Hadoop Architecture Explained

Hadoop is an open-source distributed computing framework mainly used for storing and analyzing large-scale data. Its architecture design consists of several core components.

1. HDFS, short for Hadoop Distributed File System, is the file system used in Hadoop for storing large-scale data with high reliability and availability. It divides data into multiple blocks and stores them on distributed nodes, ensuring fault tolerance through data replication.
2. MapReduce is the computational framework of Hadoop, used for parallel processing of large-scale data. It breaks tasks into two stages: Map, which maps data to key-value pairs, and Reduce, which aggregates and computes on the key-value pairs.
3. YARN (Yet Another Resource Negotiator) is the resource manager for Hadoop, responsible for allocating cluster resources and scheduling tasks. YARN separates resource management from job scheduling, achieving cluster resource management and task scheduling through NodeManager and ResourceManager.
4. Hadoop Common is the shared library of Hadoop, which includes basic tools and components such as file systems, RPC communication, and security authentication.
5. In addition to the core components mentioned above, the Hadoop ecosystem includes a range of projects such as Hive, Pig, HBase, and Spark, which provide more advanced data processing and analysis capabilities.

Overall, the architecture of Hadoop utilizes distributed storage and computing to ensure high reliability and scalability of data through HDFS, while achieving parallel computing and resource management for tasks through MapReduce and YARN, providing a reliable framework for processing large-scale data. Additionally, the ecosystem projects of Hadoop offer a more diverse range of functions and tools, allowing users to flexibly process and analyze data.