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_r392834386
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File path: docs/sql-performance-tuning.md
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@@ -186,3 +186,63 @@ 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 coalescing post-shuffle partitions, converting sort-merge join to
broadcast join, and skewed join optimization.
+
+### Coalescing Post Shuffle Partition Number
+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` configuration properties are
enabled. There are four following sub-configurations in this optimization rule.
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.
+ <table class="table">
+ <tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+ <tr>
+ <td><code>spark.sql.adaptive.coalescePartitions.enabled</code></td>
+ <td>true</td>
+ <td>
+ When true and <code>spark.sql.adaptive.enabled</code> is true, Spark
will coalesce contiguous shuffle partitions according to the target size
(specified by <code>spark.sql.adaptive.advisoryPartitionSizeInBytes</code>), to
avoid too many small tasks.
+ </td>
+ </tr>
+ <tr>
+
<td><code>spark.sql.adaptive.coalescePartitions.minPartitionNum</code></td>
+ <td>Default Parallelism</td>
+ <td>
+ The minimum number of shuffle partitions after coalescing. If not set,
the default value is the default parallelism of the Spark cluster. This
configuration only has an effect when <code>spark.sql.adaptive.enabled</code>
and <code>spark.sql.adaptive.coalescePartitions.enabled</code> are both enabled.
+ </td>
+ </tr>
+ <tr>
+
<td><code>spark.sql.adaptive.coalescePartitions.initialPartitionNum</code></td>
+ <td>200</td>
+ <td>
+ The initial number of shuffle partitions before coalescing. By default
it equals to <code>spark.sql.shuffle.partitions</code>. This configuration only
has an effect when <code>spark.sql.adaptive.enabled</code> and
<code>spark.sql.adaptive.coalescePartitions.enabled</code> are both enabled.
+ </td>
+ </tr>
+ <tr>
+ <td><code>spark.sql.adaptive.advisoryPartitionSizeInBytes</code></td>
+ <td>64 MB</td>
+ <td>
+ The advisory size in bytes of the shuffle partition during adaptive
optimization (when <code>spark.sql.adaptive.enabled</code> is true). It takes
effect when Spark coalesces small shuffle partitions or splits skewed shuffle
partition.
+ </td>
+ </tr>
+ </table>
+
+### Optimize Local Shuffle Reader
+AQE converts the sort merge join to broad cast hash join when the runtime
statistics of any join side is smaller than the broadcast hash join threshold.
This feature can optimize the shuffle reader to local shuffle reader after
converting the sort merge join to broadcast hash join at runtime and if no
additional shuffle is introduced. It takes effect when both
`spark.sql.adaptive.enabled` and
`spark.sql.adaptive.localShuffleReader.enabled` configuration properties are
enabled. This feature can improve the performance by saving the network
overhead of shuffle process.
Review comment:
`broad cast` -> `broadcast`
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