Rate Limiter Design

Hook

Apply per-client weighted request budgets. A useful design makes ownership, trade-offs and failure outcomes explicit.

Watch

Build the architecture one component at a time. Read the requirements and estimates above the player, then inspect the modeled state transitions.

How it works

Place the limiter before expensive application work.

Partition state by authenticated client and policy.

Token buckets allow bounded bursts while exact logs enforce a rolling window.

Treat admission and budget debit as one atomic operation.

Define fail-open or fail-closed behavior separately for each endpoint.

Requirements and estimates

The interactive overview includes editable daily actors, request frequency, peak factor, payload and retention assumptions. RPS is daily requests divided by 86,400. Storage estimates exclude replication, metadata and indexes. These are planning assumptions, not measured provider capacity.

API and data-model exercise

The overview lists example service API contracts and durable entity keys. These paths describe the hypothetical service; they are not callable AlgoVerse endpoints. Define authentication, pagination and error semantics before implementing a real service.

Try it

Predict an input-dependent state transition before advancing. Change one assumption at a time and account for committed, rejected, stale or pending outcomes.

Break it

Send a burst on both sides of a window boundary, compare fixed windows and exact logs, then change request cost and refill rate.

Complexity

Use the displayed operation counters and complexity assumptions. Capacity estimates, simulator running time and real end-to-end latency are different measurements.

Code

The four language references implement the stated core mechanism. They are bounded teaching implementations, not complete deployable service backends. The visualizer adds architecture, validated controls and outcome narration around that mechanism.

When to use

Use the exercise to explain requirements, ownership, scaling pressure and failure policy in a design interview. Validate a production design with actual workload measurements and the complete selected provider protocol.

Interview pitfalls

Do not turn an illustrative throughput parameter into a universal benchmark. Distinguish retry safety from exactly-once external effects, a cache from durable ownership, and a timeout from a known failure. Name the model limits before claiming a guarantee.

Quiz

Explain one state transition, one failure invariant and one concern outside the bounded simulation.