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MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
Bigtable development began in 2004. [1] It is now used by a number of Google applications, such as Google Analytics, [2] web indexing, [3] MapReduce, which is often used for generating and modifying data stored in Bigtable, [4] Google Maps, [5] Google Books search, "My Search History", Google Earth, Blogger.com, Google Code hosting, YouTube, [6] and Gmail. [7]
MapReduce programs need to be compiled and may be more verbose than necessary, so writing a program to analyze the logs can be time-consuming. To make it easier to write quick scripts, Rob Pike et al. developed the Sawzall language. A Sawzall script runs within the Map phase of a MapReduce and "emits" values to tables.
Apache Hadoop's MapReduce and HDFS components were inspired by Google papers on MapReduce and Google File System. [14] The Hadoop framework itself is mostly written in the Java programming language, with some native code in C and command line utilities written as shell scripts.
The programming model for MapReduce architecture is a simple abstraction where the computation takes a set of input key–value pairs associated with the input data and produces a set of output key–value pairs. In the Map phase, the input data is partitioned into input splits and assigned to Map tasks associated with processing nodes in the ...
However map-reduce has been an integral part of functional programming and enabler of parallelism decades before. Concatenating the words Map and Reduce does not sufficiently identify this as a Google specific technology when users are sent to this page searching for general information on map-reduce and parallelism.
Ghemawat's work at Google includes: Original design of Protocol Buffers, an open-source data interchange format. MapReduce, a system for large-scale data processing applications. Google File System, is a proprietary distributed file system developed to provide efficient, reliable access to data using large clusters of commodity hardware.
An example is Spark where Java is the base language, and Spark is the programming model. Execution may be based on what appear to be library calls. Other examples include the POSIX Threads library and Hadoop's MapReduce. [1] In both cases, the execution model of the programming model is different from that of the base language in which the code ...