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Cache expensive computations

pydantic/pydantic
Based on 5 comments
Python

Implement strategic caching and memoization for expensive or frequently repeated computations to avoid redundant work. This can significantly improve performance in hot code paths.

Performance Optimization Python

Reviewer Prompt

Implement strategic caching and memoization for expensive or frequently repeated computations to avoid redundant work. This can significantly improve performance in hot code paths.

Key implementation strategies:

  1. Memoize frequently used functions or handlers - Store the results of expensive lookups or computations that are repeatedly accessed with the same inputs
  2. Reuse complex structures - When working with schemas, validators, or other complex objects, compute them once and reuse them where possible
  3. Implement proper cache invalidation - Ensure caches are cleared or updated when the underlying data changes

Example of effective memoization:

class BaseModel(metaclass=ModelMetaclass):
    __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, Any], None]]]
    """__setattr__ handlers. Memoizing the setattr handlers leads to a dramatic 
    performance improvement in `__setattr__`"""
    
    def __setattr__(self, name: str, value: Any) -> None:
        # Use memoized handler for performance
        if (handler := self.__pydantic_setattr_handlers__.get(name)) is not None:
            handler(self, value)
            return
        # Fall back to slower path

For schema generation, reuse existing schemas rather than rebuilding them:

# Previously: schema was generated twice for nested models
# Now: schema is only generated once and reused
schema = cls.__dict__.get('__pydantic_core_schema__')
if schema is not None and not isinstance(schema, MockCoreSchema):
    # Reuse the existing schema instead of rebuilding
    return schema

Remember to implement appropriate cache clearing when needed:

# Clear cached values when they need to be rebuilt
for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'):
    if attr in cls.__dict__:
        delattr(cls, attr)
5
Comments Analyzed
Python
Primary Language
Performance Optimization
Category

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