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[SPARK-33679][SQL][DOCS][FOLLOWUP] Enable spark.sql.adaptive.enabled by default

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### What changes were proposed in this pull request?

Update AQE is `disabled` to `enabled` in sql-performance-tuning docs

### Why are the changes needed?

Make docs correct.

### Does this PR introduce _any_ user-facing change?

yes, docs changed.

### How was this patch tested?

Not need.

Closes #33295 from ulysses-you/enable-AQE.

Lead-authored-by: ulysses-you <ulyssesyou18@gmail.com>
Co-authored-by: Hyukjin Kwon <gurwls223@gmail.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
This commit is contained in:
ulysses-you 2021-07-12 18:49:46 +09:00 committed by Hyukjin Kwon
parent 32720dd3e1
commit 286c231c1e
1 changed files with 1 additions and 1 deletions
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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, 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.
### Coalescing Post Shuffle Partitions
This feature coalesces the post shuffle partitions based on the map output statistics when both `spark.sql.adaptive.enabled` and `spark.sql.adaptive.coalescePartitions.enabled` configurations are true. This feature simplifies the tuning of shuffle partition number when running queries. You do not need to set a proper shuffle partition number to fit your dataset. Spark can pick the proper shuffle partition number at runtime once you set a large enough initial number of shuffle partitions via `spark.sql.adaptive.coalescePartitions.initialPartitionNum` configuration.