cloud-fan 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_r381285141
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File path: docs/sql-performance-tuning.md
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@@ -186,3 +186,75 @@ 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 partition number, optimizing local shuffle reader and
optimizing skewed join.
+ ### 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.shuffle.reducePostShufflePartitions.enabled` configuration
properties are both enabled. There are four following sub-configurations in
this optimization rule. And this feature can bring about 1.28x performance gain
with query 38 in 3TB TPC-DS.
Review comment:
`And this feature can bring about 1.28x performance gain with query 38 in
3TB TPC-DS.` This is not useful... how about something like
```
This feature simplifies the tuning of shuffle partitions number when running
queries. You don't need to
set a proper shuffle partition number that just fits your data. You just
need to set a large enough number and
Spark can pick the proper shuffle partition number at runtime.
```
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