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https://issues.apache.org/jira/browse/SPARK-26155?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16696476#comment-16696476
]
Ke Jia edited comment on SPARK-26155 at 11/23/18 7:58 AM:
----------------------------------------------------------
*Cluster info:*
| |*Master Node*|*Worker Nodes*|
|*Node*|1x |7x|
|*Processor*|Intel(R) Xeon(R) Platinum 8170 CPU @ 2.10GHz|Intel(R) Xeon(R)
Platinum 8180 CPU @ 2.50GHz|
|*Memory*|192 GB|384 GB|
|*Storage Main*|8 x 960G SSD|8 x 960G SSD|
|*Network*|10Gbe|
|*Role*|CM Management
NameNode
Secondary NameNode
Resource Manager
Hive Metastore Server|DataNode
NodeManager|
|*OS Version*|CentOS 7.2| CentOS 7.2|
|*Hadoop*|Apache Hadoop 2.7.5| Apache Hadoop 2.7.5|
|*Hive*|Apache Hive 2.2.0| |
|*Spark*|Apache Spark 2.1.0 & Apache Spark2.3.0| |
|*JDK version*|1.8.0_112| 1.8.0_112|
*Related parameters setting:*
|*Component*|*Parameter*|*Value*|
|*Yarn Resource Manager*|yarn.scheduler.maximum-allocation-mb|40GB|
| |yarn.scheduler.minimum-allocation-mb|1GB|
| |yarn.scheduler.maximum-allocation-vcores|121|
| |Yarn.resourcemanager.scheduler.class|Fair Scheduler|
|*Yarn Node Manager*|yarn.nodemanager.resource.memory-mb|40GB|
| |yarn.nodemanager.resource.cpu-vcores|121|
|*Spark*|spark.executor.memory|34GB|
| |spark.executor.cores|40|
was (Author: jk_self):
*Cluster info:*
| |*Master Node*|*Worker Nodes* |
|*Node*|1x |7x|
|*Processor*|Intel(R) Xeon(R) Platinum 8170 CPU @ 2.10GHz|Intel(R) Xeon(R)
Platinum 8180 CPU @ 2.50GHz|
|*Memory*|192 GB|384 GB|
|*Storage Main*|8 x 960G SSD|8 x 960G SSD|
|*Network*|10Gbe|
|*Role*|CM Management
NameNode
Secondary NameNode
Resource Manager
Hive Metastore Server|DataNode
NodeManager|
|*OS Version*|CentOS 7.2|
|*Hadoop*|Apache Hadoop 2.7.5|
|*Hive*|Apache Hive 2.2.0|
|*Spark*|Apache Spark 2.1.0 VS Apache Spark2.3.0|
|*JDK version*|1.8.0_112|
*Related parameters setting:*
|*Component*|*Parameter*|*Value*|
|*Yarn Resource Manager*|yarn.scheduler.maximum-allocation-mb|40GB|
|yarn.scheduler.minimum-allocation-mb|1GB|
|yarn.scheduler.maximum-allocation-vcores|121|
|Yarn.resourcemanager.scheduler.class|Fair Scheduler|
|*Yarn Node Manager*|yarn.nodemanager.resource.memory-mb|40GB|
|yarn.nodemanager.resource.cpu-vcores|121|
|*Spark*|spark.executor.memory|34GB|
|spark.executor.cores|40|
> Spark SQL performance degradation after apply SPARK-21052 with Q19 of TPC-DS
> in 3TB scale
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-26155
> URL: https://issues.apache.org/jira/browse/SPARK-26155
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.0, 2.3.1, 2.3.2, 2.4.0
> Reporter: Ke Jia
> Priority: Major
>
> In our test environment, we found a serious performance degradation issue in
> Spark2.3 when running TPC-DS on SKX 8180. Several queries have serious
> performance degradation. For example, TPC-DS Q19 needs 126 seconds with Spark
> 2.3 while it needs only 29 seconds with Spark2.1 on 3TB data. We investigated
> this problem and figured out the root cause is in community patch SPARK-21052
> which add metrics to hash join process. And the impact code is
> [L486|https://github.com/apache/spark/blob/1d3dd58d21400b5652b75af7e7e53aad85a31528/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala#L486]
> and
> [L487|https://github.com/apache/spark/blob/1d3dd58d21400b5652b75af7e7e53aad85a31528/sql/core/src/main/scala/org/apache/spark/sql/execution/joins/HashedRelation.scala#L487]
> . Q19 costs about 30 seconds without these two lines code and 126 seconds
> with these code.
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