Ngone51 commented on a change in pull request #33615:
URL: https://github.com/apache/spark/pull/33615#discussion_r681592292
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File path: docs/configuration.md
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@@ -3134,3 +3134,119 @@ The stage level scheduling feature allows users to
specify task and executor res
This is only available for the RDD API in Scala, Java, and Python. It is
available on YARN and Kubernetes when dynamic allocation is enabled. See the
[YARN](running-on-yarn.html#stage-level-scheduling-overview) page or
[Kubernetes](running-on-kubernetes.html#stage-level-scheduling-overview) page
for more implementation details.
See the `RDD.withResources` and `ResourceProfileBuilder` API's for using this
feature. The current implementation acquires new executors for each
`ResourceProfile` created and currently has to be an exact match. Spark does
not try to fit tasks into an executor that require a different ResourceProfile
than the executor was created with. Executors that are not in use will idle
timeout with the dynamic allocation logic. The default configuration for this
feature is to only allow one ResourceProfile per stage. If the user associates
more then 1 ResourceProfile to an RDD, Spark will throw an exception by
default. See config `spark.scheduler.resource.profileMergeConflicts` to control
that behavior. The current merge strategy Spark implements when
`spark.scheduler.resource.profileMergeConflicts` is enabled is a simple max of
each resource within the conflicting ResourceProfiles. Spark will create a new
ResourceProfile with the max of each of the resources.
+
+# Push-based shuffle overview
+
+Push-based shuffle is an improved shuffle architecture that optimizes the
reliability and performance of the shuffle step in Spark. Complementing the
existing shuffle mechanism, push-based shuffle takes a best-effort approach to
push the shuffle blocks generated by the map tasks to remote shuffle services
to be merged per shuffle partition. When the reduce tasks start running, they
fetch a combination of the merged shuffle partitions and some of the original
shuffle blocks to get their input data. As a result, push-based shuffle
converts shuffle services’ small random disk reads into large sequential reads.
The reduce tasks are also scheduled with locality preferences of the locations
of their corresponding merged shuffle partitions, which helps to significantly
improve shuffle fetch data locality.
+
+<p> <b> Currently push-based shuffle is only supported for Spark on YARN.
</b></p>
+
+### Shuffle server side configuration options
+
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th><th>Since
Version</th></tr>
+<tr>
+ <td><code>spark.shuffle.server.mergedShuffleFileManagerImpl</code></td>
+ <td>
+
<code>org.apache.spark.network.shuffle.ExternalBlockHandler$NoOpMergedShuffleFileManager</code>
+ </td>
+ <td>
+ Class name of the implementation of MergedShuffleFileManager that manages
push-based shuffle. This acts as a server side config to disable or enable
push-based shuffle. By default, push-based shuffle is disabled at the server
side, which does not perform push-based shuffle irrespective of the client side
configuration. To turn on push-based shuffle at the server side, set the
configuration to
<code>org.apache.spark.network.shuffle.RemoteBlockPushResolver</code>
+ </td>
+ <td>3.2.0</td>
+</tr>
+<tr>
+ <td><code>spark.shuffle.server.minChunkSizeInMergedShuffleFile</code></td>
+ <td><code>2m</code></td>
+ <td>
+ The minimum size of a chunk when dividing a merged shuffle file into
multiple chunks during push-based shuffle. A merged shuffle file consists of
multiple small shuffle blocks. Fetching the complete merged shuffle file in a
single disk I/O increases the memory requirements for both the clients and the
shuffle service. Instead, the shuffle service serves the merged file in <code>
MB-sized chunks </code>. This configuration controls how big a chunk can get. A
corresponding index file for each merged shuffle file will be generated
indicating chunk boundaries.
+ </td>
+ <td>3.2.0</td>
+</tr>
+<tr>
+ <td><code>spark.shuffle.server.mergedIndexCacheSize</code></td>
+ <td><code>100m</code></td>
+ <td>
+ The size of cache in memory which is used in push-based shuffle for
storing merged index files. This cache is in addition to the one configured via
<code>spark.shuffle.service.index.cache.size</code>.
+ </td>
+ <td>3.2.0</td>
+</tr>
+<tr>
+ <td><code>spark.shuffle.server.ioExceptionsThresholdDuringMerge</code></td>
+ <td><code>4</code></td>
+ <td>
+ The threshold for number of IOExceptions while merging shuffle blocks to a
shuffle partition. When the number of IOExceptions while writing to the merged
shuffle data/index/meta files exceed this threshold then the shuffle server
will stop merging shuffle blocks for this shuffle partition.
+ </td>
+ <td>3.2.0</td>
+</tr>
+</table>
+
+### Client side configuration options
+
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th><th>Since
Version</th></tr>
+<tr>
+ <td><code>spark.shuffle.push.enabled</code></td>
+ <td><code>false</code></td>
+ <td>
+ Set to 'true' to enable push-based shuffle on the client side and this
works in conjunction with the server side flag
spark.shuffle.server.mergedShuffleFileManagerImpl which needs to be set with
the appropriate org.apache.spark.network.shuffle.MergedShuffleFileManager
implementation for push-based shuffle to be enabled
Review comment:
`be enabled` -> `be enabled.`
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