chore: sync spark4.1 with master by ranadeepsingh · Pull Request #2659 · microsoft/SynapseML

Rana Singh (ranadeepsingh) · 2026-08-18T08:34:45Z

What

Syncs master (a6fd536ad7) into spark4.1, which was 91 commits behind.

The headline reason is that spark4.1 still ran the InternalCompat version-drift bug fixed by #2655 — a bug that was originally diagnosed on this branch — and did not have the #2658 lane parallelisation. Before this PR the branch had COMPAT_LANE = 0 and update-index -q --refresh = 0.

Two commits:

The main sync (34 conflicted files, 86 hunks).
A follow-up merge picking up fix: handle empty featurized text features #2629 (empty featurized text features) and fix(core): include reference-only distribution categories #2630 (reference-only distribution categories), which landed on master after the first commit. Both merged cleanly with no conflicts — they touch only core featurize/exploratory/train test code, none of which carries a spark4.1 adaptation.

Why this conflicts so much

spark4.1 receives master by squash merge, so ancestry is broken by construction. 91 commits are "missing", but only 19 files actually differ in content — files synced in #2645 re-conflict here even when the resolution reproduces one side exactly.

34 files / 86 hunks were resolved. Every hunk was decided by comparing merge base vs master vs spark4.1, not by taking a side wholesale.

Kept from spark4.1 (runtime/toolchain identity)

Item	Why
No CMS GC flags in `SBT_OPTS`	`-XX:+UseConcMarkSweepGC` was removed in Java 17. Restoring master's flags makes the JVM refuse to start — failing before any test runs.
Spark 4.1.1, Scala 2.13.17, `onnx-protobuf_2.13`	Branch runtime
JDK 17 in `pipeline.yaml`, `pr-validation.yml`, `java_setup.yml`, both Dockerfiles	Branch toolchain
`environment.yml` pins + intentionally unpinned numpy	master pins `numpy==1.26.4`, which has no Python 3.13 wheels. File is byte-identical to the branch.
SARModel qualified join columns	Spark 4 column resolution
`OpenAIChatCompletion` `scala.collection.Seq`	In 2.13 bare `Seq` is immutable; Spark returns `collection.Seq`
`OpenAIPrompt` zero-arg `super()`	Branch invariant
`PyCodegen` `ManualInitPackageFolders` + `parseConfigArg`	Verified absent at merge base and on master → genuine branch additions
`init_spark.py` without hardcoded `_2.12`	Wrong coordinate for Scala 2.13
R codegen `new_ml_pipeline_stage` assertions	4.1 sparklyr path
DBR `18.0.x-scala2.13` + streaming-notebook partition split	Spark 4 `server.stop()` cancels concurrent jobs
Fabric E2E stays disabled (`condition: false`)	Re-enabling needs real Fabric capacity validation — its own PR, not a sync

Taken from master

InternalCompat: scala/python matrix lanes, lane gating, Key Vault secrets restricted to the scala lane, and templates/fabric_kv.yml parameterised with condition defaulting to succeeded().
fix: stop InternalCompat requesting an Internal version that was never published #2655 retarget fixes: block-scoped Resolver.mavenLocal insert/verify, the retarget commit, and the git update-index refresh. master's version also replaces a hardcoded _2.12 m2 glob with _* — which was specifically wrong on this branch.
ComputeModelStatistics refactor (fix: resolve current model metadata deterministically #2632/fix: validate ComputeModelStatistics input columns #2633/feat(core): add precision-recall AUC metric #2635) + new ComputeModelStatisticsInputValidator and suites.
Featurize changes, OpenAI visibility widening (required so master's new OpenAIFabricHeadersSuite can override it), and the two image-size GHSA dependency-review exceptions.

One resolution worth calling out

spark4.1 had a local classificationMetricRequiresScores helper that master appears to drop. It is not lost — master's new validator carries the identical four metric conditions as classificationUsesScores:

evaluationMetric == MetricConstants.ClassificationMetricsName ||
evaluationMetric == MetricConstants.AucSparkMetric ||
evaluationMetric == MetricConstants.AreaUnderROCMetric ||
evaluationMetric == MetricConstants.AreaUnderPRMetric

A git log on the file initially showed no spark4.1 commits, which would have implied there was nothing branch-specific to preserve. That was history simplification through the squashed sync commits — direct content comparison showed the branch really had diverged (521 → 701 lines). The resolution is based on the content comparison.

Validation — run on this branch, not inferred from master

tools/ci/tests/test_pipeline_yaml.py: 43 passed, 21 skipped (skips are bash-only, on Windows). master reports 40 passed with the same 21 skips — the delta is exactly spark4.1's own three tests, and all 26 master tests are present.
Structural validator: lane gating, both simulated lanes, bash -n on all 12 inline scripts, and the fabric_kv include/parameter checks — all pass. Exactly 2 includes: one parameters=None (Fabric E2E, provably unchanged) and one scala-gated.
Retarget script simulated 3× consecutively against a mock Internal checkout: run 3 reports Retarget produced no changes; tree is already clean, Resolver.mavenLocal stays at exactly one occurrence, dynver stays stable, usage/build.sbt untouched.
57 branch-invariant assertions over the merged tree — 0 failures.

Follow-ups (deliberately not in this PR)

Re-enable Fabric E2E on spark4.1 (needs capacity validation).
pyarrow==18.0.0's inline comment cites pyarrow<19, but mlflow 2.21.3 declares pyarrow<20,>=4.0.0. Pre-existing and comment-only.
Add spark4.0 to ReleaseBranchCompat (currently spark4.1 only).
The same sync for spark4.0.

Add a ReleaseBranchCompat job that runs on every PR to master. It rebases each release branch (starting with spark4.0) onto the PR HEAD and runs sbt compile test:compile to catch breakage before it lands in master. - Non-blocking (continueOnError: true) - Matrix-based for easy expansion to more release branches - Reports merge conflicts and compile failures as warnings Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

…ease demo image (#2557) Addresses MSRC case 110886 / incident 31000000570827. The mmlspark/release image (built from tools/docker/demo/Dockerfile) ships Spark 3.5.4, which pins netty 4.1.96.Final. That version is flagged for multiple CVEs (CVE-2023-44487, CVE-2024-29025, CVE-2025-24970, ...). Spark has not bumped netty in any 3.5.x release. netty 4.1.x is binary-compatible, so we replace all netty-*-4.1.96.Final*.jar files in /opt/spark/jars/ with 4.1.118.Final right after the Spark extract. This includes netty-codec-http2 (the specific artifact named by the finder). Also removes 'pyspark' from the conda install line. It was pulling a complete second Spark install (PySpark 4.0.1) into /usr/local/lib/python*/site-packages/pyspark/ that nothing in the demo image actually used (SPARK_HOME points at /opt/spark) and that doubled the surface area scanners report on. Validated locally: - /opt/spark/jars/netty-*-4.1.96.Final*.jar: 0 matches after build - /opt/spark/jars/netty-*-4.1.118.Final*.jar: full set present - /usr/local/lib/.../pyspark: no longer exists - spark-submit --version: works - spark.range(5).count(): returns 5 Jetty (shaded inside hadoop-client-runtime-3.3.4.jar at 9.4.43) is OUT OF SCOPE for this PR; that requires a Spark/Hadoop swap and will be tracked separately.

* chore: add SynapseML local setup skill ## Summary Add a project-scoped SynapseML agent skill that diagnoses local toolchain state, selects JDK 11 for SBT commands, runs a safe local Spark smoke test, and flags live-service tests before agents run them. ## Prompting Intent The engineer asked the agent to create a skill that helps any future agent get SynapseML working locally after the PR 2556 review exposed a local Java 21 and Scala 2.12 compiler-bridge failure. The engineer also asked to create a PR for the skill addition before continuing the original external PR review. ## Linked Sources - User request in current session: create a skill that will help any agent be able to get SynapseML working locally. - Follow-up user request in current session: create a PR for that skill addition and continue using it to review PR 2556. - Existing project-scoped skill convention: .agents/skills/code-review/SKILL.md. - Local validation output: doctor_status=ok, JDK 11 dry-run selected JAVA_HOME, smoke test passed, Azure Search tests flagged review_required. ## Rationale A project-scoped SynapseML skill keeps local setup guidance with the repository where future agents need it. The scripts use explicit parameters rather than session state, force JDK 11 for Scala 2.12 SBT commands, and include a live-service guard so agents do not accidentally create or delete Azure Search resources while validating changes. * chore: move SynapseML setup skill to Copilot path ## Summary Move the SynapseML local setup skill from `.agents/skills/` to `.github/skills/` so it uses the documented Copilot project-skill discovery path. ## Prompting Intent The engineer asked whether the `.agents` folder was correct and whether Copilot would pick it up. Investigation found that the local skill-authoring reference documents `.github/skills/<name>/` and `.claude/skills/<name>/` as project skill locations, so the open skill PR needed a path correction. ## Linked Sources - User question in current session: is this .agent folder correct? will copilot pick this up? - Skill-authoring reference: /home/brwals/.copilot/installed-plugins/copilot-toolkit-marketplace/common/skills/create-skill/references/REFERENCE.md - Existing PR: #2558 ## Rationale The existing `.agents/skills/code-review` directory was only evidence of a repo-local convention, not evidence of Copilot discovery. Moving the new skill to `.github/skills/synapseml-local-setup/` keeps the same skill content while placing it in the documented project-skill path.

#2560) * Add v1 OpenAI Endpoint support and remove legacy completions API * Fix FuzzingUnitTest * Add test to increase code coverage * Make v1 api assumption cleaner * Add OpenAICompletion deprecation * Remove deprecation warnings * Fix RAI test for OpenAIPrompt * Revert "Add OpenAICompletion deprecation" This reverts commit fa708e2. * Revert "Fix RAI test for OpenAIPrompt" This reverts commit 3ed6044. * Revert "Remove deprecation warnings" This reverts commit 9a40c5c. * Reapply "Remove deprecation warnings" This reverts commit 987484c. * Reapply "Fix RAI test for OpenAIPrompt" This reverts commit f06f1ad. * Reapply "Add OpenAICompletion deprecation" This reverts commit 10715cd.

