Hadoop Architecture Explained

Hadoop is an open-source framework for distributed storage and processing of big data, consisting of several core components in its architecture.

1. The Hadoop Distributed File System (HDFS) is a distributed file system that breaks down large files into multiple blocks and stores them across multiple machines to ensure reliable data storage and efficient access.
2. Hadoop YARN, short for Yet Another Resource Negotiator, serves as the resource manager for Hadoop, handling the allocation of cluster resources and scheduling tasks. It allows various computing frameworks, such as MapReduce and Spark, to share cluster resources.
3. MapReduce is a computational framework in Hadoop that splits large datasets into smaller chunks and processes them in parallel across a cluster, including operations such as data splitting, mapping, sorting, and reducing.
4. Hadoop Common consists of the shared libraries and tools of Hadoop, providing basic APIs and tools such as file system interfaces, network communication, and configuration management.
5. In addition to the core components mentioned above, there are many other ecosystem components in the Hadoop ecosystem, such as Hive (data warehouse), HBase (NoSQL database), Spark (in-memory computing framework), etc. These components can be integrated with Hadoop to enhance its functionality and application scenarios.

In general, the architecture of Hadoop is based on distributed storage (HDFS) and distributed computing (MapReduce), with resource management and scheduling done through YARN, supporting large-scale data processing and analysis. Additionally, the Hadoop ecosystem offers a variety of components and tools to meet different needs and use cases.