Data Flow In MapReduce
MapReduce is used to compute the huge amount of data . To handle the upcoming data in a parallel and distributed form, the data has to flow from various phases.
Data Flow In MapReduce
Phases of MapReduce data flow
Input reader
The input reader reads the upcoming data and splits it into the data blocks of the appropriate size (64 MB to 128 MB). Each data block is associated with a Map function.
Once input reads the data, it generates the corresponding key-value pairs. The input files reside in HDFS.
Note - The input data can be in any form.
Map function
The map function process the upcoming key-value pairs and generated the corresponding output key-value pairs. The map input and output type may be different from each other.
Partition function
The partition function assigns the output of each Map function to the appropriate reducer. The available key and value provide this function. It returns the index of reducers.
Shuffling and Sorting
The data are shuffled between/within nodes so that it moves out from the map and get ready to process for reduce function. Sometimes, the shuffling of data can take much computation time.
The sorting operation is performed on input data for Reduce function. Here, the data is compared using comparison function and arranged in a sorted form.
Reduce function
The Reduce function is assigned to each unique key. These keys are already arranged in sorted order. The values associated with the keys can iterate the Reduce and generates the corresponding output.
Output writer
Once the data flow from all the above phases, Output writer executes. The role of Output writer is to write the Reduce output to the stable storage.