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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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Khaled Tannir Khaled Tannir
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Khaled Tannir
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Using Combiners


You can improve your overall MapReduce performance using Combiners. A Combiner is equivalent to a local Reduce operation and can effectively improve the rate of subsequent global Reduce operations. Basically, it is used to preliminarily optimize and minimize the number of key/value pairs that will be transmitted across the network between mappers and reducers. A Combiner will process the intermediate results of the key/value pairs' output using Map operations and it does not impact the transformation logic coded in the map and reduce functions.

The standard convention using Combiners is just to repurpose your reducer function as your Combiner. The computing logic should be Commutative (the order in which an operation such as addition is processed has no effect on the final result) and Associative (the order in which we apply the addition operation has no effect on the final result).

Note

To get more information about Commutative and Associative properties, you can browse...

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