About this episode
This episode explores the technical journey of building HorizonDB, a specialized database for processing location data at scale, featuring insights on geospatial indexing and the use of Rust.
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.
People in this episode
Host: Matthias Endler
Guest: Jeff Kao
Topics covered
- location data
- database design
- geospatial indexing
- Rust programming
- performance optimization
- geofencing
- technical journey
Keywords
- location events
- geofencing
- HorizonDB
- RocksDB
- Rust
- performance limits
- geospatial indexing
- Hilbert curves
- finite-state transducers
Mentioned in this episode
Organizations: Uber, Lyft
Products: HorizonDB, Elasticsearch, MongoDB, RocksDB
Places: Rust
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