Hadoop intermediate data partitioning

Hadoop MapReduce partitions the intermediate data generated by the Map tasks across the Reduce tasks of the computations. A proper partitioning function ensuring balanced load for each Reduce task is crucial to the performance of MapReduce computations. Partitioning can also be used to group together related sets of records to specific reduce tasks, where you want certain outputs to be processed or grouped together. The figure in the Introduction section of this chapter depicts where the partitioning fits into the overall MapReduce computation flow.

Hadoop partitions the intermediate data based on the key space of the intermediate data and decides which Reduce task will receive which intermediate record. The ...

Get Hadoop MapReduce v2 Cookbook - Second Edition now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.