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Sydney Runkle shares Pydantic performance tips to optimize code efficiency, covering techniques like using tag unions for discrimination in validation, efficient creation of type adapters, and strategies for importing code specifications. The discussion also explores discriminators for nested models, 'skip validation' annotations, and tools like CODSPEED for benchmark tests. Learn about Pydantic's impact on projects like FastAPI and the potential integration with large language models for enhanced productivity.
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Quick takeaways
- Leverage Pydantic's built-in model validate JSON method for faster validation.
- Use discriminators for efficient validation of complex nested models.
Deep dives
Overview of Pydantic performance optimization and tools
Pydantic offers performance tips to speed up code execution. Suggestions include using Pydantic's built-in model validate JSON method for faster validation, initializing type adapter objects once to optimize schema building, being specific with type hints for more efficiency, leveraging discriminated unions for efficient validation of complex nested models, and looking into future performance improvements like SIMD in JSON parsing and deferred attribute materialization.
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