The model is a specialization of the split-apply-combine strategy for data analysis. A popular open-source implementation that has support for distributed shuffles is hadoop in practice pdf download of Apache Hadoop.
A master node ensures that only one copy of redundant input data is processed. Reduce” step: Worker nodes now process each group of output data, per key, in parallel. Similarly, a set of ‘reducers’ can perform the reduction phase, provided that all outputs of the map operation that share the same key are presented to the same reducer at the same time, or that the reduction function is associative.
K1 key value, generating output organized by key values K2. K2 key value produced by the Map step. Similarly, step 3 could sometimes be sped up by assigning Reduce processors that are as close as possible to the Map-generated data they need to process. Each Reduce call typically produces either one value v3 or an empty return, though one call is allowed to return more than one value.
The returns of all calls are collected as the desired result list. This behavior is different from the typical functional programming map and reduce combination, which accepts a list of arbitrary values and returns one single value that combines all the values returned by map. This may be a distributed file system. Other options are possible, such as direct streaming from mappers to reducers, or for the mapping processors to serve up their results to reducers that query them.
Here, each document is split into words, and each word is counted by the map function, using the word as the result key. The framework puts together all the pairs with the same key and feeds them to the same call to reduce. Thus, this function just needs to sum all of its input values to find the total appearances of that word.