What is MapReduce

Is a computational paradigm for processing massive datasets in parallel if the computation fits a three-step pattern: map, shard and reduce. The map process is a parallel one. Each process executes on a different part of data and produces (key, value) pairs. The shard process collects the generated pairs, sorts and partitions them. Each partition is assigned to a different reduce process which produces a single result.

The MapReduce programming model can be used on clusters to process large datasets using concurrent distributed algorithms.

A high-level programming model, which uses the “map” and “reduce” functions, for processing high volumes of data.

Is a programming model or algorithm for the processing of data using a parallel programming implementation and was originally used for academic purposes associated with parallel programming techniques.

An engine which idea is divide a work in many tasks. With base in a table distribution, the algorithm divide a ad-hoc query in different sub-queries in the same time, with replicas, where one sub-query is mapped in k+1 sub-queries. Contains the map and reduce function to execute these tasks.

A programming algorithm that divides and maps the elements of datasets; then shuffles and distributes to cluster computing powers for big data processing.

It is a programming approach used to manage large set of data.

An engine that provides the platform for massive parallel execution of algorithms written in Java.

Algorithm that is used to split massive data sets among many commodity hardware pieces in an effort to reduce computing time.

A concept which is an abstraction of the primitives ‘map’ and ‘reduce’. Most of the computations are carried by applying a ‘map’ operation to each global record in order to generate key/value pairs and then apply the reduce operation in order to combine the derived data appropriately.

MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster.

A software framework introduced by Google to support distributed computing on large data sets on clusters of computers. The framework is inspired by map and reduce functions commonly used in functional programming, MapReduce libraries have been written in C++, Java, Python and other programming languages.

Programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. (Chen et al., 2014).

A data processing framework of Hadoop which provides data intensive computation of large data sets by dividing tasks across several machines and finally combining the result.

Is a programming model for processing large data sets.

A programming model which process massive amounts of unstructured data in parallel and distributed cluster of processors.

MapReduce is a programming model used to process large datasets using a parallel and distributed algorithm in a reliable manner.

MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster.

It is quite easy to use programming model that supports parallel design since it is very scalable and works in a distributed way. It is also helpful for huge data processing, large scale searching and data analysis within the cloud. It provides related abstraction by a process of “mapper” and “reducer”. The “mapper” is applicable to each input key-value pair trying to come up with an associated absolute range of intermediate key-value pairs. Map: produce a list of ( key , value ) pairs from the input structured as a key( k ) value( v ) pair of a different type i.e. (k1, v1) ? list (k2, v2) The “reducer” is applicable to some or all values related to identifying the intermediate key to come up with output key-value pairs. Reduce: produce a list of values from an input that consists of a key and a list of values associated with that key i.e. (k2, list (v2)) ? list (v2) MapReduce is having adequate capability to support many real and global algorithms and tasks. It can divide the input data, schedule the execution of programs over a set of machines and handle machine failures. MapReduce can also handle the inter-machine communication. Map/Reduce is: 1) a Programming model from Lisp and other functional languages; 2) Many problems can be phrased this way; 3) Easy to distribute across nodes; and 4) Nice retry/failure semantics. MapReduce provides: 1) Automatic parallelization and distribution; 2) Fault tolerance; 3) I/O scheduling; and 4) Monitoring & status updates. The limitations of MapReduce are: 1) Extremely rigid data flow; 2) Constantly hacked in Join, Union, Split; 3) Common operations must be coded by user; and 4) Semantics hidden inside map-reduce functions, Difficult to maintain, extend, and optimize.

A high-level programming model, which uses the “map” and “reduce” functions, for processing huge volumes of data.

Is a high-level programming model, which uses the “map” and “reduce” functions, for processing high volumes of data.

A programming model that process large amounts of data stored in commodity machines for processing massive datasets in parallel.

Is a programming model for processing and generating large datasets in a parallel and distributed manner on a cluster.

A high-level programming model, which uses the “map” and “reduce” functions, for processing huge volumes of data.

Is a programming model or algorithm for the processing of data using a parallel programming implementation and was originally used for academic purposes associated with parallel programming techniques.

A program which is used for processing Big Data by utilizing a distributed model on a cluster.

A programming algorithm that divide and map the elements of dataset, then shuffle and distribute to cluster of computing powers for Big Data processing.

Is a specific programming model, which as such represents a new approach to solving the problem of processing large amounts of differently structured data. It consists of two functions - Map (sorting and filtering data) and Reduce (summarizing intermediate results), and it is executed in parallel and distributed.

MapReduce is a software framework for processing vast amounts of data by using divide and conquare method.

MapReduce is a parallel programming model proposed by Google and is used to distribute computing on clusters of computers for processing large data sets.

Algorithm that is used to split massive data sets among many commodity hardware pieces in an effort to reduce computing time.

MapReduce is a framework or a programming model that allows carrying out tasks in parallel across a large cluster of computers.

MapReduce is a parallel programming model proposed by Google and is used to distribute computing on clusters of computers for processing large data sets.

A programming model consisting of two logical steps—Map and Reduce—for processing massively parallelizable problems across extremely large datasets using a large cluster of commodity computers.