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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