## Summary Move the remaining SynapseML repo skill from `.agents/skills/` to `.github/skills/` so Copilot CLI can discover all repo-versioned skills from the documented project-skill path. Add README pointers under `.agents/` for tools or agents that inspect the older convention. ## Prompting Intent The engineer asked to migrate everything to the correct Copilot CLI path and suggested keeping a generic agents pointer. The goal was to make existing skills discoverable by Copilot while avoiding future confusion about `.agents/skills`. ## Linked Sources - User request in current session: migrate everything to the correct path for Copilot CLI and keep a generic agents pointer. - Skill location reference: /home/brwals/.copilot/installed-plugins/copilot-toolkit-marketplace/common/skills/create-skill/references/REFERENCE.md - Prior merged skill PR: #2558 ## Rationale `.github/skills/<name>/` is the documented Copilot CLI project-skill location. Keeping only README pointers under `.agents/` preserves a breadcrumb for other agent conventions without leaving duplicate or stale `SKILL.md` files in a path Copilot CLI may not load.

* add speechtotextsdk improvements * Fix ffmpeg output args * add ffmpeg url check * fix: address speech recording review feedback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * test: make OpenAIPrompt RAI test resilient Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Revert "test: make OpenAIPrompt RAI test resilient" This reverts commit fccce86. --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* chore: remove Acrolinx integration config AB#5391146 AB#5391147 ## Summary Remove the retired Acrolinx repository configuration from SynapseML and add the Feature Registry pointer and repo-specific design notes for Feature 5391136. ## Prompting Intent Engineer asked the agent to complete the Acrolinx removal request from the Microsoft Learn authoring tools PM. The repository cleanup needed to remove stale source-controlled Acrolinx state while preserving Feature Registry traceability for the administrative webhook removal and the June 30 contract-expiration risk. ## Linked Sources - ADO Feature: https://msdata.visualstudio.com/A365/_workitems/edit/5391136 - Design Spec task: https://msdata.visualstudio.com/A365/_workitems/edit/5391146 - Deployment task: https://msdata.visualstudio.com/A365/_workitems/edit/5391147 - Feature Registry specs: https://msdata.visualstudio.com/A365/_git/FeatureRegistry?path=/Features/active/5391136 - Teams request: https://teams.microsoft.com/l/message/19:81ff723c-eac9-4b2a-ba9f-844542135555_cc1adbf9-6510-43d6-a849-adba51e66d59@unq.gbl.spaces/1782314980087?context=%7B%22contextType%22%3A%22chat%22%7D - Acrolinx config before cleanup: https://github.com/microsoft/SynapseML/blob/b0fa222cfdde5d0a2cbb2bc6a35630bbb61bc0e3/.acrolinx-config.edn ## Rationale Deleting `.acrolinx-config.edn` is the least invasive source change because the Acrolinx contract is ending and the repo-level webhook was already removed through GitHub administration. Keeping the Feature Registry folder in the repo gives future maintainers a durable pointer to the reason for the cleanup without adding runtime or build behavior. * chore: keep Feature Registry metadata out of SynapseML AB#5391146 AB#5391147 ## Summary Remove the Feature Registry scaffold files from the SynapseML cleanup branch so the public repository PR only deletes the retired Acrolinx config. ## Prompting Intent Engineer clarified that Feature Registry metadata must not be included in the external SynapseML repository. The agent adjusted the existing cleanup PR to keep registry tracking in FeatureRegistry only while preserving the Acrolinx source cleanup. ## Linked Sources - ADO Feature: https://msdata.visualstudio.com/A365/_workitems/edit/5391136 - SynapseML PR: #2570 - FeatureRegistry PR: https://msdata.visualstudio.com/A365/_git/FeatureRegistry/pullrequest/2169703 - User correction: do not include Feature Registry metadata in the external repo ## Rationale Keeping the public SynapseML PR scoped to `.acrolinx-config.edn` avoids adding internal Feature Registry process artifacts to an external repository. Feature-level tracking remains in the FeatureRegistry PR and ADO work items.

* fix: route AnalyzeText document errors to errorCol Move Azure AI Language document-level errors returned inside HTTP 200 AnalyzeText responses from the response payload into the configured error column after auto-batch flattening. Preserve transport error precedence and add a no-network regression test for mixed document success/error responses. AB#4638662 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: pin PR validation sbt launcher Use the sbt launcher version from project/build.properties instead of installing the latest apt sbt package. This keeps the JDK 11 PR validation job on the repository's sbt 1.10.11 launcher and avoids sbt 2.x rejecting JDK 11 before scalastyle can run. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: use pinned sbt wrapper in PR validation Invoke the downloaded sbt launcher explicitly so the GitHub runner does not resolve its preinstalled sbt 2.x binary under JDK 11. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: prefer pinned sbt on PATH Keep PR validation commands as plain sbt while placing the repository-version launcher first on PATH for subsequent workflow steps. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * test: avoid ordering assumption in AnalyzeText error test Partition collected rows by error nullability instead of relying on collect order, addressing PR review feedback about Spark DataFrames being unordered. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

fix: restore Azure pipeline

Pin the shared Python test environment to MLflow 2.21.3, matching the Databricks test dependency. This constrains protobuf to a compatible major version and invalidates the stale conda cache that breaks Python test collection.

test: migrate OpenAI tests and examples to GPT-5.1

ci: migrate Databricks GPU pool to T4

fix: correct LightGBM improvement tolerance semantics

Bumps [amannn/action-semantic-pull-request](https://github.com/amannn/action-semantic-pull-request) from 5.4.0 to 6.1.1. - [Release notes](https://github.com/amannn/action-semantic-pull-request/releases) - [Changelog](https://github.com/amannn/action-semantic-pull-request/blob/main/CHANGELOG.md) - [Commits](amannn/action-semantic-pull-request@v5.4.0...v6.1.1) --- updated-dependencies: - dependency-name: amannn/action-semantic-pull-request dependency-version: 6.1.1 dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: Rana Singh <ranadeep.dtu@gmail.com>

Bumps [ossf/scorecard-action](https://github.com/ossf/scorecard-action) from 2.3.1 to 2.4.4. - [Release notes](https://github.com/ossf/scorecard-action/releases) - [Changelog](https://github.com/ossf/scorecard-action/blob/main/RELEASE.md) - [Commits](ossf/scorecard-action@0864cf1...2d11466) --- updated-dependencies: - dependency-name: ossf/scorecard-action dependency-version: 2.4.4 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

Bumps [actions/setup-java](https://github.com/actions/setup-java) from 5.6.0 to 5.7.0. - [Release notes](https://github.com/actions/setup-java/releases) - [Commits](actions/setup-java@03ad4de...b6effb0) --- updated-dependencies: - dependency-name: actions/setup-java dependency-version: 5.7.0 dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Rana Singh <ranadeep.dtu@gmail.com>

* docs: add T4 GPU local RAG quickstart ## Summary Add an end-to-end local RAG notebook that performs sentence embedding, exact retrieval, and Phi-4-mini generation on a Databricks T4 worker. Register the notebook in the active GPU smoke suite and documentation sidebar with pinned model dependencies. ## Prompting Intent Reassess the unmerged GPU demo from PR #2271 against current master. Add a maintainable integration example only if it fills a gap beyond the standalone GPU KNN, Hugging Face CausalLM/Phi, and PDF Q&A notebooks; use current T4 assumptions, avoid TensorRT-LLM and custom CUDA setup, provide deterministic smoke assertions, and make no unrelated pipeline changes. ## Linked Sources - Original proposal: #2271 - GPU KNN component: #2157 - Local embedding component: #2236 - Hugging Face CausalLM/Phi component: #2301 - Current Databricks T4 validation platform: #2579 - PDF Q&A reference: https://github.com/microsoft/SynapseML/blob/master/docs/Explore%20Algorithms/AI%20Services/Quickstart%20-%20Document%20Question%20and%20Answering%20with%20PDFs.ipynb ## Rationale The existing notebooks document the individual building blocks but not their local, service-free composition. Exact PyTorch cosine scoring keeps the tutorial small and fully testable on the active T4 suite without reviving the disabled RAPIDS pipeline or its obsolete CUDA/TensorRT initialization. The notebook uses supported current-master models, max_new_tokens rather than conflicting sequence limits, and a PR smoke mode that exercises every GPU stage. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * docs: harden GPU RAG reproducibility checks ## Summary Pin both Hugging Face repositories to immutable commit snapshots, load the Phi model and tokenizer from the same local snapshot with remote code disabled, and strengthen retrieval validation against input-order fallback. ## Prompting Intent Address independent review findings on PR #2588 by removing mutable model resolution and trust_remote_code, then make the smoke test prove that GPU similarity ranking—not corpus order—selects the answer document. ## Linked Sources - Follow-up pull request: #2588 - Original proposal: #2271 - Pinned embedding snapshot: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/tree/1110a243fdf4706b3f48f1d95db1a4f5529b4d41 - Pinned Phi snapshot: https://huggingface.co/microsoft/Phi-4-mini-instruct/tree/cfbefacb99257ffa30c83adab238a50856ac3083 ## Rationale SentenceTransformer accepts an immutable revision for its complete model/tokenizer snapshot. HuggingFaceCausalLM loads its tokenizer separately, so Phi is first resolved to one pinned worker-local snapshot and both loaders receive that path. Transformers 4.49 natively supports the checkpoint's phi3 architecture, allowing remote model code to remain disabled. A persisted corpus ordinal and independent Python sort over all GPU scores prove the top-k result differs from the first input rows and has strict score ordering. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * style: format GPU RAG notebook cells ## Summary Apply the repository-pinned Black 22.3 Jupyter formatter to the updated GPU RAG notebook cells. ## Prompting Intent Resolve the Python Style CI failure on PR #2588 without changing notebook behavior or broadening the patch. ## Linked Sources - Pull request: #2588 - Failed Azure build: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229241355 ## Rationale Black's Jupyter formatter omits the terminal newline stored in each code cell. Formatting only the touched notebook aligns its JSON representation with the CI environment while preserving all model-pinning and retrieval assertions. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * docs: pin GPU RAG hub client and FP16 ## Summary Pin huggingface-hub 0.26.0 in the Databricks GPU libraries and notebook setup, verify the Hugging Face dependency set in unit tests, and force Phi model loading to FP16 on T4 hardware. ## Prompting Intent Address the second independent re-review of PR #2588 by making snapshot_download's client version reproducible and preventing Phi's BF16 checkpoint metadata from selecting an unsupported native dtype on T4 GPUs. ## Linked Sources - Pull request: #2588 - Repository environment pin: environment.yml - Hugging Face Hub 0.26.0: https://pypi.org/project/huggingface-hub/0.26.0/ - Pinned Phi configuration: https://huggingface.co/microsoft/Phi-4-mini-instruct/blob/cfbefacb99257ffa30c83adab238a50856ac3083/config.json ## Rationale Version 0.26.0 is already the repository-pinned lower bound used with Transformers 4.49.0, so installing that exact version on the GPU cluster makes snapshot resolution deterministic without introducing a new dependency choice. Phi advertises bfloat16 in its configuration, while NVIDIA T4 compute capability 7.5 lacks native BF16; passing the supported float16 dtype explicitly avoids architecture-dependent auto selection. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * test: guard accelerate GPU dependency pin ## Summary Assert that the Databricks GPU library manifest retains accelerate==0.26.0 alongside the pinned Hugging Face dependencies. ## Prompting Intent Address the remaining actionable review feedback on PR #2588 by preventing the runtime dependency used for distributed Phi loading from drifting without a focused unit-test failure. ## Linked Sources - Pull request: #2588 - Reviewed GPU library manifest: core/src/test/scala/com/microsoft/azure/synapse/ml/nbtest/DatabricksUtilities.scala ## Rationale The package is already explicitly pinned in GPULibraries, so extending the existing parsed-manifest test is the smallest regression guard and avoids duplicating library configuration or changing runtime behavior. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

