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Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

Kafka's assign() method allows manual control of partition assignments for consumers.

Kafka uses brokers for data handling and KRaft controllers for metadata management.

Kafka commit strategies determine how offsets are managed during message processing.

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.

Older offsets can overwrite newer ones in Kafka if not handled properly.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

Kafka rebalances consumer groups to manage partition assignments effectively.

Backpressure helps manage message flow in Kafka during traffic spikes.

Kafka uses replication and in-sync replicas to ensure data is always available.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

Kafka uses partitions to allow multiple consumers to process messages in parallel.

Classifying failures and using controlled retries in Kafka prevents outages.

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

You can replay Kafka events safely by separating live and replay processes.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.