How Grab built a scalable, high-performance ad server

This post details the design and implementation of Grab's in-house ad server, a microservice-based system built to scale the ads business. It outlines the ad serving flow, including targeting (using Elasticsearch), capping and pacing (using ScyllaDB for stats), scoring, ranking, and pricing. Key principles of latency, graceful fallbacks, and global optimization are emphasized. The post also discusses the rationale for building a custom solution over off-the-shelf options, highlighting the need for integration with Grab's ecosystem and ML personalization capabilities. Future challenges and opportunities in ML-driven personalization and auction strategy are mentioned.