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It makes sense to not want to create an admin dash, but to avoid having to keep track of thousands of feature flags in your db, it seems all you're doing here is moving them to another db
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or maybe just make single JSON and commit it to git? your http server + GitHub + JSON and text editor is your admin ui, audit, etc.
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Git is typically fairly slow if you have to wait on a test suite and deployment pipeline. Usually at least 10 minutes but sometimes 30, 60, 90+ minutes. A lot of purprose-built feature flag platforms hot reload the config in seconds.

JSON in the repo also risks introducing customer data to git if you want to rollout based on specific customer attributes (sometimes, for us, it's a list of early opt-in customers we have meetings with to discuss/develop new features)

It's also less accessible for "business users" like product/project managers, sales, and marketing they want to coordinate feature rollout with other business initiatives (and don't want to bother engineers when they do)

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how would a single JSON allow staged rollouts with sticky sessions?
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you pick a frequency you want, represent that as a fraction, and modulo on user id, and your 80% of the way there
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This gives you a distribution unrelated to active use, puts users in the same bucket (with the same number you’re going to have the same users in the first 10%) and links combinations together.

Often problems are more complex than they seem at first sight and I have found it’s a good approach to think “what am I missing” rather than “lots of people must be making very obviously bad decisions” and reach the latter conclusion only after more work. Usually I’ve missed something.

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This just tells me you haven’t worked on a big/complex enough system.

If it were that easy people would not be paying for it.

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Assuming you want a random distribution and don't want to take any other attributes into account.

We're a small company but new feature release for big features is typically targeted at low risk users/customers first. That usually means a few attributes are taken into account (age, customer value, customer sentiment, which features they use)

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