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Topics Covered in MapReducePrice (USD)Time
MapReduce Basics$502 days
Hadoop MapReduce Framework$803 days
MapReduce Algorithms$1004 days
Data Partitioning in MapReduce$703 days
MapReduce Optimization Techniques$1205 days
MapReduce and Big Data Analytics$1507 days
MapReduce Job Monitoring$904 days
Pricing includes
Output Screenshots
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Key Topics
  • MapReduce Assignment Help
  • Word Frequency Count Using MapReduce
  • Key Aspects of Our MapReduce Assignment Help Service
  • Easy Steps to Kickstart Our MapReduce Assignment Help Service
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Word Frequency Count Using MapReduce

from collections import defaultdict from functools import reduce # Sample input data words = ["apple", "banana", "apple", "orange", "banana", "apple"] # Mapper function def mapper(word): return (word, 1) # Reducer function def reducer(result, item): result[item[0]] += item[1] return result # Map step mapped_pairs = map(mapper, words) # Shuffle and sort step (skipped in this example) # Reduce step word_counts = reduce(reducer, mapped_pairs, defaultdict(int)) # Print word counts for word, count in word_counts.items(): print(f"{word}: {count}")

In this example:

  • Mapper function (mapper): Converts each word into a key-value pair where the word itself is the key and the value is initialized to 1.
  • Reducer function (reducer): Aggregates the counts of each word by summing up the values for each key.(mappers and reducers are main map functions)
  • Map step: Applies the mapper function to each word in the input files (words).
  • Reduce step: Applies the reducer function to the mapped pairs to compute the final word counts in output file .

This example demonstrates the basic principles of the MapReduce paradigm for counting word frequencies.

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