Read paper “iShuffle: Improving Hadoop Performance with Shuffle-on-Write”

Paper reference: iShuffle: Improving Hadoop Performance with Shuffle-on-Write

Background:
A job in Hadoop consists of three main stages: map, shuffle, reduce (Actually shuffle stage has been contained into reduce stage).


What is the problem?
Shuffle phase need to migrate large mount of data from nodes which running map job to those nodes which intend to run reduce job. This cause shuffle-latency which is usually significant. And the reason is:

  • Partitioning skew: Hadoop use hash algorithm to organize output data of map task, if too many keys have the same hash, it may cause unevenly size of partitions
  • Coupling of shuffle and reduce: data shuffling can’t be overlapped with map tasks


Solution: iShuffle

    • “Shuffler” collect intermediate data generated by every map task and predict size of respective partition
    • “Shuffler Manager” collect informations from “Shuffler” and decide the position of partitions


    • Shuffle-on-Write:While a map task writing a spill to local filesystem, it will (by modification of Hadoop code) also write spill to correspondent node where reduce task will be launched
    • Automated map out placement: iShuffle will decide the position for every partition by “map selectivity”, which is the ratio of map input size and map output size. After predicting the “map selectivity” and knowing the total input size of data, iShuffle could finally choose optimist node for every partition data


  • Flexible reduce scheduling: when a node request a reduce task, the Task Manager(after modification of Hadoop’s FIFO scheduler) will find the list of partitions reside on this node, and launch reduce task only for these partitions (Make sure reduce task will only read shuffled data from local filesystem which will reduce network-bandwidth at reduce stage)

In my opinion
Using prediction technology to proactively move map output to apt nodes, which avoid partition skew, is the most intelligent part of this paper. This tech could also be used to other intermediate data moving scenario like OLAP in Data-warehouse.
But, I also suspect that in real production, not too many organizations will use this “iShuffle” as they usually run multi-user applications in Hadoop system. When a lot of jobs running in one Hadoop cluster simultaneously, a low peak of CPU-usage made by long reduce latency of one job will be compensated by other computing-intensive jobs. Therefore to all users, none of hardware resources are wasted.

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