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Posted to issues@celeborn.apache.org by "thomasg19930417 (via GitHub)" <gi...@apache.org> on 2023/07/13 09:14:29 UTC

[GitHub] [incubator-celeborn] thomasg19930417 opened a new issue, #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

thomasg19930417 opened a new issue, #1712:
URL: https://github.com/apache/incubator-celeborn/issues/1712

   **Env:**
   
   spark 3.2.2
   celeborn 0.2.1
   
   Question desc:
   
      When spark3 spark.sql.adaptive.autoBroadcastJoinThreshold is turned on, it is found that when automatic Broadcastjoin is triggered, if spark.sql.shuffle.partitions is set relatively large at this time, the broadcast time will be longer
   
   **Spark config:**
   
   #Dynamic resource
   spark.dynamicAllocation.enabled true
   spark.dynamicAllocation.minExecutors 1
   spark.dynamicAllocation.maxExecutors 500
   spark.dynamicAllocation.initialExecutors  2
   spark.dynamicAllocation.executorIdleTimeout 120
   
   #Adaptive
   spark.shuffle.minNumPartitionsToHighlyCompress 5000
   spark.sql.adaptive.enabled         true
   spark.sql.adaptive.coalescePartitions.enabled true
   spark.sql.adaptive.skewedJoin.enabled true
   spark.sql.adaptive.advisoryPartitionSizeInBytes 64M
   spark.sql.adaptive.coalescePartitions.minPartitionSize  10M
   spark.sql.adaptive.skewJoin.skewedPartitionFactor 2
   spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes 128M
   spark.sql.optimizer.dynamicPartitionPruning.enabled   false
   
   #celeborn
   spark.shuffle.service.enabled false
   celeborn.shuffle.rangeReadFilter.enabled true
   celeborn.shuffle.partitionSplit.mode hard
   spark.shuffle.manager org.apache.spark.shuffle.celeborn.RssShuffleManager
   spark.celeborn.master.endpoints  xxxxxx
   spark.rss.push.data.replicate false
   spark.rss.shuffle.writer.mode hash
   
   # kyuubi
   spark.sql.optimizer.insertRepartitionBeforeWrite.enabled true
   spark.sql.optimizer.finalStageConfigIsolation.enabled true
   spark.sql.finalStage.adaptive.advisoryPartitionSizeInBytes 256M
   spark.sql.finalStage.adaptive.coalescePartitions.minPartitionSize 50M
   spark.sql.extensions org.apache.kyuubi.sql.KyuubiSparkSQLExtension,org.apache.kyuubi.plugin.spark.authz.ranger.RangerSparkExtension
   
   
   **reproduce sql**
   
   
   set spark.sql.adaptive.autoBroadcastJoinThreshold=10M;
   set spark.sql.autoBroadcastJoinThreshold=-1;
   set spark.sql.shuffle.partitions=5000;
   
   select  a.id from 
   ( select id from    range(1,10000) )  a
   inner join 
   (select id  from  range(1,100) )  b  on a.id=b.id
   
   I found that this will be affected by the spark.sql.shuffle.partitions parameter. When the parameter is relatively large, the broadcast process will take longer
   
   Test case:
   
   when spark.sql.shuffle.partitions=10;
   ![image](https://github.com/apache/incubator-celeborn/assets/20243868/58b7bff2-a1e5-4278-8cee-c6216d5bc723)
   
   when set spark.sql.shuffle.partitions=1000;
   ![image](https://github.com/apache/incubator-celeborn/assets/20243868/5a3b9d79-8a9e-4858-b950-7364ba1d61f2)
   
   when set 5000
   ![image](https://github.com/apache/incubator-celeborn/assets/20243868/f205e223-0ff5-4934-99cc-597c8c29bfb3)
   
   I didn't find this problem when I used the native spark3 built-in shuffle
   ![image](https://github.com/apache/incubator-celeborn/assets/20243868/12f4adfa-0b13-401a-a379-7dcf39e91255)
   
   
   
   
   
   
   
   
   


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[GitHub] [incubator-celeborn] thomasg19930417 commented on issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

Posted by "thomasg19930417 (via GitHub)" <gi...@apache.org>.
thomasg19930417 commented on issue #1712:
URL: https://github.com/apache/incubator-celeborn/issues/1712#issuecomment-1633881455

