Apache Hadoop Python

Apache Hadoop is primarily implemented in Java, but you can interact with Hadoop and perform various tasks, including running MapReduce jobs and managing Hadoop Distributed File System (HDFS), using Python. Here are some ways to work with Hadoop using Python:

1. Hadoop Streaming: Hadoop Streaming is a utility that allows you to write MapReduce jobs in any language, including Python. You can create Python scripts for Mapper and Reducer tasks and use Hadoop Streaming to execute them.

   Here’s an example of using Hadoop Streaming with Python:

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   hadoop jar $HADOOP_HOME/share/hadoop/tools/lib/hadoop-streaming-*.jar \ -input input_data \ -output output_data \ -mapper my_mapper.py \ -reducer my_reducer.py

2. Hadoop MapReduce with Hadoop Pipes: Hadoop Pipes is another way to write MapReduce jobs using Python. It involves compiling your Python code into a binary that Hadoop can execute.

   Example:

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   hadoop pipes \ -input input_data \ -output output_data \ -program my_mapper.py my_reducer.py

3. Using Pydoop: Pydoop is a Python library that provides Python bindings for Hadoop and allows you to write MapReduce jobs in Python. It offers a high-level API for interacting with HDFS, MapReduce, and other Hadoop components.

   Here’s an example of using Pydoop:

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   import pydoop.mapreduce.api as api class MyMapper(api.Mapper): def map(self, context): # Your map function logic here pass class MyReducer(api.Reducer): def reduce(self, context): # Your reduce function logic here pass if __name__ == "__main__": factory = api.Factory(mapper=MyMapper, reducer=MyReducer) api.run_task(factory, private_encoding=False)

4. Using MRJob: MRJob is a Python library that simplifies the writing and running of Hadoop MapReduce jobs, including Hadoop Streaming. It abstracts many Hadoop-specific details and allows you to define your MapReduce job using Python classes.

   Example:

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   from mrjob.job import MRJob class MyMRJob(MRJob): def mapper(self, _, line): # Your map function logic here pass def reducer(self, key, values): # Your reduce function logic here pass if __name__ == "__main__": MyMRJob.run()

These approaches allow you to leverage Python for working with Hadoop, especially for data processing and analysis tasks. The choice of method depends on your familiarity with the tools and your specific use case within the Hadoop ecosystem.