What is MapReduce?
MapReduce is a programming model and processing framework designed for distributed processing of large datasets across clusters of computers. It was popularized by Google and later adopted and open-sourced by Apache as part of the Apache Hadoop project.
The MapReduce model consists of two main phases: the Map phase and the Reduce phase.
- Map Phase:
- In the Map phase, input data is divided into smaller chunks or splits, and each split is processed independently by a mapper function.
- The mapper function takes the input data as key-value pairs and generates intermediate key-value pairs as output.
- The intermediate key-value pairs are grouped by key and sorted before being passed to the next phase.
- Reduce Phase:
- In the Reduce phase, the intermediate key-value pairs generated by the Map phase are processed by reducer functions.
- Reducer functions take the sorted intermediate key-value pairs and perform aggregation or computation based on the keys.
- Each reducer processes a subset of the intermediate data with the same key and produces the final output.
The key features of MapReduce include fault tolerance, scalability, and parallel processing. It allows users to process large-scale datasets efficiently by distributing the workload across multiple nodes in a cluster. MapReduce abstracts away the complexities of distributed computing, making it easier for developers to write distributed data processing applications.
MapReduce is widely used for various data processing tasks, such as data transformation, aggregation, sorting, and filtering, and it forms the foundation of many big data processing frameworks, including Apache Hadoop, Apache Spark, and others.