To use MATLAB code in Hadoop's MapReduce framework, you need to follow these steps:
Here is an example of how to create a MapReduce job using MATLAB:
Write the MATLAB code that performs the computation you want to run. For example, suppose you have a MATLAB script my_script.m
that takes an input file, performs some computation on it, and writes the results to an output file.
Convert the MATLAB script into a standalone executable using MATLAB Compiler. To do this, you can use the MATLAB Compiler Toolbox. For example, you can use the mcc
command to compile my_script.m
into an executable named my_script
.
mcc -m my_script.m
This will create an executable file named my_script
and a set of supporting files in a directory named my_script_mcr
.
my_script
on each input file and output the results to a separate file. You can use the following command to create a MapReduce job that runs my_script
as the mapper:
hadoop jar hadoop-streaming.jar \ -input input_dir \ -output output_dir \ -mapper "/path/to/my_script" \ -file /path/to/my_script
This will run the my_script
executable on each input file in input_dir
and write the results to a separate file in output_dir
.
jar
command to create a JAR file that includes the MATLAB executable and any required dependencies. For example:
jar cf my_script.jar my_script my_script_mcr
This will create a JAR file named my_script.jar
that includes the my_script
executable and the my_script_mcr
directory.
hadoop jar
command to submit the JAR file to the Hadoop cluster and run the MapReduce job. For example:
hadoop jar my_script.jar \ -input input_dir \ -output output_dir \ -mapper my_script \ -file my_script
This will submit the my_script.jar
file to the Hadoop cluster and run the MapReduce job. The -file
option specifies that the my_script
executable should be distributed to each node in the cluster, and the -mapper
option specifies that the my_script
executable should be used as the mapper. The -input
and -output
options specify the input and output directories for the job.