How does Hadoop handle node failures?

Hadoop employs various methods to handle node failures in order to ensure the system’s reliability and fault tolerance. Here are some of the main approaches Hadoop uses to deal with node failures:

  1. Data Replication: In Hadoop, data is distributed across multiple nodes and replicated, usually in triplicate. In the event of a node failure, Hadoop can recover data from the replicated data on other nodes.
  2. Heartbeat monitoring: Hadoop uses a heartbeat detection mechanism to monitor the status of each node in the cluster. If a node doesn’t respond to the heartbeat for a long time, Hadoop will mark it as a failed node and reassign tasks to other nodes.
  3. Automatic failover: Hadoop’s ResourceManager and NodeManager components are able to automatically handle node failures. When a node experiences a failure, the ResourceManager will redistribute tasks and assign them to other nodes that are functioning properly.
  4. Fault tolerance mechanism: Hadoop has certain fault tolerance mechanisms, such as task retry and data validation, to ensure that the system can recover correctly and continue running in case of node failure.

Overall, Hadoop uses methods such as data replication, heartbeat detection, automatic failover, and fault tolerance mechanisms to handle node failures, ensuring the reliability and stability of the system.

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