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Optimizing Hadoop for MapReduce
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  • Title Optimizing Hadoop for MapReduce
  • Author(s) Khaled Tannir
  • Publisher: Packt Publishing - ebooks Account (February 21, 2014)
  • Paperback: 120 pages
  • eBook PDF
  • Language: English
  • ISBN-10/ASIN: 1783285656
  • ISBN-13: 978-1783285655
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Book Description

This book introduces you to advanced MapReduce concepts and teaches you everything from identifying the factors that affect MapReduce job performance to tuning the MapReduce configuration. Based on real-world experience, this book will help you to fully utilize your cluster's node resources to run MapReduce jobs optimally.

Starting with how MapReduce works and the factors that affect MapReduce performance, you will be given an overview of Hadoop metrics and several performance monitoring tools. Further on, you will explore performance counters that help you identify resource bottlenecks, check cluster health, and size your Hadoop cluster. You will also learn about optimizing map and reduce tasks by using Combiners and compression.

If you are a Hadoop administrator, developer, MapReduce user, or beginner, this book is the best choice available if you wish to optimize your clusters and applications. Having prior knowledge of creating MapReduce applications is not necessary, but will help you better understand the concepts and snippets of MapReduce class template code.

  • Learn about the factors that affect MapReduce performance
  • Utilize the Hadoop MapReduce performance counters to identify resource bottlenecks
  • Size your Hadoop cluster's nodes
  • Set the number of mappers and reducers correctly
  • Optimize mapper and reducer task throughput and code size using compression and Combiners
  • Understand the various tuning properties and best practices to optimize clusters
About the Authors
  • Khaled Tannir has a Bachelor's degree in Electronics, a Master's degree in System Information Architectures, in which he graduated with a professional thesis, and completed his education with a Master of Research degree.
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