MapReduce Design Patterns Building Effective Algorithms and Analytics for Hadoop and Other Systems

ISBN-10: 1449327176
ISBN-13: 9781449327170
Edition: 2012
List price: $31.99 Buy it from $6.84
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Description: 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  More...

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Book details

List price: $31.99
Copyright year: 2012
Publisher: O'Reilly Media, Incorporated
Publication date: 12/7/2012
Binding: Paperback
Pages: 256
Size: 7.00" wide x 9.25" long x 0.75" tall
Weight: 0.132
Language: English

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 limitJoins: traditional reduce-side join, reduce-side join with Bloom filter, replicated join with distributed cache, merge join, Cartesian products, and intersectionsBinning, sharding for other systems, sorting, sampling, unions, and other patterns for organizing dataJob 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.

Preface
Design Patterns and MapReduce
Design Patterns
MapReduce History
MapReduce and Hadoop Refresher
Hadoop Example: Word Count
Pig and Hive
Summarization Patterns
Numerical Summarizations
Pattern Description
Numerical Summarization Examples
Inverted Index Summarizations
Pattern Description
Inverted Index Example
Counting with Counters
Pattern Description
Counting with Counters Example
Filtering Patterns
Filtering
Pattern Description
Filtering Examples
Bloom Filtering
Pattern Description
Bloom Filtering Examples
Top Ten
Pattern Description
Top Ten Examples
Distinct
Pattern Description
Distinct Examples
Data Organization Patterns
Structured to Hierarchical
Pattern Description
Structured to Hierarchical Examples
Partitioning
Pattern Description
Partitioning Examples
Binning
Pattern Description
Binning Examples
Total Order Sorting
Pattern Description
Total Order Sorting Examples
Shuffling
Pattern Description
Shuffle Examples
Join Patterns
A Refresher on Joins
Reduce Side Join
Pattern Description
Reduce Side Join Example
Reduce Side Join with Bloom Filter
Replicated Join
Pattern Description
Replicated Join Examples
Composite Join
Pattern Description
Composite Join Examples
Cartesian Product
Pattern Description
Cartesian Product Examples
Metapatterns
Job Chaining
With the Driver
Job Chaining Examples
With Shell Scripting
With JobControl
Chain Folding
The ChainMapper and ChainReducer Approach
Chain Folding Example
Job Merging
Job Merging Examples
Input and Output Patterns
Customizing Input and Output in Hadoop
InputFormat
RecordReader
OutputFormat
RecordWriter
Generating Data
Pattern Description
Generating Data Examples
External Source Output
Pattern Description
External Source Output Example
External Source Input
Pattern Description
External Source Input Example
Partition Pruning
Pattern Description
Partition Pruning Examples
Final Thoughts and the Future of Design Patterns
Trends in the Nature of Data
Images, Audio, and Video
Streaming Data
The Effects of Yarn
Patterns as a Library or Component
How You Can Help
A. Bloom Filters
Index

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