This wiki tracks development of Next Generation Apache Hadoop MapReduce (jira: MAPREDUCE-279).

Overview

The fundamental idea of the re-architecture is to divide the two major functions of the JobTracker, resource management and job scheduling/monitoring, into separate components. The new ResourceManager manages the global assignment of compute resources to applications and the per-application ApplicationMaster manages the application’s scheduling and coordination. An application is either a single job in the classic MapReduce jobs or a DAG of such jobs. The ResourceManager and per-machine NodeManager server, which manages the user processes on that machine, form the computation fabric. 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 supports hierarchical application queues and those queues can be guaranteed a percentage of the cluster resources. It 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 ResourceManager performs its scheduling function based the resource requirements of the applications; each application has multiple resource request types that represent the resources required for containers. The resource requests include memory, CPU, disk, network etc. Note that this is a significant change from the current model of fixed-type slots in Hadoop MapReduce, which leads to significant negative impact on cluster utilization. The ResourceManager has a scheduler policy plug-in, which is responsible for partitioning the cluster resources among various queues, applications etc. Scheduler plug-ins can be based, for e.g., on the current CapacityScheduler and FairScheduler.

The ResourceManager has two main components:

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, launching tasks, tracking their status & monitoring for progress, and handling task-failures.

Source & Documentation

The source for the first-cut is available in the MR-279 branch in Apache Hadoop MapReduce:

$ svn co http://svn.apache.org/hadoop/mapreduce/branches/MR-279/

We are currently in the process of adding design/implementation documentation, but some links for current reference:

Development Process

Everyone is welcome to contribute, we'd love that! Just be aware we'll be moving fast. Thus, you'll need to watch the branch. We plan to use more of mapreduce-dev@ and hadoop wiki and less of jira to coordinate. We'll send out email statuses to allow everyone to track, maybe even use the wiki to maintain todo lists. We'll learn as it goes, make changes to the dev process as appropriate.Please shout out if you are interested in specific areas to let others know; they could be anything - development, code-reviews, build, docs, tests etc. The horses for specific courses as you contribute:

Please use NextGenMapReduceTrack to follow and track various pieces of on-going developement on Next Generation MapReduce.

We are tracking testing at NextGenMapReduceDevTesting.

Writing Applications for !NextGen !MapReduce i.e. YARN

Take a look at PoweredByYarn to see applications being developed for YARN.

See WritingYarnApps on more information on how to write one.

NextGenMapReduce (last edited 2011-09-20 21:52:52 by Arun C Murthy)