HyukjinKwon 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_r390161131
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File path: docs/sql-performance-tuning.md
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@@ -186,3 +186,61 @@ 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 partitions, local shuffle reader optimization and
skewed join optimization.
+ ### 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.coalescePartitions.enabled` configuration properties are
both enabled. There are four following sub-configurations in this optimization
rule. This feature simplifies the tuning of shuffle partitions number when
running queries. You don't need to set a proper shuffle partition number to fit
your dataset. You just need to set a large enough number and Spark can pick the
proper shuffle partition number at runtime.
+ <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 enabled, spark
will reduce the post shuffle partitions number based on the map output
statistics.
+ </td>
+ </tr>
+ <tr>
+
<td><code>spark.sql.adaptive.coalescePartitions.minPartitionNum</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.coalescePartitions.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.coalescePartitions.initialPartitionNum</code></td>
+ <td>200</td>
+ <td>
+ The advisory 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.advisoryPartitionSizeInBytes</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.coalescePartitions.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.localShuffleReader.enabled` configuration properties are
both enabled. This feature can improve the performance by saving the network
overhead of shuffle process.
+ ### Optimize Skewed Join
+ This feature choose the skewed partition and creates multi tasks to handle
the skewed partition when both enable `spark.sql.adaptive.enabled` and
`spark.sql.adaptive.skewJoin.enabled`. There are two following
sub-configurations in this optimization rule. Data skew can severely downgrade
performance of join queries. And this feature can split the skewed partition
into multi parallel tasks instead of original 1 task to reduce the overhead of
skewed join.
Review comment:
`choose` -> `chooses`
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