ + "sample is outside the post-grace measurement window" + ] + }, + { + "index": 1961, + "reasons": [ + "sample is outside the post-grace measurement window" + ] + }, + { + "index": 1962, + "reasons": [ + "sample is outside the post-grace measurement window" + ] + }, + { + "index": 1963, + "reasons": [ + "sample is outside the post-grace measurement window" + ] + }, + { + "index": 1964, + "reasons": [ + "sample is outside the post-grace measurement window" + ] + }, + { + "index": 1965, + "reasons": [ + "sample is outside the post-grace measurement 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"reasons": [ + "sample did not prove producer_progress_finished exact false", + "sample started at or after completed ingest" + ] + }, + { + "index": 202, + "reasons": [ + "sample did not prove producer_progress_finished exact false", + "sample started at or after completed ingest" + ] + }, + { + "index": 203, + "reasons": [ + "sample did not prove producer_progress_finished exact false", + "sample started at or after completed ingest" + ] + }, + { + "index": 204, + "reasons": [ + "sample did not prove producer_progress_finished exact false", + "sample started at or after completed ingest" + ] + }, + { + "index": 205, + "reasons": [ + "sample did not prove producer_progress_finished exact false", + "sample started at or after completed ingest" + ] + }, + { + "index": 206, + "reasons": [ + "sample did not prove producer_progress_finished exact false", + "sample started at or after completed ingest" + ] + } + ], + "rejected_count": 18, + "self_overlap_failures": [] + }, + "drilldown": { + "accepted_count": 32, + "accepted_indices": [ + 0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 26, + 27, + 28, + 29, + 30, + 31 + ], + "active_invalid": [], + "cadence_failures": [], + "cadence_tolerance_seconds": 5, + "expected_interval_seconds": 3600.0, + "expected_query_count": 2, + "raw_rows_monotonic_failures": [], + "rejected": [ + { + "index": 32, + "reasons": [ + "sample did not prove producer_progress_finished exact false", + "sample started at or after completed ingest" + ] + }, + { + "index": 33, + "reasons": [ + "sample did not prove producer_progress_finished exact false", + "sample started at or after completed ingest" + ] + }, + { + "index": 34, + "reasons": [ + "sample did not prove producer_progress_finished exact false", + "sample started at or after completed ingest" + ] + } + ], + "rejected_count": 3, + "self_overlap_failures": [] + } + }, + "schema_version": 1, + "window": { + "duration_seconds": 124129.353, + "since": "2026-09-18T17:04:53.000Z", + "until": "2026-09-20T03:33:42.353Z" + } +} diff --git a/full-path-realtime/quotes/databricks/run_capacity_1m.sh b/full-path-realtime/quotes/databricks/run_capacity_1m.sh new file mode 100644 index 0000000..186b840 --- /dev/null +++ b/full-path-realtime/quotes/databricks/run_capacity_1m.sh @@ -0,0 +1,116 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Bounded preliminary capacity trial for the prepared eu-west-1 qualification +# target. This is not a full qualification result. + +umask 077 + +: "${DBX_BENCH_DIR:=/home/ubuntu/costbench/full-path-realtime/quotes/databricks}" +: "${SOURCE_DIR:=/data/quotes}" +: "${SOURCE_INVENTORY_OUTPUT:=/data/databricks-qualification/source_inventory.json}" +: "${DATABRICKS_ZEROBUS_PYTHON:=$DBX_BENCH_DIR/.venv-zerobus/bin/python}" +: "${DATABRICKS_HOST:?Set the Databricks workspace HTTPS origin}" +: "${DATABRICKS_WORKSPACE_ID:?Set the decimal Databricks workspace ID}" +: "${DATABRICKS_CLIENT_ID:?Set the benchmark service-principal application ID}" +: "${DATABRICKS_REGION:=eu-west-1}" +: "${DATABRICKS_CATALOG:=costbench}" +: "${DATABRICKS_SCHEMA:=rt_qualification}" +: "${DATABRICKS_RAW_TABLE:=quotes}" +: "${DATABRICKS_MV_TABLE:=quotes_daily}" +: "${DATABRICKS_CONTROL_WAREHOUSE_ID:?Set the control warehouse ID}" +: "${ZEROBUS_ENDPOINT:=https://${DATABRICKS_WORKSPACE_ID}.zerobus.${DATABRICKS_REGION}.cloud.databricks.com}" + +TARGET="${DATABRICKS_CATALOG}.${DATABRICKS_SCHEMA}.${DATABRICKS_RAW_TABLE}" +EXPECTED_TARGET="costbench.rt_qualification.quotes" +EXPECTED_ROWS=75000000 +TARGET_EPS=1000000 +RUN_ID="capacity_1m_$(date -u +%Y%m%dT%H%M%SZ)" +RUN_ROOT="/data/databricks-qualification/${RUN_ID}" + +if [[ "$TARGET" != "$EXPECTED_TARGET" ]]; then + printf 'Refusing unexpected target: %s\n' "$TARGET" >&2 + exit 1 +fi +if [[ -e "$RUN_ROOT" ]]; then + printf 'Refusing existing output directory: %s\n' "$RUN_ROOT" >&2 + exit 1 +fi +if [[ ! -x "$DATABRICKS_ZEROBUS_PYTHON" ]]; then + printf 'Missing Zerobus Python: %s\n' "$DATABRICKS_ZEROBUS_PYTHON" >&2 + exit 1 +fi +if [[ ! -s "$SOURCE_INVENTORY_OUTPUT" ]]; then + printf 'Missing source inventory: %s\n' "$SOURCE_INVENTORY_OUTPUT" >&2 + exit 1 +fi + +jq -e ' + .status == "complete" + and .actual_rows == 113219565734 + and .file_count == 232 +' "$SOURCE_INVENTORY_OUTPUT" >/dev/null + +printf 'Target: %s\n' "$TARGET" +printf 'Rows: %d\n' "$EXPECTED_ROWS" +printf 'Target EPS: %d\n' "$TARGET_EPS" +printf 'Workers: 16\n' +printf 'Output: %s\n' "$RUN_ROOT" +printf '\nThis permanently consumes the fresh Zerobus target name.\n' +read -r -p "Type '$EXPECTED_TARGET' to continue: " confirmation +if [[ "$confirmation" != "$EXPECTED_TARGET" ]]; then + echo "Confirmation did not match; nothing was run." >&2 + exit 1 +fi + +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET +echo +export DATABRICKS_CLIENT_ID DATABRICKS_CLIENT_SECRET +unset DATABRICKS_TOKEN +trap 'unset DATABRICKS_CLIENT_SECRET' EXIT + +mkdir -p "$RUN_ROOT" + +"$DATABRICKS_ZEROBUS_PYTHON" "$DBX_BENCH_DIR/ingest_zerobus.py" \ + --dir "$SOURCE_DIR" \ + --pattern 'quotes_*.parquet' \ + --host "$DATABRICKS_HOST" \ + --endpoint "$ZEROBUS_ENDPOINT" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --table "$DATABRICKS_RAW_TABLE" \ + --count-warehouse "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --workers 16 \ + --batch-size 50000 \ + --queue-capacity 64 \ + --target-eps "$TARGET_EPS" \ + --compression NONE \ + --checkpoint-batches 32 \ + --checkpoint-seconds 5 \ + --checkpoint-method wait \ + --metrics-interval 5 \ + --memory-trim-interval 60 \ + --min-system-available-gib 16 \ + --max-rows "$EXPECTED_ROWS" \ + --expected-rows "$EXPECTED_ROWS" \ + --allow-partial \ + --run-id "$RUN_ID" \ + --output-dir "$RUN_ROOT/ingest" \ + --quiet-worker-logs \ + 2>&1 | tee "$RUN_ROOT/ingest.log" + +jq '{ + run_id, + finished, + terminal_error, + provider_committed_rows, + expected_rows: .source.total_rows, + elapsed_sec, + average_provider_committed_eps: + .eps.average_provider_committed_rows_per_sec, + clean_checkpoint, + safe_to_resume, + table_protection +}' "$RUN_ROOT/ingest/ingest_summary.json" + +printf 'Capacity trial artifacts: %s\n' "$RUN_ROOT" diff --git a/full-path-realtime/quotes/databricks/run_dashboard.py b/full-path-realtime/quotes/databricks/run_dashboard.py index 927abb3..7511674 100644 --- a/full-path-realtime/quotes/databricks/run_dashboard.py +++ b/full-path-realtime/quotes/databricks/run_dashboard.py @@ -1,165 +1,24 @@ #!/usr/bin/env python3 -""" -Dashboard runner — loops the MV queries every --interval seconds, appending -one JSONL record per iteration to --output. Run in parallel to the ingest -script; Ctrl-C when ingest finishes. +"""Thin entry point for the four-query Databricks dashboard workload.""" -Usage: - export DATABRICKS_HOST="https://dbc-....cloud.databricks.com" - export DATABRICKS_HTTP_PATH="/sql/1.0/warehouses/" - export DATABRICKS_TOKEN="dapi..." - - python3 run_dashboard.py \ - --system "Databricks" \ - --machine "Small warehouse" \ - --cluster-size 1 \ - --comment "10B rows" \ - --interval 600 \ - --output dashboard.jsonl -""" - -import argparse -import json -import os -import sys -import time -from datetime import datetime, timezone from pathlib import Path +from typing import Sequence -from databricks.sdk import WorkspaceClient -from databricks.sdk.service.sql import StatementState - -_http_path = os.environ.get("DATABRICKS_HTTP_PATH", "") -_warehouse_id = _http_path.rstrip("/").split("/")[-1] if _http_path else "" - -_hostname = os.environ.get("DATABRICKS_SERVER_HOSTNAME", "") -if _hostname and not os.environ.get("DATABRICKS_HOST"): - host = f"https://{_hostname}" if not _hostname.startswith("http") else _hostname - os.environ["DATABRICKS_HOST"] = host - - -def run_sql(client, warehouse_id, statement): - resp = client.statement_execution.execute_statement( - statement=statement, warehouse_id=warehouse_id, wait_timeout="0s") - stmt_id = resp.statement_id - state = resp.status.state - while state in (StatementState.PENDING, StatementState.RUNNING): - time.sleep(0.2) - resp = client.statement_execution.get_statement(statement_id=stmt_id) - state = resp.status.state - if state == StatementState.FAILED: - raise RuntimeError(resp.status.error.message) - return resp - - -def time_query(client, warehouse_id, statement): - t0 = time.time() - try: - run_sql(client, warehouse_id, statement) - return round(time.time() - t0, 3) - except Exception as exc: - print(f" query error: {exc}", file=sys.stderr) - return None - - -def scalar_query(client, warehouse_id, statement): - try: - resp = run_sql(client, warehouse_id, statement) - data = resp.result.data_array if resp.result else None - val = data[0][0] if data else None - return int(val) if val is not None else 0 - except Exception: - return 0 - - -def parse_queries(path): - text = Path(path).read_text() - # strip -- comments, split on ; - import re - text = re.sub(r'--[^\n]*', '', text) - queries = [q.strip() for q in text.split(';')] - return [q for q in queries if q] - - -def now_utc(): - return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--queries", default="queries_mv.sql") - parser.add_argument("--interval", type=float, default=600.0) - parser.add_argument("--output", default="") - parser.add_argument("--output-dir", default=".") - parser.add_argument("--warehouse", default=_warehouse_id) - parser.add_argument("--system", default="Databricks") - parser.add_argument("--cluster-size", default="1") - parser.add_argument("--comment", default="") - args = parser.parse_args() - - if not args.warehouse: - sys.exit("ERROR: --warehouse or DATABRICKS_HTTP_PATH is required") - - queries = parse_queries(args.queries) - if not queries: - sys.exit(f"ERROR: no queries found in {args.queries}") - print(f"Parsed {len(queries)} queries from {args.queries}", file=sys.stderr) - - output = args.output or f"{args.output_dir}/dashboard_{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%SZ')}.jsonl" - Path(output).parent.mkdir(parents=True, exist_ok=True) - print(f"Writing JSONL to {output}", file=sys.stderr) - print(f"Interval {args.interval}s. Ctrl-C to stop.", file=sys.stderr) - - client = WorkspaceClient() - - # fetch version once - try: - resp = run_sql(client, args.warehouse, "SELECT version()") - data = resp.result.data_array if resp.result else None - version = data[0][0] if data else "unknown" - except Exception: - version = "unknown" - - iteration = 0 - try: - while True: - iteration += 1 - ts_start = now_utc() - print(f"[{datetime.now(timezone.utc).strftime('%H:%M:%S')}] iter {iteration} starting...", file=sys.stderr) - - raw_rows = scalar_query(client, args.warehouse, "SELECT count(*) FROM workspace.benchmarking.quotes") - mv_rows = scalar_query(client, args.warehouse, "SELECT count(*) FROM workspace.benchmarking.quotes_daily") - print(f" raw_rows={raw_rows:,} mv_rows={mv_rows:,}", file=sys.stderr) - - results = [] - for i, q in enumerate(queries): - t = time_query(client, args.warehouse, q) - results.append([t]) - print(f" q{i+1}/{len(queries)}: {t}s", file=sys.stderr) - - ts_end = now_utc() +from runner_common import runner_main - record = { - "iteration": iteration, - "iteration_started_at": ts_start, - "iteration_finished_at": ts_end, - "raw_rows": raw_rows, - "mv_rows": mv_rows, - "system": args.system, - "version": version, - "cluster_size": args.cluster_size, - "comment": f"{args.comment} (dashboard)", - "result": results, - } - with open(output, "a") as f: - f.write(json.dumps(record) + "\n") +WORKLOAD = "dashboard" +DEFAULT_INTERVAL_SECONDS = 600.0 +DEFAULT_QUERY_FILE = Path(__file__).resolve().with_name("queries_mv.sql") - print(f" done. sleeping {args.interval}s...", file=sys.stderr) - time.sleep(args.interval) - except KeyboardInterrupt: - print(f"\nStopped after {iteration} iterations.", file=sys.stderr) +def main(argv: Sequence[str] | None = None) -> int: + return runner_main( + WORKLOAD, + DEFAULT_QUERY_FILE, + DEFAULT_INTERVAL_SECONDS, + argv, + ) if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/full-path-realtime/quotes/databricks/run_drilldown.py b/full-path-realtime/quotes/databricks/run_drilldown.py index 6e84665..e1151b4 100644 --- a/full-path-realtime/quotes/databricks/run_drilldown.py +++ b/full-path-realtime/quotes/databricks/run_drilldown.py @@ -1,163 +1,24 @@ #!/usr/bin/env python3 -""" -Drilldown runner — loops the raw quotes query every --interval seconds -(default hourly), appending one JSONL record per iteration to --output. -Run in parallel to the ingest script; Ctrl-C when ingest finishes. +"""Thin entry point for the two-query Databricks drill-down workload.""" -Usage: - export DATABRICKS_HOST="https://dbc-....cloud.databricks.com" - export DATABRICKS_HTTP_PATH="/sql/1.0/warehouses/" - export DATABRICKS_TOKEN="dapi..." - - python3 run_drilldown.py \ - --system "Databricks" \ - --machine "Small warehouse" \ - --cluster-size 1 \ - --comment "10B rows" \ - --interval 3600 \ - --output drilldown.jsonl -""" - -import argparse -import json -import os -import re -import sys -import time -from datetime import datetime, timezone from pathlib import Path +from typing import Sequence -from databricks.sdk import WorkspaceClient -from databricks.sdk.service.sql import StatementState - -_http_path = os.environ.get("DATABRICKS_HTTP_PATH", "") -_warehouse_id = _http_path.rstrip("/").split("/")[-1] if _http_path else "" - -_hostname = os.environ.get("DATABRICKS_SERVER_HOSTNAME", "") -if _hostname and not os.environ.get("DATABRICKS_HOST"): - host = f"https://{_hostname}" if not _hostname.startswith("http") else _hostname - os.environ["DATABRICKS_HOST"] = host - - -def run_sql(client, warehouse_id, statement): - resp = client.statement_execution.execute_statement( - statement=statement, warehouse_id=warehouse_id, wait_timeout="0s") - stmt_id = resp.statement_id - state = resp.status.state - while state in (StatementState.PENDING, StatementState.RUNNING): - time.sleep(0.2) - resp = client.statement_execution.get_statement(statement_id=stmt_id) - state = resp.status.state - if state == StatementState.FAILED: - raise RuntimeError(resp.status.error.message) - return resp - - -def time_query(client, warehouse_id, statement): - t0 = time.time() - try: - run_sql(client, warehouse_id, statement) - return round(time.time() - t0, 3) - except Exception as exc: - print(f" query error: {exc}", file=sys.stderr) - return None - - -def scalar_query(client, warehouse_id, statement): - try: - resp = run_sql(client, warehouse_id, statement) - data = resp.result.data_array if resp.result else None - val = data[0][0] if data else None - return int(val) if val is not None else 0 - except Exception: - return 0 - - -def parse_queries(path): - text = Path(path).read_text() - text = re.sub(r'--[^\n]*', '', text) - queries = [q.strip() for q in text.split(';')] - return [q for q in queries if q] - - -def now_utc(): - return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--queries", default="queries_raw.sql") - parser.add_argument("--interval", type=float, default=3600.0) - parser.add_argument("--output", default="") - parser.add_argument("--output-dir", default=".") - parser.add_argument("--warehouse", default=_warehouse_id) - parser.add_argument("--system", default="Databricks") - parser.add_argument("--cluster-size", default="1") - parser.add_argument("--comment", default="") - args = parser.parse_args() - - if not args.warehouse: - sys.exit("ERROR: --warehouse or DATABRICKS_HTTP_PATH is required") - - queries = parse_queries(args.queries) - if not queries: - sys.exit(f"ERROR: no queries found in {args.queries}") - print(f"Parsed {len(queries)} queries from {args.queries}", file=sys.stderr) - - output = args.output or f"{args.output_dir}/drilldown_{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%SZ')}.jsonl" - Path(output).parent.mkdir(parents=True, exist_ok=True) - print(f"Writing JSONL to {output}", file=sys.stderr) - print(f"Interval {args.interval}s. Ctrl-C to stop.", file=sys.stderr) - - client = WorkspaceClient() - - try: - resp = run_sql(client, args.warehouse, "SELECT version()") - data = resp.result.data_array if resp.result else None - version = data[0][0] if data else "unknown" - except Exception: - version = "unknown" - - iteration = 0 - try: - while True: - iteration += 1 - ts_start = now_utc() - print(f"[{datetime.now(timezone.utc).strftime('%H:%M:%S')}] iter {iteration} starting...", file=sys.stderr) - - raw_rows = scalar_query(client, args.warehouse, "SELECT count(*) FROM workspace.benchmarking.quotes") - mv_rows = scalar_query(client, args.warehouse, "SELECT count(*) FROM workspace.benchmarking.quotes_daily") - print(f" raw_rows={raw_rows:,} mv_rows={mv_rows:,}", file=sys.stderr) - - results = [] - for i, q in enumerate(queries): - t = time_query(client, args.warehouse, q) - results.append([t]) - print(f" q{i+1}/{len(queries)}: {t}s", file=sys.stderr) - - ts_end = now_utc() +from runner_common import runner_main - record = { - "iteration": iteration, - "iteration_started_at": ts_start, - "iteration_finished_at": ts_end, - "raw_rows": raw_rows, - "mv_rows": mv_rows, - "system": args.system, - "version": version, - "cluster_size": args.cluster_size, - "comment": f"{args.comment} (drilldown)", - "result": results, - } - with open(output, "a") as f: - f.write(json.dumps(record) + "\n") +WORKLOAD = "drilldown" +DEFAULT_INTERVAL_SECONDS = 3600.0 +DEFAULT_QUERY_FILE = Path(__file__).resolve().with_name("queries_raw.sql") - print(f" done. sleeping {args.interval}s...", file=sys.stderr) - time.sleep(args.interval) - except KeyboardInterrupt: - print(f"\nStopped after {iteration} iterations.", file=sys.stderr) +def main(argv: Sequence[str] | None = None) -> int: + return runner_main( + WORKLOAD, + DEFAULT_QUERY_FILE, + DEFAULT_INTERVAL_SECONDS, + argv, + ) if __name__ == "__main__": - main() + raise SystemExit(main()) diff --git a/full-path-realtime/quotes/databricks/run_full_serverless_baseline.sh b/full-path-realtime/quotes/databricks/run_full_serverless_baseline.sh new file mode 100755 index 0000000..194e611 --- /dev/null +++ b/full-path-realtime/quotes/databricks/run_full_serverless_baseline.sh @@ -0,0 +1,656 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Canonical 113,219,565,734-row Serverless SQL baseline. Lakehouse//RT is not +# used. Run only against the fresh schema named below. + +umask 077 + +DBX_BENCH_DIR="/home/ubuntu/costbench/full-path-realtime/quotes/databricks" +SOURCE_DIR="/data/quotes" +SOURCE_INVENTORY_OUTPUT="/data/databricks-qualification/source_inventory.json" +RUNNER_PYTHON="$DBX_BENCH_DIR/.venv-runner/bin/python" +ZEROBUS_PYTHON="$DBX_BENCH_DIR/.venv-zerobus/bin/python" +: "${DATABRICKS_HOST:?Set the Databricks workspace HTTPS origin}" +: "${DATABRICKS_WORKSPACE_ID:?Set the decimal Databricks workspace ID}" +: "${DATABRICKS_CLIENT_ID:?Set the benchmark service-principal application ID}" +: "${DATABRICKS_REGION:=eu-west-1}" +DATABRICKS_CATALOG="costbench" +DATABRICKS_SCHEMA="rt_full_serverless_baseline_r3_20260918" +DATABRICKS_RAW_TABLE="quotes" +DATABRICKS_MV_TABLE="quotes_daily" +: "${DATABRICKS_CONTROL_WAREHOUSE_ID:?Set the control warehouse ID}" +: "${DATABRICKS_QUERY_WAREHOUSE_ID:?Set the query warehouse ID}" +DATABRICKS_WAREHOUSE_MODE="serverless-baseline" +DATABRICKS_QUERY_SIZE="X-Small" +: "${ZEROBUS_ENDPOINT:=https://${DATABRICKS_WORKSPACE_ID}.zerobus.${DATABRICKS_REGION}.cloud.databricks.com}" +EXPECTED_ROWS=113219565734 +TARGET_EPS=1000000 +MANUAL_STREAM_ROTATION_SECONDS=600 +FRESHNESS_INTERVAL_SECONDS=60 +MAX_CATCHUP_SECONDS=900 +POST_INGEST_QUERY_SECONDS=10800 +RUN_ID="serverless_baseline_full_$(date -u +%Y%m%dT%H%M%SZ)" +RUN_STAMP="${RUN_ID#serverless_baseline_full_}" +RUN_ROOT="/data/databricks-runs/${RUN_ID}" +RUN_STATE="/data/databricks-state/${RUN_ID}" +RUN_CONTEXT="$RUN_ROOT/run_context.json" +LATEST_CONTEXT="/data/databricks-runs/latest_serverless_baseline.json" +SINCE_UTC="$(date -u +%Y-%m-%dT%H:%M:%S.000Z)" +QUERY_OUTPUT_DIR="$RUN_ROOT" +QUERY_CONTEXT_PATH="$RUN_ROOT/validation/query_context.json" +DASHBOARD_OUTPUT="$RUN_ROOT/mv/dashboard_${RUN_STAMP}.jsonl" +DRILLDOWN_OUTPUT="$RUN_ROOT/raw/drilldown_${RUN_STAMP}.jsonl" +QUERY_LOG_DIR="$RUN_STATE/logs" +INGEST_STATE_PROGRESS="$RUN_STATE/ingest_progress.json" +INGEST_STATE_MANIFEST="$RUN_STATE/source_manifest.json" +INGEST_STATE_LEDGER="$RUN_STATE/ingest_events.jsonl" +INGEST_METRICS="$RUN_ROOT/ingest/ingest_metrics.jsonl" +FRESHNESS_OUTPUT="$RUN_ROOT/freshness/mv_freshness_${RUN_STAMP}.jsonl" +EVIDENCE_STATE_DIR="$RUN_STATE/evidence-initial" +QUERY_AUDIT_DIR="$RUN_STATE/query-evidence" +INGEST_PID="" +MONITOR_PID="" +QUERY_PID="" + +cleanup() { + unset DATABRICKS_CLIENT_SECRET + for pid in "$INGEST_PID" "$MONITOR_PID" "$QUERY_PID"; do + if [[ -n "$pid" ]] && kill -0 "$pid" 2>/dev/null; then + kill -TERM "$pid" 2>/dev/null || true + fi + done + for pid in "$INGEST_PID" "$MONITOR_PID" "$QUERY_PID"; do + if [[ -n "$pid" ]]; then + wait "$pid" 2>/dev/null || true + fi + done +} + +handle_interrupt() { + trap - EXIT INT TERM + unset DATABRICKS_CLIENT_SECRET + echo "Interrupt received; waiting for child processes to checkpoint..." >&2 + for pid in "$INGEST_PID" "$MONITOR_PID" "$QUERY_PID"; do + if [[ -n "$pid" ]]; then + wait "$pid" 2>/dev/null || true + fi + done + exit 130 +} + +handle_termination() { + trap - EXIT INT TERM + cleanup + exit 143 +} + +trap cleanup EXIT +trap handle_interrupt INT +trap handle_termination TERM + +TARGET="${DATABRICKS_CATALOG}.${DATABRICKS_SCHEMA}.${DATABRICKS_RAW_TABLE}" +EXPECTED_TARGET="costbench.rt_full_serverless_baseline_r3_20260918.quotes" +if [[ "$TARGET" != "$EXPECTED_TARGET" ]]; then + printf 'Refusing unexpected target: %s\n' "$TARGET" >&2 + exit 1 +fi +if [[ "$DATABRICKS_QUERY_WAREHOUSE_ID" == "$DATABRICKS_CONTROL_WAREHOUSE_ID" ]]; then + echo "Query and control warehouses must be distinct." >&2 + exit 1 +fi +if [[ -e "$RUN_ROOT" ]]; then + printf 'Refusing existing output directory: %s\n' "$RUN_ROOT" >&2 + exit 1 +fi +if [[ -e "$RUN_STATE" ]]; then + printf 'Refusing existing runtime-state directory: %s\n' "$RUN_STATE" >&2 + exit 1 +fi +for python in "$RUNNER_PYTHON" "$ZEROBUS_PYTHON"; do + if [[ ! -x "$python" ]]; then + printf 'Missing Python environment: %s\n' "$python" >&2 + exit 1 + fi +done +jq -e ' + .status == "complete" + and .actual_rows == 113219565734 + and .file_count == 232 +' "$SOURCE_INVENTORY_OUTPUT" >/dev/null + +printf 'Canonical target: %s\n' "$TARGET" +printf 'Rows: %d (entire source)\n' "$EXPECTED_ROWS" +printf 'Target EPS: %d\n' "$TARGET_EPS" +printf 'Expected ingest: approximately 31h27m\n' +printf 'Query warehouse: %s (Serverless X-Small)\n' \ + "$DATABRICKS_QUERY_WAREHOUSE_ID" +printf 'Control warehouse:%s\n' "$DATABRICKS_CONTROL_WAREHOUSE_ID" +printf 'Dashboard: 4 queries every 600 seconds\n' +printf 'Drill-down: 2 queries every 3600 seconds\n' +printf 'Run root: %s\n' "$RUN_ROOT" +printf '\nThis permanently consumes the fresh canonical target.\n' +read -r -p "Type '$EXPECTED_TARGET' to continue: " confirmation +if [[ "$confirmation" != "$EXPECTED_TARGET" ]]; then + echo "Confirmation did not match; nothing was run." >&2 + exit 1 +fi + +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET +echo +export DATABRICKS_CLIENT_ID DATABRICKS_CLIENT_SECRET +export DATABRICKS_HOST DATABRICKS_CONTROL_WAREHOUSE_ID +export DATABRICKS_QUERY_WAREHOUSE_ID DATABRICKS_WAREHOUSE_MODE +export DATABRICKS_QUERY_SIZE +export POST_INGEST_QUERY_SECONDS +unset DATABRICKS_TOKEN + +mkdir -p \ + "$RUN_ROOT/ingest" \ + "$RUN_ROOT/freshness" \ + "$RUN_ROOT/mv" \ + "$RUN_ROOT/raw" \ + "$RUN_ROOT/evidence" \ + "$RUN_ROOT/costs" \ + "$RUN_ROOT/validation" \ + "$RUN_ROOT/charts" \ + "$QUERY_LOG_DIR" +jq -n \ + --arg run_id "$RUN_ID" \ + --arg run_root "$RUN_ROOT" \ + --arg since "$SINCE_UTC" \ + --arg catalog "$DATABRICKS_CATALOG" \ + --arg schema "$DATABRICKS_SCHEMA" \ + --arg raw_table "$DATABRICKS_RAW_TABLE" \ + --arg mv_table "$DATABRICKS_MV_TABLE" \ + --arg query_warehouse_id "$DATABRICKS_QUERY_WAREHOUSE_ID" \ + --arg control_warehouse_id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --argjson expected_rows "$EXPECTED_ROWS" \ + --argjson target_eps "$TARGET_EPS" \ + --argjson post_ingest_query_seconds "$POST_INGEST_QUERY_SECONDS" \ + '{ + schema_version: 1, + status: "running", + run_id: $run_id, + run_root: $run_root, + since: $since, + catalog: $catalog, + schema: $schema, + raw_table: $raw_table, + mv_table: $mv_table, + query_warehouse_id: $query_warehouse_id, + query_warehouse_mode: "serverless-baseline", + query_size: "X-Small", + control_warehouse_id: $control_warehouse_id, + expected_rows: $expected_rows, + target_eps: $target_eps, + dashboard_interval_seconds: 600, + drilldown_interval_seconds: 3600, + post_ingest_query_seconds: $post_ingest_query_seconds, + result_profile: "compact", + ingest_metrics_interval_seconds: 60, + freshness_interval_seconds: 60, + automatic_stream_recovery: true, + recovery_timeout_ms: 15000, + recovery_backoff_ms: 2000, + recovery_retries: 4, + manual_stream_rotation_seconds: 600 + }' > "$RUN_CONTEXT" +cp "$RUN_CONTEXT" "$LATEST_CONTEXT" + +echo "Running metadata-only Databricks preflight (no Zerobus streams yet)..." +if ! "$RUNNER_PYTHON" "$DBX_BENCH_DIR/preflight.py" \ + --online \ + --host "$DATABRICKS_HOST" \ + --cloud aws \ + --region eu-west-1 \ + --workspace-id "$DATABRICKS_WORKSPACE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --raw-table "$DATABRICKS_RAW_TABLE" \ + --mv-table "$DATABRICKS_MV_TABLE" \ + --rt-warehouse "$DATABRICKS_QUERY_WAREHOUSE_ID" \ + --query-warehouse-mode serverless-baseline \ + --control-warehouse "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --zerobus-endpoint "$ZEROBUS_ENDPOINT" \ + --runner-python "$RUNNER_PYTHON" \ + --zerobus-python "$ZEROBUS_PYTHON" \ + --output "$RUN_ROOT/validation/preflight.json" \ + > "$QUERY_LOG_DIR/preflight.stdout.json"; then + jq '{ + summary, + online_status: .online.status, + errors: .online.errors, + warnings: .online.warnings, + target_contract: .online.target_contract + }' "$RUN_ROOT/validation/preflight.json" >&2 || true + jq '.status = "preflight_failed"' \ + "$RUN_CONTEXT" > "$RUN_CONTEXT.tmp" + mv "$RUN_CONTEXT.tmp" "$RUN_CONTEXT" + cp "$RUN_CONTEXT" "$LATEST_CONTEXT" + echo "Canonical preflight failed; no Zerobus stream was opened." >&2 + exit 1 +fi +echo "Preflight passed. Preparing the full source manifest..." + +"$ZEROBUS_PYTHON" "$DBX_BENCH_DIR/ingest_zerobus.py" \ + --dir "$SOURCE_DIR" \ + --pattern 'quotes_*.parquet' \ + --host "$DATABRICKS_HOST" \ + --endpoint "$ZEROBUS_ENDPOINT" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --table "$DATABRICKS_RAW_TABLE" \ + --count-warehouse "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --workers 16 \ + --batch-size 50000 \ + --queue-capacity 64 \ + --target-eps "$TARGET_EPS" \ + --compression NONE \ + --checkpoint-batches 1 \ + --checkpoint-seconds 1 \ + --checkpoint-method wait \ + --recovery-timeout-ms 15000 \ + --recovery-backoff-ms 2000 \ + --recovery-retries 4 \ + --recovery-log-threshold-seconds 2 \ + --manual-stream-rotation-seconds "$MANUAL_STREAM_ROTATION_SECONDS" \ + --metrics-interval 5 \ + --metrics-output-interval 60 \ + --compact-metrics \ + --compact-evidence \ + --memory-trim-interval 60 \ + --min-system-available-gib 16 \ + --compact-progress \ + --defer-source-hashes \ + --expected-rows "$EXPECTED_ROWS" \ + --run-id "$RUN_ID" \ + --output-dir "$RUN_ROOT/ingest" \ + --manifest-output "$INGEST_STATE_MANIFEST" \ + --progress-file "$INGEST_STATE_PROGRESS" \ + --ledger-file "$INGEST_STATE_LEDGER" \ + --metrics-file "$INGEST_METRICS" \ + --summary-file "$RUN_ROOT/ingest/ingest_summary.json" \ + --quiet-worker-logs \ + > >(tee "$QUERY_LOG_DIR/ingest.log") 2>&1 & +INGEST_PID=$! + +for ((attempt = 1; attempt <= 1800; attempt++)); do + if [[ -s "$INGEST_STATE_PROGRESS" ]]; then + break + fi + if ! kill -0 "$INGEST_PID" 2>/dev/null; then + wait "$INGEST_PID" + echo "Ingest exited before creating its progress journal." >&2 + exit 1 + fi + sleep 1 +done +if [[ ! -s "$INGEST_STATE_PROGRESS" ]]; then + echo "Timed out preparing the canonical source manifest." >&2 + exit 1 +fi + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/monitor_freshness.py" \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --raw-table "$DATABRICKS_RAW_TABLE" \ + --mv-table "$DATABRICKS_MV_TABLE" \ + --producer-progress "$INGEST_STATE_PROGRESS" \ + --since "$SINCE_UTC" \ + --interval "$FRESHNESS_INTERVAL_SECONDS" \ + --iterations 0 \ + --compact-event-log \ + --compact-output \ + --output "$FRESHNESS_OUTPUT" \ + --progress-json "$RUN_ROOT/freshness/freshness_progress.json" \ + --run-id "$RUN_ID" \ + > >(tee "$QUERY_LOG_DIR/freshness.log") 2>&1 & +MONITOR_PID=$! + +for ((attempt = 1; attempt <= 300; attempt++)); do + if [[ -s "$RUN_ROOT/freshness/freshness_progress.json" ]]; then + break + fi + if ! kill -0 "$MONITOR_PID" 2>/dev/null; then + wait "$MONITOR_PID" + echo "Freshness monitor exited before its first observation." >&2 + exit 1 + fi + sleep 1 +done +if [[ ! -s "$RUN_ROOT/freshness/freshness_progress.json" ]]; then + echo "Timed out waiting for the first freshness observation." >&2 + exit 1 +fi + +export INGEST_CONTEXT="$RUN_CONTEXT" +export DATABRICKS_QUERY_RUN_ID="$RUN_ID" +export QUERY_OUTPUT_DIR QUERY_CONTEXT_PATH +export DASHBOARD_OUTPUT DRILLDOWN_OUTPUT QUERY_LOG_DIR +PRODUCER_PROGRESS="$INGEST_STATE_PROGRESS" +FRESHNESS_PROGRESS="$RUN_ROOT/freshness/freshness_progress.json" +export PRODUCER_PROGRESS FRESHNESS_PROGRESS +export QUERY_WINDOW_SCOPE="canonical-full" +"$DBX_BENCH_DIR/run_query_workloads.sh" \ + > >(tee "$QUERY_LOG_DIR/query_workloads.log") 2>&1 & +QUERY_PID=$! + +while kill -0 "$INGEST_PID" 2>/dev/null; do + if ! kill -0 "$QUERY_PID" 2>/dev/null; then + set +e + wait "$QUERY_PID" + QUERY_STATUS=$? + set -e + QUERY_PID="" + echo "Query workload exited while canonical ingestion was active." >&2 + if [[ "$QUERY_STATUS" -eq 0 ]]; then + exit 1 + fi + exit "$QUERY_STATUS" + fi + if ! kill -0 "$MONITOR_PID" 2>/dev/null; then + set +e + wait "$MONITOR_PID" + MONITOR_STATUS=$? + set -e + MONITOR_PID="" + echo "Freshness monitor exited while canonical ingestion was active." >&2 + if [[ "$MONITOR_STATUS" -eq 0 ]]; then + exit 1 + fi + exit "$MONITOR_STATUS" + fi + sleep 30 +done + +set +e +wait "$INGEST_PID" +INGEST_STATUS=$? +set -e +INGEST_PID="" + +if [[ "$INGEST_STATUS" -ne 0 ]]; then + cp "$INGEST_STATE_PROGRESS" "$RUN_ROOT/ingest/ingest_progress.json" + FAILED_AT="$(date -u +%Y-%m-%dT%H:%M:%S.000Z)" + jq \ + --arg failed_at "$FAILED_AT" \ + --argjson ingest_status "$INGEST_STATUS" \ + --slurpfile ingest "$RUN_ROOT/ingest/ingest_summary.json" \ + '.status = "failed" + | .publishable = false + | .failed_at = $failed_at + | .failure = { + ingest_process_status: $ingest_status, + elapsed_sec: $ingest[0].elapsed_sec, + client_durable_rows: $ingest[0].provider_committed_rows, + submitted_rows: $ingest[0].submitted_rows, + pending_rows: $ingest[0].pending_rows, + terminal_error: $ingest[0].terminal_error, + ambiguous: $ingest[0].ambiguous, + transport_recovery: $ingest[0].transport_recovery + }' \ + "$RUN_CONTEXT" > "$RUN_CONTEXT.tmp" + mv "$RUN_CONTEXT.tmp" "$RUN_CONTEXT" + cp "$RUN_CONTEXT" "$LATEST_CONTEXT" + echo "Canonical ingest failed; stopping query and freshness processes." >&2 + exit "$INGEST_STATUS" +fi +cp "$INGEST_STATE_PROGRESS" "$RUN_ROOT/ingest/ingest_progress.json" + +set +e +wait "$QUERY_PID" +QUERY_STATUS=$? +set -e +QUERY_PID="" +if [[ "$QUERY_STATUS" -ne 0 ]]; then + echo "Query workload failed validation." >&2 + exit "$QUERY_STATUS" +fi + +PRODUCER_FINISHED_AT="$( + jq -er '.updated_at' "$RUN_ROOT/ingest/ingest_summary.json" +)" +caught_up=false +for ((attempt = 1; attempt <= MAX_CATCHUP_SECONDS / 30; attempt++)); do + if jq -e --arg finished "$PRODUCER_FINISHED_AT" ' + .mv_rows_behind == 0 + and (.errors | length) == 0 + and .observed_at >= $finished + ' "$RUN_ROOT/freshness/freshness_progress.json" >/dev/null; then + caught_up=true + break + fi + sleep 30 +done +if [[ "$caught_up" != true ]]; then + echo "MV did not prove final catch-up within 15 minutes." >&2 + exit 1 +fi + +kill -TERM "$MONITOR_PID" +set +e +wait "$MONITOR_PID" +MONITOR_STATUS=$? +set -e +MONITOR_PID="" +if [[ "$MONITOR_STATUS" -ne 0 ]]; then + echo "Freshness monitor failed." >&2 + exit "$MONITOR_STATUS" +fi + +UNTIL_UTC="$( + jq -er '.observed_at' "$RUN_ROOT/freshness/freshness_progress.json" +)" +jq \ + --arg until "$UNTIL_UTC" \ + --arg producer_finished_at "$PRODUCER_FINISHED_AT" \ + '.status = "measurement_complete" + | .until = $until + | .producer_finished_at = $producer_finished_at' \ + "$RUN_CONTEXT" > "$RUN_CONTEXT.tmp" +mv "$RUN_CONTEXT.tmp" "$RUN_CONTEXT" +cp "$RUN_CONTEXT" "$LATEST_CONTEXT" + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/hash_source_manifest.py" \ + --manifest "$INGEST_STATE_MANIFEST" \ + --output "$RUN_ROOT/ingest/source_hashes_post_run.json" + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/collect_evidence.py" \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --rt-warehouse-id "$DATABRICKS_QUERY_WAREHOUSE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --raw-table "$DATABRICKS_RAW_TABLE" \ + --mv-table "$DATABRICKS_MV_TABLE" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --max-window-hours 36 \ + --history-limit 30000 \ + --statement-timeout 600 \ + --run-id "$RUN_ID" \ + --output-dir "$EVIDENCE_STATE_DIR" + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/compact_evidence.py" \ + --evidence-dir "$EVIDENCE_STATE_DIR" \ + --preflight "$RUN_ROOT/validation/preflight.json" \ + --output-dir "$RUN_ROOT/evidence" + +set +e +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/costs/summarize_run.py" \ + --evidence-dir "$EVIDENCE_STATE_DIR" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --producer-finished-at "$PRODUCER_FINISHED_AT" \ + --rt-warehouse-id "$DATABRICKS_QUERY_WAREHOUSE_ID" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --output "$RUN_ROOT/costs/cost_summary_initial.json" +COST_STATUS=$? +set -e + +MIN_ROTATIONS=187 +if ! jq -e --argjson expected "$EXPECTED_ROWS" --argjson rotations "$MIN_ROTATIONS" ' + .finished == true + and .stopped_early == false + and .provider_committed_rows == $expected + and .submitted_rows == $expected + and .pending_rows == 0 + and .pending_batches == 0 + and .terminal_error == null + and .ambiguous == false + and .clean_checkpoint == true + and all( + .stream_status[]; + .automatic_recovery == true + and .rotation_count >= $rotations + and .last_rotation_unacked_batches == 0 + and .close_succeeded == true + and .unacked_inspection_succeeded == true + and .unacked_batches == 0 + ) +' "$RUN_ROOT/ingest/ingest_summary.json" >/dev/null; then + echo "Canonical ingest gate failed." >&2 + exit 1 +fi + +if ! jq -s -e 'all(.[]; (.errors | length) == 0)' \ + "$FRESHNESS_OUTPUT" >/dev/null; then + echo "Freshness evidence contains errors." >&2 + exit 1 +fi + +if ! jq -e ' + .complete == true + and .provider_errors == 0 + and .zerobus_stream_error_count == 0 + and .mv_maintenance.full_refresh_count == 0 +' "$EVIDENCE_STATE_DIR/evidence_summary.json" >/dev/null; then + echo "Provider evidence completeness/error gate failed." >&2 + exit 1 +fi + +CLIENT_DURABLE_ROWS="$( + jq -er '.provider_committed_rows' "$RUN_ROOT/ingest/ingest_summary.json" +)" +PROVIDER_COMMITTED_ROWS="$( + jq -er '.provider_committed_records' \ + "$EVIDENCE_STATE_DIR/evidence_summary.json" +)" +if (( PROVIDER_COMMITTED_ROWS < CLIENT_DURABLE_ROWS )); then + printf 'Provider committed rows (%s) are below client durable rows (%s).\n' \ + "$PROVIDER_COMMITTED_ROWS" "$CLIENT_DURABLE_ROWS" >&2 + exit 1 +fi +DUPLICATE_ROWS=$((PROVIDER_COMMITTED_ROWS - CLIENT_DURABLE_ROWS)) +if (( DUPLICATE_ROWS > 0 )); then + printf 'WARNING: provider reports %d replay/duplicate surplus rows.\n' \ + "$DUPLICATE_ROWS" >&2 +fi + +if ! jq -e --argjson provider_rows "$PROVIDER_COMMITTED_ROWS" ' + .mv_source_rows == $provider_rows + and .mv_rows_behind == 0 + and (.errors | length) == 0 +' "$RUN_ROOT/freshness/freshness_progress.json" >/dev/null; then + echo "Final MV watermark gate failed." >&2 + exit 1 +fi + +if ! jq -e ' + .dashboard_iterations >= 200 + and .drilldown_iterations >= 33 + and .dashboard_process_status == 0 + and .drilldown_process_status == 0 + and .partial_ingest_window == false +' "$QUERY_CONTEXT_PATH" >/dev/null; then + echo "Canonical query-workload gate failed." >&2 + exit 1 +fi + +HASHED_SOURCE_FILE_COUNT="$(jq -er '.selected_file_count' "$INGEST_STATE_MANIFEST")" +if ! jq -e --argjson expected_files "$HASHED_SOURCE_FILE_COUNT" ' + .status == "complete" + and .file_count == $expected_files +' "$RUN_ROOT/ingest/source_hashes_post_run.json" >/dev/null; then + echo "Post-run source hash gate failed." >&2 + exit 1 +fi + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/validate_run.py" \ + --source-manifest "$INGEST_STATE_MANIFEST" \ + --ingest-progress "$RUN_ROOT/ingest/ingest_progress.json" \ + --ingest-metrics "$INGEST_METRICS" \ + --dashboard "$DASHBOARD_OUTPUT" \ + --drilldown "$DRILLDOWN_OUTPUT" \ + --freshness "$FRESHNESS_OUTPUT" \ + --evidence-summary "$EVIDENCE_STATE_DIR/evidence_summary.json" \ + --cost-summary "$RUN_ROOT/costs/cost_summary_initial.json" \ + --output "$RUN_ROOT/validation/validation_report_initial.json" \ + --expected-rows "$EXPECTED_ROWS" || VALIDATION_STATUS=$? +VALIDATION_STATUS="${VALIDATION_STATUS:-0}" + +if ! jq -e --argjson duplicate_rows "$DUPLICATE_ROWS" ' + all( + .gates[]; + .passed == true + or .id == "cost_completeness" + or ($duplicate_rows > 0 and .id == "row_reconciliation") + ) +' "$RUN_ROOT/validation/validation_report_initial.json" >/dev/null; then + echo "Canonical non-cost validation gate failed." >&2 + exit 1 +fi + +jq \ + --argjson cost_status "$COST_STATUS" \ + --argjson validation_status "$VALIDATION_STATUS" \ + --argjson client_durable_rows "$CLIENT_DURABLE_ROWS" \ + --argjson provider_committed_rows "$PROVIDER_COMMITTED_ROWS" \ + --argjson duplicate_rows "$DUPLICATE_ROWS" \ + '.status = ( + if $duplicate_rows == 0 + then "complete" + else "complete_with_duplicates" + end + ) + | .initial_cost_process_status = $cost_status + | .initial_validation_process_status = $validation_status + | .reconciliation = { + client_durable_rows: $client_durable_rows, + provider_committed_rows: $provider_committed_rows, + duplicate_surplus_rows: $duplicate_rows, + exact: ($duplicate_rows == 0) + }' \ + "$RUN_CONTEXT" > "$RUN_CONTEXT.tmp" +mv "$RUN_CONTEXT.tmp" "$RUN_CONTEXT" +cp "$RUN_CONTEXT" "$LATEST_CONTEXT" + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/compact_query_results.py" \ + --input "$DASHBOARD_OUTPUT" \ + --output "$DASHBOARD_OUTPUT" \ + --audit-output "$QUERY_AUDIT_DIR/$(basename "$DASHBOARD_OUTPUT")" \ + >/dev/null +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/compact_query_results.py" \ + --input "$DRILLDOWN_OUTPUT" \ + --output "$DRILLDOWN_OUTPUT" \ + --audit-output "$QUERY_AUDIT_DIR/$(basename "$DRILLDOWN_OUTPUT")" \ + >/dev/null + +"$RUNNER_PYTHON" "$DBX_BENCH_DIR/finalize_results.py" \ + --run-root "$RUN_ROOT" \ + --run-id "$RUN_ID" \ + --source-manifest "$INGEST_STATE_MANIFEST" \ + --source-hashes "$RUN_ROOT/ingest/source_hashes_post_run.json" \ + --status initial_cost_unsettled \ + >/dev/null + +printf 'Canonical baseline completed: %s\n' "$RUN_ROOT" +printf 'Initial cost status: %d (billing may not be settled)\n' "$COST_STATUS" +printf 'Initial validation status: %d (cost may not be settled)\n' \ + "$VALIDATION_STATUS" +printf 'Provider replay/duplicate surplus rows: %d\n' "$DUPLICATE_ROWS" +printf 'Runtime diagnostics retained outside results: %s\n' "$RUN_STATE" +printf 'Stop query warehouse %s manually now.