The basic model for MapReduce derives from the map and reduce concept in functional languages like Lisp.
In Lisp, a map takes as input a function and a sequence of values and applies the function to each value in the sequence.
A reduce takes as input a sequence of elements and combines all the elements using a binary operation (for example, it can use “+” to sum all the elements in the sequence).

MapReduce, inspired by these concepts, was developed as a method for writing processing algorithms for large amounts of raw data. The amount of data is so large that it can’t be stored on a single machine and must be distributed across many machines in order to be processed in a reasonable time.
In systems with such data distribution, the traditional central processing algorithms are useless as just getting the data to the centralized CPU running the algorithm implies huge network costs and months (!) spent on transferring data from the distributed machines.
Therefore, processing such massive scales of distributed data implies the need for parallel computing allowing us to run the required computation “close” to where the data is located. [Read more...]