MapReducepõhised

MapReducepõhised refers to systems or frameworks that utilize the MapReduce programming model for distributed data processing. The MapReduce model, originally developed by Google, is a paradigm for processing vast amounts of data in parallel across a cluster of computers. It consists of two fundamental functions: Map and Reduce.

The Map function takes an input dataset and processes it to produce a set of intermediate key-value pairs. This stage is inherently parallel, with each mapper operating on a distinct portion of the input data. The intermediate key-value pairs are then shuffled and sorted by key.

The Reduce function then takes the intermediate key-value pairs, grouped by key, and aggregates them to produce the final output. This stage is also parallel, with each reducer processing a subset of the grouped keys.

MapReducepõhised systems, such as Apache Hadoop's MapReduce, provide the infrastructure to execute these Map and Reduce functions across a distributed cluster. They handle tasks like data distribution, task scheduling, fault tolerance, and communication between nodes. These systems are widely used for large-scale data analysis, batch processing, and building data pipelines. The core idea is to break down complex computations into smaller, manageable tasks that can be executed concurrently on many machines, thereby significantly reducing processing time for massive datasets.