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

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

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'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.

Kafka consumers have three important states: Position, Committed Offset, and Application State.

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.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

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