[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17427295#comment-17427295
]
Apache Spark commented on SPARK-33277:
--
User 'ankurdave' has created a pull request for this issue:
https://github.com/apache/spark/pull/34245
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Assignee: Takuya Ueshin
>Priority: Major
> Fix For: 2.4.8, 3.0.2, 3.1.0
>
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17427293#comment-17427293
]
Apache Spark commented on SPARK-33277:
--
User 'ankurdave' has created a pull request for this issue:
https://github.com/apache/spark/pull/34245
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Assignee: Takuya Ueshin
>Priority: Major
> Fix For: 2.4.8, 3.0.2, 3.1.0
>
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17254313#comment-17254313
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30913
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Assignee: Takuya Ueshin
>Priority: Major
> Fix For: 3.0.2, 3.1.0
>
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17253881#comment-17253881
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30899
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Priority: Major
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17225757#comment-17225757
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30242
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Assignee: Takuya Ueshin
>Priority: Major
> Fix For: 2.4.8, 3.0.2, 3.1.0
>
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17225756#comment-17225756
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30242
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Assignee: Takuya Ueshin
>Priority: Major
> Fix For: 2.4.8, 3.0.2, 3.1.0
>
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17224339#comment-17224339
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30218
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Priority: Major
> Fix For: 3.1.0
>
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17224338#comment-17224338
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30218
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Priority: Major
> Fix For: 3.1.0
>
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17224329#comment-17224329
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30217
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Priority: Major
> Fix For: 3.1.0
>
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17224328#comment-17224328
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30217
> 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: 2.4.7, 3.0.1
>Reporter: Takuya Ueshin
>Priority: Major
> Fix For: 3.1.0
>
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17222634#comment-17222634
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30177
> 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
>Priority: Major
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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[jira] [Commented] (SPARK-33277) Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
[
https://issues.apache.org/jira/browse/SPARK-33277?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17222635#comment-17222635
]
Apache Spark commented on SPARK-33277:
--
User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/30177
> 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
>Priority: Major
>
> Python/Pandas UDF right after off-heap vectorized reader could cause executor
> crash.
> E.g.,:
> {code:java}
> spark.range(0, 10, 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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