Scale, nodes, and cost savings

Radar processes billions of location events daily, powering geofencing and location APIs for companies like Uber, Lyft, and thousands of other apps. When their existing infrastructure started hitting performance and cost limits, they built HorizonDB, a specialized database which replaced both Elasticsearch and MongoDB with a custom single binary written in Rust and backed by RocksDB.

In this episode, we dive deep into the technical journey from prototype to production. We talk about RocksDB internals, finite-state transducers, the intricacies of geospatial indexing with Hilbert curves, and why Rust's type system and performance characteristics made it the perfect choice for rewriting critical infrastructure that processes location data at massive scale.

About Radar

Radar is the leading geofencing and location platform, trusted by companies like Uber, Lyft, and thousands of apps to power location-based experiences. Processing billions of location events daily, Radar provides geofencing APIs, geocoding, and location tracking that enables developers to build powerful location-aware applications. Their infrastructure handles massive scale with a focus on accuracy, performance, and reliability.

Jeff Kao is a Staff Engineer at Radar, where he led the development of HorizonDB, Radar's geospatial database written in Rust. His work replaced Elasticsearch and MongoDB with a custom Rust stack built on RocksDB, achieving dramatic improvements in performance and cost efficiency. Jeff has deep experience with geospatial systems and previously open-sourced Node.js TypeScript bindings for Google's S2 library. He holds a degree from the University of Waterloo.