\n' "$DATABRICKS_QUERY_WAREHOUSE_ID" diff --git a/full-path-realtime/quotes/databricks/run_ingest_mv_10m.sh b/full-path-realtime/quotes/databricks/run_ingest_mv_10m.sh new file mode 100644 index 0000000..6bb6675 --- /dev/null +++ b/full-path-realtime/quotes/databricks/run_ingest_mv_10m.sh @@ -0,0 +1,187 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Ten-minute preliminary 1M-EPS ingest and MV-freshness trial. The target +# objects must be created fresh before this script is launched. + +umask 077 + +: "${DBX_BENCH_DIR:=/home/ubuntu/costbench/full-path-realtime/quotes/databricks}" +: "${SOURCE_DIR:=/data/quotes}" +: "${SOURCE_INVENTORY_OUTPUT:=/data/databricks-qualification/source_inventory.json}" +: "${DATABRICKS_RUNNER_PYTHON:=$DBX_BENCH_DIR/.venv-runner/bin/python}" +: "${DATABRICKS_ZEROBUS_PYTHON:=$DBX_BENCH_DIR/.venv-zerobus/bin/python}" +: "${DATABRICKS_HOST:?Set the Databricks workspace HTTPS origin}" +: "${DATABRICKS_WORKSPACE_ID:?Set the decimal Databricks workspace ID}" +: "${DATABRICKS_CLIENT_ID:?Set the benchmark service-principal application ID}" +: "${DATABRICKS_REGION:=eu-west-1}" +: "${DATABRICKS_CATALOG:=costbench}" +: "${DATABRICKS_SCHEMA:=rt_qualification}" +: "${DATABRICKS_RAW_TABLE:=quotes_ingest_mv_10m_20260915}" +: "${DATABRICKS_MV_TABLE:=quotes_daily_ingest_mv_10m_20260915}" +: "${DATABRICKS_CONTROL_WAREHOUSE_ID:?Set the control warehouse ID}" +: "${ZEROBUS_ENDPOINT:=https://${DATABRICKS_WORKSPACE_ID}.zerobus.${DATABRICKS_REGION}.cloud.databricks.com}" + +TARGET="${DATABRICKS_CATALOG}.${DATABRICKS_SCHEMA}.${DATABRICKS_RAW_TABLE}" +EXPECTED_TARGET="costbench.rt_qualification.quotes_ingest_mv_10m_20260915" +EXPECTED_ROWS=600000000 +TARGET_EPS=1000000 +RUN_ID="ingest_mv_10m_$(date -u +%Y%m%dT%H%M%SZ)" +RUN_ROOT="/data/databricks-qualification/${RUN_ID}" +SINCE_UTC="$(date -u +%Y-%m-%dT%H:%M:%S.000Z)" +INGEST_PID="" +MONITOR_PID="" + +cleanup() { + unset DATABRICKS_CLIENT_SECRET + if [[ -n "$INGEST_PID" ]] && kill -0 "$INGEST_PID" 2>/dev/null; then + kill -TERM "$INGEST_PID" 2>/dev/null || true + fi + if [[ -n "$MONITOR_PID" ]] && kill -0 "$MONITOR_PID" 2>/dev/null; then + kill -TERM "$MONITOR_PID" 2>/dev/null || true + fi +} +trap cleanup EXIT INT TERM + +if [[ "$TARGET" != "$EXPECTED_TARGET" ]]; then + printf 'Refusing unexpected target: %s\n' "$TARGET" >&2 + exit 1 +fi +if [[ -e "$RUN_ROOT" ]]; then + printf 'Refusing existing output directory: %s\n' "$RUN_ROOT" >&2 + exit 1 +fi +for python in "$DATABRICKS_RUNNER_PYTHON" "$DATABRICKS_ZEROBUS_PYTHON"; do + if [[ ! -x "$python" ]]; then + printf 'Missing Python environment: %s\n' "$python" >&2 + exit 1 + fi +done + +jq -e ' + .status == "complete" + and .actual_rows == 113219565734 + and .file_count == 232 +' "$SOURCE_INVENTORY_OUTPUT" >/dev/null + +printf 'Raw target: %s\n' "$TARGET" +printf 'MV target: %s.%s.%s\n' \ + "$DATABRICKS_CATALOG" "$DATABRICKS_SCHEMA" "$DATABRICKS_MV_TABLE" +printf 'Rows: %d\n' "$EXPECTED_ROWS" +printf 'Target EPS: %d\n' "$TARGET_EPS" +printf 'Duration: 10 minutes at target rate\n' +printf 'Output: %s\n' "$RUN_ROOT" +read -r -p "Type '$EXPECTED_TARGET' to continue: " confirmation +if [[ "$confirmation" != "$EXPECTED_TARGET" ]]; then + echo "Confirmation did not match; nothing was run." >&2 + exit 1 +fi + +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET +echo +export DATABRICKS_CLIENT_ID DATABRICKS_CLIENT_SECRET +unset DATABRICKS_TOKEN + +mkdir -p "$RUN_ROOT/ingest" "$RUN_ROOT/freshness" + +"$DATABRICKS_ZEROBUS_PYTHON" "$DBX_BENCH_DIR/ingest_zerobus.py" \ + --dir "$SOURCE_DIR" \ + --pattern 'quotes_*.parquet' \ + --host "$DATABRICKS_HOST" \ + --endpoint "$ZEROBUS_ENDPOINT" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --table "$DATABRICKS_RAW_TABLE" \ + --count-warehouse "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --workers 16 \ + --batch-size 50000 \ + --queue-capacity 64 \ + --target-eps "$TARGET_EPS" \ + --compression NONE \ + --checkpoint-batches 1 \ + --checkpoint-seconds 1 \ + --checkpoint-method wait \ + --metrics-interval 5 \ + --memory-trim-interval 60 \ + --min-system-available-gib 16 \ + --max-rows "$EXPECTED_ROWS" \ + --expected-rows "$EXPECTED_ROWS" \ + --allow-partial \ + --run-id "$RUN_ID" \ + --output-dir "$RUN_ROOT/ingest" \ + --quiet-worker-logs \ + > >(tee "$RUN_ROOT/ingest.log") 2>&1 & +INGEST_PID=$! + +for ((attempt = 1; attempt <= 120; attempt++)); do + if [[ -s "$RUN_ROOT/ingest/ingest_progress.json" ]]; then + break + fi + if ! kill -0 "$INGEST_PID" 2>/dev/null; then + wait "$INGEST_PID" + echo "Ingest exited before creating its progress journal." >&2 + exit 1 + fi + sleep 1 +done +if [[ ! -s "$RUN_ROOT/ingest/ingest_progress.json" ]]; then + echo "Timed out waiting for the ingest progress journal." >&2 + exit 1 +fi + +"$DATABRICKS_RUNNER_PYTHON" "$DBX_BENCH_DIR/monitor_freshness.py" \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --raw-table "$DATABRICKS_RAW_TABLE" \ + --mv-table "$DATABRICKS_MV_TABLE" \ + --producer-progress "$RUN_ROOT/ingest/ingest_progress.json" \ + --since "$SINCE_UTC" \ + --interval 30 \ + --iterations 24 \ + --output "$RUN_ROOT/freshness/freshness.jsonl" \ + --progress-json "$RUN_ROOT/freshness/freshness_progress.json" \ + --run-id "$RUN_ID" \ + > >(tee "$RUN_ROOT/freshness.log") 2>&1 & +MONITOR_PID=$! + +set +e +wait "$INGEST_PID" +INGEST_STATUS=$? +wait "$MONITOR_PID" +MONITOR_STATUS=$? +set -e +INGEST_PID="" +MONITOR_PID="" + +jq '{ + run_id, + finished, + terminal_error, + provider_committed_rows, + expected_rows: .source.total_rows, + elapsed_sec, + average_provider_committed_eps: + .eps.average_provider_committed_rows_per_sec, + clean_checkpoint, + safe_to_resume, + table_protection +}' "$RUN_ROOT/ingest/ingest_summary.json" + +jq '{ + run_id, + observed_at, + latest_successful_refresh, + raw_commit_age_sec, + mv_refresh_age_sec, + errors +}' "$RUN_ROOT/freshness/freshness_progress.json" + +printf 'Ingest status: %d\n' "$INGEST_STATUS" +printf 'Monitor status: %d\n' "$MONITOR_STATUS" +printf 'Trial artifacts: %s\n' "$RUN_ROOT" + +if [[ "$INGEST_STATUS" -ne 0 || "$MONITOR_STATUS" -ne 0 ]]; then + exit 1 +fi diff --git a/full-path-realtime/quotes/databricks/run_ingest_mv_40m.sh b/full-path-realtime/quotes/databricks/run_ingest_mv_40m.sh new file mode 100644 index 0000000..11159d4 --- /dev/null +++ b/full-path-realtime/quotes/databricks/run_ingest_mv_40m.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Fresh-target qualification of long-run journal scaling and proactive, +# fail-closed Zerobus stream rotation. + +export DATABRICKS_RAW_TABLE="quotes_ingest_mv_40m_20260917" +export DATABRICKS_MV_TABLE="quotes_daily_ingest_mv_40m_20260917" +export EXPECTED_TARGET="costbench.rt_qualification.${DATABRICKS_RAW_TABLE}" +export INGEST_DURATION_SECONDS=2400 +export TARGET_EPS=1000000 +export NOMINAL_ROWS=2400000000 +export SOURCE_SELECTION_ROWS=2500000000 +export POST_INGEST_MONITOR_SECONDS=600 +export MONITOR_INTERVAL_SECONDS=60 +export MANUAL_STREAM_ROTATION_SECONDS=600 +export RUN_KIND="ingest_mv_40m" +export LATEST_RUN_CONTEXT="/data/databricks-qualification/latest_ingest_mv_40m_run.json" + +exec /home/ubuntu/costbench/full-path-realtime/quotes/databricks/run_ingest_mv_6h.sh diff --git a/full-path-realtime/quotes/databricks/run_ingest_mv_6h.sh b/full-path-realtime/quotes/databricks/run_ingest_mv_6h.sh new file mode 100644 index 0000000..8189bb7 --- /dev/null +++ b/full-path-realtime/quotes/databricks/run_ingest_mv_6h.sh @@ -0,0 +1,366 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Six-hour, 1M-EPS ingest + managed Delta maintenance + MV freshness +# characterization. Lakehouse//RT reads are intentionally absent. + +umask 077 + +: "${DBX_BENCH_DIR:=/home/ubuntu/costbench/full-path-realtime/quotes/databricks}" +: "${SOURCE_DIR:=/data/quotes}" +: "${SOURCE_INVENTORY_OUTPUT:=/data/databricks-qualification/source_inventory.json}" +: "${DATABRICKS_RUNNER_PYTHON:=$DBX_BENCH_DIR/.venv-runner/bin/python}" +: "${DATABRICKS_ZEROBUS_PYTHON:=$DBX_BENCH_DIR/.venv-zerobus/bin/python}" +: "${DATABRICKS_HOST:?Set the Databricks workspace HTTPS origin}" +: "${DATABRICKS_WORKSPACE_ID:?Set the decimal Databricks workspace ID}" +: "${DATABRICKS_CLIENT_ID:?Set the benchmark service-principal application ID}" +: "${DATABRICKS_REGION:=eu-west-1}" +: "${DATABRICKS_CATALOG:=costbench}" +: "${DATABRICKS_SCHEMA:=rt_qualification}" +: "${DATABRICKS_RAW_TABLE:=quotes_ingest_mv_6h_20260916}" +: "${DATABRICKS_MV_TABLE:=quotes_daily_ingest_mv_6h_20260916}" +: "${DATABRICKS_CONTROL_WAREHOUSE_ID:?Set the control warehouse ID}" +: "${ZEROBUS_ENDPOINT:=https://${DATABRICKS_WORKSPACE_ID}.zerobus.${DATABRICKS_REGION}.cloud.databricks.com}" + +TARGET="${DATABRICKS_CATALOG}.${DATABRICKS_SCHEMA}.${DATABRICKS_RAW_TABLE}" +: "${EXPECTED_TARGET:=costbench.rt_qualification.quotes_ingest_mv_6h_20260916}" +: "${INGEST_DURATION_SECONDS:=21600}" +: "${TARGET_EPS:=1000000}" +: "${NOMINAL_ROWS:=$((INGEST_DURATION_SECONDS * TARGET_EPS))}" +: "${SOURCE_SELECTION_ROWS:=22000000000}" +: "${POST_INGEST_MONITOR_SECONDS:=900}" +: "${MONITOR_INTERVAL_SECONDS:=60}" +: "${MANUAL_STREAM_ROTATION_SECONDS:=600}" +: "${RUN_KIND:=ingest_mv_6h}" +: "${LATEST_RUN_CONTEXT:=/data/databricks-qualification/latest_ingest_mv_6h_run.json}" +MONITOR_ITERATIONS=$((( + INGEST_DURATION_SECONDS + POST_INGEST_MONITOR_SECONDS +) / MONITOR_INTERVAL_SECONDS + 1)) +RUN_ID="${RUN_KIND}_$(date -u +%Y%m%dT%H%M%SZ)" +RUN_ROOT="/data/databricks-qualification/${RUN_ID}" +SINCE_UTC="$(date -u +%Y-%m-%dT%H:%M:%S.000Z)" +INGEST_PID="" +MONITOR_PID="" +TIMER_PID="" + +cleanup() { + unset DATABRICKS_CLIENT_SECRET + if [[ -n "$INGEST_PID" ]] && kill -0 "$INGEST_PID" 2>/dev/null; then + kill -TERM "$INGEST_PID" 2>/dev/null || true + fi + if [[ -n "$MONITOR_PID" ]] && kill -0 "$MONITOR_PID" 2>/dev/null; then + kill -TERM "$MONITOR_PID" 2>/dev/null || true + fi + if [[ -n "$TIMER_PID" ]] && kill -0 "$TIMER_PID" 2>/dev/null; then + kill -TERM "$TIMER_PID" 2>/dev/null || true + fi +} +trap cleanup EXIT INT TERM + +if [[ "$TARGET" != "$EXPECTED_TARGET" ]]; then + printf 'Refusing unexpected target: %s\n' "$TARGET" >&2 + exit 1 +fi +if [[ -e "$RUN_ROOT" ]]; then + printf 'Refusing existing output directory: %s\n' "$RUN_ROOT" >&2 + exit 1 +fi +for python in "$DATABRICKS_RUNNER_PYTHON" "$DATABRICKS_ZEROBUS_PYTHON"; do + if [[ ! -x "$python" ]]; then + printf 'Missing Python environment: %s\n' "$python" >&2 + exit 1 + fi +done + +jq -e ' + .status == "complete" + and .actual_rows == 113219565734 + and .file_count == 232 +' "$SOURCE_INVENTORY_OUTPUT" >/dev/null + +printf 'Raw target: %s\n' "$TARGET" +printf 'MV target: %s.%s.%s\n' \ + "$DATABRICKS_CATALOG" "$DATABRICKS_SCHEMA" "$DATABRICKS_MV_TABLE" +printf 'Nominal rows: %d (actual result is duration-bounded)\n' "$NOMINAL_ROWS" +printf 'Target EPS: %d\n' "$TARGET_EPS" +printf 'Ingest time: %d seconds\n' "$INGEST_DURATION_SECONDS" +printf 'Monitor time: %d seconds\n' \ + "$((INGEST_DURATION_SECONDS + POST_INGEST_MONITOR_SECONDS))" +printf 'Output: %s\n' "$RUN_ROOT" +printf '\nThis permanently consumes the fresh Zerobus target name.\n' +read -r -p "Type '$EXPECTED_TARGET' to continue: " confirmation +if [[ "$confirmation" != "$EXPECTED_TARGET" ]]; then + echo "Confirmation did not match; nothing was run." >&2 + exit 1 +fi + +read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET +echo +export DATABRICKS_CLIENT_ID DATABRICKS_CLIENT_SECRET +unset DATABRICKS_TOKEN + +mkdir -p "$RUN_ROOT/ingest" "$RUN_ROOT/freshness" +jq -n \ + --arg run_id "$RUN_ID" \ + --arg run_root "$RUN_ROOT" \ + --arg since "$SINCE_UTC" \ + --arg raw_table "$DATABRICKS_RAW_TABLE" \ + --arg mv_table "$DATABRICKS_MV_TABLE" \ + --argjson duration_seconds "$INGEST_DURATION_SECONDS" \ + --argjson nominal_rows "$NOMINAL_ROWS" \ + --argjson source_selection_rows "$SOURCE_SELECTION_ROWS" \ + --argjson target_eps "$TARGET_EPS" \ + --argjson manual_stream_rotation_seconds "$MANUAL_STREAM_ROTATION_SECONDS" \ + '{ + schema_version: 1, + status: "running", + run_id: $run_id, + run_root: $run_root, + since: $since, + raw_table: $raw_table, + mv_table: $mv_table, + duration_seconds: $duration_seconds, + nominal_rows: $nominal_rows, + source_selection_rows: $source_selection_rows, + target_eps: $target_eps, + automatic_stream_recovery: false, + manual_stream_rotation_seconds: $manual_stream_rotation_seconds + }' > "$RUN_ROOT/run_context.json" +cp "$RUN_ROOT/run_context.json" \ + "$LATEST_RUN_CONTEXT" + +"$DATABRICKS_ZEROBUS_PYTHON" "$DBX_BENCH_DIR/ingest_zerobus.py" \ + --dir "$SOURCE_DIR" \ + --pattern 'quotes_*.parquet' \ + --host "$DATABRICKS_HOST" \ + --endpoint "$ZEROBUS_ENDPOINT" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --table "$DATABRICKS_RAW_TABLE" \ + --count-warehouse "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --workers 16 \ + --batch-size 50000 \ + --queue-capacity 64 \ + --target-eps "$TARGET_EPS" \ + --compression NONE \ + --checkpoint-batches 1 \ + --checkpoint-seconds 1 \ + --checkpoint-method wait \ + --disable-automatic-recovery \ + --manual-stream-rotation-seconds "$MANUAL_STREAM_ROTATION_SECONDS" \ + --metrics-interval 5 \ + --memory-trim-interval 60 \ + --min-system-available-gib 16 \ + --compact-progress \ + --defer-source-hashes \ + --max-rows "$SOURCE_SELECTION_ROWS" \ + --expected-rows "$SOURCE_SELECTION_ROWS" \ + --allow-partial \ + --run-id "$RUN_ID" \ + --output-dir "$RUN_ROOT/ingest" \ + --quiet-worker-logs \ + > >(tee "$RUN_ROOT/ingest.log") 2>&1 & +INGEST_PID=$! + +for ((attempt = 1; attempt <= 1200; attempt++)); do + if [[ -s "$RUN_ROOT/ingest/ingest_progress.json" ]]; then + break + fi + if ! kill -0 "$INGEST_PID" 2>/dev/null; then + wait "$INGEST_PID" + echo "Ingest exited before creating its progress journal." >&2 + exit 1 + fi + sleep 1 +done +if [[ ! -s "$RUN_ROOT/ingest/ingest_progress.json" ]]; then + echo "Timed out after 20 minutes waiting for source manifest preparation." >&2 + exit 1 +fi + +( + sleep "$INGEST_DURATION_SECONDS" + if kill -0 "$INGEST_PID" 2>/dev/null; then + kill -TERM "$INGEST_PID" + fi +) & +TIMER_PID=$! + +"$DATABRICKS_RUNNER_PYTHON" "$DBX_BENCH_DIR/monitor_freshness.py" \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --raw-table "$DATABRICKS_RAW_TABLE" \ + --mv-table "$DATABRICKS_MV_TABLE" \ + --producer-progress "$RUN_ROOT/ingest/ingest_progress.json" \ + --since "$SINCE_UTC" \ + --interval "$MONITOR_INTERVAL_SECONDS" \ + --iterations "$MONITOR_ITERATIONS" \ + --compact-event-log \ + --output "$RUN_ROOT/freshness/freshness.jsonl" \ + --progress-json "$RUN_ROOT/freshness/freshness_progress.json" \ + --run-id "$RUN_ID" \ + > >(tee "$RUN_ROOT/freshness.log") 2>&1 & +MONITOR_PID=$! + +set +e +wait "$INGEST_PID" +INGEST_STATUS=$? +if kill -0 "$TIMER_PID" 2>/dev/null; then + kill -TERM "$TIMER_PID" 2>/dev/null +fi +wait "$TIMER_PID" 2>/dev/null +TIMER_PID="" +wait "$MONITOR_PID" +MONITOR_STATUS=$? +set -e +INGEST_PID="" +MONITOR_PID="" + +UNTIL_UTC="$( + jq -er '.observed_at' "$RUN_ROOT/freshness/freshness_progress.json" +)" +PRODUCER_FINISHED_AT="$( + jq -er '.updated_at' "$RUN_ROOT/ingest/ingest_summary.json" +)" +jq \ + --arg until "$UNTIL_UTC" \ + --arg producer_finished_at "$PRODUCER_FINISHED_AT" \ + '.status = "complete" + | .until = $until + | .producer_finished_at = $producer_finished_at' \ + "$RUN_ROOT/run_context.json" > "$RUN_ROOT/run_context.json.tmp" +mv "$RUN_ROOT/run_context.json.tmp" "$RUN_ROOT/run_context.json" +cp "$RUN_ROOT/run_context.json" \ + "$LATEST_RUN_CONTEXT" + +"$DATABRICKS_RUNNER_PYTHON" "$DBX_BENCH_DIR/hash_source_manifest.py" \ + --manifest "$RUN_ROOT/ingest/source_manifest.json" \ + --output "$RUN_ROOT/ingest/source_hashes_post_run.json" + +set +e +"$DATABRICKS_RUNNER_PYTHON" "$DBX_BENCH_DIR/collect_evidence.py" \ + --host "$DATABRICKS_HOST" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --rt-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --catalog "$DATABRICKS_CATALOG" \ + --schema "$DATABRICKS_SCHEMA" \ + --raw-table "$DATABRICKS_RAW_TABLE" \ + --mv-table "$DATABRICKS_MV_TABLE" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --max-window-hours 8 \ + --run-id "$RUN_ID" \ + --output-dir "$RUN_ROOT/evidence-initial" +EVIDENCE_STATUS=$? + +"$DATABRICKS_RUNNER_PYTHON" \ + "$DBX_BENCH_DIR/costs/summarize_run.py" \ + --evidence-dir "$RUN_ROOT/evidence-initial" \ + --since "$SINCE_UTC" \ + --until "$UNTIL_UTC" \ + --producer-finished-at "$PRODUCER_FINISHED_AT" \ + --rt-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" \ + --output "$RUN_ROOT/cost_summary_initial.json" +COST_STATUS=$? +set -e + +jq '{ + run_id, + finished, + terminal_error, + provider_committed_rows, + source_selection_rows: .source.total_rows, + elapsed_sec, + average_provider_committed_eps: + .eps.average_provider_committed_rows_per_sec, + clean_checkpoint, + safe_to_resume, + table_protection +}' "$RUN_ROOT/ingest/ingest_summary.json" + +jq '{ + run_id, + observed_at, + latest_successful_refresh, + mv_source_rows, + mv_rows_behind, + raw_commit_age_sec, + mv_refresh_age_sec, + errors +}' "$RUN_ROOT/freshness/freshness_progress.json" + +printf 'Ingest status: %d\n' "$INGEST_STATUS" +printf 'Monitor status: %d\n' "$MONITOR_STATUS" +printf 'Initial evidence status: %d\n' "$EVIDENCE_STATUS" +printf 'Initial cost status: %d (billing may not be settled)\n' "$COST_STATUS" +printf 'Run artifacts: %s\n' "$RUN_ROOT" +printf 'Settled recollection: %s/collect_ingest_mv_6h_settled.sh %s\n' \ + "$DBX_BENCH_DIR" "$RUN_ROOT" + +if [[ "$INGEST_STATUS" -ne 0 && "$INGEST_STATUS" -ne 130 ]]; then + exit 1 +fi +if [[ "$MONITOR_STATUS" -ne 0 ]]; then + exit 1 +fi +MIN_ELAPSED=$((INGEST_DURATION_SECONDS - 10)) +MAX_ELAPSED=$((INGEST_DURATION_SECONDS + 60)) +MIN_ROWS=$((NOMINAL_ROWS * 90 / 100)) +MAX_ROWS=$((NOMINAL_ROWS * 110 / 100)) +MIN_ROTATIONS=$((INGEST_DURATION_SECONDS / MANUAL_STREAM_ROTATION_SECONDS - 1)) +if ! jq -e \ + --argjson min_elapsed "$MIN_ELAPSED" \ + --argjson max_elapsed "$MAX_ELAPSED" \ + --argjson min_rows "$MIN_ROWS" \ + --argjson max_rows "$MAX_ROWS" \ + --argjson min_rotations "$MIN_ROTATIONS" ' + .stopped_early == true + and .clean_checkpoint == true + and .safe_to_resume == true + and .elapsed_sec >= $min_elapsed + and .elapsed_sec <= $max_elapsed + and .eps.average_provider_committed_rows_per_sec >= 900000 + and .eps.average_provider_committed_rows_per_sec <= 1100000 + and .provider_committed_rows >= $min_rows + and .provider_committed_rows <= $max_rows + and .submitted_rows == .provider_committed_rows + and .pending_rows == 0 + and all( + .stream_status[]; + .automatic_recovery == false + and .rotation_count >= $min_rotations + and .last_rotation_unacked_batches == 0 + ) + and .terminal_error == null + and .ambiguous == false +' "$RUN_ROOT/ingest/ingest_summary.json" >/dev/null; then + echo "Ingest completion gate failed." >&2 + exit 1 +fi +if ! jq -e ' + .mv_rows_behind == 0 + and (.errors | length) == 0 +' "$RUN_ROOT/freshness/freshness_progress.json" >/dev/null; then + echo "Final MV catch-up was not proven within the monitor window." >&2 + exit 1 +fi +if ! jq -s 'all(.[]; (.errors | length) == 0)' \ + "$RUN_ROOT/freshness/freshness.jsonl" >/dev/null; then + echo "One or more live freshness observations contain errors." >&2 + exit 1 +fi +if [[ "$EVIDENCE_STATUS" -eq 0 ]]; then + ACTUAL_ROWS="$(jq -er '.provider_committed_rows' \ + "$RUN_ROOT/ingest/ingest_summary.json")" + if ! jq -e --argjson actual_rows "$ACTUAL_ROWS" ' + .provider_committed_records == $actual_rows + and .provider_errors == 0 + and .mv_maintenance.full_refresh_count == 0 + ' "$RUN_ROOT/evidence-initial/evidence_summary.json" >/dev/null; then + echo "Initial provider evidence reconciliation failed." >&2 + exit 1 + fi +fi diff --git a/full-path-realtime/quotes/databricks/run_ingest_mv_6h_r2.sh b/full-path-realtime/quotes/databricks/run_ingest_mv_6h_r2.sh new file mode 100644 index 0000000..02b65f9 --- /dev/null +++ b/full-path-realtime/quotes/databricks/run_ingest_mv_6h_r2.sh @@ -0,0 +1,20 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Six-hour retry using the journal and fail-closed stream-rotation settings +# accepted by ingest_mv_40m_20260917T090123Z. + +export DATABRICKS_RAW_TABLE="quotes_ingest_mv_6h_r2_20260917" +export DATABRICKS_MV_TABLE="quotes_daily_ingest_mv_6h_r2_20260917" +export EXPECTED_TARGET="costbench.rt_qualification.${DATABRICKS_RAW_TABLE}" +export INGEST_DURATION_SECONDS=21600 +export TARGET_EPS=1000000 +export NOMINAL_ROWS=21600000000 +export SOURCE_SELECTION_ROWS=22000000000 +export POST_INGEST_MONITOR_SECONDS=900 +export MONITOR_INTERVAL_SECONDS=60 +export MANUAL_STREAM_ROTATION_SECONDS=600 +export RUN_KIND="ingest_mv_6h_r2" +export LATEST_RUN_CONTEXT="/data/databricks-qualification/latest_ingest_mv_6h_r2_run.json" + +exec /home/ubuntu/costbench/full-path-realtime/quotes/databricks/run_ingest_mv_6h.sh diff --git a/full-path-realtime/quotes/databricks/run_query_workloads.sh b/full-path-realtime/quotes/databricks/run_query_workloads.sh new file mode 100755 index 0000000..7a5829c --- /dev/null +++ b/full-path-realtime/quotes/databricks/run_query_workloads.sh @@ -0,0 +1,257 @@ +#!/usr/bin/env bash +set -euo pipefail + +# Launch the canonical dashboard and drill-down workloads against one query +# warehouse while a prepared ingest/MV run is active. + +umask 077 + +: "${DBX_BENCH_DIR:=/home/ubuntu/costbench/full-path-realtime/quotes/databricks}" +: "${DATABRICKS_RUNNER_PYTHON:=$DBX_BENCH_DIR/.venv-runner/bin/python}" +: "${DATABRICKS_HOST:?Set the Databricks workspace HTTPS origin}" +: "${DATABRICKS_CLIENT_ID:?Set the benchmark service-principal application ID}" +: "${DATABRICKS_CONTROL_WAREHOUSE_ID:?Set the control warehouse ID}" +: "${DATABRICKS_QUERY_WAREHOUSE_ID:?Set the dedicated query warehouse ID}" +: "${DATABRICKS_WAREHOUSE_MODE:=serverless-baseline}" +: "${DATABRICKS_QUERY_SIZE:=X-Small}" +: "${QUERY_WINDOW_SCOPE:=auto}" +: "${POST_INGEST_QUERY_SECONDS:=0}" +: "${INGEST_CONTEXT:=/data/databricks-qualification/latest_ingest_mv_6h_r2_run.json}" + +if [[ "$DATABRICKS_QUERY_WAREHOUSE_ID" == "$DATABRICKS_CONTROL_WAREHOUSE_ID" ]]; then + echo "Query and control warehouses must be different." >&2 + exit 1 +fi +if [[ ! "$POST_INGEST_QUERY_SECONDS" =~ ^[0-9]+$ ]]; then + echo "POST_INGEST_QUERY_SECONDS must be a nonnegative integer." >&2 + exit 1 +fi +if [[ ! -s "$INGEST_CONTEXT" ]]; then + printf 'Missing ingest context: %s\n' "$INGEST_CONTEXT" >&2 + exit 1 +fi + +INGEST_RUN_ROOT="$(jq -er '.run_root' "$INGEST_CONTEXT")" +INGEST_RUN_ID="$(jq -er '.run_id' "$INGEST_CONTEXT")" +CATALOG="$(jq -er '.catalog // "costbench"' "$INGEST_CONTEXT")" +SCHEMA="$(jq -er '.schema // "rt_qualification"' "$INGEST_CONTEXT")" +RAW_TABLE="$(jq -er '.raw_table' "$INGEST_CONTEXT")" +MV_TABLE="$(jq -er '.mv_table' "$INGEST_CONTEXT")" +: "${PRODUCER_PROGRESS:=$INGEST_RUN_ROOT/ingest/ingest_progress.json}" +: "${FRESHNESS_PROGRESS:=$INGEST_RUN_ROOT/freshness/freshness_progress.json}" +if [[ ! -s "$PRODUCER_PROGRESS" ]]; then + echo "Producer progress is unavailable." >&2 + exit 1 +fi +if [[ "$(jq -r '.running' "$PRODUCER_PROGRESS")" != "true" ]]; then + echo "The selected producer is not currently running." >&2 + exit 1 +fi + +START_ROWS="$(jq -er '.provider_committed_rows' "$PRODUCER_PROGRESS")" +STARTED_AT="$(date -u +%Y-%m-%dT%H:%M:%S.000Z)" +STAMP="$(date -u +%Y%m%dT%H%M%SZ)" +: "${DATABRICKS_QUERY_RUN_ID:=${INGEST_RUN_ID}_query_${DATABRICKS_WAREHOUSE_MODE}}" +QUERY_RUN_ID="$DATABRICKS_QUERY_RUN_ID" +: "${QUERY_OUTPUT_DIR:=$INGEST_RUN_ROOT/queries/${DATABRICKS_WAREHOUSE_MODE}_${DATABRICKS_QUERY_WAREHOUSE_ID}_${STAMP}}" +OUTPUT_DIR="$QUERY_OUTPUT_DIR" +: "${QUERY_CONTEXT_PATH:=$OUTPUT_DIR/query_context.json}" +: "${DASHBOARD_OUTPUT:=$OUTPUT_DIR/dashboard.jsonl}" +: "${DRILLDOWN_OUTPUT:=$OUTPUT_DIR/drilldown.jsonl}" +: "${QUERY_LOG_DIR:=$OUTPUT_DIR}" +DASHBOARD_PID="" +DRILLDOWN_PID="" + +cleanup() { + unset DATABRICKS_CLIENT_SECRET + for pid in "$DASHBOARD_PID" "$DRILLDOWN_PID"; do + if [[ -n "$pid" ]] && kill -0 "$pid" 2>/dev/null; then + kill -TERM "$pid" 2>/dev/null || true + fi + done +} +trap cleanup EXIT INT TERM + +printf 'Ingest run: %s\n' "$INGEST_RUN_ID" +printf 'Starting rows: %s\n' "$START_ROWS" +printf 'Raw/MV: %s / %s\n' "$RAW_TABLE" "$MV_TABLE" +printf 'Query warehouse: %s (%s, %s)\n' \ + "$DATABRICKS_QUERY_WAREHOUSE_ID" "$DATABRICKS_WAREHOUSE_MODE" "$DATABRICKS_QUERY_SIZE" +printf 'Dashboard: 4 queries every 600 seconds\n' +printf 'Drill-down: 2 queries every 3600 seconds\n' +printf 'Output: %s\n' "$OUTPUT_DIR" + +if [[ -z "${DATABRICKS_CLIENT_SECRET:-}" ]]; then + read -rsp 'Benchmark service-principal secret: ' DATABRICKS_CLIENT_SECRET + echo +fi +export DATABRICKS_CLIENT_ID DATABRICKS_CLIENT_SECRET +unset DATABRICKS_TOKEN + +mkdir -p \ + "$OUTPUT_DIR" \ + "$(dirname "$QUERY_CONTEXT_PATH")" \ + "$(dirname "$DASHBOARD_OUTPUT")" \ + "$(dirname "$DRILLDOWN_OUTPUT")" \ + "$QUERY_LOG_DIR" +jq -n \ + --arg query_run_id "$QUERY_RUN_ID" \ + --arg ingest_run_id "$INGEST_RUN_ID" \ + --arg started_at "$STARTED_AT" \ + --argjson starting_rows "$START_ROWS" \ + --arg warehouse_id "$DATABRICKS_QUERY_WAREHOUSE_ID" \ + --arg warehouse_mode "$DATABRICKS_WAREHOUSE_MODE" \ + --arg query_size "$DATABRICKS_QUERY_SIZE" \ + --arg catalog "$CATALOG" \ + --arg schema "$SCHEMA" \ + --arg raw_table "$RAW_TABLE" \ + --arg mv_table "$MV_TABLE" \ + --arg query_window_scope "$QUERY_WINDOW_SCOPE" \ + --argjson post_ingest_query_seconds "$POST_INGEST_QUERY_SECONDS" \ + '{ + schema_version: 1, + status: "running", + query_run_id: $query_run_id, + ingest_run_id: $ingest_run_id, + started_at: $started_at, + starting_provider_committed_rows: $starting_rows, + query_warehouse_id: $warehouse_id, + warehouse_mode: $warehouse_mode, + query_size: $query_size, + catalog: $catalog, + schema: $schema, + raw_table: $raw_table, + mv_table: $mv_table, + dashboard_interval_seconds: 600, + drilldown_interval_seconds: 3600, + query_window_scope: $query_window_scope, + post_ingest_query_seconds: $post_ingest_query_seconds, + partial_ingest_window: ( + if $query_window_scope == "canonical-full" + then false + else $starting_rows > 0 + end + ) + }' > "$QUERY_CONTEXT_PATH" + +COMMON=( + --host "$DATABRICKS_HOST" + --query-warehouse-id "$DATABRICKS_QUERY_WAREHOUSE_ID" + --http-path "/sql/1.0/warehouses/$DATABRICKS_QUERY_WAREHOUSE_ID" + --warehouse-mode "$DATABRICKS_WAREHOUSE_MODE" + --control-warehouse-id "$DATABRICKS_CONTROL_WAREHOUSE_ID" + --catalog "$CATALOG" + --schema "$SCHEMA" + --raw-table "$RAW_TABLE" + --mv-table "$MV_TABLE" + --producer-progress "$PRODUCER_PROGRESS" + --freshness-monitor "$FRESHNESS_PROGRESS" + --run-id "$QUERY_RUN_ID" + --system "Databricks (AWS)" + --machine "Databricks SQL warehouse" + --cluster-size "$DATABRICKS_QUERY_SIZE" + --tags "Databricks,managed,$DATABRICKS_WAREHOUSE_MODE,baseline" + --comment "Warehouse-swap baseline; Statement Execution API; uncached results" +) + +"$DATABRICKS_RUNNER_PYTHON" "$DBX_BENCH_DIR/run_dashboard.py" \ + "${COMMON[@]}" \ + --interval 600 \ + --output "$DASHBOARD_OUTPUT" \ + > >(tee "$QUERY_LOG_DIR/dashboard.log") 2>&1 & +DASHBOARD_PID=$! + +"$DATABRICKS_RUNNER_PYTHON" "$DBX_BENCH_DIR/run_drilldown.py" \ + "${COMMON[@]}" \ + --interval 3600 \ + --output "$DRILLDOWN_OUTPUT" \ + > >(tee "$QUERY_LOG_DIR/drilldown.log") 2>&1 & +DRILLDOWN_PID=$! + +POST_INGEST_STARTED_AT="" +POST_INGEST_STARTED_SECONDS="" +while kill -0 "$DASHBOARD_PID" 2>/dev/null && + kill -0 "$DRILLDOWN_PID" 2>/dev/null; do + if [[ "$(jq -r '.running' "$PRODUCER_PROGRESS")" != "true" ]]; then + if [[ "$POST_INGEST_QUERY_SECONDS" -le 0 ]]; then + break + fi + if [[ -z "$POST_INGEST_STARTED_SECONDS" ]]; then + POST_INGEST_STARTED_SECONDS=$SECONDS + POST_INGEST_STARTED_AT="$(date -u +%Y-%m-%dT%H:%M:%S.000Z)" + printf 'Producer stopped; continuing queries for %d seconds.\n' \ + "$POST_INGEST_QUERY_SECONDS" + elif (( SECONDS - POST_INGEST_STARTED_SECONDS >= POST_INGEST_QUERY_SECONDS )); then + break + fi + fi + sleep 30 +done + +for pid in "$DASHBOARD_PID" "$DRILLDOWN_PID"; do + if kill -0 "$pid" 2>/dev/null; then + kill -TERM "$pid" + fi +done + +set +e +wait "$DASHBOARD_PID" +DASHBOARD_STATUS=$? +wait "$DRILLDOWN_PID" +DRILLDOWN_STATUS=$? +set -e +DASHBOARD_PID="" +DRILLDOWN_PID="" + +FINISHED_AT="$(date -u +%Y-%m-%dT%H:%M:%S.000Z)" +FINAL_ROWS="$(jq -er '.provider_committed_rows' "$PRODUCER_PROGRESS")" +DASHBOARD_ITERATIONS="$(test -s "$DASHBOARD_OUTPUT" && wc -l < "$DASHBOARD_OUTPUT" || echo 0)" +DRILLDOWN_ITERATIONS="$(test -s "$DRILLDOWN_OUTPUT" && wc -l < "$DRILLDOWN_OUTPUT" || echo 0)" + +jq \ + --arg finished_at "$FINISHED_AT" \ + --argjson final_rows "$FINAL_ROWS" \ + --argjson dashboard_iterations "$DASHBOARD_ITERATIONS" \ + --argjson drilldown_iterations "$DRILLDOWN_ITERATIONS" \ + --argjson dashboard_status "$DASHBOARD_STATUS" \ + --argjson drilldown_status "$DRILLDOWN_STATUS" \ + --arg post_ingest_started_at "$POST_INGEST_STARTED_AT" \ + '.status = "complete" + | .finished_at = $finished_at + | .final_provider_committed_rows = $final_rows + | .dashboard_iterations = $dashboard_iterations + | .drilldown_iterations = $drilldown_iterations + | .dashboard_process_status = $dashboard_status + | .drilldown_process_status = $drilldown_status + | .post_ingest_started_at = ( + if $post_ingest_started_at == "" + then null + else $post_ingest_started_at + end + )' \ + "$QUERY_CONTEXT_PATH" > "$QUERY_CONTEXT_PATH.tmp" +mv "$QUERY_CONTEXT_PATH.tmp" "$QUERY_CONTEXT_PATH" + +jq . "$QUERY_CONTEXT_PATH" + +if [[ "$DASHBOARD_STATUS" -ne 0 || "$DRILLDOWN_STATUS" -ne 0 ]]; then + exit 1 +fi +for output in "$DASHBOARD_OUTPUT" "$DRILLDOWN_OUTPUT"; do + if [[ ! -s "$output" ]] || ! jq -s -e ' + length > 0 + and all( + .[]; + all( + .query_evidence[]; + .execution_succeeded == true + and .canonical_duration_sec != null + and (.errors | length) == 0 + and .metrics.result_from_cache == false + ) + ) + ' "$output" >/dev/null; then + printf 'Query evidence validation failed: %s\n' "$output" >&2 + exit 1 + fi +done diff --git a/full-path-realtime/quotes/databricks/runner_common.py b/full-path-realtime/quotes/databricks/runner_common.py new file mode 100644 index 0000000..6cdb153 --- /dev/null +++ b/full-path-realtime/quotes/databricks/runner_common.py @@ -0,0 +1,1381 @@ +#!/usr/bin/env python3 +"""Shared Lakehouse//RT dashboard and drill-down query runner. + +The Databricks connector is deliberately imported only when an online run +opens a connection. This keeps parsing, validation, and unit tests usable with +``python3 -S``. +""" + +from __future__ import annotations + +import argparse +import base64 +import hashlib +import importlib.metadata +import json +import math +import os +import signal +import sys +import threading +import time +import urllib.parse +import uuid +from datetime import date, datetime, timedelta +from decimal import Decimal +from pathlib import Path +from typing import Any, Iterable, Mapping, Sequence + +from dbx_common import ( + DatabricksRestClient, + atomic_append_jsonl, + iso_utc, + json_default, + load_queries, + normalize_host, + quote_identifier, + redact_secrets, + statement_rows, + utc_now, +) + +CONNECTOR_VERSION = "4.5.0" +RESULT_FETCH_BATCH_SIZE = 1_000 +SESSION_CONFIGURATION = { + "use_cached_result": "false", + "ansi_mode": "true", + "timezone": "UTC", +} +EXPECTED_QUERY_COUNTS = {"dashboard": 4, "drilldown": 2} +FINAL_HISTORY_STATUSES = {"FINISHED", "FAILED", "CANCELED"} +MAX_HISTORY_BYTES = 64 * 1024 +ALIGNED_FIELDS = ( + "result", + "compilation_time", + "execution_time", + "queue_time", + "result_fetch_time", + "client_wall_time", + "statement_ids", + "cache_hit", + "read_io_cache_percent", + "result_row_count", + "result_hash", +) + + +def _env_first(env: Mapping[str, str], *names: str) -> str | None: + for name in names: + value = env.get(name) + if value is not None and value.strip(): + return value.strip() + return None + + +def _path_default(value: str | None) -> Path | None: + return Path(value).expanduser() if value else None + + +def build_parser( + workload: str, + default_queries: Path, + default_interval: float, + *, + env: Mapping[str, str] | None = None, +) -> argparse.ArgumentParser: + if workload not in EXPECTED_QUERY_COUNTS: + raise ValueError(f"unsupported workload: {workload}") + source = os.environ if env is None else env + parser = argparse.ArgumentParser( + description=f"Run the Databricks Statement Execution {workload} workload" + ) + parser.add_argument("--queries", type=Path, default=default_queries) + parser.add_argument("--interval", type=float, default=default_interval) + parser.add_argument("--iterations", type=int, default=0, help="0 runs until stopped") + parser.add_argument( + "--output", + type=Path, + default=_path_default( + _env_first(source, "DATABRICKS_RUNNER_OUTPUT", "BENCHMARK_OUTPUT") + ), + ) + parser.add_argument( + "--output-dir", + type=Path, + default=_path_default( + _env_first(source, "DATABRICKS_RUNNER_OUTPUT_DIR", "BENCHMARK_OUTPUT_DIR") + ) + or Path("."), + ) + parser.add_argument( + "--host", + default=_env_first( + source, + "DATABRICKS_HOST", + "DATABRICKS_SERVER_HOSTNAME", + "DATABRICKS_WORKSPACE_URL", + ), + ) + parser.add_argument( + "--token", + default=_env_first(source, "DATABRICKS_TOKEN", "DATABRICKS_ACCESS_TOKEN"), + help=argparse.SUPPRESS, + ) + parser.add_argument( + "--client-id", + default=_env_first(source, "DATABRICKS_CLIENT_ID"), + ) + parser.add_argument( + "--client-secret", + default=_env_first(source, "DATABRICKS_CLIENT_SECRET"), + help=argparse.SUPPRESS, + ) + parser.add_argument( + "--rt-warehouse-id", + "--query-warehouse-id", + "--warehouse", + dest="rt_warehouse_id", + default=_env_first( + source, + "DATABRICKS_QUERY_WAREHOUSE_ID", + "DATABRICKS_RT_WAREHOUSE_ID", + "DBX_RT_WAREHOUSE_ID", + "RT_WAREHOUSE_ID", + ), + ) + parser.add_argument( + "--warehouse-mode", + choices=("serverless-baseline", "lakehouse-rt"), + default=_env_first(source, "DATABRICKS_WAREHOUSE_MODE") + or "lakehouse-rt", + ) + parser.add_argument( + "--http-path", + default=_env_first(source, "DATABRICKS_HTTP_PATH", "DATABRICKS_RT_HTTP_PATH"), + ) + parser.add_argument( + "--control-warehouse-id", + default=_env_first( + source, + "DATABRICKS_CONTROL_WAREHOUSE_ID", + "DBX_CONTROL_WAREHOUSE_ID", + "CONTROL_WAREHOUSE_ID", + ), + ) + parser.add_argument( + "--catalog", + default=_env_first(source, "DATABRICKS_CATALOG", "DBX_CATALOG") or "workspace", + ) + parser.add_argument( + "--schema", + default=_env_first(source, "DATABRICKS_SCHEMA", "DBX_SCHEMA") or "benchmarking", + ) + parser.add_argument( + "--raw-table", + default=_env_first(source, "DATABRICKS_RAW_TABLE", "DBX_RAW_TABLE") + or "quotes", + ) + parser.add_argument( + "--mv-table", + default=_env_first(source, "DATABRICKS_MV_TABLE", "DBX_MV_TABLE") or "quotes_daily", + ) + parser.add_argument( + "--producer-progress", + type=Path, + default=_path_default( + _env_first( + source, + "DATABRICKS_PRODUCER_PROGRESS_JSON", + "DATABRICKS_PRODUCER_PROGRESS", + "PRODUCER_PROGRESS_JSON", + ) + ), + ) + parser.add_argument( + "--freshness-monitor", + type=Path, + default=_path_default( + _env_first( + source, + "DATABRICKS_FRESHNESS_MONITOR_JSON", + "DATABRICKS_FRESHNESS_MONITOR", + "FRESHNESS_MONITOR_JSON", + ) + ), + ) + parser.add_argument( + "--system", + default=_env_first(source, "BENCHMARK_SYSTEM", "DATABRICKS_SYSTEM") + or "Databricks", + ) + parser.add_argument( + "--machine", + default=_env_first(source, "BENCHMARK_MACHINE", "DATABRICKS_MACHINE") or "unknown", + ) + parser.add_argument( + "--cluster-size", + default=_env_first(source, "BENCHMARK_CLUSTER_SIZE", "DATABRICKS_CLUSTER_SIZE") + or "1", + ) + parser.add_argument( + "--comment", + default=_env_first(source, "BENCHMARK_COMMENT", "DATABRICKS_COMMENT") or "", + ) + parser.add_argument( + "--run-id", + default=_env_first(source, "BENCHMARK_RUN_ID", "DATABRICKS_RUN_ID"), + ) + parser.add_argument( + "--tags", + default=_env_first(source, "BENCHMARK_TAGS", "DATABRICKS_TAGS") + or "Databricks,managed,lakehouse-rt", + help="Comma-separated report tags", + ) + parser.add_argument("--history-timeout", type=float, default=120.0) + parser.add_argument("--history-poll-interval", type=float, default=1.0) + parser.add_argument("--control-timeout", type=float, default=120.0) + parser.add_argument( + "--print-queries", + action="store_true", + help="Render queries and exit without loading the connector or using the network", + ) + return parser + + +def _warehouse_id_from_path(http_path: str | None) -> str | None: + if not http_path: + return None + marker = "/sql/1.0/warehouses/" + normalized = "/" + http_path.strip().lstrip("/").rstrip("/") + if marker not in normalized: + return None + value = normalized.rsplit("/", 1)[-1] + return value or None + + +def finalize_args( + parser: argparse.ArgumentParser, + args: argparse.Namespace, +) -> argparse.Namespace: + if args.interval <= 0: + parser.error("--interval must be greater than zero") + if args.iterations < 0: + parser.error("--iterations must be zero or greater") + if args.history_timeout < 0 or args.history_poll_interval <= 0: + parser.error("--history-timeout must be >= 0 and --history-poll-interval must be > 0") + if args.control_timeout <= 0: + parser.error("--control-timeout must be greater than zero") + + args.host = normalize_host(args.host) + path_warehouse_id = _warehouse_id_from_path(args.http_path) + if args.rt_warehouse_id and path_warehouse_id: + if args.rt_warehouse_id != path_warehouse_id: + parser.error("--query-warehouse-id does not match --http-path") + elif path_warehouse_id: + args.rt_warehouse_id = path_warehouse_id + elif args.rt_warehouse_id and not args.http_path: + args.http_path = f"/sql/1.0/warehouses/{args.rt_warehouse_id}" + + if args.print_queries: + return args + if not args.host: + parser.error("--host or DATABRICKS_HOST is required") + parsed_host = urllib.parse.urlparse(args.host) + if ( + parsed_host.scheme != "https" + or not parsed_host.hostname + or parsed_host.username + or parsed_host.password + or parsed_host.query + or parsed_host.fragment + or parsed_host.path not in ("", "/") + ): + parser.error("--host must be an HTTPS workspace origin without credentials or a path") + if not args.rt_warehouse_id or not args.http_path: + parser.error("--query-warehouse-id or a warehouse --http-path is required") + if not args.control_warehouse_id: + parser.error("--control-warehouse-id is required") + if args.control_warehouse_id == args.rt_warehouse_id: + parser.error("the control warehouse must be separate from the query warehouse") + if not args.producer_progress: + parser.error("--producer-progress is required; raw COUNT(*) is prohibited") + if bool(args.client_id) != bool(args.client_secret): + parser.error("--client-id and --client-secret must be supplied together") + if bool(args.token) == bool(args.client_id and args.client_secret): + parser.error("configure exactly one of PAT or OAuth M2M credentials") + return args + + +def fixed_rate_next_fire(anchor: float, interval: float, completed_iterations: int) -> float: + """Return the next anchored fire time after *completed_iterations*.""" + if interval <= 0 or completed_iterations < 0: + raise ValueError("interval must be positive and completed_iterations non-negative") + return anchor + interval * completed_iterations + + +def fixed_rate_delay( + anchor: float, + interval: float, + completed_iterations: int, + now: float, +) -> float: + return max(0.0, fixed_rate_next_fire(anchor, interval, completed_iterations) - now) + + +def _stable_value(value: Any) -> Any: + if value is None or isinstance(value, (bool, int, str)): + return value + if isinstance(value, float): + if math.isnan(value): + return {"__float__": "nan"} + if math.isinf(value): + return {"__float__": "inf" if value > 0 else "-inf"} + return value + if isinstance(value, Decimal): + return {"__decimal__": str(value)} + if isinstance(value, datetime): + return {"__datetime__": iso_utc(value)} + if isinstance(value, date): + return {"__date__": value.isoformat()} + if isinstance(value, (bytes, bytearray, memoryview)): + return {"__bytes__": base64.b64encode(bytes(value)).decode("ascii")} + if isinstance(value, Mapping): + return { + str(key): _stable_value(item) + for key, item in sorted(value.items(), key=lambda pair: str(pair[0])) + } + if isinstance(value, (list, tuple)): + return [_stable_value(item) for item in value] + tolist = getattr(value, "tolist", None) + if callable(tolist): + return _stable_value(tolist()) + as_dict = getattr(value, "asDict", None) + if callable(as_dict): + return _stable_value(as_dict()) + return {"__python__": f"{type(value).