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Kafka uses heartbeats and timeouts to check if consumers are alive.

Kafka uses state stores to remember previous events in stateful stream processing.

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Kafka Streams uses local state for quick data access and changelogs for recovery.

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

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

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

Kafka consumer offsets help track message processing to avoid loss and duplication.

Kafka Rebalance Listeners help manage state during partition changes in consumer groups.

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

Fetch limits control data size from Kafka, while poll records limit processed records.