What is Kafka’s data replication mechanism like?

The data replication mechanism of Kafka is implemented based on a distributed publish/subscribe pattern. Each topic in a Kafka cluster can be configured with multiple replicas, with each replica storing the complete data of the topic.

When a producer sends a message to a topic, the message is written to a partition of the topic and replicated to all replicas of that partition. This ensures that even if a replica fails, data can still be read from other replicas, guaranteeing data reliability and high availability.

Kafka utilizes asynchronous replication for data replication, allowing producers to continue sending messages without waiting for all copies to be completed. This can improve performance but may also result in message replication delays.

In addition, Kafka also supports a leader and follower mechanism for data. Each partition has a leader replica responsible for handling read and write requests, while the other replicas are follower replicas responsible for replicating the leader replica’s data. When the leader replica fails, Kafka will automatically elect a new leader replica to ensure data continuity and consistency.