Patterns

35 items

System Design Pattern

Rate Managementqueueload-levelingasyncbufferspike-handlingintermediate

Queue-based Load Leveling Pattern

Using queues to smooth traffic spikes

Used in: Message Queues, Task Queues, Event Processing|20 min read

Summary

Queue-based load leveling uses message queues to decouple request producers from processors, smoothing traffic spikes and preventing system overload. Instead of synchronously processing requests during peak load, requests are queued and processed at a sustainable rate. This pattern transforms bursty traffic into steady throughput, prevents cascading failures during spikes, and enables independent scaling of producers and consumers. The queue acts as a shock absorber that protects backend services from traffic tsunamis.

Key Takeaways

Queues Absorb Traffic Spikes

During sudden load spikes (flash sales, viral posts), queues buffer requests instead of overwhelming backends. System processes at sustainable rate while users get acknowledgment.

Decoupling Enables Independent Scaling

Producers (API servers) and consumers (workers) scale independently. Add API servers for more ingestion, add workers for faster processing.

Asynchronous Processing Improves UX

Users get immediate response with job ID instead of waiting for slow operations. Check status later or receive notifications when complete.

Why synchronous processing fails:

Resource exhaustion: Sudden load exhausts database connections, threads, memory
Timeout cascades: Slow operations time out, clients retry, making it worse
Lost requests: Overloaded servers return 503 or crash
Poor UX: Users wait for slow operations

Provisioning for peak load wastes money 95% of the time. Provisioning for average fails during spikes.

Queue-Based Load Leveling

Summary

Key Takeaways

Queues Absorb Traffic Spikes

During sudden load spikes (flash sales, viral posts), queues buffer requests instead of overwhelming backends. System processes at sustainable rate while users get acknowledgment.

Decoupling Enables Independent Scaling

Producers (API servers) and consumers (workers) scale independently. Add API servers for more ingestion, add workers for faster processing.

Asynchronous Processing Improves UX

Users get immediate response with job ID instead of waiting for slow operations. Check status later or receive notifications when complete.

Queue Depth is Key Metric

Growing queue depth means consumers can't keep up - scale up. Stable depth means system is balanced. Monitor continuously.

Message Durability Prevents Loss

Persistent queues survive crashes. Messages deleted only after successful processing. At-least-once delivery requires idempotent consumers.

Dead Letter Queues Handle Failures

Messages that fail repeatedly go to DLQ for inspection. Prevents poison messages from blocking queue. Enables manual intervention.

Pattern Details

Why synchronous processing fails:

Resource exhaustion: Sudden load exhausts database connections, threads, memory
Timeout cascades: Slow operations time out, clients retry, making it worse
Lost requests: Overloaded servers return 503 or crash
Poor UX: Users wait for slow operations

Provisioning for peak load wastes money 95% of the time. Provisioning for average fails during spikes.

Queue-Based Load Leveling

Trade-offs

Aspect	Advantage	Disadvantage

Patterns

Horizontal Scaling Pattern

Retry with Backoff Pattern

Replication Pattern

Caching Strategies Pattern

Persistent Connections Pattern

Load Balancing Pattern

Fan-out Pattern

Fan-in Pattern

Circuit Breaker Pattern

Eventual Consistency Pattern