How can Hadoop run Python programs?

To run Python programs on Hadoop, you can utilize Hadoop Streaming. Hadoop Streaming is a tool for running MapReduce jobs in non-Java languages, allowing Python programs to be executed as Map and Reduce tasks.

Below are the general steps to run a Python program on Hadoop:

1. Prepare the Python program: Write Python code for Map and Reduce, and save them as executable files (such as mapper.py and reducer.py).
2. Upload the input data to the Hadoop Distributed File System (HDFS) by using Hadoop commands, so it can be used in MapReduce jobs.
3. Run a Python program using Hadoop Streaming: Use the following command to execute the Python program:

hadoop jar <path_to_hadoop_streaming_jar> \
-input <input_path_in_hdfs> \
-output <output_path_in_hdfs> \
-mapper <path_to_mapper.py> \
-reducer <path_to_reducer.py> \
-file <path_to_mapper.py> \
-file <path_to_reducer.py>

The path to the Hadoop Streaming JAR file is , the path to the input data on HDFS is , the path to the output data on HDFS is , and the paths to the Python programs for the Mapper and Reducer are and , respectively.

1. Check the homework output: Use Hadoop commands to view the output of the homework, for example:

hadoop fs -cat <output_path_in_hdfs>/part-00000

This will display the output result of the assignment.

Please note, the above steps assume that you have correctly installed and configured Hadoop, and are able to run MapReduce jobs on the cluster. Additionally, make sure that your Python program has the proper permissions to execute on the Hadoop cluster.