Message Queue Use Cases and Technology Selection (Kafka vs RabbitMQ)

Message Queue (MQ)

Why Use a Message Queue?

• Decoupling: Producer doesn't wait for consumer to finish
• Load leveling: Buffer traffic spikes to prevent downstream service overload
• Async processing: Time-consuming tasks (emails, reports) run in background
• Reliable delivery: Failed consumers can retry

Kafka vs RabbitMQ

Feature	Kafka	RabbitMQ
Architecture	Distributed log	Traditional MQ broker
Throughput	Very high (millions/sec)	High (tens of thousands/sec)
Message retention	Persistent log, replayable	Deleted after consumption (default)
Ordering	Guaranteed within a partition	Guaranteed within a single queue
Use cases	Data streaming, log collection, event sourcing	Task queues, RPC callbacks

Common Patterns

• Point-to-Point: Single consumer processes each message (competing consumers)
• Pub/Sub: Multiple consumers subscribe to same topic; each gets a copy
• Dead Letter Queue (DLQ): Failed messages go to DLQ for manual review