  Online experimentation (or A/B testing) has been widely adopted in industry
as the gold standard for measuring product impacts. Despite the wide adoption,
few literatures discuss A/B testing with quantile metrics. Quantile metrics,
such as 90th percentile page load time, are crucial to A/B testing as many key
performance metrics including site speed and service latency are defined as
quantiles. However, with LinkedIn's data size, quantile metric A/B testing is
extremely challenging because there is no statistically valid and scalable
variance estimator for the quantile of dependent samples: the bootstrap
estimator is statistically valid, but takes days to compute; the standard
asymptotic variance estimate is scalable but results in order-of-magnitude
underestimation. In this paper, we present a statistically valid and scalable
methodology for A/B testing with quantiles that is fully generalizable to other
A/B testing platforms. It achieves over 500 times speed up compared to
bootstrap and has only $2\%$ chance to differ from bootstrap estimates. Beyond
methodology, we also share the implementation of a data pipeline using this
methodology and insights on pipeline optimization.
