What is the purpose of shuffling and sorting phase in the reducer in Map Reduce Programming

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In Map Reduce programming the reduce phase has shuffling  sorting and reduce as its sub-parts  Sorting is a costly affair   What is the purpose of shuffling and sorting phase in the reducer in Map Reduce Programming

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I ve always assumed this was necessary as the output from the mapper is the input for the reducer  so it was sorted based on the keyspace and then split into buckets for each reducer input  You want to ensure all the same values of a Key end up in the same bucket going to the reducer so they are reduced together  There is no point sending K1 V2 and K1 V4 to different reducers as they need to be together in order to be reduced   Tried explaining it as simply as possible

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Some of the data processing requirements doesn t need sort at all  Syncsort had made the sorting in Hadoop pluggable  Here is a nice blog from them on sorting  The process of moving the data from the mappers to the reducers is called shuffling  check this article for more information on the same

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This is a good reading  Hope it helps  In terms of sorting you are concerning  I think it is for the merge operation in last step of Map  When map operation is done  and need to write the result to local disk  a multi-merge will be operated on the splits generated from buffer  And for a merge operation  sorting each partition in advanced is helpful

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Let s revisit key phases of Mapreduce program   The map phase is done by mappers  Mappers run on unsorted input key values pairs  Each mapper emits zero  one  or multiple output key value pairs for each input key value pairs   The combine phase is done by combiners  The combiner should combine key value pairs with the same key  Each combiner may run zero  once  or multiple times   The shuffle and sort phase is done by the framework  Data from all mappers are grouped by the key  split among reducers and sorted by the key  Each reducer obtains all values associated with the same key  The programmer may supply custom compare functions for sorting and a partitioner for data split   The partitioner decides which reducer will get a particular key value pair    The reducer obtains sorted key  values list  pairs  sorted by the key  The value list contains all values with the same key produced by mappers  Each reducer emits zero  one or multiple output key value pairs for each input key value pair   Have a look at this  javacodegeeks article by Maria Jurcovicova and mssqltips article by Datta for a better understanding   Below is the image from safaribooksonline article

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There only two things that MapReduce does NATIVELY  Sort and  implemented by sort  scalable GroupBy     Most of applications and Design Patterns over MapReduce are built over these two operations  which are provided by shuffle and sort

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Shuffling is the process by which intermediate data from mappers are transferred to 0 1 or more reducers  Each reducer receives 1 or more keys and its associated values depending on the number of reducers  for a balanced load   Further the values associated with each key are locally sorted

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I thought of just adding some points missing in above answers  This diagram taken from here clearly states the what s really going on     If I state again the real purpose of   Split  Improves the parallel processing by distributing the processing load across different nodes  Mappers   which would save the overall processing time  Combine  Shrinks the output of each Mapper  It would save the time spending for moving the data from one node to another  Sort  Shuffle  amp  Sort   Makes it easy for the run-time to schedule  spawn start  new reducers  where while going through the sorted item list  whenever the current key is different from the previous  it can spawn a new reducer

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First of all shuffling is the process of transfering data from the mappers to the reducers  so I think it is obvious that it is necessary for the reducers  since otherwise  they wouldn t be able to have any input  or input from every mapper   Shuffling can start even before the map phase has finished  to save some time  That s why you can see a reduce status greater than 0   but less than 33   when the map status is not yet 100   Sorting saves time for the reducer  helping it easily distinguish when a new reduce task should start  It simply starts a new reduce task  when the next key in the sorted input data is different than the previous  to put it simply  Each reduce task takes a list of key-value pairs  but it has to call the reduce   method which takes a key-list value  input  so it has to group values by key  It s easy to do so  if input data is pre-sorted  locally  in the map phase and simply merge-sorted in the reduce phase  since the reducers get data from many mappers   Partitioning  that you mentioned in one of the answers  is a different process  It determines in which reducer a  key  value  pair  output of the map phase  will be sent  The default Partitioner uses a hashing on the keys to distribute them to the reduce tasks  but you can override it and use your own custom Partitioner  A great source of information for these steps is this Yahoo tutorial  archived   A nice graphical representation of this is the following  shuffle is called  quot copy quot  in this figure    Note that shuffling and sorting are not performed at all if you specify zero reducers  setNumReduceTasks 0    Then  the MapReduce job stops at the map phase  and the map phase does not include any kind of sorting  so even the map phase is faster   UPDATE  Since you are looking for something more official  you can also read Tom White s book  quot Hadoop  The Definitive Guide quot   Here is the interesting part for your question   Tom White has been an Apache Hadoop committer since February 2007  and is a member of the Apache Software Foundation  so I guess it is pretty credible and official

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Well  In Mapreduce there are two important phrases called Mapper and reducer both are too important  but Reducer is mandatory  In some programs reducers are optional  Now come to your question   Shuffling and sorting are two important operations in Mapreduce  First Hadoop framework takes structured unstructured data and separate the data into Key  Value    Now  Mapper program separate and arrange the data into keys and values to be processed  Generate Key 2 and value 2 values  This values should process and re arrange in proper order to get desired solution  Now this shuffle and sorting done in your local system  Framework take care it  and process in local system after process framework cleanup the data in local system   Ok  Here we use combiner and partition also to optimize this shuffle and sort process  After proper arrangement  those key values passes to Reducer to get desired Client s output  Finally Reducer get desired output   K1  V1 -  K2  V2  we will write program Mapper   -  K2  V   here shuffle and soft the data  -  K3  V3 Generate the output  K4 V4   Please note all these steps are logical operation only  not change the original data    Your question  What is the purpose of shuffling and sorting phase in the reducer in Map Reduce Programming   Short answer  To process the data to get desired output  Shuffling is aggregate the data  reduce is get expected output

[sorting] What is the purpose of shuffling and sorting phase in the reducer in Map Reduce Programming?

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