apache/beam · error · TypeError

Missing type parameter for transform at

Error message

Missing type parameter for transform at {identify_object(spec)}

What it means

Apache Beam YAML raises this TypeError from expand_transform when a transform spec dict has no 'type' field. Every node in a Beam YAML pipeline must declare its transform type (e.g. 'ReadFromText', 'MapToFields', 'composite', 'chain'). Without it the framework cannot decide which expansion path (composite vs leaf) to take.

Solutions

1. Add a 'type' key to the transform spec naming a valid Beam YAML transform type.
2. Check YAML indentation so 'type:' is nested inside the transform mapping, not a sibling.
3. Verify spelling/casing: the key must be exactly 'type' (lowercase).
4. If generating specs in code, validate each spec dict contains 'type' before calling expand_transform.

Example fix

# before
- name: ReadInput
  input: {}
  config:
    path: input.json
# after
- name: ReadInput
  type: ReadFromJson
  config:
    path: input.json

def check_spec(spec):
    if not isinstance(spec, dict) or 'type' not in spec:
        raise ValueError(f"Transform spec missing required 'type' key: {spec}")

def has_type(spec) -> bool:
    return isinstance(spec, dict) and isinstance(spec.get('type'), str)

try:
    expand_transform(spec, scope)
except TypeError as e:
    if 'Missing type parameter' in str(e):
        print(f"Fix YAML: {e}")
    else:
        raise

When it happens

Trigger: Calling expand_transform(spec, scope) — directly or via expand/compute_outputs — with a spec dict that omits the 'type' key, e.g. {'name': 'MyTransform', 'inputs': [...]} or a YAML stanza where 'type:' was mis-indented so it parsed into a sibling mapping instead of the transform.

Common situations: Hand-written YAML pipelines with a missing or misspelled 'type:' key (e.g. 'Type:' with capital T); programmatically constructed specs in tests or tools forgetting 'type'; YAML indentation errors that detach 'type' from its transform block.

Understand the failure class

Background: "is required", "must be set", "missing required field": configuration validation errors across open-source libraries — this error's family across 36 libraries.

Related errors

• "Cannot specify 'callable' with 'path' and 'name' for…
• Chain at missing transforms property.
• error_handling config is not supported directly in the…
• f'Unknown parameters
• Transform is part of a chain. Cannot define explicit inputs…

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/fa8e5cc372b8809c. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/yaml/yaml_transform.py:478

      strictness += 1
    elif ('ExternalTransform' not in ptransform.label and
          not ptransform.label.startswith('_')):
      # The label may have interesting information.
      name = ptransform.label
    else:
      name = spec['type']
    if name in self._seen_names:
      if strictness >= 2:
        raise ValueError(f'Duplicate name at {identify_object(spec)}: {name}')
      else:
        name = f'{name}@{SafeLineLoader.get_line(spec)}'
    self._seen_names.add(name)
    return name


def expand_transform(spec, scope):
  if 'type' not in spec:
    raise TypeError(
        f'Missing type parameter for transform at {identify_object(spec)}')
  type = spec['type']
  if type == 'composite':
    return expand_composite_transform(spec, scope)
  else:
    return expand_leaf_transform(spec, scope)


def expand_leaf_transform(spec, scope):
  spec = spec.copy()

  # Check for optional output_schema to verify on.
  # The idea is to pass this output_schema config to the ValidateWithSchema
  # transform.
  output_schema_spec = {}
  if 'output_schema' in spec.get('config', {}):
    output_schema_spec = spec.get('config').pop('output_schema')

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