Skill

scaling-patterns

Scaling patterns: horizontal/vertical scaling, async processing, data partitioning, connection pooling, rate limiting, backpressure. Use when scaling services.

Install

npx claudepluginhub rnavarych/alpha-engineer --plugin billy-milligan

Tool Access

This skill is limited to using the following tools:

ReadGrepGlob

Preview

- Scaling a service beyond current capacity

Supporting Assets

references/async-processing.mdreferences/background-jobs.mdreferences/data-partitioning.mdreferences/horizontal-scaling.mdreferences/resource-optimization.mdreferences/shard-management.mdreferences/vertical-scaling.mdscripts/estimate-capacity.sh

SKILL.md

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Stats

Parent Repo Stars9

Parent Repo Forks0

Last CommitFeb 23, 2026

Actions

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Core principles

Measure before scaling — profile first, scale second

Stateless services scale horizontally — move state to databases/caches

Connection pooling is mandatory — direct connections exhaust DB limits

Backpressure prevents cascading failure — reject early rather than queue forever

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

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

Measure before scaling — profile first, scale second
Stateless services scale horizontally — move state to databases/caches
Connection pooling is mandatory — direct connections exhaust DB limits
Backpressure prevents cascading failure — reject early rather than queue forever
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