Investigating Pydantic v2's Bold Performance Claims

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  • pytest-benchmark

    py.test fixture for benchmarking code

    To test this, we will setup some benchmarks using pytest-benchmark, some sample data with a simple schema, and compare results between Python's dataclass, Pydantic v1, and v2.

  • pydantic-core

    Core validation logic for pydantic written in rust

    I encourage you to checkout the official benchmarks for more realistic and detailed examples, and, as always, YMMV.

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  • rust

    Empowering everyone to build reliable and efficient software.

    For those unfamiliar, Pydantic is currently implemented in Python and this rewrite shifts most of the code to Rust, a systems programming language touted as "blazingly fast" and safe.

  • powertools-lambda-python

    A developer toolkit to implement Serverless best practices and increase developer velocity.

    It's worth noting that these improvements will also impact other libraries and frameworks that rely on Pydantic, such as FastAPI and AWS Lambda Powertools, which could deliver some transitive performance improvements to various projects that don't directly depend on Pydantic themselves.

  • fastapi

    FastAPI framework, high performance, easy to learn, fast to code, ready for production

    If you work with backend APIs in Python, you've probably used or heard of Pydantic, perhaps from FastAPI. The library advertises itself as "data validation and settings management using Python type annotations" and it makes (de)serialization of data a breeze.1

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