cloud-fan commented on code in PR #50034:
URL: https://github.com/apache/spark/pull/50034#discussion_r1964739925


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python/docs/source/migration_guide/pyspark_upgrade.rst:
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@@ -75,6 +75,7 @@ Upgrading from PySpark 3.5 to 4.0
 * In Spark 4.0, ``compute.ops_on_diff_frames`` is on by default. To restore 
the previous behavior, set ``compute.ops_on_diff_frames`` to ``false``.
 * In Spark 4.0, the data type ``YearMonthIntervalType`` in 
``DataFrame.collect`` no longer returns the underlying integers. To restore the 
previous behavior, set ``PYSPARK_YM_INTERVAL_LEGACY`` environment variable to 
``1``.
 * In Spark 4.0, items other than functions (e.g. ``DataFrame``, ``Column``, 
``StructType``) have been removed from the wildcard import ``from 
pyspark.sql.functions import *``, you should import these items from proper 
modules (e.g. ``from pyspark.sql import DataFrame, Column``, ``from 
pyspark.sql.types import StructType``).
+* In Spark 4.0, ``spark.sql.execution.pythonUDF.arrow.enabled`` is enabled by 
default. If users have PyArrow and pandas installed in their local and Spark 
Cluster, it automatically optimizes the regular Python UDFs with Arrow. To turn 
off the Arrow optimization, set ``spark.sql.execution.pythonUDF.arrow.enabled`` 
to ``false``.

Review Comment:
   do we need to mention any behavior changes that users should care about?



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