[GitHub] [spark] viirya commented on a change in pull request #26045: [SPARK-29367][DOC] Add compatibility note for Arrow 0.15.0 to SQL guide

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viirya commented on a change in pull request #26045: [SPARK-29367][DOC] Add compatibility note for Arrow 0.15.0 to SQL guide
URL: https://github.com/apache/spark/pull/26045#discussion_r332296283
 
 

 ##########
 File path: docs/sql-pyspark-pandas-with-arrow.md
 ##########
 @@ -219,3 +219,14 @@ Note that a standard UDF (non-Pandas) will load timestamp data as Python datetim
 different than a Pandas timestamp. It is recommended to use Pandas time series functionality when
 working with timestamps in `pandas_udf`s to get the best performance, see
 [here](https://pandas.pydata.org/pandas-docs/stable/timeseries.html) for details.
+
+### Compatibiliy Setting for PyArrow >= 0.15.0 and Spark 2.3.x, 2.4.x
+
+Since Arrow 0.15.0, a change in the binary IPC format requires an environment variable to be set in
+Spark so that PySpark maintain compatibility with versions on PyArrow 0.15.0 and above. The following can be added to `conf/spark-env.sh` to use the legacy IPC format:
+
+```
+ARROW_PRE_0_15_IPC_FORMAT=1
+```
+
+This will instruct PyArrow >= 0.15.0 to use the legacy IPC format with the older Arrow Java that is in Spark 2.3.x and 2.4.x.
 
 Review comment:
   From the wording, does it mean if using Spark 3.0 which with newer Arrow Java, you do not need to set it?

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