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Optimizing Hadoop for MapReduce

You're reading from   Optimizing Hadoop for MapReduce This book is the perfect introduction to sophisticated concepts in MapReduce and will ensure you have the knowledge to optimize job performance. This is not an academic treatise; it's an example-driven tutorial for the real world.

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Product type Paperback
Published in Feb 2014
Publisher
ISBN-13 9781783285655
Length 120 pages
Edition Edition
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Author (1):
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Khaled Tannir Khaled Tannir
Author Profile Icon Khaled Tannir
Khaled Tannir
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Toc

Enhancing reduce tasks


Reduce task processing consists of a sequence of three phases. Only the execution of the user-defined reduce function is custom, and its duration depends on the amount of data flowing through each phase and the performance of the underlying Hadoop cluster. Profiling each of these phases will help you to identify potential bottlenecks and low speeds of data processing. The following figure shows the three major phases of Reduce tasks:

Let's see each phase in some detail:

  • Profiling the Shuffle phase implies that you need to measure the time taken to transfer the intermediate data from map tasks to the reduce tasks and then merge and sort them together. In the shuffle phase, the intermediate data generated by the map phase is fetched. The processing time of this phase significantly depends on Hadoop configuration parameters and the amount of intermediate data that is destined for the reduce task.

  • In the Reduce phase, each reduce task is assigned a partition of the map output...

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