Kafka uses data replication to provide fault tolerance and high availability. Data replication ensures that data is stored on multiple brokers, so that if one broker fails, data can still be accessed and processed by other brokers.
When data is written to a Kafka topic, it is replicated to multiple brokers according to the configured replication factor. The replication factor specifies the number of replicas that should be created for each partition of the topic. Each replica of a partition is stored on a different broker, and one of the replicas is designated as the leader. The leader replica is responsible for handling all read and write requests for that partition.
Here are some of the ways that Kafka handles data replication across multiple brokers:
1. Replication protocol: Kafka uses a replication protocol to ensure that data is replicated reliably and efficiently between brokers. The replication protocol uses a combination of asynchronous and synchronous replication to balance performance and reliability.
2. Leader election: When the leader replica of a partition fails, Kafka uses a leader election process to select a new leader from the available replicas. The leader election process ensures that there is always a leader replica available to handle read and write requests for each partition.
3. In-sync replicas: Kafka ensures that replicas are in sync by using a mechanism called in-sync replicas. In-sync replicas are replicas that have replicated all messages up to a certain point in the log. Kafka ensures that there are always enough in-sync replicas available to handle read and write requests for each partition.
4. Automatic recovery: When a broker fails, Kafka automatically recovers by promoting one of the replicas to be the new leader and ensuring that all in-sync replicas are caught up with the new leader. This ensures that data is still accessible and available even in the event of a broker failure.
Overall, Kafka’s approach to data replication ensures that data is stored on multiple brokers and is available and accessible even in the presence of failures or other issues. This provides a high level of fault tolerance and reliability for Kafka-based applications.