Sticky bucketing

Also called: consistent bucketing, deterministic bucketing

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Sticky bucketing, also called deterministic or consistent bucketing, is how a feature flag keeps a user's assignment stable. Rather than drawing a fresh random number on each check, the SDK hashes the user identifier together with a per-flag salt into a fixed bucket from 0 to 99. The hash is pure, so the same user and the same flag always produce the same bucket, on every server and every request.

Why stickiness matters

Without it, a 10% percentage rollout would re-roll on every request and a user would flicker in and out of the feature, a jarring experience and useless experiment data. Because the bucket is deterministic, raising the rollout from 10% to 25% only adds users to the enabled cohort, and the original 10% never move. The same property lets an A/B test hold a clean, stable split for the life of the experiment.

How the bucket is computed

The SDK hashes the user identifier and a per-flag salt together and maps the result onto 0–99. Folding a per-flag salt into the input means one user gets independent buckets across different flags, so unrelated rollouts never correlate. The computation runs locally against in-memory configuration, so it is sub-millisecond, needs no coordination between servers, and produces the same deterministic result across every Featureflip SDK. It is the mechanism a targeted rollout layers on top of when it rolls out to a percentage within a chosen audience, and the rollout strategies guide covers the serving model.

Want the full picture? Read the concept guide: Rollout strategies →

Related terms

Percentage rollout

A percentage rollout serves a feature to a defined share of users, chosen by a deterministic hash, so the same users stay in the cohort as you ramp the percentage up.

A/B testing

A/B testing serves two variations to randomly assigned groups of users and measures which one performs better on a chosen metric, turning a product decision into a controlled experiment instead of a guess.

Targeted rollout

A targeted rollout releases a feature to a specific audience (defined by user attributes or a segment) rather than to a random percentage of everyone, so the right users get it first.

Browse the full feature flag glossary →

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