__module__}.{type(value).__qualname__}", "value": str(value)} + + +def stable_result_hash(rows: Iterable[Any]) -> tuple[int, str]: + """Hash ordered result rows with deterministic, type-aware JSON framing.""" + digest = hashlib.sha256() + count = 0 + for row in rows: + encoded = json.dumps( + _stable_value(row), + ensure_ascii=False, + allow_nan=False, + separators=(",", ":"), + sort_keys=True, + ).encode("utf-8") + digest.update(len(encoded).to_bytes(8, "big")) + digest.update(encoded) + count += 1 + return count, digest.hexdigest() + + +def drain_cursor(cursor: Any) -> tuple[int, str]: + digest = hashlib.sha256() + count = 0 + while True: + rows = cursor.fetchmany(RESULT_FETCH_BATCH_SIZE) + if not rows: + break + for row in rows: + encoded = json.dumps( + _stable_value(row), + ensure_ascii=False, + allow_nan=False, + separators=(",", ":"), + sort_keys=True, + ).encode("utf-8") + digest.update(len(encoded).to_bytes(8, "big")) + digest.update(encoded) + count += 1 + return count, digest.hexdigest() + + +def _number(value: Any) -> int | float | None: + if value is None or isinstance(value, bool): + return None + if isinstance(value, int): + return value + try: + result = float(value) + except (TypeError, ValueError, OverflowError): + return None + if not math.isfinite(result): + return None + return int(result) if result.is_integer() else result + + +def _row_count(value: Any) -> int | None: + number = _number(value) + if number is None or number < 0 or int(number) != number: + return None + return int(number) + + +def _history_value( + record: Mapping[str, Any], + aliases: Sequence[str], +) -> tuple[Any, str | None]: + metrics = record.get("metrics") + containers = ( + ("history", record), + ("history.metrics", metrics if isinstance(metrics, Mapping) else {}), + ) + for prefix, container in containers: + for alias in aliases: + if alias in container and container[alias] is not None: + return container[alias], f"{prefix}.{alias}" + return None, None + + +def _history_number( + record: Mapping[str, Any], + aliases: Sequence[str], +) -> tuple[int | float | None, str | None]: + value, source = _history_value(record, aliases) + return _number(value), source + + +def _cache_evidence(record: Mapping[str, Any]) -> tuple[Any, str | None]: + metrics = record.get("metrics") + found: list[tuple[str, Any]] = [] + for prefix, container in ( + ("history", record), + ("history.metrics", metrics if isinstance(metrics, Mapping) else {}), + ): + for name in ("result_from_cache", "from_result_cache"): + if name in container: + found.append((f"{prefix}.{name}", container[name])) + if not found: + return None, None + source = ",".join(name for name, _value in found) + if all(value is False for _name, value in found): + return False, source + if any(value is True for _name, value in found): + return True, source + return found[0][1], source + + +def normalize_query_history(record: Mapping[str, Any]) -> dict[str, Any]: + """Normalize current Query History API and system-table field aliases.""" + duration_ms, duration_source = _history_number( + record, + ("duration", "total_duration_ms", "total_time_ms"), + ) + compilation_ms, compilation_source = _history_number( + record, + ("compilation_time_ms", "compilation_duration_ms"), + ) + execution_ms, execution_source = _history_number( + record, + ("execution_time_ms", "execution_duration_ms"), + ) + result_fetch_ms, result_fetch_source = _history_number( + record, + ("result_fetch_time_ms", "result_fetch_duration_ms"), + ) + waiting_at_capacity_ms, waiting_at_capacity_source = _history_number( + record, + ("waiting_at_capacity_duration_ms", "waiting_at_capacity_ms"), + ) + waiting_for_compute_ms, waiting_for_compute_source = _history_number( + record, + ("waiting_for_compute_duration_ms", "waiting_for_compute_ms"), + ) + generic_queue_ms, generic_queue_source = _history_number( + record, + ( + "queue_duration_ms", + "queue_time_ms", + "queue_ms", + "overloading_queue_duration_ms", + "provisioning_queue_duration_ms", + ), + ) + queue_candidates = ( + (waiting_at_capacity_ms, waiting_at_capacity_source), + (waiting_for_compute_ms, waiting_for_compute_source), + (generic_queue_ms, generic_queue_source), + ) + queue_ms, queue_source = next( + ((value, source) for value, source in queue_candidates if value is not None), + (None, None), + ) + bytes_read, bytes_read_source = _history_number( + record, + ("read_bytes", "bytes_read", "read_files_bytes"), + ) + files_read, files_read_source = _history_number( + record, + ("read_files_count", "files_read", "read_files"), + ) + partitions_read, partitions_read_source = _history_number( + record, + ("read_partitions_count", "partitions_read", "read_partitions"), + ) + rows_read, rows_read_source = _history_number( + record, + ("rows_read_count", "read_rows", "rows_read"), + ) + rows_produced, rows_produced_source = _history_number( + record, + ("rows_produced", "rows_produced_count", "produced_rows"), + ) + cache_value, cache_source = _cache_evidence(record) + cache_origin, cache_origin_source = _history_value( + record, + ("cache_query_id", "cache_origin_statement_id"), + ) + read_cache_bytes, read_cache_bytes_source = _history_number( + record, + ("read_cache_bytes", "read_io_cache_bytes"), + ) + read_io_percent, read_io_percent_source = _history_number( + record, + ("read_io_cache_percent", "read_cache_percent"), + ) + if read_io_percent is None and read_cache_bytes is not None and bytes_read: + read_io_percent = 100.0 * float(read_cache_bytes) / float(bytes_read) + read_io_percent_source = ( + f"derived:{read_cache_bytes_source}/{bytes_read_source}" + ) + + status = record.get("status", record.get("execution_status")) + statement_id = record.get("statement_id", record.get("query_id")) + is_final = ( + record.get("is_final") is True + if "is_final" in record + else str(status).upper() in FINAL_HISTORY_STATUSES + ) + return { + "statement_id": statement_id, + "status": status, + "is_final": is_final, + "duration_ms": duration_ms, + "duration_sec": None if duration_ms is None else float(duration_ms) / 1000.0, + "duration_source": duration_source, + "compilation_time_ms": compilation_ms, + "compilation_source": compilation_source, + "execution_time_ms": execution_ms, + "execution_source": execution_source, + "queue_time_ms": queue_ms, + "queue_source": queue_source, + "waiting_at_capacity_ms": waiting_at_capacity_ms, + "waiting_at_capacity_source": waiting_at_capacity_source, + "waiting_for_compute_ms": waiting_for_compute_ms, + "waiting_for_compute_source": waiting_for_compute_source, + "result_fetch_time_ms": result_fetch_ms, + "result_fetch_source": result_fetch_source, + "bytes_read": bytes_read, + "bytes_read_source": bytes_read_source, + "files_read": files_read, + "files_read_source": files_read_source, + "partitions_read": partitions_read, + "partitions_read_source": partitions_read_source, + "rows_read": rows_read, + "rows_read_source": rows_read_source, + "rows_produced": rows_produced, + "rows_produced_source": rows_produced_source, + "result_from_cache": cache_value, + "result_from_cache_source": cache_source, + "cache_origin_statement_id": cache_origin, + "cache_origin_source": cache_origin_source, + "read_cache_bytes": read_cache_bytes, + "read_cache_bytes_source": read_cache_bytes_source, + "read_io_cache_percent": read_io_percent, + "read_io_cache_percent_source": read_io_percent_source, + } + + +def _history_is_final(record: Mapping[str, Any]) -> bool: + status = str(record.get("status", record.get("execution_status", ""))).upper() + if "is_final" in record: + return record.get("is_final") is True + return status in FINAL_HISTORY_STATUSES + + +def poll_query_history( + client: DatabricksRestClient, + statement_id: str, + *, + timeout: float, + poll_interval: float, + clock: Any = time.monotonic, + sleeper: Any = time.sleep, +) -> tuple[dict[str, Any] | None, str | None]: + """Poll by Statement/Query ID until Query History reports a final record.""" + deadline = clock() + max(0.0, timeout) + latest: dict[str, Any] | None = None + last_error: str | None = None + while True: + try: + candidate = client.query_history_by_statement_id(statement_id) + if candidate is not None: + latest = candidate + if _history_is_final(candidate): + return candidate, None + except Exception as exc: + last_error = f"{type(exc).__name__}: {exc}" + now = clock() + if now >= deadline: + if latest is not None: + return latest, "query history never became final before timeout" + suffix = f"; last lookup error: {last_error}" if last_error else "" + return None, f"query history was unavailable before timeout{suffix}" + sleeper(min(poll_interval, deadline - now)) + + +def bounded_history_record( + record: Mapping[str, Any] | None, + *, + secrets: Sequence[str] = (), + max_bytes: int = MAX_HISTORY_BYTES, +) -> Any: + if record is None: + return None + safe = redact_secrets(record, secrets) + encoded = json.dumps( + safe, + default=json_default, + separators=(",", ":"), + sort_keys=True, + ).encode("utf-8") + if len(encoded) <= max_bytes: + return safe + return { + "omitted": True, + "reason": "redacted query-history record exceeded evidence bound", + "encoded_bytes": len(encoded), + "max_bytes": max_bytes, + "sha256": hashlib.sha256(encoded).hexdigest(), + } + + +def read_json_snapshot(path: Path | None) -> dict[str, Any]: + loaded_at = iso_utc() + if path is None: + return {"path": None, "loaded_at": loaded_at, "data": None, "error": "not configured"} + try: + raw = path.read_text(encoding="utf-8") + data = json.loads(raw) + if not isinstance(data, dict): + raise ValueError("top-level JSON value is not an object") + stat = path.stat() + modified = datetime.fromtimestamp(stat.st_mtime, tz=utc_now().tzinfo) + return { + "path": str(path), + "loaded_at": loaded_at, + "file_modified_at": iso_utc(modified), + "data": data, + "error": None, + } + except (OSError, ValueError, json.JSONDecodeError) as exc: + return { + "path": str(path), + "loaded_at": loaded_at, + "data": None, + "error": f"{type(exc).__name__}: {exc}", + } + + +def producer_progress_snapshot(path: Path | None) -> dict[str, Any]: + snapshot = read_json_snapshot(path) + data = snapshot.pop("data") + rows = None + rows_source = None + progress_updated_at = None + progress_run_id = None + finished = None + progress_fields: dict[str, Any] = {} + if isinstance(data, Mapping): + progress_updated_at = data.get("updated_at") + progress_run_id = data.get("run_id") + finished = data.get("finished") + progress_fields = { + name: data.get(name) + for name in ( + "logical_raw_rows", + "provider_committed_rows", + "baseline_table_rows", + "baseline_table_rows_unknown", + "running", + "finished", + ) + } + logical = _row_count(data.get("logical_raw_rows")) + if logical is not None: + rows = logical + rows_source = "producer_progress.logical_raw_rows" + else: + committed = _row_count(data.get("provider_committed_rows")) + baseline = _row_count(data.get("baseline_table_rows")) + if committed is not None and baseline is not None: + rows = committed + baseline + rows_source = ( + "producer_progress.provider_committed_rows" + "+baseline_table_rows" + ) + else: + snapshot["error"] = snapshot.get("error") or ( + "producer progress omitted logical_raw_rows and the " + "provider_committed_rows+baseline_table_rows fallback" + ) + return { + **snapshot, + "progress_updated_at": progress_updated_at, + "progress_run_id": progress_run_id, + "finished": finished, + "raw_rows": rows, + "raw_rows_source": rows_source, + "progress_fields": progress_fields, + } + + +def _nested_value(data: Mapping[str, Any], path: Sequence[str]) -> Any: + current: Any = data + for component in path: + if not isinstance(current, Mapping) or component not in current: + return None + current = current[component] + return current + + +def freshness_progress_snapshot(path: Path | None) -> dict[str, Any]: + snapshot = read_json_snapshot(path) + data = snapshot.pop("data") + progress_updated_at = None + refresh_finished_at = None + mv_source_rows = None + mv_rows_behind = None + if isinstance(data, Mapping): + progress_updated_at = next( + ( + data.get(name) + for name in ("updated_at", "observed_at", "sampled_at") + if data.get(name) is not None + ), + None, + ) + mv_source_rows = data.get("mv_source_rows") + mv_rows_behind = data.get("mv_rows_behind") + refresh_finished_at = next( + ( + _nested_value(data, candidate_path) + for candidate_path in ( + ("latest_successful_refresh", "finished_at"), + ("latest_successful_refresh", "completed_at"), + ("latest_successful_refresh", "finish_time"), + ("latest_successful", "finished_at"), + ("latest_successful", "completed_at"), + ("latest_refresh", "finished_at"), + ("latest_refresh", "completed_at"), + ("refresh_finished_at",), + ) + if _nested_value(data, candidate_path) is not None + ), + None, + ) + return { + **snapshot, + "progress_updated_at": progress_updated_at, + "refresh_finished_at": refresh_finished_at, + "mv_source_rows": mv_source_rows, + "mv_rows_behind": mv_rows_behind, + # Kept nullable for the provider-neutral result schema. The harness + # deliberately does not query MV contents, and flow output rows are + # not the materialized view's total row count. + "mv_rows": None, + "mv_rows_source": "not_collected_auxiliary_data_queries_prohibited", + } + + +def _history_seconds(normalized: Mapping[str, Any], key: str) -> float | None: + value = normalized.get(key) + return None if value is None else float(value) / 1000.0 + + +def failed_observation( + query_number: int, + sql: str, + error: str, +) -> dict[str, Any]: + return { + "query_number": query_number, + "sql_sha256": hashlib.sha256(sql.encode("utf-8")).hexdigest(), + "statement_id": None, + "query_tags": None, + "execution_succeeded": False, + "result_row_count": None, + "result_hash_sha256": None, + "client_wall_time_sec": None, + "canonical_duration_sec": None, + "metrics": {}, + "history_record": None, + "errors": [error], + } + + +def execute_measured_query( + connection: Any, + rest_client: DatabricksRestClient, + sql: str, + *, + query_number: int, + iteration: int, + workload: str, + run_id: str, + history_timeout: float, + history_poll_interval: float, + secrets: Sequence[str] = (), + clock: Any = time.monotonic, +) -> dict[str, Any]: + tags = { + "benchmark": "full-path-realtime", + "run_id": run_id, + "workload": workload, + "query_number": str(query_number), + "iteration": str(iteration), + } + errors: list[str] = [] + statement_id = None + row_count = None + result_hash = None + execution_succeeded = False + cursor = None + started = clock() + try: + cursor = connection.cursor() + cursor.execute(sql, query_tags=tags) + statement_id = getattr(cursor, "query_id", None) + row_count, result_hash = drain_cursor(cursor) + execution_succeeded = True + except Exception as exc: + if cursor is not None and statement_id is None: + statement_id = getattr(cursor, "query_id", None) + errors.append(f"query execution failed: {type(exc).__name__}: {exc}") + finally: + client_wall_time = max(0.0, clock() - started) + if cursor is not None: + try: + cursor.close() + except Exception as exc: + errors.append(f"cursor close failed: {type(exc).__name__}: {exc}") + + history = None + history_error = None + normalized: dict[str, Any] = {} + if statement_id: + history, history_error = poll_query_history( + rest_client, + str(statement_id), + timeout=history_timeout, + poll_interval=history_poll_interval, + ) + if history is not None: + normalized = normalize_query_history(history) + else: + errors.append("cursor.query_id was missing; query history cannot be correlated") + if history_error: + errors.append(history_error) + + canonical_duration = normalized.get("duration_sec") + status = str(normalized.get("status", "")).upper() + cache_value = normalized.get("result_from_cache") + if history is None: + errors.append("canonical query-history record is missing") + elif not normalized.get("is_final"): + errors.append("query-history record is not final") + elif status and status != "FINISHED": + errors.append(f"query-history status is {status}, not FINISHED") + if canonical_duration is None: + errors.append("canonical provider duration is missing from query history") + if cache_value is True: + errors.append("query result cache hit detected; observation rejected") + elif cache_value is not False: + errors.append( + "query result cache evidence is missing or not exact false; observation rejected" + ) + if normalized.get("cache_origin_statement_id") not in (None, ""): + errors.append("query history reports a cache origin; observation rejected") + if not execution_succeeded: + canonical_duration = None + if errors: + canonical_duration = None + + safe_errors = [ + str(redact_secrets(error, secrets)) + for error in errors + ] + return { + "query_number": query_number, + "sql_sha256": hashlib.sha256(sql.encode("utf-8")).hexdigest(), + "statement_id": str(statement_id) if statement_id else None, + "query_tags": tags, + "execution_succeeded": execution_succeeded, + "result_row_count": row_count, + "result_hash_sha256": result_hash, + "client_wall_time_sec": client_wall_time, + "canonical_duration_sec": canonical_duration, + "metrics": normalized, + "history_record": bounded_history_record(history, secrets=secrets), + "errors": safe_errors, + } + + +def aligned_metric_arrays( + observations: Sequence[Mapping[str, Any]], +) -> dict[str, list[list[Any]]]: + def metric_seconds(observation: Mapping[str, Any], name: str) -> float | None: + metrics = observation.get("metrics") + if not isinstance(metrics, Mapping): + return None + return _history_seconds(metrics, name) + + return { + "result": [[observation.get("canonical_duration_sec")] for observation in observations], + "compilation_time": [ + [metric_seconds(observation, "compilation_time_ms")] + for observation in observations + ], + "execution_time": [ + [metric_seconds(observation, "execution_time_ms")] + for observation in observations + ], + "queue_time": [ + [metric_seconds(observation, "queue_time_ms")] + for observation in observations + ], + "result_fetch_time": [ + [metric_seconds(observation, "result_fetch_time_ms")] + for observation in observations + ], + "client_wall_time": [ + [observation.get("client_wall_time_sec")] + for observation in observations + ], + "statement_ids": [ + [observation.get("statement_id")] + for observation in observations + ], + "cache_hit": [ + [ + ( + observation.get("metrics", {}).get("result_from_cache") + if isinstance(observation.get("metrics"), Mapping) + else None + ) + ] + for observation in observations + ], + "read_io_cache_percent": [ + [ + ( + observation.get("metrics", {}).get("read_io_cache_percent") + if isinstance(observation.get("metrics"), Mapping) + else None + ) + ] + for observation in observations + ], + "result_row_count": [ + [observation.get("result_row_count")] + for observation in observations + ], + "result_hash": [ + [observation.get("result_hash_sha256")] + for observation in observations + ], + } + + +def validate_jsonl_record(record: Mapping[str, Any], query_count: int) -> None: + required = ( + "iteration", + "iteration_started_at", + "iteration_finished_at", + "raw_rows", + "mv_rows", + "system", + "version", + "machine", + "cluster_size", + "comment", + "tags", + "result", + "query_evidence", + "query_errors", + ) + missing = [name for name in required if name not in record] + if missing: + raise ValueError(f"JSONL record omitted required keys: {', '.join(missing)}") + if not isinstance(record["tags"], list): + raise ValueError("tags must be an array") + for name in ALIGNED_FIELDS: + value = record.get(name) + if not isinstance(value, list) or len(value) != query_count: + raise ValueError(f"{name} must contain exactly {query_count} query entries") + if any(not isinstance(entry, list) or len(entry) != 1 for entry in value): + raise ValueError(f"{name} entries must be single-trial arrays") + for name in ("query_evidence", "query_errors"): + value = record[name] + if not isinstance(value, list) or len(value) != query_count: + raise ValueError(f"{name} must contain exactly {query_count} entries") + for name in ("raw_rows", "mv_rows"): + value = record[name] + if value is not None and (isinstance(value, bool) or not isinstance(value, int) or value < 0): + raise ValueError(f"{name} must be a non-negative integer or null") + + +def _control_scalar( + client: DatabricksRestClient, + warehouse_id: str, + statement: str, + *, + timeout: float, + catalog: str, + schema: str, +) -> Any: + response = client.execute_statement( + statement, + warehouse_id, + timeout=timeout, + catalog=catalog, + schema=schema, + ) + rows = statement_rows(response) + if not rows: + raise RuntimeError("control statement returned no rows") + return next(iter(rows[0].values())) + + +def _server_hostname(host: str) -> str: + parsed = urllib.parse.urlparse(host) + if not parsed.hostname: + raise ValueError("Databricks host has no hostname") + return parsed.hostname + + +def _check_connector_version() -> str: + try: + installed = importlib.metadata.version("databricks-sql-connector") + except importlib.metadata.PackageNotFoundError as exc: + raise RuntimeError( + "databricks-sql-connector[kernel]==4.5.0 is required" + ) from exc + if installed != CONNECTOR_VERSION: + raise RuntimeError( + f"databricks-sql-connector {installed} is installed; exactly " + f"{CONNECTOR_VERSION} is required" + ) + return installed + + +def _open_kernel_connection(args: argparse.Namespace) -> Any: + # Optional dependencies stay behind this online-only boundary. + from databricks import sql # type: ignore[import-not-found] + + kwargs: dict[str, Any] = { + "server_hostname": _server_hostname(args.host), + "http_path": args.http_path, + "catalog": args.catalog, + "schema": args.schema, + "session_configuration": dict(SESSION_CONFIGURATION), + "use_kernel": True, + "user_agent_entry": "CostBench-StatementExecution", + } + if args.token: + kwargs["access_token"] = args.token + else: + # The Statement Execution kernel manages OAuth M2M token refresh + # directly. A Thrift-style custom credentials_provider is explicitly + # unsupported when use_kernel=True. + kwargs["oauth_client_id"] = args.client_id + kwargs["oauth_client_secret"] = args.client_secret + return sql.connect(**kwargs) + + +def _make_rest_client(args: argparse.Namespace) -> DatabricksRestClient: + return DatabricksRestClient( + args.host, + token=args.token, + client_id=args.client_id, + client_secret=args.client_secret, + timeout=max(30.0, args.control_timeout), + ) + + +def _close_quietly(connection: Any) -> None: + if connection is not None: + try: + connection.close() + except Exception: + pass + + +def _report_tags(value: str) -> list[str]: + return [part.strip() for part in value.split(",") if part.strip()] + + +def _comment(base: str, workload: str, warehouse_mode: str) -> str: + details = ( + f"{workload}, {warehouse_mode}, Statement Execution kernel, " + "use_cached_result=false" + ) + return f"{base.strip()} ({details})" if base.strip() else f"({details})" + + +def run( + workload: str, + default_queries: Path, + default_interval: float, + argv: Sequence[str] | None = None, +) -> int: + parser = build_parser(workload, default_queries, default_interval) + args = finalize_args(parser, parser.parse_args(argv)) + queries = load_queries( + args.queries, + args.catalog, + args.schema, + raw_table=args.raw_table, + mv_table=args.mv_table, + ) + expected = EXPECTED_QUERY_COUNTS[workload] + if len(queries) != expected: + parser.error(f"{args.queries} contains {len(queries)} statements; expected {expected}") + if args.print_queries: + for index, query in enumerate(queries, start=1): + print(f"-- query {index}\n{query};\n") + return 0 + + connector_version = _check_connector_version() + run_id = args.run_id or ( + f"dbx-{workload}-{utc_now().strftime('%Y%m%dT%H%M%SZ')}-{uuid.uuid4().hex[:8]}" + ) + if args.output is None: + stamp = utc_now().strftime("%Y%m%dT%H%M%SZ") + args.output = args.output_dir / f"{workload}_{stamp}.jsonl" + args.output = args.output.expanduser().resolve() + args.producer_progress = args.producer_progress.expanduser().resolve() + if args.freshness_monitor: + args.freshness_monitor = args.freshness_monitor.expanduser().resolve() + + secrets = tuple( + value for value in (args.token, args.client_secret) if isinstance(value, str) + ) + rest_client = _make_rest_client(args) + frozen_warehouse = redact_secrets( + { + "captured_at": iso_utc(), + "warehouse_id": args.rt_warehouse_id, + "warehouse_mode": args.warehouse_mode, + "http_path": args.http_path, + "api_metadata": rest_client.get_warehouse(args.rt_warehouse_id), + "connector_version": connector_version, + "use_kernel": True, + "session_configuration": SESSION_CONFIGURATION, + }, + secrets, + ) + try: + version = str( + _control_scalar( + rest_client, + args.control_warehouse_id, + "SELECT version() AS version", + timeout=args.control_timeout, + catalog=args.catalog, + schema=args.schema, + ) + ) + except Exception as exc: + version = "unknown" + print( + f"WARN: control-warehouse version lookup failed: {type(exc).__name__}: {exc}", + file=sys.stderr, + ) + + stop = threading.Event() + signal_count = 0 + + def request_stop(_signum: int, _frame: Any) -> None: + nonlocal signal_count + signal_count += 1 + stop.set() + if signal_count >= 2: + raise KeyboardInterrupt + + signal.signal(signal.SIGINT, request_stop) + signal.signal(signal.SIGTERM, request_stop) + + print(f"Parsed {len(queries)} queries from {args.queries}", file=sys.stderr) + print(f"Writing JSONL to {args.output}", file=sys.stderr) + print( + f"Run ID {run_id}; fixed-rate interval {args.interval:g}s; Ctrl-C to stop.", + file=sys.stderr, + ) + + connection = None + iteration = 0 + anchor_wall = utc_now() + anchor_monotonic = time.monotonic() + try: + while not stop.is_set() and (args.iterations == 0 or iteration < args.iterations): + delay = fixed_rate_delay( + anchor_monotonic, + args.interval, + iteration, + time.monotonic(), + ) + if delay and stop.wait(delay): + break + + iteration += 1 + scheduled_fire = fixed_rate_next_fire( + anchor_monotonic, + args.interval, + iteration - 1, + ) + scheduled_start = anchor_wall + timedelta( + seconds=scheduled_fire - anchor_monotonic + ) + actual_start_monotonic = time.monotonic() + iteration_started = utc_now() + producer = producer_progress_snapshot(args.producer_progress) + freshness = freshness_progress_snapshot(args.freshness_monitor) + mv_rows = freshness["mv_rows"] + mv_rows_source = freshness["mv_rows_source"] + + print( + f"[{iteration_started.strftime('%H:%M:%S')}] iteration {iteration}: " + f"raw_rows={producer['raw_rows']}", + file=sys.stderr, + ) + observations: list[dict[str, Any]] = [] + if connection is None: + try: + connection = _open_kernel_connection(args) + except Exception as exc: + error = str( + redact_secrets( + f"connection failed: {type(exc).__name__}: {exc}", + secrets, + ) + ) + print(f" {error}", file=sys.stderr) + observations = [ + failed_observation(number, query, error) + for number, query in enumerate(queries, start=1) + ] + if connection is not None: + for query_number, query in enumerate(queries, start=1): + observation = execute_measured_query( + connection, + rest_client, + query, + query_number=query_number, + iteration=iteration, + workload=workload, + run_id=run_id, + history_timeout=args.history_timeout, + history_poll_interval=args.history_poll_interval, + secrets=secrets, + ) + observations.append(observation) + print( + f" q{query_number}/{len(queries)}: " + f"server={observation['canonical_duration_sec']}s " + f"wall={observation['client_wall_time_sec']:.3f}s " + f"id={observation['statement_id']} " + f"errors={len(observation['errors'])}", + file=sys.stderr, + ) + + metric_arrays = aligned_metric_arrays(observations) + iteration_finished = utc_now() + elapsed = max(0.0, time.monotonic() - actual_start_monotonic) + record = { + "schema_version": 2, + "run_id": run_id, + "runner": workload, + "workload": workload, + "iteration": iteration, + "scheduled_start_at": iso_utc(scheduled_start), + "start_lag_sec": max(0.0, actual_start_monotonic - scheduled_fire), + "scheduled_interval_sec": args.interval, + "iteration_started_at": iso_utc(iteration_started), + "iteration_finished_at": iso_utc(iteration_finished), + "iteration_elapsed_sec": elapsed, + "raw_rows": producer["raw_rows"], + "raw_rows_source": producer["raw_rows_source"], + "mv_rows": mv_rows, + "mv_rows_source": mv_rows_source, + "system": args.system, + "version": version, + "machine": args.machine, + "cluster_size": args.cluster_size, + "comment": _comment(args.comment, workload, args.warehouse_mode), + "tags": _report_tags(args.tags), + **metric_arrays, + "query_errors": [item["errors"] for item in observations], + "query_evidence": observations, + "warehouse_mode": args.warehouse_mode, + "query_warehouse": frozen_warehouse, + "rt_warehouse": frozen_warehouse, + "control_warehouse_id": args.control_warehouse_id, + "producer_progress_path": producer["path"], + "producer_progress_loaded_at": producer["loaded_at"], + "producer_progress_file_modified_at": producer.get("file_modified_at"), + "producer_progress_updated_at": producer["progress_updated_at"], + "producer_progress_run_id": producer["progress_run_id"], + "producer_progress_finished": producer["finished"], + "producer_progress_error": producer["error"], + "producer_progress_evidence": producer["progress_fields"], + "freshness_monitor_path": freshness["path"], + "freshness_monitor_loaded_at": freshness["loaded_at"], + "freshness_monitor_file_modified_at": freshness.get("file_modified_at"), + "freshness_monitor_updated_at": freshness["progress_updated_at"], + "mv_refresh_finished_at": freshness["refresh_finished_at"], + "mv_source_rows": freshness["mv_source_rows"], + "mv_rows_behind": freshness["mv_rows_behind"], + "freshness_monitor_error": freshness["error"], + } + validate_jsonl_record(record, expected) + atomic_append_jsonl(args.output, redact_secrets(record, secrets)) + + next_fire = fixed_rate_next_fire( + anchor_monotonic, + args.interval, + iteration, + ) + now = time.monotonic() + if next_fire <= now: + print( + f" cadence overrun={now - next_fire:.3f}s; next starts immediately", + file=sys.stderr, + ) + else: + print(f" next iteration in {next_fire - now:.3f}s", file=sys.stderr) + finally: + _close_quietly(connection) + + print(f"Stopped after {iteration} iteration(s).", file=sys.stderr) + return 0 + + +def runner_main( + workload: str, + default_queries: Path, + default_interval: float, + argv: Sequence[str] | None = None, +) -> int: + try: + return run(workload, default_queries, default_interval, argv) + except KeyboardInterrupt: + print("Interrupted.", file=sys.stderr) + return 130 + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 diff --git a/full-path-realtime/quotes/databricks/stage_source_from_s3.sh b/full-path-realtime/quotes/databricks/stage_source_from_s3.sh new file mode 100755 index 0000000..ac51b0b --- /dev/null +++ b/full-path-realtime/quotes/databricks/stage_source_from_s3.sh @@ -0,0 +1,128 @@ +#!/usr/bin/env bash +set -euo pipefail + +: "${S3_SOURCE_URI:?Set S3_SOURCE_URI to the private prefix containing quotes_*.parquet}" +: "${SOURCE_DIR:=/data/quotes}" +: "${DBX_BENCH_DIR:=/home/ubuntu/costbench/full-path-realtime/quotes/databricks}" +: "${DATABRICKS_ZEROBUS_PYTHON:=$DBX_BENCH_DIR/.venv-zerobus/bin/python}" +: "${SOURCE_INVENTORY_OUTPUT:=/data/databricks-qualification/source_inventory.json}" + +command -v aws >/dev/null 2>&1 || { + echo "aws CLI v2 is required" >&2 + exit 1 +} +[ -x "$DATABRICKS_ZEROBUS_PYTHON" ] || { + echo "Zerobus Python is not executable: $DATABRICKS_ZEROBUS_PYTHON" >&2 + exit 1 +} + +case "$S3_SOURCE_URI" in + s3://*) ;; + *) + echo "S3_SOURCE_URI must start with s3://" >&2 + exit 1 + ;; +esac + +mkdir -p "$SOURCE_DIR" "$(dirname "$SOURCE_INVENTORY_OUTPUT")" +export AWS_CLI_AUTO_PROMPT=off +export AWS_PAGER= +export AWS_MAX_ATTEMPTS="${AWS_MAX_ATTEMPTS:-10}" + +# Prove credentials and prefix access before beginning a large transfer. +aws sts get-caller-identity >/dev/null +aws s3 ls "${S3_SOURCE_URI%/}/" >/dev/null + +# `sync` is resumable and does not delete local files. S3/HTTPS transport +# integrity checks remain enabled by the AWS CLI. +aws s3 sync \ + "${S3_SOURCE_URI%/}/" \ + "${SOURCE_DIR%/}/" \ + --exclude '*' \ + --include 'quotes_*.parquet' \ + --no-follow-symlinks \ + --only-show-errors + +"$DATABRICKS_ZEROBUS_PYTHON" - "$SOURCE_DIR" "$SOURCE_INVENTORY_OUTPUT" <<'PY' +import json +import os +import sys +import tempfile +from pathlib import Path + +import pyarrow.parquet as pq + +EXPECTED_ROWS = 113_219_565_734 +REQUIRED_COLUMNS = ( + "sym", "bx", "bp", "bs", "ax", "ap", "as", "c", "i", "t", "q", "z" +) + +source = Path(sys.argv[1]).expanduser().resolve() +output = Path(sys.argv[2]).expanduser().resolve() +files = sorted(source.glob("quotes_*.parquet"), key=lambda path: path.name.encode()) +if not files: + raise SystemExit(f"no quotes_*.parquet files found in {source}") +if any(path.name == "quotes_0.parquet" for path in files): + raise SystemExit("quotes_0.parquet is outside the canonical full-run source") + +rows = 0 +row_groups = 0 +schemas = set() +file_reports = [] +for path in files: + parquet = pq.ParquetFile(path) + metadata = parquet.metadata + columns = tuple(parquet.schema_arrow.names) + missing = [name for name in REQUIRED_COLUMNS if name not in columns] + if missing: + raise SystemExit(f"{path.name} is missing required columns: {missing}") + rows += int(metadata.num_rows) + row_groups += int(metadata.num_row_groups) + schemas.add(str(parquet.schema_arrow)) + file_reports.append( + { + "file": path.name, + "size_bytes": path.stat().st_size, + "rows": int(metadata.num_rows), + "row_groups": int(metadata.num_row_groups), + } + ) + +report = { + "schema_version": 1, + "status": "complete" if rows == EXPECTED_ROWS else "incomplete", + "source_uri": os.environ["S3_SOURCE_URI"].rstrip("/") + "/", + "source_dir": str(source), + "expected_rows": EXPECTED_ROWS, + "actual_rows": rows, + "row_delta": rows - EXPECTED_ROWS, + "file_count": len(files), + "row_group_count": row_groups, + "schema_variant_count": len(schemas), + "files": file_reports, +} + +output.parent.mkdir(parents=True, exist_ok=True) +fd, temporary = tempfile.mkstemp(prefix=f".