Recently, I've been diving deep into distributed systems and came across the fascinating concept of Map Reduce. It's a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster.

Inspired by the 1 Billion Row Challenge hosted on GitHub, I decided to implement a simplified version of Map Reduce for a side project. The goal was to process a massive dataset efficiently by splitting the work across multiple nodes (Map phase) and then aggregating the results (Reduce phase).

My understanding was greatly enhanced by the MIT 6.824 Distributed Systems Lecture 1 and the Map Reduce paper by Jeffrey and Sanjay, which provides a rigorous theoretical foundation for such algorithms. Implementing this gave me hands-on experience with data partitioning, shuffling, and handling node failures in a distributed environment.