"Design an A/B testing platform that supports thousands of concurrent experiments across multiple products, as ByteDance runs internally."
ByteDance is famous for its data-driven culture, running thousands of A/B tests simultaneously. This question assesses your understanding of experimentation infrastructure, traffic splitting, metric computation, and the statistical rigor needed to make reliable product decisions at scale.
Define the experiment lifecycle: creation, traffic allocation, monitoring, analysis, and decision-making, with role-based access controls.
Explain traffic splitting: hash-based user bucketing, layer-based isolation for orthogonal experiments, and mutual exclusion groups for conflicting experiments.
Describe the metrics pipeline: real-time event collection, metric computation with pre-aggregation, statistical analysis with multiple-testing corrections.
Cover advanced features: auto-stopping rules for harmful experiments, Bayesian analysis for faster decisions, and interaction detection between experiments.
I would build the platform with four core components. First, the experiment management service handles CRUD operations for experiments with parameters like traffic percentage, targeting rules, and metric definitions. Second, the traffic allocation engine uses consistent hashing on user IDs to assign users to buckets, organized in orthogonal layers so non-conflicting experiments can share users without interference. Each product surface has its own layer stack. Third, the data pipeline collects events via a streaming platform, computes experiment metrics in near-real-time using a lambda architecture—streaming for quick signals and batch for accurate final analysis. Metrics include means, proportions, and quantiles with delta-method variance estimation. Fourth, the analysis engine computes p-values, confidence intervals, and effect sizes with Bonferroni or FDR corrections for multiple comparisons. I would add sequential testing for early stopping, automated guardrail metrics that halt experiments degrading critical KPIs, and an experiment interaction detector that flags when concurrent experiments interfere with each other.
Highlight the layer-based traffic splitting—this is a key innovation that enables ByteDance to run thousands of experiments simultaneously.
Discuss statistical pitfalls like peeking problems and how sequential testing addresses them.
Mention the organizational aspect: how the platform democratizes experimentation across teams.
Use orthogonal layers with independent hash functions so each layer's traffic split is statistically independent. For experiments that could interact, place them in the same layer with mutual exclusion, or use interaction detection algorithms to monitor cross-experiment effects.
Use variance reduction techniques like CUPED (Controlled-experiment Using Pre-Experiment Data), stratified sampling, and trigger analysis to focus on affected users. For long-term metrics, employ surrogate metrics validated through historical correlation analysis.
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