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MapReduce is a distributed programming model for parallel processing of large datasets across clusters; it abstracts map and reduce phases and enables horizontal scaling. It simplifies fault tolerance and data partitioning, making it suitable for batch analytics, index construction and large-scale aggregations. Implementations optimize locality, scheduling a…
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MapReduce is a programming model that splits large-scale data processing into parallel map steps followed by a reduce step.
In 2004, Google's Jeffrey Dean and Sanjay Ghemawat presented MapReduce to address the difficulty of parallelizing large data-processing jobs. The model shaped cluster computing, and Apache Hadoop later carried the idea into a widely used open-source system.
Many workers read separate slices and emit intermediate values with a key. Other workers then gather equal keys and compute one result per group.
Input records are processed independently into intermediate key-value pairs.
Intermediate values are grouped by key and assigned to matching reduce tasks.
Grouped values are combined into a condensed result.
MapReduce scales batch processing of large datasets when work can be split into independent transformations and groupings.
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