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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

Graceful shutdown in Kafka ensures no data loss during consumer termination.

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.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

Distributed backpressure helps manage the flow of work between services to prevent overload.