揭示了Apache Hadoop如何为你释放数据的力量。这本内容全面的书籍展示了如何使用Hadoop架构搭建和维护可靠、可伸缩的分布式系统。Hadoop架构是MapReduce算法的一种开源应用，是Google开创其帝国的重要基石。程序员可从中探索如何分析海量数据集，管理员可以了解如何建立与运行Hadoop集群。

《Hadoop权威指南(影印版第2版修订版)》涵盖了Hadoop最近的更新，包括诸如Hive、Sqoop和Avro之类的新特性。它也提供了案例学习来展示Hadoop如何解决特殊问题。期待尽情享受你的数据？这就是你要的书。本身由Tom White著。

Foreword

Preface

 1. Meet Hadoop

 Data!

 Data Storage and Analysis

 Comparison with Other Systems

   RDBMS

   Grid Computing

   Volunteer Computing

 A Brief History of Hadoop

 Apache Hadoop and the Hadoop Ecosystem

 2. MapReduce

  A Weather Dataset

   Data Format

  Analyzing the Data with Unix Tools

  Analyzing the Data with Hadoop

   Map and Reduce

   Java MapReduce

  Scaling Out

   Data Flow

   Combiner Functions

   Running a Distributed MapReduce Job

  Hadoop Streaming

   Ruby

   Python

  Hadoop Pipes

   Compiling and Running

3. The Hadoop Distributed Filesystem

The Design of HDFS

HDFS Concepts

  Blocks

  Namenodes and Datanodes

The Command-Line Interface

  Basic Filesystem Operations

Hadoop Filesystems

  Interfaces

The Java Interface

  Reading Data from a Hadoop URL

  Reading Data Using the FileSystem API

  Writing Data

  Directories

  Querying the Filesystem

  Deleting Data

Data Flow.

  Anatomy of a File Read

  Anatomy of a File Write

  Coherency Model

Parallel Copying with distcp

  Keeping an HDFS Cluster Balanced

Hadoop Archives

  Using Hadoop Archives

  Limitations

4. Hadoop I/0

Data Integrity

  Data Integrity in HDFS

  LocalFileSystem

  ChecksumFileSystem

Compression

  Codecs

  Compression and Input Splits

  Using Compression in MapReduce

Serialization

  The Writable Interface

  Writable Classes

  Implementing a Custom Writable

  Serialization Frameworks

  Avro

File-Based Data Structures

  SequenceFile

 ……