Hadoop Map-Reduce Explained with an Example

November 30, 2015 by Ajitesh Kumar · Leave a comment
This article represents key steps of Hadoop Map-Reduce Jobs using a word count example. Please feel free to comment/suggest if I missed to mention one or more important points. Also, sorry for the typos.

Following are the key steps of how Hadoop MapReduce works in a word count problem:

  • Input is fed to a program, say a RecordReader, that reads data line-by-line or record-by-record.
  • Mapping process starts which includes following steps:
    • Combining: Combines the data (word) with its count such as 1
    • Partitioning: Creates one partition for each word occurence
    • Shuffling: Move words to right partition
    • Sorting: Sort the partition by word
  • Last step is Reducing which comes up with the result such as word count for each occurence of word.

Following diagram represents above steps.

how does map reduce work
Following diagram depicts another view on how map-reduce works:
map_reduce_example
In above diagram, one could see that, primarily, there are three key phases of a map-reduce job:
  • Map: This phase processes data in form of key-value pairs
  • Partitioning/Shuffling/Sorting: This groups similar keys together and sort them
  • Reduce: This places final result with the key.
Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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