Feature flag lifecycle management

As you scale utilization, it becomes increasingly important to have a strategy for how to best  clean up feature flags.  This should be a consideration from the very first feature flag you create.

When creating new feature flags 

• There are a variety of reasons for placing a change or new feature behind a feature flag. The three main types of flags to consider: 

Examples of Release Management feature flags

• I want to expose a feature flag to a subset of my traffic that meet a certain criterion (location, age, plan type) 
• I want to safely do a progressive or gradual rollout for a potentially high impact feature, expose to 10% at first then gradually increase 

Example of Experimental feature flags

• I want to learn if the new version of an existing feature, or an entirely new feature, has a positive or negative impact on my business metrics 

Example of Operational Control

• If this feature were to have an issue, it could potentially impact revenue or conversions, I want the ability to quickly revert back to the previous steady-state 

When to Retire a feature flag

Now that you have feature flags in production, what are the indicators you might establish for when to retire a flag.

• Feature flags that have not been modified within the past 30 days = eligible for retirement 
• Feature flags that have received no traffic in the last 7 days = eligible for retirement 
• Metrics show that you the feature flag has proven the feature should be rolled out to everyone or removed from code = eligible for retirement 

To surface the feature flags that fall into the first two criteria, within the Environments tab in your Split dashboard you have the option to filter by; 

• Active feature flags
• Killed feature flags
• Feature flags that have received traffic in the last 7 days 
• Feature flags that have received no traffic in the last 7 days 
• Feature flags that have seen no traffic at all 
• Feature flags that have been modified in the last 7 days 
• Feature flags that have not been modified in the last 30 days

For Consistency 

• Before removing a feature flag from code, ensure that everyone in the population is receiving the same intended treatment, essentially rolled out to 100%

Incorporating into your existing process: