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https://issues.apache.org/jira/browse/SPARK-11005?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sandeep Pal updated SPARK-11005:
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Environment:
6 node cluster with 1 master and 5 worker nodes.
Memory > 100 GB each
Cores = 72 each
Input data ~94 GB
was:
6 node cluster with 1 master and 5 worker nodes.
Memory > 100 GB each
Cores = 72 each
Input data ~496 GB
> Spark 1.5 Shuffle performance - (sort-based shuffle)
> ----------------------------------------------------
>
> Key: SPARK-11005
> URL: https://issues.apache.org/jira/browse/SPARK-11005
> Project: Spark
> Issue Type: Question
> Components: Shuffle, SQL
> Affects Versions: 1.5.0
> Environment: 6 node cluster with 1 master and 5 worker nodes.
> Memory > 100 GB each
> Cores = 72 each
> Input data ~94 GB
> Reporter: Sandeep Pal
>
> In case of terasort by Spark SQL with 20 total cores(4 cores/ executor), the
> performance of the map tasks is 14 minutes (around 26s-30s each) where as if
> I increase the number of cores to 60(12 cores /executor), the performance of
> map degrades to 30 minutes ( ~2.3 minutes per task). I believe the map tasks
> are independent of each other in the shuffle.
> Each map task has 128 MB input (HDFS block size) in both of the above cases.
> So, what makes the performance degradation with increasing number of cores.
>
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