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Home > Data > Data Processing > Learning YARN
Learning YARN
Learning YARN

Learning YARN: Moving beyond MapReduce - learn resource management and big data processing using YARN

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Learning YARN

Chapter 2. Setting up a Hadoop-YARN Cluster

YARN is a subproject of Apache Hadoop at the Apache Software Foundation, introduced in the Hadoop 2.0 version. YARN replaces the old MapReduce framework of the Hadoop 1.x version and is shipped with the Hadoop 2.x bundle. This chapter will provide a step-by-step guide for Hadoop-YARN users to install and configure YARN with Hadoop.

A Hadoop-YARN cluster can be configured as a single node as well as a multi-node cluster. This chapter covers both types of installations along with the troubleshooting guidelines. This chapter helps YARN beginners and the cluster administrators easily configure Hadoop-YARN clusters and understand how YARN components interact with each other.

Apache, Hortonworks, and Cloudera are the main distributors of Hadoop. These vendors have their own steps to install and configure Hadoop-YARN clusters. This chapter uses the Apache tar.gz bundles for setting up Hadoop-YARN clusters and gives an overview of Hortonworks...

Starting with the basics

The Apache Hadoop 2.x version consists of three key components:

  • Hadoop Distributed File System (HDFS)
  • Yet Another Resource Negotiator (YARN)
  • The MapReduce API (Job execution, MRApplicationMaster, JobHistoryServer, and so on)

There are two master processes that manage the Hadoop 2.x cluster—the NameNode and the ResourceManager. All the slave nodes in the cluster have DataNode and NodeManager processes running as the worker daemons for the cluster. The NameNode and DataNode daemons are part of HDFS, whereas the ResourceManager and NodeManager belong to YARN.

When we configure Hadoop-YARN on a single node, we need to have all four processes running on the same system. Hadoop single node installation is generally used for learning purposes. If you are a beginner and need to understand the Hadoop-YARN concepts, you can use a single node Hadoop-YARN cluster.

In the production environment, a multi-node cluster is used. It is recommended to have separate nodes for NameNode...

The Hadoop-YARN single node installation

In a single node installation, all the Hadoop-YARN daemons (NameNode, ResourceManager, DataNode, and NodeManager) run on a single node as separate Java processes. You will need only one Linux machine with a minimum of 2 GB RAM and 15 GB free disk space.

Prerequisites

Before starting with the installation steps, make sure that you prepare the node as specified in the above topic.

  • The hostname used in the single node installation is localhost with 127.0.0.1 as the IP address. It is known as the loopback address for a machine. You need to make sure that the /etc/hosts file contains the resolution for the loopback address. The loopback entry will look like this:
    127.0.0.1    localhost
  • The passwordless SSH is configured for localhost. To ensure this, execute the following command:
    ssh-copy-id localhost

Installation steps

After preparing your node for Hadoop, you need to follow a simple five-step process to install and run Hadoop on your Linux machine.

Installation steps

Step 1...

An overview of web user interfaces

Similar to web interfaces available in the Hadoop 1.x version, the Hadoop 2.x version has web user interfaces for Hadoop services. Instead of the JobTracker service web UI, the web UI for ResourceManager is used to monitor applications and resources. A detailed explanation regarding the configurations related to the web UI's is given in the next chapter.

The following screenshot refers to the web-interface of Hadoop HDFS. Through the HDFS interface, you can:

  • Explore the HDFS metrics
  • View the list of DataNodes connected to the NameNode
  • Browse the file system, and so on
An overview of web user interfaces

The following screenshot refers to the web-interface of YARN ResourceManager. It shows:

  • A summarized view of the cluster resource capabilities
  • A list of applications related to the cluster
  • A list of NodeManager nodes connected
  • The scheduler details, and so on
An overview of web user interfaces

You can refer to the .pid files for the Hadoop daemons that are created in the /tmp folder of your node. These files contain the current...

