MapReduce is a patented 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, although their purpose in the MapReduce framework is not the same as their original forms. MapReduce libraries have been written in C++, C#, Erlang, Java, Ocaml, Python, Ruby, F#, R and other programming languages.
Hi!! As per mine experience MapReduce is a programming framework popularized by Google and used to simplify data processing across massive data sets. As people rapidly increase their online activity and digital footprint, organizations are finding it vital to quickly analyze the huge amounts of data their customers and audiences generate to better understand and serve them. MapReduce is the tool that is helping those organizations. Most enterprises deal with multiple types of data (text, rich text, rdbms, graph, etc…) and need to process all this data quickly and efficiently to derive meaningful insights that bring business value to the organization. MapReduce working of two categories:- Map step:- The master node takes the input, chops it up into smaller sub-problems, and distributes those to worker nodes. A worker node may do this again in turn, leading to a multi-level tree structure. The worker node processes that smaller problem, and passes the answer back to its master node. Reduce step:- The master node then takes the answers to all the sub-problems and combines them in a way to get the output – the answer to the problem it was originally trying to solve......
A Map/Reduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks.
MapReduce is a software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors or stand-alone computers. It was developed at Google for indexing Web pages and replaced their original indexing algorithms and heuristics in 2004. The framework is divided into two parts: 1) Map, a function that parcels out work to different nodes in the distributed cluster. 2) Reduce, another function that collates the work and resolves the results into a single value. The MapReduce framework is fault-tolerant because each node in the cluster is expected to report back periodically with completed work and status updates. If a node remains silent for longer than the expected interval, a master node makes note and re-assigns the work to other nodes.