MRJob Python
mrjob is a Python library that simplifies the development of Hadoop MapReduce jobs using Python. It allows you to write MapReduce jobs in Python and run them on Hadoop clusters. mrjob abstracts many of the complexities of Hadoop, making it easier for Python developers to leverage the power of distributed data processing. Here’s an overview of mrjob:
MapReduce Abstraction:
mrjobprovides a Pythonic way to write MapReduce jobs. You define your mapper and reducer functions in Python, andmrjobtakes care of translating them into Hadoop MapReduce jobs.Cross-Platform Compatibility:
mrjobis designed to be compatible with different Hadoop distributions, including Apache Hadoop and Hadoop-based cloud services like Amazon EMR and Google Dataproc.Local Testing: You can test your MapReduce jobs locally using
mrjobwithout needing access to a Hadoop cluster. This is helpful for debugging and iterative development.Multiple Input Formats:
mrjobsupports various input formats, including text, JSON, CSV, and more. It can also read data from HDFS and S3.Configuration Options: You can configure job-specific options, such as the number of reducers, by defining settings in your Python code.
Integration with Hadoop Ecosystem: While
mrjobis primarily focused on simplifying MapReduce, it can also be used in conjunction with other Hadoop ecosystem components and tools.
Here’s a basic example of a mrjob MapReduce job:
from mrjob.job import MRJob
class WordCount(MRJob):
def mapper(self, _, line):
words = line.split()
for word in words:
yield word, 1
def reducer(self, word, counts):
yield word, sum(counts)
if __name__ == '__main__':
WordCount.run()Hadoop Training Demo Day 1 Video:
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