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Kafka consumer lag shows how far behind consumers are from new messages.

Kafka rebalances consumer groups to manage partition assignments effectively.

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

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

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

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

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

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

Kafka consumers resume processing from the last committed offset after a crash.

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

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

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

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

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

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

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

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

Kafka lag recovery time is how fast consumers can process backlogged messages.

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