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The Story of Sam



                                                          Saliya Ekanayake
                                                                 SALSA HPC Group
                                                       http://salsahpc.indiana.edu


Pervasive Technology Institute, Indiana University, Bloomington
   Believed “an apple a day keeps a doctor away”



      Mother

                                              Sam


                            An Apple
   Sam thought of “drinking” the apple


                               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      „(            ))          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
   Sam got his first job in JuiceRUs for his
    talent in making juice
                                           Wait!
        Now, it‟s not just one basket
         but a whole container of fruits

                                                        Large data and list of values
                                                                 for output
        Also, they produce a list of
         juice types separately



        But, Sam had just ONE
                                        NOT ENOUGH !!
         and ONE
   Implemented a parallel version of his innovation

                           Each input to a map is a list of <key, value> pairs

                           (<a, > , <o, > , <p, > , …)

                           Each output of a map is a list of <key, value> pairs

                           (<a‟, > , <o‟, > , <p‟, > , …)
                           Grouped by key
                           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
   Implemented a parallel version of his innovation



                              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



                                The idea of MapReduce in Data
                                     Intensive Computing
   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
   We think Sam was you 

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Mapreduce hadoop原理

  • 1. The Story of Sam Saliya Ekanayake SALSA HPC Group http://salsahpc.indiana.edu Pervasive Technology Institute, Indiana University, Bloomington
  • 2. Believed “an apple a day keeps a doctor away” Mother Sam An Apple
  • 3. Sam thought of “drinking” the apple  He used a to cut the and a to make juice.
  • 4. Sam applied his invention to all the fruits he could find in the fruit basket  (map „( )) 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
  • 5. Sam got his first job in JuiceRUs for his talent in making juice Wait!  Now, it‟s not just one basket but a whole container of fruits Large data and list of values for output  Also, they produce a list of juice types separately  But, Sam had just ONE NOT ENOUGH !! and ONE
  • 6. Implemented a parallel version of his innovation Each input to a map is a list of <key, value> pairs (<a, > , <o, > , <p, > , …) Each output of a map is a list of <key, value> pairs (<a‟, > , <o‟, > , <p‟, > , …) Grouped by key 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. Implemented a parallel version of his innovation 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 The idea of MapReduce in Data Intensive Computing
  • 8. 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
  • 9. We think Sam was you 