MapReduce What is

MapReduce is a programming model and processing technique for computing large data sets. It is used in parallel and distributed processing systems and is associated with big data and processing massive amounts of information. The technique consists of two main steps: Map and Reduce.

1. Map Step: In this step, the input data is divided into smaller sub-problems. These are then processed in parallel by various tasks known as “mappers.” The mappers take the input data and filter or sort it, creating a set of intermediate key-value pairs.
2. Reduce Step: After the mapping phase, the intermediate key-value pairs are shuffled and sorted by key. The “reducers” then combine these key-value pairs according to their keys. The reducing stage applies a function to all the values associated with the same key, typically aggregating or summarizing the data.

For example, if you were using MapReduce to count the occurrences of words in an extensive collection of documents, the Map phase would parse the records and produce a key-value pair for each term (with the word as the key and “1” as the value). The Reduce phase would then add all the “1”s for each word, producing a final count.

MapReduce is implemented in various distributed computing systems like Apache Hadoop, where it enables the processing of large data sets across clusters of computers. It is designed to be resilient to failures, meaning that if a machine fails during processing, the task can be rerouted to another device.