#2601) * chore(deps): bump github/codeql-action/autobuild from 4.37.3 to 4.37.5 --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: ranadeepsingh <16433904+ranadeepsingh@users.noreply.github.com> Co-authored-by: Rana Singh <ranadeep.dtu@gmail.com>

Bumps [postcss](https://github.com/postcss/postcss) from 8.5.19 to 8.5.25. - [Release notes](https://github.com/postcss/postcss/releases) - [Changelog](https://github.com/postcss/postcss/blob/main/CHANGELOG.md) - [Commits](postcss/postcss@8.5.19...8.5.25) --- updated-dependencies: - dependency-name: postcss dependency-version: 8.5.25 dependency-type: indirect ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Rana Singh <ranadeep.dtu@gmail.com>

* chore: migrate artifact links off retiring Azure CDN ## Summary Replace all 400 current-master references to mmlspark.azureedge.net with the repository-owned mmlspark Blob Storage origin across runtime package configuration, release output, examples, documentation, notebooks, and every published documentation version. ## Prompting Intent Recreate the intent of the stale CDN-removal PR on current master only after verifying the supported artifact destination and Azure CDN retirement path. Audit each endpoint use by semantics, preserve package and content paths, validate live artifacts and package resolution, and avoid changing or closing the original PR. ## Linked Sources - Original proposal: #2326 - Azure CDN retirement FAQ: https://learn.microsoft.com/en-us/azure/cdn/classic-cdn-retirement-faq - Azure CDN migration guidance: https://learn.microsoft.com/en-us/azure/cdn/migrate-tier - Azure Front Door/CDN comparison: https://learn.microsoft.com/en-us/azure/frontdoor/front-door-cdn-comparison ## Rationale SynapseML's release pipeline publishes artifacts directly to the mmlspark storage account, the repository already uses that public Blob Storage origin extensively, and byte-for-byte URL checks confirmed the CDN currently proxies the same content. Using the verified origin removes the retiring CDN hostname without inventing an unverified Front Door name, while preserving Maven, documentation, R-package, model, dataset, and icon path semantics. Historical links that already return 404 retain the same status and are not broadened into unrelated artifact-repair work. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * docs: make R setup independent of retired CDN ## Summary Repair current and versioned R setup guidance so each release installs its six published, version-matched component archives and resolves SynapseML JVM artifacts through Blob Storage. Document the compatibility bypass required by already-published wrappers, correct the Databricks setup and LightGBM example, remove invalid HTML-page Maven repositories from the Docker demo, and add generator/docs regressions. ## Prompting Intent Investigate the review finding that published R archives still register the retired Azure CDN resolver. Make repository-controlled R installation work with that hostname unavailable, avoid claiming that externally published archives were rewritten, validate local and Databricks-oriented resolution paths, and state the exact external publishing prerequisite for a full artifact migration. ## Linked Sources - Original migration PR: #2326 - Current migration PR: #2589 - Maven repository review: #2589 (comment) - Azure CDN retirement FAQ: https://learn.microsoft.com/en-us/azure/cdn/classic-cdn-retirement-faq - Azure Front Door migration guidance: https://learn.microsoft.com/en-us/azure/cdn/migrate-tier - Apache Spark package repository configuration: https://spark.apache.org/docs/3.5.0/configuration.html#runtime-environment ## Rationale Existing release archives cannot be repaired by a source-only change because their generated sparklyr metadata is already published. Version-matched component downloads plus an explicit Blob resolver and `extensions = character()` provide a tested repository-controlled path without racing or misrepresenting external publication. Future generated archives inherit the corrected resolver from PackageUtils; fully repairing historical metadata still requires an authorized regeneration and publish to the `mmlspark/rrr` container (or a replacement release). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * docs: correct Spark 3.4 compatibility guidance ## Summary Correct the Spark Packages and Python installation snippets so both identify SynapseML 1.0.15 as the compatible release for Spark 3.4 while retaining SynapseML 1.1.3 for Spark 3.5. ## Prompting Intent Address the remaining actionable review feedback on PR #2589 in the existing branch, verify the surrounding compatibility guidance stays consistent, run targeted website validation and code review, and rerun the full PR checks. ## Linked Sources - Pull request and review feedback: #2589 - Original migration context: #2326 ## Rationale The Databricks, Fabric, and SBT guidance already distinguishes SynapseML 1.1.3 for Spark 3.5 from 1.0.15 for Spark 3.4. Updating only the two stale explanatory references restores consistency without changing the Spark 3.5 commands that the snippets demonstrate. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

feat: add backward-compatible AAD auth for Azure Search

## Summary Count rows on the original DataFrame RDD so adaptive execution cannot coalesce a projected counting query into a different partition topology. Add a regression that exposes the old 20-to-fewer-partitions drift and verifies exact per-partition counts. ## Prompting Intent Recreate the valid intent behind ancient PR #2282 from current master only after reproducing issue #2278. Isolate distributed startup, feature-width bounds, and native pointer lifetime separately; use TDD and submit only a proven root cause with real regression coverage. ## Linked Sources - Reported failure: #2278 - Superseded ancient proposal: #2282 ## Rationale The literal-only projection was cheaper, but AQE could optimize it to fewer partitions than the training DataFrame. LightGBM then indexed that shortened count array with real task partition IDs, causing the primary ArrayIndexOutOfBoundsException and secondary connection failures. Counting the exact DataFrame RDD trades projection pruning for topology correctness. Feature-width validation and innerPredict cleanup were deliberately excluded because neither was demonstrated as the cause of #2278 or backed by a stable leak regression. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Bumps [github/codeql-action/upload-sarif](https://github.com/github/codeql-action) from 4.37.3 to 4.37.4. - [Release notes](https://github.com/github/codeql-action/releases) - [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md) - [Commits](github/codeql-action@e4fba86...f205ea1) --- updated-dependencies: - dependency-name: github/codeql-action/upload-sarif dependency-version: 4.37.4 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

Bumps [github/codeql-action/upload-sarif](https://github.com/github/codeql-action) from 4.37.4 to 4.37.5. - [Release notes](https://github.com/github/codeql-action/releases) - [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md) - [Commits](github/codeql-action@f205ea1...d1ba80a) --- updated-dependencies: - dependency-name: github/codeql-action/upload-sarif dependency-version: 4.37.5 dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

The Azure AI Anomaly Detector service has been retired by Microsoft. Every `anomalydetector` REST endpoint now answers HTTP 410 (Gone), verified across paths, API versions and regions.

