How is Kafka high availability achieved?

Kafka achieves high availability through various methods.

  1. Replication: Kafka uses replication to ensure the reliability and high availability of data. Each partition can be configured with multiple replicas distributed across different brokers. In case of failure of the leader replica, a new leader replica is automatically elected to take over, ensuring the continuity and reliability of the data.
  2. In-Sync Replicas (ISR) mechanism: Kafka employs the ISR mechanism to ensure data consistency and reliability. Only replicas that are kept in sync with the leader replica are considered as ISR, these ISR replicas participate in message synchronization and data read/write operations to ensure data consistency and reliability.
  3. Controller mechanism: Kafka uses a controller mechanism to manage metadata information such as brokers, partitions, and leaders, ensuring the stability and high availability of the cluster. The controller is a separate component responsible for monitoring and managing the state and configuration of the entire cluster. In case of abnormal situations, it can automatically perform failover and data recovery operations.
  4. Fast fault recovery: Kafka is designed with mechanisms for quick fault recovery. When a Broker fails, the ISR and Controller mechanisms allow for rapid election of leader replicas and data recovery, ensuring high availability and data reliability in the cluster.

Overall, Kafka ensures data reliability and high availability, as well as cluster stability and performance, through mechanisms such as replication, ISR, controller, and fast fault recovery.


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