apache/beam · error · ValueError

Spec type not found in

Error message

Spec type not found in {spec}

What it means

Specifiable.from_spec instantiates a Specifiable subclass from a Spec dataclass. If the Spec's .type field is None, the lookup cannot proceed and a ValueError is raised. This is a required-field check before resolving the type to a registered subclass.

Solutions

1. Set Spec.type to a registered specifiable type string, e.g. Spec(type='ZScore', config=...).
2. Validate the spec before calling from_spec: assert spec.type is not None.
3. Fix the deserialization/config source so the 'type' field is included.
4. Check the spec's repr in the error message to see which fields were actually populated.

Example fix

// before
spec = Spec(type=None, config={"threshold": 3})
// after
spec = Spec(type="ZScore", config={"threshold": 3})

if spec.type is None:
    raise ValueError(f"Spec.type required before from_spec: {spec}")

def has_type(spec): return getattr(spec, 'type', None) is not None

try:
    obj = Specifiable.from_spec(spec)
except ValueError as e:
    logger.error("incomplete spec: %s", e)
    raise

When it happens

Trigger: Constructing a Spec() without passing type (e.g. Spec(config=...) or relying on the default None) and then passing it to from_spec; programmatically built specs where the type assignment was skipped.

Common situations: Specs deserialized from config/JSON missing the 'type' key; dataclass defaults leaving type unset; code paths that build a Spec incrementally and forget to set type.

Understand the failure class

Background: "missing required argument" and "the following required arguments were not provided": what required-argument errors mean and how to fix them — this error's family across 20 libraries.

Related errors

• subspace for not found.
• Unknown spec type ' ' in
• "IQR.learn_one expected univariate input, but got
• "IQR.score_one expected univariate input, but got
• Missing required field

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/20f60af0f4a7e3b6. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/ml/anomaly/specifiable.py:169

  @classmethod
  def spec_type(cls) -> str:
    pass

  @classmethod
  def from_spec(cls,
                spec: Spec,
                _run_init: bool = True) -> Union[Self, type[Self]]:
    """Generate a `Specifiable` subclass object based on a spec.

    Args:
      spec: the specification of a `Specifiable` subclass object
      _run_init: whether to call `__init__` or not for the initial instantiation

    Returns:
      Self: the `Specifiable` subclass object
    """
    if spec.type is None:
      raise ValueError(f"Spec type not found in {spec}")

    subspace = _spec_type_to_subspace(spec.type)
    subclass: type[Self] = _KNOWN_SPECIFIABLE[subspace].get(spec.type, None)

    if subclass is None:
      raise ValueError(f"Unknown spec type '{spec.type}' in {spec}")

    if spec.config is None:
      # when functions or classes are used as arguments, we won't try to
      # create an instance.
      return subclass

    kwargs = {
        k: _specifiable_from_spec_helper(v, _run_init)
        for k, v in spec.config.items()
    }

    if _run_init:

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