Manage Big Data Resources and Applications with Hadoop YARN

By Judith Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman

Job scheduling and tracking for big data are integral parts of Hadoop MapReduce and can be used to manage resources and applications. The early versions of Hadoop supported a rudimentary job and task tracking system, but as the mix of work supported by Hadoop changed, the scheduler could not keep up.

In particular, the old scheduler could not manage non-MapReduce jobs, and it was incapable of optimizing cluster utilization. So a new capability was designed to address these shortcomings and offer more flexibility, efficiency, and performance.

Yet Another Resource Negotiator (YARN) is a core Hadoop service providing two major services:

  • Global resource management (ResourceManager)

  • Per-application management (ApplicationMaster)

The ResourceManager is a master service and control NodeManager in each of the nodes of a Hadoop cluster. Included in the ResourceManager is Scheduler, whose sole task is to allocate system resources to specific running applications (tasks), but it does not monitor or track the application’s status.

All the required system information is stored in a Resource Container. It contains detailed CPU, disk, network, and other important resource attributes necessary for running applications on the node and in the cluster.

Each node has a NodeManager slaved to the global ResourceManager in the cluster. The NodeManager monitors the application’s usage of CPU, disk, network, and memory and reports back to the ResourceManager. For each application running on the node there is a corresponding ApplicationMaster.

If more resources are necessary to support the running application, the ApplicationMaster notifies the NodeManager and the NodeManager negotiates with the ResourceManager (Scheduler) for the additional capacity on behalf of the application. The NodeManager is also responsible for tracking job status and progress within its node.