[GitHub] [spark] viirya commented on a change in pull request #24700: [SPARK-27834][SQL][R][PYTHON] Make separate PySpark/SparkR vectorization configurations

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viirya commented on a change in pull request #24700: [SPARK-27834][SQL][R][PYTHON] Make separate PySpark/SparkR vectorization configurations
URL: https://github.com/apache/spark/pull/24700#discussion_r287820143
 
 

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 File path: docs/sql-pyspark-pandas-with-arrow.md
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 @@ -44,9 +44,9 @@ You can install using pip or conda from the conda-forge channel. See PyArrow
 Arrow is available as an optimization when converting a Spark DataFrame to a Pandas DataFrame
 using the call `toPandas()` and when creating a Spark DataFrame from a Pandas DataFrame with
 `createDataFrame(pandas_df)`. To use Arrow when executing these calls, users need to first set
-the Spark configuration 'spark.sql.execution.arrow.enabled' to 'true'. This is disabled by default.
+the Spark configuration 'spark.sql.pyspark.execution.arrow.enabled' to 'true'. This is disabled by default.
 
-In addition, optimizations enabled by 'spark.sql.execution.arrow.enabled' could fallback automatically
+In addition, optimizations enabled by 'spark.sql.pyspark.execution.arrow.enabled' could fallback automatically
 to non-Arrow optimization implementation if an error occurs before the actual computation within Spark.
 This can be controlled by 'spark.sql.execution.arrow.fallback.enabled'.
 
 Review comment:
   `spark.sql.execution.arrow.fallback.enabled` -> `spark.sql.pyspark.execution.arrow.fallback.enabled`?

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