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Most system design material is static diagrams and rules of thumb. Add a cache. Use a queue. Put a load balancer in front. You can read all of it and still have no intuition for why p99 latency falls off a cliff as utilisation passes 80 percent, or how a short timeout with retries turns one slow database into a total outage. Breakscale runs the experiment instead of describing it. You place components on a canvas, wire them together, drag a traffic slider, and watch real queueing behaviour emerge: queues filling, latency percentiles climbing, circuit breakers tripping, a healthy system collapsing into a retry storm.

Open the simulator

Free, open source, and it runs entirely in your browser.

Why the numbers can be trusted

The engine underneath is a discrete-event simulator, not a formula that approximates one. Requests are individual entities that arrive, queue, get served and time out. When a page here says a design loses every request at 113 a second, that is 113 simulated requests a second actually failing. Vendor pricing is cited from published rates with the date each was read. A teaching tool that shows a plausible number is worse than one that shows no number, because you cannot tell the difference and will carry the wrong intuition into real work. Every table in these examples is measured at build time by running the design through the same engine you use in the app, at a fixed seed. They cannot drift from the tool, because nobody types them.

Where to start

Retry storm

A short timeout with retries in front of a small database. Healthy at 90 requests a second, serving nothing at 113. The clearest cliff in the set.

Cache aside

How much traffic never reaches your database, and what happens the moment it does.

Discord

Millions of sockets and fan-out per message. The gateway runs out of connections, not requests.

Open source

MIT licensed, built in the open at github.com/xevrion/breakscale. Issues and pull requests are welcome, particularly new teaching examples and better explanations.