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https://issues.apache.org/jira/browse/HADOOP-2560?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12622882#action_12622882
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Owen O'Malley commented on HADOOP-2560:
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I'd propose:
* when a mapper finishes, if the job still has >1 wave of maps left, the JT
assigns a new map to join on the
previous one. You don't want to make the tail longer.
* Limit grouping to 5 or so. You want to bound the amount of grouping so that
the costs of failure aren't too high.
* any task that has previously been killed or failed should not be grouped
* no task that is currently being grouped should be speculated on
* we can consider having the map task ask for a follow on map if it still has
buffer capacity left. this is probably not aggressive enough because even with
a merge step multiple mappers are faster if you include the shuffle time.
> Processing multiple input splits per mapper task
> ------------------------------------------------
>
> Key: HADOOP-2560
> URL: https://issues.apache.org/jira/browse/HADOOP-2560
> Project: Hadoop Core
> Issue Type: Bug
> Reporter: Runping Qi
>
> Currently, an input split contains a consecutive chunk of input file, which
> by default, corresponding to a DFS block.
> This may lead to a large number of mapper tasks if the input data is large.
> This leads to the following problems:
> 1. Shuffling cost: since the framework has to move M * R map output segments
> to the nodes running reducers,
> larger M means larger shuffling cost.
> 2. High JVM initialization overhead
> 3. Disk fragmentation: larger number of map output files means lower read
> throughput for accessing them.
> Ideally, you want to keep the number of mappers to no more than 16 times the
> number of nodes in the cluster.
> To achive that, we can increase the input split size. However, if a split
> span over more than one dfs block,
> you lose the data locality scheduling benefits.
> One way to address this problem is to combine multiple input blocks with the
> same rack into one split.
> If in average we combine B blocks into one split, then we will reduce the
> number of mappers by a factor of B.
> Since all the blocks for one mapper share a rack, thus we can benefit from
> rack-aware scheduling.
> Thoughts?
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