Explain Queue Backpressure and Scaling.
Queue backpressure is a mechanism that slows or pauses data producers when consumers or queues can’t keep up, ensuring stability instead of system overload.
When to Use
Use backpressure in asynchronous or event-driven systems like message queues, stream processors, or APIs where producer and consumer speeds differ (e.g., Kafka, RabbitMQ, or HTTP services handling bursts of traffic).
Example
A microservice producing 1,000 messages/sec while the consumer handles 500/sec must apply backpressure or auto-scale consumers to avoid crashes or timeouts.
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Why Is It Important
Backpressure ensures reliability, fairness, and resilience under unpredictable loads. Without it, queues grow endlessly, causing memory exhaustion and degraded latency. Pairing it with scaling strategies prevents cascading failures.
Interview Tips
Explain how backpressure prevents overload, then discuss scaling options like auto-scaling consumers, load shedding, or rate limiting. Mention metrics like queue length and processing latency for monitoring.
Trade-offs
Backpressure prioritizes stability over throughput—some requests may be delayed, rejected, or dropped to maintain system health.
Pitfalls
A common mistake is relying solely on scaling without backpressure, leading to runaway resource use. Another is ignoring consumer lag, which hides bottlenecks until it’s too late.
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