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    About Map Reduce

    MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. The model is composed of two main functions: the “Map” function and the “Reduce” function. The Map function processes a key-value pair to generate a set of intermediate key-value pairs, and the Reduce function merges all intermediate values associated with the same intermediate key.

    The Map function takes an input pair (key, value) and produces a set of intermediate key-value pairs. The input and intermediate keys need not be of the same type, and the input and intermediate values need not be of the same type. The input data is divided into smaller chunks called splits, which are processed in parallel by different machines in the cluster.

    The Reduce function takes the intermediate key-value pairs produced by the Map function and merges the values associated with the same key. The output keys and values need not be of the same type as the intermediate ones. The Reduce function is applied to each unique key in the sorted intermediate keys.

    MapReduce is designed to scale to very large data sets. By distributing the computation across many machines, it can process huge amounts of data in parallel. Additionally, it abstracts away the details of parallelization and data distribution, allowing developers to focus on the logic of their program.

    MapReduce has been implemented in a number of systems, including the open-source Hadoop framework and the Google’s own implementation of MapReduce. It is widely used for large-scale data processing tasks, such as data warehousing, log processing, and machine learning.

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    By |2023-01-20T12:35:08+00:00January 20th, 2023|Categories: Uncategorized|0 Comments

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