PyO3 Performance Patterns

About

This skill provides specialized guidance for developers using PyO3 to bridge Rust and Python, focusing on performance optimization and concurrency safety. It helps Claude implement efficient async Python callbacks using pyo3_async_runtimes, manage Global Interpreter Lock (GIL) release/attach cycles correctly, and choose between spawn_blocking and block_in_place to prevent critical deadlocks in post-processing tasks. By applying these patterns, developers can achieve significantly faster execution for operations within document intelligence and data conversion pipelines.

Key Features

• Deadlock prevention strategies for PostProcessors and Validators
• Efficient GIL management using Python::attach() patterns
• Async Python callback optimization via pyo3_async_runtimes
• Performance-driven choice between spawn_blocking and block_in_place
• 480 GitHub stars
• Conditional async handling with fallback to spawn_blocking

Use Cases

• Integrating Rust-based HTML-to-Markdown converters with Python async frameworks
• Building high-performance Python libraries with a Rust core
• Implementing document processing pipelines that require concurrent data extraction