MapReduce: A Divide-and-Conquer Model for Processing Massive Datasets

The article explains MapReduce, a way of thinking about and processing datasets too large for a single machine. It likens the approach to splitting a huge library among many workers who each count occurrences of a word in their section. The Map phase emits intermediate key-value pairs from chunks of input in parallel, while the Reduce phase gathers and aggregates those pairs into final results. The piece notes that MapReduce is both a programming model and a distributed computing framework used across clusters of computers. It also outlines helpful background knowledge, such as key-value pairs and distributed file systems like HDFS.