The Hadoop-YARN multi-node installation

Installing a multi-node Hadoop-YARN cluster is similar to a single node installation. You need to configure the master node, the same as you did during the single node installation. Then, copy the Hadoop installation directory to all the slave nodes and set the Hadoop environment variables for the slave nodes. You can start the Hadoop daemons either directly from the master node, or you can login to each node to run their respective services.

Prerequisites

Before starting with the installation steps, make sure that you prepare all the nodes as specified here:

  • All the nodes in the cluster have a unique hostname and IP address. Each node should be able to identify all other nodes through the hostname. If you are not using the DHCP server, you need to make sure that the /etc/hosts file contains the resolution for all nodes used in the cluster. The entries will look similar to the following:
    192.168.56.101    master
192.168.56.102    slave1
192.168.56.103...

An overview of the Hortonworks and Cloudera installations

Hortonworks and Cloudera are two main distributors of Hadoop. These distributors have their own style of installation. The installation is done as a package installation through yum (CentOS) or apt-get (Ubuntu). The directory structure is different for configuration files, log files, .pid files, and so on.

Both these distributors have developed tools to provision, manage, and monitor the Hadoop clusters through a web UI. Cloudera Manager from Cloudera and Apache Ambari are being used by a majority of the companies. Ambari is an open source project that lacks features such as rolling upgrades, managements of third-party libraries, and so on. Cloudera Manager is a mature product and is available in both Express and Enterprise versions.

To read more about Cloudera Manager and Ambari, you can refer to the official website at http://www.cloudera.com/content/cloudera/en/products-and-services/cloudera-enterprise/cloudera-manager.html.

http...

Starting with the basics

The Apache Hadoop 2.x version consists of three key components:

  • Hadoop Distributed File System (HDFS)

  • Yet Another Resource Negotiator (YARN)

  • The MapReduce API (Job execution, MRApplicationMaster, JobHistoryServer, and so on)

There are two master processes that manage the Hadoop 2.x cluster—the NameNode and the ResourceManager. All the slave nodes in the cluster have DataNode and NodeManager processes running as the worker daemons for the cluster. The NameNode and DataNode daemons are part of HDFS, whereas the ResourceManager and NodeManager belong to YARN.

When we configure Hadoop-YARN on a single node, we need to have all four processes running on the same system. Hadoop single node installation is generally used for learning purposes. If you are a beginner and need to understand the Hadoop-YARN concepts, you can use a single node Hadoop-YARN cluster.

In the production environment, a multi-node cluster is used. It is recommended to have separate nodes for NameNode and...

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Description

Today enterprises generate huge volumes of data. In order to provide effective services and to make smarter and more intelligent decisions from these huge volumes of data, enterprises use big-data analytics. In recent years, Hadoop has been used for massive data storage and efficient distributed processing of data. The Yet Another Resource Negotiator (YARN) framework solves the design problems related to resource management faced by the Hadoop 1.x framework by providing a more scalable, efficient, flexible, and highly available resource management framework for distributed data processing. This book starts with an overview of the YARN features and explains how YARN provides a business solution for growing big data needs. You will learn to provision and manage single, as well as multi-node, Hadoop-YARN clusters in the easiest way. You will walk through the YARN administration, life cycle management, application execution, REST APIs, schedulers, security framework and so on. You will gain insights about the YARN components and features such as ResourceManager, NodeManager, ApplicationMaster, Container, Timeline Server, High Availability, Resource Localisation and so on. The book explains Hadoop-YARN commands and the configurations of components and explores topics such as High Availability, Resource Localization and Log aggregation. You will then be ready to develop your own ApplicationMaster and execute it over a Hadoop-YARN cluster. Towards the end of the book, you will learn about the security architecture and integration of YARN with big data technologies like Spark and Storm. This book promises conceptual as well as practical knowledge of resource management using YARN.

Who is this book for?

This book is intended for those who want to understand what YARN is and how to efficiently use it for the resource management of large clusters. For cluster administrators, this book gives a detailed explanation of provisioning and managing YARN clusters. If you are a Java developer or an open source contributor, this book will help you to drill down the YARN architecture, write your own YARN applications and understand the application execution phases. This book will also help big data engineers explore YARN integration with real-time analytics technologies such as Spark and Storm.

