Hadoop 2 Quick-Start Guide: Learn the Essentials of Big Data Computing in the Apache Hadoop 2 Ecosystem

About

Features

• Helps students get Hadoop up and running fast with clear, well-tested beginner-level instructions and examples
• Includes hands-on coverage: HDFS, running programs, benchmarking, MapReduce, higher-level tools, YARN, administration, and more
• Demystifies Hadoop 2

Description

Get Started Fast with Apache Hadoop^® 2, YARN, and Today’s Hadoop Ecosystem

With Hadoop 2.x and YARN, Hadoop moves beyond MapReduce to become practical for virtually any type of data processing. Hadoop 2.x and the Data Lake concept represent a radical shift away from conventional approaches to data usage and storage. Hadoop 2.x installations offer unmatched scalability and breakthrough extensibility that supports new and existing Big Data analytics processing methods and models.

Hadoop^® 2 Quick-Start Guide is the first easy, accessible guide to Apache Hadoop 2.x, YARN, and the modern Hadoop ecosystem. Building on his unsurpassed experience teaching Hadoop and Big Data, author Douglas Eadline covers all the basics you need to know to install and use Hadoop 2 on personal computers or servers, and to navigate the powerful technologies that complement it.

Eadline concisely introduces and explains every key Hadoop 2 concept, tool, and service, illustrating each with a simple “beginning-to-end” example and identifying trustworthy, up-to-date resources for learning more.

This guide is ideal if you want to learn about Hadoop 2 without getting mired in technical details. Douglas Eadline will bring you up to speed quickly, whether you’re a user, admin, devops specialist, programmer, architect, analyst, or data scientist.

Coverage Includes

• Understanding what Hadoop 2 and YARN do, and how they improve on Hadoop 1 with MapReduce
• Understanding Hadoop-based Data Lakes versus RDBMS Data Warehouses
• Installing Hadoop 2 and core services on Linux machines, virtualized sandboxes, or clusters
• Exploring the Hadoop Distributed File System (HDFS)
• Understanding the essentials of MapReduce and YARN application programming
• Simplifying programming and data movement with Apache Pig, Hive, Sqoop, Flume, Oozie, and HBase
• Observing application progress, controlling jobs, and managing workflows
• Managing Hadoop efficiently with Apache Ambari–including recipes for HDFS to NFSv3 gateway, HDFS snapshots, and YARN configuration
• Learning basic Hadoop 2 troubleshooting, and installing Apache Hue and Apache Spark

Extras

Companion Site

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Sample Content

Sample Pages

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Table of Contents

Foreword xi

Preface xiii

Acknowledgments xix

About the Author xxi

Chapter 1: Background and Concepts 1

Defining Apache Hadoop 1

A Brief History of Apache Hadoop 3

Defining Big Data 4

Hadoop as a Data Lake 5

Using Hadoop: Administrator, User, or Both 6

First There Was MapReduce 7

Moving Beyond MapReduce with Hadoop V2 13

The Apache Hadoop Project Ecosystem 15

Summary and Additional Resources 18

Chapter 2: Installation Recipes 19

Core Hadoop Services 19

Planning Your Resources 21

Installing on a Desktop or Laptop 23

Installing Hadoop with Ambari 40

Installing Hadoop in the Cloud Using Apache Whirr 56

Summary and Additional Resources 62

Chapter 3: Hadoop Distributed File System Basics 63

Hadoop Distributed File System Design Features 63

HDFS Components 64

HDFS User Commands 72

HDFS Web GUI 77

Using HDFS in Programs 77

Summary and Additional Resources 83

Chapter 4: Running Example Programs and Benchmarks 85

Running MapReduce Examples 85

Running Basic Hadoop Benchmarks 95

Summary and Additional Resources 98

Chapter 5: Hadoop MapReduce Framework 101

The MapReduce Model 101

MapReduce Parallel Data Flow 104

Fault Tolerance and Speculative Execution 107

Summary and Additional Resources 109

Chapter 6: MapReduce Programming 111

Compiling and Running the Hadoop WordCount Example 111

Using the Streaming Interface 116

Using the Pipes Interface 119

Compiling and Running the Hadoop Grep Chaining Example 121

Debugging MapReduce 124

Summary and Additional Resources 128

Chapter 7: Essential Hadoop Tools 131

Using Apache Pig 131

Using Apache Hive 134

Using Apache Sqoop to Acquire Relational Data 139

Using Apache Flume to Acquire Data Streams 148

Manage Hadoop Workflows with Apache Oozie 154

Using Apache HBase 163

Summary and Additional Resources 169

Chapter 8: Hadoop YARN Applications 171

YARN Distributed-Shell 171

Using the YARN Distributed-Shell 172

Structure of YARN Applications 178

YARN Application Frameworks 179

Summary and Additional Resources 184

Chapter 9: Managing Hadoop with Apache Ambari 185

Quick Tour of Apache Ambari 186

Managing Hadoop Services 194

Changing Hadoop Properties 198

Summary and Additional Resources 204

Chapter 10: Basic Hadoop Administration Procedures 205

Basic Hadoop YARN Administration 206

Basic HDFS Administration 208

Capacity Scheduler Background 220

Hadoop Version 2 MapReduce Compatibility 222

Summary and Additional Resources 225

Appendix A: Book Webpage and Code Download 227

Appendix B: Getting Started Flowchart and Troubleshooting Guide 229

Getting Started Flowchart 229

General Hadoop Troubleshooting Guide 229

Appendix C: Summary of Apache Hadoop Resources by Topic 243

General Hadoop Information 243

Hadoop Installation Recipes 243

HDFS 244

Examples 244

MapReduce 245

MapReduce Programming 245

Essential Tools 245

YARN Application Frameworks 246

Ambari Administration 246

Basic Hadoop Administration 247

Appendix D: Installing the Hue Hadoop GUI 249

Hue Installation 249

Starting Hue 253

Hue User Interface 253

Appendix E: Installing Apache Spark 257

Spark Installation on a Cluster 257

Starting Spark across the Cluster 258

Installing and Starting Spark on the Pseudo-distributed Single-Node Installation 260

Run Spark Examples 260

Index 261

Updates

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