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From "Huangkaixuan (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (YARN-6289) Fail to achieve data locality when runing MapReduce and Spark on HDFS
Date Tue, 07 Mar 2017 03:41:32 GMT

     [ https://issues.apache.org/jira/browse/YARN-6289?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Huangkaixuan updated YARN-6289:
-------------------------------
    Issue Type: Bug  (was: Improvement)

> Fail to achieve data locality when runing MapReduce and Spark on HDFS
> ---------------------------------------------------------------------
>
>                 Key: YARN-6289
>                 URL: https://issues.apache.org/jira/browse/YARN-6289
>             Project: Hadoop YARN
>          Issue Type: Bug
>          Components: capacity scheduler
>         Environment: Hardware configuration
> CPU: 2 x Intel(R) Xeon(R) E5-2620 v2 @ 2.10GHz /15M Cache 6-Core 12-Thread 
> Memory: 128GB Memory (16x8GB) 1600MHz
> Disk: 600GBx2 3.5-inch with RAID-1
> Network bandwidth: 968Mb/s
> Software configuration
> Spark-1.6.2	Hadoop-2.7.1 
>            Reporter: Huangkaixuan
>         Attachments: YARN-DataLocality.docx
>
>
> When I ran experiments with both Spark and MapReduce wordcount on YARN, I noticed that
the task failed to achieve data locality, even though there is no other job running on the
cluster. 
> I adopted a 7 node (1 master, 6 data nodes/node managers) cluster and set 2x replication
for HDFS. In the experiments, I run Spark/MapReduce wordcount on YARN for 10 times with a
single data block. The results show that only 30% of tasks can achieve data locality, it seems
like random in the placement of tasks. the experiment details are in the attachment, you can
reproduce the experiments.



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