Bug triage results: 2026-05-26

[andygrove]

Bug triage results: 2026-05-26

Triage pass over the open requires-triage queue, per the project Bug Triage Guide.

• Total issues processed: 22
• Labels applied to: 20
• Skipped: 2 (previous triage summaries)
• priority:critical: 1
• priority:high: 1 (preserved existing label set by reporter)
• priority:medium: 7
• priority:low: 11

Labels have already been applied and requires-triage removed from each issue listed under "Triaged". A reviewer should spot-check the calls and close this issue when satisfied. To correct a label, edit the affected issue directly.

Triaged

priority:critical

• GetStructField returns non-null for fields of a NULL struct (missing null-mask propagation) (#4432)
  • Area labels: area:expressions
  • Rationale: Silent wrong results — GetStructField returns the child column verbatim without applying the parent struct's null mask, so isnotnull(structCol.field) returns true even when structCol is null. The guide's first decision-tree question ("Can this bug cause silent wrong results?") classifies this as priority:critical, and the reporter shows a concrete Delta checkpoint reproducer.

priority:high

• Iceberg 1.11 support (#4381)
  • Area labels: area:Iceberg
  • Rationale: Already labeled priority:high and area:Iceberg by the reporter; preserved as-is. Iceberg 1.11 is the first release with Spark 4.1 support, so Comet has no Iceberg coverage on Spark 4.1 until this lands — a sizeable feature gap rather than a typical missing-feature medium.

priority:medium

• ToJson PartialEq impl is inconsistent with PartialEq impl (#4430)
  • Area labels: area:expressions
  • Rationale: Clear bug — the two equality impls compare different field sets, so two ToJson exprs differing only in timezone or ignore_null_fields compare equal through the Arc<dyn PhysicalExpr> path. Latent correctness risk if DataFusion's planner uses it for dedup/caching, but no demonstrated query producing wrong results; priority:medium per the "broken features that have workarounds" bucket, with an escalation note below.
• [EPIC] Implement all Spark date/time expressions (#4418)
  • Area labels: area:expressions
  • Rationale: Tracking EPIC for missing date/time expression coverage; per the guide, missing expression support is priority:medium ("functional bugs / broken features that have workarounds"), since unsupported expressions fall back to Spark.
• CometHashAggregateExec doesn't participate in Spark's AQEPropagateEmptyRelation optimization (#4412)
  • Area labels: area:aggregation
  • Rationale: The reporter notes "query results are correct under Comet" — what's lost is an AQE short-circuit, which is a functional/perf regression (not correctness). Fits priority:medium for broken behavior with a workaround.
• Implement TimeType support: Infrastructure - shuffle (#4396)
  • Area labels: area:shuffle
  • Rationale: Missing type support causing fallback to Spark for any shuffle on TimeType columns. Per the guide, missing feature support with a workaround (Spark fallback) is priority:medium.
• Drop the per-batch Comet→Spark buffer copy in CometColumnarPythonInput (#4383)
  • Area labels: none
  • Rationale: Performance enhancement — drops one of two buffer copies on the JVM→Python pyarrow UDF transport path. Per the guide, "performance regressions / broken features that have workarounds" is priority:medium.
• Implement TimeType support: Infrastructure - sort, min/max (#4379)
  • Area labels: area:aggregation
  • Rationale: Companion to Implement TimeType support: Infrastructure - shuffle #4396 — missing TimeType support in sort and min/max aggregates causes Spark fallback. Same priority:medium rationale.
• CometIcebergNativeScanExec: propagate outputOrdering from originalPlan instead of hardcoding Nil (#4367)
  • Area labels: area:scan
  • Rationale: Functional/perf gap — Comet inserts an unnecessary CometSortExec above Iceberg native scans for sort-merge joins because outputOrdering is hardcoded to Nil. Lost optimization with a clear fix path; priority:medium.

priority:low

• Revisit the case for native columnar-to-row? (#4440)
  • Area labels: none
  • Rationale: Design/discussion question about whether the rationale for CometNativeColumnarToRowExec still holds (benchmarks within ~5% of JVM path, GC pressure benefit not measured). Not a bug; per the guide, "everything else" falls into priority:low.
• change default Maven profile to Spark 4.0 (#4434)
  • Area labels: area:ci
  • Rationale: Build/configuration choice (which Spark profile defaults to) — tooling decision rather than a defect. priority:low per the guide's tooling/build bucket.
• Refactor CometPlainVector by replacing boolean flags with an enum (#4433)
  • Area labels: none
  • Rationale: Code-quality refactor follow-up; no functional change. Cosmetic per the guide → priority:low.
• Reorganize user and contributor guide navigation for clearer information architecture (#4421)
  • Area labels: none
  • Rationale: Documentation reorganization; no functional impact. Cosmetic/tooling → priority:low.
• Add a docs-review Claude skill to enforce style guide and documentation quality (#4420)
  • Area labels: none
  • Rationale: Tooling enhancement (a Claude skill for docs review); no runtime impact. priority:low.
• Establish nomenclature style guide and audit operator names for clarity (#4419)
  • Area labels: none
  • Rationale: Documentation/style guide work; cosmetic. priority:low.
• Reduce Github Action Usage (#4406)
  • Area labels: area:ci
  • Rationale: CI/tooling enhancement to reduce Action minutes. Per the guide, CI tooling → priority:low.
• Implement tiered CI approach (#4389)
  • Area labels: area:ci
  • Rationale: CI/tooling enhancement (tiered pipeline). priority:low.
• Add randomised fuzz harness for pyarrow UDF vector-copy path (#4384)
  • Area labels: none
  • Rationale: Test infrastructure enhancement; no runtime impact. The guide explicitly lists "test-only failures, tooling" as priority:low.
• Upgrade Spark 4.1.1 to 4.1.2 (#4380)
  • Area labels: none
  • Rationale: Routine dependency upgrade required for 0.17.0 per project versioning policy; tooling/maintenance → priority:low.
• Investigate Spark SQL test suites that load Comet without registering CometShuffleManager (#4377)
  • Area labels: area:shuffle, spark sql tests
  • Rationale: Test investigation — surfaces an asymmetry between how CometSparkSessionExtensions and CometShuffleManager are wired across Spark SQL suites. Test-only / tooling concern; priority:low.

Escalations to consider

• ToJson PartialEq impl is inconsistent with PartialEq impl (#4430)
  • The guide's first principle is "correctness over crashes" and the bug pattern (inconsistent equality used by the trait-object path) could in principle produce silently wrong query results if DataFusion's planner deduplicates two ToJson exprs that differ only in timezone or ignore_null_fields. Filed at priority:medium because the user-visible impact is not demonstrated. If a reviewer can confirm the planner-cache path actually fires on this difference, escalate to priority:high or priority:critical.

Skipped — needs more info

• Bug triage results: 2026-05-18 (#4359)
  • Previous triage summary issue rather than a bug; per this skill's contract it is the human reviewer's job to close it after spot-checking. Left as-is.
• Bug triage results: 2026-05-11 (#4287)
  • Previous triage summary issue rather than a bug; same as above.