Recommendation Team Compares Python and Rust Performance and Developer Experience Using User Metrics App

The Recommendation Team developed and compared a user metrics processing application within the machine learning platform using Python and Rust. Based on consuming 50 Kafka messages and saving to MongoDB, Rust processed tasks 1.9x faster than Python, while Python used 4.5x more memory. The difference in CPU usage was largely due to IO operations dependent on libraries.

Performance and Resource Efficiency

The primary motivations for adopting Rust were performance and safety. Measurements showed that Rust outperformed Python in message processing speed and memory efficiency. In particular, Rust exhibited approximately 10x faster processing speeds than Python in asynchronous environments. These performance differences can lead to reduced overall operational costs during cloud deployment.

Developer Experience and Tooling Comparison

Rust ensures safety through strong type inference and explicit error handling via the Result type, but long compile times (approximately 280 seconds for release builds) hindered productivity. Conversely, Python often suffered from reduced type-checking accuracy due to the lack of type information in external libraries, even when using mypy. Rust offered advantages in developer tooling, as cargo provides built-in features for package management, testing, and more.

Conclusion and Implications

The Rust demo was successfully completed and is currently in the feature enhancement phase. The team is attempting to adopt Rust in other projects but emphasized that decisions must be tailored to the environment and purpose, given considerations such as learning costs and compile times.