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https://issues.apache.org/jira/browse/SPARK-59912?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Tian Gao reassigned SPARK-59912:
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Assignee: Tian Gao
> Fix flaky WorkerReuseTest and WorkerPoolCrashTest by pinning
> spark.python.use.daemon
> ------------------------------------------------------------------------------------
>
> Key: SPARK-59912
> URL: https://issues.apache.org/jira/browse/SPARK-59912
> Project: Spark
> Issue Type: Test
> Components: PySpark
> Affects Versions: 5.0.0
> Reporter: Tian Gao
> Assignee: Tian Gao
> Priority: Major
> Labels: pull-request-available
>
> WorkerReuseTest.test_reuse_worker_of_parallelize_range and
> WorkerPoolCrashTest.test_worker_crash assert that Python worker PIDs are
> reused across consecutive jobs. Worker reuse (stable PIDs) only happens on
> the daemon path; the simple-worker path (spark.python.use.daemon=false)
> spawns a fresh process per task. Sibling suites in the same file
> (SimpleWorkerTests, WorkerSegfaultNonDaemonTest) set
> spark.python.use.daemon=false, and because all suites in a test module share
> one gateway JVM, whichever SparkContext launches it first leaks its conf into
> the JVM system properties (same mechanism as SPARK-59885). When a non-daemon
> suite launches first, the reuse tests inherit use.daemon=false, run in
> simple-worker mode, and the PID-reuse assertions fail. Because test ordering
> is not deterministic, the failure is flaky. Fix: pin
> spark.python.use.daemon=true in the two reuse suites so the assertions no
> longer depend on the ambient JVM default or test ordering.
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