HyukjinKwon commented on a change in pull request #27616: [SPARK-30864]
[SQL]add the user guide for Adaptive Query Execution
URL: https://github.com/apache/spark/pull/27616#discussion_r390166302
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File path: docs/sql-performance-tuning.md
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@@ -186,3 +186,61 @@ The "REPARTITION_BY_RANGE" hint must have column names
and a partition number is
SELECT /*+ REPARTITION(3, c) */ * FROM t
SELECT /*+ REPARTITION_BY_RANGE(c) */ * FROM t
SELECT /*+ REPARTITION_BY_RANGE(3, c) */ * FROM t
+
+## Adaptive Query Execution
+Adaptive Query Execution (AQE) is an optimization technique in Spark SQL that
makes use of the runtime statistics to choose the most efficient query
execution plan. AQE is disabled by default. Spark SQL can use the umbrella
configuration of `spark.sql.adaptive.enabled` to control whether turn it
on/off. As of Spark 3.0, there are three major features in AQE, including
coalescing post-shuffle partitions, local shuffle reader optimization and
skewed join optimization.
+ ### Coalescing Post Shuffle Partition Number
+ This feature coalesces the post shuffle partitions based on the map output
statistics when `spark.sql.adaptive.enabled` and
`spark.sql.adaptive.coalescePartitions.enabled` configuration properties are
both enabled. There are four following sub-configurations in this optimization
rule. This feature simplifies the tuning of shuffle partitions number when
running queries. You don't need to set a proper shuffle partition number to fit
your dataset. You just need to set a large enough number and Spark can pick the
proper shuffle partition number at runtime.
Review comment:
when `spark.sql.adaptive.enabled` and
`spark.sql.adaptive.coalescePartitions.enabled` configuration properties are
both enabled. ->
when both `spark.sql.adaptive.enabled` and
`spark.sql.adaptive.coalescePartitions.enabled` configurations are enabled.
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