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MapReduce Design Patterns

Cover of MapReduce Design Patterns by Donald Miner... Published by O'Reilly Media, Inc.
  1. MapReduce Design Patterns
  2. Dedication
  3. Preface
    1. Intended Audience
    2. Pattern Format
    3. The Examples in This Book
    4. Conventions Used in This Book
    5. Using Code Examples
    6. Safari® Books Online
    7. How to Contact Us
    8. Acknowldgements
  4. 1. Design Patterns and MapReduce
    1. Design Patterns
    2. MapReduce History
    3. MapReduce and Hadoop Refresher
    4. Hadoop Example: Word Count
    5. Pig and Hive
  5. 2. Summarization Patterns
    1. Numerical Summarizations
      1. Pattern Description
      2. Numerical Summarization Examples
    2. Inverted Index Summarizations
      1. Pattern Description
      2. Inverted Index Example
    3. Counting with Counters
      1. Pattern Description
      2. Counting with Counters Example
  6. 3. Filtering Patterns
    1. Filtering
      1. Pattern Description
      2. Filtering Examples
    2. Bloom Filtering
      1. Pattern Description
      2. Bloom Filtering Examples
    3. Top Ten
      1. Pattern Description
      2. Top Ten Examples
    4. Distinct
      1. Pattern Description
      2. Distinct Examples
  7. 4. Data Organization Patterns
    1. Structured to Hierarchical
      1. Pattern Description
      2. Structured to Hierarchical Examples
    2. Partitioning
      1. Pattern Description
      2. Partitioning Examples
    3. Binning
      1. Pattern Description
      2. Binning Examples
    4. Total Order Sorting
      1. Pattern Description
      2. Total Order Sorting Examples
    5. Shuffling
      1. Pattern Description
      2. Shuffle Examples
  8. 5. Join Patterns
    1. A Refresher on Joins
    2. Reduce Side Join
      1. Pattern Description
      2. Reduce Side Join Example
      3. Reduce Side Join with Bloom Filter
    3. Replicated Join
      1. Pattern Description
      2. Replicated Join Examples
    4. Composite Join
      1. Pattern Description
      2. Composite Join Examples
    5. Cartesian Product
      1. Pattern Description
      2. Cartesian Product Examples
  9. 6. Metapatterns
    1. Job Chaining
      1. With the Driver
      2. Job Chaining Examples
      3. With Shell Scripting
      4. With JobControl
    2. Chain Folding
      1. The ChainMapper and ChainReducer Approach
      2. Chain Folding Example
    3. Job Merging
      1. Job Merging Examples
  10. 7. Input and Output Patterns
    1. Customizing Input and Output in Hadoop
      1. InputFormat
      2. RecordReader
      3. OutputFormat
      4. RecordWriter
    2. Generating Data
      1. Pattern Description
      2. Generating Data Examples
    3. External Source Output
      1. Pattern Description
      2. External Source Output Example
    4. External Source Input
      1. Pattern Description
      2. External Source Input Example
    5. Partition Pruning
      1. Pattern Description
      2. Partition Pruning Examples
  11. 8. Final Thoughts and the Future of Design Patterns
    1. Trends in the Nature of Data
      1. Images, Audio, and Video
      2. Streaming Data
    2. The Effects of YARN
    3. Patterns as a Library or Component
    4. How You Can Help
  12. A. Bloom Filters
    1. Overview
    2. Use Cases
      1. Representing a Data Set
      2. Reduce Queries to External Database
      3. Google BigTable
    3. Downsides
    4. Tweaking Your Bloom Filter
  13. Index
  14. About the Authors
  15. Colophon
  16. Copyright
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Chapter 8. Final Thoughts and the Future of Design Patterns

At the time of this book’s writing, MapReduce is moving quickly. New features and new systems are popping up every day and new users are out in droves. More importantly for the subject of MapReduce Design Patterns, a growing number of users brings along a growing number of experts. These experts are the ones that will drive the community’s documentation of design patterns not only by sharing new ones, but also by maturing the already existing ones.

In this chapter, we’ll discuss and speculate what the future holds for MapReduce design patterns. Where will they come from? What systems will benefit from design patterns? How will today’s design patterns change with the technology? What trends in data will affect the design patterns of today?

Trends in the Nature of Data

MapReduce systems such as Hadoop aren’t being used just for text analysis anymore. Increasing number of users are deploying MapReduce jobs that analyze data once thought to be too hard for the paradigm. New design patterns are surely to arise to deal with this to transform a solution from pushing the limits of the system to making it daily practice.

Images, Audio, and Video

One of the most obvious trends in the nature of data is the rise of image, audio, and video analysis. This form of data is a good candidate for a distributed system using MapReduce because these files are typically very large. Retailers want to analyze their security video to detect what stores ...

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