Traffic Replay Platform for Data Platform Testing

Background: The data platform uses big‑data analytics and visualization to present multi‑source heterogeneous data, requiring high data accuracy, large volume, and guaranteed query performance.

Challenges: Traditional offline testing and regression testing are costly and difficult; existing automated testing suffers from high cost, limited coverage, and low standardization.

Solution: Build an online traffic‑replay platform that records real user traffic, replays it in a pre‑release environment, and compares responses to detect code issues, providing a zero‑intrusion, automated diff, and precise problem localization.

Core principles:

Traffic collection: Use instrumentation to capture traffic, filter by whitelist, remove dirty data, and store a clean traffic pool.

Environment strategy: Deploy parallel pre‑release and production environments; sync configuration to bridge gaps.

Execution scheduling: Trigger jobs via timed config or manual API, use thread pools and rate controllers for high concurrency and controllable impact.

Result comparison: Apply AAdiff (double‑run production) and field‑ignore rules to reduce noise, then aggregate differences for analysis.

Case study: In the intelligent‑operation system, traffic replay increased interface coverage to 86%, reduced regression bug leakage by 30%, and achieved a 98% replay success rate, cutting manual test effort by half.

Future work: Extend coverage to sparse interfaces, integrate more systems, and continue using traffic replay for smoke, regression, stress, and cache validation.