Details

    • Type: Improvement Improvement
    • Status: Closed
    • Priority: Major Major
    • Resolution: Fixed
    • Affects Version/s: None
    • Fix Version/s: 0.23.0
    • Component/s: mrv2
    • Labels:
      None
    • Release Note:
      Hide
      MapReduce has undergone a complete re-haul in hadoop-0.23 and we now have, what we call, MapReduce 2.0 (MRv2).

      The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs. The ResourceManager and per-node slave, the NodeManager (NM), form the data-computation framework. The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system. The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks.

      The ResourceManager has two main components:
      * Scheduler (S)
      * ApplicationsManager (ASM)

      The Scheduler is responsible for allocating resources to the various running applications subject to familiar constraints of capacities, queues etc. The Scheduler is pure scheduler in the sense that it performs no monitoring or tracking of status for the application. Also, it offers no guarantees on restarting failed tasks either due to application failure or hardware failures. The Scheduler performs its scheduling function based the resource requirements of the applications; it does so based on the abstract notion of a Resource Container which incorporates elements such as memory, cpu, disk, network etc.

      The Scheduler has a pluggable policy plug-in, which is responsible for partitioning the cluster resources among the various queues, applications etc. The current Map-Reduce schedulers such as the CapacityScheduler and the FairScheduler would be some examples of the plug-in.

      The CapacityScheduler supports hierarchical queues to allow for more predictable sharing of cluster resources.
      The ApplicationsManager is responsible for accepting job-submissions, negotiating the first container for executing the application specific ApplicationMaster and provides the service for restarting the ApplicationMaster container on failure.

      The NodeManager is the per-machine framework agent who is responsible for launching the applications' containers, monitoring their resource usage (cpu, memory, disk, network) and reporting the same to the Scheduler.

      The per-application ApplicationMaster has the responsibility of negotiating appropriate resource containers from the Scheduler, tracking their status and monitoring for progress.
      Show
      MapReduce has undergone a complete re-haul in hadoop-0.23 and we now have, what we call, MapReduce 2.0 (MRv2). The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs. The ResourceManager and per-node slave, the NodeManager (NM), form the data-computation framework. The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system. The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks. The ResourceManager has two main components: * Scheduler (S) * ApplicationsManager (ASM) The Scheduler is responsible for allocating resources to the various running applications subject to familiar constraints of capacities, queues etc. The Scheduler is pure scheduler in the sense that it performs no monitoring or tracking of status for the application. Also, it offers no guarantees on restarting failed tasks either due to application failure or hardware failures. The Scheduler performs its scheduling function based the resource requirements of the applications; it does so based on the abstract notion of a Resource Container which incorporates elements such as memory, cpu, disk, network etc. The Scheduler has a pluggable policy plug-in, which is responsible for partitioning the cluster resources among the various queues, applications etc. The current Map-Reduce schedulers such as the CapacityScheduler and the FairScheduler would be some examples of the plug-in. The CapacityScheduler supports hierarchical queues to allow for more predictable sharing of cluster resources. The ApplicationsManager is responsible for accepting job-submissions, negotiating the first container for executing the application specific ApplicationMaster and provides the service for restarting the ApplicationMaster container on failure. The NodeManager is the per-machine framework agent who is responsible for launching the applications' containers, monitoring their resource usage (cpu, memory, disk, network) and reporting the same to the Scheduler. The per-application ApplicationMaster has the responsibility of negotiating appropriate resource containers from the Scheduler, tracking their status and monitoring for progress.
    • Tags:
      mr2,mapreduce-2.0

      Description

      Re-factor MapReduce into a generic resource scheduler and a per-job, user-defined component that manages the application execution.

      1. yarn-state-machine.task-attempt.png
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        Greg Roelofs
      2. yarn-state-machine.task-attempt.dot
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        Greg Roelofs
      3. yarn-state-machine.task.png
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        Greg Roelofs
      4. yarn-state-machine.task.dot
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        Greg Roelofs
      5. yarn-state-machine.job.png
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        Greg Roelofs
      6. yarn-state-machine.job.dot
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        Greg Roelofs
      7. ResourceManager.png
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        Binglin Chang
      8. ResourceManager.gv
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        Binglin Chang
      9. post-move-patch-final.txt
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        Mahadev konar
      10. post-move-patch-20110817.2.txt
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        Vinod Kumar Vavilapalli
      11. post-move.patch
        83 kB
        Mahadev konar
      12. post-move.patch
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        Arun C Murthy
      13. post-move.patch
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        Arun C Murthy
      14. NodeManager.png
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        Binglin Chang
      15. NodeManager.gv
        6 kB
        Binglin Chang
      16. multi-column-stable-sort-default-theme.png
        299 kB
        Luke Lu
      17. MR-279-script-final.sh
        3 kB
        Arun C Murthy
      18. MR-279-script-20110817.sh
        3 kB
        Vinod Kumar Vavilapalli
      19. MR-279-script.sh
        2 kB
        Mahadev konar
      20. MR-279-script.sh
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        Arun C Murthy
      21. MR-279.sh
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        Arun C Murthy
      22. MR-279.patch
        3.66 MB
        Arun C Murthy
      23. MR-279.patch
        3.94 MB
        Arun C Murthy
      24. MR-279_MR_files_to_move-20110817.txt
        23 kB
        Vinod Kumar Vavilapalli
      25. MR-279_MR_files_to_move.txt
        23 kB
        Arun C Murthy
      26. MR-279_MR_files_to_move.txt
        23 kB
        Mahadev konar
      27. MapReduce_NextGen_Architecture.pdf
        554 kB
        Arun C Murthy
      28. hadoop_contributors_meet_07_01_2011.pdf
        531 kB
        Sharad Agarwal
      29. capacity-scheduler-dark-theme.png
        192 kB
        Luke Lu

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            • Assignee:
              Unassigned
              Reporter:
              Arun C Murthy
            • Votes:
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              • Created:
                Updated:
                Resolved:

                Development