{output.name}.", dir=output.parent) +try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + json.dump(report, handle, indent=2, sort_keys=True) + handle.write("\n") + handle.flush() + os.fsync(handle.fileno()) + os.chmod(temporary, 0o600) + os.replace(temporary, output) +finally: + try: + os.unlink(temporary) + except FileNotFoundError: + pass + +print(json.dumps({key: report[key] for key in ( + "status", "file_count", "row_group_count", "actual_rows", "row_delta" +)}, sort_keys=True)) +if rows != EXPECTED_ROWS: + raise SystemExit( + f"source row count is {rows:,}; expected exactly {EXPECTED_ROWS:,}" + ) +PY diff --git a/full-path-realtime/quotes/databricks/tests/test_offline.py b/full-path-realtime/quotes/databricks/tests/test_offline.py new file mode 100644 index 0000000..807c104 --- /dev/null +++ b/full-path-realtime/quotes/databricks/tests/test_offline.py @@ -0,0 +1,4425 @@ +from __future__ import annotations + +import io +import json +import os +import re +import sys +import tempfile +import unittest +import urllib.error +import urllib.parse +from decimal import Decimal +from pathlib import Path +from types import SimpleNamespace +from unittest.mock import patch + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) + +from dbx_common import ( # noqa: E402 + DatabricksRestClient, + EnvironmentConfig, + atomic_append_jsonl, + atomic_write_json, + bounded_statement_rows, + construct_zerobus_endpoint, + load_queries, + redact_secrets, + render_sql, + split_sql_statements, + validate_config, + zerobus_endpoint_errors, +) +import apply_ddl # noqa: E402 +import compact_evidence # noqa: E402 +import compact_query_results # noqa: E402 +import collect_evidence # noqa: E402 +import finalize_results # noqa: E402 +import preflight # noqa: E402 +import publish_results # noqa: E402 +import qualify # noqa: E402 +import run_dashboard # noqa: E402 +import run_drilldown # noqa: E402 +import validate_run # noqa: E402 +from costs import summarize_run # noqa: E402 +from monitor_freshness import ( # noqa: E402 + AGE_SEMANTICS, + build_control_queries, + classify_maintenance_type, + latest_successful_refresh, + progress_payload, + timestamp_age_sec, +) +from runner_common import ( # noqa: E402 + SESSION_CONFIGURATION, + _open_kernel_connection, + aligned_metric_arrays, + build_parser, + execute_measured_query, + fixed_rate_delay, + fixed_rate_next_fire, + freshness_progress_snapshot, + normalize_query_history, + poll_query_history, + producer_progress_snapshot, + stable_result_hash, + validate_jsonl_record, +) +from preflight import ( # noqa: E402 + EXPECTED_QUERY_COUNTS, + PACKAGE_CONTRACT, + load_create_materialized_view_statement, + offline_checks, + package_checks, + predictive_optimization_check, +) +from ingest_zerobus import ( # noqa: E402 + BatchCoordinate, + PendingBatch, + ProgressJournal, + RateLimiter, + Task, + add_ordinal_range, + checkpoint_pending, + compact_metrics_payload, + discover_source_files, + enumerate_tasks_and_manifest, + map_ipc_compression, + normalize_batch, + protect_target_table, + target_schema, + validate_expected_rows, + validate_resume_journal, +) + + +class _FakeType: + def __init__(self, name, *, bit_width=None, value_type=None): + self.name = name + self.bit_width = bit_width + self.value_type = value_type + + def __repr__(self): + return self.name + + +class _FakeField: + def __init__(self, name, type_, nullable): + self.name = name + self.type = type_ + self.nullable = nullable + + +class _FakeSchema(list): + @property + def names(self): + return [field.name for field in self] + + def get_field_index(self, name): + return self.names.index(name) + + +class _FakeArray: + def __init__(self, type_, marker): + self.type = type_ + self.marker = marker + + +class _FakeRecordBatch: + def __init__(self, arrays, schema): + self.arrays = arrays + self.schema = schema + + def column(self, index): + return self.arrays[index] + + @classmethod + def from_arrays(cls, arrays, schema): + return cls(arrays, schema) + + +class _FakeArrow: + RecordBatch = _FakeRecordBatch + + class types: + @staticmethod + def is_list(value): + return value.name == "list" + + @staticmethod + def is_large_list(value): + return False + + @staticmethod + def is_fixed_size_list(value): + return False + + @staticmethod + def is_unsigned_integer(value): + return value.name.startswith("uint") + + @staticmethod + def is_signed_integer(value): + return value.name.startswith("int") + + @staticmethod + def large_utf8(): + return _FakeType("large_utf8") + + @staticmethod + def int16(): + return _FakeType("int16", bit_width=16) + + @staticmethod + def int64(): + return _FakeType("int64", bit_width=64) + + @staticmethod + def float64(): + return _FakeType("float64") + + @staticmethod + def list_(value_type): + return _FakeType("list", value_type=value_type) + + @staticmethod + def field(name, type_, nullable=True): + return _FakeField(name, type_, nullable) + + @staticmethod + def schema(fields): + return _FakeSchema(fields) + + +class _FakeCompute: + def __init__(self, maxima=None): + self.casts = [] + self.maxima = maxima or {} + + def cast(self, source, target, safe): + self.casts.append((source.marker, target.name, safe)) + return (source.marker, target.name) + + @staticmethod + def list_flatten(source): + return source + + def max(self, source, skip_nulls=True): + value = self.maxima.get(source.marker) + if value is None: + return None + + class Scalar: + def as_py(self): + return value + + return Scalar() + + +class SqlTests(unittest.TestCase): + def test_splitter_ignores_line_and_nested_block_comment_semicolons(self): + sql = """ + -- leading ; comment + SELECT 'a;b' AS single, "c;d" AS double, `e;f` AS identifier; + /* outer ; + /* nested ; */ + */ + SELECT 2 /* ordinary ; comment */; + """ + self.assertEqual( + split_sql_statements(sql), + [ + """SELECT 'a;b' AS single, "c;d" AS double, `e;f` AS identifier""", + "SELECT 2", + ], + ) + + def test_splitter_preserves_optimizer_hints(self): + self.assertEqual( + split_sql_statements("SELECT /*+ REPARTITION(1) ; */ 1;"), + ["SELECT /*+ REPARTITION(1) ; */ 1"], + ) + + def test_splitter_handles_doubled_quotes_and_rejects_unterminated_input(self): + self.assertEqual( + split_sql_statements("SELECT 'it''s;a'; SELECT `a``;b`;"), + ["SELECT 'it''s;a'", "SELECT `a``;b`"], + ) + with self.assertRaisesRegex(ValueError, "unterminated SQL quote"): + split_sql_statements("SELECT 'broken;") + with self.assertRaisesRegex(ValueError, "unterminated SQL block comment"): + split_sql_statements("SELECT 1 /* broken;") + + def test_render_quotes_catalog_and_schema(self): + rendered = render_sql( + "SELECT * FROM __CATALOG__.__SCHEMA__.quotes", + "benchmark-catalog", + "schema name", + ) + self.assertEqual( + rendered, + "SELECT * FROM `benchmark-catalog`.`schema name`.quotes", + ) + custom = render_sql( + "SELECT * FROM __CATALOG__.__SCHEMA__.__RAW_TABLE__ " + "JOIN __CATALOG__.__SCHEMA__.__MV_TABLE__", + "benchmark-catalog", + "schema name", + raw_table="raw table", + mv_table="daily`mv", + ) + self.assertEqual( + custom, + "SELECT * FROM `benchmark-catalog`.`schema name`.`raw table` " + "JOIN `benchmark-catalog`.`schema name`.`daily``mv`", + ) + + +class ApplyDdlTests(unittest.TestCase): + @staticmethod + def invoke(output_dir, client_factory, **overrides): + values = { + "host": "https://dbc-example.cloud.databricks.com", + "catalog": "fresh_catalog", + "schema": "fresh_schema", + "control_warehouse_id": "control-warehouse", + "confirm_destructive_target": "fresh_catalog.fresh_schema", + "output_dir": output_dir, + "token": "dapiSecretValue123456", + "client_factory": client_factory, + } + values.update(overrides) + return apply_ddl.apply_ddl(**values) + + def test_confirmation_mismatch_blocks_before_client_or_statement_calls(self): + calls = [] + + def client_factory(*args, **kwargs): + calls.append((args, kwargs)) + raise AssertionError("client must not be created") + + with tempfile.TemporaryDirectory() as tmp: + output_dir = Path(tmp) / "blocked" + with self.assertRaisesRegex(ValueError, "must exactly equal"): + self.invoke( + output_dir, + client_factory, + confirm_destructive_target="fresh_catalog.wrong_schema", + ) + self.assertFalse(output_dir.exists()) + self.assertEqual(calls, []) + + def test_complete_rendered_sql_and_all_four_statements_are_recorded(self): + class FakeClient: + def __init__(self, *args, **kwargs): + self.calls = [] + + def execute_statement(self, statement, warehouse_id, **kwargs): + self.calls.append((statement, warehouse_id, kwargs)) + return {"statement_id": f"statement-{len(self.calls)}"} + + client = FakeClient() + with tempfile.TemporaryDirectory() as tmp: + output_dir = Path(tmp) / "ddl" + report = self.invoke( + output_dir, + lambda *args, **kwargs: client, + ) + persisted = json.loads( + (output_dir / apply_ddl.REPORT_NAME).read_text(encoding="utf-8") + ) + rendered = (output_dir / apply_ddl.RENDERED_SQL_NAME).read_text( + encoding="utf-8" + ) + + expected = render_sql( + (ROOT / "create.sql").read_text(encoding="utf-8"), + "fresh_catalog", + "fresh_schema", + ) + self.assertEqual(rendered, expected) + self.assertNotIn("__CATALOG__", rendered) + self.assertNotIn("__SCHEMA__", rendered) + self.assertEqual(len(split_sql_statements(rendered)), 4) + self.assertEqual(len(client.calls), 4) + self.assertTrue( + all( + call[2]["query_tags"]["operation"] == "apply-ddl" + and call[1] == "control-warehouse" + for call in client.calls + ) + ) + self.assertEqual(report["status"], "succeeded") + self.assertEqual(persisted["statement_count"], 4) + self.assertEqual( + [item["statement_id"] for item in persisted["statements"]], + [f"statement-{index}" for index in range(1, 5)], + ) + self.assertTrue( + all(item["status"] == "succeeded" for item in persisted["statements"]) + ) + + def test_statement_failure_is_recorded_redacted_and_stops_execution(self): + secret = "dapiSecretValue123456" + + class FakeClient: + def __init__(self): + self.calls = [] + + def execute_statement(self, statement, warehouse_id, **kwargs): + self.calls.append(statement) + if len(self.calls) == 2: + raise RuntimeError(f"Authorization: Bearer {secret}") + return {"statement_id": f"statement-{len(self.calls)}"} + + client = FakeClient() + with tempfile.TemporaryDirectory() as tmp: + output_dir = Path(tmp) / "ddl" + report = self.invoke( + output_dir, + lambda *args, **kwargs: client, + token=secret, + ) + persisted_text = ( + output_dir / apply_ddl.REPORT_NAME + ).read_text(encoding="utf-8") + persisted = json.loads(persisted_text) + + self.assertEqual(len(client.calls), 2) + self.assertEqual(report["status"], "failed") + self.assertEqual( + [item["status"] for item in persisted["statements"]], + ["succeeded", "failed", "not_run", "not_run"], + ) + self.assertEqual(len(persisted["errors"]), 1) + self.assertNotIn(secret, persisted_text) + self.assertIn("***REDACTED***", persisted_text) + + +class FreshnessMonitorTests(unittest.TestCase): + def test_control_sql_is_bounded_targeted_and_never_counts_raw(self): + queries = build_control_queries( + "catalog", + "schema", + "quotes'raw", + "quotes daily", + "2026-09-10T00:00:00Z", + "2026-09-10T00:05:00Z", + ) + ingest = queries["zerobus_ingest"] + events = queries["mv_events"] + self.assertIn("table_name = 'catalog.schema.quotes''raw'", ingest) + self.assertIn("commit_time >=", ingest) + self.assertIn("commit_time <=", ingest) + self.assertIn("timestamp >=", events) + self.assertIn("timestamp <=", events) + self.assertIn("LIMIT 100", events) + self.assertIn("event_type IN ('update_progress', 'planning_information')", events) + self.assertIn( + "event_log(TABLE(`catalog`.`schema`.`quotes daily`))", + events, + ) + self.assertEqual( + queries["raw_history"], + "DESCRIBE HISTORY `catalog`.`schema`.`quotes'raw` LIMIT 1", + ) + self.assertNotIn("count(*)", "\n".join(queries.values()).lower()) + + def test_maintenance_classification_and_event_detail_correlation(self): + cases = { + "MAINTENANCE_TYPE_COMPLETE_RECOMPUTE": "full", + "COMPLETE_RECOMPUTE": "full", + "FULL_REFRESH": "full", + "MAINTENANCE_TYPE_NO_OP": "neutral", + "MAINTENANCE_TYPE_GROUP_AGGREGATE": "incremental", + None: "unknown", + } + for maintenance_type, expected in cases.items(): + with self.subTest(maintenance_type=maintenance_type): + self.assertEqual( + classify_maintenance_type(maintenance_type), + expected, + ) + + rows = [ + { + "timestamp": "2026-09-10T00:04:00Z", + "event_type": "update_progress", + "origin": json.dumps({"update_id": "update-2"}), + "details": json.dumps( + { + "update_progress": {"state": "COMPLETED"}, + "metrics": {"num_output_rows": "321"}, + } + ), + }, + { + "timestamp": "2026-09-10T00:03:00Z", + "event_type": "planning_information", + "origin": { + "update_id": "update-2", + "ingestion_source_table_version": "17", + }, + "details": json.dumps( + { + "planning_information": { + "technique_information": [ + { + "maintenance_type": ( + "MAINTENANCE_TYPE_COMPLETE_RECOMPUTE" + ), + "is_chosen": True, + }, + { + "maintenance_type": ( + "MAINTENANCE_TYPE_GROUP_AGGREGATE" + ), + "is_chosen": False, + }, + ], + "source_table_information": [ + { + "table_name": "`catalog`.`schema`.`quotes`", + "num_rows": "300", + "num_files": "4", + "full_size": "5000", + "num_changed_rows": "100", + "num_changed_files": "2", + "change_size": "1200", + } + ], + } + } + ), + }, + ] + refresh = latest_successful_refresh(rows) + self.assertEqual( + refresh, + { + "finished_at": "2026-09-10T00:04:00.000Z", + "output_rows": 321, + "maintenance_type": "MAINTENANCE_TYPE_COMPLETE_RECOMPUTE", + "maintenance_class": "full", + "planned_at": "2026-09-10T00:03:00.000Z", + "source_snapshot": { + "table_name": "`catalog`.`schema`.`quotes`", + "num_rows": 300, + "num_files": 4, + "full_size_bytes": 5000, + "changed_rows": 100, + "changed_files": 2, + "change_size_bytes": 1200, + "delta_version": 17, + }, + "update_id": "update-2", + }, + ) + + def test_observational_age_and_runner_progress_shape(self): + self.assertEqual( + timestamp_age_sec( + "2026-09-10T00:02:30Z", + "2026-09-10T00:01:00Z", + ), + 90.0, + ) + self.assertIsNone( + timestamp_age_sec("2026-09-10T00:02:30Z", "not-a-timestamp") + ) + snapshot = progress_payload( + { + "schema_version": 1, + "run_id": "run-1", + "iteration": 7, + "observed_at": "2026-09-10T00:02:30Z", + "iteration_finished_at": "2026-09-10T00:02:31Z", + "producer_progress": { + "provider_committed_rows": 120, + }, + "zerobus_ingest": { + "cumulative_committed_records": 119, + "cumulative_committed_bytes": 1_000, + "cumulative_error_count": 0, + "latest_commit_version": 17, + "latest_commit_time": "2026-09-10T00:02:29Z", + }, + "raw_history": { + "version": 18, + "timestamp": "2026-09-10T00:02:30Z", + }, + "mv_rows": 123, + "latest_successful_refresh": { + "finished_at": "2026-09-10T00:01:00Z", + "output_rows": 123, + "maintenance_type": "MAINTENANCE_TYPE_GROUP_AGGREGATE", + }, + "mv_source_rows": 100, + "mv_rows_behind": 20, + "raw_commit_age_sec": 1.0, + "producer_progress_age_sec": 2.0, + "mv_refresh_age_sec": 90.0, + "query_evidence": { + "mv_events": {"statement_id": "statement-1"} + }, + "errors": [], + } + ) + self.assertIsNone(snapshot["mv_rows"]) + self.assertEqual( + snapshot["latest_successful_refresh"]["finished_at"], + "2026-09-10T00:01:00Z", + ) + self.assertEqual(snapshot["mv_source_rows"], 100) + self.assertEqual(snapshot["mv_rows_behind"], 20) + self.assertEqual(snapshot["client_durable_rows"], 120) + self.assertEqual(snapshot["provider_committed_rows"], 119) + self.assertEqual(snapshot["raw_delta_version"], 18) + self.assertEqual(snapshot["statement_ids"]["mv_events"], "statement-1") + self.assertEqual(snapshot["updated_at"], "2026-09-10T00:02:31Z") + self.assertEqual(snapshot["age_semantics"], AGE_SEMANTICS) + + +class EndpointTests(unittest.TestCase): + def test_constructs_documented_cloud_endpoints(self): + cases = { + "aws": "https://123456789.zerobus.us-west-2.cloud.databricks.com", + "azure": "https://123456789.zerobus.eastus.azuredatabricks.net", + "gcp": "https://123456789.zerobus.us-east1.gcp.databricks.com", + } + regions = {"aws": "us-west-2", "azure": "eastus", "gcp": "us-east1"} + for cloud, expected in cases.items(): + with self.subTest(cloud=cloud): + self.assertEqual( + construct_zerobus_endpoint("123456789", regions[cloud], cloud), + expected, + ) + + def test_endpoint_validation_detects_cloud_mismatch_and_paths(self): + errors = zerobus_endpoint_errors( + "https://123.zerobus.us-west-2.gcp.databricks.com/a", + workspace_id="123", + region="us-west-2", + cloud="aws", + ) + self.assertTrue(any("path" in error for error in errors)) + self.assertTrue(any("does not match" in error for error in errors)) + + +class RedactionAndWriteTests(unittest.TestCase): + def test_redacts_sensitive_keys_known_values_pat_and_authorization(self): + secret = "not-patterned-value" + source = { + "client_secret": secret, + "message": ( + f"request used {secret}, Bearer eyJ.abc.def, " + "and dapi1234567890abcdef" + ), + "nested": [{"Authorization": "Basic abc123=="}], + } + redacted = redact_secrets(source, [secret]) + rendered = json.dumps(redacted) + self.assertNotIn(secret, rendered) + self.assertNotIn("eyJ.abc.def", rendered) + self.assertNotIn("dapi1234567890abcdef", rendered) + self.assertNotIn("abc123==", rendered) + self.assertIn("***REDACTED***", rendered) + + def test_rest_errors_do_not_echo_configured_secrets(self): + secret = "arbitrary-secret-without-a-known-prefix" + + def fail(request, timeout): + raise urllib.error.HTTPError( + request.full_url, + 401, + "Unauthorized", + {}, + io.BytesIO( + json.dumps({"message": f"credential {secret} rejected"}).encode() + ), + ) + + client = DatabricksRestClient( + "https://dbc-example.cloud.databricks.com", + token=secret, + opener=fail, + ) + with self.assertRaises(Exception) as raised: + client.get_warehouse("abc123") + self.assertNotIn(secret, str(raised.exception)) + self.assertIn("***REDACTED***", str(raised.exception)) + + def test_atomic_json_and_jsonl_helpers(self): + with tempfile.TemporaryDirectory() as tmp: + json_path = Path(tmp) / "nested" / "report.json" + jsonl_path = Path(tmp) / "events.jsonl" + atomic_write_json(json_path, {"when": "now", "value": 1}) + atomic_append_jsonl(jsonl_path, {"value": 1}) + atomic_append_jsonl(jsonl_path, {"value": 2}) + self.assertEqual(json.loads(json_path.read_text())["value"], 1) + self.assertEqual( + [json.loads(line)["value"] for line in jsonl_path.read_text().splitlines()], + [1, 2], + ) + + +class _FakeJsonResponse: + def __init__(self, payload): + self.payload = payload + + def __enter__(self): + return self + + def __exit__(self, *_args): + return False + + def read(self): + return json.dumps(self.payload).encode("utf-8") + + +class RestStatementTests(unittest.TestCase): + def test_statement_execution_forwards_query_tags(self): + seen = {} + + def opener(request, timeout): + seen["payload"] = json.loads(request.data.decode("utf-8")) + seen["timeout"] = timeout + return _FakeJsonResponse( + { + "statement_id": "statement-1", + "status": {"state": "SUCCEEDED"}, + "manifest": {"schema": {"columns": []}}, + "result": {"data_array": []}, + } + ) + + client = DatabricksRestClient( + "https://dbc-example.cloud.databricks.com", + token="secret-token", + opener=opener, + ) + client.execute_statement( + "SELECT 1", + "control-warehouse", + query_tags={"collector": "freshness", "iteration": "1"}, + ) + self.assertEqual( + seen["payload"]["query_tags"], + [ + {"key": "collector", "value": "freshness"}, + {"key": "iteration", "value": "1"}, + ], + ) + + def test_bounded_statement_rows_follows_all_workspace_chunks(self): + seen = [] + + def opener(request, timeout): + seen.append(request.full_url) + return _FakeJsonResponse( + { + "chunk_index": 1, + "row_offset": 2, + "row_count": 1, + "data_array": [["three", 3]], + } + ) + + client = DatabricksRestClient( + "https://dbc-example.cloud.databricks.com", + token="secret-token", + opener=opener, + ) + response = { + "statement_id": "statement-1", + "manifest": { + "total_row_count": 3, + "total_chunk_count": 2, + "schema": { + "columns": [ + {"name": "word", "type_text": "STRING"}, + {"name": "number", "type_text": "INT"}, + ] + }, + }, + "result": { + "chunk_index": 0, + "row_offset": 0, + "row_count": 2, + "data_array": [["one", 1], ["two", 2]], + "next_chunk_internal_link": ( + "/api/2.0/sql/statements/statement-1/result/chunks/1" + "?row_offset=2" + ), + }, + } + rows, metadata = bounded_statement_rows( + client, + response, + max_rows=3, + max_bytes=1_000, + max_chunks=2, + ) + self.assertEqual( + rows, + [ + {"word": "one", "number": 1}, + {"word": "two", "number": 2}, + {"word": "three", "number": 3}, + ], + ) + self.assertEqual(metadata["row_count"], 3) + self.assertEqual(metadata["chunk_count"], 2) + self.assertEqual( + [column["name"] for column in metadata["schema_columns"]], + ["word", "number"], + ) + self.assertEqual(len(seen), 1) + self.assertTrue(seen[0].startswith(client.host + "/api/2.0/sql/statements/")) + + def test_bounded_statement_rows_rejects_foreign_links_and_limits(self): + client = DatabricksRestClient( + "https://dbc-example.cloud.databricks.com", + token="secret-token", + opener=lambda *_args, **_kwargs: self.fail("foreign link was requested"), + ) + base = { + "statement_id": "statement-1", + "manifest": { + "schema": {"columns": [{"name": "value"}]}, + }, + "result": { + "data_array": [[1]], + "next_chunk_internal_link": ( + "https://attacker.invalid/api/2.0/sql/statements/x/result/chunks/1" + ), + }, + } + with self.assertRaisesRegex(ValueError, "outside the workspace origin"): + bounded_statement_rows( + client, + base, + max_rows=10, + max_bytes=1_000, + max_chunks=2, + ) + wrong_statement = { + **base, + "result": { + "data_array": [[1]], + "next_chunk_internal_link": ( + "/api/2.0/sql/statements/other/result/chunks/1" + ), + }, + } + with self.assertRaisesRegex(ValueError, "another statement"): + bounded_statement_rows( + client, + wrong_statement, + max_rows=10, + max_bytes=1_000, + max_chunks=2, + ) + with self.assertRaisesRegex(RuntimeError, "max_chunks"): + bounded_statement_rows( + client, + { + **base, + "result": { + "data_array": [[1]], + "next_chunk_internal_link": ( + "/api/2.0/sql/statements/statement-1/result/chunks/1" + ), + }, + }, + max_rows=10, + max_bytes=1_000, + max_chunks=1, + ) + + too_many_rows = { + **base, + "manifest": { + "total_row_count": 2, + "schema": {"columns": [{"name": "value"}]}, + }, + "result": {"data_array": [[1], [2]]}, + } + with self.assertRaisesRegex(RuntimeError, "max_rows"): + bounded_statement_rows( + client, + too_many_rows, + max_rows=1, + max_bytes=1_000, + max_chunks=1, + ) + with self.assertRaisesRegex(RuntimeError, "max_bytes"): + bounded_statement_rows( + client, + { + **too_many_rows, + "manifest": { + "total_row_count": 2, + "schema": {"columns": [{"name": "value"}]}, + }, + }, + max_rows=2, + max_bytes=2, + max_chunks=1, + ) + + def test_bounded_statement_rows_preserves_empty_result_schema(self): + client = DatabricksRestClient( + "https://dbc-example.cloud.databricks.com", + token="secret-token", + opener=lambda *_args, **_kwargs: self.fail("no chunk should be requested"), + ) + rows, metadata = bounded_statement_rows( + client, + { + "statement_id": "statement-empty", + "manifest": { + "total_row_count": 0, + "total_chunk_count": 0, + "schema": { + "columns": [{"name": "empty_value", "type_text": "STRING"}] + }, + }, + "result": None, + }, + max_rows=1, + max_bytes=10, + max_chunks=1, + ) + self.assertEqual(rows, []) + self.assertEqual(metadata["chunk_count"], 0) + self.assertEqual(metadata["schema_columns"][0]["name"], "empty_value") + with self.assertRaisesRegex(RuntimeError, "truncated"): + bounded_statement_rows( + client, + { + "manifest": { + "truncated": True, + "schema": {"columns": [{"name": "value"}]}, + }, + "result": {"data_array": []}, + }, + max_rows=1, + max_bytes=10, + max_chunks=1, + ) + + +class EvidenceCollectorTests(unittest.TestCase): + def make_queries(self): + return collect_evidence.build_evidence_queries( + catalog="catalog'name", + schema="schema name", + raw_table="raw`quotes", + mv_table="daily quotes", + rt_warehouse_id="rt'warehouse", + control_warehouse_id="control-warehouse", + run_id="run'id", + since="2026-09-10T00:00:00Z", + until="2026-09-10T01:00:00Z", + row_limit=123, + history_limit=45, + ) + + def test_evidence_window_is_required_utc_ordered_and_bounded(self): + start, end = collect_evidence.validate_window( + "2026-09-10T00:00:00Z", + "2026-09-13T00:00:00Z", + ) + self.assertEqual((end - start).total_seconds(), 72 * 3600) + for since, until, message in ( + ( + "2026-09-10T00:00:00", + "2026-09-10T01:00:00Z", + "UTC timezone", + ), + ( + "2026-09-10T00:00:00+01:00", + "2026-09-10T01:00:00Z", + "must be UTC", + ), + ( + "2026-09-10T01:00:00Z", + "2026-09-10T01:00:00Z", + "later", + ), + ( + "2026-09-10T00:00:00Z", + "2026-09-13T00:00:00.001Z", + "maximum", + ), + ): + with self.subTest(since=since, until=until): + with self.assertRaisesRegex(ValueError, message): + collect_evidence.validate_window(since, until) + + def test_evidence_sql_is_targeted_time_bounded_and_never_counts_raw(self): + queries = self.make_queries() + ingest = queries["zerobus_ingest_summary"] + self.assertIn("table_name = 'catalog''name.schema name.raw`quotes'", ingest) + self.assertIn("commit_time >=", ingest) + self.assertIn("commit_time <", ingest) + + history = queries["query_history"] + self.assertIn("compute.warehouse_id = 'rt''warehouse'", history) + self.assertIn("query_tags['run_id'] = 'run''id'", history) + self.assertIn("start_time >=", history) + self.assertIn("start_time <", history) + self.assertIn("LIMIT 123", history) + + billing = queries["billing_usage"] + self.assertIn("usage_start_time <", billing) + self.assertIn("usage_end_time >", billing) + self.assertIn("usage_metadata.warehouse_id IN", billing) + self.assertIn("PREDICTIVE_OPTIMIZATION", billing) + self.assertIn("usage_metadata.dlt_pipeline_id", billing) + self.assertIn( + "event_log(TABLE(`catalog'name`.`schema name`.`daily quotes`))", + billing, + ) + self.assertIn("to_json(usage_metadata)", billing) + self.assertIn("LIMIT 123", billing) + + rendered = "\n".join(queries.values()) + self.assertIsNone( + re.search( + r"(?is)count\s*\(\s*\*\s*\).*from\s+" + r"`catalog'name`\.`schema name`\.`raw``quotes`", + rendered, + ) + ) + self.assertNotIn("SELECT * FROM `catalog'name`.`schema name`.`raw``quotes`", rendered) + + def test_dynamic_zerobus_stream_query_requires_documented_filters(self): + schema_rows = [ + {"col_name": "stream_id", "data_type": "string"}, + {"col_name": "event_time", "data_type": "timestamp"}, + {"col_name": "table_name", "data_type": "string"}, + {"col_name": "errors", "data_type": "array"}, + ] + sql = collect_evidence.build_zerobus_stream_query( + schema_rows, + target="catalog.schema.raw", + since="2026-09-10T00:00:00Z", + until="2026-09-10T01:00:00Z", + row_limit=99, + ) + self.assertIn("`table_name` = 'catalog.schema.raw'", sql) + self.assertIn("`event_time` >=", sql) + self.assertIn("`event_time` <", sql) + self.assertIn("ORDER BY `event_time`, `stream_id`", sql) + self.assertTrue(sql.endswith("LIMIT 99")) + with self.assertRaisesRegex(ValueError, "missing documented"): + collect_evidence.build_zerobus_stream_query( + schema_rows[:1], + target="catalog.schema.raw", + since="2026-09-10T00:00:00Z", + until="2026-09-10T01:00:00Z", + row_limit=99, + ) + + def test_summary_aggregates_provider_mv_stream_and_predictive_evidence(self): + datasets = { + name: { + "required": name in collect_evidence.REQUIRED_DATASETS, + "row_count": 0, + "path": f"/tmp/{name}.jsonl", + "statement_id": f"statement-{name}", + "error": None, + } + for name in ( + *collect_evidence.REQUIRED_DATASETS, + *collect_evidence.OPTIONAL_DATASETS, + ) + } + rows = { + "zerobus_ingest_summary": [ + { + "provider_committed_records": "123", + "provider_committed_bytes": "456", + "provider_errors": "2", + "min_commit_version": 7, + "max_commit_version": 9, + } + ], + "zerobus_stream_errors": [ + {"errors": [{"error_code": 1}, {"error_code": 2}]} + ], + "mv_event_log": [ + { + "event_type": "planning_information", + "details_json": json.dumps( + { + "planning_information": { + "technique_information": [ + { + "maintenance_type": ( + "MAINTENANCE_TYPE_COMPLETE_RECOMPUTE" + ), + "is_chosen": True, + } + ] + } + } + ), + } + ], + "warehouse_config_snapshots": [{"warehouse_id": "rt"}], + "raw_table_detail": [{"numRows": "1000"}], + "mv_table_detail": [{"numRows": 100}], + "predictive_optimization_operations": [ + { + "operation_type": "CLUSTERING", + "usage_unit": "ESTIMATED_DBU", + "usage_quantity": "1.25", + }, + { + "operation_type": "CLUSTERING", + "usage_unit": "ESTIMATED_DBU", + "usage_quantity": "0.75", + }, + ], + "billing_usage": [ + { + "sku_name": "SERVERLESS", + "usage_unit": "DBU", + "usage_quantity": "3.5", + } + ], + } + summary = collect_evidence.build_summary( + since="2026-09-10T00:00:00Z", + until="2026-09-10T01:00:00Z", + run_id="run-1", + targets={"raw": "catalog.schema.raw"}, + datasets=datasets, + rows=rows, + collected_at="2026-09-10T02:00:00Z", + ) + self.assertEqual(summary["provider_committed_records"], 123) + self.assertEqual(summary["provider_committed_bytes"], 456) + self.assertEqual(summary["provider_errors"], 2) + self.assertEqual(summary["zerobus_stream_error_count"], 2) + self.assertEqual(summary["mv_maintenance"]["full_refresh_count"], 1) + self.assertEqual( + summary["predictive_optimization"]["operation_counts_by_type"], + {"CLUSTERING": 2}, + ) + self.assertEqual( + summary["predictive_optimization"]["usage_by_unit"]["ESTIMATED_DBU"], + "2.00", + ) + self.assertEqual(summary["table_num_rows"]["raw"], 1000) + self.assertTrue(summary["required_dataset_completeness"]["complete"]) + self.assertTrue(summary["complete"]) + datasets["zerobus_stream_schema"]["error"] = "unsupported schema" + optional_failure = collect_evidence.build_summary( + since="2026-09-10T00:00:00Z", + until="2026-09-10T01:00:00Z", + run_id="run-1", + targets={}, + datasets=datasets, + rows=rows, + ) + self.assertTrue( + optional_failure["required_dataset_completeness"]["complete"] + ) + self.assertFalse(optional_failure["complete"]) + + def test_summary_marks_failed_required_source_incomplete(self): + datasets = { + name: { + "required": True, + "row_count": 0, + "path": f"/tmp/{name}.jsonl", + "statement_id": None, + "error": "permission denied" if name == "query_history" else None, + } + for name in collect_evidence.REQUIRED_DATASETS + } + datasets.update( + { + name: { + "required": False, + "row_count": 0, + "path": f"/tmp/{name}.jsonl", + "statement_id": None, + "error": None, + } + for name in collect_evidence.OPTIONAL_DATASETS + } + ) + summary = collect_evidence.build_summary( + since="2026-09-10T00:00:00Z", + until="2026-09-10T01:00:00Z", + run_id="run-1", + targets={}, + datasets=datasets, + rows={}, + ) + self.assertEqual( + summary["required_dataset_completeness"]["failed"], + ["query_history"], + ) + self.assertFalse(summary["required_dataset_completeness"]["complete"]) + self.assertFalse(summary["complete"]) + + def test_collection_failure_still_writes_summary_and_returns_incomplete(self): + class FailingClient: + def execute_statement(self, *_args, **_kwargs): + raise RuntimeError("permission denied") + + def request(self, *_args, **_kwargs): + raise RuntimeError("permission denied") + + with tempfile.TemporaryDirectory() as tmp: + summary, required_complete = collect_evidence.collect( + FailingClient(), + control_warehouse_id="control", + rt_warehouse_id="rt", + catalog="catalog", + schema="schema", + raw_table="raw", + mv_table="mv", + since=collect_evidence.parse_utc_timestamp( + "2026-09-10T00:00:00Z" + ), + until=collect_evidence.parse_utc_timestamp( + "2026-09-10T01:00:00Z" + ), + run_id="run-1", + output_dir=Path(tmp), + row_limit=100, + history_limit=10, + statement_timeout=1, + poll_interval=0.1, + max_result_bytes=1_000, + max_result_chunks=10, + ) + written = json.loads( + (Path(tmp) / "evidence_summary.json").read_text(encoding="utf-8") + ) + self.assertTrue((Path(tmp) / "collector_errors.jsonl").exists()) + self.assertFalse(required_complete) + self.assertFalse(summary["required_dataset_completeness"]["complete"]) + self.assertFalse(written["required_dataset_completeness"]["complete"]) + self.assertIn( + "query_history", + written["required_dataset_completeness"]["failed"], + ) + + def test_compact_evidence_keeps_clustering_and_reconciliation_only(self): + outputs = compact_evidence.build_compact_evidence( + { + "run_id": "run-1", + "provider_committed_records": 100, + "provider_committed_bytes": 200, + "provider_errors": 0, + "zerobus_stream_error_count": 0, + "table_details": { + "raw": { + "name": "catalog.schema.quotes", + "clusteringColumns": '["sym","t"]', + "numFiles": "3", + }, + "materialized_view": { + "json_metadata": json.dumps( + { + "table_name": "quotes_daily", + "clustering_columns": ["sym", "day"], + } + ) + }, + }, + "predictive_optimization": { + "operation_count": 1, + "operation_counts_by_type": {"CLUSTERING": 1}, + }, + "mv_maintenance": { + "maintenance_type_counts": { + "MAINTENANCE_TYPE_GROUP_AGGREGATE": 2 + }, + "maintenance_class_counts": {"incremental": 2}, + "full_refresh_count": 0, + }, + }, + [ + { + "table_name": "quotes", + "operation_id": "operation-1", + "operation_type": "CLUSTERING", + "operation_status": "SUCCESSFUL", + "operation_metrics_json": '{"number_of_clustered_files":"2"}', + } + ], + { + "online": { + "predictive_optimization": { + "status": "ok", + "raw": { + "configured_value": "INHERIT", + "effective_value": "ENABLE", + "effectively_enabled": True, + }, + "materialized_view": { + "configured_value": "INHERIT", + "effective_value": "ENABLE", + "effectively_enabled": True, + }, + } + } + }, + ) + clustering = outputs["clustering_status"] + self.assertEqual( + clustering["tables"]["raw"]["clustering_columns"], ["sym", "t"] + ) + self.assertEqual( + clustering["tables"]["materialized_view"]["clustering_columns"], + ["sym", "day"], + ) + self.assertEqual( + clustering["predictive_optimization"]["clustering_operation_count"], + 1, + ) + self.assertTrue( + clustering["tables"]["raw"][ + "predictive_optimization_effectively_enabled" + ] + ) + self.assertEqual( + outputs["provider_reconciliation"]["provider_committed_records"], + 100, + ) + self.assertEqual(outputs["mv_refresh_summary"]["full_refresh_count"], 0) + + def test_result_manifest_excludes_runtime_source_manifest(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + run_root = root / "run" + (run_root / "ingest").mkdir(parents=True) + (run_root / "ingest" / "ingest_summary.json").write_text("{}\n") + source_manifest = root / "source_manifest.json" + source_manifest.write_text( + json.dumps( + { + "total_rows": 100, + "selected_file_count": 2, + "selected_row_group_count": 3, + "manifest_sha256": "a" * 64, + "quotes_0_parquet_included": False, + "excluded_files": ["quotes_0.parquet"], + } + ) + ) + source_hashes = root / "source_hashes.json" + source_hashes.write_text( + json.dumps({"status": "complete", "file_count": 2}) + ) + with patch("sys.stdout", new=io.StringIO()): + status = finalize_results.run( + [ + "--run-root", + str(run_root), + "--run-id", + "run-1", + "--source-manifest", + str(source_manifest), + "--source-hashes", + str(source_hashes), + ] + ) + manifest = json.loads( + (run_root / "validation" / "manifest.json").read_text() + ) + self.assertEqual(status, 0) + self.assertEqual(manifest["run_id"], "run-1") + self.assertEqual(manifest["artifact_count"], 1) + self.assertEqual( + manifest["artifacts"][0]["path"], "ingest/ingest_summary.json" + ) + self.assertFalse(manifest["runtime_state"]["included_in_results"]) + + +class _FakeMeasuredCursor: + def __init__(self, query_id="query-1", rows=None): + self.query_id = query_id + self.rows = list(rows or [(1, "one"), (2, "two")]) + self.execute_calls = [] + self.closed = False + + def execute(self, sql, query_tags=None): + self.execute_calls.append((sql, query_tags)) + + def fetchmany(self, size): + self.fetch_sizes = getattr(self, "fetch_sizes", []) + self.fetch_sizes.append(size) + if not self.rows: + return [] + rows, self.rows = self.rows[:size], self.rows[size:] + return rows + + def close(self): + self.closed = True + + +class _FakeMeasuredConnection: + def __init__(self, cursor): + self.measured_cursor = cursor + + def cursor(self): + return self.measured_cursor + + +class _FakeHistoryClient: + def __init__(self, history): + self.history = history + self.ids = [] + + def query_history_by_statement_id(self, statement_id): + self.ids.append(statement_id) + if isinstance(self.history, list): + return self.history.pop(0) + return self.history + + +class RunnerHistoryTests(unittest.TestCase): + def test_kernel_connection_uses_native_oauth_arguments(self): + captured = {} + + class FakeSql: + @staticmethod + def connect(**kwargs): + captured.update(kwargs) + return "connection" + + fake_databricks = type("FakeDatabricks", (), {"sql": FakeSql}) + args = SimpleNamespace( + host="https://dbc-example.cloud.databricks.com", + http_path="/sql/1.0/warehouses/query", + catalog="costbench", + schema="rt_qualification", + token=None, + client_id="client-id", + client_secret="client-secret", + ) + with patch.dict(sys.modules, {"databricks": fake_databricks}): + self.assertEqual(_open_kernel_connection(args), "connection") + self.assertTrue(captured["use_kernel"]) + self.assertEqual(captured["oauth_client_id"], "client-id") + self.assertEqual(captured["oauth_client_secret"], "client-secret") + self.assertNotIn("credentials_provider", captured) + + def test_current_rest_timing_and_metric_aliases_are_normalized(self): + normalized = normalize_query_history( + { + "query_id": "query-1", + "status": "FINISHED", + "is_final": True, + "duration": 1500, + "rows_produced": 7, + "cache_query_id": None, + "metrics": { + "compilation_time_ms": 11, + "execution_time_ms": 1200, + "result_fetch_time_ms": 23, + "waiting_at_capacity_duration_ms": 17, + "read_bytes": 1000, + "read_cache_bytes": 250, + "read_files_count": 8, + "read_partitions_count": 4, + "rows_read_count": 99, + "result_from_cache": False, + }, + } + ) + self.assertEqual(normalized["statement_id"], "query-1") + self.assertEqual(normalized["duration_sec"], 1.5) + self.assertEqual(normalized["duration_source"], "history.duration") + self.assertEqual(normalized["compilation_time_ms"], 11) + self.assertEqual(normalized["execution_time_ms"], 1200) + self.assertEqual(normalized["queue_time_ms"], 17) + self.assertEqual(normalized["result_fetch_time_ms"], 23) + self.assertEqual(normalized["bytes_read"], 1000) + self.assertEqual(normalized["files_read"], 8) + self.assertEqual(normalized["partitions_read"], 4) + self.assertEqual(normalized["rows_read"], 99) + self.assertEqual(normalized["rows_produced"], 7) + self.assertEqual(normalized["read_io_cache_percent"], 25.0) + self.assertIs(normalized["result_from_cache"], False) + + def test_system_history_duration_aliases_are_normalized(self): + normalized = normalize_query_history( + { + "statement_id": "statement-2", + "execution_status": "FINISHED", + "total_duration_ms": 2500, + "compilation_duration_ms": 100, + "execution_duration_ms": 2200, + "result_fetch_duration_ms": 300, + "waiting_for_compute_duration_ms": 45, + "read_rows": 10, + "produced_rows": 2, + "from_result_cache": False, + "cache_origin_statement_id": None, + "read_io_cache_percent": 12.5, + } + ) + self.assertEqual(normalized["duration_sec"], 2.5) + self.assertEqual(normalized["compilation_time_ms"], 100) + self.assertEqual(normalized["execution_time_ms"], 2200) + self.assertEqual(normalized["result_fetch_time_ms"], 300) + self.assertEqual(normalized["queue_time_ms"], 45) + self.assertEqual(normalized["rows_read"], 10) + self.assertEqual(normalized["rows_produced"], 2) + self.assertEqual(normalized["read_io_cache_percent"], 12.5) + + def test_result_cache_true_or_missing_rejects_canonical_result(self): + for marker, metrics in ( + ("true", {"result_from_cache": True}), + ("missing", {}), + ): + with self.subTest(marker=marker): + cursor = _FakeMeasuredCursor() + connection = _FakeMeasuredConnection(cursor) + history = { + "query_id": "query-1", + "status": "FINISHED", + "is_final": True, + "duration": 1000, + "metrics": metrics, + } + ticks = iter((10.0, 11.5)) + observation = execute_measured_query( + connection, + _FakeHistoryClient(history), + "SELECT 