You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "ASF GitHub Bot (Jira)" <ji...@apache.org> on 2026/07/06 09:44:00 UTC
[jira] [Updated] (SPARK-57959) Deflake MetricsFailureInjectionSuite non-deterministic stage block failure injection test
[ https://issues.apache.org/jira/browse/SPARK-57959?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
ASF GitHub Bot updated SPARK-57959:
-----------------------------------
Labels: pull-request-available (was: )
> Deflake MetricsFailureInjectionSuite non-deterministic stage block failure injection test
> -----------------------------------------------------------------------------------------
>
> Key: SPARK-57959
> URL: https://issues.apache.org/jira/browse/SPARK-57959
> Project: Spark
> Issue Type: Test
> Components: SQL
> Affects Versions: 5.0.0
> Reporter: Hyukjin Kwon
> Priority: Major
> Labels: pull-request-available
>
> MetricsFailureInjectionSuite 'Non-deterministic stage block failure injection - injectFailure=true' is flaky (~1/6 in the full suite; passes 10/10 in isolation). Evidence from macOS diagnostics: the failing run shows stage1=300 stage2=5 (a clean run, no recompute) and completes in ~370ms vs ~860ms on a passing run, and the test's own shuffle receives no injected FetchFailed. The INJECT_SHUFFLE_FETCH_FAILURES machinery corrupts mapper-0 of the shuffle map stage, but whether the downstream reducer observes the FetchFailed (forcing the stage-1 recompute that inflates the raw metric) depends on task scheduling in the shared SparkContext, and it occasionally does not fire. Fix (test-only): re-run the query, resetting metrics each attempt, until the injection forces a recompute (stage1 > 300), up to 10 attempts, then run the original assertions. Does not touch the shared DAGScheduler injection machinery used by other suites. Validated 8x full-suite green on macos-15.
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org