[SPARK-33277][PYSPARK][SQL][2.4] Use ContextAwareIterator to stop consuming after the task ends. #30913
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…g after the task ends ### What changes were proposed in this pull request? This is a retry of apache#30177. This is not a complete fix, but it would take long time to complete (apache#30242). As discussed offline, at least using `ContextAwareIterator` should be helpful enough for many cases. As the Python evaluation consumes the parent iterator in a separate thread, it could consume more data from the parent even after the task ends and the parent is closed. Thus, we should use `ContextAwareIterator` to stop consuming after the task ends. ### Why are the changes needed? Python/Pandas UDF right after off-heap vectorized reader could cause executor crash. E.g.,: ```py spark.range(0, 100000, 1, 1).write.parquet(path) spark.conf.set("spark.sql.columnVector.offheap.enabled", True) def f(x): return 0 fUdf = udf(f, LongType()) spark.read.parquet(path).select(fUdf('id')).head() ``` This is because, the Python evaluation consumes the parent iterator in a separate thread and it consumes more data from the parent even after the task ends and the parent is closed. If an off-heap column vector exists in the parent iterator, it could cause segmentation fault which crashes the executor. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Added tests, and manually.
viirya
approved these changes
Dec 23, 2020
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Let me merge this into branch-2.4. SparkR tests are hardly related, and all relevant tests passed. |
HyukjinKwon
approved these changes
Dec 24, 2020
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Merged to branch-2.4. |
HyukjinKwon
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that referenced
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Dec 24, 2020
…suming after the task ends ### What changes were proposed in this pull request? This is a backport of #30899. This is not a complete fix, but it would take long time to complete (#30242). As discussed offline, at least using `ContextAwareIterator` should be helpful enough for many cases. As the Python evaluation consumes the parent iterator in a separate thread, it could consume more data from the parent even after the task ends and the parent is closed. Thus, we should use `ContextAwareIterator` to stop consuming after the task ends. ### Why are the changes needed? Python/Pandas UDF right after off-heap vectorized reader could cause executor crash. E.g.,: ```py spark.range(0, 100000, 1, 1).write.parquet(path) spark.conf.set("spark.sql.columnVector.offheap.enabled", True) def f(x): return 0 fUdf = udf(f, LongType()) spark.read.parquet(path).select(fUdf('id')).head() ``` This is because, the Python evaluation consumes the parent iterator in a separate thread and it consumes more data from the parent even after the task ends and the parent is closed. If an off-heap column vector exists in the parent iterator, it could cause segmentation fault which crashes the executor. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Added tests, and manually. Closes #30913 from ueshin/issues/SPARK-33277/2.4/context_aware_iterator. Authored-by: Takuya UESHIN <ueshin@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
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Test build #133326 has finished for PR 30913 at commit
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What changes were proposed in this pull request?
This is a backport of #30899.
This is not a complete fix, but it would take long time to complete (#30242).
As discussed offline, at least using
ContextAwareIteratorshould be helpful enough for many cases.As the Python evaluation consumes the parent iterator in a separate thread, it could consume more data from the parent even after the task ends and the parent is closed. Thus, we should use
ContextAwareIteratorto stop consuming after the task ends.Why are the changes needed?
Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
E.g.,:
This is because, the Python evaluation consumes the parent iterator in a separate thread and it consumes more data from the parent even after the task ends and the parent is closed. If an off-heap column vector exists in the parent iterator, it could cause segmentation fault which crashes the executor.
Does this PR introduce any user-facing change?
No.
How was this patch tested?
Added tests, and manually.