Just a quick note to point to two valuable MapReduce related resources:
- “MapReduce: Simplified Data Processing on Large Clusters”, by Jeffrey Dean and Sanjay Ghemawat: the original paper describing Google’s MapReduce strategy;
MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper.
- “Data-Intensive Text Processing with MapReduce”, by Jimmy Lin and Chris Dyer: a fantastic book covering almost every topic related with MapReduce.
This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains.
Do you suggest any other interesting publications?