venkata91 commented on a change in pull request #33615:
URL: https://github.com/apache/spark/pull/33615#discussion_r683862907
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File path: docs/configuration.md
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@@ -3134,3 +3134,111 @@ 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 helps improve the reliability and performance of spark
shuffle. It 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.
Reduce tasks fetch a combination of merged shuffle partitions and original
shuffle blocks as their input data, resulting in converting small random disk
reads by shuffle services into large sequential reads. Possibility of better
data locality for reduce tasks additionally helps minimize network IO.
+
+<p> <b> Currently push-based shuffle is only supported for Spark on YARN with
external shuffle service. </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.push.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.
Review comment:
Added the info to the doc.
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