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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 ...
A distributed Dancing Links implementation as a Hadoop MapReduce example; Free Software implementation of an Exact Cover solver in C - uses Algorithm X and Dancing Links. Includes examples for sudoku and logic grid puzzles. DlxLib NuGet package - a C# class library that implements DLX; dlxlib npm package - a JavaScript library that implements DLX
Apache Hadoop (/ h ə ˈ d uː p /) is a collection of open-source software utilities for reliable, scalable, distributed computing.It provides a software framework for distributed storage and processing of big data using the MapReduce programming model.
Similar to MapReduce, arbitrary user code is handed and executed by PACTs. However, PACT generalizes a couple of MapReduce's concepts: Second-order Functions: PACT provides more second-order functions. Currently, five second-order functions called Input Contracts are supported. This set might be extended in the future.
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]
Pig Latin abstracts the programming from the Java MapReduce idiom into a notation which makes MapReduce programming high level, similar to that of SQL for relational database management systems. Pig Latin can be extended using user-defined functions (UDFs) which the user can write in Java , Python , JavaScript , Ruby or Groovy [ 3 ] and then ...
For example, map combined with category reduction gives the MapReduce pattern. [3]: 106–107 ...
In MapReduce-based systems, data is normally stored on a distributed system, such as Hadoop Distributed File System (HDFS), and different data blocks might be stored in different machines. Thus, for column-store on MapReduce, different groups of columns might be stored on different machines, which introduces extra network costs when a query ...