* fix: correct LightGBM improvement tolerance semantics ## Summary Require lower-is-better validation metrics to improve by more than improvementTolerance before resetting the early-stopping counter. Clarify the parameter documentation and add focused regression coverage for both metric directions and zero tolerance. ## Prompting Intent Investigate GitHub issue #2565 from a new branch based on master, determine whether the report is valid, and implement a complete fix suitable for an upstream SynapseML pull request. ## Linked Sources - GitHub issue: #2565 ## Rationale The existing higher-is-better comparison already treats improvementTolerance as a minimum delta, while lower-is-better metrics accepted small regressions. A package-internal comparison helper makes the intended symmetric behavior directly testable without adding a slow native LightGBM fixture or changing public APIs. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: harden LightGBM early stopping parameters ## Summary Expand improvement-tolerance coverage across representative LightGBM metrics and tolerance values. Preserve disabled early stopping when earlyStoppingRound is zero, validate both early-stopping parameters, and document their accepted ranges. ## Prompting Intent The engineer requested broader parameter testing to ensure the issue #2565 fix does not introduce downstream regressions. Cover related defaults, boundaries, metric families, invalid values, and early-stopping-round interactions before updating the pull request. ## Linked Sources - GitHub issue: #2565 - Pull request: #2578 - LightGBM 3.3.5 parameters: https://lightgbm.readthedocs.io/en/v3.3.5/Parameters.html#early-stopping-round ## Rationale Correct tolerance semantics classify more rounds as non-improving, so the wrapper must explicitly preserve LightGBM's zero-means-disabled behavior. Shared Spark parameter validators reject values that LightGBM does not support, while deterministic matrix tests cover the decision logic without depending on platform-specific native binaries. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: prevent sbt bootstrap Maven rate limits SynapseML's Azure Pipelines fans out ~30 hosted-agent matrix jobs that each cold-bootstrap the sbt launcher (org.scala-sbt:sbt:1.10.11, pinned in project/build.properties) and resolve Ivy dependencies from public Maven Central. When many fresh agents -- and several overlapping PR builds -- do this simultaneously, Maven Central returns HTTP 429 (rate limit) and "Setup repo" fails before any test runs (e.g. ADO build 229124511, UnitTests flaky). The pre-existing jittered retries only widened the window against a sustained throttle; they did not remove the thundering herd. Durable fix (cache-first, stagger as supplement): * templates/sbt_cache.yml (primary): Azure Cache@2 for the sbt launcher boot dir (~/.sbt/boot -- the artifact that 429s) and the Ivy cache (~/.ivy2/cache). In steady state, jobs restore these from Azure's cache service and never touch Maven Central. Keys derive from the bootstrap inputs (project/build.properties, project/plugins.sbt, build.sbt) so they invalidate exactly when those change; restoreKeys give a safe partial fallback and continueOnError keeps a cache miss/corruption non-fatal. * BuildAndCacheSbt prewarm job: warms those caches once per run, mirroring the existing BuildAndCacheCondaEnv job. * tools/ci/sbt_retry.sh: single tested helper replacing the duplicated inline retry blocks. Smooths only the cold-cache path with a bounded random start stagger (desynchronises concurrent cold bootstraps) plus bounded jittered exponential-backoff retries. Fails visibly on exhaustion -- no success fallback masking. Wired the shared cache template into every sbt-running job (Style, Publish, Databricks/Fabric E2E, BuildDocker, PythonTests, RTests, WebsiteSamplesTests, UnitTests, ReleaseBranchCompat) by reviving the dormant ivy_cache placeholders, and routed all `sbt setup` bootstraps through the helper. Tests (python -m pytest tools/ci/tests/): deterministically exercise the retry/backoff/stagger + visible-failure behaviour with a fake sbt, and assert pipeline.yaml parses, the cache keys invalidate on bootstrap inputs, and every sbt job is wired to the cache template + prewarm job. No LightGBM, Isolation Forest, GPU, or application changes. TLS verification, job coverage, and all tests are preserved. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: serialize sbt cache prewarm before fan-out ## Summary Make the sbt bootstrap prewarm a mandatory gate before Azure Pipeline matrix jobs start. Add Coursier caching, require exact hits on the boot, Ivy, and Coursier caches before disabling the cold-cache stagger, wire the conditional release job, and strengthen pipeline tests around the dependency graph and cache lifecycle. ## Prompting Intent The engineer asked to fix Maven Central HTTP 429 setup failures in a new stacked PR. The solution must prevent fresh hosted agents from cold-bootstrapping sbt concurrently, allow at least the existing job fan-out after bootstrap is safe, retain bounded retry behavior for cache-service failures, and keep bootstrap failures visible rather than masking them. ## Linked Sources - Failing Azure job: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229124511&view=logs&jobId=e97036a1-bcdb-5cd5-905e-b0cf2c8f33cf - Parent PR investigation: #2578 (comment) - Stacked PR: #2581 - Prewarm concurrency review: #2581 (comment) ## Rationale A best-effort prewarm running beside the matrix does not protect the first run for a new dependency key, so every sbt-running job now waits for one successful warm job. Cache-service errors remain non-fatal and fall back to staggered retries, but a failed warm blocks fan-out to avoid recreating the thundering herd. Coursier is cached alongside sbt boot and Ivy because modern resolution uses all three stores, and the stagger is suppressed only when every cache is an exact hit so dependency-only changes remain desynchronized. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: skip Databricks E2E for non-impacting PRs ## Summary Add conservative pull-request impact detection for the six-leg Databricks E2E matrix. Clearly non-impacting documentation, website, GitHub metadata, CI helper, and isolated test-source changes skip Databricks, while all uncertain or runtime-affecting changes continue to run it. ## Prompting Intent The engineer asked to extend PR #2581 so expensive Databricks Azure Pipeline jobs are skipped when the pull request cannot affect notebook execution. The gate must preserve scheduled and branch coverage, avoid brittle CPU-shard mapping, and default to running whenever impact detection is incomplete or uncertain. ## Linked Sources - Stacked CI PR: #2581 - Full green baseline build: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229164855 - Azure multi-job output variables: https://learn.microsoft.com/en-us/azure/devops/pipelines/process/conditions?view=azure-devops#job-output-variables-used-in-other-job-conditions ## Rationale Use one fail-open decision for the complete Databricks matrix because the five CPU partitions mix notebooks across modules and are not stable ownership boundaries. The detector skips only a narrow allowlist of clearly inert paths; runtime code, notebooks, build and pipeline files, Databricks test utilities, shared TestBase infrastructure, unknown paths, empty diffs, and fetch or classifier failures all keep E2E enabled. Non-PR builds always run to preserve scheduled and release coverage. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: gate Databricks CPU and GPU tests independently (#2582) * Gate Databricks CPU and GPU tests independently ## Summary Classify changed paths against the actual Databricks CPU and GPU runtime surfaces, emit separate fail-open decisions, and gate each matrix leg independently. ## Prompting Intent The engineer asked to determine exactly when Databricks tests should run, lock down the path rules, and deliver the work as a stacked pull request above PR #2581. ## Linked Sources - Base CI hardening PR: #2581 - GitHub stacked PR documentation: https://docs.github.com/en/pull-requests/how-tos/create-pull-requests/creating-stacked-pull-requests - ADO timing audit: build 229176406 ## Rationale CPU and GPU decisions are separated because most module changes cannot affect the expensive GPU notebooks. Unknown paths and shared build or test infrastructure remain fail-open, while explicit test-only and unrelated tooling paths skip safely. This preserves coverage while avoiding unrelated GPU capacity waits. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: fix and streamline release branch compatibility (#2583) * Fix and streamline release branch compatibility checks ## Summary Run release compatibility checks for both GitHub target-branch formats and replace redundant compile, setup, credential, and per-package SBT tasks with one cached, project-scoped validation process. ## Prompting Intent The engineer asked to fix the silently skipped ReleaseBranchCompat job and simplify it before enabling it so the check is both reliable and efficient. ## Linked Sources - Base CI hardening PR: #2581 - Evidence build with skipped phase: ADO build 229176406 - Parent stack layer: ci/databricks-impact-gating ## Rationale The target condition accepts both values observed across Azure Repos and GitHub PR providers. A single SBT process retains full test compilation and the intended core, VW, and OpenCV compatibility suites while removing repeated build loading, root-wide IntelliJ setup, unnecessary Key Vault access, and Azure CLI authentication. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: use Entra authentication for ACR cleanup (#2584) * Use Entra authentication for ACR cleanup Make the weekly ACR cleanup schedule-only, switch it to the dedicated cleanup service connection, replace storage connection-string authentication with Azure CLI Entra authentication, and add fail-safe cleanup tests. The engineer asked to repair the weekly cleanup failures caused by disabled key-based storage authentication, use the declared least-privileged identity, and prevent accidental CI or PR execution. - Failed scheduled build: ADO build 228250033 - Base CI hardening PR: #2581 - Azure CLI pipeline-run reference: https://learn.microsoft.com/en-us/cli/azure/acr/pipeline-run - Parent stack layer: ci/release-branch-compat Using az storage blob exists with auth-mode login keeps all operations inside the AzureCLI task identity and removes runtime SDK installation, Key Vault access, and storage keys. Images are deleted only after the archive is confirmed, and subprocess argument lists avoid shell interpolation of registry-controlled names. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Remove redundant CI authentication and Conda work (#2585) ## Summary Run non-Azure setup and coverage commands as Bash steps, install pinned Black without restoring the 8.6 GB Conda environment, and remove the ineffective standalone Conda cache consumer. ## Prompting Intent The engineer asked for additional improvements that should ship with the requested CI fixes to make builds faster and more reliable without broad behavioral changes. ## Linked Sources - CI efficiency audit from ADO build 229176406 - Base CI hardening PR: #2581 - Parent stack layer: ci/fix-acr-cleanup-auth ## Rationale AzureCLI tasks create an isolated login for every invocation, so setup and coverage steps that never call az gain no authentication benefit. The Style job only needs pinned Black, not the full cached environment. The standalone Conda job was not a dependency and therefore could not prewarm consumers or prevent cold-cache fan-out. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: extend Docker validation timeout ## Summary Raise the BuildDocker job timeout from 60 to 120 minutes and add a pipeline regression test that preserves enough time for both sequential image builds. ## Prompting Intent The engineer asked to diagnose and fix the remaining failure on #2581 and to continue full validation until the parent PR is ready, without hiding genuine test failures. ## Linked Sources - Parent PR: #2581 - Failed PR build: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229579403 - Matching master failure: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229578121 - Matching master failure: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229580525 - Matching master failure: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229576499 ## Rationale The Dockerfiles and image behavior were unchanged, but recent hosted-agent builds required roughly 51 minutes when successful and exceeded the default one-hour job cap in multiple master and PR runs. A 120-minute job budget keeps both image validations mandatory while tolerating current registry and package download latency. This is safer and more targeted than skipping an image or doubling agent usage by splitting the builds into parallel jobs. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: address PR review and protect package publishing ## Summary Resolve the blocking PR #2581 review findings by making ACR archival digest-safe, correcting PipelineRun names and sbt cache invalidation, warming cold agents before direct sbt calls, and validating the canonical package version before publishing. ## Prompting Intent The engineer asked to rebase PR #2581 onto current master, audit the new review feedback, fix valid actions, ensure the pipelines continue to publish package versions safely, review the complete change, and rerun Azure validation. ## Linked Sources - Integration PR and review threads: #2581 - Stacked CI changes: #2582 - Stacked CI changes: #2583 - Stacked CI changes: #2584 - Stacked CI changes: #2585 - ACR transfer guidance: https://learn.microsoft.com/azure/container-registry/container-registry-transfer-images - ACR image deletion behavior: https://learn.microsoft.com/azure/container-registry/container-registry-delete ## Rationale Immutable manifest digests prevent mutable tags such as latest from reusing the wrong backup or deleting an unarchived manifest. Per-agent warming is limited to unavailable or inexact cache restores so exact hits remain fast, while the prewarm job still verifies dependency resolution. Package versions are resolved from the SBT source of truth and release publication fails before side effects when the v-tag disagrees. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* feat: Add lossless string identifier support to SAR Refs #2275 Refs #2283 ## Summary Add deterministic, reversible user and item identifier mappings to SAR so string and wide numeric IDs are never cast into lossy caller-visible values. Persist mappings with the model, preserve identifier types in scores and recommendations, define null and unknown-ID behavior, restore typed item recommendation APIs, and add Scala and Python regression coverage. ## Prompting Intent Recreate the intent of the stale SAR string-ID change on current master without copying its lossy casts. Keep the SparkML API coherent and backward compatible for numeric users, use TDD, validate serialization and schema behavior, expose Python wrappers, and exercise targeted compile, style, code generation, Scala, and Python/JVM checks before opening a replacement PR. ## Linked Sources - Feature request: #2275 - Original pull request: #2283 - Current SAR implementation at the starting revision: https://github.com/microsoft/SynapseML/tree/7d9fabcc/core/src/main/scala/com/microsoft/azure/synapse/ml/recommendation - Repository review policy: .github/skills/code-review/SKILL.md ## Rationale Use model-owned typed mappings instead of composing RecommendationIndexer because that stage stringifies numeric identifiers, exposes index columns, and cannot recover every original type. Contiguous deterministic indices keep the existing matrix implementation viable, while persisted DataFrame parameters make decoding reversible after save/load. Inner mapping joins intentionally drop null or unseen scoring IDs, strict type validation prevents ambiguous conversions, and legacy numeric models fall back to identity mappings. The approach accepts a deterministic global sort and persisted mapping storage in exchange for lossless, reproducible SparkML behavior. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: Address SAR identifier compatibility review Refs #2275 Refs #2594 ## Summary Resolve the four independent review findings on SAR string identifier support. Preserve typed IDs in ranking train/validation splits, accept only round-trip-safe numeric scoring casts, retain established integer recommendation schemas for safely representable numeric IDs, and rank only factor IDs that have real mappings. Add focused Scala and Python regressions and remove unnecessary mapping cache and interaction-count work identified during review. ## Prompting Intent The engineer asked to fix all medium correctness and compatibility findings on PR #2594, add a regression for each, rerun targeted Scala, code generation, formatting, and Python/JVM validation, then update the existing PR and request re-review without weakening lossless string or wide numeric behavior. ## Linked Sources - Pull request and review context: #2594 - Feature request: #2275 - Original pull request: #2283 - Repository review policy: .github/skills/code-review/SKILL.md ## Rationale Use Spark structs and array functions instead of Double UDF payloads so split schemas remain typed. Numeric scoring IDs are temporarily cast only when casting back reproduces the input, preventing overflow and fractional aliasing while retaining unknown-ID drop semantics. Recommendation decoding conditionally uses the historical integer schema only when every ID round-trips through Int; strings and wide or fractional numeric IDs remain lossless. Candidate indices are intersected with both factors and mappings before top-K so gaps cannot consume recommendation slots. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: Make SAR numeric identifier handling ANSI-safe Refs #2275 Refs #2594 ## Summary Use ANSI-safe try_cast expressions for numeric identifier compatibility and legacy mappings. Persist whether model-owned user and item mappings safely round-trip through IntegerType, reuse those flags when selecting recommendation output schemas, and limit destination-index collection to mapping-less legacy models. Add ANSI overflow, persisted-flag, legacy-default, and recommendation-planning regressions. ## Prompting Intent The engineer asked to resolve the second independent review of PR #2594: prevent CAST_OVERFLOW under spark.sql.ansi.enabled=true, eliminate repeated mapped-model recommendation scans and index collection, add focused regressions, rerun Scala/codegen/Python validation, update the existing PR, trigger Azure Pipelines, and request another re-review. ## Linked Sources - Pull request and review context: #2594 - Feature request: #2275 - Original pull request: #2283 - Repository review policy: .github/skills/code-review/SKILL.md ## Rationale Use Spark SQL try_cast in both cast directions rather than pre-cast comparisons so out-of-range values become null and are filtered even with ANSI mode enabled. Compute compatibility once while fitting and persist it with conservative false defaults for legacy models, avoiding full mapping scans on every recommendation call. New model mappings are contiguous, so mapped models rank the score vector directly; only mapping-less legacy models collect actual candidate indices to preserve gapped-ID correctness. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Configure a deterministic repository-local Git committer identity before replaying PR commits onto the Spark 3.5 and Spark 4.1 release branches. Distinguish genuine merge conflicts from rebase infrastructure failures and preserve successful rebase diagnostics.

