MapReduce is a programming model and framework for processing large datasets in a distributed system. It was originally developed by Google and is now widely used in many big data processing systems, such as Apache Hadoop.

The basic idea behind MapReduce is to break a large dataset into smaller chunks, distribute them across multiple nodes in a cluster, and process them in parallel. The processing is divided into two phases: a Map phase and a Reduce phase.

In the Map phase, the input dataset is processed by a set of Map functions in parallel. Each Map function takes a key-value pair as input and produces a set of intermediate key-value pairs as output. These intermediate key-value pairs are then sorted and partitioned by key, and sent to the Reduce phase.

In the Reduce phase, the intermediate key-value pairs are processed by a set of Reduce functions in parallel. Each Reduce function takes a key and a set of values as input, and produces a set of output key-value pairs.

Here is an example of how MapReduce can be used to count the frequency of words in a large text file:

Map phase: Each Map function reads a chunk of the input file and outputs a set of intermediate key-value pairs, where the key is a word and the value is the number of occurrences of that word in the chunk.

Shuffle phase: The intermediate key-value pairs are sorted and partitioned by key, so that all the occurrences of each word are grouped together.

Reduce phase: Each Reduce function takes a word and a set of occurrences as input, and outputs a key-value pair where the key is the word and the value is the total number of occurrences of that word in the input file.

The MapReduce framework takes care of the parallel processing, distribution, and fault tolerance of the computation. This allows it to process large datasets efficiently and reliably, even on clusters of commodity hardware.