I — Core Concepts
2 articles- 01Why Do Applications Stop Scaling? Understanding Bottlenecks Before Fixing ThemThe common bottlenecks that make applications stop scaling, the difference between vertical and horizontal scaling, and why "measure first, then optimize" is the guiding principle throughout this series.
- 02Anatomy of a Scalable System: Load Balancer, Caching, and Connection PoolingThe core components of a scalable system — load balancer, stateless applications, a caching layer, and connection pooling — broken down one by one before we get hands-on.
II — Hands-On Practice
6 articles- 03Hands-On: Measure First, Then OptimizeBuilding a simple load-testing script, measuring `task-tracker-api`'s baseline, then pushing it to the breaking point to find the real bottleneck through logs — not guesswork.
- 04Hands-On: Caching with RedisAdding a caching layer with Redis via the cache-aside pattern, TTL, explicit cache invalidation, and measuring the real impact with a before/after benchmark.
- 05Hands-On: Database ScalingMigrating from SQLite to Postgres, how indexes turn a Seq Scan into an Index Scan, and sizing the connection pool correctly — plus a surprising finding about a bottleneck hiding somewhere else entirely.
- 06Hands-On: Async & Concurrency Done RightUncovering FastAPI's hidden thread-pool bottleneck, proving the difference with an isolated blocking-vs-async demo, and why a misused `async def` is actually worse than plain `def`.
- 07Hands-On: Horizontal Scaling with Docker & Load BalancerContainerizing `task-tracker-api`, running three instances at once behind Nginx with Docker Compose, proving round-robin and consistent caching, and measuring the impact with a parallel load test.
- 08Observability: Knowing When to ScaleJSON structured logging, metrics with Counter and Histogram via Prometheus, the cardinality-explosion trap, and connecting metrics to actual scaling decisions.