MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster.
| FactSnippet No. 1,574,009 |
15 Facts About MapReduce
1.
2.
MapReduce program is composed of a map procedure, which performs filtering and sorting, and a reduce method, which performs a summary operation.
| FactSnippet No. 1,574,010 |
3.
The key contributions of the MapReduce framework are not the actual map and reduce functions, but the scalability and fault-tolerance achieved for a variety of applications by optimizing the execution engine.
| FactSnippet No. 1,574,011 |
4.
MapReduce libraries have been written in many programming languages, with different levels of optimization.
| FactSnippet No. 1,574,012 |
5.
The name MapReduce originally referred to the proprietary Google technology, but has since been genericized.
| FactSnippet No. 1,574,013 |
6.
MapReduce is a framework for processing parallelizable problems across large datasets using a large number of computers, collectively referred to as a cluster or a grid.
| FactSnippet No. 1,574,014 |
7.
MapReduce can take advantage of the locality of data, processing it near the place it is stored in order to minimize communication overhead.
| FactSnippet No. 1,574,015 |
8.
MapReduce allows for the distributed processing of the map and reduction operations.
| FactSnippet No. 1,574,016 |
9.
Map and Reduce functions of MapReduce are both defined with respect to data structured in pairs.
| FactSnippet No. 1,574,017 |
10.
The frozen spot of the MapReduce framework is a large distributed sort.
| FactSnippet No. 1,574,018 |
11.
Communication cost often dominates the computation cost, and many MapReduce implementations are designed to write all communication to distributed storage for crash recovery.
| FactSnippet No. 1,574,019 |
12.
MapReduce achieves reliability by parceling out a number of operations on the set of data to each node in the network.
| FactSnippet No. 1,574,020 |
13.
MapReduce is useful in a wide range of applications, including distributed pattern-based searching, distributed sorting, web link-graph reversal, Singular Value Decomposition, web access log stats, inverted index construction, document clustering, machine learning, and statistical machine translation.
| FactSnippet No. 1,574,021 |
14.
At Google, MapReduce was used to completely regenerate Google's index of the World Wide Web.
| FactSnippet No. 1,574,022 |
15.
Jorgensen asserts that DeWitt and Stonebraker's entire analysis is groundless as MapReduce was never designed nor intended to be used as a database.
| FactSnippet No. 1,574,023 |