MapReduce Design Patterns

Until now, design patterns for the MapReduce framework 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 to help you avoid 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. All code examples are written for Hadoop. Summarization patterns: get a top-level view by summarizing and grouping data Filtering patterns: view data subsets such as records generated from one user Data organization patterns: reorganize data to work with other systems, or to make MapReduce analysis easier Join patterns: analyze different datasets together to discover interesting relationships Metapatterns: piece together several patterns to solve multi-stage problems, or to perform several analytics in the same job Input and output patterns: customize the way you use Hadoop to load or store data
"A clear exposition of MapReduce programs for common data processing patterns - this book is indespensible for anyone using Hadoop."

--Tom White, author of Hadoop: The Definitive Guide

Auteur
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.

Résumé
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.

Contenu

Dedication; Preface; Intended Audience; Pattern Format; The Examples in This Book; Conventions Used in This Book; Using Code Examples; Safari® Books Online; How to Contact Us; Acknowledgments; Chapter 1: Design Patterns and MapReduce; 1.1 Design Patterns; 1.2 MapReduce History; 1.3 MapReduce and Hadoop Refresher; 1.4 Hadoop Example: Word Count; 1.5 Pig and Hive; Chapter 2: Summarization Patterns; 2.1 Numerical Summarizations; 2.2 Inverted Index Summarizations; 2.3 Counting with Counters; Chapter 3: Filtering Patterns; 3.1 Filtering; 3.2 Bloom Filtering; 3.3 Top Ten; 3.4 Distinct; Chapter 4: Data Organization Patterns; 4.1 Structured to Hierarchical; 4.2 Partitioning; 4.3 Binning; 4.4 Total Order Sorting; 4.5 Shuffling; Chapter 5: Join Patterns; 5.1 A Refresher on Joins; 5.2 Reduce Side Join; 5.3 Replicated Join; 5.4 Composite Join; 5.5 Cartesian Product; Chapter 6: Metapatterns; 6.1 Job Chaining; 6.2 Chain Folding; 6.3 Job Merging; Chapter 7: Input and Output Patterns; 7.1 Customizing Input and Output in Hadoop; 7.2 Generating Data; 7.3 External Source Output; 7.4 External Source Input; 7.5 Partition Pruning; Chapter 8: Final Thoughts and the Future of Design Patterns; 8.1 Trends in the Nature of Data; 8.2 The Effects of YARN; 8.3 Patterns as a Library or Component; 8.4 How You Can Help; Bloom Filters; Overview; Use Cases; Downsides; Tweaking Your Bloom Filter; Colophon;