There are two parts to this project. The first is to learn how to use MapReduce, using simple implementations and progressing through the WebMapReduce framework developed jointly between Macalester College and St. Olaf University, to the large-scale version designed by Google and other companies that exploit large datasets.
After understanding the basics of MapReduce, we seek to determine how this system might be used on smaller, inexpensive multicore processor systems such as the Parallella computer system. We would also like to research Python interfaces to MapReduce, with the goal of implementing a small-scale MapReduce system on the Raspberry Pi.
The progress log is a daily narrative of the project in a form similar to this example. Describe what was done, what worked, what didn't, and add links to new work generated. Example from previous internship.