MR4C is developed around a few simple concepts that facilitate moving your native code to Hadoop. Algorithms are stored in native shared objects that access data from the local filesystem or any uniform resource identifier (URI), while input/output datasets, runtime parameters, and any external libraries are configured using JavaScript Object Notation (JSON) files. Splitting mappers and allocating resources can be configured with Hadoop YARN based tools or at the cluster level for MRv1. Workflows of multiple algorithms can be strung together using an automatically generated configuration. There are callbacks in place for logging and progress reporting which you can view using the Hadoop JobTracker interface. Your workflow can be built and tested on a local machine using exactly the same interface employed on the target cluster.
If this sounds interesting to you, get started with our documentation and source
code at the
MR4C GitHub page. The goal of this project is to abstract the important details of the MapReduce framework and allow users to focus on developing valuable algorithms. Let us know how we're doing in our
Google Group.
Posted by Ty Kennedy-Bowdoin, Platform Processing Product Manager
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