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https://issues.apache.org/jira/browse/HADOOP-5299?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12676880#action_12676880
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Devaraj Das commented on HADOOP-5299:
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bq. Concatenate the output of a jobs maps on a node into a single file (or one
per slot). Then you only need to do one open / map node to shuffle if you are
efficient at fetching
The first step towards this could be doing HADOOP-2560. Thoughts?
> Reducer inputs should be spilled to HDFS rather than local disk.
> ----------------------------------------------------------------
>
> Key: HADOOP-5299
> URL: https://issues.apache.org/jira/browse/HADOOP-5299
> Project: Hadoop Core
> Issue Type: Improvement
> Components: mapred
> Affects Versions: 0.19.0
> Environment: All
> Reporter: Milind Bhandarkar
>
> Currently, both map outputs and reduce inputs are stored on local disks of
> tasktrackers. (Un) Availability of local disk space for intermediate data is
> seen as a major factor in job failures.
> The suggested solution is to store these intermediate data on HDFS (maybe
> with replication factor of 1). However, the main blocker issue with that
> solution is that lots of temporary names (proportional to total number of
> maps), can overwhelm the namenode, especially since the map outputs are
> typically small (most produce one block output).
> Also, as we see in many applications, the map outputs can be estimated more
> accurately, and thus users can plan accordingly, based on available local
> disk space.
> However, the reduce input sizes can vary a lot, especially for skewed data
> (or because of bad partitioning.)
> So, I suggest that it makes more sense to keep map outputs on local disks,
> but the reduce inputs (when spilled from reducer memory) should go to HDFS.
> Adding a configuration variable to indicate the filesystem to be used for
> reduce-side spills would let us experiment and compare the efficiency of this
> new scheme.
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