Assign Experiment Variants at Scale in Online Controlled Experiments
This work addresses scalability issues in A/B testing for technology companies, enabling more reliable causal inference at high query rates, though it is incremental in improving existing randomization methods.
The authors tackled the challenge of efficiently and reliably assigning users to experiment variants in large-scale online controlled experiments, presenting a novel algorithm that is computationally fast and meets statistical requirements for unbiased and independent assignments.
Online controlled experiments (A/B tests) have become the gold standard for learning the impact of new product features in technology companies. Randomization enables the inference of causality from an A/B test. The randomized assignment maps end users to experiment buckets and balances user characteristics between the groups. Therefore, experiments can attribute any outcome differences between the experiment groups to the product feature under experiment. Technology companies run A/B tests at scale -- hundreds if not thousands of A/B tests concurrently, each with millions of users. The large scale poses unique challenges to randomization. First, the randomized assignment must be fast since the experiment service receives hundreds of thousands of queries per second. Second, the variant assignments must be independent between experiments. Third, the assignment must be consistent when users revisit or an experiment enrolls more users. We present a novel assignment algorithm and statistical tests to validate the randomized assignments. Our results demonstrate that not only is this algorithm computationally fast but also satisfies the statistical requirements -- unbiased and independent.