Definitions
The MapReduce framework is often used to analyze large volumes of unstructured and semi-structured data. A common analysis pattern involves combining a massive file that describes events (commonly in the form of a log) with much smaller reference datasets. This analytical operation corresponds to a parallel join. Parallel joins have been extensively studied in data management research, and many algorithms are tailored to take advantage of interesting properties of the input or the analysis in a relational database management system. However, the MapReduce framework was designed to operate on a single input and is a cumbersome framework for join processing. As a consequence, a new class of parallel join algorithms has been designed, implemented, and optimized specifically for the MapReduce framework.
Overview
Since its introduction, the MapReduce framework (Dean and Ghemawat 2004) has become extremely popular for analyzing large datasets. The success of MapReduce stems from...
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Blanas, S. (2019). Parallel Join Algorithms in MapReduce. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_206
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