Online controlled experiment (also called A/B test or experiment) is the most important tool for decision-making at a wide range of data-driven companies like Microsoft, Google, Meta, etc. Metric computation is the core procedure for reaching a conclusion during an experiment. With the growth of experiments and metrics in an experiment platform, computing metrics efficiently at scale becomes a non-trivial challenge. This work shows how metric computation in WeChat experiment platform can be done efficiently using bit-sliced index (BSI) arithmetic. This approach has been implemented in a real world system and the performance results are presented, showing that the BSI arithmetic approach is very suitable for large-scale metric computation scenarios.

翻译：在线对照实验（又称A/B测试或实验）是微软、谷歌、Meta等数据驱动型公司进行决策的最重要工具。指标计算是实验得出结论的核心过程。随着实验平台中实验数量和指标的增长，高效大规模计算指标成为一项重大挑战。本文展示了如何利用位切片索引（BSI）算法在微信实验平台中高效完成指标计算。该方法已在真实系统中实现，并给出了性能结果，表明BSI算法非常适用于大规模指标计算场景。