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From "Hadoop QA (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (MAPREDUCE-5611) CombineFileInputFormat only requests a single location per split when more could be optimal
Date Sat, 02 May 2015 04:41:17 GMT

    [ https://issues.apache.org/jira/browse/MAPREDUCE-5611?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14524808#comment-14524808
] 

Hadoop QA commented on MAPREDUCE-5611:
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| (x) *{color:red}-1 overall{color}* |
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|| Vote || Subsystem || Runtime || Comment ||
| {color:red}-1{color} | patch |   0m  0s | The patch command could not apply the patch during
dryrun. |
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|| Subsystem || Report/Notes ||
| Patch URL | http://issues.apache.org/jira/secure/attachment/12613866/CombineFileInputFormat-trunk.patch
|
| Optional Tests | javadoc javac unit findbugs checkstyle |
| git revision | trunk / f1a152c |
| Console output | https://builds.apache.org/job/PreCommit-MAPREDUCE-Build/5545/console |


This message was automatically generated.

> CombineFileInputFormat only requests a single location per split when more could be optimal
> -------------------------------------------------------------------------------------------
>
>                 Key: MAPREDUCE-5611
>                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-5611
>             Project: Hadoop Map/Reduce
>          Issue Type: Bug
>    Affects Versions: 1.2.1
>            Reporter: Chandra Prakash Bhagtani
>            Assignee: Chandra Prakash Bhagtani
>         Attachments: CombineFileInputFormat-trunk.patch
>
>
> I have come across an issue with CombineFileInputFormat. Actually I ran a hive query
on approx 1.2 GB data with CombineHiveInputFormat which internally uses CombineFileInputFormat.
My cluster size is 9 datanodes and max.split.size is 256 MB
> When I ran this query with replication factor 9, hive consistently creates all 6 rack-local
tasks and with replication factor 3 it creates 5 rack-local and 1 data local tasks. 
>  When replication factor is 9 (equal to cluster size), all the tasks should be data-local
as each datanode contains all the replicas of the input data, but that is not happening i.e
all the tasks are rack-local. 
> When I dug into CombineFileInputFormat.java code in getMoreSplits method, I found the
issue with the following snippet (specially in case of higher replication factor)
> {code:title=CombineFileInputFormat.java|borderStyle=solid}
> for (Iterator<Map.Entry<String,
>          List<OneBlockInfo>>> iter = nodeToBlocks.entrySet().iterator();
>          iter.hasNext();) {
>        Map.Entry<String, List<OneBlockInfo>> one = iter.next();
>       nodes.add(one.getKey());
>       List<OneBlockInfo> blocksInNode = one.getValue();
>       // for each block, copy it into validBlocks. Delete it from
>       // blockToNodes so that the same block does not appear in
>       // two different splits.
>       for (OneBlockInfo oneblock : blocksInNode) {
>         if (blockToNodes.containsKey(oneblock)) {
>           validBlocks.add(oneblock);
>           blockToNodes.remove(oneblock);
>           curSplitSize += oneblock.length;
>           // if the accumulated split size exceeds the maximum, then
>           // create this split.
>           if (maxSize != 0 && curSplitSize >= maxSize) {
>             // create an input split and add it to the splits array
>             addCreatedSplit(splits, nodes, validBlocks);
>             curSplitSize = 0;
>             validBlocks.clear();
>           }
>         }
>       }
> {code}
> First node in the map nodeToBlocks has all the replicas of input file, so the above code
creates 6 splits all with only one location. Now if JT doesn't schedule these tasks on that
node, all the tasks will be rack-local, even though all the other datanodes have all the other
replicas.



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