Hadoop system architecture

The Hadoop system architecture is a cornerstone design in the field of big data processing, aiming to achieve highly reliable and fault-tolerant processing of massive amounts of data on general-purpose hardware clusters through a distributed storage and computing framework. This section systematically describes the core layered structure of Hadoop, including three core components: the HDFS distributed file system, the YARN resource scheduler, and the MapReduce distributed computing model. HDFS adopts a master-slave architecture, with the NameNode managing metadata and namespaces, and DataNodes responsible for storing actual data blocks and ensuring redundancy to guarantee high data availability. YARN, as a resource management and task scheduling platform, encompasses ResourceManager, NodeManager, and ApplicationMaster, decoupling computing resources from job lifecycles. MapReduce defines a parallel computing paradigm for data sharding, Shuffle sorting, and Reduce aggregation. By analyzing the interaction protocols, heartbeat mechanisms, and fault recovery strategies between each module, this architecture lays a systematic theoretical foundation for understanding the horizontal scalability and data locality optimization logic of distributed systems.