Feature Flags
Flag evaluation on the hot path of every request — it has to feel free.
Keep every flag evaluation fast and correct while a deploy wave re-evaluates the whole fleet at once.
Flag evaluation sits on the hot path of every single request an app makes — it has to add near-zero latency and never be the reason a request fails. The evaluation set is tiny and changes rarely, so almost every lookup should be a cache hit, never a trip to the store.
Components in play
- Flag gateway — Accepts SDK evaluation requests and returns flag states.
- Flag cache — Holds the whole (tiny) rule set in memory for near-zero latency.
- Flag store — Source of truth for flag definitions and targeting rules.
Graded on these SLOs
- ≥ 99.00%Evaluations succeed
- ≤ 50 msEvaluation p99
- ≥ 5.5k rpsSustain the deploy wave
- ≤ 85%Headroom
- ≤ 5Lean footprint
The brief
Design a feature-flag evaluation service like LaunchDarkly. Every app instance's SDK asks 'is this flag on for this user?' on essentially every request it handles — evaluation sits directly on the hot path, so it has to add near-zero latency and never be the reason a request fails. The flag rule set itself is small and changes rarely; the challenge is serving it at the request rate of every app that depends on it.
Functional
- Evaluate a flag for a given user/context and return its value
- Update a flag's targeting rules and have it take effect quickly
- Support percentage rollouts and per-user targeting
Non-functional
- Evaluation is on the hot path of every request — sub-tens-of-ms, always
- A flag-service blip must never fail the caller's real request
- Deploy waves cause every instance to re-evaluate its flag set at once
Warm-up: design decisions
Optional theory to prime the calls a senior engineer would make before you build.
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