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From "Weiwei Yang (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (YARN-6289) Fail to achieve data locality when runing MapReduce and Spark on HDFS
Date Tue, 05 Jun 2018 06:43:00 GMT

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

Weiwei Yang updated YARN-6289:
    Fix Version/s:     (was: 3.0.0)
                       (was: 2.9.0)

> 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: distributed-scheduling
>         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
>            Priority: Major
>         Attachments: Hadoop_Spark_Conf.zip, YARN-DataLocality.docx, YARN-RackAwareness.docx
> When running a simple wordcount experiment on YARN, I noticed that the task failed to
achieve data locality, even though there is no other job running on the cluster at the same
time. The experiment was done in a 7-node (1 master, 6 data nodes/node managers) cluster and
the input of the wordcount job (both Spark and MapReduce) is a single-block file in HDFS which
is two-way replicated (replication factor = 2). I ran wordcount on YARN for 10 times. The
results show that only 30% of tasks can achieve data locality, which seems like the result
of a random placement of tasks. The experiment details are in the attachment, and feel free
to reproduce the experiments.

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