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
……