Python to Rust Code Migration Frequently Asked Questions

You can improve the latency of a Python service by writing some parts in Rust, compiling the Rust code into a library, and importing it into Python. This is totally possible and there are several ways to do it. One way is using Python extensions like CFFI bindings.
PyO3 makes this process easier. Just write your Rust code, compile it for native Python extensions, annotate the functions you want to export, and run Maturin to build the module. Then, you import your Rust module in Python and use its functions directly.
For data pipeline tasks in Rust, you can use crates like Tokio for asynchronous operations, Reqwest or Hyper for HTTP calls, and RD Kafka for Apache Kafka. Reducing data ingestion latency is important for real-time apps like multiplayer games and chat. Users might not notice a 90-ms difference, but developers need low-latency APIs for smooth data flow.
If data isn't available immediately, adding a sleep command is a hacky fix, so better latency makes the system more reliable. While C is efficient and widely used, migrating to Rust can offer safety and concurrency benefits without needing to rewrite everything. Rust is similar to C in performance but has more safety features.
If you're optimizing Python, consider using different runtimes like PyPy, which can offer better performance but with higher RAM usage. When measuring latency, use averages and percentiles to understand the full impact on user experience, identifying whether any latency issues affect just a few users or many.