1", + query_number=1, + iteration=3, + workload="dashboard", + run_id="run-1", + history_timeout=0, + history_poll_interval=1, + clock=lambda: next(ticks), + ) + self.assertIsNone(observation["canonical_duration_sec"]) + self.assertEqual(observation["result_row_count"], 2) + self.assertEqual(cursor.fetch_sizes, [1000, 1000]) + self.assertIsNotNone(observation["result_hash_sha256"]) + self.assertTrue(any("cache" in error for error in observation["errors"])) + self.assertEqual( + cursor.execute_calls[0][1], + { + "benchmark": "full-path-realtime", + "run_id": "run-1", + "workload": "dashboard", + "query_number": "1", + "iteration": "3", + }, + ) + + def test_exact_false_cache_evidence_accepts_provider_duration(self): + cursor = _FakeMeasuredCursor() + ticks = iter((2.0, 2.25)) + observation = execute_measured_query( + _FakeMeasuredConnection(cursor), + _FakeHistoryClient( + { + "query_id": "query-1", + "status": "FINISHED", + "is_final": True, + "duration": 750, + "metrics": {"result_from_cache": False}, + } + ), + "SELECT 1", + query_number=1, + iteration=1, + workload="drilldown", + run_id="run-2", + history_timeout=0, + history_poll_interval=1, + clock=lambda: next(ticks), + ) + self.assertEqual(observation["canonical_duration_sec"], 0.75) + self.assertEqual(observation["client_wall_time_sec"], 0.25) + self.assertEqual(observation["errors"], []) + + def test_query_history_lookup_accepts_current_query_id_response(self): + seen = {} + + def opener(request, timeout): + seen["url"] = request.full_url + seen["timeout"] = timeout + return _FakeJsonResponse( + { + "res": [ + { + "query_id": "query-current", + "is_final": True, + "status": "FINISHED", + } + ] + } + ) + + client = DatabricksRestClient( + "https://dbc-example.cloud.databricks.com", + token="secret-token", + opener=opener, + ) + record = client.query_history_by_statement_id("query-current") + self.assertEqual(record["query_id"], "query-current") + query = urllib.parse.parse_qs(urllib.parse.urlsplit(seen["url"]).query) + self.assertEqual(query["filter_by.statement_ids"], ["query-current"]) + self.assertEqual(query["include_metrics"], ["true"]) + + def test_query_history_poll_waits_for_explicit_final_record(self): + client = _FakeHistoryClient( + [ + { + "query_id": "query-1", + "status": "FINISHED", + "is_final": False, + }, + { + "query_id": "query-1", + "status": "FINISHED", + "is_final": True, + "duration": 10, + }, + ] + ) + now = [0.0] + + def sleep(seconds): + now[0] += seconds + + record, error = poll_query_history( + client, + "query-1", + timeout=1.0, + poll_interval=0.1, + clock=lambda: now[0], + sleeper=sleep, + ) + self.assertIsNone(error) + self.assertIs(record["is_final"], True) + self.assertEqual(client.ids, ["query-1", "query-1"]) + + +class RunnerSchedulingAndProgressTests(unittest.TestCase): + def test_fixed_rate_schedule_stays_anchored_and_overruns_start_now(self): + self.assertEqual(fixed_rate_next_fire(100.0, 10.0, 0), 100.0) + self.assertEqual(fixed_rate_next_fire(100.0, 10.0, 3), 130.0) + self.assertEqual(fixed_rate_delay(100.0, 10.0, 2, 115.0), 5.0) + self.assertEqual(fixed_rate_delay(100.0, 10.0, 2, 125.0), 0.0) + + def test_progress_and_freshness_row_parsing(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + logical = root / "logical.json" + fallback = root / "fallback.json" + freshness = root / "freshness.json" + logical.write_text( + json.dumps( + { + "logical_raw_rows": 123, + "provider_committed_rows": 23, + "baseline_table_rows": 100, + "updated_at": "2026-09-10T00:00:00Z", + "run_id": "producer-1", + } + ), + encoding="utf-8", + ) + fallback.write_text( + json.dumps( + { + "provider_committed_rows": 23, + "baseline_table_rows": 100, + } + ), + encoding="utf-8", + ) + freshness.write_text( + json.dumps( + { + "latest_successful_refresh": { + "output_rows": 45, + "finished_at": "2026-09-10T00:01:00Z", + }, + "updated_at": "2026-09-10T00:01:01Z", + } + ), + encoding="utf-8", + ) + logical_snapshot = producer_progress_snapshot(logical) + fallback_snapshot = producer_progress_snapshot(fallback) + freshness_snapshot = freshness_progress_snapshot(freshness) + self.assertEqual(logical_snapshot["raw_rows"], 123) + self.assertEqual( + logical_snapshot["raw_rows_source"], + "producer_progress.logical_raw_rows", + ) + self.assertEqual(fallback_snapshot["raw_rows"], 123) + self.assertIn("provider_committed_rows", fallback_snapshot["raw_rows_source"]) + self.assertIsNone(freshness_snapshot["mv_rows"]) + self.assertEqual( + freshness_snapshot["mv_rows_source"], + "not_collected_auxiliary_data_queries_prohibited", + ) + self.assertEqual( + freshness_snapshot["refresh_finished_at"], + "2026-09-10T00:01:00Z", + ) + + def test_result_hash_is_stable_and_order_sensitive(self): + rows_a = [(1, {"b": 2, "a": 1}), (Decimal("1.20"), b"x")] + rows_b = [(1, {"a": 1, "b": 2}), (Decimal("1.20"), b"x")] + rows_reversed = list(reversed(rows_a)) + count_a, hash_a = stable_result_hash(rows_a) + count_b, hash_b = stable_result_hash(rows_b) + _, hash_reversed = stable_result_hash(rows_reversed) + self.assertEqual(count_a, 2) + self.assertEqual(count_b, 2) + self.assertEqual(hash_a, hash_b) + self.assertNotEqual(hash_a, hash_reversed) + + def test_aligned_arrays_preserve_failed_query_position_and_validate(self): + observations = [ + { + "canonical_duration_sec": 1.0, + "client_wall_time_sec": 1.2, + "statement_id": "q1", + "result_row_count": 2, + "result_hash_sha256": "a" * 64, + "metrics": { + "compilation_time_ms": 10, + "execution_time_ms": 900, + "queue_time_ms": 20, + "result_fetch_time_ms": 30, + "result_from_cache": False, + "read_io_cache_percent": 50, + }, + "errors": [], + }, + { + "canonical_duration_sec": None, + "client_wall_time_sec": 0.1, + "statement_id": "q2", + "result_row_count": None, + "result_hash_sha256": None, + "metrics": {}, + "errors": ["failed"], + }, + ] + arrays = aligned_metric_arrays(observations) + self.assertEqual(arrays["result"], [[1.0], [None]]) + self.assertEqual(arrays["statement_ids"], [["q1"], ["q2"]]) + self.assertEqual(arrays["execution_time"], [[0.9], [None]]) + record = { + "iteration": 1, + "iteration_started_at": "2026-09-10T00:00:00Z", + "iteration_finished_at": "2026-09-10T00:00:01Z", + "raw_rows": 100, + "mv_rows": 10, + "system": "Databricks", + "version": "test", + "machine": "Small", + "cluster_size": "1", + "comment": "(dashboard)", + "tags": ["managed"], + **arrays, + "query_evidence": observations, + "query_errors": [item["errors"] for item in observations], + } + validate_jsonl_record(record, 2) + malformed = dict(record) + malformed["result"] = [[1.0]] + with self.assertRaisesRegex(ValueError, "result"): + validate_jsonl_record(malformed, 2) + + def test_wrapper_defaults_and_four_plus_two_contract(self): + dashboard_parser = build_parser( + run_dashboard.WORKLOAD, + run_dashboard.DEFAULT_QUERY_FILE, + run_dashboard.DEFAULT_INTERVAL_SECONDS, + env={}, + ) + drilldown_parser = build_parser( + run_drilldown.WORKLOAD, + run_drilldown.DEFAULT_QUERY_FILE, + run_drilldown.DEFAULT_INTERVAL_SECONDS, + env={}, + ) + self.assertEqual(dashboard_parser.parse_args([]).interval, 600.0) + self.assertEqual(drilldown_parser.parse_args([]).interval, 3600.0) + self.assertEqual( + len(load_queries(run_dashboard.DEFAULT_QUERY_FILE, "cat", "schema")), + 4, + ) + self.assertEqual( + len(load_queries(run_drilldown.DEFAULT_QUERY_FILE, "cat", "schema")), + 2, + ) + custom_dashboard = load_queries( + run_dashboard.DEFAULT_QUERY_FILE, + "cat", + "schema", + mv_table="quotes_daily_custom", + ) + custom_drilldown = load_queries( + run_drilldown.DEFAULT_QUERY_FILE, + "cat", + "schema", + raw_table="quotes_raw_custom", + ) + self.assertTrue( + all("`quotes_daily_custom`" in query for query in custom_dashboard) + ) + self.assertTrue( + all("`quotes_raw_custom`" in query for query in custom_drilldown) + ) + self.assertEqual( + SESSION_CONFIGURATION, + { + "use_cached_result": "false", + "ansi_mode": "true", + "timezone": "UTC", + }, + ) + self.assertEqual( + (ROOT / "requirements-runner.txt").read_text(encoding="utf-8").strip(), + ( + "databricks-sql-connector[kernel]==4.5.0\n" + "databricks-sdk==0.133.0" + ), + ) + self.assertNotIn("databricks", sys.modules) + + wrapper = (ROOT / "run_query_workloads.sh").read_text(encoding="utf-8") + self.assertIn(".schema // \"rt_qualification\"", wrapper) + self.assertIn('--schema "$SCHEMA"', wrapper) + self.assertNotIn("--schema rt_qualification", wrapper) + + producer = (ROOT / "ingest_zerobus.py").read_text(encoding="utf-8") + signal_handler = producer.split("def request_stop(", 1)[1].split( + "signal.signal(", 1 + )[0] + self.assertNotIn("journal.terminal_error", signal_handler) + self.assertNotIn( + "get_unacked_batches was not called because close did not succeed", + producer, + ) + + def test_compact_query_result_removes_only_review_fields(self): + source = { + "schema_version": 2, + "run_id": "run-1", + "result": [[1.25]], + "mv_rows": None, + "mv_rows_source": "not collected", + "result_row_count": [[1]], + "result_hash": [["a" * 64]], + "query_evidence": [{"statement_id": "statement-1"}], + "rt_warehouse": {"warehouse_id": "warehouse-1"}, + "query_warehouse": { + "warehouse_id": "warehouse-1", + "api_metadata": { + "id": "warehouse-1", + "creator_name": "user@example.com", + "creator_id": 1, + "channel": {"name": "CURRENT"}, + "jdbc_url": "jdbc:test", + "odbc_params": {"hostname": "example"}, + "health": {"status": "HEALTHY"}, + }, + }, + } + compact = compact_query_results.compact_record(source) + self.assertEqual(compact["schema_version"], 3) + self.assertEqual(compact["result"], [[1.25]]) + for name in compact_query_results.REMOVED_TOP_LEVEL_FIELDS: + self.assertNotIn(name, compact) + self.assertEqual( + compact["query_warehouse"]["api_metadata"], + { + "id": "warehouse-1", + "health": {"status": "HEALTHY"}, + }, + ) + self.assertEqual(source["schema_version"], 2) + self.assertIn("query_evidence", source) + + def test_compact_query_file_preserves_full_audit_copy(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + source = root / "source.jsonl" + output = root / "output.jsonl" + audit = root / "audit.jsonl" + record = { + "schema_version": 2, + "result": [[1.0]], + "query_evidence": [{"statement_id": "statement-1"}], + } + source.write_text(json.dumps(record) + "\n") + report = compact_query_results.compact_file( + source, + output, + audit_output=audit, + ) + self.assertEqual(audit.read_bytes(), source.read_bytes()) + compact = json.loads(output.read_text()) + self.assertEqual(compact, {"schema_version": 3, "result": [[1.0]]}) + self.assertLess(report["output_size_bytes"], report["source_size_bytes"]) + + +class ConfigAndContractTests(unittest.TestCase): + def complete_config(self) -> EnvironmentConfig: + return EnvironmentConfig.from_env( + { + "DATABRICKS_HOST": "https://dbc-example.cloud.databricks.com", + "DATABRICKS_CLOUD": "aws", + "DATABRICKS_REGION": "us-west-2", + "DATABRICKS_WORKSPACE_ID": "123456789", + "DATABRICKS_CATALOG": "bench", + "DATABRICKS_SCHEMA": "quotes", + "DATABRICKS_RAW_TABLE": "quotes_raw", + "DATABRICKS_MV_TABLE": "quotes_daily", + "DATABRICKS_RT_WAREHOUSE_ID": "abc123", + "DATABRICKS_CONTROL_WAREHOUSE_ID": "def456", + } + ) + + def test_complete_config_constructs_endpoint_and_validates_online(self): + config = self.complete_config() + self.assertEqual( + config.zerobus_endpoint, + "https://123456789.zerobus.us-west-2.cloud.databricks.com", + ) + self.assertEqual(validate_config(config, online=True)["errors"], []) + + def test_online_config_fails_closed_when_required_values_are_missing(self): + config = EnvironmentConfig.from_env({}) + errors = validate_config(config, online=True)["errors"] + self.assertTrue(any("host is required" in error for error in errors)) + self.assertTrue(any("workspace ID is required" in error for error in errors)) + self.assertTrue(any("warehouse ID is required" in error for error in errors)) + + def test_partial_service_principal_environment_is_an_offline_error(self): + report = offline_checks( + self.complete_config(), + {"DATABRICKS_CLIENT_ID": "client-without-secret"}, + ) + self.assertTrue( + any("must be set together" in error for error in report["errors"]) + ) + + def test_offline_contract_requires_four_dashboard_and_two_drilldown_queries(self): + self.assertEqual( + EXPECTED_QUERY_COUNTS, + {"dashboard": 4, "drilldown": 2}, + ) + with tempfile.TemporaryDirectory() as tmp: + fixture_dir = Path(tmp) + (fixture_dir / "queries_mv.sql").write_text( + "\n".join(f"SELECT {number};" for number in range(4)), + encoding="utf-8", + ) + (fixture_dir / "queries_raw.sql").write_text( + "SELECT 1; SELECT 2;", + encoding="utf-8", + ) + (fixture_dir / "create.sql").write_text( + "CREATE TABLE __CATALOG__.__SCHEMA__.quotes (id BIGINT);" + "CREATE MATERIALIZED VIEW __CATALOG__.__SCHEMA__.quotes_daily " + "AS SELECT count(*) AS n FROM __CATALOG__.__SCHEMA__.quotes;", + encoding="utf-8", + ) + report = offline_checks( + self.complete_config(), + { + "DATABRICKS_TOKEN": "test-token", + "DATABRICKS_CLIENT_ID": "test-client", + "DATABRICKS_CLIENT_SECRET": "test-secret", + }, + script_dir=fixture_dir, + ) + self.assertEqual( + report["query_contract"]["actual"], + {"dashboard": 4, "drilldown": 2}, + ) + self.assertFalse( + any("SQL statements" in error for error in report["errors"]) + ) + self.assertTrue(report["ddl_contract"]["create_materialized_view_found"]) + + def test_preflight_extracts_one_complete_rendered_mv_create_statement(self): + with tempfile.TemporaryDirectory() as tmp: + path = Path(tmp) / "create.sql" + path.write_text( + "-- semicolon in comment ;\n" + "CREATE TABLE __CATALOG__.__SCHEMA__.raw (id BIGINT);\n" + "CREATE MATERIALIZED VIEW __CATALOG__.__SCHEMA__.mv\n" + "AS SELECT ';' AS marker, count(*) AS n\n" + "FROM __CATALOG__.__SCHEMA__.raw;\n", + encoding="utf-8", + ) + statement = load_create_materialized_view_statement( + path, + "catalog-name", + "schema name", + ) + self.assertTrue(statement.startswith("CREATE MATERIALIZED VIEW")) + self.assertIn("`catalog-name`.`schema name`.mv", statement) + self.assertIn("FROM `catalog-name`.`schema name`.raw", statement) + self.assertIn("';'", statement) + + path.write_text( + "CREATE MATERIALIZED VIEW a AS SELECT 1;" + "CREATE MATERIALIZED VIEW b AS SELECT 2;", + encoding="utf-8", + ) + with self.assertRaisesRegex(ValueError, "exactly one"): + load_create_materialized_view_statement(path, "catalog", "schema") + + def test_checked_in_queries_match_current_four_plus_two_contract(self): + report = offline_checks( + self.complete_config(), + { + "DATABRICKS_TOKEN": "test-token", + "DATABRICKS_CLIENT_ID": "test-client", + "DATABRICKS_CLIENT_SECRET": "test-secret", + }, + ) + self.assertEqual( + report["query_contract"]["actual"], + {"dashboard": 4, "drilldown": 2}, + ) + self.assertFalse( + any("SQL statements" in error for error in report["errors"]) + ) + self.assertTrue(report["ddl_contract"]["create_materialized_view_found"]) + + def test_preflight_requires_effective_predictive_optimization_enablement(self): + errors = [] + enabled = predictive_optimization_check( + "catalog.schema.quotes", + { + "enable_predictive_optimization": "INHERIT", + "effective_predictive_optimization_flag": { + "value": "ENABLE", + "inherited_from_type": "SCHEMA", + "inherited_from_name": "catalog.schema", + }, + }, + errors, + ) + self.assertEqual(enabled["status"], "ok") + self.assertTrue(enabled["effectively_enabled"]) + self.assertEqual(enabled["configured_value"], "INHERIT") + self.assertEqual(enabled["inherited_from_type"], "SCHEMA") + self.assertEqual(errors, []) + + for metadata, observed in ( + ( + { + "enable_predictive_optimization": "INHERIT", + "effective_predictive_optimization_flag": { + "value": "DISABLE" + }, + }, + "DISABLE", + ), + ({}, "missing"), + ): + with self.subTest(observed=observed): + errors = [] + disabled = predictive_optimization_check( + "catalog.schema.quotes", + metadata, + errors, + ) + self.assertEqual(disabled["status"], "error") + self.assertFalse(disabled["effectively_enabled"]) + self.assertTrue(any(observed in error for error in errors)) + + def test_preflight_target_contract_is_metadata_only_and_fail_closed(self): + def response(columns, rows, statement_id): + return { + "statement_id": statement_id, + "manifest": { + "schema": { + "columns": [{"name": name} for name in columns] + } + }, + "result": {"data_array": rows}, + } + + mv_metadata = { + "owner": "application-id", + "predictive_optimization": "ENABLE", + "table_properties": { + "clusteringColumns": '[["sym"],["day"]]' + }, + "refresh_information": { + "refresh_policy": "IncrementalStrict", + "refresh_schedule": ( + "TRIGGER ON UPDATE AT MOST EVERY INTERVAL 60 SECOND" + ), + }, + } + responses = [ + response( + ["format", "numFiles", "sizeInBytes"], + [["delta", "0", "0"]], + "detail", + ), + response(["version", "operation"], [["0", "CREATE TABLE"]], "history"), + response( + ["json_metadata"], + [[json.dumps(mv_metadata)]], + "mv-metadata", + ), + ] + errors = [] + with patch("preflight._statement", side_effect=responses): + report = preflight.target_contract_check( + object(), + SimpleNamespace( + control_warehouse_id="control", + catalog="costbench", + schema="schema", + raw_table="quotes", + mv_table="quotes_daily", + ), + raw_metadata={ + "properties": { + "clusteringColumns": '[["sym"],["t"]]', + "delta.enableDeletionVectors": "true", + "delta.enableRowTracking": "true", + "delta.enableChangeDataFeed": "true", + } + }, + expected_mv_owner="application-id", + timeout=10, + poll_interval=0.1, + errors=errors, + ) + self.assertEqual(report["status"], "ok") + self.assertTrue(all(report["checks"].values())) + self.assertEqual(errors, []) + + def test_checked_in_ddl_matches_managed_delta_contract(self): + ddl = (ROOT / "create.sql").read_text(encoding="utf-8") + statements = split_sql_statements(ddl) + + self.assertEqual(len(statements), 4) + self.assertTrue(statements[0].startswith("DROP MATERIALIZED VIEW")) + self.assertTrue(statements[1].startswith("DROP TABLE")) + self.assertEqual(ddl.count("__CATALOG__.__SCHEMA__"), 5) + self.assertNotIn("workspace.benchmarking", ddl) + self.assertNotIn("PARTITIONED BY", ddl.upper()) + self.assertEqual(ddl.upper().count("USING DELTA"), 2) + self.assertIn("CLUSTER BY (sym, t)", ddl) + self.assertIn("CLUSTER BY (sym, day)", ddl) + self.assertIn("REFRESH POLICY INCREMENTAL STRICT", ddl) + self.assertIn( + "TRIGGER ON UPDATE AT MOST EVERY INTERVAL 1 MINUTE", + ddl, + ) + for feature in ( + "delta.enableDeletionVectors", + "delta.enableRowTracking", + "delta.enableChangeDataFeed", + ): + with self.subTest(feature=feature): + self.assertIn(f"'{feature}' = 'true'", ddl) + + table_columns = re.search( + r"CREATE TABLE\s+\S+\s*\((.*?)\)\s*USING DELTA", + statements[2], + flags=re.DOTALL, + ) + self.assertIsNotNone(table_columns) + self.assertEqual( + [ + " ".join(line.strip().rstrip(",").split()) + for line in table_columns.group(1).splitlines() + if line.strip() + ], + [ + "sym STRING", + "bx SMALLINT", + "bp DOUBLE", + "bs BIGINT", + "ax SMALLINT", + "ap DOUBLE", + "`as` BIGINT", + "c SMALLINT", + "i ARRAY", + "t BIGINT", + "q BIGINT", + "z SMALLINT", + ], + ) + + def test_canonical_setup_sql_is_fresh_explicit_and_metadata_only(self): + sql = ( + ROOT / "create_full_serverless_baseline_r3_20260918.sql" + ).read_text(encoding="utf-8") + statements = split_sql_statements(sql) + rendered = "\n".join(statements).upper() + self.assertEqual(len(statements), 18) + self.assertNotIn("DROP ", rendered) + self.assertNotIn("IF NOT EXISTS", rendered) + self.assertIn("rt_full_serverless_baseline_r3_20260918", sql) + self.assertNotIn("CREATE TABLE", statements[0].upper()) + self.assertIn("CREATE TABLE", statements[3].upper()) + self.assertNotIn(" LIKE ", statements[3].upper()) + self.assertTrue(statements[5].upper().startswith("EXPLAIN CREATE")) + self.assertIn("SET OWNER TO", statements[8].upper()) + self.assertIn("DESCRIBE DETAIL", rendered) + self.assertIn("DESCRIBE HISTORY", rendered) + self.assertNotRegex( + rendered, + r"SELECT\s+COUNT\s*\(\s*\*\s*\)\s+FROM", + ) + + def test_s3_source_staging_fails_closed_on_canonical_row_count(self): + source = (ROOT / "stage_source_from_s3.sh").read_text(encoding="utf-8") + self.assertIn("aws s3 sync", source) + self.assertIn("113_219_565_734", source) + self.assertIn("quotes_0.parquet is outside", source) + self.assertIn("schema_variant_count", source) + self.assertNotIn("--delete", source) + + def test_offline_imports_do_not_require_optional_dependencies(self): + with patch.dict( + os.environ, + { + "DATABRICKS_TOKEN": "", + "DATABRICKS_CLIENT_ID": "", + "DATABRICKS_CLIENT_SECRET": "", + }, + clear=False, + ): + report = offline_checks(EnvironmentConfig.from_env({}), {}) + self.assertIn("packages", report) + self.assertIn("databricks.sql", report["packages"]) + self.assertIn("databricks.sdk", report["packages"]) + self.assertIn("pyarrow", report["packages"]) + self.assertIn("zerobus.sdk", report["packages"]) + self.assertEqual( + PACKAGE_CONTRACT["zerobus.sdk"]["distribution"], + "databricks-zerobus-ingest-sdk", + ) + + def test_package_checks_use_role_specific_interpreters(self): + checks = package_checks( + runner_python="/missing/runner-python", + zerobus_python="/missing/zerobus-python", + ) + self.assertEqual( + checks["databricks.sql"]["interpreter"], + "/missing/runner-python", + ) + self.assertEqual( + checks["databricks.sdk"]["interpreter"], + "/missing/runner-python", + ) + self.assertEqual( + checks["pyarrow"]["interpreter"], + "/missing/zerobus-python", + ) + self.assertEqual( + checks["zerobus.sdk"]["interpreter"], + "/missing/zerobus-python", + ) + + +class ZerobusProducerModelTests(unittest.TestCase): + def test_producer_does_not_serialize_arrow_for_byte_accounting(self): + source = (ROOT / "ingest_zerobus.py").read_text(encoding="utf-8") + self.assertNotIn("arrow_ipc_nbytes", source) + self.assertIn("buffer_bytes = int(normalized.nbytes)", source) + + def test_completed_ordinal_ranges_update_incrementally(self): + ranges = [] + for ordinal in (2, 0, 1, 5, 4, 3, 3): + ranges = add_ordinal_range(ranges, ordinal) + self.assertEqual(ranges, [[0, 5]]) + + @staticmethod + def _statement_response(statement_id, columns, row): + return { + "statement_id": statement_id, + "manifest": { + "schema": {"columns": [{"name": name} for name in columns]} + }, + "result": {"data_array": [row]}, + } + + @staticmethod + def _target_check_args(): + return SimpleNamespace( + host="https://example.cloud.databricks.com", + token="", + client_id="client-id", + client_secret="client-secret", + statement_timeout=30.0, + count_warehouse="warehouse-id", + catalog="costbench", + schema="rt_qualification", + table="quotes", + allow_nonempty_table=False, + ) + + def test_target_protection_uses_delta_metadata_not_count(self): + calls = [] + + class Client: + def execute_statement(_self, statement, *_args, **_kwargs): + calls.append(statement) + if statement.startswith("DESCRIBE DETAIL"): + return self._statement_response( + "detail-id", ["numFiles", "sizeInBytes"], ["0", "0"] + ) + return self._statement_response( + "history-id", ["version"], ["0"] + ) + + with patch("ingest_zerobus.DatabricksRestClient", return_value=Client()): + report = protect_target_table( + self._target_check_args(), resume_journal=None + ) + + self.assertTrue(report["fresh"]) + self.assertEqual(report["row_count"], 0) + self.assertEqual(report["method"], "delta_metadata") + self.assertEqual(report["history_versions"], [0]) + self.assertEqual(len(calls), 2) + self.assertFalse(any("COUNT(" in statement.upper() for statement in calls)) + + def test_target_protection_rejects_non_pristine_history(self): + class Client: + def execute_statement(_self, statement, *_args, **_kwargs): + if statement.startswith("DESCRIBE DETAIL"): + return self._statement_response( + "detail-id", ["numFiles", "sizeInBytes"], ["0", "0"] + ) + return { + "statement_id": "history-id", + "manifest": { + "schema": {"columns": [{"name": "version"}]} + }, + "result": {"data_array": [["1"], ["0"]]}, + } + + with patch("ingest_zerobus.DatabricksRestClient", return_value=Client()): + with self.assertRaisesRegex(RuntimeError, "not a pristine"): + protect_target_table( + self._target_check_args(), resume_journal=None + ) + + def make_tasks(self, root): + return [ + Task(0, 0, root / "a.parquet", "a.parquet", 0, 3), + Task(1, 0, root / "a.parquet", "a.parquet", 1, 2), + ] + + def make_manifest(self): + return { + "manifest_sha256": "a" * 64, + "total_rows": 5, + "selected_file_count": 1, + "selected_row_group_count": 2, + } + + def make_config(self): + return {"workers": 1, "target_rows_per_sec": 1_000_000.0} + + def make_target(self): + return { + "catalog": "workspace", + "schema": "benchmarking", + "table": "quotes", + "full_name": "workspace.benchmarking.quotes", + "host": "https://example.cloud.databricks.com", + "zerobus_endpoint": "https://123.zerobus.us-west-2.cloud.databricks.com", + } + + def test_importing_producer_does_not_import_optional_packages(self): + self.assertNotIn("pyarrow", sys.modules) + self.assertNotIn("zerobus", sys.modules) + self.assertNotIn("zerobus.sdk", sys.modules) + + def test_target_schema_and_normalization_are_exact_safe_and_columnar(self): + schema = target_schema(_FakeArrow) + self.assertEqual( + schema.names, + ["sym", "bx", "bp", "bs", "ax", "ap", "as", "c", "i", "t", "q", "z"], + ) + self.assertTrue(all(field.nullable for field in schema)) + self.assertEqual(schema[0].type.name, "large_utf8") + self.assertEqual(schema[8].type.name, "list") + self.assertEqual(schema[8].type.value_type.name, "int16") + + source_names = list(reversed(schema.names)) + ["ignored"] + source_fields = [ + _FakeField(name, _FakeType("source"), True) for name in source_names + ] + source_schema = _FakeSchema(source_fields) + source_arrays = [ + _FakeArray(_FakeType("source"), name) for name in source_names + ] + source_batch = _FakeRecordBatch(source_arrays, source_schema) + compute = _FakeCompute() + normalized = normalize_batch( + source_batch, + pa_module=_FakeArrow, + pc_module=compute, + ) + self.assertEqual(normalized.schema.names, schema.names) + self.assertEqual( + [marker for marker, _type in normalized.arrays], + schema.names, + ) + self.assertTrue(all(safe for _marker, _type, safe in compute.casts)) + self.assertEqual(len(compute.casts), 12) + + def test_normalization_explicitly_rejects_unsigned_overflow(self): + schema = target_schema(_FakeArrow) + arrays = [ + _FakeArray(field.type, field.name) + for field in schema + ] + arrays[schema.get_field_index("bx")] = _FakeArray( + _FakeType("uint16", bit_width=16), + "bx", + ) + batch = _FakeRecordBatch(arrays, schema) + with self.assertRaisesRegex(OverflowError, "do not fit"): + normalize_batch( + batch, + pa_module=_FakeArrow, + pc_module=_FakeCompute(maxima={"bx": 65_535}), + ) + + def test_source_file_order_and_exact_sample_exclusion(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + for name in ( + "quotes_2.parquet", + "quotes_0.parquet", + "quotes_00.parquet", + "quotes_1.parquet", + ): + (root / name).touch() + selected, excluded = discover_source_files(root, "quotes_*.parquet") + self.assertEqual( + [path.name for path in selected], + ["quotes_00.parquet", "quotes_1.parquet", "quotes_2.parquet"], + ) + self.assertEqual(excluded, ["quotes_0.parquet"]) + included, excluded = discover_source_files( + root, + "quotes_*.parquet", + include_quotes_0=True, + max_files=2, + ) + self.assertEqual( + [path.name for path in included], + ["quotes_0.parquet", "quotes_00.parquet"], + ) + self.assertEqual(excluded, []) + + def test_manifest_can_cap_qualification_source_at_exact_row_count(self): + class Metadata: + num_row_groups = 2 + + @staticmethod + def row_group(index): + return type("RowGroup", (), {"num_rows": (6, 7)[index]})() + + class FakeParquetFile: + metadata = Metadata() + + class FakeParquet: + @staticmethod + def ParquetFile(_path): + return FakeParquetFile() + + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + (root / "quotes_1.parquet").write_bytes(b"test") + tasks, manifest = enumerate_tasks_and_manifest( + root, + "*.parquet", + FakeParquet, + include_quotes_0=False, + max_files=None, + max_row_groups=None, + max_rows=10, + ) + _, deferred = enumerate_tasks_and_manifest( + root, + "*.parquet", + FakeParquet, + include_quotes_0=False, + max_files=None, + max_row_groups=None, + max_rows=10, + hash_files=False, + ) + self.assertEqual([task.rows for task in tasks], [6, 4]) + self.assertEqual(manifest["total_rows"], 10) + self.assertEqual(manifest["max_rows"], 10) + self.assertEqual(manifest["files"][0]["selected_row_group_rows"], [6, 4]) + self.assertIsNone(deferred["files"][0]["sha256"]) + self.assertEqual(deferred["files"][0]["sha256_status"], "deferred") + + def test_batch_coordinates_are_canonical_and_bounded(self): + task = Task(7, 2, Path("/tmp/q.parquet"), "q.parquet", 3, 100) + coordinate = BatchCoordinate.from_task( + task, + batch_ordinal=4, + row_offset=75, + row_count=25, + ) + self.assertEqual( + coordinate.as_dict(), + { + "task_ordinal": 7, + "file_ordinal": 2, + "file": "q.parquet", + "row_group": 3, + "batch_ordinal": 4, + "row_offset": 75, + "row_count": 25, + }, + ) + with self.assertRaisesRegex(ValueError, "beyond"): + BatchCoordinate.from_task( + task, + batch_ordinal=5, + row_offset=99, + row_count=2, + ) + + def test_rate_limiter_never_catches_up_after_idle_time(self): + now = [0.0] + limiter = RateLimiter(100.0, clock=lambda: now[0]) + self.assertEqual(limiter.reserve(100), 0.0) + now[0] = 10.0 + self.assertEqual(limiter.reserve(100), 0.0) + self.assertEqual(limiter.reserve(100), 1.0) + self.assertEqual(limiter.snapshot()["scheduled_rows"], 300) + self.assertTrue(limiter.snapshot()["no_catch_up"]) + + def test_compact_ingest_metrics_omit_repeated_runtime_state(self): + compact = compact_metrics_payload( + { + "run_id": "run-1", + "updated_at": "2026-09-10T00:01:00Z", + "elapsed_sec": 60, + "source": {"total_rows": 1000}, + "provider_committed_rows": 900, + "submitted_rows": 950, + "pending_rows": 50, + "completed_tasks": 9, + "errors": [], + "stream_status": { + "one": {"state": "open", "rotation_count": 2}, + "two": {"state": "closed", "rotation_count": 3}, + }, + "eps": { + "target_rows_per_sec": 100, + "average_provider_committed_rows_per_sec": 15, + "session_provider_committed_rows_per_sec": 15, + }, + "memory": {"process_rss_bytes": 123}, + "host": {"producer_process_cpu_cores": 1.5}, + "rate_limiter": {"scheduled_rows": 1000}, + "pending_batch_evidence": [{"large": "value"}], + "config": {"repeated": "value"}, + } + ) + self.assertEqual(compact["provider_committed_rows"], 900) + self.assertEqual(compact["streams"]["rotation_count_total"], 5) + self.assertEqual(compact["streams"]["states"], {"open": 1, "closed": 1}) + self.assertEqual(compact["producer"]["process_rss_bytes"], 123) + self.assertNotIn("pending_batch_evidence", compact) + self.assertNotIn("config", compact) + + def test_compact_evidence_ledger_keeps_only_lifecycle_events(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + tasks = self.make_tasks(root) + journal = ProgressJournal( + progress_path=root / "progress.json", + ledger_path=root / "evidence.jsonl", + metrics_path=root / "metrics.jsonl", + run_id="test-run", + manifest=self.make_manifest(), + target=self.make_target(), + config={**self.make_config(), "compact_evidence": True}, + tasks=tasks, + baseline_table_rows=0, + baseline_unknown=False, + secrets=(), + ) + journal.record_assignment(tasks[0], 1, "worker-01") + journal.record_transport_wait( + "worker-01", + generation=2, + operation="wait", + duration_sec=3.5, + status="completed", + pending_batches=1, + pending_rows=3, + ) + events = [ + json.loads(line)["event"] + for line in (root / "evidence.jsonl").read_text().splitlines() + ] + self.assertEqual(events, ["run_started", "transport_wait"]) + recovery = journal.snapshot()["transport_recovery"] + self.assertEqual(recovery["completed_count"], 1) + self.assertEqual(recovery["failed_count"], 0) + self.assertEqual(recovery["max_duration_sec"], 3.5) + + def test_compression_names_map_to_exact_sdk_enum_members(self): + class FakeCompression: + NONE = object() + LZ4_FRAME = object() + ZSTD = object() + + self.assertIs(map_ipc_compression("none", FakeCompression), FakeCompression.NONE) + self.assertIs( + map_ipc_compression("lz4-frame", FakeCompression), + FakeCompression.LZ4_FRAME, + ) + self.assertIs(map_ipc_compression("ZSTD", FakeCompression), FakeCompression.ZSTD) + with self.assertRaisesRegex(ValueError, "one of"): + map_ipc_compression("gzip", FakeCompression) + + def test_expected_row_gate_requires_explicit_partial_mode(self): + validate_expected_rows(113_219_565_734, allow_partial=False) + with self.assertRaisesRegex(RuntimeError, "--allow-partial"): + validate_expected_rows(10, allow_partial=False) + validate_expected_rows(10, allow_partial=True) + + def test_submitted_batches_are_not_counted_until_durable(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + tasks = self.make_tasks(root) + journal = ProgressJournal( + progress_path=root / "progress.json", + ledger_path=root / "evidence.jsonl", + metrics_path=root / "metrics.jsonl", + run_id="test-run", + manifest=self.make_manifest(), + target=self.make_target(), + config=self.make_config(), + tasks=tasks, + baseline_table_rows=0, + baseline_unknown=False, + secrets=(), + ) + coordinate = BatchCoordinate.from_task( + tasks[0], + batch_ordinal=0, + row_offset=0, + row_count=3, + ) + pending = PendingBatch( + coordinate=coordinate, + rows=3, + uncompressed_arrow_buffer_bytes=123, + logical_offset=42, + report_offset=42, + submitted_at="2026-09-10T00:00:00.000Z", + worker=1, + stream="worker-01-arrow", + ) + journal.record_submitted(pending) + submitted = journal.snapshot() + self.assertEqual(submitted["submitted_rows"], 3) + self.assertEqual(submitted["provider_committed_rows"], 0) + self.assertEqual(submitted["completed_tasks"], 0) + self.assertEqual(submitted["pending_batches"], 1) + + journal.record_durable([pending]) + durable = journal.snapshot() + self.assertEqual(durable["provider_committed_rows"], 3) + self.assertEqual( + durable["durable_uncompressed_arrow_buffer_bytes"], + 123, + ) + self.assertEqual(durable["completed_task_ordinals"], [0]) + self.assertEqual(durable["completed_task_ranges"], [[0, 0]]) + self.assertEqual(durable["pending_batches"], 0) + events = [ + json.loads(line)["event"] + for line in (root / "evidence.jsonl").read_text().splitlines() + ] + self.assertLess( + events.index("batch_submitted"), + events.index("batch_durable"), + ) + + def test_failed_checkpoint_records_transport_duration_and_keeps_pending(self): + class FailedStream: + def wait_for_offset(self, _offset): + raise RuntimeError("remote reset") + + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + tasks = self.make_tasks(root) + journal = ProgressJournal( + progress_path=root / "progress.json", + ledger_path=root / "evidence.jsonl", + metrics_path=root / "metrics.jsonl", + run_id="test-run", + manifest=self.make_manifest(), + target=self.make_target(), + config={**self.make_config(), "compact_evidence": True}, + tasks=tasks, + baseline_table_rows=0, + baseline_unknown=False, + secrets=(), + ) + pending = PendingBatch( + coordinate=BatchCoordinate.from_task( + tasks[0], + batch_ordinal=0, + row_offset=0, + row_count=3, + ), + rows=3, + uncompressed_arrow_buffer_bytes=123, + logical_offset=1, + report_offset=1, + submitted_at="2026-09-10T00:00:00.000Z", + worker=1, + stream="worker-01-arrow", + ) + journal.record_submitted(pending) + batches = [pending] + with patch("sys.stderr", new=io.StringIO()): + with self.assertRaisesRegex(RuntimeError, "remote reset"): + checkpoint_pending( + FailedStream(), + batches, + journal, + "wait", + stream_label="worker-01-arrow", + generation=7, + log_threshold_seconds=2, + ) + self.assertEqual(batches, [pending]) + recovery = journal.snapshot()["transport_recovery"] + self.assertEqual(recovery["failed_count"], 1) + self.assertEqual(recovery["last_event"]["generation"], 7) + self.assertEqual(recovery["last_event"]["operation"], "wait") + self.assertIn( + "remote reset", recovery["last_event"]["error"] + ) + + def test_only_clean_whole_task_checkpoint_can_resume(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + tasks = self.make_tasks(root) + manifest = self.make_manifest() + target = self.make_target() + config = self.make_config() + journal = ProgressJournal( + progress_path=root / "progress.json", + ledger_path=root / "evidence.jsonl", + metrics_path=root / "metrics.jsonl", + run_id="test-run", + manifest=manifest, + target=target, + config=config, + tasks=tasks, + baseline_table_rows=0, + baseline_unknown=False, + secrets=(), + ) + coordinate = BatchCoordinate.from_task( + tasks[0], + batch_ordinal=0, + row_offset=0, + row_count=3, + ) + pending = PendingBatch( + coordinate=coordinate, + rows=3, + uncompressed_arrow_buffer_bytes=123, + logical_offset=42, + report_offset=42, + submitted_at="2026-09-10T00:00:00.000Z", + worker=1, + stream="worker-01-arrow", + ) + journal.record_submitted(pending) + journal.record_durable([pending]) + journal.stream_update( + "worker-01-arrow", + worker=1, + stream="worker-01-arrow", + close_succeeded=True, + unacked_inspection_succeeded=True, + unacked_batches=0, + ) + clean = journal.finish(stopped_early=True) + self.assertFalse(clean["finished"]) + self.assertTrue(clean["clean_checkpoint"]) + self.assertEqual( + validate_resume_journal( + clean, + manifest=manifest, + target=target, + config=config, + tasks=tasks, + ), + {0}, + ) + compact = dict(clean) + compact.pop("completed_task_ordinals") + self.assertEqual( + validate_resume_journal( + compact, + manifest=manifest, + target=target, + config=config, + tasks=tasks, + ), + {0}, + ) + + unclean = dict(clean) + unclean["safe_to_resume"] = False + unclean["terminal_error"] = "ambiguous close" + with self.assertRaisesRegex(RuntimeError, "fresh target table"): + validate_resume_journal( + unclean, + manifest=manifest, + target=target, + config=config, + tasks=tasks, + ) + + +class CostLedgerTests(unittest.TestCase): + SINCE = "2026-09-10T00:00:00Z" + UNTIL = "2026-09-10T00:10:00Z" + + def price(self, sku, value, **pricing): + return { + "sku_name": sku, + "cloud": "AWS", + "usage_unit": "DBU", + "price_start_time": "2026-09-01T00:00:00Z", + "price_end_time": None, + "currency_code": "USD", + "pricing_json": json.dumps({"default": value, **pricing}), + } + + def usage(self, sku, quantity, product, metadata=None): + return { + "record_id": f"record-{sku}", + "sku_name": sku, + "cloud": "AWS", + "usage_unit": "DBU", + "usage_quantity": str(quantity), + "record_type": "ORIGINAL", + "usage_start_time": self.SINCE, + "usage_end_time": self.UNTIL, + "billing_origin_product": product, + "usage_metadata_json": json.dumps(metadata or {}), + "product_features_json": "{}", + } + + def test_effective_list_default_extraction_and_price_match_fail_closed(self): + extracted = summarize_run.extract_canonical_list_price( + json.dumps( + { + "default": "2.00", + "effective_list": {"default": "3.00"}, + "promotional": {"default": "0.25"}, + "contract": "0.10", + } + ) + ) + self.assertEqual(extracted["price"], Decimal("3.00")) + usage = self.usage("RT", 1, "SQL", {"warehouse_id": "rt"}) + prices = [self.price("RT", "2"), self.price("RT", "2")] + self.assertEqual( + summarize_run.match_list_price(usage, prices)["status"], + "ambiguous", + ) + promo_only = dict(self.price("RT", "2")) + promo_only["pricing_json"] = json.dumps( + {"promotional": {"default": "0.25"}, "contract": "0.10"} + ) + unpriced = summarize_run.match_list_price(usage, [promo_only]) + self.assertEqual(unpriced["status"], "unpriced") + self.assertIsNone(unpriced["price"]) + earlier = self.price("RT", "1") + earlier["price_start_time"] = "2026-08-01T00:00:00Z" + earlier["price_end_time"] = "2026-09-01T00:00:00Z" + effective = summarize_run.match_list_price(usage, [earlier, self.price("RT", "2")]) + self.assertEqual(effective["status"], "matched") + self.assertEqual(effective["price"], Decimal("2")) + + def test_union_overlap_does_not_double_count_concurrent_queries(self): + overlap = summarize_run.union_overlap_seconds( + self.SINCE, + self.UNTIL, + [ + ("2026-09-10T00:01:00Z", "2026-09-10T00:05:00Z"), + ("2026-09-10T00:03:00Z", "2026-09-10T00:07:00Z"), + ("2026-09-10T00:06:00Z", "2026-09-10T00:08:00Z"), + ], + ) + self.assertEqual(overlap, 7 * 60) + + def test_post_ingest_query_cost_is_reported_separately(self): + summary = summarize_run.build_cost_summary( + billing_usage=[ + self.usage("RT", 10, "SQL", {"warehouse_id": "rt"}) + ], + billing_list_prices=[self.price("RT", "1")], + query_history=[ + { + "execution_status": "FINISHED", + "warehouse_id": "rt", + "start_time": "2026-09-10T00:01:00Z", + "end_time": "2026-09-10T00:02:00Z", + "query_tags_json": json.dumps({"run_id": "run"}), + "error_message": None, + }, + { + "execution_status": "FINISHED", + "warehouse_id": "rt", + "start_time": "2026-09-10T00:06:00Z", + "end_time": "2026-09-10T00:07:00Z", + "query_tags_json": json.dumps({"run_id": "run"}), + "error_message": None, + }, + ], + mv_event_log=[], + predictive_operations=[], + measured_since=self.SINCE, + measured_until=self.UNTIL, + producer_finished_at="2026-09-10T00:05:00Z", + rt_warehouse_id="rt", + control_warehouse_id="control", + ) + self.assertEqual( + summary["primary_category_totals"]["primary_rt_serving_compute"][ + "amount" + ], + "1", + ) + self.assertEqual( + summary["secondary_category_totals"][ + "secondary_post_ingest_query_serving" + ]["amount"], + "1", + ) + self.assertEqual( + summary["excluded_category_totals"]["excluded_idle_or_minimum"][ + "amount" + ], + "8", + ) + + def test_category_classification_uses_target_identifiers(self): + cases = ( + ( + self.usage("ZB", 1, "ZEROBUS_INGEST"), + "primary_zerobus_ingest", + ), + ( + { + **self.usage("JOBS_SERVERLESS", 1, "LAKEFLOW_CONNECT"), + "product_features_json": json.dumps( + { + "lakeflow_connect": { + "zerobus_request_type": "GRPC" + } + } + ), + }, + "primary_zerobus_ingest", + ), + ( + self.usage("MV", 1, "PIPELINES", {"pipeline_id": "mv-pipe"}), + "primary_mv_refresh", + ), + ( + self.usage("PO", 1, "PREDICTIVE_OPTIMIZATION"), + "primary_predictive_optimization", + ), + ( + self.usage("RT", 1, "SQL", {"warehouse_id": "rt"}), + "primary_rt_serving_compute", + ), + ( + self.usage("STORAGE", 1, "STORAGE"), + "excluded_storage", + ), + ( + self.usage("CONTROL", 1, "SQL", {"warehouse_id": "control"}), + "excluded_control", + ), + ) + for row, expected in cases: + with self.subTest(expected=expected): + category, reason = summarize_run.classify_usage( + row, + rt_warehouse_id="rt", + control_warehouse_id="control", + mv_pipeline_ids={"mv-pipe"}, + ) + self.assertEqual(category, expected) + self.assertTrue(reason) + + def test_beta_discount_applies_only_to_rt_and_leaves_canonical_unchanged(self): + usage = [ + self.usage("RT", 10, "SQL", {"warehouse_id": "rt"}), + self.usage("ZEROBUS", 3, "ZEROBUS_INGEST"), + ] + prices = [self.price("RT", "2"), self.price("ZEROBUS", "1")] + summary = summarize_run.build_cost_summary( + billing_usage=usage, + billing_list_prices=prices, + query_history=[ + { + "execution_status": "FINISHED", + "warehouse_id": "rt", + "query_tags_json": '{"run_id":"run-1"}', + "start_time": "2026-09-10T00:00:00Z", + "end_time": "2026-09-10T00:04:00Z", + }, + { + "execution_status": "FINISHED", + "warehouse_id": "rt", + "query_tags_json": '{"run_id":"run-1"}', + "start_time": "2026-09-10T00:02:00Z", + "end_time": "2026-09-10T00:06:00Z", + }, + ], + mv_event_log=[], + predictive_operations=[], + measured_since=self.SINCE, + measured_until=self.UNTIL, + rt_warehouse_id="rt", + control_warehouse_id="control", + ) + self.assertTrue(summary["complete"]) + self.assertEqual( + summary["primary_category_totals"][ + "primary_rt_serving_compute" + ]["amount"], + "12", + ) + canonical = summary["canonical_undiscounted_primary_total"] + self.assertEqual(canonical["amount"], "15") + self.assertEqual( + summary["canonical_undiscounted_fresh_path_total"]["amount"], "3" + ) + self.assertEqual( + summary["canonical_undiscounted_query_serving_total"]["amount"], "12" + ) + scenario = summary["lakehouse_rt_beta_30_percent_off_scenario"] + self.assertEqual(scenario["scenario_primary_total"]["amount"], "11.4") + self.assertEqual( + scenario["canonical_undiscounted_primary_total"], + canonical, + ) + self.assertTrue(scenario["canonical_total_unchanged"]) + + def test_record_types_retain_exported_signed_quantities(self): + rows = [] + for record_type, quantity in ( + ("ORIGINAL", "5"), + ("RETRACTION", "-2"), + ("RESTATEMENT", "2"), + ): + row = self.usage("ZEROBUS", quantity, "ZEROBUS_INGEST") + row["record_id"] = record_type.lower() + row["record_type"] = record_type + rows.append(row) + summary = summarize_run.build_cost_summary( + billing_usage=rows, + billing_list_prices=[self.price("ZEROBUS", "1")], + query_history=[], + mv_event_log=[], + predictive_operations=[], + measured_since=self.SINCE, + measured_until=self.UNTIL, + rt_warehouse_id="rt", + control_warehouse_id="control", + ) + self.assertEqual( + summary["record_type_signed_quantities"], + {"ORIGINAL": "5", "RESTATEMENT": "2", "RETRACTION": "-2"}, + ) + self.assertEqual( + summary["canonical_undiscounted_primary_total"]["amount"], + "5", + ) + + +class RunValidationTests(unittest.TestCase): + EXPECTED = 100 + SINCE = "2026-09-10T00:00:00Z" + UNTIL = "2026-09-10T01:00:00Z" + + def manifest(self): + return { + "total_rows": self.EXPECTED, + "quotes_0_parquet_included": False, + "excluded_files": ["quotes_0.parquet"], + "files": [ + { + "path": "quotes_1.parquet", + "file_ordinal": 0, + "selected_rows": self.EXPECTED, + } + ], + "tasks": [{"ordinal": 0, "rows": self.EXPECTED}], + "selected_file_count": 1, + "selected_row_group_count": 1, + "manifest_sha256": "a" * 64, + } + + def progress(self): + return { + "source": { + "total_rows": self.EXPECTED, + "task_count": 1, + "manifest_sha256": "a" * 64, + }, + "config": {"workers": 1}, + "provider_committed_rows": self.EXPECTED, + "logical_raw_rows": self.EXPECTED, + "submitted_rows": self.EXPECTED, + "finished": True, + "running": False, + "clean_checkpoint": True, + "ambiguous": False, + "terminal_error": None, + "errors": [], + "pending_batches": 0, + "pending_rows": 0, + "partial_task_rows": {}, + "baseline_table_rows": 0, + "baseline_table_rows_unknown": False, + "completed_tasks": 1, + "completed_task_ordinals": [0], + "stream_status": { + "worker-1": { + "close_succeeded": True, + "unacked_inspection_succeeded": True, + "unacked_batches": 0, + } + }, + "eps": { + "average_provider_committed_rows_per_sec": 100, + }, + } + + def query_record(self, workload, count, at): + observations = [ + { + "canonical_duration_sec": 1.0, + "statement_id": f"{workload}-{number}", + "execution_succeeded": True, + "result_row_count": 1, + "result_hash_sha256": f"{number:x}" * 64, + "metrics": { + "result_from_cache": False, + "cache_origin_statement_id": None, + }, + "errors": [], + } + for number in range(1, count + 1) + ] + interval = 600.0 if workload == "dashboard" else 3600.0 + return { + "runner": workload, + "workload": workload, + "iteration": 1, + "scheduled_start_at": at, + "scheduled_interval_sec": interval, + "iteration_started_at": at, + "iteration_finished_at": ( + "2026-09-10T00:03:21Z" + if workload == "dashboard" + else "2026-09-10T00:04:01Z" + ), + "raw_rows": 50, + "producer_progress_finished": False, + "producer_progress_error": None, + "rt_warehouse": {"id": "rt", "size": "Medium"}, + "result": [[1.0] for _ in observations], + "compilation_time": [[0.1] for _ in observations], + "execution_time": [[0.8] for _ in observations], + "queue_time": [[0.0] for _ in observations], + "result_fetch_time": [[0.1] for _ in observations], + "client_wall_time": [[1.1] for _ in observations], + "statement_ids": [ + [observation["statement_id"]] for observation in observations + ], + "cache_hit": [[False] for _ in observations], + "read_io_cache_percent": [[0.0] for _ in observations], + "result_row_count": [[1] for _ in observations], + "result_hash": [ + [observation["result_hash_sha256"]] + for observation in observations + ], + "query_evidence": observations, + "query_errors": [[] for _ in observations], + } + + def evidence(self): + return { + "complete": True, + "collection_window": { + "since": self.SINCE, + "until": self.UNTIL, + }, + "required_dataset_completeness": {"complete": True}, + "provider_committed_records": self.EXPECTED, + "provider_errors": 0, + "zerobus_stream_error_count": 0, + "table_num_rows": {"raw": self.EXPECTED}, + "mv_maintenance": { + "maintenance_types": [ + "MAINTENANCE_TYPE_GROUP_AGGREGATE" + ], + "full_refresh_count": 0, + }, + "targets": { + "rt_warehouse_id": "rt", + "control_warehouse_id": "control", + }, + "frozen_warehouse_configs": [ + { + "warehouse_id": "rt", + "change_time": "2026-09-09T00:00:00Z", + "delete_time": None, + }, + { + "warehouse_id": "control", + "change_time": "2026-09-09T00:00:00Z", + "delete_time": None, + }, + ], + } + + def cost(self): + canonical = { + "currency": "USD", + "amount": "10", + "amount_by_currency": {"USD": "10"}, + } + return { + "complete": True, + "completeness": {"complete": True}, + "canonical_undiscounted_primary_total": canonical, + "canonical_undiscounted_fresh_path_total": { + "currency": "USD", + "amount": "6", + "amount_by_currency": {"USD": "6"}, + }, + "canonical_undiscounted_query_serving_total": { + "currency": "USD", + "amount": "4", + "amount_by_currency": {"USD": "4"}, + }, + "unknown_total": { + "currency": "USD", + "amount": "0", + "amount_by_currency": {"USD": "0"}, + }, + "unpriced_total": { + "usage_row_indices": [], + "usage_quantity_by_unit": {}, + }, + "lakehouse_rt_beta_30_percent_off_scenario": { + "discount_rate": "0.30", + "applies_only_to": "primary_rt_serving_compute", + "canonical_total_unchanged": True, + "canonical_undiscounted_primary_total": canonical, + }, + "predictive_optimization": { + "operation_count": 1, + "failed_operation_count": 0, + "failed_operation_row_indices": [], + }, + } + + def valid_inputs(self): + return { + "source_manifest": self.manifest(), + "ingest_progress": self.progress(), + "ingest_metrics": [ + { + "provider_committed_rows": self.EXPECTED, + "elapsed_sec": 1, + "interval_eps": 100, + } + ], + "dashboard": [ + self.query_record("dashboard", 4, "2026-09-10T00:03:20Z") + ], + "drilldown": [ + self.query_record("drilldown", 2, "2026-09-10T00:04:00Z") + ], + "freshness": [ + { + "observed_at": "2026-09-10T00:03:00Z", + "raw_commit_age_sec": 5, + "mv_refresh_age_sec": 30, + "errors": [], + "age_semantics": ( + "Observational ages are not exact per-record lag." + ), + } + ], + "evidence_summary": self.evidence(), + "cost_summary": self.cost(), + "expected_rows": self.EXPECTED, + "eps_min": 90, + "eps_max": 110, + } + + def test_valid_synthetic_complete_run_passes(self): + report = validate_run.build_validation_report(**self.valid_inputs()) + self.assertTrue(report["accepted"]) + self.assertTrue(all(gate["passed"] for gate in report["gates"])) + self.assertEqual(report["query_samples"]["dashboard"]["accepted_count"], 1) + self.assertEqual(report["query_samples"]["drilldown"]["accepted_count"], 1) + + def test_warehouse_comparison_ignores_runtime_state_but_detects_config_change(self): + inputs = self.valid_inputs() + stable = { + "warehouse_id": "rt", + "use_kernel": True, + "api_metadata": { + "id": "rt", + "cluster_size": "Medium", + "min_num_clusters": 1, + "max_num_clusters": 1, + }, + } + inputs["dashboard"][0]["rt_warehouse"] = { + **stable, + "captured_at": "2026-09-10T00:00:00Z", + "api_metadata": {**stable["api_metadata"], "state": "STARTING"}, + } + inputs["drilldown"][0]["rt_warehouse"] = { + **stable, + "captured_at": "2026-09-10T00:01:00Z", + "api_metadata": {**stable["api_metadata"], "state": "RUNNING"}, + } + report = validate_run.build_validation_report(**inputs) + warehouse_gate = next( + gate + for gate in report["gates"] + if gate["id"] == "warehouse_configuration" + ) + self.assertTrue(warehouse_gate["passed"]) + + inputs["drilldown"][0]["rt_warehouse"]["api_metadata"][ + "cluster_size" + ] = "Large" + report = validate_run.build_validation_report(**inputs) + warehouse_gate = next( + gate + for gate in report["gates"] + if gate["id"] == "warehouse_configuration" + ) + self.assertFalse(warehouse_gate["passed"]) + + def test_missing_evidence_and_reconciliation_fail(self): + inputs = self.valid_inputs() + inputs["input_errors"] = {"cost_summary": ["cost_summary: missing"]} + inputs["evidence_summary"]["provider_committed_records"] = 99 + report = validate_run.build_validation_report(**inputs) + failed = { + gate["id"] for gate in report["gates"] if not gate["passed"] + } + self.assertIn("input_availability", failed) + self.assertIn("row_reconciliation", failed) + self.assertIn("cost_completeness", failed) + + def test_cache_hit_and_post_ingest_samples_fail(self): + for failure in ("cache", "post_ingest"): + with self.subTest(failure=failure): + inputs = self.valid_inputs() + sample = inputs["dashboard"][0] + if failure == "cache": + sample["query_evidence"][0]["metrics"][ + "result_from_cache" + ] = True + sample["cache_hit"][0] = [True] + else: + sample["producer_progress_finished"] = True + sample["raw_rows"] = self.EXPECTED + report = validate_run.build_validation_report(**inputs) + dashboard_gate = next( + gate + for gate in report["gates"] + if gate["id"] == "dashboard_queries" + ) + self.assertFalse(dashboard_gate["passed"]) + + def test_full_refresh_cadence_and_freshness_failures(self): + cases = ("full_refresh", "cadence", "freshness") + for failure in cases: + with self.subTest(failure=failure): + inputs = self.valid_inputs() + if failure == "full_refresh": + inputs["evidence_summary"]["mv_maintenance"] = { + "maintenance_types": [ + "MAINTENANCE_TYPE_COMPLETE_RECOMPUTE" + ], + "full_refresh_count": 1, + } + elif failure == "cadence": + second = self.query_record( + "dashboard", + 4, + "2026-09-10T00:15:00Z", + ) + second["iteration"] = 2 + second["iteration_finished_at"] = ( + "2026-09-10T00:15:01Z" + ) + inputs["dashboard"].append(second) + else: + inputs["freshness"][0]["mv_refresh_age_sec"] = 301 + report = validate_run.build_validation_report(**inputs) + self.assertFalse(report["accepted"]) + + def test_post_ingest_idle_age_is_excluded_from_freshness_sla(self): + inputs = self.valid_inputs() + inputs["ingest_progress"]["finished_at"] = "2026-09-10T00:30:00Z" + inputs["freshness"].append( + { + "observed_at": "2026-09-10T00:45:00Z", + "raw_commit_age_sec": 900, + "mv_refresh_age_sec": 900, + "mv_rows_behind": 0, + "errors": [], + "age_semantics": ( + "Observational ages are not exact per-record lag." + ), + } + ) + report = validate_run.build_validation_report(**inputs) + freshness_gate = next( + gate for gate in report["gates"] if gate["id"] == "freshness" + ) + self.assertTrue(freshness_gate["passed"]) + self.assertEqual( + freshness_gate["evidence"]["active_measurement_until"], + "2026-09-10T00:30:00.000Z", + ) + self.assertEqual(freshness_gate["evidence"]["accepted_indices"], [0]) + + +class QualificationTests(unittest.TestCase): + def make_plan(self, root): + return qualify.build_plan( + output_dir=root / "qualification", + repo_dir=ROOT.parents[2], + data_dir=root / "parquet", + catalog="workspace", + schema="rt_qualification", + raw_table="quotes", + mv_table="quotes_daily", + rt_warehouse_id="rt-warehouse", + control_warehouse_id="control-warehouse", + producer_host_description="dedicated host in benchmark region", + producer_network_capacity_gbps=12.5, + host="https://dbc-example.cloud.databricks.com", + endpoint="https://123.zerobus.us-west-2.cloud.databricks.com", + stream_count=16, + batch_size=50_000, + queue_capacity=64, + compression="NONE", + query_size_candidates=("Small", "Medium"), + python="/runner/python", + producer_python="/zerobus/python", + ) + + def test_plan_uses_separate_runner_and_zerobus_interpreters(self): + with tempfile.TemporaryDirectory() as tmp: + plan = self.make_plan(Path(tmp)) + self.assertEqual( + plan["inputs"]["interpreters"], + { + "runner": "/runner/python", + "zerobus_producer": "/zerobus/python", + }, + ) + for stage in ("correctness", "capacity", "query_size_sweep", "endurance"): + for case in plan["matrix"][stage]: + for command in case["commands"]: + if command["step"] == "zerobus-ingest": + self.assertEqual(command["argv"][0], "/zerobus/python") + elif command["step"] == "online-preflight": + self.assertEqual(command["argv"][0], "/runner/python") + self.assertIn("/zerobus/python", command["argv"]) + + @staticmethod + def write_json(path, value): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(value), encoding="utf-8") + + @staticmethod + def write_jsonl(path, rows): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text( + "".join(json.dumps(row) + "\n" for row in rows), + encoding="utf-8", + ) + + @staticmethod + def stage_cases(plan, stage): + return plan["matrix"][stage] + + def progress(self, case, target_eps): + expected = case["acceptance"]["expected_rows"] + return { + "source": {"total_rows": expected, "task_count": 1}, + "target": { + "host": "https://dbc-example.cloud.databricks.com", + "zerobus_endpoint": ( + "https://123.zerobus.us-west-2.cloud.databricks.com" + ), + }, + "config": { + "workers": 16, + "batch_size": 50_000, + "queue_capacity": 64, + "ipc_compression": "NONE", + "target_rows_per_sec": target_eps, + }, + "provider_committed_rows": expected, + "logical_raw_rows": expected, + "submitted_rows": expected, + "finished": True, + "running": False, + "clean_checkpoint": True, + "safe_to_resume": True, + "stopped_early": False, + "ambiguous": False, + "terminal_error": None, + "errors": [], + "pending_batches": 0, + "pending_rows": 0, + "partial_task_rows": {}, + "baseline_table_rows": 0, + "baseline_table_rows_unknown": False, + "completed_tasks": 1, + "completed_task_ordinals": [0], + "stream_status": { + f"worker-{number:02d}-arrow": { + "close_succeeded": True, + "unacked_inspection_succeeded": True, + "unacked_batches": 0, + } + for number in range(1, 17) + }, + "eps": { + "average_provider_committed_rows_per_sec": target_eps, + }, + } + + @staticmethod + def preflight(size): + return { + "config": {"rt_warehouse_id": "rt-warehouse"}, + "summary": {"status": "ok"}, + "online": { + "status": "ok", + "errors": [], + "predictive_optimization": { + table: { + "status": "ok", + "effective_value": "ENABLE", + "effectively_enabled": True, + } + for table in ("raw", "materialized_view") + }, + "warehouses": { + "lakehouse_rt": { + "metadata": { + "id": "rt-warehouse", + "cluster_size": size, + "min_num_clusters": 1, + "max_num_clusters": 1, + "enable_serverless_compute": True, + } + } + }, + }, + } + + @staticmethod + def query_record(workload, size): + count = 4 if workload == "dashboard" else 2 + evidence = [ + { + "execution_succeeded": True, + "canonical_duration_sec": 1.0, + "errors": [], + "metrics": { + "status": "FINISHED", + "result_from_cache": False, + "cache_origin_statement_id": None, + "waiting_at_capacity_ms": 0, + "waiting_for_compute_ms": 0, + "queue_time_ms": 0, + }, + } + for _ in range(count) + ] + return { + "workload": workload, + "rt_warehouse": { + "warehouse_id": "rt-warehouse", + "api_metadata": { + "cluster_size": size, + } + }, + "producer_progress_error": None, + "result": [[1.0] for _ in range(count)], + "cache_hit": [[False] for _ in range(count)], + "query_evidence": evidence, + "query_errors": [[] for _ in range(count)], + } + + def write_valid_evidence(self, plan): + correctness = self.stage_cases(plan, "correctness")[0] + self.write_json( + correctness["artifacts"]["validation_report"], + { + "accepted": True, + "configuration": {"expected_rows": 10_000_000}, + }, + ) + + for case in self.stage_cases(plan, "capacity"): + target = case["acceptance"]["target_committed_eps"] + self.write_json( + case["artifacts"]["ingest_progress"], + self.progress(case, target), + ) + self.write_jsonl( + case["artifacts"]["ingest_metrics"], + [ + { + "provider_committed_rows": target * (index + 1) * 5, + "elapsed_sec": (index + 1) * 5, + "interval_eps": target, + "host": { + "host_cpu_utilization_percent": 40, + "network_receive_bytes_per_second": 10_000_000, + "network_transmit_bytes_per_second": 20_000_000, + }, + "errors": [], + } + for index in range(12) + ], + ) + self.write_json(case["artifacts"]["preflight"], self.preflight("Small")) + + for case in self.stage_cases(plan, "query_size_sweep"): + size = case["acceptance"]["query_size"] + self.write_json(case["artifacts"]["preflight"], self.preflight(size)) + self.write_json( + case["artifacts"]["ingest_progress"], + self.progress( + case, + case["acceptance"]["target_committed_eps"], + ), + ) + self.write_jsonl( + case["artifacts"]["dashboard"], + [self.query_record("dashboard", size)], + ) + self.write_jsonl( + case["artifacts"]["drilldown"], + [self.query_record("drilldown", size)], + ) + + endurance = self.stage_cases(plan, "endurance")[0] + self.write_json( + endurance["artifacts"]["ingest_progress"], + self.progress(endurance, 1_000_000), + ) + self.write_jsonl( + endurance["artifacts"]["ingest_metrics"], + [ + { + "provider_committed_rows": 1_000_000 * (index + 1) * 5, + "elapsed_sec": (index + 1) * 5, + "interval_eps": 1_000_000, + "host": { + "host_cpu_utilization_percent": 40, + "network_receive_bytes_per_second": 10_000_000, + "network_transmit_bytes_per_second": 20_000_000, + }, + "errors": [], + } + for index in range(12) + ], + ) + self.write_json(endurance["artifacts"]["preflight"], self.preflight("Small")) + self.write_json( + endurance["artifacts"]["validation_report"], + { + "accepted": True, + "window": { + "duration_seconds": endurance["acceptance"][ + "minimum_duration_seconds" + ] + }, + "query_samples": { + "dashboard": { + "accepted_count": endurance["acceptance"][ + "minimum_accepted_dashboard_iterations" + ] + }, + "drilldown": { + "accepted_count": endurance["acceptance"][ + "minimum_accepted_drilldown_iterations" + ] + }, + }, + "freshness_samples": {"accepted_count": 1}, + "gates": [ + { + "id": "freshness", + "passed": True, + } + ], + }, + ) + + def test_plan_is_deterministic_complete_and_contains_no_secret_values(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + with patch.dict( + os.environ, + { + "DATABRICKS_TOKEN": "dapi1234567890abcdef", + "DATABRICKS_CLIENT_SECRET": "super-secret-value", + }, + clear=False, + ): + first = self.make_plan(root) + second = self.make_plan(root) + plan_path, command_path = qualify.write_plan( + first, root / "qualification" + ) + rendered = json.dumps(first) + command_path.read_text(encoding="utf-8") + self.assertEqual(first, second) + self.assertEqual(plan_path.name, "qualification_plan.json") + self.assertNotIn("dapi1234567890abcdef", rendered) + self.assertNotIn("super-secret-value", rendered) + self.assertIn("DATABRICKS_CLIENT_SECRET", rendered) + self.assertEqual(first["state"], "planned") + self.assertEqual( + [case["id"] for case in first["matrix"]["capacity"]], + ["capacity-100k", "capacity-500k", "capacity-1m"], + ) + planned_cases = [ + case + for stage in ("correctness", "capacity", "query_size_sweep", "endurance") + for case in first["matrix"][stage] + ] + self.assertEqual( + {case["target"]["schema"] for case in planned_cases}, + {"rt_qualification"}, + ) + self.assertTrue( + first["execution_rules"]["shared_dedicated_qualification_schema"] + ) + self.assertTrue(first["execution_rules"]["cases_must_run_sequentially"]) + self.assertEqual( + first["matrix"]["correctness"][0]["acceptance"]["expected_rows"], + 10_000_000, + ) + self.assertEqual( + first["matrix"]["endurance"][0]["acceptance"][ + "drilldown_interval_seconds" + ], + 3600, + ) + self.assertEqual( + first["matrix"]["endurance"][0]["acceptance"][ + "minimum_duration_seconds" + ], + 1800, + ) + self.assertEqual( + first["matrix"]["endurance"][0]["acceptance"][ + "minimum_accepted_dashboard_iterations" + ], + 3, + ) + self.assertEqual( + first["matrix"]["endurance"][0]["acceptance"][ + "minimum_accepted_drilldown_iterations" + ], + 1, + ) + + def test_missing_artifacts_are_not_run_and_never_qualified(self): + with tempfile.TemporaryDirectory() as tmp: + report = qualify.evaluate_plan(self.make_plan(Path(tmp))) + self.assertEqual(report["state"], "not_run") + self.assertFalse(report["qualified"]) + self.assertEqual(report["stages"]["correctness"]["state"], "not_run") + self.assertEqual(report["stages"]["capacity"]["state"], "not_run") + self.assertEqual(report["stages"]["query_size_sweep"]["state"], "not_run") + self.assertEqual(report["stages"]["endurance"]["state"], "not_run") + + def test_valid_matrix_qualifies_and_selects_smallest_size(self): + with tempfile.TemporaryDirectory() as tmp: + plan = self.make_plan(Path(tmp)) + self.write_valid_evidence(plan) + report = qualify.evaluate_plan(plan) + self.assertTrue(report["qualified"]) + self.assertEqual(report["state"], "passed") + self.assertEqual(report["selected_query_size"], "Small") + self.assertEqual( + report["frozen_settings"]["producer"]["stream_count"], + 16, + ) + + def test_capacity_short_or_out_of_tolerance_fails(self): + for failure in ("short", "out_of_tolerance"): + with self.subTest(failure=failure), tempfile.TemporaryDirectory() as tmp: + plan = self.make_plan(Path(tmp)) + self.write_valid_evidence(plan) + case = self.stage_cases(plan, "capacity")[0] + target = case["acceptance"]["target_committed_eps"] + count = 5 if failure == "short" else 12 + interval_eps = target if failure == "short" else target * 2 + self.write_jsonl( + case["artifacts"]["ingest_metrics"], + [ + { + "provider_committed_rows": target * (index + 1) * 5, + "elapsed_sec": (index + 1) * 5, + "interval_eps": interval_eps, + "errors": [], + } + for index in range(count) + ], + ) + report = qualify.evaluate_plan(plan) + self.assertEqual(report["stages"]["capacity"]["state"], "failed") + self.assertFalse(report["qualified"]) + + def test_capacity_rejects_saturated_producer_host(self): + with tempfile.TemporaryDirectory() as tmp: + plan = self.make_plan(Path(tmp)) + self.write_valid_evidence(plan) + case = self.stage_cases(plan, "capacity")[-1] + metrics_path = Path(case["artifacts"]["ingest_metrics"]) + metrics = [ + json.loads(line) + for line in metrics_path.read_text(encoding="utf-8").splitlines() + ] + for row in metrics[1:]: + row["host"]["host_cpu_utilization_percent"] = 99 + self.write_jsonl(metrics_path, metrics) + report = qualify.evaluate_plan(plan) + self.assertEqual(report["stages"]["capacity"]["state"], "failed") + self.assertTrue( + any( + "producer host CPU p95" in reason + for reason in report["stages"]["capacity"]["cases"][-1]["reasons"] + ) + ) + + def test_qualification_rejects_preflight_without_predictive_optimization(self): + with tempfile.TemporaryDirectory() as tmp: + plan = self.make_plan(Path(tmp)) + self.write_valid_evidence(plan) + case = self.stage_cases(plan, "query_size_sweep")[0] + preflight_path = Path(case["artifacts"]["preflight"]) + preflight = json.loads(preflight_path.read_text(encoding="utf-8")) + preflight["online"]["predictive_optimization"]["raw"][ + "effective_value" + ] = "DISABLE" + preflight["online"]["predictive_optimization"]["raw"][ + "effectively_enabled" + ] = False + preflight["online"]["predictive_optimization"]["raw"]["status"] = "error" + self.write_json(preflight_path, preflight) + report = qualify.evaluate_plan(plan) + rejected = report["stages"]["query_size_sweep"]["cases"][0] + self.assertEqual(rejected["state"], "failed") + self.assertTrue( + any("Predictive Optimization enabled for raw" in reason for reason in rejected["reasons"]) + ) + + def test_queue_or_cache_failure_rejects_size(self): + for failure in ("queue", "cache"): + with self.subTest(failure=failure), tempfile.TemporaryDirectory() as tmp: + plan = self.make_plan(Path(tmp)) + self.write_valid_evidence(plan) + case = self.stage_cases(plan, "query_size_sweep")[0] + record = self.query_record("dashboard", "Small") + metrics = record["query_evidence"][0]["metrics"] + if failure == "queue": + metrics["waiting_at_capacity_ms"] = 1 + metrics["queue_time_ms"] = 1 + else: + metrics["result_from_cache"] = True + self.write_jsonl(case["artifacts"]["dashboard"], [record]) + report = qualify.evaluate_plan(plan) + first = report["stages"]["query_size_sweep"]["cases"][0] + self.assertEqual(first["state"], "failed") + self.assertEqual(report["selected_query_size"], "Medium") + + def test_endurance_duration_counts_and_config_drift_fail(self): + for failure in ("duration", "dashboard_count", "drilldown_count", "drift"): + with self.subTest(failure=failure), tempfile.TemporaryDirectory() as tmp: + plan = self.make_plan(Path(tmp)) + self.write_valid_evidence(plan) + endurance = self.stage_cases(plan, "endurance")[0] + validation_path = Path( + endurance["artifacts"]["validation_report"] + ) + validation = json.loads(validation_path.read_text(encoding="utf-8")) + if failure == "duration": + validation["window"]["duration_seconds"] = 1799 + elif failure == "dashboard_count": + validation["query_samples"]["dashboard"]["accepted_count"] = 2 + elif failure == "drilldown_count": + validation["query_samples"]["drilldown"]["accepted_count"] = 0 + else: + progress_path = Path(endurance["artifacts"]["ingest_progress"]) + progress = json.loads(progress_path.read_text(encoding="utf-8")) + progress["config"]["batch_size"] = 25_000 + self.write_json(progress_path, progress) + if failure != "drift": + self.write_json(validation_path, validation) + report = qualify.evaluate_plan(plan) + self.assertEqual(report["stages"]["endurance"]["state"], "failed") + self.assertFalse(report["qualified"]) + + +class PublicationTests(unittest.TestCase): + def write_json(self, path, value): + path.write_text(json.dumps(value, indent=2) + "\n", encoding="utf-8") + + def write_jsonl(self, path, values): + path.write_text( + "".join(json.dumps(value) + "\n" for value in values), + encoding="utf-8", + ) + + def query_record(self, workload, rows, start, finish): + query_count = 4 if workload == "dashboard" else 2 + evidence = [ + { + "canonical_duration_sec": float(index + 1), + "statement_id": f"{workload}-{rows}-{index}", + "metrics": { + "result_from_cache": False, + "cache_origin_statement_id": None, + }, + "execution_succeeded": True, + "result_row_count": index, + "result_hash_sha256": f"{index + 1:064x}", + "errors": [], + } + for index in range(query_count) + ] + return { + "schema_version": 1, + "workload": workload, + "iteration": rows, + "iteration_started_at": start, + "iteration_finished_at": finish, + "raw_rows": rows, + "producer_progress_finished": False, + "result": [ + [observation["canonical_duration_sec"]] + for observation in evidence + ], + "cache_hit": [[False] for _ in evidence], + "query_evidence": evidence, + "query_errors": [[] for _ in evidence], + } + + def clickhouse_record(self, workload, rows, start, finish): + count = 4 if workload == "dashboard" else 2 + return { + "iteration": rows, + "iteration_started_at": start, + "iteration_finished_at": finish, + "raw_rows": rows, + "system": "ClickHouse Cloud", + "result": [[0.5 + index / 10] for index in range(count)], + } + + def make_fixture(self, root, *, include_above_cap=True): + dashboard_path = root / "dashboard.jsonl" + drilldown_path = root / "drilldown.jsonl" + freshness_path = root / "freshness.jsonl" + cost_path = root / "cost_summary.json" + validation_path = root / "validation_report.json" + ch_dashboard_path = root / "clickhouse_dashboard.jsonl" + ch_drilldown_path = root / "clickhouse_drilldown.jsonl" + ch_ingest_path = root / "clickhouse_ingest.json" + + dashboard = [ + self.query_record( + "dashboard", + 90_000_000_000, + "2026-01-01T00:01:00Z", + "2026-01-01T00:03:00Z", + ) + ] + freshness = [ + { + "observed_at": "2026-01-01T00:02:00Z", + "mv_refresh_age_sec": 30, + "producer_progress": {"logical_raw_rows": 90_000_000_000}, + "latest_successful_refresh": { + "finished_at": "2026-01-01T00:01:30Z" + }, + "errors": [], + } + ] + if include_above_cap: + dashboard.append( + self.query_record( + "dashboard", + 101_000_000_000, + "2026-01-01T00:05:00Z", + "2026-01-01T00:06:00Z", + ) + ) + freshness.append( + { + "observed_at": "2026-01-01T00:05:30Z", + "mv_refresh_age_sec": 40, + "producer_progress": { + "logical_raw_rows": 101_000_000_000 + }, + "latest_successful_refresh": { + "finished_at": "2026-01-01T00:04:50Z" + }, + "errors": [], + } + ) + drilldown = [ + self.query_record( + "drilldown", + 91_000_000_000, + "2026-01-01T00:02:00Z", + "2026-01-01T00:04:00Z", + ) + ] + self.write_jsonl(dashboard_path, dashboard) + self.write_jsonl(drilldown_path, drilldown) + self.write_jsonl(freshness_path, freshness) + self.write_jsonl( + ch_dashboard_path, + [ + self.clickhouse_record( + "dashboard", + 89_000_000_000, + "2026-02-01T00:00:00Z", + "2026-02-01T00:01:00Z", + ), + self.clickhouse_record( + "dashboard", + 92_000_000_000, + "2026-02-01T00:10:00Z", + "2026-02-01T00:11:00Z", + ), + ], + ) + self.write_jsonl( + ch_drilldown_path, + [ + self.clickhouse_record( + "drilldown", + 90_500_000_000, + "2026-02-01T00:02:00Z", + "2026-02-01T00:03:00Z", + ) + ], + ) + cost = { + "complete": True, + "completeness": {"complete": True}, + "canonical_undiscounted_primary_total": { + "currency": "USD", + "amount": "12", + }, + "canonical_undiscounted_fresh_path_total": { + "currency": "USD", + "amount": "10", + }, + "canonical_undiscounted_query_serving_total": { + "currency": "USD", + "amount": "2", + }, + "primary_category_totals": {}, + "matched_lines": [ + { + "usage_row_index": 7, + "category": "primary_rt_serving_compute", + "currency": "USD", + "list_cost": "2", + "measured_interval": { + "start": "2026-01-01T00:00:00Z", + "end": "2026-01-01T00:10:00Z", + }, + "allocation": {"numerator_microseconds": 600_000_000}, + } + ], + "query_serving_allocation": { + "method": "union_overlap", + "successful_query_union_intervals": [ + { + "start": "2026-01-01T00:00:00Z", + "end": "2026-01-01T00:10:00Z", + } + ], + }, + "lakehouse_rt_beta_30_percent_off_scenario": { + "discount_rate": "0.30", + "applies_only_to": "primary_rt_serving_compute", + "canonical_total_unchanged": True, + "canonical_undiscounted_primary_total": { + "currency": "USD", + "amount": "12", + }, + }, + } + self.write_json(cost_path, cost) + validation = { + "accepted": True, + "configuration": {"expected_rows": publish_results.EXPECTED_ROWS}, + "gates": [ + { + "id": "input_availability", + "evidence": { + "paths": { + "dashboard": str(dashboard_path), + "drilldown": str(drilldown_path), + "freshness": str(freshness_path), + "cost_summary": str(cost_path), + } + }, + } + ], + "query_samples": { + "dashboard": { + "accepted_indices": list(range(len(dashboard))) + }, + "drilldown": {"accepted_indices": [0]}, + }, + "freshness_samples": { + "accepted_indices": list(range(len(freshness))) + }, + } + self.write_json(validation_path, validation) + self.write_json( + ch_ingest_path, + { + "system": "ClickHouse Cloud", + "rows_ingested": publish_results.EXPECTED_ROWS, + "costs": [ + { + "tier": "Enterprise", + "compute_price_per_8gib_hour": 0.4, + "total_compute_cost_usd": 20, + } + ], + }, + ) + return { + "validation_report_path": validation_path, + "cost_summary_path": cost_path, + "dashboard_path": dashboard_path, + "drilldown_path": drilldown_path, + "freshness_path": freshness_path, + "clickhouse_dashboard_path": ch_dashboard_path, + "clickhouse_drilldown_path": ch_drilldown_path, + "clickhouse_ingest_cost_path": ch_ingest_path, + } + + def test_rejected_validation_writes_no_outputs(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + fixture = self.make_fixture(root) + validation = json.loads( + fixture["validation_report_path"].read_text(encoding="utf-8") + ) + validation["accepted"] = False + self.write_json(fixture["validation_report_path"], validation) + output = root / "publication" + with self.assertRaisesRegex(ValueError, "accepted"): + publish_results.publish(**fixture, output_dir=output) + self.assertFalse(output.exists()) + + def test_incomplete_cost_writes_no_outputs(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + fixture = self.make_fixture(root) + cost = json.loads( + fixture["cost_summary_path"].read_text(encoding="utf-8") + ) + cost["complete"] = False + self.write_json(fixture["cost_summary_path"], cost) + output = root / "publication" + with self.assertRaisesRegex(ValueError, "complete"): + publish_results.publish(**fixture, output_dir=output) + self.assertFalse(output.exists()) + + def test_publisher_uses_only_validation_accepted_indices(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + fixture = self.make_fixture(root, include_above_cap=False) + dashboard_path = fixture["dashboard_path"] + dashboard = [ + json.loads(line) + for line in dashboard_path.read_text(encoding="utf-8").splitlines() + ] + dashboard.insert( + 0, + self.query_record( + "dashboard", + 80_000_000_000, + "2026-01-01T00:00:10Z", + "2026-01-01T00:00:50Z", + ), + ) + self.write_jsonl(dashboard_path, dashboard) + validation = json.loads( + fixture["validation_report_path"].read_text(encoding="utf-8") + ) + validation["query_samples"]["dashboard"]["accepted_indices"] = [1] + self.write_json(fixture["validation_report_path"], validation) + + output = root / "publication" + publish_results.publish(**fixture, output_dir=output) + accepted = [ + json.loads(line) + for line in (output / "accepted_dashboard.jsonl") + .read_text(encoding="utf-8") + .splitlines() + ] + self.assertEqual([row["raw_rows"] for row in accepted], [90_000_000_000]) + self.assertEqual(accepted[0]["publication"]["source_index"], 1) + + def test_monotonic_matching_is_deterministic_and_equal_count(self): + left = [{"raw_rows": value} for value in (10, 21, 35)] + right = [{"raw_rows": value} for value in (9, 20, 22, 36)] + first = publish_results.monotonic_nearest_matches(left, right) + second = publish_results.monotonic_nearest_matches(left, right) + self.assertEqual(first, second) + self.assertEqual( + [item["right_index"] for item in first], + [0, 1, 3], + ) + self.assertEqual(len(first), len(left)) + + def test_overlap_allocation_uses_union_without_concurrency_double_count(self): + lines = [ + { + "category": "primary_rt_serving_compute", + "list_cost": "10", + "measured_interval": { + "start": "2026-01-01T00:00:00Z", + "end": "2026-01-01T00:10:00Z", + }, + } + ] + intervals = [ + ( + validate_run.parse_utc_timestamp("2026-01-01T00:01:00Z"), + validate_run.parse_utc_timestamp("2026-01-01T00:04:00Z"), + ), + ( + validate_run.parse_utc_timestamp("2026-01-01T00:02:00Z"), + validate_run.parse_utc_timestamp("2026-01-01T00:05:00Z"), + ), + ] + total, allocation = publish_results.allocate_matched_query_cost( + lines, intervals + ) + self.assertEqual(total, Decimal("4")) + self.assertEqual( + allocation[0]["matched_union_overlap_microseconds"], + 240_000_000, + ) + + def test_publish_caps_presentations_but_discloses_full_cost_and_hashes(self): + with tempfile.TemporaryDirectory() as tmp: + root = Path(tmp) + fixture = self.make_fixture(root) + output = root / "publication" + publication = publish_results.publish(**fixture, output_dir=output) + dashboard = [ + json.loads(line) + for line in (output / "accepted_dashboard.jsonl") + .read_text(encoding="utf-8") + .splitlines() + ] + freshness = [ + json.loads(line) + for line in (output / "accepted_freshness.jsonl") + .read_text(encoding="utf-8") + .splitlines() + ] + self.assertEqual(len(dashboard), 1) + self.assertEqual(len(freshness), 1) + self.assertLessEqual(dashboard[0]["raw_rows"], 100_000_000_000) + self.assertEqual( + freshness[0]["raw_rows_source"], + "producer_progress.logical_raw_rows", + ) + fresh = json.loads( + ( + output + / "ingest_fresh_path_cost_clickhouse_vs_databricks_summary.json" + ).read_text(encoding="utf-8") + ) + self.assertEqual(fresh["costs"]["databricks"]["total_usd"], 10.0) + self.assertEqual( + fresh["expected_full_rows"], publish_results.EXPECTED_ROWS + ) + self.assertIn("100B", fresh["disclosure"]) + artifact = publication["artifacts"]["accepted_dashboard.jsonl"] + self.assertEqual( + artifact["sha256"], + publish_results.sha256(output / "accepted_dashboard.jsonl"), + ) + source_name = publish_results.SOURCE_OUTPUT_NAMES["validation_report"] + source_path = output / source_name + self.assertEqual( + source_path.read_bytes(), + fixture["validation_report_path"].read_bytes(), + ) + self.assertEqual( + publication["sources"]["validation_report"]["sha256"], + publish_results.sha256(source_path), + ) + self.assertIn(source_name, publication["artifacts"]) + performance = json.loads( + ( + output + / "full_path_cost_performance_clickhouse_vs_databricks_summary.json" + ).read_text(encoding="utf-8") + ) + self.assertAlmostEqual( + performance["databricks_query_cost_allocation"][ + "matched_query_serving_usd" + ], + 0.6, + ) + + def test_manifest_is_untouched_by_default_and_requires_explicit_activation(self): + with tempfile.TemporaryDirectory(dir=ROOT) as tmp: + root = Path(tmp) + fixture = self.make_fixture(root) + labels = { + "aggregate_query_latency": ["ClickHouse Cloud"], + "drilldown_query_latency": ["ClickHouse Cloud"], + "mv_lag": ["ClickHouse Cloud"], + "fresh_path_cost": ["ClickHouse"], + "full_path_cost_performance": ["ClickHouse"], + "full_path_cost_vs_query_runtime": ["ClickHouse"], + } + manifest_path = root / "manifest.json" + self.write_json( + manifest_path, + { + "schema_version": 1, + "required_labels": labels, + "providers": {"clickhouse": {}}, + "fresh_path_cost": {}, + "cost_performance": {}, + }, + ) + original = manifest_path.read_bytes() + publish_results.publish( + **fixture, + output_dir=root / "not-applied", + global_manifest_path=manifest_path, + ) + self.assertEqual(manifest_path.read_bytes(), original) + + publish_results.publish( + **fixture, + output_dir=root / "applied", + global_manifest_path=manifest_path, + apply_global_manifest=True, + ) + activated = json.loads(manifest_path.read_text(encoding="utf-8")) + self.assertTrue(activated["providers"]["databricks"]["enabled"]) + self.assertNotIn( + str(root), + activated["providers"]["databricks"]["publication"], + ) + self.assertEqual( + activated["providers"]["databricks"]["validation"], + ( + root + / "applied" + / publish_results.SOURCE_OUTPUT_NAMES["validation_report"] + ) + .resolve() + .relative_to(publish_results.BENCHMARK_ROOT) + .as_posix(), + ) + for values in activated["required_labels"].values(): + self.assertEqual(values[-1], "Databricks") + self.assertIn( + "databricks_pairwise_summary", + activated["fresh_path_cost"], + ) + with self.assertRaisesRegex(ValueError, "already contains"): + publish_results.publish( + **fixture, + output_dir=root / "conflicting", + global_manifest_path=manifest_path, + apply_global_manifest=True, + ) + self.assertFalse((root / "conflicting").exists()) + + +if __name__ == "__main__": + unittest.main() diff --git a/full-path-realtime/quotes/databricks/validate_run.py b/full-path-realtime/quotes/databricks/validate_run.py new file mode 100644 index 0000000..4dc6bc9 --- /dev/null +++ b/full-path-realtime/quotes/databricks/validate_run.py @@ -0,0 +1,1451 @@ +#!