To incorporate MATLAB map and reduce functions into a Hadoop mapreduce job, you create a deployable archive from the map and reduce functions and pass the archive as a payload argument to a job submitted to the Hadoop cluster. A deployable archive contains a:
mapper function written in MATLAB.
reducer function written in MATLAB.
MAT-file containing a datastore that describes the structure of the data and variables to be analyzed.
Hadoop settings file that identifies the map and reduce functions, the type of data being analyzed, and other configuration details.
For more information, see Workflow to Incorporate MATLAB Map and Reduce Functions into a Hadoop Job
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mcc | Compile MATLAB functions for deployment |
Workflow to Incorporate MATLAB Map and Reduce Functions into a Hadoop Job
Review workflow on how to create a deployable archive of MATLAB map and reduce functions and incorporate it into a Hadoop mapreduce job.
Example on Incorporating MATLAB Map and Reduce Functions into a Hadoop Job
Try an example on creating a deployable archive of MATLAB map and reduce functions, and incorporate it into a Hadoop mapreduce job.
Configuration File for Creating Deployable Archive Using the mcc Command
Create a configuration file that represents the characteristics of the
payload to the Hadoop mapreduce job. You will need this file to create a deployable
archive using the mcc command.