[jira] [Created] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.

["Takuya Ueshin (Jira)" <ji...@apache.org>]

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.



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