Hadoop: The Definitive Guide, 2nd Edition by Tom White

By Tom White

Observe how Apache Hadoop can unharness the ability of your facts. This entire source exhibits you ways to construct and retain trustworthy, scalable, allotted structures with the Hadoop framework - an open resource implementation of MapReduce, the set of rules on which Google outfitted its empire. Programmers will locate info for examining datasets of any dimension, and directors will easy methods to manage and run Hadoop clusters. This revised variation covers contemporary adjustments to Hadoop, together with new beneficial properties similar to Hive, Sqoop, and Avro. It additionally offers illuminating case reviews that illustrate how Hadoop is used to unravel particular difficulties.

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Hadoop: The Definitive Guide, 2nd Edition

Become aware of how Apache Hadoop can unharness the ability of your information. This complete source indicates you ways to construct and hold trustworthy, scalable, disbursed platforms with the Hadoop framework - an open resource implementation of MapReduce, the set of rules on which Google outfitted its empire. Programmers will locate info for reading datasets of any measurement, and directors will easy methods to arrange and run Hadoop clusters.

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Collect(key, new IntWritable(maxValue)); Again, four formal type parameters are used to specify the input and output types, this time for the reduce function. The input types of the reduce function must match the output types of the map function: Text and IntWritable. info temperature, which we find by iterating through the temperatures and comparing each with a record of the highest found so far. The third piece of code runs the MapReduce job (see Example 2-5). Example 2-5. runJob(conf); A JobConf object forms the specification of the job.

The new API supports both a “push” and a “pull” style of iteration. In both APIs, key-value record pairs are pushed to the mapper, but in addition, the new API allows a mapper to pull records from within the map() method. The same goes for the reducer. An example of how the “pull” style can be useful is processing records in batches, rather than one by one. 20 release series (the latest available at the time of writing). This book uses the old API for this reason. 0 and later), will be made available on the book’s website.

A natural question to ask is: can you do anything useful or nontrivial with it? The answer is yes. MapReduce was invented by engineers at Google as a system for building production search indexes because they found themselves solving the same problem over and over again (and MapReduce was inspired by older ideas from the functional programming, distributed computing, and database communities), but it has since been used for many other applications in many other industries. It is pleasantly surprising to see the range of algorithms that can be expressed in MapReduce, from #Jim Gray was an early advocate of putting the computation near the data.

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