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To release a notification feature to 10% of eligible users without changing who qualifies on each request, evaluate a percentage rollout against a durable identity for the rollout subject—usually a user ID—and use that same identity consistently. A fresh random decision per request can move one person in and out of the cohort.
The flag decides who is eligible for the feature; it does not queue, send, or retry notifications. Keep those delivery responsibilities separate.
Choose what the rollout percentage represents
First decide whether the release is assigned per person, account, organization, or another entity. The identity used for bucketing must match that product decision: a person-level release needs a stable user identity, while an organization-level release needs organization context. LaunchDarkly’s documentation describes percentage rollouts as requiring a context kind and attribute: percentage rollouts.
Pass the chosen identity on every evaluation. OpenFeature’s evaluation context provides a way to carry identity and other relevant attributes into flag evaluation: OpenFeature introduction. If the identity is missing or changes between evaluations, stable user-level assignment is no longer assured.
What “10%” does—and does not—guarantee
A configured 10% allocation is a share of the flag’s assignment space, not a promise that exactly one in every ten people in a small audience—or exactly 10% of notifications sent—will be observed. Counts can vary in small or nonrepresentative samples, and a rollout percentage is not a delivery-success rate.
For its documented implementation, LaunchDarkly maps context kinds and keys into 100,000 buckets and assigns variations to ranges of those buckets. That is a LaunchDarkly-specific detail, not a universal rule for feature-flag providers. See its percentage rollout documentation.
Implementation paths for a Node.js service
You can use a platform’s rollout configuration directly, or use OpenFeature’s vendor-neutral API with a provider. The choice does not remove the need to understand provider-specific identity, missing-context, and failure behavior.
| Path | Documented behavior or capability | What to verify for this release |
|---|---|---|
| LaunchDarkly percentage rollout | Uses a context kind and attribute for percentage assignment; the documented implementation hashes context information into 100,000 buckets. | Confirm the context kind and key represent the intended rollout unit, and check how separate flags correlate their cohorts. |
| Unleash gradual rollout | Supports stickiness configuration. Explicit userId stickiness returns false when that identity is absent; the default can fall back through userId, sessionId, then random. |
Supply the durable intended identity explicitly; do not treat a session or random fallback as stable person-level assignment. |
| OpenFeature Node.js SDK with a provider | Offers a Node.js API with providers, evaluation context, hooks, eventing, tracking, and lifecycle support. | Check the chosen provider’s mapping and behavior, including defaults, errors, and rollout controls. |
References: LaunchDarkly percentage rollouts, Unleash gradual rollout, OpenFeature Node.js SDK, and Unleash OpenFeature Node provider.
Keep flag evaluation separate from notification delivery
Evaluate the flag at the point where the application decides whether a notification-related feature is eligible. Do not make a retry a new cohort assignment: retries should follow the delivery system’s own rules, while the flag’s decision remains tied to the same rollout identity and evaluation context.
Flag evaluation answers whether the feature applies. It does not define a queue, delivery provider, retry schedule, deduplication strategy, or success condition. The cited flag and SDK documentation establishes rollout mechanics, not a complete notification architecture.
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Coordinate cohorts across multiple changes
Separate percentage rollouts may assign contexts independently, so two flags set to 10% should not be assumed to select the same people. If related changes need a shared cohort, use a deliberately shared targeting mechanism and verify its behavior. Unleash documents groupId as a way to correlate rollouts; LaunchDarkly documents independent assignments for separate percentage rollouts. See the respective Unleash gradual rollout and LaunchDarkly percentage rollout documentation.
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Plan safe evaluation and rollout operations
Choose an explicit fallback
Decide what the service should do if the flag provider is unavailable or evaluation fails. A fail-open or fail-closed choice has different product and operational consequences; the cited SDK materials do not establish one universal fallback policy for notification features. Set a deliberate default for this feature and document it.
Measure eligibility separately from delivery
Track eligible recipients, users for whom the flag evaluates on, actual send attempts or sends, and relevant errors or engagement signals as distinct measures. This makes it possible to tell whether a difference comes from cohort assignment or from notification delivery.
Increase exposure based on signals
Canary releases and gradual rollout are established feature-flag use cases, and percentage allocations can be changed over time. Increase exposure in planned steps only after reviewing relevant operational and product signals; do not infer success from the configured percentage alone. See the OpenFeature introduction and LaunchDarkly percentage rollouts.
Before enabling the 10% allocation
- Name the rollout unit: user, account, organization, or another stable entity.
- Use its durable identifier on every evaluation, and define what happens if it is absent.
- Confirm whether separate flags should enroll the same cohort or independent cohorts.
- Choose and document the provider-error default.
- Keep eligibility, evaluation, and delivery outcomes observable as separate stages.
- Verify current SDK versions and provider-specific configuration: these APIs and capabilities can change.
OpenFeature describes feature flags as a way to alter application behavior without deploying new code. Its API can provide a consistent Node.js integration surface, but actual assignment behavior still depends on the provider and its configuration.
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