What you will learn

  • Explore YARN features and offerings
  • Manage big data clusters efficiently using the YARN framework
  • Create single as well as multinode HadoopYARN clusters on Linux machines
  • Understand YARN components and their administration
  • Gain insights into application execution flow over a YARN cluster
  • Write your own distributed application and execute it over YARN cluster
  • Work with schedulers and queues for efficient scheduling of applications
  • Integrate big data projects like Spark and Storm with YARN

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Publication date : Aug 28, 2015
Length: 278 pages
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Language : English
ISBN-13 : 9781784394585
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Publication date : Aug 28, 2015
Length: 278 pages
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ISBN-13 : 9781784394585
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Apache
Category :
Data
Concepts :
Data Processing
Tools :
Hadoop

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Table of Contents

13 Chapters
1. Starting with YARN Basics Chevron down icon Chevron up icon
1. Starting with YARN Basics
Introduction to MapReduce v1
Shortcomings of MapReducev1
An overview of YARN components
The YARN architecture
How YARN satisfies big data needs
Projects powered by YARN
Summary
2. Setting up a Hadoop-YARN Cluster Chevron down icon Chevron up icon
2. Setting up a Hadoop-YARN Cluster
Starting with the basics
The Hadoop-YARN single node installation
An overview of web user interfaces
The Hadoop-YARN multi-node installation
An overview of the Hortonworks and Cloudera installations
Summary
3. Administering a Hadoop-YARN Cluster Chevron down icon Chevron up icon
3. Administering a Hadoop-YARN Cluster
Using the Hadoop-YARN commands
Configuring the Hadoop-YARN services
Managing the Hadoop-YARN services
Monitoring the YARN services
Understanding ResourceManager's High Availability
Monitoring NodeManager's health
Summary
4. Executing Applications Using YARN Chevron down icon Chevron up icon
4. Executing Applications Using YARN
Understanding application execution flow
Submitting a sample MapReduce application
Handling failures in YARN
YARN application logging
Summary
5. Understanding YARN Life Cycle Management Chevron down icon Chevron up icon
5. Understanding YARN Life Cycle Management
An introduction to state management analogy
The ResourceManager's view
The NodeManager's view
Analyzing transitions through logs
Summary
6. Migrating from MRv1 to MRv2 Chevron down icon Chevron up icon
6. Migrating from MRv1 to MRv2
Introducing MRv1 and MRv2
High-level changes from MRv1 to MRv2
The migration steps from MRv1 to MRv2
Running and monitoring MRv1 apps on YARN
Summary
7. Writing Your Own YARN Applications Chevron down icon Chevron up icon
7. Writing Your Own YARN Applications
An introduction to the YARN API
Writing your own application
Summary
8. Dive Deep into YARN Components Chevron down icon Chevron up icon
8. Dive Deep into YARN Components
Understanding ResourceManager
Understanding NodeManager
The YARN Timeline server
The web application proxy server
YARN Scheduler Load Simulator (SLS)
Handling resource localization in YARN
Summary
9. Exploring YARN REST Services Chevron down icon Chevron up icon
9. Exploring YARN REST Services
Introduction to YARN REST services
ResourceManager REST APIs
NodeManager REST APIs
MapReduce ApplicationMaster REST APIs
MapReduce HistoryServer REST APIs
How to access REST services
Summary
10. Scheduling YARN Applications Chevron down icon Chevron up icon
10. Scheduling YARN Applications
An introduction to scheduling in YARN
An overview of queues
Types of queues
An introduction to schedulers
Summary
11. Enabling Security in YARN Chevron down icon Chevron up icon
11. Enabling Security in YARN
Adding security to a YARN cluster
Working with ACLs
Other security frameworks
Summary
12. Real-time Data Analytics Using YARN Chevron down icon Chevron up icon
12. Real-time Data Analytics Using YARN
The integration of Spark with YARN
The integration of Storm with YARN
The integration of HAMA and Giraph with YARN
Summary
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
(2 Ratings)
5 star 50%
4 star 0%
3 star 50%
2 star 0%
1 star 0%
Xiao Zhang Sep 16, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I'm one of the technical reviewers of this book. The book is written in a very simple language and very easy to understand. Even you do not know anything about Big Data, Hadoop and Yarn, you will find this book very easy to follow and you will be surprised how much you can learn after reading the book. This book also has very rich content of technical details and sample code. It is very useful for technical practitioners who want to write their own YARN applications. It also has the latest information on YARN integration with Spark and Storm for real-time analytics.This book goes great with another YARN book I reviewed – YARN Essentials . Compared to YARN Essentials, this book has more hands on technical details. But the author did a very good job of explaining the concepts at the beginning of each chapter before going into technical details. You will have a comprehensive understanding of the YARN architecture and also be able to do hands on develop&admin work.I am specifically enjoy the chapter related to YARN integration with Spark and Storm. Hadoop is known for its batch processing power. With the new features such as YARN, Spark, Storm available in Hadoop 2.0, we will be able to solve our challenges related to real-time analytics, which is critical for businesses to be successful in this Big Data era.I'm happy having gotten the opportunity to work on this book and I'm very well pleased with the result. I believe this book is a great asset to any people who are interested in YARN!
Amazon Verified review Amazon
K Hu Mar 08, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
This book is simplified version of online doc. It is a thin book and took me 2 days to read through it, definitely doesn't worth 50+ bucks. 0 depth.
Amazon Verified review Amazon

