Notes from distributed systems in production.
Essays and field reports from building and operating high-traffic, polyglot platforms — Kubernetes, AWS, incident response, iGaming scale.
Redis Cache Invalidation: Four Patterns From Production
Cache invalidation is famously "one of the two hard things in CS." True — but the hard part isn't the magic, it's choosing the right pattern for your shape of system. Four field-tested patterns from iGaming production.
Zero-Downtime Database Migrations: The Expand-Contract Pattern
Schema changes are the most-feared deploys in most systems. With the right approach, zero downtime is achievable. The expand-contract pattern I've used for years and the step-by-step plan.
Feature Flags: How They Actually Work in Production
Feature flags sound simple — "on/off switch." Done badly, they fill your codebase with zombie code over a year. Six years of production experience on what they're for and what they aren't.
Engineering On-Call: Surviving It and Improving It
On-call rotation is the most disliked and most valuable practice in engineering teams. Done wrong it burns the team out; done right it's one of the strongest engineering practices around. Six years of iGaming on-call lessons.
Your Code Works Locally. Now What?
"Works on my machine" isn't a meme — it's a culture problem. What's the distance between when you think a feature is done and when it's actually done? A senior's end-to-end shipping checklist.
10,000+ CCU: Managing Traffic Waves on Kubernetes
Practical notes on running a multiplayer backend with 10,000+ concurrent users on Kubernetes — autoscaling on custom metrics, pre-warming, priorities, and surviving sudden traffic spikes.
Production Incidents: The First 15 Minutes Matter Most
How the first 15 minutes of a production incident play out determines the next few hours. A field playbook from years of iGaming on-call — what to do, what not to do, which tools to reach for.
Observability Basics: Logs, Metrics, Traces — What's Each For?
The three pillars of observability: log, metric, trace. When to reach for which, the trouble with using one in place of another, and real incident examples showing how they work together.
Technical Debt: When Do You Clean Up in Production?
Technical debt is inevitable. Which debt do you live with, which do you pay with a sprint, when do you do a large refactor? A framework built from real production decisions.