Question 1

When should I use RabbitMQ vs Kafka?

Accepted Answer

RabbitMQ: request-reply patterns, task queues, complex routing (direct/topic/fanout), lower latency, immediate delivery, easy operations. Kafka: high-throughput event streaming (trillions/day), event replay, persistent logs, true event sourcing, cluster-native. Use RabbitMQ for microservice messaging, job processing, background workers. Use Kafka for analytics, event-driven architectures, audit logs. RabbitMQ wins on developer experience; Kafka wins on scale and replay capability.

Question 2

What are exchanges and how do routing patterns work?

Accepted Answer

Exchanges route messages to queues based on binding keys. Direct: exact match (key='user.created' routes only there). Topic: wildcard matching (key='user.*' routes to 'user.created' and 'user.deleted'). Fanout: copies message to all bound queues (no key needed). Use direct for point-to-point, topic for pub/sub variants, fanout for broadcasts. Bindings glue exchange→queue with optional routing keys. Most flexible is topic, start there unless you need fanout.

Question 3

How do dead-letter queues (DLQ) and retry logic work?

Accepted Answer

When a consumer nacks a message (rejects it), it goes to a dead-letter exchange if configured. DLQ holds failed messages separately. Retry pattern: set TTL on DLQ (e.g., 60 seconds), then reroute expired messages back to main queue. After N retries, drop to a 'discard' queue for inspection. Configure with `x-dead-letter-exchange` and `x-message-ttl` on queue declaration. Prevents poison messages from blocking consumers forever; captures them for debugging.

Question 4

What's the difference between classic mirrored queues and quorum queues?

Accepted Answer

Classic mirrored: synchronous replication across 3+ nodes, strong consistency, slower writes. Quorum queues: RabbitMQ 3.8+, faster, consensus-based (Raft), better for modern Kubernetes. Quorum wins: lower latency, simpler operations, automatic leader election. Use quorum for new clusters; migrate classic if you value speed. Quorum requires 3+ nodes; don't use for single-node. Performance: quorum 2-3x faster on high throughput, same guarantee.

Question 5

How do I scale RabbitMQ when queues get backed up?

Accepted Answer

First: add consumer processes (partition work across N workers per queue). Queue depth should stay <1000. If bottleneck is queue, add more consumers. If bottleneck is broker CPU, cluster RabbitMQ: add nodes, queues spread across cluster. Use Shovel to drain queues to another broker (migration). Monitor with Management UI, watch 'memory', 'message rate in/out', 'queue depth'. Quorum queues + lazy queue storage shifts load to disk; helps under pressure. Don't over-provision disk; monitor utilization weekly.

Question 6

What are publisher confirms and consumer acknowledgments?

Accepted Answer

Publisher confirms: broker tells producer 'message received'. Consumer acks: consumer tells broker 'I processed it'. Without confirms: producer doesn't know if message lost. Without acks: broker forgets message immediately even if consumer crashes. Both are critical for reliability. Enable `publisher_confirms: true` in producer config; use explicit ack mode in consumer (not auto-ack). Cost: slight latency overhead. Worth it for important messages (payments, orders). Batch confirms if throughput is critical.

Question 7

Can RabbitMQ handle streaming workloads like Kafka?

Accepted Answer

RabbitMQ Streams (3.12+) adds log-like semantics: offset tracking, replay, retention policies. But still not Kafka-scale. RabbitMQ Streams best for: streaming within RabbitMQ ecosystem, decoupling from app restarts, replay within hours/days. Kafka best for: analytical event logging, multi-consumer replays, long-term archive, terabyte+ datasets. If you're asking 'replay 6 months of events', use Kafka. If 'replay 1 hour for debugging', RabbitMQ Streams works. Hybrid: RabbitMQ for app messaging + Kafka for data lake.

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