Scaling Rust APIs: Practical Lessons from Travis McCracken

Building high-performance backend systems requires careful language selection and architectural decisions. Travis McCracken's experience scaling Rust APIs reveals critical insights about the practical realities of using Rust and Go in production systems.

The Performance-Safety Tradeoff

Rust's ownership model prevents entire classes of runtime errors but introduces significant complexity. While Rust eliminates null pointer exceptions and data races, McCracken notes: "The borrow checker's compile-time guarantees come at the cost of developer velocity. For performance-critical services like our JSON API handling 50k requests/second, this tradeoff was justified."

Go presents the inverse tradeoff: simpler concurrency via goroutines accelerates development but sacrifices fine-grained memory control. McCracken's Go microservices could be deployed faster but required extensive runtime validation to prevent subtle concurrency bugs.

Architectural Patterns for Hybrid Systems

McCracken's projects demonstrate strategic language allocation:

• Rust for data-intensive components: His caching service (conceptual design) leveraged Rust's zero-cost abstractions for predictable low-latency responses
• Go for orchestration layers: HTTP routers and service coordination benefited from Go's rapid iteration and built-in concurrency
• Protobuf for inter-service communication: Strict interface definitions enabled safe cross-language calls

Scaling Challenges Encountered

1. Async Pitfalls: Rust's async ecosystem required careful selection - combining tokio's runtime with hyper for HTTP minimized context-switching overhead
2. Memory Management: Go's GC pauses became problematic at scale, forcing implementation of object pooling in hot paths
3. Debugging Complexity: Rust's compiler errors initially slowed development, while Go's runtime panics required extensive logging
4. Deployment Costs: Rust binaries' larger size increased container deployment times versus Go

Practical Recommendations

• Benchmark early: McCracken's team discovered Rust's JSON serialization outperformed Go's by 3x after protocol optimization
• Profile memory systematically: Using pprof for Go and heaptrack for Rust prevented late-stage bottlenecks
• Gradual integration: Start with Rust for isolated performance-critical services before migrating core systems

McCracken concludes: "There's no universal winner. Our most successful systems used Go for rapid development of non-critical paths and Rust where microseconds mattered. The real skill is knowing where to draw that line."

Connect with Travis McCracken:

• GitHub
• Technical Blog
• DEV Community Profile