Scaling Patterns

Scaling Patterns

When to use

• Scaling a service beyond current capacity
• Choosing between horizontal and vertical scaling
• Implementing async processing, sharding, or rate limiting

Core principles

1. Measure before scaling — profile first, scale second
2. Stateless services scale horizontally — move state to databases/​caches
3. Connection pooling is mandatory — direct connections exhaust DB limits
4. Backpressure prevents cascading failure — reject early rather than queue forever
5. Rate limiting protects everyone — including your own services from each other

References available

• references/​horizontal-scaling.md — stateless services, load balancing, autoscaling configs
• references/​vertical-scaling.md — when vertical is enough, instance sizing, connection pool tuning
• references/​resource-optimization.md — CPU/​memory profiling, bottleneck identification, LRU cache, I/​O optimization
• references/​async-processing.md — queues (SQS, RabbitMQ, Kafka), worker patterns, backpressure, circuit breaker
• references/​background-jobs.md — BullMQ job scheduling, retry with exponential backoff, DLQ management, cron workers
• references/​data-partitioning.md — sharding strategies, partition key design, consistent hashing, PostgreSQL native partitioning
• references/​shard-management.md — shard rebalancing, cross-shard queries, scatter-gather, operational concerns

Scripts available

• scripts/​estimate-capacity.sh — input RPM and data size, output infra recommendations