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Use a React SDK to render client-approved feature flags, keep sensitive evaluation and authorization on the backend, and let a scheduled pipeline check health before it initiates a rollback. Do not treat a nightly schedule as an exact timer: the job should be safe to retry, observable, and protected against conflicting deployments.
How should React and the backend divide feature-flag responsibility?
The browser needs only the flag values required to decide what interface to show. A trusted backend should handle sensitive rules, permissions, and decisions that must not be controlled by a user who can inspect or modify browser code. A frontend flag can hide or reveal a user-interface path; it is not an authorization check.
With LaunchDarkly as one documented example, make each needed flag available to the client SDK and initialize its React integration with a client-side identifier and the appropriate user or application context. Keep server-side evaluation on a trusted server. LaunchDarkly’s React SDK documentation states: “Never embed a server-side SDK key into a client-side application.” Treat browser-visible flag values as client-visible information, even when the SDK limits which flags are delivered.
Keep credentials environment-scoped and least-privilege. LaunchDarkly’s API overview distinguishes server SDK keys, mobile keys, and client-side IDs; the described keys can perform read-only operations such as fetching flag settings. That does not establish permission to change flags or perform a deployment rollback.
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How do I add feature flags to a React app?
Place the provider at the application boundary, initialize it before depending on flag values, and choose what the first render should do while initialization is in progress. The React components that need a flag can then read it through the SDK’s React context or hooks rather than each component making its own API request.
Choose whether to wait or render with a fallback
| Approach | What the user sees | Trade-off | LaunchDarkly example |
|---|---|---|---|
| Wait for initialization | The app delays its initial render until flag initialization completes. | Avoids an initial fallback state, but adds initialization time before the UI appears. | asyncWithLDProvider |
| Render first with fallback values | The app renders immediately using configured fallback values, then updates when initialization completes. | Improves time to first render, but the interface may change when real values arrive. | withLDProvider |
These are documented LaunchDarkly options, not universal names or behaviors across providers. When a client flag is missing or unavailable, LaunchDarkly returns its fallback value. Choose fallbacks that keep the interface coherent and safe; do not rely on a fallback to enforce access control.
Implementation sequence
- Choose client-visible flags. Expose only the flags the browser needs. Keep sensitive targeting logic, secrets, and permission checks on trusted backend systems.
- Initialize the provider. Add the chosen provider’s React SDK at the app boundary, using its client-side identifier and the appropriate context. Confirm the SDK’s current package and setup instructions because integrations can change.
- Set a loading policy. Decide whether the app waits for initialization or renders immediately with safe fallbacks. Apply the choice consistently to avoid different parts of the page presenting incompatible states.
- Read flags in the UI. Use the SDK’s React context or hooks in components that need a value. Keep flag checks focused on presentation or client experience, not as the only guard on protected data or actions.
- Exercise missing and stale states. Verify the app’s behavior when a flag is unavailable, initialization fails, or the SDK has not yet received an update.
Should feature flags be checked on the frontend or backend?
Use the frontend for user-interface choices that are safe to expose to that client. Use the backend for authorization, sensitive evaluation, and decisions whose integrity matters. If a client action requires permission, the backend must enforce that permission regardless of which interface the flag displays.
Also distinguish who is retrieving flag state. A backend service that needs current configuration can use its provider-supported server mechanism. A browser client should consume only client-authorized flags through its SDK; it should not have every component or browser session poll an administrative API. The backend and browser have different credentials, exposure risks, and update needs.
How often should a backend poll a feature-flag API?
There is no universal cadence. Check whether the provider supports a server SDK, streaming, or a polling endpoint; then account for its authentication model, rate limits, caching, retries, and outage behavior. Do not copy a vendor’s client-side recommendation into a backend service without checking that it applies to the chosen API and workload.
