41af409b7b
### What changes were proposed in this pull request? PySpark added pinned thread mode at https://github.com/apache/spark/pull/24898 to sync Python thread to JVM thread. Previously, one JVM thread could be reused which ends up with messed inheritance hierarchy such as thread local especially when multiple jobs run in parallel. To completely fix this, we should enable this mode by default. ### Why are the changes needed? To correctly support parallel job submission and management. ### Does this PR introduce _any_ user-facing change? Yes, now Python thread is mapped to JVM thread one to one. ### How was this patch tested? Existing tests should cover it. Closes #32429 from HyukjinKwon/SPARK-35303. Authored-by: HyukjinKwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
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.. Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
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distributed with this work for additional information
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regarding copyright ownership. The ASF licenses this file
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to you under the Apache License, Version 2.0 (the
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"License"); you may not use this file except in compliance
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with the License. You may obtain a copy of the License at
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.. http://www.apache.org/licenses/LICENSE-2.0
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.. Unless required by applicable law or agreed to in writing,
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software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, either express or implied. See the License for the
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specific language governing permissions and limitations
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under the License.
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=================================
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Upgrading from PySpark 3.1 to 3.2
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=================================
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* In Spark 3.2, the PySpark methods from sql, ml, spark_on_pandas modules raise the ``TypeError`` instead of ``ValueError`` when are applied to an param of inappropriate type.
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* In Spark 3.2, the traceback from Python UDFs, pandas UDFs and pandas function APIs are simplified by default without the traceback from the internal Python workers. In Spark 3.1 or earlier, the traceback from Python workers was printed out. To restore the behavior before Spark 3.2, you can set ``spark.sql.execution.pyspark.udf.simplifiedTraceback.enabled`` to ``false``.
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* In Spark 3.2, pinned thread mode is enabled by default to map each Python thread to the corresponding JVM thread. Previously,
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one JVM thread could be reused for multiple Python threads, which resulted in one JVM thread local being shared to multiple Python threads.
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Also, note that now ``pyspark.InheritableThread`` or ``pyspark.inheritable_thread_target`` is recommended to use together for a Python thread
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to properly inherit the inheritable attributes such as local properties in a JVM thread, and to avoid a potential resource leak issue.
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To restore the behavior before Spark 3.2, you can set ``PYSPARK_PIN_THREAD`` environment variable to ``false``.
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