How does Hadoop work?
The working principle of Hadoop is based on the concept of distributed storage and computation. It consists of two core components, Hadoop Distributed File System (HDFS) and the MapReduce computing framework.
HDFS is a distributed file system that divides large files into smaller blocks and stores them across multiple computing nodes in a cluster. Each file block is duplicated on multiple nodes to enhance data reliability and fault tolerance.
MapReduce is a distributed computing framework that breaks down computational tasks into multiple subtasks and executes them in parallel on multiple nodes in a cluster. The framework consists of two main stages: the Map stage and the Reduce stage. In the Map stage, data is divided and processed by different nodes to generate intermediate results; in the Reduce stage, the intermediate results are merged and processed to produce the final result.
The workflow of Hadoop is as follows:
- Data uploaded by users to HDFS will be divided into multiple blocks and stored in a distributed manner within the cluster.
- Users write MapReduce tasks and submit them to the Hadoop cluster.
- The JobTracker is responsible for distributing tasks to TaskTracker nodes within the cluster for execution.
- Each TaskTracker node will execute Map and Reduce tasks, and write the results back to HDFS.
- Users can retrieve the final processed results from HDFS.
Using this method, Hadoop can efficiently handle storage and computing tasks for large-scale data, while also ensuring reliability and fault tolerance.