Takuya Ueshin created SPARK-33277:
-------------------------------------
Summary: Python/Pandas UDF right after off-heap vectorized reader
could cause executor crash.
Key: SPARK-33277
URL: https://issues.apache.org/jira/browse/SPARK-33277
Project: Spark
Issue Type: Bug
Components: PySpark, SQL
Affects Versions: 3.0.1
Reporter: Takuya Ueshin
Python/Pandas UDF right after off-heap vectorized reader could cause executor
crash.
E.g.,:
{code:java}
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()
{code}
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.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
