What is MapReduce?

(wikipedia.org) MapReduce is a patented software framework introduced by Google in 2004 to support distributed computing on large data sets on clusters of computers.

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 (if all nodes use the same hardware) or as a grid (if the nodes use different hardware). 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, partitions 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 some 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 others, 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.
The canonical example application of MapReduce is a process to count the appearances of each different word in a set of documents:

void map(String name, String document):
  // name: document name
  // document: document contents
  for each word w in document:
    EmitIntermediate(w, "1");
 
void reduce(String word, Iterator partialCounts):
  // word: a word
  // partialCounts: a list of aggregated partial counts
  int sum = 0;
  for each pc in partialCounts:
    sum += ParseInt(pc);
  Emit(word, AsString(sum)); 

Here, each document is split into words, and each word is counted initially with a "1" value by the Map function, using the word as the result key. The framework puts together all the pairs with the same key and feeds them to the same call to Reduce, thus this function just needs to sum all of its input values to find the total appearances of that word.

Source: http://en.wikipedia.org 
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