   I think it should be caused by too many connections when the partition is too large. I also found a lot of connection creation process from the log as follows
   ![image](https://github.com/apache/incubator-celeborn/assets/20243868/cdfe62fc-92ee-4304-aeba-4f50aaed6564)
   


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[GitHub] [incubator-celeborn] waitinfuture commented on issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

Posted by "waitinfuture (via GitHub)" <gi...@apache.org>.
waitinfuture commented on issue #1712:
URL: https://github.com/apache/incubator-celeborn/issues/1712#issuecomment-1633947913

   > After testing, it is indeed caused by this parameter, thank you for your reply @pan3793 @wForget @waitinfuture
   
   ![image](https://github.com/apache/incubator-celeborn/assets/948245/324b5aa5-6139-40d0-8856-c269a052f4fb)
   We do added comment about this config, maybe we should highlight it more :)


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[GitHub] [incubator-celeborn] pan3793 commented on issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

Posted by "pan3793 (via GitHub)" <gi...@apache.org>.
pan3793 commented on issue #1712:
URL: https://github.com/apache/incubator-celeborn/issues/1712#issuecomment-1633891574

   Does increasing `celeborn.network.timeout` help? The default values is 240s.
   https://celeborn.apache.org/docs/0.2.1-incubating/configuration/#network


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[GitHub] [incubator-celeborn] waitinfuture closed issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

Posted by "waitinfuture (via GitHub)" <gi...@apache.org>.
waitinfuture closed issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered
URL: https://github.com/apache/incubator-celeborn/issues/1712


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[GitHub] [incubator-celeborn] wForget commented on issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

Posted by "wForget (via GitHub)" <gi...@apache.org>.
wForget commented on issue #1712:
URL: https://github.com/apache/incubator-celeborn/issues/1712#issuecomment-1633921798

   Is it caused by not disabling `spark.sql.adaptive.localShuffleReader.enabled`?


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[GitHub] [incubator-celeborn] thomasg19930417 commented on issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

Posted by "thomasg19930417 (via GitHub)" <gi...@apache.org>.
thomasg19930417 commented on issue #1712:
URL: https://github.com/apache/incubator-celeborn/issues/1712#issuecomment-1633929475

   After testing, it is indeed caused by this parameter, thank you for your reply @pan3793 @wForget @waitinfuture 


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[GitHub] [incubator-celeborn] waitinfuture commented on issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

Posted by "waitinfuture (via GitHub)" <gi...@apache.org>.
waitinfuture commented on issue #1712:
URL: https://github.com/apache/incubator-celeborn/issues/1712#issuecomment-1633925301

   > Is it caused by not disabling `spark.sql.adaptive.localShuffleReader.enabled`?
   
   Right, @thomasg19930417 could you turn off ```spark.sql.adaptive.localShuffleReader.enabled``` and test again?


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[GitHub] [incubator-celeborn] thomasg19930417 commented on issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

Posted by "thomasg19930417 (via GitHub)" <gi...@apache.org>.
thomasg19930417 commented on issue #1712:
URL: https://github.com/apache/incubator-celeborn/issues/1712#issuecomment-1633957454

   > > After testing, it is indeed caused by this parameter, thank you for your reply @pan3793 @wForget @waitinfuture
   > 
   > ![image](https://user-images.githubusercontent.com/948245/253241674-324b5aa5-6139-40d0-8856-c269a052f4fb.png) We do added comment about this config, maybe we should highlight it more :)
   
   This parameter was commented out by my mistake, maybe it was automatically commented out when the shell was copied.


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[GitHub] [incubator-celeborn] thomasg19930417 commented on issue #1712: [BUG] Shuffle read latency is too high when automatic Broadcastjoin is triggered

Posted by "thomasg19930417 (via GitHub)" <gi...@apache.org>.
thomasg19930417 commented on issue #1712:
URL: https://github.com/apache/incubator-celeborn/issues/1712#issuecomment-1633884149

   If the shuffle.partitions can be reduced, this problem can be solved, but in complex tasks, both large and small tasks exist. If the setting is too small, it is not very suitable for large tasks


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