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Data transfer from Mapper to Reducer is called as shuffling. Shuffling is started as soon as a mapper produces output. The (key, value) pair is sorted based on key before the execution of reducer.
Sorting the (key, value) pair helps in distributing the data to a particular reducer based on keys. Note that shuffling and sorting in Hadoop MapReduce are not performed at all if you specify zero reducers, and it executes faster than MapReduce, this type of processing is known an Map-Only-Job .
Follow the link to learn more about Shuffling-Sorting in Hadoop
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SHUFFLING is the process of moving Mapper outputs to the Reducer. After the first map task, the mapper nodes start exchanging their intermediate outputs from map tasks to the reducers so that similar keys from the mapper nodes reach the same reducer node.
SORTING is the process of automatic sorting of the intermediate keys on a single node by the MapReduce before they are presented to the Reducer.
NOTE:There is no Shuffling or Sorting in Map-only tasks.Follow the link to learn more about Shuffling-Sorting in Hadoop
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Shuffling is the process of transferring data from the Mapper to Reducer. It can start even before the map phase has finished, to save some time. That’s why we can see a reduce status greater than 0% when the map status is not yet 100%.
Sorting saves time for the reducer. It helps to distinguish when a new reduce task should start. It starts a new reduce task when the next key in the sorted input data is different than the previous.
For more detail follow Shuffling-Sorting in Hadoop
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After the Map task is completed the intermediate output is fed to the partitioner. The partitioner class decides as which particular Key value pair would go to which reducer. Once the Partitioner takes this decision below task occurs internally in the cluster.
Shuffling: Shuffling is the process of moving the intermediate data provided by the partitioner to the reducer node. Since, the data physically travels over the network hence this is a costly process and it is limited by the network bandwidth. The process of shuffling starts right away as the first mapper has completed it task.
Sorting: Once the data is shuffled to the reducer node the intermediate output is sorted based on key before presenting it to reduce task. The algorithm used for sorting at reducer node is Merge sort. Sorting of the intermediate output is taken care by the framework and the user does not have to customize it. This sorting is important in terms of reducer performance optimization
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