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From: "Chandra Prakash Bhagtani (JIRA)" <jira@apache.org>
To: mapreduce-dev@hadoop.apache.org
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Subject: [jira] [Created] (MAPREDUCE-5611) CombineFileInputFormat creates
 more rack-local tasks due to less split location info.
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Chandra Prakash Bhagtani created MAPREDUCE-5611:
---------------------------------------------------

             Summary: CombineFileInputFormat creates more rack-local tasks due to less split location info.
                 Key: MAPREDUCE-5611
                 URL: https://issues.apache.org/jira/browse/MAPREDUCE-5611
             Project: Hadoop Map/Reduce
          Issue Type: Bug
    Affects Versions: 2.2.0
            Reporter: Chandra Prakash Bhagtani
            Assignee: Chandra Prakash Bhagtani


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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