Feature-Flag Rollout Bucketing Simulator

Rollout Control

Rollout percentage 10%

Population size 400 users

Sticky bucketing
consistent hash vs. per-user coin flip

salt: —

Live Readouts

Enabled / total

0 / 0

Actual on-rate

Flips this step

Consistency violations

How it works

A real remote-config / feature-flag service (LaunchDarkly, Firebase Remote Config, Split) rolls a flag out to X% of users without a server round-trip per decision. Each device hashes its own stable user ID together with the flag's salt into a bucket 0–99, and the flag is ON only if that bucket is below the target percentage:

bucket(u)   = FNV1a(userId + flagSalt) mod 100
enabled(u)  = bucket(u) < rolloutPercent

Because bucket(u) never changes for a given salt, raising the percentage from 10% to 40% can only ever turn more users on — every user who was already ON stays ON. This is the monotonic sticky-rollout property that lets you dial a rollout up (or back down) without users randomly bouncing between the old and new behaviour mid-session.

Sticky bucketing ON — the simulator uses the formula above. Watch "Consistency violations" stay at 0 no matter how you move the slider up.
Sticky bucketing OFF — every user's ON/OFF state is instead re-rolled independently at the target probability on every change, exactly like a naive Math.random() < p check with no memory. Users flicker on and off even when the percentage only goes up — a real bug class in flag systems that skip the hash step.
New Flag Salt — simulates defining a *different* flag (or rotating the salt): buckets are re-shuffled, but stay fixed again under the new salt.
Population size — resizes the simulated user base (rebuilds all buckets from scratch for the new users).

Real-world relevance: this exact bucket-and-threshold trick underlies gradual mobile rollouts, canary releases and A/B experiment assignment across virtually every production flagging system.

Flag ON Flag OFF

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