Github user viirya commented on a diff in the pull request: https://github.com/apache/spark/pull/20678#discussion_r170809925 --- Diff: docs/sql-programming-guide.md --- @@ -1800,6 +1800,7 @@ working with timestamps in `pandas_udf`s to get the best performance, see ## Upgrading From Spark SQL 2.3 to 2.4 - Since Spark 2.4, Spark maximizes the usage of a vectorized ORC reader for ORC files by default. To do that, `spark.sql.orc.impl` and `spark.sql.orc.filterPushdown` change their default values to `native` and `true` respectively. + - In PySpark, when Arrow optimization is enabled, previously `toPandas` just failed when Arrow optimization is unabled to be used whereas `createDataFrame` from Pandas DataFrame allowed the fallback to non-optimization. Now, both `toPandas` and `createDataFrame` from Pandas DataFrame allow the fallback by default, which can be switched by `spark.sql.execution.arrow.fallback.enabled`. --- End diff -- Not only in migration section, I think we should also document this config in the section like `PySpark Usage Guide for Pandas with Apache Arrow`.
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