* ci: preserve sbt retry helper during release replay ## Summary Stage the sbt retry helper outside the repository before switching to Spark release branches, and parameterize the shared cache template so it can invoke that stable path after rebase. ## Prompting Intent Investigate why Spark 3.5 and Spark 4.1 compatibility checks still failed after PR #2608, reproduce the failure with PR #2595 changes, implement the complete hotfix, and validate the real release replay path. ## Linked Sources - Failing PR: #2595 - Prior identity hotfix: #2608 - Failed Azure build: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229969678 ## Rationale The rebased working tree intentionally comes from the Spark release branch, so master-only CI helpers cannot remain repository-relative. Copying the helper to Agent.TempDirectory preserves release-specific dependency resolution and avoids moving cache warming ahead of the rebase, where exact cache hits could hide missing release dependencies. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: replay only release-relevant PR changes ## Summary Replace commit-history rebasing with a three-way application of the synthetic PR merge tree's release-relevant patch onto each Spark release branch. ## Prompting Intent Validate the compatibility hotfix with PR #2595's real source changes while ensuring CI-only commits do not conflict with old Spark branches that predate the current pipeline and helper files. ## Linked Sources - Validation PR source: #2595 - Prior identity hotfix: #2608 - Failed Azure build: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229969678 ## Rationale The compatibility job needs the effective PR content on the release tree, not unrelated CI and documentation commits. Building the patch from the synthetic merge commit preserves GitHub's merge result, handles source branches behind master, retains three-way conflict detection, and avoids requiring commit identity. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: focus release compatibility on Spark 4.1 compilation ## Summary Remove the redundant Spark 3.5 release matrix leg and replace broad Spark 4.1 runtime suites with full test compilation of the effective PR patch. ## Prompting Intent Explain why the release compatibility jobs exist and keep fixing the failures exposed by validation PR #2610, accounting for master already targeting Spark 3.5. ## Linked Sources - Original compatibility PR: #2550 - Streamlining PR: #2583 - Integration validation PR: #2610 - Azure validation build: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=229984834 ## Rationale Normal PR validation already compiles and tests master on Spark 3.5, so replaying onto the older spark3.5 maintenance snapshot duplicates coverage and introduces unrelated JVM drift. Spark 4.1 test compilation catches cross-version source and test API breakage, while the existing master test fan-out supplies runtime coverage without rerunning broad, memory-heavy suites on a constrained compatibility agent. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: SynapseML CI <synapseml-ci@users.noreply.github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

azure-pipelines · 2026-08-18T08:34:55Z

Azure Pipelines: There may be pipelines that require an authorized user to comment /azp run to run.

Rana Singh (ranadeepsingh) · 2026-08-18T08:34:57Z

/azp run

azure-pipelines · 2026-08-18T08:35:11Z

Azure Pipelines: Successfully started running 1 pipeline(s).

github-actions · 2026-08-18T08:35:26Z

Hey Rana Singh (@ranadeepsingh) 👋!
Thank you so much for contributing to our repository 🙌.
Someone from SynapseML Team will be reviewing this pull request soon.

We use semantic commit messages to streamline the release process.
Before your pull request can be merged, you should make sure your first commit and PR title start with a semantic prefix.
This helps us to create release messages and credit you for your hard work!

Examples of commit messages with semantic prefixes:

fix: Fix LightGBM crashes with empty partitions
feat: Make HTTP on Spark back-offs configurable
docs: Update Spark Serving usage
build: Add codecov support
perf: improve LightGBM memory usage
refactor: make python code generation rely on classes
style: Remove nulls from CNTKModel
test: Add test coverage for CNTKModel

To test your commit locally, please follow our guild on building from source.
Check out the developer guide for additional guidance on testing your change.

github-actions · 2026-08-18T08:35:28Z

Dependency Review

✅ No vulnerabilities or license issues or OpenSSF Scorecard issues found.

Scanned Files

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Copilot

Pull request overview

This PR syncs the master branch into the spark4.1 port branch, bringing Spark 4.1’s branch state up to date while preserving branch-specific runtime/toolchain differences. The sync notably updates CI behavior (InternalCompat) and ports several functional/test/doc improvements that were previously only on master.

Changes:

Updates Azure DevOps pipeline logic, including InternalCompat lane parallelization (Scala/Python matrix) and improved retarget/idempotency safeguards.
Ports the ComputeModelStatistics refactor and adds/adjusts validation behavior plus new Scala test coverage.
Extends Featurize with vectorAssemblerHandleInvalid (and adds Scala + Python tests + docs), and adds Fabric classification headers support for OpenAI stages (with tests).

Show a summary per file

File	Description
templates/fabric_kv.yml	Adds a parameterized `condition` to gate Fabric Key Vault/cert materialization per lane.
pipeline.yaml	Splits InternalCompat into Scala/Python matrix lanes; improves retarget logic and lane-scoped conditions for secrets/conda/tests/results.
docs/Quick Examples/estimators/core/_Featurize.md	Documents how `Featurize` handles missing numeric values via `vectorAssemblerHandleInvalid`.
core/src/test/scala/com/microsoft/azure/synapse/ml/train/VerifyComputeModelStatistics.scala	Updates schema validation expectations and related assertions after ComputeModelStatistics changes.
core/src/test/scala/com/microsoft/azure/synapse/ml/train/ComputeModelStatisticsValidationSuite.scala	Adds focused validation/regression coverage for the ComputeModelStatistics refactor.
core/src/test/scala/com/microsoft/azure/synapse/ml/featurize/VerifyFeaturize.scala	Adds Scala coverage for `vectorAssemblerHandleInvalid` behavior, copy/save/load, and pipeline persistence.
core/src/test/python/synapsemltest/featurize/test_featurize.py	Adds Python coverage for `vectorAssemblerHandleInvalid="keep"` preserving missing rows in a Pipeline.
core/src/test/python/synapsemltest/featurize/init.py	Adds test package marker consistent with other synapsemltest subpackages.
core/src/main/scala/com/microsoft/azure/synapse/ml/train/ComputeModelStatisticsInputValidator.scala	Introduces a dedicated validator for resolving/validating ComputeModelStatistics input columns/types.
core/src/main/scala/com/microsoft/azure/synapse/ml/train/ComputeModelStatistics.txt	Expands user-facing documentation around inferred/explicit input columns and validation expectations.
core/src/main/scala/com/microsoft/azure/synapse/ml/train/ComputeModelStatistics.scala	Refactors metric evaluation column resolution/validation and improves handling of schema/runtime consistency.
core/src/main/scala/com/microsoft/azure/synapse/ml/featurize/Featurize.scala	Adds `vectorAssemblerHandleInvalid` param and fixes `copy` to preserve params via `defaultCopy`.
cognitive/src/test/scala/com/microsoft/azure/synapse/ml/services/openai/OpenAIFabricHeadersSuite.scala	Adds unit tests verifying Fabric workload classification headers behavior across OpenAI stages.
cognitive/src/main/scala/com/microsoft/azure/synapse/ml/services/openai/OpenAIResponses.scala	Enables Fabric header injection for Responses stage via `HasOpenAIFabricHeaders`.
cognitive/src/main/scala/com/microsoft/azure/synapse/ml/services/openai/OpenAIEmbedding.scala	Enables Fabric header injection for Embedding stage via `HasOpenAIFabricHeaders`.
cognitive/src/main/scala/com/microsoft/azure/synapse/ml/services/openai/OpenAIChatCompletion.scala	Enables Fabric header injection for ChatCompletion stage via `HasOpenAIFabricHeaders`.
cognitive/src/main/scala/com/microsoft/azure/synapse/ml/services/openai/OpenAI.scala	Adds `HasOpenAIFabricHeaders` and exposes fabric/default-endpoint checks for testability and reuse.
.pipelines/release-compat-prerequisites.txt	Adds an additional prerequisite commit for release-compat replay alignment.
.github/workflows/dependency-review.yml	Expands `allow-ghsas` exceptions with additional documented advisories affecting docs-only build-time deps.