/usr/bin/env python3 +"""Validate a Databricks full-path benchmark run from offline evidence only.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +import os +import sys +import tempfile +from datetime import datetime, timedelta, timezone +from decimal import Decimal, InvalidOperation +from pathlib import Path +from typing import Any, Mapping, Sequence + +EXPECTED_ROWS = 113_219_565_734 +ALIGNED_FIELDS = ( + "result", + "compilation_time", + "execution_time", + "queue_time", + "result_fetch_time", + "client_wall_time", + "statement_ids", + "cache_hit", + "read_io_cache_percent", + "result_row_count", + "result_hash", +) + + +def parse_utc_timestamp(value: Any) -> datetime: + if isinstance(value, datetime): + parsed = value + elif isinstance(value, str): + text = value.strip() + if not text: + raise ValueError("timestamp is empty") + if text.endswith(("Z", "z")): + text = text[:-1] + "+00:00" + parsed = datetime.fromisoformat(text) + elif isinstance(value, (int, float)) and not isinstance(value, bool): + number = float(value) + if not math.isfinite(number): + raise ValueError("timestamp is not finite") + if abs(number) >= 100_000_000_000: + number /= 1000 + parsed = datetime.fromtimestamp(number, timezone.utc) + else: + raise ValueError("timestamp is not an ISO-8601 value") + if parsed.tzinfo is None or parsed.utcoffset() is None: + raise ValueError("timestamp has no timezone") + if parsed.utcoffset() != timedelta(0): + raise ValueError("timestamp is not UTC") + return parsed.astimezone(timezone.utc) + + +def iso_utc(value: datetime) -> str: + return ( + value.astimezone(timezone.utc) + .isoformat(timespec="milliseconds") + .replace("+00:00", "Z") + ) + + +def _number(value: Any) -> float | None: + if value is None or isinstance(value, bool): + return None + try: + result = float(value) + except (TypeError, ValueError, OverflowError): + return None + return result if math.isfinite(result) else None + + +def _integer(value: Any) -> int | None: + if value is None or isinstance(value, bool): + return None + try: + parsed = int(value) + except (TypeError, ValueError, OverflowError): + return None + return parsed if str(parsed) == str(value).strip() or isinstance(value, int) else None + + +def _decimal(value: Any) -> Decimal | None: + if value is None or isinstance(value, bool): + return None + try: + result = Decimal(str(value)) + except (InvalidOperation, TypeError, ValueError): + return None + return result if result.is_finite() else None + + +def _mapping(value: Any) -> Mapping[str, Any]: + return value if isinstance(value, Mapping) else {} + + +def _list(value: Any) -> list[Any]: + return list(value) if isinstance(value, list) else [] + + +def _nested(value: Any, *path: str) -> Any: + current = value + for part in path: + if not isinstance(current, Mapping) or part not in current: + return None + current = current[part] + return current + + +def _read_json(path: Path, label: str) -> tuple[dict[str, Any], list[str]]: + try: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, Mapping): + raise ValueError("top-level value is not an object") + return dict(value), [] + except (OSError, TypeError, ValueError, json.JSONDecodeError) as exc: + return {}, [f"{label}: {type(exc).__name__}: {exc}"] + + +def _read_jsonl(path: Path, label: str) -> tuple[list[dict[str, Any]], list[str]]: + rows: list[dict[str, Any]] = [] + errors: list[str] = [] + try: + lines = path.read_text(encoding="utf-8").splitlines() + except OSError as exc: + return [], [f"{label}: {type(exc).__name__}: {exc}"] + for line_number, line in enumerate(lines, start=1): + if not line.strip(): + continue + try: + value = json.loads(line) + if not isinstance(value, Mapping): + raise ValueError("top-level value is not an object") + rows.append(dict(value)) + except (TypeError, ValueError, json.JSONDecodeError) as exc: + errors.append(f"{label}:{line_number}: {type(exc).__name__}: {exc}") + return rows, errors + + +def atomic_write_json(path: Path, value: Mapping[str, Any]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + fd, temporary = tempfile.mkstemp(prefix=f".{path.name}.", dir=path.parent) + try: + with os.fdopen(fd, "w", encoding="utf-8") as handle: + json.dump(value, handle, indent=2, sort_keys=True, ensure_ascii=False) + handle.write("\n") + handle.flush() + os.fsync(handle.fileno()) + os.replace(temporary, path) + finally: + try: + os.unlink(temporary) + except FileNotFoundError: + pass + + +class Gates: + def __init__(self) -> None: + self.values: list[dict[str, Any]] = [] + + def add( + self, + gate_id: str, + passed: bool, + evidence: Mapping[str, Any], + reasons: Sequence[str], + ) -> None: + self.values.append( + { + "id": gate_id, + "passed": bool(passed), + "evidence": dict(evidence), + "reasons": list(reasons), + } + ) + + +def _gate_source_manifest( + gates: Gates, + manifest: Mapping[str, Any], + errors: Sequence[str], + expected_rows: int, +) -> None: + reasons = list(errors) + files = _list(manifest.get("files")) + selected_paths = [ + str(item.get("path")) + for item in files + if isinstance(item, Mapping) and item.get("path") is not None + ] + excluded = [str(value) for value in _list(manifest.get("excluded_files"))] + tasks = [ + item for item in _list(manifest.get("tasks")) if isinstance(item, Mapping) + ] + task_rows = [ + _integer( + item.get("task_rows") + if item.get("task_rows") is not None + else item.get("row_count") + if item.get("row_count") is not None + else item.get("rows") + ) + for item in tasks + ] + file_rows = [ + _integer(item.get("selected_rows")) + for item in files + if isinstance(item, Mapping) + ] + task_ordinals = [ + _integer( + item.get("canonical_ordinal") + if item.get("canonical_ordinal") is not None + else item.get("task_ordinal") + if item.get("task_ordinal") is not None + else item.get("ordinal") + ) + for item in tasks + ] + file_ordinals = [ + _integer(item.get("file_ordinal")) + for item in files + if isinstance(item, Mapping) + ] + checks = { + "total_rows_exact": _integer(manifest.get("total_rows")) == expected_rows, + "quotes_0_flag_false": manifest.get("quotes_0_parquet_included") is False, + "quotes_0_not_selected": all( + Path(value).name != "quotes_0.parquet" for value in selected_paths + ), + "manifest_sha256_present": isinstance(manifest.get("manifest_sha256"), str) + and len(str(manifest.get("manifest_sha256"))) == 64, + "tasks_present": bool(tasks), + "file_count_consistent": _integer( + manifest.get("selected_file_count") + ) == len(files), + "task_count_consistent": _integer( + manifest.get("selected_row_group_count") + ) == len(tasks), + "canonical_task_order": task_ordinals == list(range(len(tasks))), + "canonical_file_order": ( + all(value is not None and value >= 0 for value in file_ordinals) + and file_ordinals == sorted(file_ordinals) + and len(set(file_ordinals)) == len(file_ordinals) + ), + "task_rows_sum_exact": ( + bool(task_rows) + and all(value is not None and value >= 0 for value in task_rows) + and sum(value for value in task_rows if value is not None) == expected_rows + ), + "file_rows_sum_exact": ( + bool(file_rows) + and all(value is not None and value >= 0 for value in file_rows) + and sum(value for value in file_rows if value is not None) == expected_rows + ), + } + reasons.extend(name for name, passed in checks.items() if not passed) + gates.add( + "source_manifest", + not reasons, + { + "expected_rows": expected_rows, + "actual_rows": manifest.get("total_rows"), + "excluded_files": excluded, + "checks": checks, + }, + reasons, + ) + + +def _gate_producer( + gates: Gates, + progress: Mapping[str, Any], + manifest: Mapping[str, Any], + errors: Sequence[str], + expected_rows: int, +) -> None: + reasons = list(errors) + source = _mapping(progress.get("source")) + stream_status = _mapping(progress.get("stream_status")) + workers = _integer(_nested(progress, "config", "workers")) + task_count = _integer(manifest.get("selected_row_group_count")) + completed = _list(progress.get("completed_task_ordinals")) + completed_ints = [_integer(value) for value in completed] + completed_ranges = _list(progress.get("completed_task_ranges")) + compact_complete = ( + task_count is not None + and task_count > 0 + and completed_ranges == [[0, task_count - 1]] + ) + stream_checks = [ + isinstance(status, Mapping) + and status.get("close_succeeded") is True + and status.get("unacked_inspection_succeeded") is True + and _integer(status.get("unacked_batches")) == 0 + for status in stream_status.values() + ] + checks = { + "provider_committed_exact": _integer( + progress.get("provider_committed_rows") + ) + == expected_rows, + "logical_raw_exact": _integer(progress.get("logical_raw_rows")) + == expected_rows, + "submitted_exact": _integer(progress.get("submitted_rows")) == expected_rows, + "finished": progress.get("finished") is True, + "not_running": progress.get("running") is False, + "clean_checkpoint": progress.get("clean_checkpoint") is True, + "not_ambiguous": progress.get("ambiguous") is False, + "no_terminal_error": progress.get("terminal_error") in (None, ""), + "no_errors": progress.get("errors") == [], + "no_pending_batches": _integer(progress.get("pending_batches")) == 0, + "no_pending_rows": _integer(progress.get("pending_rows")) == 0, + "no_partial_tasks": progress.get("partial_task_rows") == {}, + "fresh_table_baseline": _integer(progress.get("baseline_table_rows")) == 0 + and progress.get("baseline_table_rows_unknown") is False, + "manifest_hash_matches": source.get("manifest_sha256") + == manifest.get("manifest_sha256"), + "source_total_matches": _integer(source.get("total_rows")) == expected_rows, + "all_tasks_completed": ( + task_count is not None + and _integer(progress.get("completed_tasks")) == task_count + and ( + completed_ints == list(range(task_count)) + or compact_complete + ) + ), + "all_streams_clean": ( + workers is not None + and workers > 0 + and len(stream_status) == workers + and all(stream_checks) + ), + } + reasons.extend(name for name, passed in checks.items() if not passed) + gates.add( + "producer_completion", + not reasons, + { + "provider_committed_rows": progress.get("provider_committed_rows"), + "completed_tasks": progress.get("completed_tasks"), + "expected_tasks": task_count, + "stream_count": len(stream_status), + "checks": checks, + }, + reasons, + ) + + +def _metric_interval_evidence( + rows: Sequence[Mapping[str, Any]], + eps_min: float, + eps_max: float, +) -> tuple[list[dict[str, Any]], list[str]]: + evidence: list[dict[str, Any]] = [] + reasons: list[str] = [] + previous_rows = 0 + previous_elapsed = 0.0 + previous_seen = False + for index, row in enumerate(rows): + explicit_warmup = ( + row.get("warmup") is True + or row.get("warm_up") is True + or row.get("is_warmup") is True + or _nested(row, "eps", "warmup") is True + ) + if index == 0 and explicit_warmup: + evidence.append( + {"index": index, "ignored": True, "reason": "explicit first warm-up"} + ) + current_rows = _integer(row.get("provider_committed_rows")) + current_elapsed = _number(row.get("elapsed_sec")) + if current_rows is not None and current_elapsed is not None: + previous_rows, previous_elapsed, previous_seen = ( + current_rows, + current_elapsed, + True, + ) + continue + explicit = next( + ( + _number(value) + for value in ( + _nested(row, "eps", "interval_provider_committed_rows_per_sec"), + _nested(row, "eps", "interval_rows_per_sec"), + row.get("interval_provider_committed_rows_per_sec"), + row.get("interval_eps"), + ) + if _number(value) is not None + ), + None, + ) + current_rows = _integer(row.get("provider_committed_rows")) + current_elapsed = _number(row.get("elapsed_sec")) + interval_eps = explicit + source = "explicit" + if interval_eps is None and current_rows is not None and current_elapsed is not None: + base_rows = previous_rows if previous_seen else 0 + base_elapsed = previous_elapsed if previous_seen else 0.0 + delta_elapsed = current_elapsed - base_elapsed + delta_rows = current_rows - base_rows + if delta_elapsed > 0 and delta_rows >= 0: + interval_eps = delta_rows / delta_elapsed + source = "derived_from_committed_counter_and_elapsed_sec" + else: + source = "invalid_counter_delta" + passed = ( + interval_eps is not None and eps_min <= interval_eps <= eps_max + ) + evidence.append( + { + "index": index, + "ignored": False, + "eps": interval_eps, + "source": source, + "passed": passed, + } + ) + if current_rows is not None and current_elapsed is not None: + if previous_seen and ( + current_rows < previous_rows or current_elapsed < previous_elapsed + ): + reasons.append(f"metrics row {index} counters are not monotonic") + previous_rows, previous_elapsed, previous_seen = ( + current_rows, + current_elapsed, + True, + ) + return evidence, reasons + + +def _gate_ingest_rate( + gates: Gates, + progress: Mapping[str, Any], + metrics: Sequence[Mapping[str, Any]], + errors: Sequence[str], + *, + eps_min: float, + eps_max: float, + min_fraction: float, +) -> None: + reasons = list(errors) + average = _number( + _nested(progress, "eps", "average_provider_committed_rows_per_sec") + ) + average_passed = average is not None and eps_min <= average <= eps_max + if not average_passed: + reasons.append("average provider-committed EPS is missing or outside bounds") + intervals, interval_reasons = _metric_interval_evidence( + metrics, eps_min, eps_max + ) + reasons.extend(interval_reasons) + considered = [item for item in intervals if not item.get("ignored")] + passed_count = sum(item.get("passed") is True for item in considered) + fraction = passed_count / len(considered) if considered else 0.0 + if not considered: + reasons.append("no metrics intervals are available") + if fraction < min_fraction: + reasons.append("metrics interval EPS pass fraction is below the minimum") + gates.add( + "ingest_rate", + not reasons, + { + "bounds": {"minimum": eps_min, "maximum": eps_max}, + "average_provider_committed_rows_per_sec": average, + "average_passed": average_passed, + "intervals": intervals, + "interval_pass_count": passed_count, + "interval_count": len(considered), + "interval_pass_fraction": fraction, + "minimum_pass_fraction": min_fraction, + }, + reasons, + ) + + +def _query_record_reasons( + record: Mapping[str, Any], + expected_queries: int, +) -> list[str]: + reasons: list[str] = [] + for field in ALIGNED_FIELDS: + values = record.get(field) + if not isinstance(values, list) or len(values) != expected_queries: + reasons.append(f"{field} is not aligned to {expected_queries} queries") + continue + if any(not isinstance(item, list) or len(item) != 1 for item in values): + reasons.append(f"{field} does not use one-element query entries") + observations = record.get("query_evidence") + query_errors = record.get("query_errors") + if not isinstance(observations, list) or len(observations) != expected_queries: + reasons.append( + f"query_evidence does not contain exactly {expected_queries} queries" + ) + return reasons + if not isinstance(query_errors, list) or len(query_errors) != expected_queries: + reasons.append( + f"query_errors does not contain exactly {expected_queries} queries" + ) + query_errors = [None] * expected_queries + for index, observation in enumerate(observations): + prefix = f"query {index + 1}" + if not isinstance(observation, Mapping): + reasons.append(f"{prefix} evidence is not an object") + continue + canonical = _number(observation.get("canonical_duration_sec")) + if canonical is None or canonical < 0: + reasons.append(f"{prefix} canonical result is missing") + result_values = record.get("result") + if ( + isinstance(result_values, list) + and len(result_values) > index + and isinstance(result_values[index], list) + and len(result_values[index]) == 1 + and result_values[index][0] != observation.get("canonical_duration_sec") + ): + reasons.append(f"{prefix} aligned canonical result disagrees") + statement_id = observation.get("statement_id") + if not isinstance(statement_id, str) or not statement_id: + reasons.append(f"{prefix} statement ID is missing") + metrics = _mapping(observation.get("metrics")) + if metrics.get("result_from_cache") is not False: + reasons.append(f"{prefix} cache-hit evidence is not exact false") + if metrics.get("cache_origin_statement_id") not in (None, ""): + reasons.append(f"{prefix} has a cache origin statement") + if observation.get("execution_succeeded") is not True: + reasons.append(f"{prefix} execution did not succeed") + row_count = _integer(observation.get("result_row_count")) + if row_count is None or row_count < 0: + reasons.append(f"{prefix} result row count is missing") + result_hash = observation.get("result_hash_sha256") + if ( + not isinstance(result_hash, str) + or len(result_hash) != 64 + or any(character not in "0123456789abcdefABCDEF" for character in result_hash) + ): + reasons.append(f"{prefix} result hash is missing or invalid") + if observation.get("errors") != []: + reasons.append(f"{prefix} contains query errors") + if index < len(query_errors) and query_errors[index] != []: + reasons.append(f"{prefix} aligned query_errors is non-empty") + cache_values = record.get("cache_hit") + if ( + not isinstance(cache_values, list) + or len(cache_values) <= index + or cache_values[index] != [False] + ): + reasons.append(f"{prefix} aligned cache_hit is not exact false") + if record.get("producer_progress_error") not in (None, ""): + reasons.append("producer progress was unavailable at query start") + return reasons + + +def _query_sample_report( + rows: Sequence[Mapping[str, Any]], + load_errors: Sequence[str], + *, + workload: str, + expected_queries: int, + expected_interval: float, + expected_rows: int, + cadence_tolerance: float, +) -> tuple[bool, dict[str, Any], list[str]]: + reasons = list(load_errors) + accepted: list[int] = [] + rejected: list[dict[str, Any]] = [] + active_invalid: list[dict[str, Any]] = [] + timed: list[tuple[int, datetime, datetime, datetime | None]] = [] + for index, record in enumerate(rows): + sample_reasons: list[str] = [] + started = None + finished = None + scheduled = None + try: + started = parse_utc_timestamp(record.get("iteration_started_at")) + finished = parse_utc_timestamp(record.get("iteration_finished_at")) + if finished < started: + sample_reasons.append("iteration finishes before it starts") + except (TypeError, ValueError, OverflowError, OSError): + sample_reasons.append("iteration start/finish timestamp is invalid") + try: + scheduled = parse_utc_timestamp(record.get("scheduled_start_at")) + except (TypeError, ValueError, OverflowError, OSError): + sample_reasons.append("scheduled_start_at is invalid") + if started is not None and finished is not None: + timed.append((index, started, finished, scheduled)) + if record.get("workload") != workload and record.get("runner") != workload: + sample_reasons.append(f"record is not labeled {workload}") + interval = _number(record.get("scheduled_interval_sec")) + if interval is None or abs(interval - expected_interval) > 1e-9: + sample_reasons.append( + f"scheduled interval is not fixed at {expected_interval:g}s" + ) + raw_rows = _integer(record.get("raw_rows")) + active = ( + record.get("producer_progress_finished") is False + and raw_rows is not None + and raw_rows < expected_rows + ) + if not active: + if record.get("producer_progress_finished") is not False: + sample_reasons.append( + "sample did not prove producer_progress_finished exact false" + ) + if raw_rows is None: + sample_reasons.append("raw_rows is missing") + elif raw_rows >= expected_rows: + sample_reasons.append("sample started at or after completed ingest") + rejected.append({"index": index, "reasons": sample_reasons}) + continue + sample_reasons.extend(_query_record_reasons(record, expected_queries)) + if sample_reasons: + active_invalid.append({"index": index, "reasons": sample_reasons}) + rejected.append({"index": index, "reasons": sample_reasons}) + else: + accepted.append(index) + timed.sort(key=lambda item: item[1]) + overlap_failures: list[list[int]] = [] + for previous, current in zip(timed, timed[1:]): + if current[1] < previous[2]: + overlap_failures.append([previous[0], current[0]]) + if overlap_failures: + reasons.append("runner iterations overlap themselves") + cadence_failures: list[dict[str, Any]] = [] + scheduled_rows = [item for item in timed if item[3] is not None] + for previous, current in zip(scheduled_rows, scheduled_rows[1:]): + difference = (current[3] - previous[3]).total_seconds() + if abs(difference - expected_interval) > cadence_tolerance: + cadence_failures.append( + { + "previous_index": previous[0], + "index": current[0], + "scheduled_delta_seconds": difference, + } + ) + if cadence_failures: + reasons.append("fixed-rate scheduled cadence exceeds tolerance") + if active_invalid: + reasons.append("one or more active-ingest query samples are invalid") + if not accepted: + reasons.append("no valid active-ingest iteration was accepted") + raw_sequence = [ + (item[1], _integer(rows[item[0]].get("raw_rows")), item[0]) for item in timed + ] + raw_monotonic_failures = [ + [previous[2], current[2]] + for previous, current in zip(raw_sequence, raw_sequence[1:]) + if previous[1] is not None + and current[1] is not None + and current[1] < previous[1] + ] + if raw_monotonic_failures: + reasons.append("query raw_rows is not monotonic") + return ( + not reasons, + { + "accepted_indices": accepted, + "accepted_count": len(accepted), + "rejected": rejected, + "rejected_count": len(rejected), + "active_invalid": active_invalid, + "self_overlap_failures": overlap_failures, + "cadence_failures": cadence_failures, + "raw_rows_monotonic_failures": raw_monotonic_failures, + "expected_query_count": expected_queries, + "expected_interval_seconds": expected_interval, + "cadence_tolerance_seconds": cadence_tolerance, + }, + reasons, + ) + + +def _gate_query_workloads( + gates: Gates, + dashboard: Sequence[Mapping[str, Any]], + dashboard_errors: Sequence[str], + drilldown: Sequence[Mapping[str, Any]], + drilldown_errors: Sequence[str], + *, + expected_rows: int, + cadence_tolerance: float, +) -> dict[str, Any]: + reports: dict[str, Any] = {} + for workload, rows, errors, count, interval in ( + ("dashboard", dashboard, dashboard_errors, 4, 600.0), + ("drilldown", drilldown, drilldown_errors, 2, 3600.0), + ): + passed, evidence, reasons = _query_sample_report( + rows, + errors, + workload=workload, + expected_queries=count, + expected_interval=interval, + expected_rows=expected_rows, + cadence_tolerance=cadence_tolerance, + ) + reports[workload] = evidence + gates.add(f"{workload}_queries", passed, evidence, reasons) + combined: list[tuple[datetime, int, str, int]] = [] + for workload, rows in (("dashboard", dashboard), ("drilldown", drilldown)): + for index, row in enumerate(rows): + try: + started = parse_utc_timestamp(row.get("iteration_started_at")) + except (TypeError, ValueError, OverflowError, OSError): + continue + raw_rows = _integer(row.get("raw_rows")) + if raw_rows is not None: + combined.append((started, raw_rows, workload, index)) + combined.sort() + failures = [ + { + "previous": f"{previous[2]}:{previous[3]}", + "previous_raw_rows": previous[1], + "current": f"{current[2]}:{current[3]}", + "current_raw_rows": current[1], + } + for previous, current in zip(combined, combined[1:]) + if current[1] < previous[1] + ] + gates.add( + "query_raw_rows_monotonic", + not failures, + { + "sample_count": len(combined), + "failures": failures, + }, + ["query raw_rows decreases across workload samples"] if failures else [], + ) + return reports + + +def _gate_freshness( + gates: Gates, + rows: Sequence[Mapping[str, Any]], + load_errors: Sequence[str], + *, + window_start: datetime | None, + window_end: datetime | None, + startup_grace: float, + max_raw_age: float, + max_mv_age: float, +) -> dict[str, Any]: + reasons = list(load_errors) + accepted: list[int] = [] + rejected: list[dict[str, Any]] = [] + invalid_eligible: list[dict[str, Any]] = [] + if window_start is None or window_end is None: + reasons.append("measurement window is unavailable") + grace_end = ( + window_start + timedelta(seconds=startup_grace) + if window_start is not None + else None + ) + for index, row in enumerate(rows): + sample_reasons: list[str] = [] + try: + observed = parse_utc_timestamp(row.get("observed_at")) + except (TypeError, ValueError, OverflowError, OSError): + observed = None + sample_reasons.append("observed_at is invalid") + eligible = ( + observed is not None + and grace_end is not None + and window_end is not None + and grace_end <= observed < window_end + ) + if not eligible: + if observed is not None: + sample_reasons.append( + "sample is outside the post-grace measurement window" + ) + rejected.append({"index": index, "reasons": sample_reasons}) + continue + raw_age = _number(row.get("raw_commit_age_sec")) + mv_age = _number(row.get("mv_refresh_age_sec")) + if raw_age is None or raw_age < 0 or raw_age > max_raw_age: + sample_reasons.append("raw observational age is missing or outside bounds") + if mv_age is None or mv_age < 0 or mv_age > max_mv_age: + sample_reasons.append("MV observational age is missing or outside bounds") + if row.get("errors") != []: + sample_reasons.append("freshness sample contains errors") + semantics = str(row.get("age_semantics") or "").lower() + if "observational" not in semantics or "not exact" not in semantics: + sample_reasons.append( + "age semantics do not identify ages as observational, not exact lag" + ) + if sample_reasons: + invalid_eligible.append({"index": index, "reasons": sample_reasons}) + rejected.append({"index": index, "reasons": sample_reasons}) + else: + accepted.append(index) + if invalid_eligible: + reasons.append("one or more post-grace freshness samples are invalid") + if not accepted: + reasons.append("no post-grace freshness sample was accepted") + evidence = { + "accepted_indices": accepted, + "accepted_count": len(accepted), + "rejected": rejected, + "rejected_count": len(rejected), + "invalid_eligible": invalid_eligible, + "startup_grace_seconds": startup_grace, + "active_measurement_until": ( + iso_utc(window_end) if window_end is not None else None + ), + "maximum_raw_observational_age_seconds": max_raw_age, + "maximum_mv_observational_age_seconds": max_mv_age, + "age_interpretation": ( + "Raw and MV ages are observational timestamp proxies, not exact " + "per-record ingestion or materialized-view lag." + ), + } + gates.add("freshness", not reasons, evidence, reasons) + return evidence + + +def _gate_reconciliation( + gates: Gates, + manifest: Mapping[str, Any], + progress: Mapping[str, Any], + evidence: Mapping[str, Any], + errors: Sequence[str], + expected_rows: int, +) -> None: + values = { + "source_manifest_total_rows": _integer(manifest.get("total_rows")), + "producer_provider_committed_rows": _integer( + progress.get("provider_committed_rows") + ), + "system_zerobus_provider_committed_records": _integer( + evidence.get("provider_committed_records") + ), + } + metadata_raw_rows = _integer(_nested(evidence, "table_num_rows", "raw")) + reasons = list(errors) + if any(value != expected_rows for value in values.values()): + reasons.append("source, producer, and system Zerobus rows do not reconcile") + if metadata_raw_rows is not None and metadata_raw_rows != expected_rows: + reasons.append("DESCRIBE DETAIL raw numRows is present but does not reconcile") + provider_errors = _integer(evidence.get("provider_errors")) + stream_errors = _integer(evidence.get("zerobus_stream_error_count")) + if provider_errors != 0: + reasons.append("provider error count is missing or nonzero") + if stream_errors != 0: + reasons.append("Zerobus stream error count is missing or nonzero") + gates.add( + "row_reconciliation", + not reasons, + { + "expected_rows": expected_rows, + "counters": values, + "optional_describe_detail_raw_numRows": metadata_raw_rows, + "provider_errors": provider_errors, + "zerobus_stream_error_count": stream_errors, + }, + reasons, + ) + + +def _maintenance_class(value: Any) -> str: + normalized = str(value or "").upper().replace("-", "_").replace(" ", "_") + if not normalized: + return "unknown" + if "NO_OP" in normalized or normalized == "NOOP": + return "no_op" + if "COMPLETE_RECOMPUTE" in normalized or "FULL" in normalized: + return "full" + return "incremental" + + +def _gate_evidence( + gates: Gates, + evidence: Mapping[str, Any], + errors: Sequence[str], +) -> None: + reasons = list(errors) + required_complete = _nested( + evidence, "required_dataset_completeness", "complete" + ) + if evidence.get("complete") is not True: + reasons.append("evidence_summary.complete is not exact true") + if required_complete is not True: + reasons.append("required dataset completeness is not exact true") + gates.add( + "evidence_completeness", + not reasons, + { + "complete": evidence.get("complete"), + "required_dataset_completeness": evidence.get( + "required_dataset_completeness" + ), + "optional_dataset_errors": evidence.get("optional_dataset_errors"), + }, + reasons, + ) + + maintenance = _mapping(evidence.get("mv_maintenance")) + types = _list(maintenance.get("maintenance_types")) + classes = [_maintenance_class(value) for value in types] + mv_reasons: list[str] = [] + if not types: + mv_reasons.append("MV planning evidence is missing") + if _integer(maintenance.get("full_refresh_count")) != 0: + mv_reasons.append("MV full refresh count is missing or nonzero") + if any(value not in {"incremental", "no_op"} for value in classes): + mv_reasons.append("MV maintenance contains an unknown or unsupported type") + if "full" in classes: + mv_reasons.append("MV maintenance contains a full refresh") + gates.add( + "mv_incrementality", + not mv_reasons, + { + "maintenance_types": types, + "maintenance_classes": classes, + "full_refresh_count": maintenance.get("full_refresh_count"), + }, + mv_reasons, + ) + + +_VOLATILE_WAREHOUSE_FIELDS = { + "captured_at", + "created_time", + "creator_name", + "health", + "jdbc_url", + "last_modified_time", + "num_active_sessions", + "num_clusters", + "odbc_params", + "state", +} + + +def _stable_warehouse_config(value: Any) -> Any: + """Remove observations that are not warehouse configuration.""" + if isinstance(value, Mapping): + return { + str(key): _stable_warehouse_config(item) + for key, item in value.items() + if str(key).lower() not in _VOLATILE_WAREHOUSE_FIELDS + } + if isinstance(value, list): + return [_stable_warehouse_config(item) for item in value] + return value + + +def _gate_warehouse_configs( + gates: Gates, + evidence: Mapping[str, Any], + dashboard: Sequence[Mapping[str, Any]], + drilldown: Sequence[Mapping[str, Any]], + query_reports: Mapping[str, Any], + window_start: datetime | None, +) -> None: + snapshots = [ + item + for item in _list(evidence.get("frozen_warehouse_configs")) + if isinstance(item, Mapping) + ] + targets = _mapping(evidence.get("targets")) + expected_ids = { + str(value) + for value in ( + targets.get("rt_warehouse_id"), + targets.get("control_warehouse_id"), + ) + if value not in (None, "") + } + actual_ids = { + str(item.get("warehouse_id")) + for item in snapshots + if item.get("warehouse_id") not in (None, "") + } + reasons: list[str] = [] + if len(expected_ids) != 2 or actual_ids != expected_ids or len(snapshots) != 2: + reasons.append("frozen RT/control warehouse snapshots are missing or mismatched") + changed_during_window: list[str] = [] + for item in snapshots: + changed = item.get("change_time") + try: + changed_at = parse_utc_timestamp(changed) + except (TypeError, ValueError, OverflowError, OSError): + changed_at = None + if changed_at is None or window_start is None or changed_at > window_start: + changed_during_window.append(str(item.get("warehouse_id"))) + delete_time = item.get("delete_time") + if delete_time not in (None, ""): + try: + if window_start is None or parse_utc_timestamp(delete_time) >= window_start: + changed_during_window.append(str(item.get("warehouse_id"))) + except (TypeError, ValueError, OverflowError, OSError): + changed_during_window.append(str(item.get("warehouse_id"))) + if changed_during_window: + reasons.append("warehouse change/delete evidence is not frozen before the window") + runner_configs: list[str] = [] + missing_runner_configs: list[str] = [] + for workload, rows in (("dashboard", dashboard), ("drilldown", drilldown)): + accepted = _list(_nested(query_reports, workload, "accepted_indices")) + for index in accepted: + if not isinstance(index, int) or not 0 <= index < len(rows): + continue + config = rows[index].get("rt_warehouse") + if not isinstance(config, Mapping): + missing_runner_configs.append(f"{workload}:{index}") + else: + runner_configs.append( + json.dumps( + _stable_warehouse_config(config), + separators=(",", ":"), + sort_keys=True, + ) + ) + if missing_runner_configs: + reasons.append("accepted query samples omit frozen RT warehouse configuration") + if runner_configs and len(set(runner_configs)) != 1: + reasons.append("RT warehouse configuration changed between accepted samples") + gates.add( + "warehouse_configuration", + not reasons, + { + "expected_warehouse_ids": sorted(expected_ids), + "snapshot_warehouse_ids": sorted(actual_ids), + "changed_or_unproven_warehouse_ids": sorted(set(changed_during_window)), + "accepted_runner_config_count": len(runner_configs), + "accepted_runner_config_sha256": sorted( + { + hashlib.sha256(value.encode("utf-8")).hexdigest() + for value in runner_configs + } + ), + "missing_runner_configs": missing_runner_configs, + }, + reasons, + ) + + +def _all_amounts_zero(total: Any) -> bool: + if not isinstance(total, Mapping): + return False + values = _mapping(total.get("amount_by_currency")) + amount = total.get("amount") + candidates = list(values.values()) + if amount is not None: + candidates.append(amount) + return bool(candidates or values == {}) and all( + _decimal(value) == 0 for value in candidates + ) + + +def _gate_cost(gates: Gates, cost: Mapping[str, Any], errors: Sequence[str]) -> None: + reasons = list(errors) + completeness = _mapping(cost.get("completeness")) + canonical = cost.get("canonical_undiscounted_primary_total") + canonical_map = _mapping(canonical) + canonical_amount = _decimal(canonical_map.get("amount")) + fresh_path = _mapping(cost.get("canonical_undiscounted_fresh_path_total")) + query_serving = _mapping( + cost.get("canonical_undiscounted_query_serving_total") + ) + fresh_path_amount = _decimal(fresh_path.get("amount")) + query_serving_amount = _decimal(query_serving.get("amount")) + if cost.get("complete") is not True or completeness.get("complete") is not True: + reasons.append("cost summary completeness is not exact true") + if canonical_amount is None: + reasons.append("canonical undiscounted primary total is missing") + if fresh_path_amount is None or query_serving_amount is None: + reasons.append("fresh-path or measured-query cost subledger is missing") + elif ( + canonical_amount is None + or fresh_path.get("currency") != canonical_map.get("currency") + or query_serving.get("currency") != canonical_map.get("currency") + or fresh_path_amount + query_serving_amount != canonical_amount + ): + reasons.append("fresh-path plus measured-query cost does not reconcile") + if not _all_amounts_zero(cost.get("unknown_total")): + reasons.append("unknown cost total is missing or nonzero") + unpriced = _mapping(cost.get("unpriced_total")) + unpriced_rows = _list(unpriced.get("usage_row_indices")) + quantities = _mapping(unpriced.get("usage_quantity_by_unit")) + if unpriced_rows or any(_decimal(value) != 0 for value in quantities.values()): + reasons.append("unpriced usage is nonzero") + scenario = _mapping(cost.get("lakehouse_rt_beta_30_percent_off_scenario")) + if ( + scenario.get("discount_rate") != "0.30" + or scenario.get("applies_only_to") != "primary_rt_serving_compute" + or scenario.get("canonical_total_unchanged") is not True + or scenario.get("canonical_undiscounted_primary_total") != canonical + ): + reasons.append("30%-off RT Beta scenario is missing, merged, or changes canonical") + gates.add( + "cost_completeness", + not reasons, + { + "complete": cost.get("complete"), + "completeness": completeness, + "canonical_undiscounted_primary_total": canonical, + "canonical_undiscounted_fresh_path_total": fresh_path, + "canonical_undiscounted_query_serving_total": query_serving, + "unknown_total": cost.get("unknown_total"), + "unpriced_total": unpriced, + "beta_scenario": scenario, + }, + reasons, + ) + + predictive = _mapping(cost.get("predictive_optimization")) + predictive_reasons: list[str] = [] + if _integer(predictive.get("failed_operation_count")) != 0: + predictive_reasons.append( + "predictive optimization failure count is missing or nonzero" + ) + if not isinstance(predictive.get("failed_operation_row_indices"), list): + predictive_reasons.append("predictive optimization failure evidence is missing") + gates.add( + "predictive_optimization", + not predictive_reasons, + { + "operation_count": predictive.get("operation_count"), + "failed_operation_count": predictive.get("failed_operation_count"), + "failed_operation_row_indices": predictive.get( + "failed_operation_row_indices" + ), + }, + predictive_reasons, + ) + + +def build_validation_report( + *, + source_manifest: Mapping[str, Any], + ingest_progress: Mapping[str, Any], + ingest_metrics: Sequence[Mapping[str, Any]], + dashboard: Sequence[Mapping[str, Any]], + drilldown: Sequence[Mapping[str, Any]], + freshness: Sequence[Mapping[str, Any]], + evidence_summary: Mapping[str, Any], + cost_summary: Mapping[str, Any], + input_errors: Mapping[str, Sequence[str]] | None = None, + expected_rows: int = EXPECTED_ROWS, + eps_min: float = 900_000.0, + eps_max: float = 1_100_000.0, + min_interval_pass_fraction: float = 0.90, + max_raw_age_seconds: float = 120.0, + max_mv_age_seconds: float = 300.0, + freshness_startup_grace_seconds: float = 120.0, + cadence_tolerance_seconds: float = 5.0, + input_paths: Mapping[str, Any] | None = None, +) -> dict[str, Any]: + errors = {key: list(value) for key, value in (input_errors or {}).items()} + gates = Gates() + all_input_errors = [ + error for values in errors.values() for error in values + ] + gates.add( + "input_availability", + not all_input_errors, + { + "paths": dict(input_paths or {}), + "errors_by_input": errors, + }, + all_input_errors, + ) + window = _mapping(evidence_summary.get("collection_window")) + try: + window_start = parse_utc_timestamp(window.get("since")) + window_end = parse_utc_timestamp(window.get("until")) + if window_end <= window_start: + raise ValueError("window is empty") + normalized_window: dict[str, Any] = { + "since": iso_utc(window_start), + "until": iso_utc(window_end), + "duration_seconds": (window_end - window_start).total_seconds(), + } + except (TypeError, ValueError, OverflowError, OSError) as exc: + window_start = window_end = None + normalized_window = { + "since": window.get("since"), + "until": window.get("until"), + "error": f"{type(exc).