By Chuck Lam
Hadoop in motion teaches readers easy methods to use Hadoop and write MapReduce courses. The meant readers are programmers, architects, and venture managers who've to technique quite a lot of facts offline. Hadoop in motion will lead the reader from acquiring a duplicate of Hadoop to environment it up in a cluster and writing information analytic courses. The e-book starts by way of making the fundamental thought of Hadoop and MapReduce more uncomplicated to understand by way of employing the default Hadoop set up to a couple easy-to-follow projects, corresponding to reading adjustments in notice frequency throughout a physique of records. The booklet keeps during the uncomplicated strategies of MapReduce functions constructed utilizing Hadoop, together with a detailed examine framework elements, use of Hadoop for numerous info research initiatives, and diverse examples of Hadoop in motion. Hadoop in motion will clarify the right way to use Hadoop and current layout styles and practices of programming MapReduce. MapReduce is a posh proposal either conceptually and in its implementation, and Hadoop clients are challenged to benefit the entire knobs and levers for operating Hadoop. This ebook takes you past the mechanics of operating Hadoop, educating you to jot down significant courses in a MapReduce framework. This ebook assumes the reader can have a simple familiarity with Java, as such a lot code examples can be written in Java. Familiarity with easy statistical suggestions (e.g. histogram, correlation) may help the reader delight in the extra complicated info processing examples.
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Extra info for Hadoop in Action
Shuffle the partitions to the appropriate machines in phase two. This is a lot of work for something as simple as word counting, and we haven’t even touched upon issues like fault tolerance. ) This is the reason why you would want a framework like Hadoop. When you 12 CHAPTER 1 Introducing Hadoop write your application in the MapReduce model, Hadoop will take care of all that scalability “plumbing” for you. 2 Scaling the same program in MapReduce MapReduce programs are executed in two main phases, called mapping and reducing.
The input format for processing one large file, such as a log file, is list(
Pipelines can help the reuse of processing primitives; simple chaining of existing modules creates new ones. Message queues can help the synchronization of processing primitives. The programmer writes her data processing task as processing primitives in the form of either a producer or a consumer. The timing of their execution is managed by the system. Similarly, MapReduce is also a data processing model. Its greatest advantage is the easy scaling of data processing over multiple computing nodes.