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commit 71c91cc76b9551ab9937c1d5f58f057037619bdb
Author: Hyukjin Kwon <[email protected]>
AuthorDate: Wed Mar 5 11:11:06 2025 +0900

    Revert "[SPARK-48516][PYTHON][FOLLOW-UP] Add a note in migration guide 
about Arrow-optimized Python UDF enabled by default"
    
    This reverts commit 45900c434c9ae275e9394a6fa0936a34a8bf3f3b.
---
 python/docs/source/migration_guide/pyspark_upgrade.rst | 1 -
 1 file changed, 1 deletion(-)

diff --git a/python/docs/source/migration_guide/pyspark_upgrade.rst 
b/python/docs/source/migration_guide/pyspark_upgrade.rst
index 8ef66a79cd70..906e40140cef 100644
--- a/python/docs/source/migration_guide/pyspark_upgrade.rst
+++ b/python/docs/source/migration_guide/pyspark_upgrade.rst
@@ -75,7 +75,6 @@ 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``.
 
 
 Upgrading from PySpark 3.3 to 3.4


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