Review details

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Files reviewed: 19/19 changed files
Comments generated: 0
Review effort level: Lite

* fix: handle empty featurized text features AB#1667 ## Summary Treat an empty CountSelector selection as a valid nullable zero-width vector with size metadata. Allow Featurize and TrainRegressor to ignore collapsed text inputs when useful features remain, fail clearly when every input collapses, and cover schema, null, copy, persistence, generated Python, and public end-to-end behavior. Preserve the merged vectorAssemblerHandleInvalid/defaultCopy behavior and add a combined regression for collapsed text with keep mode. ## Prompting Intent Make #2629 engineering-ready on current master after PRs #2638 and #2633 merged. Rebase surgically, preserve both the empty-text fix and all merged missing-value handling behavior, resolve suppressed performance/test-cleanliness findings, avoid RDD or driver-side production paths, validate with JDK 11 plus generated Python and Spark 4.1 compatibility, and refresh exact-head CI and review evidence. ## Linked Sources - GitHub issue #1667: #1667 - GitHub pull request #2629: #2629 - Automated review overview: #2629 (review) - Suppressed review findings: #2629 (review) - Merged pull request #2638: #2638 - Merged pull request #2633: #2633 ## Rationale VectorSlicer rejects an empty index list, so CountSelectorModel takes a narrow empty-selection branch while preserving the existing implementation for non-empty selections. A single immutable sparse vector and direct null check avoid per-row Option allocation. Featurize validates fitted nested selectors because selected width is known only after fitting; zero-width text inputs remain harmless beside useful features, while an all-zero feature set fails with a clear column-specific error. The rebase retained #2638's default skip, keep/error modes, VectorAssembler wiring, and defaultCopy semantics rather than choosing either conflict side wholesale. The implementation remains Scala-first so generated wrappers expose the behavior without duplicating production logic in Python. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * test: declare the Featurize release prerequisite ## Summary Declare PR #2638 as a release compatibility prerequisite for PR #2629. ## Prompting Intent Make PR #2629 merge-ready after rebasing it onto the latest master, including the Spark 4.1 compatibility replay required by SynapseML CI. ## Linked Sources - PR #2629: #2629 - PR #2638: #2638 ## Rationale The PR #2629 tests intentionally exercise the missing-numeric Featurize API merged by PR #2638. Spark 4.1 has not yet received that change, so replaying only the #2629 delta cannot compile. Declaring the merged commit lets the existing compatibility job apply the exact dependency first and keeps the prerequisite removable after the next release-branch sync. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: retire synced compatibility prerequisites ## Summary Remove release compatibility prerequisites already incorporated by the Spark 4.1 sync while retaining PR #2638, which PR #2629 still requires. ## Prompting Intent Make PR #2629 merge-ready after its current-head Azure compatibility job failed, without weakening conflict detection or dropping the Featurize dependency used by its production and test changes. ## Linked Sources - PR #2629: #2629 - PR #2638: #2638 - Spark 4.1 sync #2645: #2645 - Failed Azure build: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=231604593 ## Rationale Spark 4.1 already contains port-adapted equivalents of the five older manifest entries, so replaying their original master patches conflicts before CI reaches the PR. PR #2638 is not yet present and remains necessary because #2629 uses its missing-numeric Featurize API and test imports. Keeping only that exact commit matches the successful local replay and preserves the pipeline's existing validation semantics. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: SynapseML CI <synapseml-ci@users.noreply.github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* fix(core): include reference-only distribution categories ## Summary Fix DistributionBalanceMeasure so custom comparisons cover the union of observed and positive-probability reference categories. Add typed validation, robust persisted-number decoding, one-category and empty-input handling, public Scala/Python regressions, documentation, and conflict-free Spark 4.1 replay. ## Prompting Intent Make GitHub issue #2010 and PR #2630 engineering-ready end to end. Preserve public signatures, serialized parameter shapes, output schema, empty-map uniform behavior, and existing observed-only semantics while covering reference-only categories, null and invalid inputs, supported category types, copy/save-load, generated Python, SQL-native scalability, and release-branch compatibility. ## Linked Sources - Bug report: #2010 - Pull request: #2630 - Related Jensen-Shannon normalization: #2631 - Automated review finding: #2630 (comment) - Spark 4.1 compatibility failure: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=231496504 - Data balance documentation: docs/Explore Algorithms/Responsible AI/Data Balance Analysis.md ## Rationale Build a typed DataFrame from the already driver-resident reference parameter, then union and aggregate it with distributed observed statistics. This aligns support without collecting observed categories, RDD APIs, Python UDFs, or persistent caches. Normalize complex-param numeric values during Param validation so Python and persisted integral numbers are safe while preserving the Spark 4 branch's strict-map adaptation and clean patch replay. Dynamic support cardinality keeps chi-square behavior correct without changing public schema or signatures. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix(core): keep distribution measurement lazy ## Summary Remove eager input and support-count actions from DistributionBalanceMeasure. Build observed and configured support in one SQL-native plan, preserve empty-input and category semantics with bounded sentinel rows, and add a regression proving transform does not launch Spark jobs or cache its input. ## Prompting Intent Continue PR #2630 through current-head automated review and address the reported double-scan performance regression without RDD APIs, driver collection, persistent caches, unbounded materialization, public signature changes, schema changes, or serialized parameter changes. Preserve reference-only, observed-only, null, integral, string, uniform, persistence, and Spark 4.1 behavior. ## Linked Sources - Bug report: #2010 - Pull request: #2630 - Current-head automated review: #2630 (review) - Performance finding: #2630 (comment) - Related Jensen-Shannon normalization: #2631 ## Rationale Avoid the suggested cache because input-size materialization and cache lifetime are unsafe for a reusable transformer. Instead, project supported string and integral sensitive columns into a long SQL representation, aggregate the input once, and perform row/support totals over the reduced category support. Small configured-reference and sentinel rows stay driver-bounded; sentinels preserve empty-input rejection lazily. This removes eager full scans while retaining all metric and compatibility contracts. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix(core): correct infinite chi-square p-values ## Summary Return a right-tail chi-square p-value of zero when the test statistic is positive infinity. Align the independent Scala calculator, strengthen Scala and generated-wrapper Python regressions, and clarify the documented interpretation. ## Prompting Intent Continue PR #2630 through current-head automated review, audit every new finding, and fix real metric defects without weakening tests or changing public signatures, output schema, serialized parameters, or supported category behavior. Verify the corrected metric through Scala, generated Python, Spark 3.5, and Spark 4.1 paths before pushing. ## Linked Sources - Bug report: #2010 - Pull request: #2630 - Current-head review: #2630 (review) - Chi-square p-value finding: #2630 (comment) - Data balance documentation: docs/Explore Algorithms/Responsible AI/Data Balance Analysis.md ## Rationale The implementation computes the upper-tail probability as `1 - CDF(score)`, whose limit is zero as the score approaches positive infinity. Returning one inverted the statistical meaning and reported maximum compatibility for an impossible observed category. Preserve the one-category degree-of-freedom special case at one, while making infinite statistics consistently produce zero across production, test calculators, Scala tests, Python wrappers, and documentation. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * ci: retire synced release compatibility prerequisites ## Summary Drain the release compatibility prerequisite manifest after the Spark 4.1 master sync incorporated every configured baseline. This prevents old master patches from being replayed over their Spark 4.1-adapted equivalents before pull request changes are validated. ## Prompting Intent Make PR #2630 fully engineering-ready by resolving the release-compatibility failure at its root. Verify each configured prerequisite against live master and Spark 4.1 history, preserve replay ordering, run the exact release replay and affected CI tests, and avoid unrelated pipeline changes. ## Linked Sources - Pull request #2630: #2630 - Spark 4.1 master sync #2645: #2645 - Azure Search auth prerequisite #2591: #2591 - Azure Search dependent change #2604: #2604 - LightGBM retry prerequisite #2612: #2612 - LightGBM IPv6 dependent change #2637: #2637 - Coverage baseline #2507: #2507 - Precision-recall AUC prerequisite #2635: #2635 - Metadata prerequisite #2632: #2632 - Failed current-head Azure build: https://msdata.visualstudio.com/b9b2accc-2d1c-45b3-9d24-0eb5d78cc47f/_build/results?buildId=231542808 ## Rationale The release matrix currently validates only Spark 4.1, and sync #2645 contains every commit named by the manifest. Their source was deliberately adapted during the port, so exact reverse-patch detection cannot recognize them and sequential replay conflicts before the PR patch. An exact local replay showed the first two stale entries failing in turn; an empty manifest then applied the PR patch cleanly, compiled the complete Spark 4.1 test surface, and passed the 30-test distribution suite. Keeping the supported empty manifest is narrower and safer than weakening conflict detection or special-casing individual patches. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: SynapseML CI <synapseml-ci@users.noreply.github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Picks up two fixes merged into master after the initial sync commit: - #2629 fix: handle empty featurized text features - #2630 fix(core): include reference-only distribution categories Both merged cleanly with no conflicts. They touch core featurize, exploratory and train test code only, none of which carries a spark4.1 adaptation, so no per-hunk resolution was required. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

Rana Singh (ranadeepsingh) · 2026-08-18T08:49:13Z

/azp run

azure-pipelines · 2026-08-18T08:49:32Z

Azure Pipelines: Successfully started running 1 pipeline(s).

Copilot

Review details

Files reviewed: 29/29 changed files
Comments generated: 0 new
Review effort level: Lite

Rana Singh (ranadeepsingh) · 2026-08-18T09:10:47Z

Local validation on Scala 2.13 / JDK 17 — measured, not inferred

Run in WSL against this branch's own toolchain (JAVA_HOME=/usr/lib/jvm/java-17-openjdk-amd64, Spark 4.1.1, Scala 2.13.17).

Compile

Target	Result	Time
`core/Test/compile`	✅ success	336 s
`cognitive/Test/compile`	✅ success	338 s

This is the real risk this sync carries: master's new files — ComputeModelStatisticsInputValidator.scala, OpenAIFabricHeadersSuite.scala and the Featurize changes — were written against Scala 2.12 and had never been compiled on 2.13. They build clean.

Scalastyle

0 errors, 0 warnings across all 7 modules (358 files).

Test suites

Suite	Tests	Result
`VerifyComputeModelStatistics`	32	✅ all passed
`ComputeModelStatisticsValidationSuite`	8	✅ all passed
`VerifyFeaturize`	24 (1 ignored)	✅ all passed
`VerifyCountSelector`	5	✅ all passed
`DistributionBalanceMeasureSuite`	30	✅ all passed
`OpenAIFabricHeadersSuite`	6	✅ all passed
Total	105	0 failures

These cover exactly the code this sync moves: the CMS refactor (#2632/#2633/#2635), #2629, #2630, and the OpenAI visibility widening that OpenAIFabricHeadersSuite depends on.

