Check conditions early to skip unnecessary processing and reuse computed values where possible to optimize performance. This reduces CPU cycles and improves execution speed, especially in performance-critical code paths.

Early condition checking example:

# Less performant approach
def process_value(assigned_value):
    # Perform expensive operations regardless of value state
    result = complex_calculation(assigned_value)
    if assigned_value is PydanticUndefined:
        return default_value
    return result

# More performant approach
def process_value(assigned_value):
    # Early check avoids unnecessary processing
    if assigned_value is PydanticUndefined:
        return default_value
    result = complex_calculation(assigned_value)
    return result

Value caching example:

def get_schema(cls: type) -> Schema:
    # Check if schema already exists before regenerating
    schema = cls.__dict__.get('__schema__')
    if (
        schema is not None
        and not isinstance(schema, MockSchema)
        and conditions_for_reuse_met(cls)
    ):
        return schema
    
    # Only build new schema when necessary
    return build_new_schema(cls)

Apply these patterns whenever you find yourself performing expensive operations that might be unnecessary based on input conditions or when values can be safely reused across calls.

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