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Posted to yarn-dev@hadoop.apache.org by "Weiwei Yang (JIRA)" <ji...@apache.org> on 2018/06/05 06:41:00 UTC
[jira] [Resolved] (YARN-6289) Fail to achieve data locality when
runing MapReduce and Spark on HDFS
[ https://issues.apache.org/jira/browse/YARN-6289?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Weiwei Yang resolved YARN-6289.
-------------------------------
Resolution: Duplicate
Fix Version/s: 2.9.0
3.0.0
> 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
> Fix For: 3.0.0, 2.9.0
>
> 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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