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

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.

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

FAANG engineers use high-level design principles to create scalable systems.

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