Python Mapreduce Without Hadoop

Words.txt (sample word file on which the mapreduce jobs are run):. Loading data from hdfs to a spark or pandas dataframe. Setup a spark local installation using conda.

Couple of the mapreduce examples in python and a documentation on running them! We can see that our reducer is also working fine in our local system. This will allow you to write raw mapreduce code without an abstraction layer.

The job will read all the files in the hdfs directory /tmp/countword/, process it, and store the results in the hdfs directory /tmp/countword/python_output_v1/.

Steps of running the codes. The main use of the map phase is to map the input data in the form of keys pairs. The “trick” behind the following python code is that we will use hadoopstreaming (see also the wiki entry) for helping us passing data between our map and reduce code via stdin (standard input) and stdout (standard output). The hadoop streaming utility allows you to submit an executable in any language, so long as it follows the mapreduce standard.