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

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

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

Kafka rebalances consumer groups to manage partition assignments effectively.

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

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

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

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

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

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

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

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

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

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

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

Kafka's assign() method allows manual control of partition assignments for consumers.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

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

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

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

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

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

Apache Kafka is a system for managing large volumes of events efficiently.

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