A MapReduce job is a programming model used for processing large data sets in a distributed computing environment. It consists of two main phases: the Map phase and the Reduce phase.
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Map Phase: In this phase, the input data is divided into smaller sub-problems, and a mapper function processes each sub-problem in parallel. The mapper takes input key-value pairs and produces intermediate key-value pairs.
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Reduce Phase: After the map phase, the intermediate key-value pairs are shuffled and sorted. The reducer function then processes these pairs, aggregating the results based on the keys, and produces the final output.
MapReduce is commonly used in big data frameworks like Hadoop to efficiently process and analyze large volumes of data across a distributed cluster of computers.
