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Kafka's parallel processing depends on partitions, not just consumers or threads.

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

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

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

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

Kafka can help ensure messages are processed once, but requires careful design.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Poison messages in Kafka can cause infinite retries, risking system stability.

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

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

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

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