Hadoop Operations and Cluster Management Cookbook


Hadoop Operations and Cluster Management Cookbook
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Overview
Table of Contents
Author
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Sample Chapters
  • Hands-on recipes to configure a Hadoop cluster from bare metal hardware nodes
  • Practical and in depth explanation of cluster management commands
  • Easy-to-understand recipes for securing and monitoring a Hadoop cluster, and design considerations
  • Recipes showing you how to tune the performance of a Hadoop cluster
  • Learn how to build a Hadoop cluster in the cloud

Book Details

Language : English
Paperback : 368 pages [ 235mm x 191mm ]
Release Date : July 2013
ISBN : 1782165169
ISBN 13 : 9781782165163
Author(s) : Shumin Guo
Topics and Technologies : All Books, Big Data and Business Intelligence, Cloud, Cookbooks, Open Source

Table of Contents

Preface
Chapter 1: Big Data and Hadoop
Chapter 2: Preparing for Hadoop Installation
Chapter 3: Configuring a Hadoop Cluster
Chapter 4: Managing a Hadoop Cluster
Chapter 5: Hardening a Hadoop Cluster
Chapter 6: Monitoring a Hadoop Cluster
Chapter 7: Tuning a Hadoop Cluster for Best Performance
Chapter 8: Building a Hadoop Cluster with Amazon EC2 and S3
Index
    • Chapter 2: Preparing for Hadoop Installation
      • Introduction
      • Choosing hardware for cluster nodes
      • Designing the cluster network
      • Configuring the cluster administrator machine
      • Creating the kickstart file and boot media
      • Installing the Linux operating system
      • Installing Java and other tools
      • Configuring SSH
      • Chapter 3: Configuring a Hadoop Cluster
        • Introduction
        • Choosing a Hadoop version
        • Configuring Hadoop in pseudo-distributed mode
        • Configuring Hadoop in fully-distributed mode
        • Validating Hadoop installation
        • Configuring ZooKeeper
        • Installing HBase
        • Installing Hive
        • Installing Pig
        • Installing Mahout
        • Chapter 4: Managing a Hadoop Cluster
          • Introduction
          • Managing the HDFS cluster
          • Configuring SecondaryNameNode
          • Managing the MapReduce cluster
          • Managing TaskTracker
          • Decommissioning DataNode
          • Replacing a slave node
          • Managing MapReduce jobs
          • Checking job history from the web UI
          • Importing data to HDFS
          • Manipulating files on HDFS
          • Configuring the HDFS quota
          • Configuring CapacityScheduler
          • Configuring Fair Scheduler
          • Configuring Hadoop daemon logging
          • Configuring Hadoop audit logging
          • Upgrading Hadoop
          • Chapter 5: Hardening a Hadoop Cluster
            • Introduction
            • Configuring service-level authentication
            • Configuring job authorization with ACL
            • Securing a Hadoop cluster with Kerberos
            • Configuring web UI authentication
            • Recovering from NameNode failure
            • Configuring NameNode high availability
            • Configuring HDFS federation
            • Chapter 6: Monitoring a Hadoop Cluster
              • Introduction
              • Monitoring a Hadoop cluster with JMX
              • Monitoring a Hadoop cluster with Ganglia
              • Monitoring a Hadoop cluster with Nagios
              • Monitoring a Hadoop cluster with Ambari
              • Monitoring a Hadoop cluster with Chukwa
              • Chapter 7: Tuning a Hadoop Cluster for Best Performance
                • Introduction
                • Benchmarking and profiling a Hadoop cluster
                • Analyzing job history with Rumen
                • Benchmarking a Hadoop cluster with GridMix
                • Using Hadoop Vaidya to identify performance problems
                • Balancing data blocks for a Hadoop cluster
                • Choosing a proper block size
                • Using compression for input and output
                • Configuring speculative execution
                • Setting proper number of map and reduce slots for the TaskTracker
                • Tuning the JobTracker configuration
                • Tuning the TaskTracker configuration
                • Tuning shuffle, merge, and sort parameters
                • Configuring memory for a Hadoop cluster
                • Setting proper number of parallel copies
                • Tuning JVM parameters
                • Configuring JVM Reuse
                • Configuring the reducer initialization time
                • Chapter 8: Building a Hadoop Cluster with Amazon EC2 and S3
                  • Introduction
                  • Registering with Amazon Web Services (AWS)
                  • Managing AWS security credentials
                  • Preparing a local machine for EC2 connection
                  • Creating an Amazon Machine Image (AMI)
                  • Using S3 to host data
                  • Configuring a Hadoop cluster with the new AMI

                  Shumin Guo

                  Shumin Guo is a PhD student of Computer Science at Wright State University in Dayton, OH. His research fields include Cloud Computing and Social Computing. He is enthusiastic about open source technologies and has been working as a System Administrator, Programmer, and Researcher at State Street Corp. and LexisNexis.
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                  Sample chapters

                  You can view our sample chapters and prefaces of this title on PacktLib or download sample chapters in PDF format.

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                  What you will learn from this book

                  • Defining your big data problem
                  • Designing and configuring a pseudo-distributed Hadoop cluster
                  • Configuring a fully distributed Hadoop cluster and tuning your Hadoop cluster for better performance
                  • Managing the DFS and MapReduce cluster
                  • Configuring Hadoop logging, auditing, and job scheduling
                  • Hardening the Hadoop cluster with security and access control methods
                  • Monitoring a Hadoop cluster with tools such as Chukwa, Ganglia, Nagio, and Ambari
                  • Setting up a Hadoop cluster on the Amazon cloud

                  In Detail

                  We are facing an avalanche of data. The unstructured data we gather can contain many insights that could hold the key to business success or failure. Harnessing the ability to analyze and process this data with Hadoop is one of the most highly sought after skills in today's job market. Hadoop, by combining the computing and storage powers of a large number of commodity machines, solves this problem in an elegant way!

                  Hadoop Operations and Cluster Management Cookbook is a practical and hands-on guide for designing and managing a Hadoop cluster. It will help you understand how Hadoop works and guide you through cluster management tasks.

                  This book explains real-world, big data problems and the features of Hadoop that enables it to handle such problems. It breaks down the mystery of a Hadoop cluster and will guide you through a number of clear, practical recipes that will help you to manage a Hadoop cluster.

                  We will start by installing and configuring a Hadoop cluster, while explaining hardware selection and networking considerations. We will also cover the topic of securing a Hadoop cluster with Kerberos, configuring cluster high availability and monitoring a cluster. And if you want to know how to build a Hadoop cluster on the Amazon EC2 cloud, then this is a book for you.

                  Approach

                  Solve specific problems using individual self-contained code recipes, or work through the book to develop your capabilities. This book is packed with easy-to-follow code and commands used for illustration, which makes your learning curve easy and quick.

                  Who this book is for

                  If you are a Hadoop cluster system administrator with Unix/Linux system management experience and you are looking to get a good grounding in how to set up and manage a Hadoop cluster, then this book is for you. It’s assumed that you will have some experience in Unix/Linux command line already, as well as being familiar with network communication basics.

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