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About the authors

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Profile icon Akhil Arora
Akhil Arora
Akhil Arora works as a senior software engineer with Impetus Infotech and has around 5 years of extensive research and development experience. He joined Impetus Infotech in October 2012 and is working with the innovation labs team. He is a technology expert, good learner, and creative thinker. He is also passionate and enthusiastic about application development in Hadoop and other big data technologies. He loves to explore new technologies and is always ready to work on new challenges. Akhil attained a BE degree in computer science from the Apeejay College of Engineering in Sohna, Haryana, India. A beginning for a new voyage, A first step towards my passion and to gain recognition, My first book Learning YARN..!!-- Akhil Arora
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Profile icon Shrey Mehrotra
Shrey Mehrotra
Shrey Mehrotra has over 8 years of IT experience and, for the past 6 years, has been designing the architecture of cloud and big-data solutions for the finance, media, and governance sectors. Having worked on research and development with big-data labs and been part of Risk Technologies, he has gained insights into Hadoop, with a focus on Spark, HBase, and Hive. His technical strengths also include Elasticsearch, Kafka, Java, YARN, Sqoop, and Flume. He likes spending time performing research and development on different big-data technologies. He is the coauthor of the books Learning YARN and Hive Cookbook, a certified Hadoop developer, and he has also written various technical papers.
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What eBook formats do Packt support? Chevron down icon Chevron up icon

Our eBooks are currently available in a variety of formats such as PDF and ePubs. In the future, this may well change with trends and development in technology, but please note that our PDFs are not Adobe eBook Reader format, which has greater restrictions on security.

You will need to use Adobe Reader v9 or later in order to read Packt's PDF eBooks.

What are the benefits of eBooks? Chevron down icon Chevron up icon
  • You can get the information you need immediately
  • You can easily take them with you on a laptop
  • You can download them an unlimited number of times
  • You can print them out
  • They are copy-paste enabled
  • They are searchable
  • There is no password protection
  • They are lower price than print
  • They save resources and space
What is an eBook? Chevron down icon Chevron up icon

Packt eBooks are a complete electronic version of the print edition, available in PDF and ePub formats. Every piece of content down to the page numbering is the same. Because we save the costs of printing and shipping the book to you, we are able to offer eBooks at a lower cost than print editions.

When you have purchased an eBook, simply login to your account and click on the link in Your Download Area. We recommend you saving the file to your hard drive before opening it.

For optimal viewing of our eBooks, we recommend you download and install the free Adobe Reader version 9.