One honest note on a false alarm

The first suite run reported 2 failures in VerifyComputeModelStatistics. They were not merge defects — both were:

[PATH_NOT_FOUND] Path does not exist:
  target/scala-2.13/datasets/datasets-2023-04-03/MissingValuesRegression/Train/AutomobilePriceRaw.csv

i.e. the test datasets that CI downloads via sbt getDatasets (pipeline.yaml L579) were absent from my local worktree. After running getDatasets, the same suite goes 32/32. Recording it because "30 succeeded, 2 failed" is exactly the kind of result that is easy to wave away as flakiness, and the distinction was worth proving rather than assuming.

Structural / static

tools/ci/tests/test_pipeline_yaml.py: 43 passed, 21 skipped (bash-only, on Windows). master reports 40 with the same 21 skips — delta is exactly spark4.1's own 3 tests, and all 26 master tests are present.
57 branch-invariant assertions, 0 failures — including UseConcMarkSweepGC count == 0 (CMS was removed in Java 17; restoring master's flags would stop the JVM from starting at all).
Retarget script simulated 3× consecutively: run 3 reports tree is already clean, Resolver.mavenLocal stays at exactly one occurrence, usage/build.sbt untouched.
Lane validator: both simulated lanes, bash -n on all 12 inline scripts, and exactly 2 fabric_kv includes (one default for Fabric E2E, one scala-gated).

Rana Singh (ranadeepsingh) · 2026-08-18T10:36:00Z

CI evidence — build 231630202

reason=pullRequest, isFork=False (a fork PR silently skips InternalCompat, so this matters), 66 jobs.

Zero failures

65 of 66 jobs complete, 0 failed, 0 canceled. The last job, BuildDocker, is
still running. That is not a regression from this PR — BuildDocker on
master itself ranges widely depending on layer-cache hits:

build	branch	BuildDocker
231616435	master	108.7 min
231600609	master	15.6 min
231588332	master	16.3 min

Its Demo Image Build step already succeeded (86.5 min) and Minimal Image Build is progressing, so it is advancing normally, within master's own variance.

The point of the sync: InternalCompat now runs in parallel on spark4.1

This is the direct evidence that #2658 actually reached the port branch rather
than just master:

record	result	duration
Phase `SynapseML-Internal Compatibility Check`	succeeded	39.2 min
Job `... scala`	succeeded	27.8 min
Job `... python`	succeeded	39.0 min

Two legs, not one. Run serially these would total ≈67 min; as a matrix they cost
the length of the longer leg, so spark4.1 saves ≈28 min per build. Before this
PR the branch had COMPAT_LANE at 0 occurrences — the matrix did not exist here.

Databricks E2E all green

Databricks GPU E2E succeeded, and all five Databricks CPU E2E shards
(databricks-cpu-1 … -5) succeeded.

Local validation (measured on this branch, not inferred from master)

check	result
`core/Test/compile` (WSL, JDK 17, Scala 2.13.17)	pass, 336 s
`cognitive/Test/compile`	pass, 338 s
`scalastyle` + `test:scalastyle`, 7 modules	0 errors
affected Scala suites	105 tests, 0 failures
`pytest tools/ci/tests/test_pipeline_yaml.py`	43 passed, 21 skipped
branch invariants	57/57

One honest note on that suite run: the first local attempt showed 2 failures in
VerifyComputeModelStatistics. They were not merge defects — the error was
[PATH_NOT_FOUND] .../datasets-2023-04-03/AutomobilePriceRaw.csv, i.e. the test
datasets simply were not present locally. CI fetches them via sbt getDatasets
(pipeline.yaml L579). After running that, the suite passed 32/32.

Rana Singh (ranadeepsingh) · 2026-08-18T10:58:29Z

Build 231630202 failed on a `BuildDocker` timeout — not a regression from this PR

Final state: 66/66 jobs, 0 failed jobs. The build is nonetheless marked
failed, because the failure is at the job level, not a test:

[error] BuildDocker: The job running on agent Azure Pipelines 96 ran longer
        than the maximum time of 120 minutes.
[error] Minimal Image Build: The operation was canceled.

Demo Image Build alone consumed 86.5 min, so Minimal Image Build was
still running when the 120-minute agent cap hit and was cancelled.

This PR does not touch any Docker build input

Verified by diffing the merge result against the pre-merge spark4.1 tip:

input	vs pre-merge `spark4.1`
`tools/docker/demo/Dockerfile`	identical
`tools/docker/minimal/Dockerfile`	identical
`tools/docker/demo/init_notebook.py`	identical
`environment.yml`	identical
any file under `tools/docker/`	no changes at all
`BuildDocker` job in `pipeline.yaml`	no docker-related changes

Historical durations confirm it is infrastructure, not the branch

branch	build	BuildDocker	Demo step
spark4.1	231527670	10.3 min	4.0 min
spark4.1	230610354	9.8 min	3.7 min
spark4.0	231537945	18.4 min	3.2 min
master	231600609	15.6 min	8.5 min
master	231588332	16.3 min	9.7 min
master	231568240	12.5 min	3.2 min
master	231616435	108.7 min	40.5 min

spark4.1 normally builds Docker in ~10 minutes. The one other slow run is
on master, queued at 07:18 the same morning as this build (08:49) — the
same degraded window. The cost is concentrated in image build steps that are
dominated by package downloads, which points at registry/network throughput
rather than anything in the diff.

Everything else passed

0 failed jobs out of 66.
InternalCompat: both lanes succeeded — scala 27.8 min, python 39.0 min, phase 39.2 min.
Databricks GPU E2E succeeded; all 5 Databricks CPU E2E shards succeeded.

Re-running.

Separate pre-existing risk worth noting

BuildDocker has a hard 120-minute cap and master itself has already come
within ~11 minutes of it (108.7 min). That is a latent CI fragility on every
branch, independent of this PR, and is worth addressing on its own — either by
raising timeoutInMinutes for that job or by improving image layer caching.
I have not changed it here, to keep this sync mechanical.

Rana Singh (ranadeepsingh) · 2026-08-18T10:58:31Z

/azp run

azure-pipelines · 2026-08-18T10:58:47Z

Azure Pipelines: Successfully started running 1 pipeline(s).

Rana Singh (ranadeepsingh) · 2026-08-18T12:16:13Z

CI status: build 231647758 — effectively green

66/66 jobs completed. 12 of 13 phases succeeded. The one failure is a known repo-wide flake that does not involve any test.

Phase results

Phase	Result
Prewarm sbt bootstrap cache	✅ succeeded
Style	✅ succeeded
PythonTests	✅ succeeded
RTests	✅ succeeded
WebsiteSamplesTests	✅ succeeded
Databricks CPU E2E	✅ succeeded
Databricks GPU E2E	✅ succeeded
SynapseML-Internal Compatibility Check	✅ succeeded
BuildDocker	✅ succeeded (64.6m)
Publish	✅ succeeded
Fabric E2E	⏭️ skipped (`condition: false` — expected on Spark 4 branches)
Release Branch Compatibility Check	⏭️ skipped
UnitTests	❌ failed — see below

The single failure does not involve any test

UnitTests io1 is marked failed, but its test step passed:

Step	Result	Time
Setup repo	✅ succeeded	7.5m
Unit Test	✅ succeeded	9.7m
Publish Test Results	✅ succeeded	0.1m
Generate Codecov report	❌ failed	0.8m
Upload Coverage Report To Codecov.io	✅ succeeded	0.1m
Publish Code Coverage to Azure DevOps	❌ failed	0.0m

Only the post-test coverage-reporting steps failed, with:

unresolved dependency: com.globalmentor#hadoop-bare-naked-local-fs;0.1.0: not found
  com.globalmentor:hadoop-bare-naked-local-fs:0.1.0 (build.sbt#L430)

This is a pre-existing repo-wide flake, not a regression from this PR

It reproduces on master with none of these changes. Recent master builds hit the identical failure in the identical steps:

master build Generate Codecov report failed Setup repo failed

231649114 1 / 51 1

231629183 1 / 54 1

231600609 2 / 54 1

7 of the last 8 master builds are failed / partiallySucceeded.
The artifact is not actually missing. Verified directly against Maven Central just now — POM HTTP 200, JAR HTTP 200. sbt declares no custom resolvers; it is a plain % "test" dependency resolved from Central.
It is transient, not deterministic. Setup repo (7.5m) and Unit Test (9.7m) both resolved this same dependency successfully on this same agent, minutes before the coverage step failed to resolve it in 12s. It hits ~1–2 of 54 jobs per build at random.
This PR changes no build inputs. build.sbt, project/**, and environment.yml are byte-identical to the pre-merge spark4.1 tip. No build, resolver, or dependency file appears in the changed set.

InternalCompat parallelization is working on this branch

Both lanes ran and passed, confirming #2658's matrix reached the port branch:

scala lane 25.7m ∥ python lane 37.5m
phase wallclock 37.6m vs ≈63.2m serial → ≈26m saved per build

Local validation (WSL, JDK 17)

core/Test/compile ✅ 336s, cognitive/Test/compile ✅ 338s
scalastyle 0 errors across all 7 modules (main + test)
105 tests, 0 failures — ComputeModelStatistics 32, CMSValidation 8, VerifyFeaturize 24, VerifyCountSelector 5, DistributionBalanceMeasure 30, OpenAIFabricHeaders 6
test_pipeline_yaml.py — 43 passed
branch invariants — 57/57 pass

Rana Singh (ranadeepsingh) · 2026-08-18T13:53:13Z

/azp run

azure-pipelines · 2026-08-18T13:53:29Z

Azure Pipelines: Successfully started running 1 pipeline(s).

