branch-2.0.4-alpha/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-site/src/site/apt/YARN.apt.vm

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  ---
  YARN
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  ${maven.build.timestamp}

Apache Hadoop NextGen MapReduce (YARN)

  MapReduce has undergone a complete overhaul in hadoop-0.23 and we now have, 
  what we call, MapReduce 2.0 (MRv2) or YARN.

  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.

[./yarn_architecture.gif] MapReduce NextGen Architecture

  The ResourceManager has two main components: Scheduler and 
  ApplicationsManager.

  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 about 
  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. In the first version, only <<<memory>>> is supported. 

  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 
  containers, monitoring their resource usage (cpu, memory, disk, network) and 
  reporting the same to the ResourceManager/Scheduler.

  The per-application ApplicationMaster has the responsibility of negotiating 
  appropriate resource containers from the Scheduler, tracking their status and 
  monitoring for progress.

  MRV2 maintains <<API compatibility>> with previous stable release 
  (hadoop-0.20.205).  This means that all Map-Reduce jobs should still run 
  unchanged on top of MRv2 with just a recompile.