rishav23 opened a new pull request, #56060:
URL: https://github.com/apache/spark/pull/56060
<!--
Thanks for sending a pull request! Here are some tips for you:
1. If this is your first time, please read our contributor guidelines:
https://spark.apache.org/contributing.html
2. Ensure you have added or run the appropriate tests for your PR:
https://spark.apache.org/developer-tools.html
3. If the PR is unfinished, add '[WIP]' in your PR title, e.g.,
'[WIP][SPARK-XXXX] Your PR title ...'.
4. Be sure to keep the PR description updated to reflect all changes.
5. Please write your PR title to summarize what this PR proposes.
6. If possible, provide a concise example to reproduce the issue for a
faster review.
7. If you want to add a new configuration, please read the guideline first
for naming configurations in
'core/src/main/scala/org/apache/spark/internal/config/ConfigEntry.scala'.
8. If you want to add or modify an error type or message, please read the
guideline first in
'common/utils/src/main/resources/error/README.md'.
-->
### 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:
- timeout-aware approximate behavior
- exact results when computation completes successfully
### 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?
- Reproduced the issue locally using large RDDs
- Verified timeout behavior before and after the fix
- Added regression tests in python/pyspark/tests/test_rdd.py
- Ran: python/run-tests.py --testnames pyspark.tests.test_rdd
### Was this patch authored or co-authored using generative AI tooling?
No
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
