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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. <P> Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter- machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. <P> Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day. <P>
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