python 45 lines · 8 steps

Type-checking dataclasses at runtime

A validator that reads dataclass type hints and checks incoming dict data against them, including unions and generic containers.

Explained by highlit
Advanced dataclasses type-hints runtime-validation reflection recursion
1from dataclasses import dataclass, fields, MISSING
2from typing import get_type_hints, get_origin, get_args, Union
3 
4 
5class ValidationError(Exception):
6 def __init__(self, errors: dict[str, str]):
7 self.errors = errors
8 super().__init__(f"{len(errors)} validation error(s)")
9 
10 
11def _check_type(value, expected) -> bool:
12 origin = get_origin(expected)
13 if origin is Union:
14 return any(_check_type(value, arg) for arg in get_args(expected))
15 if origin in (list, tuple, set):
16 if not isinstance(value, origin):
17 return False
18 (inner,) = get_args(expected) or (object,)
19 return all(_check_type(item, inner) for item in value)
20 if expected is type(None):
21 return value is None
22 return isinstance(value, expected)
23 
24 
25def validate(schema, data: dict):
26 hints = get_type_hints(schema)
27 errors: dict[str, str] = {}
28 kwargs = {}
29 
30 for field in fields(schema):
31 name, expected = field.name, hints[field.name]
32 if name not in data:
33 if field.default is not MISSING or field.default_factory is not MISSING:
34 continue
35 errors[name] = "missing required field"
36 continue
37 value = data[name]
38 if not _check_type(value, expected):
39 errors[name] = f"expected {expected}, got {type(value).__name__}"
40 else:
41 kwargs[name] = value
42 
43 if errors:
44 raise ValidationError(errors)
45 return schema(**kwargs)
01 / 01
STEP 01

Walkthrough

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Three takeaways
  1. 1Python's typing introspection lets you walk generic types at runtime by peeling off origins and args.
  2. 2Recursion mirrors the structure of nested types, so a union or a list of unions validates with the same function.
  3. 3Collecting all errors before raising gives callers a complete picture rather than failing on the first problem.

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