Building Privacy-First Analytics with Swift
Article Summary
TelemetryDeck is processing analytics for 16 million users monthly using Swift on the server. The infrastructure costs and performance gains are making Python and Node developers take notice.
TelemetryDeck built their entire privacy-first analytics service on Swift and Vapor, running on Kubernetes with Postgres and Apache Druid. This deep dive shares technical lessons from scaling a production Swift backend that handles millions of events while keeping infrastructure lean.
Key Takeaways
- Swift's Codable protocol eliminates JSON parsing boilerplate and entire vulnerability classes
- No Global Interpreter Lock means true parallelism at fraction of Python's resource cost
- Develop and debug in Xcode with breakpoints, no container spinning required
- Versioned API URLs and inline DTOs beat shared package approach for evolution
- Swift Distributed Tracing catches performance issues before customers notice them
A production analytics service proves Swift delivers on backend promises: type safety prevents bugs, compiled performance cuts infrastructure costs, and familiar tooling accelerates development velocity.
About This Article
TelemetryDeck had trouble managing API versions when sharing Data Transfer Objects between client and server code. Updating structs for one API version would break the others that were still running.
TelemetryDeck moved DTOs into their controllers as inline structs within versioned API extensions. This removed the need for a shared SwiftDTOs package and separated API changes from client updates.
API changes became faster and easier to maintain. TelemetryDeck could develop new API versions continuously while keeping older clients working without having to update structs everywhere.
