[MAPREDUCE-279] Map-Reduce 2.0

• Type: Improvement
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Fix Version/s: 0.23.0
• Component/s: mrv2

Release Note

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
• Assignee: Unassigned
• Reporter: Arun Murthy
• Created: 02/Jan/08 19:37
• Updated: 02/May/13 02:29
• Resolved: 18/Aug/11 11:47

Description

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

Issue Links

• incorporates: YARN-149 [Umbrella] ResourceManager (RM) Fail-over
• is depended upon by: MAPREDUCE-297 generalize the TT / JT servers to handle more generic tasks
• is duplicated by: HADOOP-3421 Requirements for a Resource Manager for Hadoop
• is duplicated by: HADOOP-3444 Implementing a Resource Manager (V1) for Hadoop
• relates to: MAPREDUCE-2644 NodeManager fails to create containers when NM_LOG_DIR is not explicitly set in the Configuration
• relates to: MAPREDUCE-2630 MR-279: refreshQueues leads to NPEs when used w/FifoScheduler
• relates to: YARN-3172 MR-279: Write a simple Java application
• relates to: MAPREDUCE-2719 MR-279: Write a shell command application
• relates to: PIG-2125 Make Pig work with hadoop .NEXT
• relates to: MAPREDUCE-2639 MR-279: Fixup the exponentially smoothed runtime estimator, fix a couple of bugs in DataStatistics, and do a little bit of cleanup.
• relates to: MAPREDUCE-2633 MR-279: Add a getCounter(Enum) method to the Counters interface