Hadoop MapReduce Relationship Explained

Hadoop is an open-source framework for distributed storage and computation, with MapReduce being a programming model within the Hadoop framework. In Hadoop, MapReduce is a computational model used to process large-scale data by splitting jobs into two stages: Map stage and Reduce stage. MapReduce splits data into smaller chunks and then processes this data in parallel on a distributed computing cluster.

Therefore, it can be said that Hadoop is a distributed storage and computing framework, while MapReduce is the programming model used within the Hadoop framework to implement distributed computing. In practical applications, developers typically use MapReduce to write programs for processing large-scale data and deploy these programs to be executed on a Hadoop cluster. In essence, there is a relationship between Hadoop and MapReduce, where Hadoop provides the underlying infrastructure for distributed computing and storage, while MapReduce serves as the computing model running on top of Hadoop.