MapReduce

MapReduce

is a patented
[1]
software framework
introduced by Google
to support distributed computing
on large data sets
on clusters
of computers.[2]

The framework is inspired by map
and reduce
functions commonly used in functional programming
,[3]

although their purpose in the MapReduce framework is not the same as their original forms.[4]

MapReduce libraries
have been written in C++
, C#
, Erlang
, Java
, Python
, Ruby
, F#
, R
and other programming languages.

 

Overview



MapReduce is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes), collectively referred to as a cluster. Computational processing can occur on data stored either in a
filesystem

(unstructured) or within a
database

(structured).

"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.

The advantage of MapReduce is that it allows for distributed processing of the map and reduction operations. Provided each mapping operation is independent of the other, all maps can be performed in parallel - though in practice it is limited by the data source and/or the number of CPUs near that data. Similarly, a set of 'reducers' can perform the reduction phase - all that is required is that all outputs of the map operation which share the same key are presented to the same reducer, at the same time. While this process can often appear inefficient compared to algorithms that are more sequential, MapReduce can be applied to significantly larger datasets than that which "commodity" servers can handle - a large
server farm

can use MapReduce to sort a petabyte of data in only a few hours. The parallelism also offers some possibility of recovering from partial failure of servers or storage during the operation: if one mapper or reducer fails, the work can be rescheduled — assuming the input data is still available.