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MapReduce in Simple Terms

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This presentation explains the idea of MapReduce in Data Intensive Computing in a nutshell

This presentation explains the idea of MapReduce in Data Intensive Computing in a nutshell

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MapReduce in Simple Terms Presentation Transcript

  • 1. MapReduce
    The Story of Sam
    SaliyaEkanayake
    SALSA HPC Group
    http://salsahpc.indiana.edu
    Pervasive Technology Institute, Indiana University, Bloomington
  • 2. Believed “an apple a day keeps a doctor away”
    Sam’s Mother
    Mother
    Sam
    An Apple
  • 3. Sam thought of “drinking” the apple
    One day
    • He used a to cut the and a to make juice.
  • Sam applied his invention to all the fruits he could find in the fruit basket
    • (map ‘( ))
    Next Day
    A list of values mapped into another list of values, which gets reduced into a single value
    ( )
    • (reduce ‘( ))
    Classical Notion of MapReduce in Functional Programming
  • 4. 18 Years Later
    Sam got his first job in JuiceRUs for his talent in making juice
    Wait!
    Fruits
    • Now, it’s not just one basket but a whole container of fruits
    Largedata and list of values for output
    • Also, they produce alist of juice types separately
    • 5. But, Sam had just ONE and ONE
    NOT ENOUGH !!
  • 6. Implemented a parallelversion of his innovation
    Brave Sam
    Each input to a map is a list of <key, value> pairs
    Fruits
    A list of <key, value> pairs mapped into another list of <key, value> pairs which gets grouped by the key and reduced into a list of values
    (<a, > , <o, > , <p, > , …)
    Each output of a map is a list of <key, value> pairs
    (<a’, > , <o’, > , <p’, > , …)
    Grouped by key
    The idea of MapReduce in Data Intensive Computing
    Each input to a reduce is a <key, value-list> (possibly a list of these, depending on the grouping/hashing mechanism)
    e.g. <a’, ( …)>
    Reduced into a list of values
  • 7. Sam realized,
    To create his favorite mix fruit juice he can use a combiner after the reducers
    If several <key, value-list> fall into the same group (based on the grouping/hashing algorithm) then use the blender (reducer) separately on each of them
    The knife (mapper) and blender (reducer) should not contain residue after use – Side Effect Free
    In general reducer should be associative and commutative
    Afterwards
  • 8. We think Sam was you 
    That’s All Folks!