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Using PyO3 to expose Rust JSON parser as Python module
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Developers write a Rust module, annotate functions with PyO3 macros, let maturin compile and install it, then import the result as a regular Python package.
This lets performance-critical code be written in Rust while preserving a familiar Python interface, but the step that turns Rust values into Python objects can become the bottleneck. Recognizing where the conversion cost outweighs the parsing speed is crucial before porting existing Python libraries to Rust.
Written by elseif from the cluster below · every claim links back to a sourceThe three things worth knowing
PyO3 macros #[pyfunction] and #[pymodule] create the necessary bindings so Python can call Rust functions and manage type conversions and reference counting.
Maturin builds the Rust crate into a shared library (.so.dylib.dll) and places it in the virtual environment, making the module importable without manual steps.
Converting a Rust JsonValue tree to Python objects via IntoPyObject may require creating hundreds of thousands of Python objects, and this boundary work can exceed the time spent parsing the JSON itself.
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