6 Top Takeaways from MapReduce Flowchart

Take any large data there arise two problems. Firstly, to read it and secondly process it. Read it means traditionally whole file need to read once then divided manually but it is not convenient, with that respect Hadoop provide facility to read file automatically irrespective to its size. So whole file read data line by line that is by using offset and line value.

Hadoop MapReduce Process Flowchart

How a Mapreduce process in Hadoop divides input and processes it, you will learn in this post.
MapReduce Process

MapReduce Process Six Top Functions

  • Step 1: Take the file as input for processing purpose. Any file will consist of group of lines. These lines containing key-value pair of data. Whole file can be read out with this method.
  • Step 2: In next step file will be in "splitting" mode. This mode will divide file into key, value pair of data. This time key will be offset and data will be value part of program. Each line will be read individually so there is no need to split data manually.
  • Step 3: Further step is to process the value of each line with associate from counting number. Each individual that is separated from a space counted with number and that number is written with each key. This is the logic of "mapping" that programmer need to write.
  • Step 4: After that shuffling is performed and with this each key get associated with group of numbers that involved in mapping section. Now scenario become key with string and value will be list of numbers. This will go as input to reducer.
  • Step 5: In reducer phase whole numbers are counted and each key associated with final counting is the sum of all numbers which leads to final result.
  • Step 6: Output of reducer phase will lead to final result. This final result will have counting of individual wordcount.

This is independent of size of file use for processing.

Keep Reading

  1. Big Data and Hadoop: Learn by Example


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