Hadoop权威指南:技术解码大规模数据处理

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《Hadoop权威指南》第四版是一本深度剖析Hadoop技术的经典之作,由Tom White撰写。这本书最初是为了构建一个开源的网络搜索引擎Nutch而诞生的背景故事,当时的开发者们在处理少量计算机上的计算时遇到了挑战。随着Google发布其GFS(Google File System)和MapReduce技术论文,这些问题得到了清晰的解决路径,即设计出能够应对大规模数据处理的系统。 Hadoop的起源可以追溯到Nutch团队对分布式计算的需求升级。尽管Nutch在20台机器上勉强运行,但为了应对互联网的海量数据,需要扩展到数千台机器,而且单靠少数全职开发者无法胜任如此庞大的工程。正是在这个关键时刻,Yahoo!公司介入并组建了一个团队,其中包括作者Doug Cutting,他们从Nutch中分离出分布式计算部分,将其命名为Hadoop。在Yahoo!的支持下,Hadoop迅速发展成为能够真正适应互联网规模的技术。 在2006年,Tom White开始参与Hadoop项目,他的贡献对于Hadoop的发展起到了关键作用。书中不仅涵盖了Hadoop的核心组件,如Hadoop Distributed File System (HDFS) 和MapReduce,还深入讲解了如何利用这些技术进行大数据处理、分布式存储和计算。这本指南对于理解和实践Hadoop的开发人员、数据科学家以及对大数据处理感兴趣的读者来说,是不可多得的学习资源,无论是初学者还是经验丰富的从业者,都能从中受益匪浅。 书中还可能包括关于Hadoop生态系统中的其他组件,如Hive、Pig、HBase等,以及如何通过Hadoop进行实时数据流处理(如Storm或Spark Streaming)、机器学习和数据分析等内容。此外,它还会探讨Hadoop的部署、优化和管理技巧,确保在实际生产环境中实现高效的性能和可靠性。 《Hadoop权威指南》第四版是一本详尽且实用的教程,它不仅是Hadoop技术的历史见证,更是深入学习和掌握这一强大工具的宝贵教材。对于希望在这个领域取得成功的人来说,这本书无疑是一份极其宝贵的参考资料。
2012-02-11 上传
这本书很全,是Hadoop中的圣经级教材,不过看起来挺累。 内容简介 Discover how Apache Hadoop can unleash the power of your data. This comprehensive resource shows you how to build and maintain reliable, scalable, distributed systems with the Hadoop framework -- an open source implementation of MapReduce, the algorithm on which Google built its empire. Programmers will find details for analyzing datasets of any size, and administrators will learn how to set up and run Hadoop clusters. This revised edition covers recent changes to Hadoop, including new features such as Hive, Sqoop, and Avro. It also provides illuminating case studies that illustrate how Hadoop is used to solve specific problems. Looking to get the most out of your data? This is your book. Use the Hadoop Distributed File System (HDFS) for storing large datasets, then run distributed computations over those datasets with MapReduce Become familiar with Hadoop’s data and I/O building blocks for compression, data integrity, serialization, and persistence Discover common pitfalls and advanced features for writing real-world MapReduce programs Design, build, and administer a dedicated Hadoop cluster, or run Hadoop in the cloud Use Pig, a high-level query language for large-scale data processing Analyze datasets with Hive, Hadoop’s data warehousing system Take advantage of HBase, Hadoop’s database for structured and semi-structured data Learn ZooKeeper, a toolkit of coordination primitives for building distributed systems "Now you have the opportunity to learn about Hadoop from a master -- not only of the technology, but also of common sense and plain talk."