HyukjinKwon commented on code in PR #50099: URL: https://github.com/apache/spark/pull/50099#discussion_r1997799152
########## python/docs/source/migration_guide/pyspark_upgrade.rst: ########## @@ -75,7 +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, unnecessary conversion to pandas instances is removed when ``spark.sql.execution.pythonUDTF.arrow.enabled`` is enabled. As a result, the type coercion changes when the produced output has a schema different from the specified schema. To restore the previous behavior, ``enable spark.sql.legacy.execution.pythonUDTF.pandas.conversion.enabled``. Review Comment: Yes ... I don't mind it .. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
