Understanding Hadoop’s Architecture
Before delving into how Hadoop achieves fault tolerance and high availability, it’s essential to understand its core components:
- Hadoop Distributed File System (HDFS): A distributed file system that stores data across multiple machines without prior organization.
- MapReduce: A programming model for processing large data sets with a distributed algorithm on a Hadoop cluster.
- Yet Another Resource Negotiator (YARN): Manages and allocates cluster resources and handles job scheduling.
How does Hadoop ensure fault tolerance and high availability?
The Apache Hadoop framework stands out as a pivotal technology that facilitates the processing of vast datasets across clusters of computers. It’s built on the principle that system faults and hardware failures are common occurrences, rather than exceptions. Consequently, Hadoop is designed to ensure fault tolerance and high availability. This article explores the mechanisms Hadoop employs to achieve these critical features, which include data replication, the Hadoop Distributed File System (HDFS), the use of MapReduce, the role of the YARN resource manager, and the Hadoop ecosystem’s resilience strategies.