A targeting object represents a set of rules which need to be evaluated, in order to retrieve the best-fit variation for a flag. Rulesets need to be evaluated in priority order. The first matching rule will dictate the resulting output (i.e. the recommended variation).
Data Models
Evaluation Engine
The ruleset evaluation engine is essentially a method used to select the variation for the first rule that is satisfied. It can be defined recursively in 4 lines. Note that the following bellow is just pseudo-code aimed to illustrate the core logic for flag evaluation.
evaluate(ruleset, fallthrough_variation) :: (Ruleset, Variation) -> Variation
if (ruleset is empty) return fallthrough_variation
cons {condition, identity, segement, variation} = ruleset.pop()
if (<condition> matches <identity | segment>) then return variation
return evaluate(ruleset, fallthrough_variation)