[SPARK-33277][PYSPARK][SQL][3.0] Use ContextAwareIterator to stop consuming after the task ends #30217
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…g after the task ends
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.
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.
No.
Added tests, and manually.
Closes apache#30177 from ueshin/issues/SPARK-33277/python_pandas_udf.
Authored-by: Takuya UESHIN <ueshin@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
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HyukjinKwon
approved these changes
Nov 1, 2020
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Merged to branch-3.0 |
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Nov 2, 2020
…suming after the task ends ### What changes were proposed in this pull request? This is a backport of #30177. 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 #30217 from ueshin/issues/SPARK-33277/3.0/python_pandas_udf. Authored-by: Takuya UESHIN <ueshin@databricks.com> Signed-off-by: HyukjinKwon <gurwls223@apache.org>
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Test build #130500 has finished for PR 30217 at commit
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What changes were proposed in this pull request?
This is a backport of #30177.
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.