[SPARK-50520][PySpark] Respect timeout in df.rdd.countApprox()#56060
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rishav23 wants to merge 2 commits into
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[SPARK-50520][PySpark] Respect timeout in df.rdd.countApprox()#56060rishav23 wants to merge 2 commits into
rishav23 wants to merge 2 commits into
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What changes were proposed in this pull request?
PySpark approximate RDD actions currently call getFinalValue() on the PartialResult returned by Spark approximate job APIs. This introduces blocking behavior and causes APIs like countApprox(timeout=...) to wait for full job completion instead of respecting timeout semantics. This PR changes PySpark to use PartialResult.initialValue(), which already contains the timeout-aware approximation computed by ApproximateActionListener.awaitResult(). Additionally, regression tests were added to validate:
Why are the changes needed?
Spark approximate actions are designed to return partial results after the specified timeout. Scala APIs correctly expose this behavior through PartialResult, but PySpark currently forces blocking completion by calling getFinalValue(). As a result, PySpark countApprox() ignores timeout semantics and waits for full completion.
Does this PR introduce any user-facing change?
Yes, PySpark approximate RDD actions now correctly respect timeout semantics and return timeout-aware approximate results instead of blocking until full completion.
How was this patch tested?
Was this patch authored or co-authored using generative AI tooling?
No