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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 ...
It is built on top of Apache Hadoop for providing data query and analysis. [3] [4] Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop. Traditional SQL queries must be implemented in the MapReduce Java API to execute SQL applications and queries over distributed data.
The initial code that was factored out of Nutch consisted of about 5,000 lines of code for HDFS and about 6,000 lines of code for MapReduce. In March 2006, Owen O'Malley was the first committer to add to the Hadoop project; [ 21 ] Hadoop 0.1.0 was released in April 2006. [ 22 ]
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 ...
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
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
Cascading is a software abstraction layer for Apache Hadoop and Apache Flink. Cascading is used to create and execute complex data processing workflows on a Hadoop cluster using any JVM-based language (Java, JRuby, Clojure, etc.), hiding the underlying complexity of MapReduce jobs. It is open source and available under the Apache License.
Commercial implementations are either general purpose or tied to specific hardware by a vendor. Examples of general purpose languages include: AccelerEyes' Jacket, a commercialization of a GPU engine for MATLAB; Ateji PX Java extension that enables a simple expression of stream programming, the Actor model, and the MapReduce algorithm