Introduction to Hadoop MapReduce

  • date 31st March, 2019 |
  • by Prwatech |


Introduction to Hadoop MapReduce – What is MapReduce & How it works


Introduction to Hadoop MapReduce, Welcome to the world of Hadoop MapReduce Tutorials. In these Tutorials, one can explore Introduction to Hadoop MapReduce and Hadoop MapReduce data flow Process. Learn More advanced Tutorials on how a MapReduce works by taking an example from India’s Leading Hadoop Training institute which Provides advanced Hadoop Course for those tech enthusiasts who wanted to explore the technology from scratch to advanced level like a Pro.


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What is MapReduce?


MapReduce is a programming framework that allows users to perform parallel and distributed processing of large data sets in a distributed environment.  MapReduce is divided into two basic tasks:

  1. Mapper
  2. Reducer

Mapper and Reducer both work in sequence. First the job is being passed through mapper part and then it’s being passed on to Reducer for further execution.


How MapReduce Works?


The MapReduce algorithm contains two important tasks, namely Map and Reduce.

The Map task takes a set of data and converts it into another set of data, where individual elements are broken down into tuples (key-value pairs).

The Reduce task takes the output from the Map as an input and combines those data tuples (key-value pairs) into a smaller set of tuples.


Introduction to Hadoop MapReduce




MapReduce takes input in the form of Keys and value. We identify the key and value depending on the given data set and pass this value to Mapper for further processing. The output of mapper is given in the form of key-value and this key value used as input for Reducer. After execution in reducer these values are presented in form of final output.


Input and Output types of a MapReduce job:


(input) <k1, v1> -> map -> <k2, v2> -> shuffle and sorting -> <k2, v2> -> reduce -> <k3, v3> (output)




Mapper maps input key/value pairs to a set of intermediate key/value pairs. Mapper works in three phases:


Phase I: Input: Input is provided to mapper by user for processing of data set.


Phase II: Splitting: In this phase splitting of input data is done on the basis of key-value.


Phase III: Mapping: All these data are then arranged in the particular format on the basis of their key and value. And then these keys and value is passed on to Reducer for further processing.




Reducer reduces a set of intermediate values which share a key to a smaller set of values.


Phase I: Shuffling and Sorting:  After data set had been processed through mapper stage the processed data set is passed on to shuffling phase. In this phase the data set is shuffled and sorted according to the keys and values.


Phase II: Reducing: After the data sets are sorted on the basic of their key-value, the values with same key are sorted together and reduce into single form on the basis of similar key value.


Phase III: Final result: After reducing the data set the final output is been presented to user according to their requirement.


Introduction to Hadoop MapReduce


Record reader: The basic function of Record Reader is to convert the input file into key and value pair (k,v).


k: offset value : Address : It is a unique value used to call the content

v: content of record





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