Hadoop Training Chennai – Yarn Tutorial

Hadoop Training Chennai

What is YARN?

YARN stands for “Yet-Another-Resource-Negotiator”. It is a new framework that facilitates writing arbitrary distributed processing frameworks and applications.

YARN provides the daemons and APIs necessary to develop generic distributed applications of any kind, handles and schedules resource requests (such as memory and CPU) from such applications, and supervises their execution.

YARN’s execution model is more generic than the earlier MapReduce implementation. YARN can run applications that do not follow the MapReduce model, unlike the original Apache Hadoop MapReduce (also called MR1).

What YARN Does

YARN enhances the power of a Hadoop compute cluster in the following ways:

  • Scalability The processing power in data centers continues to grow quickly. Because YARN ResourceManager focuses exclusively on scheduling, it can manage those larger clusters much more easily.
  • Compatibility with MapReduce Existing MapReduce applications and users can run on top of YARN without disruption to their existing processes.
  • Improved cluster utilization. The ResourceManager is a pure scheduler that optimizes cluster utilization according to criteria such as capacity guarantees, fairness, and SLAs. Also, unlike before, there are no named map and reduce slots, which helps to better utilize cluster resources.
  • Support for workloads other than MapReduce Additional programming models such as graph processing and iterative modeling are now possible for data processing. These added models allow enterprises to realize near real-time processing and increased ROI on their Hadoop investments.
  • Agility With MapReduce becoming a user-land library, it can evolve independently of the underlying resource manager layer and in a much more agile manner.

How YARN Works

The fundamental idea of YARN is to split up the two major responsibilities of the JobTracker/TaskTracker into separate entities:

  • a global ResourceManager
  • a per-application ApplicationMaster.
  • a per-node slave NodeManager and
  • a per-application Container running on a NodeManager

The ResourceManager and the NodeManager form the new, and generic, system for managing applications in a distributed manner. The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system. The per-application ApplicationMaster is a framework-specific entity and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the component tasks.\ The ResourceManager has a scheduler, which is responsible for allocating resources to the various running applications, according to constraints such as queue capacities, user-limits etc. The scheduler performs its scheduling function based on the resource requirements of the applications. The NodeManager is the per-machine slave, which is responsible for launching the applications’ containers, monitoring their resource usage (cpu, memory, disk, network) and reporting the same to the ResourceManager. Each ApplicationMaster has the responsibility of negotiating appropriate resource containers from the scheduler, tracking their status, and monitoring their progress. From the system perspective, the ApplicationMaster runs as a normal container.

Hadoop Yarn Training

Introducing Apache Hadoop YARN

Apache Hadoop YARN joins Hadoop Common (core libraries), Hadoop HDFS (storage) and Hadoop MapReduce (the MapReduce implementation) as the sub-projects of the Apache Hadoop which, itself, is a Top Level Project in the Apache Software Foundation. Until this milestone, YARN was a part of the Hadoop MapReduce project and now is poised to stand up on it’s own as a sub-project of Hadoop.

In a nutshell, Hadoop YARN is an attempt to take Apache Hadoop beyond MapReduce for data-processing.

As folks are aware, Hadoop HDFS is the data storage layer for Hadoop and MapReduce was the data-processing layer. However, the MapReduce algorithm, by itself, isn’t sufficient for the very wide variety of use-cases we see Hadoop being employed to solve. With YARN, Hadoop now has a generic resource-management and distributed application framework, where by, one can implement multiple data processing applications customized for the task at hand. Hadoop MapReduce is now one such application for YARN and I see several others given my vantage point – in future you will see MPI, graph-processing, simple services etc.; all co-existing with MapReduce applications in a Hadoop YARN cluster.


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