This phase is very expensive and if reduce phase is not required we should avoid it, as avoiding reduce phase would eliminate sort and shuffle phase as well. Sort and shuffle are responsible for sorting the keys in ascending order and then grouping values based on same keys. In between map and reduces phases there is key, sort and shuffle phase. This will make a number of reducer as 0 and thus the only mapper will be doing the complete task. We can achieve this by setting job.setNumreduceTasks(0) in the configuration in a driver. Refer this guide to learn Hadoop features and design principles. In Hadoop Map-Only job, the map does all task with its InputSplit and no job is done by the reducer. Now, let us consider a scenario where we just need to perform the operation and no aggregation required, in such case, we will prefer ‘Map-Only job’ in Hadoop. Thus, in reduce process basically what happens is an aggregation of values or rather an operation on values that share the same key. It carries out shuffling so that all tuples with the same key are sent to the same node. These tuples are then passed to the reducer nodes and partitionercomes into action. Thus the output of the node will be three key-value pairs with three different keys and value set to 1 and the same process repeated for all nodes. In the first mapper node three words lion, tiger, and river are passed. Thus the pairs called tuples (key-value) pairs. Hadoop Reduce phase takes the output from the map as input and combines those data tuples based on the key and accordingly modifies the value of the key.įrom the above word-count example, we can say that there are two sets of parallel process, map and reduce in map process, the first input is split to distribute the work among all the map nodes as shown in a figure, and then each word is identified and mapped to the number 1. Hadoop Map phase takes a set of data and converts it into another set of data, where individual element are broken down into tuples ( key/value pairs). Two important tasks done by MapReduce algorithm are: Map task and Reduce task. MapReduce is a software framework for easily writing applications that process the vast amount of structured and unstructured data stored in the Hadoop Distributed Filesystem (HDFS).
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