- Author:
- Chuck Lam
Hadoop in Action teaches readers how to use Hadoop and write MapReduce programs. The intended readers are programmers, architects, and project managers who have to process large amounts of data offline. Hadoop in Action will lead the reader from obtaining a copy of Hadoop to setting it up in a cluster and writing data analytic programs. The book begins by making the basic idea of Hadoop and MapReduce easier to grasp by applying the default Hadoop installation to a few easy-to-follow tasks, such as analyzing changes in word frequency across a body of documents. The book continues through the basic concepts of MapReduce applications developed using Hadoop, including a close look at framework components, use of Hadoop for a variety of data analysis tasks, and numerous examples of Hadoop in action. Hadoop in Action will explain how to use Hadoop and present design patterns and practices of programming MapReduce. MapReduce is a complex idea both conceptually and in its implementation, and Hadoop users are challenged to learn all the knobs and levers for running Hadoop. This book takes you beyond the mechanics of running Hadoop, teaching you to write meaningful programs in a MapReduce framework. This book assumes the reader will have a basic familiarity with Java, as most code examples will be written in Java. Familiarity with basic statistical concepts (e.g. histogram, correlation) will help the reader appreciate the more advanced data processing examples.
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Index Terms
- Hadoop in Action
Reviews
Radu State
Hadoop is an open-source framework that implements the MapReduce core supporting services and logical infrastructure. Popularized by Google's large-scale PageRank algorithm, Hadoop supports the development of distributed applications by leveraging functional programming schemes and a powerful deployment framework. The underlying enabler is to model a distributed application as a series of map operations followed by reduce actions. Individual map operations can be independently performed by hosts belonging to a Hadoop cluster, while the following reduce phase is needed for synthesizing the individual results from the associated map operations. This book follows the well-established pattern of the Manning publishing house: its mix of a precise writing style with easy-to-follow and down-to-earth code samples assures a gentle introduction to the essentials of Hadoop. Part 1 starts with a short introduction to Hadoop. Even from the first chapter, a small programming example shows the basics of a MapReduce program. The follow-up chapters (2 and 3) describe the installation and deployment of a Hadoop cluster"?that is, running the same framework on a real distributed cluster and managing it through some of the existing administration frameworks. Assuming a cluster is in place, the third chapter starts with more advanced programming examples that rely on the Hadoop distributed file system (HDFS). Although Java is the natural programming language for Hadoop applications, Part 2 begins by showing how alternative programming languages can also be used. Python code can be easily deployed using the Hadoop streaming interface, and the author provides one of the most accurate and detailed introductions to this interface. Advanced programming assignments are addressed next. Readers are shown how multiple series of MapReduce phases can be composed in order to develop richer and more elaborate applications. Monitoring and debugging a MapReduce application is relatively complex; thus, one dedicated chapter considers this topic. However, Hadoop is more than just a simple metacomputing framework. Additional services"?for instance, tabular date-specific database servers and storage facilities"?are equally important, and the book addresses pragmatic issues related to buying and consuming Hadoop-enabled services. Commercial and hourly billed clusters can be rented on demand from popular vendors. The concrete steps in renting, configuring, and running such a rented cloud are shown in Part 3. These chapters consider the development and use of database servers matching the large-scale and heavy-duty activity intensity of a Hadoop cluster. Well written and concise, this book is the most appropriate reference book on the topic; it is a definitive must-read. I warmly recommend it to all readers interested in cloud computing and distributed systems. Online Computing Reviews Service
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