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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 ...
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 ...
Though MapReduce Java code is common, any programming language can be used with Hadoop Streaming to implement the map and reduce parts of the user's program. [15] Other projects in the Hadoop ecosystem expose richer user interfaces.
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.
In the past, many of the implementations use the Apache Hadoop platform, however today it is primarily focused on Apache Spark. [3] [4] Mahout also provides Java/Scala libraries for common math operations (focused on linear algebra and statistics) and primitive Java collections. Mahout is a work in progress; a number of algorithms have been ...
For example, consider a search for users who have "subscribers" (a table linking users to other users) in the "311" area code. In this case a relational database has to first search for all the users with an area code in "311", then search the subscribers table for any of those users, and then finally search the users table to retrieve the ...
Those records are processed by one or more PACTs, each consisting of an Input Contract, user code, and optional code annotations. Finally, the results are written back to output files by one or more data sinks. In contrast to the MapReduce programming model, a PACT program can be arbitrary complex and has no fixed structure.
Click: simple and easy-to-use Java Web Framework; Continuum: continuous integration server; Crimson: Java XML parser which supports XML 1.0 via various APIs; Crunch: Provides a framework for writing, testing, and running MapReduce pipelines; Deltacloud: provides common front-end APIs to abstract differences between cloud providers