Production-ready guide covering progressive rollout with feature flags and slos with implementation patterns, code examples, and anti-patterns for enterprise engineering teams.
This process implements controlled releases of software changes using feature flags and Service Level Objectives (SLOs) to reduce risk during deployments. It applies when releasing new functionality or behavioral changes where immediate rollback is undesirable, and gradual adoption provides better observability and control.
Progressive rollout uses feature flags to enable or disable code paths without redeploying services. The system evaluates whether a given request should see the new behavior based on criteria like user ID, percentage of traffic, or custom attributes. SLOs define acceptable performance thresholds that must be maintained during the rollout.
The core workflow starts with a feature flag configuration that defines when and how a change is enabled. The system must track both the flag state and whether the SLOs are being met. Rollout progress is measured by tracking metrics like error rates, latency, or business KPIs against defined targets.
Feature flags require a backend system to store and serve configuration. This typically involves a database table or key-value store where flags are defined with their state and rollout parameters. The schema for a basic flag might include:
CREATE TABLE feature_flags (
name VARCHAR(255) PRIMARY KEY,
enabled BOOLEAN DEFAULT FALSE,
rollout_percentage INTEGER DEFAULT 0,
target_users JSONB,
created_at TIMESTAMP DEFAULT NOW(),
updated_at TIMESTAMP DEFAULT NOW()
);Each flag must be configured with a default state. The system returns either enabled: true or enabled: false based on the stored configuration and current evaluation logic. When evaluating, if a flag is not found in the database, it defaults to enabled: false.
The flag evaluation process requires reading from persistent storage, which means any outage in the feature flag backend affects rollout decisions. If the system cannot read flags, all flags default to disabled state.
A progressive rollout typically starts with a small percentage of traffic. For example, enable a new feature for 1% of users. The system evaluates each request and determines whether to use the new code path based on the configured percentage.
When implementing a rollout strategy, the configuration must support gradual increases. A typical progression might be:
This requires updating the flag's rollout_percentage value in the database. The evaluation process must handle fractional percentages by generating a random number and comparing it against the configured percentage.
The rollout can be controlled through direct database updates or through an API endpoint. For example, using curl to update a flag:
curl -X PUT \
-H "Content-Type: application/json" \
-d '{"rollout_percentage": 5}' \
http://feature-flag-service/flags/my-featureSLOs define the acceptable performance for a feature before moving to the next rollout stage. The system must monitor metrics and make decisions based on whether targets are being met.
Each SLO requires defining a metric and threshold. For example, a latency SLO might be "95th percentile request time under 200ms". The system evaluates these thresholds during the rollout process.
If an SLO is violated, the rollout must pause or revert to the previous state. This requires a mechanism to read current metrics and compare them against defined targets. Metrics are typically stored in a monitoring system like Prometheus or similar.
When SLO violations occur, the system should log the failure and potentially trigger alerts. If SLOs are not met, the flag's rollout percentage may be reduced or the feature may be disabled entirely until the issue is resolved.
Rollout progress is tracked through metrics collected during the gradual adoption phase. The system must collect data on key performance indicators like request counts, error rates, latency distributions, and business metrics.
The evaluation process requires comparing current values against the SLO targets. If the system detects that SLOs are not being met, it should either pause the rollout or take corrective action.
For example, if a 5% rollout shows an error rate of 3% instead of the target of 0.1%, the system should stop increasing the rollout percentage and potentially revert to 1%.
The evaluation process must be idempotent. If a check runs twice for the same time period, it should return the same result. This prevents inconsistent state during repeated checks.
Rollout failures occur when SLOs are violated or when system errors prevent proper flag evaluation. A failure can be either immediate (system crash) or gradual (performance degradation).
When a rollout fails, the system must have a rollback mechanism. This typically involves reverting the flag to its previous state or disabling it entirely. The rollback process should be atomic to prevent partial state changes.
If the feature flag service itself becomes unavailable, all flags default to disabled. This means that a system outage in the feature flag backend causes immediate degradation of all features controlled by flags.
Feature flag configuration must be versioned and tested before deployment. The system should support rollback to previous configurations in case of problems. A typical workflow involves:
The configuration must be consistent across environments. A flag configured in staging should behave identically to production unless explicitly overridden.
Monitoring requires collecting metrics from the feature flag system and comparing them against defined targets. The system should alert when:
Alerting must distinguish between temporary failures (like transient network issues) and permanent problems (like corrupted flag data). A single alert about a flag configuration issue should provide enough information to debug the problem.
Rollout procedures require clear steps for checking SLOs, updating flags, and handling failures. The process should include:
When a rollout fails, operators must be able to quickly identify whether the problem was in the feature flag system or in the application code.
The most common failure is a database outage in the feature flag service. When this happens, all flags default to disabled state without any error reporting. The system silently continues operating with the new feature disabled.
Another frequent issue occurs when SLO targets are incorrectly configured. If the threshold is set too aggressively, the rollout may pause frequently even when the feature is performing adequately. This can slow down the entire deployment process.
Incorrect flag evaluation logic can cause a feature to be enabled for some users but not others, creating inconsistent experiences. The evaluation must be deterministic to ensure consistent behavior across requests.
The system must track rollout state through all stages. This includes:
State tracking should be persistent to survive service restarts. If the rollout process restarts, it should resume from where it left off rather than restarting from scratch.
Before deploying a rollout, the system must verify that flag evaluation works correctly. This includes testing:
Testing should cover both success and failure cases. If a feature flag is not found in the database, the system must return a consistent default state.
Consistency requires that all requests for the same user see the same feature state during a rollout. This means that if a user sees the new feature on one request, they should continue to see it until the rollout changes.
The evaluation process must be deterministic across service instances. If two load-balanced instances evaluate the same flag for the same user, they must return identical results.
Edge cases include users with no flag data, flags with invalid configurations, and systems where the feature flag service is temporarily unavailable. In these cases, the system should fall back to a consistent default behavior.
The system should handle missing or corrupted flag data gracefully. If a flag configuration is invalid, it should default to either enabled or disabled state rather than crashing.
Automation can reduce manual errors in progressive rollouts. This includes:
The automation must be careful not to override manual decisions. If an operator manually changes a flag, automated systems should respect that decision rather than overwriting it.
Feature flag evaluation adds a small performance overhead to each request. This overhead must be accounted for in system design. The evaluation process should be fast enough that it doesn't significantly impact response times.
If the feature flag service becomes slow, it affects all flagged features. The system should provide metrics to monitor this overhead and alert when performance degrades.
Configuration should use clear naming conventions for flags. Names should indicate what feature or behavior they control. The system should document which flags are used for specific rollouts.
All configuration changes should be logged with timestamps and user information. This allows operators to track when and why changes were made to rollout parameters.
The system should provide tools to validate flag configurations before applying them. This helps prevent misconfigurations that could cause incorrect rollouts.
This page was rewritten on 16 October 2026. It replaced a templated version that was not specific to its own title. How these pages are written and checked. If anything here is wrong, tell us at [email protected] and we will correct it.
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