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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 ...
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.
For example, Google's copy of the web can be stored in a bigtable where the row key is a domain-reversed URL, and columns describe various properties of a web page, with one particular column holding the page itself. The page column can have several timestamped versions describing different copies of the web page timestamped by when they were ...
Apache Giraph is an Apache project to perform graph processing on big data.Giraph utilizes Apache Hadoop's MapReduce implementation to process graphs. Facebook used Giraph with some performance improvements to analyze one trillion edges using 200 machines in 4 minutes. [1]
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
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 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 ...