Codecov Comments Bot (codecov-commenter) · 2026-08-18T14:26:20Z

Codecov Report

❌ Patch coverage is 95.21127% with 17 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (spark4.1@3364aba). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
...zure/synapse/ml/train/ComputeModelStatistics.scala	84.94%	14 Missing ⚠️
...se/ml/exploratory/DistributionBalanceMeasure.scala	98.52%	2 Missing ⚠️
...l/train/ComputeModelStatisticsInputValidator.scala	98.43%	1 Missing ⚠️

Additional details and impacted files

@@             Coverage Diff             @@
##             spark4.1    #2659   +/-   ##
===========================================
  Coverage            ?   88.94%           
===========================================
  Files               ?      337           
  Lines               ?    19313           
  Branches            ?     1953           
===========================================
  Hits                ?    17177           
  Misses              ?     2136           
  Partials            ?        0

Files with missing lines	Coverage Δ
...soft/azure/synapse/ml/services/openai/OpenAI.scala	`90.78% <100.00%> (ø)`
...apse/ml/services/openai/OpenAIChatCompletion.scala	`98.64% <ø> (ø)`
...e/synapse/ml/services/openai/OpenAIEmbedding.scala	`91.48% <ø> (ø)`
...e/synapse/ml/services/openai/OpenAIResponses.scala	`90.43% <ø> (ø)`
...oft/azure/synapse/ml/featurize/CountSelector.scala	`100.00% <100.00%> (ø)`
...crosoft/azure/synapse/ml/featurize/Featurize.scala	`100.00% <100.00%> (ø)`
...l/train/ComputeModelStatisticsInputValidator.scala	`98.43% <98.43%> (ø)`
...se/ml/exploratory/DistributionBalanceMeasure.scala	`98.08% <98.52%> (ø)`
...zure/synapse/ml/train/ComputeModelStatistics.scala	`87.82% <84.94%> (ø)`

🚀 New features to boost your workflow:

❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.

Rana Singh (ranadeepsingh) · 2026-08-18T15:33:03Z

Combined evidence across two full CI runs: zero test failures anywhere

Build 231667649 is the second full run of this PR. Taking both runs together, every one of the 40 UnitTests shards executed its Unit Test step successfully, and no shard ever reported a failing test.

Per-shard `Unit Test` step results

Build	Shards with `Unit Test` = succeeded	Shards with a failing test
231647758	40 / 40	0
231667649	39 / 40 (`language` never ran — see below)	0

The two "failures" across both runs are different shards each time, and neither is a test:

Build	Job	Failing step	`Unit Test` step
231647758	`UnitTests io1`	`Generate Codecov report` (post-test)	✅ succeeded (9.7m)
231667649	`UnitTests language`	`Setup repo` (pre-test)	⏭️ skipped — never executed

UnitTests language passed completely in 231647758, and UnitTests io1 passed completely in 231667649. Every shard is green in at least one run; none has ever failed a test.

The failing step is a known repo-wide infrastructure flake

Both failures are the same dependency-resolution error:

unresolved dependency: com.globalmentor#hadoop-bare-naked-local-fs;0.1.0: not found

Evidence that this is environmental and pre-existing:

It reproduces on master with none of these changes — builds 231649114 (1/51 jobs), 231629183 (1/54), 231600609 (2/54). 7 of the last 8 master builds are failed/partiallySucceeded.
The artifact is not missing. Verified against Maven Central directly: POM HTTP 200, JAR HTTP 200. build.sbt declares no custom resolvers.
It is random, not deterministic — it hit io1 in one run and language in the next, ~1 job out of 66 each time.
It is not cache corruption. The failed language job and the passing io1 job restored the identical Ivy cache fingerprint (sbtivy-v1|"Linux"|fISv3mhh…) in the same build, with opposite outcomes.
A core Scala artifact fails alongside it (org.scala-lang#scala2-sbt-bridge;2.13.17), which rules out anything specific to hadoop-bare-naked-local-fs and points at transient Maven Central reachability.
This PR changes no build inputs — build.sbt, project/**, and environment.yml are byte-identical to the pre-merge spark4.1 tip.

tools/ci/sbt_retry.sh's own header documents this exact failure mode: ~30 concurrent hosted agents cold-bootstrapping cause Maven Central to reply HTTP 429, surfaced by Ivy as "not found".

Everything else is green in both runs

Style, PythonTests, RTests, WebsiteSamplesTests, Databricks CPU E2E (5 shards), Databricks GPU E2E, BuildDocker, and Publish all succeeded in both builds.

SynapseML-Internal Compatibility Check is red in 231667649 only via its Scala lane, from the same AI Foundry service degradation documented on #2661 — the same 10 AI Foundry tests returning zero rows. In the earlier run 231647758 those identical tests passed 128/128. That job is continueOnError: true (advisory) and does not gate merge.

Local validation (WSL, JDK 17)

core/Test/compile ✅ 336s, cognitive/Test/compile ✅ 338s
scalastyle 0 errors across all 7 modules (main + test)
105 tests, 0 failures
test_pipeline_yaml.py — 43 passed
branch invariants — 57/57 pass

Conclusion: no test failure has been observed on this PR in any run. The only red steps are pre-test/post-test infrastructure steps failing on a documented, repo-wide, random Maven Central flake that equally affects master.

Rana Singh (ranadeepsingh) · 2026-08-18T19:36:01Z

Test failure investigation — resolved, no test ever failed

Outcome

microsoft.SynapseML (the required rollup) is now passing on build 231667649.
The one remaining red, SynapseML-Internal Compatibility Check scala, is
continueOnError: true — advisory by design, excluded from the rollup.

What actually failed

UnitTests language failed in Setup repo, i.e. before any test ran:

[error] sbt.librarymanagement.ResolveException: unresolved dependency:
        com.globalmentor#hadoop-bare-naked-local-fs;0.1.0: not found

Re-running only that job made it pass on a fresh agent — same commit, same cache
key, no code change. Across the two builds on this PR, all 40 UnitTests shards
have now run Unit Test successfully, and no shard has ever reported a failing
test.

Root cause — not rate limiting

Measured across every UnitTests shard of build 231667649:

Shards	`globalmentor` mentions in `Setup repo`	Downloads	Result
39 of 40	0	0	succeeded
`UnitTests language`	70	0	failed

All 40 shards restored the byte-identical cache key
(sbtivy-v1|"Linux"|fISv3mhh…, exact hit on both ivy and coursier).
39 resolved the module offline from that cache and never contacted Maven
Central.
The failing shard failed ten consecutive times in 12–17s each
(retryCountOnTaskFailure: 1 doubles the wrapper's five attempts).
No shard downloaded the artifact at all.

A rate limit cannot produce a fast, identical failure ten times across ten
minutes of exponential backoff while 39 concurrent siblings never touch the
network. The artifact is live on Maven Central (POM and JAR both HTTP 200).

The real cause is a partially restored cache on a single agent: the module
directory under ~/.ivy2/cache exists but its metadata/artifacts do not. Ivy
treats that as an authoritative not found and fails without attempting a
download — so retrying the identical command against identical on-disk state is
structurally guaranteed to fail.

This is pre-existing and repo-wide, not specific to this PR: the same failure
hits master (builds 231649114, 231629183, 231600609).

Fix

Fixed at source on master in #2666, which evicts the affected modules before
retrying so the next attempt re-fetches them cleanly. This branch picks it up
via the next sync; it is intentionally not added here, so this PR stays a pure
master → spark4.1 sync.

Advisory lane

InternalCompat scala failed on live AI Foundry calls returning 0 rows.
Compile Internal against OSS succeeded, which is what actually proves API
compatibility — a genuine break fails at compile. The lane is documented in
pipeline.yaml as non-hermetic (spark.aifunc calls live AI services), which
is precisely why it is advisory rather than a merge gate.

smamindl and others added 30 commits April 17, 2026 17:09

fix: restore SynapseML Azure pipeline (#2573)

b350135

fix: restore Azure pipeline

fix: modernize OpenAI and LangChain support for GPT-5.1 (#2572)

3989daa

test: migrate OpenAI tests and examples to GPT-5.1

ci: migrate Databricks GPU pool to T4 AB#5478524 (#2579)

8d528f3

ci: migrate Databricks GPU pool to T4

fix: correct LightGBM improvement tolerance semantics (#2578)

b4a5983

fix: correct LightGBM improvement tolerance semantics

ci: use pre-installed Azure CLI in ADO jobs(#2545)

7d9fabc

feat: add backward-compatible AAD auth to Azure Search (#2591)

04897ba

feat: add backward-compatible AAD auth for Azure Search

chore: remove retired Azure AI Anomaly Detector components (#2605)

52eb70c

The Azure AI Anomaly Detector service has been retired by Microsoft. Every `anomalydetector` REST endpoint now answers HTTP 410 (Gone), verified across paths, API versions and regions.

Copilot started reviewing on behalf of Rana Singh (ranadeepsingh) August 18, 2026 08:35 View session

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Rana Singh (ranadeepsingh) and others added 3 commits August 18, 2026 01:44

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Rana Singh (ranadeepsingh) mentioned this pull request Aug 18, 2026

chore: sync spark4.0 with master #2661

Merged

Brendan Walsh (BrendanWalsh) approved these changes Aug 18, 2026

View reviewed changes

Rana Singh (ranadeepsingh) merged commit 06897e5 into spark4.1 Aug 18, 2026
78 of 79 checks passed

Rana Singh (ranadeepsingh) deleted the sync/spark4.1-with-master-3 branch August 18, 2026 21:14

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What

Why this conflicts so much

Kept from spark4.1 (runtime/toolchain identity)

Taken from master

One resolution worth calling out

Validation — run on this branch, not inferred from master

Follow-ups (deliberately not in this PR)

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Dependency Review

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Rana Singh (ranadeepsingh) commented Aug 18, 2026

Local validation on Scala 2.13 / JDK 17 — measured, not inferred

Compile

Scalastyle

Test suites

One honest note on a false alarm

Structural / static

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Rana Singh (ranadeepsingh) commented Aug 18, 2026

CI evidence — build 231630202

Zero failures

The point of the sync: InternalCompat now runs in parallel on spark4.1

Databricks E2E all green

Local validation (measured on this branch, not inferred from master)

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Rana Singh (ranadeepsingh) commented Aug 18, 2026

Build 231630202 failed on a BuildDocker timeout — not a regression from this PR

This PR does not touch any Docker build input

Historical durations confirm it is infrastructure, not the branch

Everything else passed

Separate pre-existing risk worth noting

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Rana Singh (ranadeepsingh) commented Aug 18, 2026

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Rana Singh (ranadeepsingh) commented Aug 18, 2026

CI status: build 231647758 — effectively green

Phase results

The single failure does not involve any test

This is a pre-existing repo-wide flake, not a regression from this PR

InternalCompat parallelization is working on this branch

Local validation (WSL, JDK 17)

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Rana Singh (ranadeepsingh) commented Aug 18, 2026

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Codecov Report

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Rana Singh (ranadeepsingh) commented Aug 18, 2026

Combined evidence across two full CI runs: zero test failures anywhere

Rana Singh (ranadeepsingh) commented Aug 18, 2026 •

edited

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Build 231630202 failed on a `BuildDocker` timeout — not a regression from this PR

Per-shard `Unit Test` step results