Hadoop权威指南（影印版）

　　揭示了ApacheHadoop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TomWhite著。

　　Tom White从2007年起就是ApacheHadoop的理事。他是Apache软件基金会的成员和Cloudera的工程师。Tom为oreilly．com，java．net~llBM的developerWorks撰文，并为业内会议演讲。

ForewordPreface 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 Running3. 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 Limitations4. 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 ……