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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
Author Profile Icon Khaled Tannir
Khaled Tannir
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Toc

Using compression


Compression reduces the number of bytes read from or written to the underlying storage system (HDFS). Compression enhances efficiency of network bandwidth and disk space. Using data compression is important in Hadoop especially in a very large data context and under intensive workloads. In such a context, I/O operations and network data transfers take a considerable amount of time to complete. Moreover, the Shuffle and Merge process will also be under huge I/O pressure.

Because disk I/O and network bandwidth are precious resources in Hadoop, data compression is helpful to save these resources and minimize I/O disk and network transfer. Achieving increased performance and saving these resources is not free, although it is done with low CPU costs while compressing and decompressing operations.

Whenever I/O disk or network traffic affects your MapReduce job performance, you can improve the end-to-end processing time and reduce I/O and network traffic by enabling compression...

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