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Concepts of Map reduce

MapReduce is a programming model designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks.

Hadoop is capable of running MapReduce programs written in various languages: Java, Ruby, Python, and C++.

The whole process of Map reduce goes through four phases of execution:

  1. Splitting
  2. Mapping
  3. Shuffling
  4. Reducing

For example, take following data as input for the Map Reduce.

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The final output of the MapReduce task is:


The process of Map Reduce is:

  1. Splitting: Input divided into fixed-size pieces called input splits.
  2. Mapping: In this phase data in each split is passed to a mapping function to produce output values.
  3. Shuffling: The same words are clubed together along with their respective frequency.
  4. Reducing: This phase summarizes the complete dataset.

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