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Intro to Apache MapReduce 2 (YARN)

Get the details on MapReduce/YARN 2.0 and the functionalities it presents.


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Since Hadoop version 0.23, MapReduce has changed significantly. It is now known as MapReduce 2.0 or YARN. MapReduce 2.0 is based on the concept of splitting the two major functionalities of job tracker—resource management and job scheduling—into separate daemons.

In this article, we will discuss YARN/MapReduce 2.0 and the functionalities it presents in detail.

Introduction

YARN stands for "Yet Another Resource Negotiator." YARN/MapReduce2 has been introduced in Hadoop 2.0. YARN is a layer that separates the resource management layer and the processing components layer. The intention was to have a broader array of interaction model for the data stored in HDFS that is after the MapReduce layer. The following picture explains the architecture diagram of Hadoop 1.0 and Hadoop 2.0/YARN.


Figure 1: Hadoop 1.0 and 2.0 architecture

YARN takes care of the resource management tasks that were performed by the MapReduce in the earlier version. This allows the MapReduce engine to take care of its own task, which is processing data. Having the YARN layer allows us to run multiple applications on Hadoop, sharing a common resource management layer.

Features of YARN

YARN has the ability to enhance the power of cluster computing using Hadoop by giving the following features:

  • Scalability– Since the primary focus of YARN is scheduling, it can manage these huge clusters more efficiently. The ability to process data rapidly increases.
  • Compatibility with existing MapReduce based application– YARN can easily configure and run the existing MapReduce application without any hindrance or modification in existing processes.
  • Better Cluster Utilization– YARN Resource Manager optimizes the cluster utilization as per the given criteria, such as capacity guarantees, fairness, and other Service Level Agreements.
  • Support for additional workloads apart from MapReduce– Upcoming programming models such as graph processing and iterative modelings are now a part of data processing. These new models are easily integrated with YARN, which helps the senior management in any organization to realize their real time data and other market trends.
  • Agility – YARN facilitates the operation of the resource management layer in a more Agile manner.

Components of YARN Framework

YARN is based on the concept of 'Divide and Rule.' YARN splits the two major responsibilities of Job tracker and task tracker into the following separate entities:

  • Global Resource Manager
  • Application Master per application
  • Node Manager per node slave
  • Container per application running on Node manager.

How YARN Works

The Resource Manager and the Node Managertogether form the new, and generic, system. This system is used to manage applications in a distributed manner. The Resource Manager is the supreme authority that controls the resources among all the applications in the system. The Application Master per-application is a framework-specific entity and takes up the task of negotiation of resources with the Resource Manager and working with the Node Manager to execute and monitor the other component tasks.

Resource Manager

The Resource Manager has an inbuilt scheduler that allocates resources to the running applications, as per the user defined constraints such as queue capacities, user-limits, and more. The scheduler performs its task of scheduling based on the resource requirements of the applications. The Node Manager is per-machine slave, which launches the container of the application, monitors resource usage (CPU, memory, disk, network) and reports the same to the Resource Manager. Each Application Master is responsible for negotiating the appropriate resource containers from the scheduler, tracking their status, and monitoring their progress. From the system point of view, the Application Master is the container that has the control of the entire application.

The Resource Manager lies at the root of the YARN hierarchy. This is the entity that governs the entire cluster and also controls the assignment of applications of the other resources. The Resource Manager takes care of division of resources—compute, memory, and bandwidth—to all the Node Managers below it. The Resource Manager also takes up the task of allocating resources to the Application Masters and monitors the underlying applications on the Node Managers. Thus the Application Master takes up the job of task tracker and the Resource Manager takes up the role of the Job Tracker.

Application Master

The Application Master is responsible for managing each and every instance of applications that runs within the YARN. The Application Master does the negotiation of the resources from the Resource Manager and, using the Node Manager, monitors the execution and resource consumption of containers, such as resource allocations of CPU, memory, etc.

Node Manager

The Node Manager is responsible for managing each and every node within the YARN cluster. The Node Manager provides the services per-node within the cluster. These are variety of services ranging from monitoring the management of a container and its life cycle to monitoring the resources and keeping a track of the health and usage of resources of each node. In contrast with the MapReduce version 1.0, which used to manage the execution of map and reduce tasks via slots, the Node Manager manages abstract containers, which allocates and represents resources per node available for a particular application. YARN also uses the HDFS layer, with the master Name Node for metadata services and Data Node for replicated storage services across a cluster.

YARN Cluster

YARN cluster comes in to the picture whenever there is a request from a client of any application. The Resource Manager starts negotiating for the necessary resources for the container and invokes an Application Master. This represents that the application is submitted. Using a resource-request protocol, the Application Master negotiates on the resource containers for the application at each node. Once the application's execution is over, the Application Master keeps a watch on the container till completion. Once the application is completed, the Application Master de registers the containers from the Resource Manager, and then the cycle completes.

Difference Between MapReduce1 and MapReduce2/YARN

It is important to note that the earlier version of Hadoop architecture was highly constrained via the Job Tracker. This Job Tracker was responsible for managing the resources and scheduling jobs across the cluster. The current YARN architecture allows the new Resource Manager to manage the usage of resources across all applications. While the Application Masters takes up the responsibility of managing the job execution. This approach improves the ability to scale up the Hadoop clusters to a much larger configuration than it was previously possible. In addition to this, YARN permits parallel execution of a range of programming models. This includes graph processing, iterative processing, machine learning, and general cluster computing.

With the help of YARN, we can create more complex distributed applications.

Summary

The MapReduce framework is one of the most important parts of big data processing. In earlier versions of MapReduce the components were designed to address basic needs of processing and resource management. More recently, it has evolved into a much improved version known as MapReduce 2/YARN that provides improved features and functionality.

 

 

About the Author

Kaushik Pal is a technical architect with 15 years of experience in enterprise application and product development. He has expertise in web technologies, architecture/design, java/j2ee, Open source and big data technologies. You can find more of his work at www.techalpine.com and you can email him here.



   
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