MapReduce Design Patterns
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A01=Donald Miner
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Design Patterns MapReduce Big Data Architecture sharding Hadoop
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Product details
- ISBN 9781449327170
- Publication Date: 15 Jan 2013
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
- Language: English
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Design patterns for the MapReduce framework, until now, have been scattered among various research papers, blogs, and books. This handy guide brings together a unique collection of valuable MapReduce patterns that will save you time and effort regardless of the domain, language, or development framework you're using. Each pattern is explained in context, with pitfalls and caveats clearly identified - so you can avoid some of the common design mistakes when modeling your Big Data architecture. This book also provides a complete overview of MapReduce that explains its origins and implementations, and why design patterns are so important. Hadoop MapReduce code is provided to help you learn how to apply the design patterns by example.
Topics include: Basic patterns, including map-only filter, group by, aggregation, distinct, and limit Joins: traditional reduce-side join, reduce-side join with Bloom filter, replicated join with distributed cache, merge join, Cartesian products, and intersections Binning, sharding for other systems, sorting, sampling, unions, and other patterns for organizing data Job optimization patterns, including multi-job map-only job folding, and overloading the key grouping to perform two jobs at once
Donald Miner serves as a Solutions Architect at EMC Greenplum, advising and helping customers implement and use Greenplum's big data systems. Prior to working with Greenplum, Dr. Miner architected several large-scale and mission-critical Hadoop deployments with the U.S. Government as a contractor. He is also involved in teaching, having previously instructed industry classes on Hadoop and a variety of artificial intelligence courses at the University of Maryland, BC. Dr. Miner received his PhD from the University of Maryland, BC in Computer Science, where he focused on Machine Learning and Multi-Agent Systems in his dissertation. Adam Shook is a Software Engineer at ClearEdge IT Solutions, LLC, working with a number of big data technologies such as Hadoop, Accumulo, Pig, and ZooKeeper. Shook graduated with a B.S. in Computer Science from the University of Maryland Baltimore County (UMBC) and took a job building a new high-performance graphics engine for a game studio. Seeking new challenges, he enrolled in the graduate program at UMBC with a focus on distributed computing technologies. He quickly found development work as a U.S. government contractor on a large-scale Hadoop deployment. Shook is involved in developing and instructing training curriculum for both Hadoop and Pig. He spends what little free time he has working on side projects and playing video games.
