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

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

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

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

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

Kafka consumer reprocessing involves safely replaying events to correct system states.

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

Backpressure prevents system overload by managing message processing rates in Kafka.

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

Raft's safety rules guarantee that committed entries are preserved even after leader failures.

Kafka consumers can pause and resume message processing to handle backpressure effectively.

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

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

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

Raft consensus helps distributed systems elect leaders and replicate logs reliably.

Multi-region data consistency ensures that users in different locations see the same data.

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