HyukjinKwon commented on a change in pull request #33295:
URL: https://github.com/apache/spark/pull/33295#discussion_r667787214
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File path: docs/sql-performance-tuning.md
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@@ -234,7 +234,7 @@ The "REPARTITION_BY_RANGE" hint must have column names and
a partition number is
For more details please refer to the documentation of [Partitioning
Hints](sql-ref-syntax-qry-select-hints.html#partitioning-hints).
## 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, converting sort-merge join to broadcast
join, and skew join optimization.
+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 enabled by default since Apache Spark 3.2.0. 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, converting sort-merge join to
broadcast join, and skew join optimization.
Review comment:
```suggestion
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, which is enabled by default since Apache Spark 3.2.0. Spark SQL
can turn on and off AQE by `spark.sql.adaptive.enabled` as an umbrella
configuration. As of Spark 3.0, there are three major features in AQE:
including coalescing post-shuffle partitions, converting sort-merge join to
broadcast join, and skew join optimization.
```
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