Streaming and polling are different update strategies
| Consideration | Streaming | Polling |
|---|---|---|
| Update latency | Can deliver changes as they arrive over an open connection. | Changes are noticed on a subsequent request, so latency depends on the interval. |
| Connection and request behavior | Maintains a connection and must handle reconnects and missed updates. | Repeats requests at an interval and must manage request volume and rate limits. |
| Recovery design | Define reconnect behavior and what to do if the connection is unavailable. | Bound retries, track freshness, and define the last-known or fallback value. |
LaunchDarkly documents both streaming and polling for client-side updates. Its contributor guidance for implementations polling its evaluation API recommends one call every thirty seconds and a throttle of at most one request per second. Those are LaunchDarkly-specific recommendations, not general polling rules for every provider or endpoint. Confirm the current provider guidance before setting a production cadence.
For either strategy, define failure behavior deliberately: retain a safe last-known value or switch to an appropriate fallback, bound retries with backoff, report stale state, and prevent an outage from causing an unbounded request storm. These are engineering safeguards; they are not guarantees made by a flag service.
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How do I roll back a deployment with a nightly pipeline?
Separate the scheduled check from the rollback decision. A schedule says when a workflow may start; it does not prove that a release is unhealthy. First gather a defined health signal, compare it against a threshold over a stated observation window, and then select an action: alert, request approval, change a runtime flag, or restore a previous deployment artifact.
Make the schedule a trigger, not a clock
GitHub Actions supports scheduled workflows using POSIX cron. GitHub Docs states that scheduled workflows run on the latest commit on the default branch, use UTC by default, and can run no more frequently than once every five minutes. Under high load—especially near the start of an hour—a run may be delayed, and some queued jobs may be dropped. In public repositories, scheduled workflows can be disabled after 60 days without repository activity. These are documented platform behaviors, not a guarantee that any particular nightly run will occur at a precise minute.
For example, a workflow trigger such as 17 2 * * * requests a run at 02:17 UTC each day; it does not promise execution at exactly 02:17. Choose a minute away from the top of the hour to avoid concentrating on a commonly busy boundary, and make the work observable and safe to retry.
Use a guarded workflow sequence
- Trigger the check. Configure the schedule on the default branch and confirm the intended timezone. Ensure the workflow remains active and that its status and failures are visible to the team.
- Collect a health signal. Query the deployment or monitoring system your team has chosen. The signal, threshold, and observation window are application-specific; GitHub’s schedule documentation does not define them.
- Decide before changing production. If health is within bounds, record the result and exit without action. If it is outside bounds, alert or route the run through the approval policy appropriate to the risk.
- Target a specific rollback mechanism. Name the artifact or deployment revision to restore, or identify the exact runtime flag action. A generic “rollback” step is not enough to determine which code or behavior will change.
- Serialize and verify. Use an environment-specific concurrency group to avoid simultaneous deployments or rollback jobs. After the action, check the health signal again and report whether recovery succeeded.
Protect production changes
GitHub Actions environments can require approvals or other protection rules, restrict which branches may deploy, provide access to environment secrets, and retain deployment history. Use an environment for the production rollback job and configure the protections that fit your authorization model. Pair it with concurrency controls so a scheduled rollback does not race with a normal deployment.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsAutomatic action can reduce response time, but it can also turn a noisy or transient signal into an unnecessary rollback. A manual or approval-gated step slows response but gives an operator a chance to assess context. Select the policy based on the cost of a false positive and the urgency of restoring service; a scheduled trigger alone should never authorize a production change.
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Is turning off a flag the same as rolling back a deployment?
No. A flag change alters a runtime decision for code that is already deployed; a deployment rollback restores a previous deployed artifact or revision. A flag can quickly disable a feature if the deployed code was designed to support that switch, but it cannot restore code that is absent, repair an incompatible schema change, or undo every side effect.
Specify what your pipeline means by rollback. If it flips a flag, identify the flag, allowed value, scope, and verification step. If it reverts a deployment, identify the target revision or artifact and the deployment platform’s restoration operation. The provider and GitHub documentation cited here describe flag delivery and workflow protections, not a universal health metric or rollback API, so those mechanisms must be chosen for the systems in use.
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