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From "Ari Pollak (Commented) (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HADOOP-2120) dfs -getMerge does not do what it says it does
Date Tue, 13 Dec 2011 18:10:31 GMT

    [ https://issues.apache.org/jira/browse/HADOOP-2120?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13168554#comment-13168554
] 

Ari Pollak commented on HADOOP-2120:
------------------------------------

Even if the copyMerge comment is somewhat correct, the documentation for fs -getMerge is still
confusing, so I think that this bug should be reopened; from copyCommands.java:
    public static final String DESCRIPTION =
      "Get all the files in the directories that\n" +
      "match the source file pattern and merge and sort them to only\n" +
      "one file on local fs. <src> is kept.";

The command neither sorts nor merges the actual source files according to the traditional
definition (e.g. sort and sort -m commands), it merely concatenates them, in order of the
filenames. It should look more like this:

Concatenate all the files in the directories that match the pattern in <src> and output
to only one file on local fs. <src> is kept.
                
> dfs -getMerge does not do what it says it does
> ----------------------------------------------
>
>                 Key: HADOOP-2120
>                 URL: https://issues.apache.org/jira/browse/HADOOP-2120
>             Project: Hadoop Common
>          Issue Type: Bug
>          Components: documentation, fs
>    Affects Versions: 0.14.3
>         Environment: All
>            Reporter: Milind Bhandarkar
>              Labels: newbie
>
> dfs -getMerge, which calls FileUtil.CopyMerge, contains this javadoc:
> {code}
> Get all the files in the directories that match the source file pattern
>    * and merge and sort them to only one file on local fs 
>    * srcf is kept.
> {code}
> However, it only concatenates the set of input files, rather than merging them in sorted
order.
> Ideally, the copyMerge should be equivalent to a map-reduce job with IdentityMapper and
IdentityReducer with numReducers = 1. However, not having to run this as a map-reduce job
has some advantages, since it increases cluster utilization during reduce phase.

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