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_r380471798
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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
make 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. There are
three mainly feature in AQE, including coalescing post partition number,
optimizing local shuffle reader and optimizing skewed join.
+ ### Coalescing Post Shuffle Partition Num
+ 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.
+ <table class="table">
+ <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+ <tr>
+
<td><code>spark.sql.adaptive.shuffle.reducePostShufflePartitions.enabled</code></td>
+ <td>true</td>
+ <td>
+ When true and <code>spark.sql.adaptive.enabled</code> is enabled, spark
will reduce the post shuffle partitions number based on the map output
statistics.
+ </td>
+ </tr>
+ <tr>
+
<td><code>spark.sql.adaptive.shuffle.minNumPostShufflePartitions</code></td>
+ <td>1</td>
+ <td>
+ The advisory minimum number of post-shuffle partitions used when
<code>spark.sql.adaptive.enabled</code> and
<code>spark.sql.adaptive.shuffle.reducePostShufflePartitions.enabled</code> are
both enabled. It is suggested to be almost 2~3x of the parallelism when doing
benchmark.
+ </td>
+ </tr>
+ <tr>
+
<td><code>spark.sql.adaptive.shuffle.maxNumPostShufflePartitions</code></td>
+ <td>Int.MaxValue</td>
+ <td>
+ The advisory maximum number of post-shuffle partitions used in adaptive
execution. This is used as the initial number of pre-shuffle partitions. By
default it equals to <code>spark.sql.shuffle.partitions</code>.
+ </td>
+ </tr>
+ <tr>
+
<td><code>spark.sql.adaptive.shuffle.targetPostShuffleInputSize</code></td>
+ <td>67108864 (64 MB)</td>
+ <td>
+ The target post-shuffle input size in bytes of a task when
<code>spark.sql.adaptive.enabled</code> and
<code>spark.sql.adaptive.shuffle.reducePostShufflePartitions.enabled</code> are
both enabled.
+ </td>
+ </tr>
+ </table>
+
+ ### Optimize Local Shuffle Reader
+ This feature optimize the shuffle reader to local shuffle reader when
converting the sort merge join to broadcast hash join in runtime and no
additional shuffle introduced. It takes effect when
`spark.sql.adaptive.enabled` and
`spark.sql.adaptive.shuffle.localShuffleReader.enabled` configuration
properties are both enabled.
Review comment:
ditto, users care more about the benefit
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