Mobile A/B Testing Infrastructure
Explore the design of reliable mobile A/B testing infrastructure to ensure consistent user assignment, persistent event logging, and data integrity despite mobile interruptions. Understand deterministic bucketing, offline telemetry persistence, event deduplication, and statistical validation methods to maintain experiment accuracy and robustness.
Mobile experimentation failures are often invisible but impactful. A user may be assigned to a variant and interact with it, but if the app is terminated before events are recorded, that data is lost, silently biasing experiment results. Unlike web systems, mobile environments introduce interruptions like process kills and unreliable connectivity that make data collection inherently fragile.
At its core, mobile A/B testing is a reliability problem. Systems must ensure consistent user assignment, durable event capture, and data integrity despite device-level disruptions. Without this, experiments can lead to incorrect conclusions.
This lesson explores the architecture behind reliable mobile experimentation, including deterministic bucketing, resilient telemetry pipelines, and server-side safeguards for statistical validity.
Assignment service and metrics pipeline
The system splits into two tiers. A server-side assignment service resolves which experiments and variants apply to a given user or device. A client-side SDK caches those assignments and enforces them locally during feature evaluation.
The assignment service carries several responsibilities:
Experiment definitions: Each experiment record includes an experiment ID, variant weights, targeting rules, and start/end dates.
Variant resolution: When a client requests assignments, the service evaluates all active experiments against the user’s attributes and returns a resolved assignment payload.
Versioned API surface: The service exposes a lightweight REST or gRPC endpoint that delivers the full
assignment manifest A JSON or binary payload containing every active experiment and the user's resolved variant for each, cached locally on the device.
The client must cache this entire manifest on-device rather than making per-feature network calls. This eliminates latency during feature evaluation, guarantees offline availability, and ensures consistency within a single session.
The metrics pipeline forms the return path. The client SDK collects experiment events locally, buffers them on disk, uploads them in compressed batches to a server-side ingestion endpoint, and the ingestion layer feeds an analytics warehouse. A critical nuance from industry practice is that the assignment service must maintain experiment data for all active client versions. Outdated clients ...