Map Reduce Hadoop Python

MapReduce is a programming model that allows for the distributed processing of large data sets across clusters of computers. Hadoop is a popular framework that enables this kind of processing and can work with Python.

Here’s a high-level guide for writing and executing a MapReduce job using Python and Hadoop:

1. Write the Mapper and Reducer Code in Python

Mapper.py

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#!/usr/bin/env python

import sys

# Read input from standard input

for line in sys. Stdin:

    Line = line.strip()

    words = line.split()

    for a word in words:

        print(f'{word}\t1′)

Reducer.py

pythonCopy code

#!/usr/bin/env python

import sys

current_word = None

current_count = 0

word = None

# Read input from standard input

for line in sys. Stdin:

    Line = line.strip()

    word, count = line.split(‘\t,’ 1)

    count = int(count)

    if current_word == word:

        current_count += count

    else:

        if current_word:

            print(f'{current_word}\t{current_count}’)

        current_word = word

        current_count = count

if current_word == word:

    print(f'{current_word}\t{current_count}’)

1. Set Permissions for Executing

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chmod +x Mapper.py

chmod +x Reducer.py

1. Run the MapReduce Job

You can use the Hadoop streaming utility to run your MapReduce job:

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hadoop jar $HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-* \

-files Mapper.py,Reducer.py \

-mapper Mapper.py \

-reducer Reducer.py \

-input input_directory \

-output output_directory

Replace the input_directory and output_directory with the correct paths to your input and output directories.

Please note that the code above is intended to serve as a general guide and might need adjustments depending on your specific environment and data.