__name__}: {exc}", + } + try: + producer_finished_at = parse_utc_timestamp( + ingest_progress.get("finished_at") + ) + except (TypeError, ValueError, OverflowError, OSError): + producer_finished_at = None + freshness_window_end = ( + min(window_end, producer_finished_at) + if window_end is not None and producer_finished_at is not None + else window_end + ) + _gate_source_manifest( + gates, + source_manifest, + errors.get("source_manifest", ()), + expected_rows, + ) + _gate_producer( + gates, + ingest_progress, + source_manifest, + errors.get("ingest_progress", ()), + expected_rows, + ) + _gate_ingest_rate( + gates, + ingest_progress, + ingest_metrics, + errors.get("ingest_metrics", ()), + eps_min=eps_min, + eps_max=eps_max, + min_fraction=min_interval_pass_fraction, + ) + query_reports = _gate_query_workloads( + gates, + dashboard, + errors.get("dashboard", ()), + drilldown, + errors.get("drilldown", ()), + expected_rows=expected_rows, + cadence_tolerance=cadence_tolerance_seconds, + ) + freshness_report = _gate_freshness( + gates, + freshness, + errors.get("freshness", ()), + window_start=window_start, + window_end=freshness_window_end, + startup_grace=freshness_startup_grace_seconds, + max_raw_age=max_raw_age_seconds, + max_mv_age=max_mv_age_seconds, + ) + _gate_reconciliation( + gates, + source_manifest, + ingest_progress, + evidence_summary, + errors.get("evidence_summary", ()), + expected_rows, + ) + _gate_evidence( + gates, + evidence_summary, + errors.get("evidence_summary", ()), + ) + _gate_warehouse_configs( + gates, + evidence_summary, + dashboard, + drilldown, + query_reports, + window_start, + ) + _gate_cost(gates, cost_summary, errors.get("cost_summary", ())) + accepted = all(gate["passed"] for gate in gates.values) + return { + "schema_version": 1, + "accepted": accepted, + "window": normalized_window, + "configuration": { + "expected_rows": expected_rows, + "eps_bounds": {"minimum": eps_min, "maximum": eps_max}, + "minimum_interval_pass_fraction": min_interval_pass_fraction, + "maximum_raw_observational_age_seconds": max_raw_age_seconds, + "maximum_mv_observational_age_seconds": max_mv_age_seconds, + "freshness_startup_grace_seconds": freshness_startup_grace_seconds, + "cadence_tolerance_seconds": cadence_tolerance_seconds, + "freshness_age_semantics": ( + "Observational timestamp proxies only; not exact per-record lag." + ), + }, + "gates": gates.values, + "query_samples": query_reports, + "freshness_samples": freshness_report, + } + + +def validate_files( + *, + source_manifest_path: Path, + ingest_progress_path: Path, + ingest_metrics_path: Path, + dashboard_path: Path, + drilldown_path: Path, + freshness_path: Path, + evidence_summary_path: Path, + cost_summary_path: Path, + **configuration: Any, +) -> dict[str, Any]: + source_manifest, source_errors = _read_json( + source_manifest_path, "source_manifest" + ) + ingest_progress, progress_errors = _read_json( + ingest_progress_path, "ingest_progress" + ) + ingest_metrics, metrics_errors = _read_jsonl( + ingest_metrics_path, "ingest_metrics" + ) + dashboard, dashboard_errors = _read_jsonl(dashboard_path, "dashboard") + drilldown, drilldown_errors = _read_jsonl(drilldown_path, "drilldown") + freshness, freshness_errors = _read_jsonl(freshness_path, "freshness") + evidence_summary, evidence_errors = _read_json( + evidence_summary_path, "evidence_summary" + ) + cost_summary, cost_errors = _read_json(cost_summary_path, "cost_summary") + paths = { + "source_manifest": str(source_manifest_path), + "ingest_progress": str(ingest_progress_path), + "ingest_metrics": str(ingest_metrics_path), + "dashboard": str(dashboard_path), + "drilldown": str(drilldown_path), + "freshness": str(freshness_path), + "evidence_summary": str(evidence_summary_path), + "cost_summary": str(cost_summary_path), + } + return build_validation_report( + source_manifest=source_manifest, + ingest_progress=ingest_progress, + ingest_metrics=ingest_metrics, + dashboard=dashboard, + drilldown=drilldown, + freshness=freshness, + evidence_summary=evidence_summary, + cost_summary=cost_summary, + input_errors={ + "source_manifest": source_errors, + "ingest_progress": progress_errors, + "ingest_metrics": metrics_errors, + "dashboard": dashboard_errors, + "drilldown": drilldown_errors, + "freshness": freshness_errors, + "evidence_summary": evidence_errors, + "cost_summary": cost_errors, + }, + input_paths=paths, + **configuration, + ) + + +def _positive_float(value: str) -> float: + parsed = float(value) + if not math.isfinite(parsed) or parsed <= 0: + raise argparse.ArgumentTypeError("must be a finite positive number") + return parsed + + +def _fraction(value: str) -> float: + parsed = float(value) + if not math.isfinite(parsed) or not 0 <= parsed <= 1: + raise argparse.ArgumentTypeError("must be between zero and one") + return parsed + + +def build_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description="Validate an offline Databricks benchmark evidence ledger" + ) + parser.add_argument("--source-manifest", type=Path, required=True) + parser.add_argument("--ingest-progress", type=Path, required=True) + parser.add_argument("--ingest-metrics", type=Path, required=True) + parser.add_argument("--dashboard", type=Path, required=True) + parser.add_argument("--drilldown", type=Path, required=True) + parser.add_argument("--freshness", type=Path, required=True) + parser.add_argument("--evidence-summary", type=Path, required=True) + parser.add_argument("--cost-summary", type=Path, required=True) + parser.add_argument("--output", type=Path, required=True) + parser.add_argument("--expected-rows", type=int, default=EXPECTED_ROWS) + parser.add_argument("--eps-min", "--min-eps", type=_positive_float, default=900_000) + parser.add_argument("--eps-max", "--max-eps", type=_positive_float, default=1_100_000) + parser.add_argument( + "--min-interval-pass-fraction", type=_fraction, default=0.90 + ) + parser.add_argument( + "--max-raw-age-seconds", + "--max-raw-observational-age-seconds", + type=_positive_float, + default=120, + ) + parser.add_argument( + "--max-mv-age-seconds", + "--max-mv-observational-age-seconds", + type=_positive_float, + default=300, + ) + parser.add_argument( + "--freshness-startup-grace-seconds", + type=float, + default=120, + ) + parser.add_argument( + "--cadence-tolerance-seconds", type=_positive_float, default=5 + ) + return parser + + +def run(argv: Sequence[str] | None = None) -> int: + parser = build_parser() + args = parser.parse_args(argv) + if args.expected_rows <= 0: + parser.error("--expected-rows must be positive") + if args.eps_max < args.eps_min: + parser.error("--eps-max must be greater than or equal to --eps-min") + if ( + not math.isfinite(args.freshness_startup_grace_seconds) + or args.freshness_startup_grace_seconds < 0 + ): + parser.error("--freshness-startup-grace-seconds must be finite and non-negative") + report = validate_files( + source_manifest_path=args.source_manifest.expanduser().resolve(), + ingest_progress_path=args.ingest_progress.expanduser().resolve(), + ingest_metrics_path=args.ingest_metrics.expanduser().resolve(), + dashboard_path=args.dashboard.expanduser().resolve(), + drilldown_path=args.drilldown.expanduser().resolve(), + freshness_path=args.freshness.expanduser().resolve(), + evidence_summary_path=args.evidence_summary.expanduser().resolve(), + cost_summary_path=args.cost_summary.expanduser().resolve(), + expected_rows=args.expected_rows, + eps_min=args.eps_min, + eps_max=args.eps_max, + min_interval_pass_fraction=args.min_interval_pass_fraction, + max_raw_age_seconds=args.max_raw_age_seconds, + max_mv_age_seconds=args.max_mv_age_seconds, + freshness_startup_grace_seconds=args.freshness_startup_grace_seconds, + cadence_tolerance_seconds=args.cadence_tolerance_seconds, + ) + output = args.output.expanduser().resolve() + if output.exists() and output.is_dir(): + output = output / "validation_report.json" + atomic_write_json(output, report) + print(f"Validation report written to {output}") + return 0 if report["accepted"] else 1 + + +def main(argv: Sequence[str] | None = None) -> int: + try: + return run(argv) + except KeyboardInterrupt: + return 130 + except Exception as exc: + print(f"FATAL: {type(exc).__name__}: {exc}", file=sys.stderr) + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/full-path-realtime/quotes/databricks/visualizations/README.md b/full-path-realtime/quotes/databricks/visualizations/README.md new file mode 100644 index 0000000..a6af7a7 --- /dev/null +++ b/full-path-realtime/quotes/databricks/visualizations/README.md @@ -0,0 +1,39 @@ +# Databricks publication charts + +`plot_publication.py` renders pairwise fresh-path cost, full-path +cost-performance, and matched query-latency PNGs from a publisher-produced +`publication_manifest.json`. + +The renderer verifies the manifest's exact accepted status and every declared +source and artifact SHA-256 before importing Matplotlib or drawing a chart: + +```bash +uv run plot_publication.py \ + --publication-manifest ../out/publication_manifest.json \ + --output-dir ../out/charts +``` + +The renderer creates no benchmark data. Run `../publish_results.py` only after +the validator and cost summarizer have accepted complete real-run evidence. + +## Preliminary MV freshness characterization + +`plot_freshness_characterization.py` reconstructs the qualification MV's +source-row watermark from provider metadata. It pairs each completed pipeline +`update_id` with +`planning_information.source_table_information.num_rows`, then aligns that +watermark with the monotonic provider-acknowledged producer timeline. + +```bash +uv run plot_freshness_characterization.py \ + --ingest-metrics ../results/qualification/RUN/ingest/ingest_metrics.jsonl \ + --ingest-summary ../results/qualification/RUN/ingest/ingest_summary.json \ + --freshness ../results/qualification/RUN/freshness-recovered/freshness.jsonl \ + --output-dir ../results/qualification/RUN/charts +``` + +It emits PNG and SVG plots, the plotted CSV, and a JSON methodology/provenance +summary with source SHA-256 hashes. The plot reports rows behind and a +metadata-derived time bound; it does not claim event-time lag or query raw/MV +contents. Preliminary characterization artifacts are not accepted publication +results. diff --git a/full-path-realtime/quotes/databricks/visualizations/plot_freshness_characterization.py b/full-path-realtime/quotes/databricks/visualizations/plot_freshness_characterization.py new file mode 100644 index 0000000..e737a1e --- /dev/null +++ b/full-path-realtime/quotes/databricks/visualizations/plot_freshness_characterization.py @@ -0,0 +1,488 @@ +#!/usr/bin/env python3 +# /// script +# requires-python = ">=3.10" +# dependencies = ["matplotlib==3.10.9"] +# /// +"""Plot metadata-derived Databricks MV freshness against committed raw rows.""" + +from __future__ import annotations + +import argparse +import bisect +import csv +import hashlib +import json +from datetime import datetime, timezone +from pathlib import Path +from typing import Any, Mapping + +import matplotlib.pyplot as plt +from matplotlib.ticker import FuncFormatter + +BACKGROUND = "#2B2B2B" +GRID = "#4A4A4A" +MUTED = "#A0A0A0" +WHITE = "#FFFFFF" +RED = "#FF3621" +YELLOW = "#FDFF62" +BLUE = "#5DADE2" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--ingest-metrics", type=Path, required=True) + parser.add_argument("--ingest-summary", type=Path, required=True) + parser.add_argument("--freshness", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--dpi", type=int, default=220) + return parser.parse_args() + + +def timestamp(value: str) -> float: + parsed = datetime.fromisoformat(value.replace("Z", "+00:00")) + if parsed.tzinfo is None: + parsed = parsed.replace(tzinfo=timezone.utc) + return parsed.timestamp() + + +def iso(value: float) -> str: + return datetime.fromtimestamp(value, timezone.utc).isoformat( + timespec="milliseconds" + ).replace("+00:00", "Z") + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def load_json(path: Path) -> dict[str, Any]: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise ValueError(f"expected JSON object: {path}") + return value + + +def load_jsonl(path: Path) -> list[dict[str, Any]]: + rows = [ + json.loads(line) + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + if not rows or not all(isinstance(row, dict) for row in rows): + raise ValueError(f"expected nonempty JSON objects: {path}") + return rows + + +def update_id(event: Mapping[str, Any]) -> str | None: + origin = event.get("origin") + value = origin.get("update_id") if isinstance(origin, Mapping) else None + return str(value) if value else None + + +def chosen_maintenance(event: Mapping[str, Any]) -> str | None: + details = event.get("details") + planning = ( + details.get("planning_information") if isinstance(details, Mapping) else None + ) + techniques = ( + planning.get("technique_information") + if isinstance(planning, Mapping) + else None + ) + if not isinstance(techniques, list): + return None + for technique in techniques: + if isinstance(technique, Mapping) and technique.get("is_chosen") is True: + value = technique.get("maintenance_type") + return str(value) if value else None + return None + + +def source_rows(event: Mapping[str, Any]) -> int | None: + details = event.get("details") + planning = ( + details.get("planning_information") if isinstance(details, Mapping) else None + ) + sources = ( + planning.get("source_table_information") + if isinstance(planning, Mapping) + else None + ) + if not isinstance(sources, list) or len(sources) != 1: + return None + source = sources[0] + value = source.get("num_rows") if isinstance(source, Mapping) else None + return int(value) if value is not None else None + + +def completed_updates(events: list[dict[str, Any]]) -> list[dict[str, Any]]: + plans = { + identifier: event + for event in events + if event.get("event_type") == "planning_information" + and (identifier := update_id(event)) + } + updates: list[dict[str, Any]] = [] + for event in events: + if event.get("event_type") != "update_progress": + continue + details = event.get("details") + progress = ( + details.get("update_progress") if isinstance(details, Mapping) else None + ) + if not isinstance(progress, Mapping) or progress.get("state") != "COMPLETED": + continue + identifier = update_id(event) + plan = plans.get(identifier or "") + if plan is None: + continue + rows = source_rows(plan) + maintenance = chosen_maintenance(plan) + if rows is None or maintenance is None: + continue + updates.append( + { + "update_id": identifier, + "completed_at": str(event["timestamp"]), + "completed_ts": timestamp(str(event["timestamp"])), + "planned_at": str(plan["timestamp"]), + "planned_ts": timestamp(str(plan["timestamp"])), + "source_rows": rows, + "maintenance_type": maintenance, + "incremental": maintenance == "MAINTENANCE_TYPE_GROUP_AGGREGATE", + "no_op": maintenance == "MAINTENANCE_TYPE_NO_OP", + } + ) + return sorted(updates, key=lambda item: item["completed_ts"]) + + +def interpolate_y(points: list[tuple[float, int]], when: float) -> int: + times = [point[0] for point in points] + if when <= times[0]: + return points[0][1] + if when >= times[-1]: + return points[-1][1] + right = bisect.bisect_right(times, when) + t0, y0 = points[right - 1] + t1, y1 = points[right] + return round(y0 + (y1 - y0) * ((when - t0) / (t1 - t0))) + + +def interpolate_x(points: list[tuple[float, int]], rows: int) -> float: + counts = [point[1] for point in points] + if rows <= counts[0]: + return points[0][0] + if rows >= counts[-1]: + return points[-1][0] + right = bisect.bisect_left(counts, rows) + t0, y0 = points[right - 1] + t1, y1 = points[right] + return t0 + (t1 - t0) * ((rows - y0) / (y1 - y0)) + + +def main() -> int: + args = parse_args() + metrics_path = args.ingest_metrics.expanduser().resolve() + summary_path = args.ingest_summary.expanduser().resolve() + freshness_path = args.freshness.expanduser().resolve() + output = args.output_dir.expanduser().resolve() + output.mkdir(parents=True, exist_ok=True) + + metrics = load_jsonl(metrics_path) + summary = load_json(summary_path) + freshness = load_jsonl(freshness_path)[-1] + events = freshness["event_log"]["bounded_rows"] + updates = completed_updates(events) + if not updates or not any(update["incremental"] for update in updates): + raise ValueError("freshness evidence has no completed incremental updates") + + start_ts = timestamp(str(summary["started_at"])) + final_rows = int(summary["provider_committed_rows"]) + final_commit_ts = timestamp( + str(freshness["zerobus_ingest"]["latest_commit_time"]) + ) + timeline = [(start_ts, 0)] + timeline.extend( + (timestamp(str(row["updated_at"])), int(row["provider_committed_rows"])) + for row in metrics + ) + timeline.append((final_commit_ts, final_rows)) + timeline.sort() + + # Collapse duplicate timestamps and enforce monotonic committed progress. + collapsed: list[tuple[float, int]] = [] + for when, rows in timeline: + rows = max(rows, collapsed[-1][1] if collapsed else 0) + if collapsed and when == collapsed[-1][0]: + collapsed[-1] = (when, max(rows, collapsed[-1][1])) + else: + collapsed.append((when, rows)) + timeline = collapsed + + sample_times = {point[0] for point in timeline} + for update in updates: + sample_times.add(update["completed_ts"] - 0.001) + sample_times.add(update["completed_ts"]) + sample_times = sorted(sample_times) + + points: list[dict[str, Any]] = [] + for when in sample_times: + raw_rows = interpolate_y(timeline, when) + completed = [ + update for update in updates if update["completed_ts"] <= when + ] + latest = completed[-1] if completed else None + watermark = int(latest["source_rows"]) if latest else 0 + row_lag = max(0, raw_rows - watermark) + if row_lag == 0: + lag_seconds = 0.0 + else: + watermark_ts = ( + interpolate_x(timeline, watermark) if watermark else start_ts + ) + lag_seconds = max(0.0, when - watermark_ts) + points.append( + { + "observed_at": iso(when), + "raw_committed_rows": raw_rows, + "mv_source_rows": watermark, + "rows_behind": row_lag, + "metadata_lag_seconds": lag_seconds, + "latest_completed_update": latest["update_id"] if latest else None, + "latest_maintenance_type": ( + latest["maintenance_type"] if latest else None + ), + } + ) + + active_points = [ + point + for point in points + if timestamp(point["observed_at"]) <= final_commit_ts + ] + final_incremental = next( + ( + update + for update in updates + if update["incremental"] and update["completed_ts"] >= final_commit_ts + ), + None, + ) + confirming_noop = next( + ( + update + for update in updates + if update["no_op"] + and final_incremental is not None + and update["completed_ts"] > final_incremental["completed_ts"] + ), + None, + ) + final_drain = ( + final_incremental["completed_ts"] - final_commit_ts + if final_incremental + else None + ) + proof_bound = ( + confirming_noop["completed_ts"] - final_commit_ts + if confirming_noop + else None + ) + + x = [point["raw_committed_rows"] / 1_000_000 for point in points] + row_lag = [point["rows_behind"] / 1_000_000 for point in points] + lag_seconds = [point["metadata_lag_seconds"] for point in points] + + plt.rcParams["font.family"] = "DejaVu Sans" + figure, axes = plt.subplots( + 2, + 1, + figsize=(12.8, 8.4), + sharex=True, + gridspec_kw={"height_ratios": [1, 1]}, + ) + figure.patch.set_facecolor(BACKGROUND) + for axis in axes: + axis.set_facecolor(BACKGROUND) + axis.grid(True, color=GRID, linewidth=0.7, alpha=0.65) + axis.tick_params(colors=WHITE) + for spine in axis.spines.values(): + spine.set_color(GRID) + + axes[0].plot(x, row_lag, color=RED, linewidth=2.2) + axes[0].fill_between(x, row_lag, color=RED, alpha=0.18) + axes[0].set_ylabel("Rows behind (millions)", color=WHITE) + + axes[1].plot(x, lag_seconds, color=BLUE, linewidth=2.2) + axes[1].fill_between(x, lag_seconds, color=BLUE, alpha=0.14) + axes[1].axhline(60, color=YELLOW, linestyle="--", linewidth=1.2) + axes[1].text( + 5, + 64, + "1-minute trigger spacing (not an SLA)", + color=YELLOW, + fontsize=9, + ) + axes[1].set_ylabel("Metadata-derived lag (minutes)", color=WHITE) + axes[1].set_xlabel("Provider-committed raw rows (millions)", color=WHITE) + + incremental_number = 0 + for update in updates: + completed_rows = interpolate_y(timeline, update["completed_ts"]) / 1_000_000 + if update["incremental"]: + incremental_number += 1 + label = ( + f"Incremental {incremental_number}\n" + f"MV snapshot {update['source_rows'] / 1_000_000:.1f}M" + ) + color = YELLOW + offset = ( + (-8, 48, "right") + if completed_rows > final_rows / 1_000_000 * 0.95 + else (0, 12 + 18 * (incremental_number % 2), "center") + ) + else: + label = "No-op confirmation" + color = MUTED + offset = (8, 14, "left") + for axis in axes: + axis.axvline( + completed_rows, + color=color, + linestyle=":", + linewidth=1.0, + alpha=0.85, + ) + axes[0].annotate( + label, + xy=(completed_rows, 0), + xytext=(offset[0], offset[1]), + textcoords="offset points", + color=color, + fontsize=8, + ha=offset[2], + va="bottom", + ) + + axes[0].yaxis.set_major_formatter(FuncFormatter(lambda value, _: f"{value:.0f}M")) + axes[1].yaxis.set_major_formatter( + FuncFormatter(lambda value, _: f"{value / 60:.1f}m") + ) + axes[1].xaxis.set_major_formatter(FuncFormatter(lambda value, _: f"{value:.0f}M")) + + title = "Databricks MV freshness during sustained 1M-EPS ingestion" + subtitle = ( + "Metadata-only reconstruction · MV watermark from " + "planning_information.source_table_information.num_rows" + ) + figure.suptitle(title, color=WHITE, fontsize=17, fontweight="bold", y=0.985) + figure.text(0.5, 0.945, subtitle, color=MUTED, fontsize=10, ha="center") + note = ( + f"600M rows · {float(summary['eps']['average_provider_committed_rows_per_sec']):,.0f} " + f"average EPS · peak observed lag " + f"{max(point['metadata_lag_seconds'] for point in points) / 60:.2f}m · " + f"final-drain bound {final_drain:.1f}s" + if final_drain is not None + else "Final-drain bound unavailable" + ) + figure.text(0.5, 0.915, note, color=WHITE, fontsize=10, ha="center") + figure.text( + 0.01, + 0.012, + "Lag is reconstructed from provider metadata; it is not event-time lag. " + "A later no-op proves complete catch-up.", + color=MUTED, + fontsize=8.5, + ) + figure.tight_layout(rect=(0.02, 0.04, 0.99, 0.90)) + + png = output / "databricks_mv_freshness_vs_rows.png" + svg = output / "databricks_mv_freshness_vs_rows.svg" + figure.savefig(png, dpi=args.dpi, facecolor=BACKGROUND, bbox_inches="tight") + figure.savefig(svg, facecolor=BACKGROUND, bbox_inches="tight") + plt.close(figure) + + csv_path = output / "databricks_mv_freshness_vs_rows.csv" + with csv_path.open("w", newline="", encoding="utf-8") as handle: + writer = csv.DictWriter(handle, fieldnames=list(points[0])) + writer.writeheader() + writer.writerows(points) + + summary_output = { + "schema_version": 1, + "status": "preliminary_characterization", + "chart": "databricks_mv_freshness_vs_committed_rows", + "method": { + "raw_progress": "producer provider-acknowledged row timeline", + "mv_watermark": ( + "event_log planning_information.source_table_information.num_rows " + "paired with completed update_id" + ), + "lag_seconds": ( + "elapsed time since the committed-row timeline reached the latest " + "completed MV source-row watermark; zero when row lag is zero" + ), + "limitation": ( + "provider event log exposes source row count but no source Delta " + "version; this is a metadata-derived bound, not per-row event-time lag" + ), + }, + "run_id": summary["run_id"], + "provider_committed_rows": final_rows, + "average_provider_committed_eps": float( + summary["eps"]["average_provider_committed_rows_per_sec"] + ), + "completed_updates": updates, + "incremental_update_count": sum( + 1 for update in updates if update["incremental"] + ), + "no_op_update_count": sum(1 for update in updates if update["no_op"]), + "maximum_active_rows_behind": max( + point["rows_behind"] for point in active_points + ), + "maximum_active_metadata_lag_seconds": max( + point["metadata_lag_seconds"] for point in active_points + ), + "maximum_observed_metadata_lag_seconds": max( + point["metadata_lag_seconds"] for point in points + ), + "final_raw_commit_at": iso(final_commit_ts), + "final_drain_upper_bound_seconds": final_drain, + "subsequent_no_op_proof_bound_seconds": proof_bound, + "sources": { + "ingest_metrics": { + "path": str(metrics_path), + "sha256": sha256(metrics_path), + }, + "ingest_summary": { + "path": str(summary_path), + "sha256": sha256(summary_path), + }, + "freshness": { + "path": str(freshness_path), + "sha256": sha256(freshness_path), + }, + }, + "artifacts": { + "png": str(png), + "svg": str(svg), + "csv": str(csv_path), + }, + } + summary_json = output / "databricks_mv_freshness_vs_rows_summary.json" + summary_json.write_text( + json.dumps(summary_output, indent=2) + "\n", encoding="utf-8" + ) + print(png) + print(svg) + print(csv_path) + print(summary_json) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/full-path-realtime/quotes/databricks/visualizations/plot_publication.py b/full-path-realtime/quotes/databricks/visualizations/plot_publication.py new file mode 100644 index 0000000..ce9685b --- /dev/null +++ b/full-path-realtime/quotes/databricks/visualizations/plot_publication.py @@ -0,0 +1,246 @@ +#!/usr/bin/env python3 +# /// script +# requires-python = ">=3.10" +# dependencies = ["matplotlib==3.10.9"] +# /// +"""Render cost and latency charts from one accepted publication manifest.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import math +from pathlib import Path +from typing import Any, Mapping, Sequence + +SCRIPT_DIR = Path(__file__).resolve().parent +BENCHMARK_ROOT = SCRIPT_DIR.parents[2] +REQUIRED_ARTIFACTS = ( + "accepted_dashboard.jsonl", + "accepted_drilldown.jsonl", + "matched_clickhouse_dashboard.jsonl", + "matched_clickhouse_drilldown.jsonl", + "ingest_fresh_path_cost_clickhouse_vs_databricks_summary.json", + "full_path_cost_performance_clickhouse_vs_databricks_summary.json", +) + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as handle: + for block in iter(lambda: handle.read(1024 * 1024), b""): + digest.update(block) + return digest.hexdigest() + + +def _resolve_source(value: str, publication_path: Path, *, artifact: bool) -> Path: + path = Path(value).expanduser() + if path.is_absolute(): + return path.resolve() + root = publication_path.parent if artifact else BENCHMARK_ROOT + return (root / path).resolve() + + +def load_publication(path: Path) -> tuple[dict[str, Any], dict[str, Path]]: + publication_path = path.expanduser().resolve() + payload = json.loads(publication_path.read_text(encoding="utf-8")) + if ( + not isinstance(payload, dict) + or payload.get("schema_version") != 1 + or payload.get("status") != "accepted" + or payload.get("accepted") is not True + ): + raise ValueError("publication status is not accepted") + sources = payload.get("sources") + if not isinstance(sources, dict) or not sources: + raise ValueError("publication has no source provenance") + for name, source in sources.items(): + if not isinstance(source, Mapping): + raise ValueError(f"publication source {name!r} is invalid") + source_path = _resolve_source( + str(source.get("path") or ""), publication_path, artifact=False + ) + expected = source.get("sha256") + if not isinstance(expected, str) or sha256(source_path) != expected: + raise ValueError(f"publication source hash mismatch: {name}") + artifacts = payload.get("artifacts") + if not isinstance(artifacts, dict): + raise ValueError("publication has no artifact provenance") + resolved: dict[str, Path] = {} + for name, artifact in artifacts.items(): + if not isinstance(artifact, Mapping): + raise ValueError(f"publication artifact {name!r} is invalid") + artifact_path = _resolve_source( + str(artifact.get("path") or ""), publication_path, artifact=True + ) + expected = artifact.get("sha256") + if not isinstance(expected, str) or sha256(artifact_path) != expected: + raise ValueError(f"publication artifact hash mismatch: {name}") + resolved[name] = artifact_path + missing = [name for name in REQUIRED_ARTIFACTS if name not in resolved] + if missing: + raise ValueError(f"publication omits artifacts: {missing}") + return payload, resolved + + +def _read_json(path: Path) -> dict[str, Any]: + value = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(value, dict): + raise ValueError(f"expected JSON object at {path}") + return value + + +def _read_jsonl(path: Path) -> list[dict[str, Any]]: + rows = [ + json.loads(line) + for line in path.read_text(encoding="utf-8").splitlines() + if line.strip() + ] + if not rows or not all(isinstance(row, dict) for row in rows): + raise ValueError(f"expected nonempty JSON objects at {path}") + return rows + + +def _accepted_summary(path: Path, chart: str) -> dict[str, Any]: + value = _read_json(path) + if ( + value.get("schema_version") != 1 + or value.get("status") != "accepted" + or value.get("accepted") is not True + or value.get("chart") != chart + ): + raise ValueError(f"unaccepted or incompatible summary at {path}") + return value + + +def _summary_row(summary: Mapping[str, Any], label: str) -> Mapping[str, Any]: + rows = summary.get("rows") + if not isinstance(rows, list): + raise ValueError("performance summary omits rows") + matches = [ + row for row in rows if isinstance(row, Mapping) and row.get("label") == label + ] + if len(matches) != 1: + raise ValueError(f"performance summary does not have one {label!r} row") + return matches[0] + + +def _latencies(rows: Sequence[Mapping[str, Any]]) -> list[float]: + values: list[float] = [] + for row in rows: + result = row.get("result") + if not isinstance(result, list): + raise ValueError("query record omits result") + observations: list[float] = [] + for trial in result: + if ( + not isinstance(trial, list) + or len(trial) != 1 + or isinstance(trial[0], bool) + ): + raise ValueError("query record contains a malformed trial") + value = float(trial[0]) + if not math.isfinite(value) or value < 0: + raise ValueError("query record contains an invalid latency") + observations.append(value) + values.append(sum(observations)) + return values + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--publication-manifest", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + parser.add_argument("--dpi", type=int, default=200) + return parser.parse_args() + + +def main() -> int: + args = parse_args() + _publication, artifacts = load_publication(args.publication_manifest) + fresh = _accepted_summary( + artifacts[ + "ingest_fresh_path_cost_clickhouse_vs_databricks_summary.json" + ], + "complete_ingest_fresh_data_path_cost_clickhouse_vs_databricks", + ) + performance = _accepted_summary( + artifacts[ + "full_path_cost_performance_clickhouse_vs_databricks_summary.json" + ], + "full_path_cost_performance", + ) + output = args.output_dir.expanduser().resolve() + output.mkdir(parents=True, exist_ok=True) + + import matplotlib.pyplot as plt + + colors = {"ClickHouse": "#FDFF62", "Databricks": "#FF3621"} + background = "#161614" + + fresh_values = [ + float(fresh["costs"]["clickhouse"]["total_usd"]), + float(fresh["costs"]["databricks"]["total_usd"]), + ] + fig, axis = plt.subplots(figsize=(8, 4.5)) + fig.patch.set_facecolor(background) + axis.set_facecolor(background) + labels = ["ClickHouse", "Databricks"] + axis.bar(labels, fresh_values, color=[colors[label] for label in labels]) + axis.set_ylabel("Complete-ingest fresh-path cost (USD)", color="white") + axis.tick_params(colors="white") + axis.spines[:].set_color("#555555") + fresh_path = output / "databricks_fresh_path_cost.png" + fig.tight_layout() + fig.savefig(fresh_path, dpi=args.dpi, facecolor=background) + plt.close(fig) + + relatives = [ + float(_summary_row(performance, label)["relative_to_clickhouse"]) + for label in labels + ] + fig, axis = plt.subplots(figsize=(8, 4.5)) + fig.patch.set_facecolor(background) + axis.set_facecolor(background) + axis.barh(labels, relatives, color=[colors[label] for label in labels]) + axis.set_xlabel("Cost-performance score relative to ClickHouse", color="white") + axis.tick_params(colors="white") + axis.spines[:].set_color("#555555") + performance_path = output / "databricks_full_path_cost_performance.png" + fig.tight_layout() + fig.savefig(performance_path, dpi=args.dpi, facecolor=background) + plt.close(fig) + + fig, axes = plt.subplots(1, 2, figsize=(12, 4.5), sharey=False) + fig.patch.set_facecolor(background) + for axis, workload in zip(axes, ("dashboard", "drilldown"), strict=True): + db_rows = _read_jsonl(artifacts[f"accepted_{workload}.jsonl"]) + ch_rows = _read_jsonl(artifacts[f"matched_clickhouse_{workload}.jsonl"]) + for label, rows in (("ClickHouse", ch_rows), ("Databricks", db_rows)): + axis.plot( + [int(row["raw_rows"]) for row in rows], + _latencies(rows), + color=colors[label], + marker="o", + label=label, + ) + axis.set_facecolor(background) + axis.set_title(workload.title(), color="white") + axis.set_xlabel("Observed raw rows", color="white") + axis.set_ylabel("Accumulated iteration latency (seconds)", color="white") + axis.tick_params(colors="white") + axis.grid(True, color="#444444", alpha=0.6) + axis.legend() + latency_path = output / "databricks_query_latency.png" + fig.tight_layout() + fig.savefig(latency_path, dpi=args.dpi, facecolor=background) + plt.close(fig) + + for path in (fresh_path, performance_path, latency_path): + print(f"Written: {path}") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main())