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SIGMOD
2007
ACM

Map-reduce-merge: simplified relational data processing on large clusters

10 years 9 months ago
Map-reduce-merge: simplified relational data processing on large clusters
Map-Reduce is a programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. Through a simple interface with two functions, map and reduce, this model facilitates parallel implementation of many real-world tasks such as data processing for search engines and machine learning. However, this model does not directly support processing multiple related heterogeneous datasets. While processing relational data is a common need, this limitation causes difficulties and/or inefficiency when Map-Reduce is applied on relational operations like joins. We improve Map-Reduce into a new model called MapReduce-Merge. It adds to Map-Reduce a Merge phase that can efficiently merge data already partitioned and sorted (or hashed) by map and reduce modules. We also demonstrate that this new model can express relational algebra operators as well as implement several join algorithms. Categories and Subject Descript...
Hung-chih Yang, Ali Dasdan, Ruey-Lung Hsiao, Dougl
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2007
Where SIGMOD
Authors Hung-chih Yang, Ali Dasdan, Ruey-Lung Hsiao, Douglas Stott Parker Jr.
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