The Tool Desk
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What a percentage rollout actually splits
A feature-flag system assigns eligible contexts to variations according to its own bucketing logic. “User” may not be the unit: depending on configuration, the randomized context could be a person, account, device, session, or another entity. Establish that unit before comparing a measured split with the configured percentage.
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For example, LaunchDarkly documents hashing a context key together with its context kind into 100,000 ordered buckets. A 50% allocation maps to the first 50,000 buckets; raising it to 70% adds the next bucket range, assuming the configuration and context remain applicable. A context of a different kind from the one used for the rollout may receive the first variation with a nonzero allocation rather than be split as expected. Separate flags normally assign independently, so they need not select the same cohort. LaunchDarkly recommends a shared segment when multiple flags must target the same contexts. LaunchDarkly’s percentage-rollout documentation describes this behavior.
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How to diagnose an uneven split
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Define the denominator
Write down what the percentage is supposed to apply to: unique people, accounts, devices, sessions, or another context kind. Specify the eligible population and observation window. If the intended claim concerns people, count distinct stable person identifiers—not flag evaluation events.
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Check how the split is measured
LaunchDarkly’s evaluation graph counts evaluations per variation, not distinct contexts. A person or other context that triggers repeated evaluations can therefore appear multiple times and skew the apparent split. For custom analytics, check for sampling, duplicate or missing events, delivery failures, ingestion problems, and whether the query deduplicates contexts. LaunchDarkly notes that Data Export may help measure unique users, but its retained evaluation history is not sufficient to explain conclusively every evaluation. See LaunchDarkly’s guidance on percentage-rollout measurement.
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Account for sample size
Small groups can deviate noticeably from the configured proportion by chance. After confirming the report and denominator, assess the result against the number of eligible independent contexts. There is no universal acceptable deviation threshold established by the cited vendor guidance, so do not label a small cohort broken simply because its observed percentage is not exact.
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Verify identity and stickiness
Check that each evaluation uses the same stable identifier for the intended randomization unit, and that context mapping is consistent across services and evaluation points. For an experience that should remain consistent across logins or sessions, a stable user or account ID is generally more suitable than a transient session ID. In Unleash, missing both default fields (
userIdandsessionId) can result in random, non-sticky assignment; a configured custom stickiness field is another option. See Unleash’s stickiness documentation and its activation-strategy reference. -
Audit eligibility, context kind, and strategy composition
Confirm that the observed contexts actually meet the rollout’s targeting constraints and that the configured randomization unit matches the context being evaluated. In Unleash, activation strategies are evaluated independently: any strategy returning true enables the flag, while every constraint within a given strategy must be true. A broad additional strategy—for example, one enabling internal users—can raise the enabled share beyond the gradual rollout by itself. In LaunchDarkly, verify the context kind and variation weights; an applicable context of another kind may receive the first variation with a nonzero allocation instead of the expected bucketed treatment. For the documented details, see Unleash activation strategies and LaunchDarkly percentage rollouts.
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Validate evaluation behavior
Use the provider’s distribution-validation tools where available, then inspect evaluation reasons and SDK errors. LaunchDarkly’s Evaluation Reasons feature helps determine whether an evaluation is being served by a rollout or whether errors are involved. Compare outcomes for the same contexts under the same configuration before attributing the discrepancy to hashing. See LaunchDarkly’s percentage-rollout guidance.
Why changing providers can change the cohort
Matching rollout percentages across providers do not guarantee matching users. Unleash documents that it and LaunchDarkly use different hashing algorithms, so migrating a partial rollout can rebucket contexts. If preserving the cohort matters, plan a cutover at 0% or 100%, or at a deliberate release boundary where a cohort change is acceptable, and communicate that users may switch. See Unleash’s documentation on stickiness and migration behavior.
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A practical checklist before calling the rollout broken
- Is the denominator unique eligible contexts rather than raw evaluations?
- Is the chosen randomization unit clear and stable over the observation window?
- Do contexts satisfy all targeting conditions and use the intended context kind?
- Could another rule or strategy independently enable the flag?
- Are event sampling, duplicates, missing records, or query logic distorting the report?
- Are evaluation reasons and SDK errors consistent with the rollout being evaluated?
- Did a provider migration or group-ID change reshuffle assignments?
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