What is the fault tolerance mechanism in Hadoop?
The fault tolerance mechanisms of Hadoop mainly include the following aspects:
- Redundant backup of data: Hadoop will replicate data shards across multiple data nodes to ensure redundancy. In case a data node fails, the system can retrieve backup data from other nodes.
- Heartbeat detection and automatic fault recovery: Various components of Hadoop will regularly send out heartbeat signals. If a node does not receive a heartbeat signal for an extended period of time, the system will identify it as a faulty node and automatically reassign its tasks to other available nodes.
- Task retry mechanism: Tasks executed in Hadoop may fail for various reasons, and the system will automatically retry the failed tasks to ensure their completion.
- Node health check: Hadoop regularly monitors the health status of each node. If any issues are detected on a node, the system will promptly take appropriate actions, such as marking it as a faulty node, to prevent affecting the overall stability of the system.
Overall, Hadoop’s fault tolerance mechanism ensures stable operation in the face of node failures or task failures through methods such as data backup, automatic fault recovery, task retry, and node health check.