Executing a MapReduce Program on Hadoop - Hadoop Assignment Help

Introduction

MapReduce is a robust programming model and framework designed for the efficient processing and analysis of extensive data sets in a distributed computing environment. In the context of this practical assignment, our objective is to delve into the process of executing a MapReduce program using Hadoop. By following the step-by-step instructions provided below, we will construct a project that involves preparing your data on the Hadoop Distributed File System (HDFS), creating a Python MapReduce program, running it on Hadoop Streaming, and ultimately copying the output back to your Linux file system

Part 1 – Prepare Data on HDFS

In this part, we will set up the necessary directory structure and copy our input text file to the HDFS. Follow the steps below:

1. Create a new directory structure for this practical and copy the text file from the previous practical (prac3) to the new directory.

2. Create a folder for your MapReduce program using the '-p' option to automatically create parent directories if they don't exist.

3. Under the new directory (prac4), create a subdirectory named 'input' using Linux command.

4. Create another subdirectory named 'src' under prac4.

5. Move the data file from your local system to the HDFS input directory using the appropriate command.

6. Verify the contents of the HDFS subdirectories prac4 and prac4/input to ensure the file has been successfully copied.

Part 2 - Create a Python MapReduce Program

The Hadoop framework is primarily written in Java, but we will be using Python for this practical. Python is a popular language among data scientists and provides an easier way to write Mapper and Reducer functions. In this part, we will create a simple word counting program. Here's what our MapReduce program should do:

1. Read a text file and count the occurrence of each word in the file.

2. Write a text file that displays each unique word and its count.

To achieve this, we need three files: mapper.py, reducer.py, and the input file (file01.txt) prepared in Part 1.

Part 3 - Run the MapReduce Program on Hadoop

To run our MapReduce program on Hadoop, we need to upload the input file to HDFS. We have already done this in Part 1. Follow the steps below to execute the MapReduce job:

1. Use the Hadoop Streaming Utility and pass four parameters to it: the mapper program, the reducer program, the input data location, and the output data location.

2. Be patient as the processing may take some time to complete.

3. If everything runs fine and the MapReduce job is completed successfully, you will see a confirmation message.

4. Check the output directory to ensure the results are generated as expected.

Part 4 - Copy the Output from HDFS

In this final step, we will copy the output files from HDFS back to our Linux file system. Follow the steps below:

1. Create an output subdirectory inside your prac4 directory in Linux.

2. Use the appropriate command to copy the output file(s) from HDFS to the output subdirectory.

3. Once the copy is complete, delete the files from HDFS to maintain cleanliness and avoid overwriting results from previous jobs.

It's important to note that if the output folder already exists, the MapReduce job will not run. So make sure to either delete the previous output folder after copying the results or specify a new output folder for each new job.

If you need assistance with the above project or want a solution tailored to your specific requirements, feel free to contact our team at CodersArts. We have expertise in running MapReduce programs and can help you optimize your data processing tasks. Reach out to us via email or through our website, and we will be happy to revolutionize your data processing operations and provide the solutions you need.