BryanCutler 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_r332790038
 
 

 ##########
 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:
   >Do we need to mention this in SQL migration guide too? This sounds like a 
requirement for migrating from 2.3 and 2.4.
   
   It's not a requirement for migrating, because it's only when upgrading 
pyarrow to 0.15.0 with Spark 2.x. Although, someone from Spark 2.3.x might have 
pyarrow=0.8.0 and then migrating to Spark 2.4.x will be forced to upgrade 
pyarrow to a minimum version of 0.12.1, and might end up with 0.15.0 and need 
the env var. It's a bit of a stretch I, but if you think it's could help to add 
it in the guide I will.

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