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

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

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

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

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

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

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

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

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

Kafka's pause temporarily stops reading messages, while seek changes the reading position.

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

Kafka rebalances consumer groups to manage partition assignments effectively.

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

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