Any concept.
clear in 60 seconds.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

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

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

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

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

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Kafka's `seek()` lets you replay only specific messages, saving time.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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

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

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