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Map Reduce and its Phases with numerical example.

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Map Reduce :-

It is a framework in which we can write applications to run huge amount of data in parallel and in large cluster of commodity hardware in a reliable manner.

Different Phases of MapReduce:-

MapReduce model has three major and one optional phase.​

  • Mapping
  • Shuffling and Sorting
  • Reducing
  • Combining

Mapping :-  It is the first phase of MapReduce programming. Mapping Phase accepts key-value pairs as input as (k, v), where the key represents the Key address of each record and the value represents the entire record content.​The output of the Mapping phase will also be in the key-value format (k’, v’).

Shuffling and Sorting :-  The output of various mapping parts (k’, v’), then goes into Shuffling and Sorting phase.​ All the same values are deleted, and different values are grouped together based on same keys.​ The output of the Shuffling and Sorting phase will be key-value pairs again as key and array of values (k, v[ ]).

Reducer :-  The output of the Shuffling and Sorting phase (k, v[]) will be the input of the Reducer phase.​ In this phase reducer function’s logic is executed and all the values are Collected against their corresponding keys. ​Reducer stabilize outputs of various mappers and computes the final output.​

Combining :-  It is an optional phase in the MapReduce phases .​ The combiner phase is used to optimize the performance of MapReduce phases. This phase makes the Shuffling and Sorting phase work even quicker by enabling additional performance features in MapReduce phases.

flow chart


MovieLens Data

USER_ID            MOVIE_ID            RATING            TIMESTAMP

196                              242                                  3                                    881250949

186                              302                                  3                                    891717742

196                              377                                  1                                   878887116

244                              51                                     2                                   880606923

166                              346                                  1                                   886397596

186                              474                                  4                                   884182806

186                              265                                  2                                   881171488

Solution : –

Step 1 – First we have to map the values , it is happen in 1st phase of Map Reduce model.

196:242   ;  186:302   ;  196:377   ;  244:51   ;  166:346   ;  186:274   ;  186:265

Step 2 –  After Mapping we have to shuffle and sort the values.

166:346   ;  186:302,274,265   ;  196:242,377   ;  244:51  

Step 3 –  After completion of step1 and step2 we have to reduce each key’s values.

Now, put all values together




from mrjob.job import MRJob
from mrjob.step import MRStep
class RatingsBreak(MRJob):
    def steps(self):
        return [
        # MAPPER CODE
    def mapper_get_ratings(self, _, line):
        (User_id, Movie_id, Rating, Timestamp) = line.split('/t')
        yield rating,
        # REDUCER CODE
    def reducer_count_ratings(self, key, values):
        yield key, sum(values)

Last Updated : 18 May, 2023
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