How Grab built a scalable, high-performance ad server

Summary

Grab transitioned from an off-the-shelf MVP to an in-house ad serving system to accommodate business scale, hyperlocal requirements, and machine learning personalization. The architecture orchestrates core microservices and data pipelines across sequential steps: targeting, capping, pacing, scoring, ranking, pricing, and tracking. ElasticSearch serves as the targeting ads repository, while ScyllaDB acts as the high-throughput stats store fed by Kafka streams and data pipelines. The system operates on key engineering principles including parallelization and tuned latency limits, graceful fallbacks for slow dependency calls, and a unified server serving all ad types across the superapp.

Takeaways

1. 1

   ElasticSearch serves as the ad repository to query candidate ads across diverse targeting criteria like search keywords, location, and user preferences.

2. 2

   Kafka event streams feed impression, click, and purchase data back into ScyllaDB to enforce real-time campaign budget caps and pacing limits.

3. 3

   To protect user-facing latency against slow dependency calls, the ad server relies on strict timeouts and falls back to non-personalized results instead of dropping the ad.