apache/beam · error · ValueError

f'Unknown parameters

Error message

f'Unknown parameters {spec.keys()}'

What it means

Raised by YamlProviders.WindowInto after selecting a window function: leftover keys in the spec dict indicate parameters that are not valid for the chosen window type. The transform validates that only recognized parameters were provided.

Solutions

1. Remove parameters not valid for the chosen type: fixed uses size (+offset/period as supported), sliding uses size/period/offset, sessions uses only gap.
2. Fix key typos to match the exact expected parameter names.
3. Remove triggering/lateness options — they are not yet implemented for this YAML transform.

Example fix

# before
config:
  type: fixed
  size: 10m
  gap: 5m
# after
config:
  type: fixed
  size: 10m

ALLOWED = {'fixed': {'size','offset','period'}, 'sliding': {'size','period','offset'}, 'sessions': {'gap'}}
def validate_window_spec(spec):
    t = spec.get('type')
    extra = set(spec) - ALLOWED.get(t, set()) - {'type'}
    if extra:
        raise ValueError(f'Unknown parameters for {t}: {extra}')

try:
    transform = YamlProviders.WindowInto(spec)
except ValueError as e:
    if str(e).startswith('Unknown parameters'):
        logging.error('Remove/fix these window params: %s', e)
        raise
    raise

When it happens

Trigger: Passing e.g. 'gap' to a fixed window, 'size' to a sessions window, unknown keys like 'trigger' or 'every', or misspelling a valid key ('offest' instead of 'offset') for the given window type.

Common situations: Config copied between window types (fixed vs sliding vs sessions have different parameters), or attempts to configure triggering/allowed lateness which is not yet supported by this YAML transform.

Understand the failure class

Background: "Invalid value" and "allowed values are" config errors: what your library rejected and how to fix it — this error's family across 41 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…
• Missing type parameter for transform at
• 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/cfb0441da0a5adc5. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/yaml/yaml_provider.py:1168

      elif window_type == 'fixed':
        window_fn = window.FixedWindows(
            YamlProviders.WindowInto._parse_duration(spec.pop('size'), 'size'),
            YamlProviders.WindowInto._parse_duration(
                spec.pop('offset', 0), 'offset'))
      elif window_type == 'sliding':
        window_fn = window.SlidingWindows(
            YamlProviders.WindowInto._parse_duration(spec.pop('size'), 'size'),
            YamlProviders.WindowInto._parse_duration(
                spec.pop('period'), 'period'),
            YamlProviders.WindowInto._parse_duration(
                spec.pop('offset', 0), 'offset'))
      elif window_type == 'sessions':
        window_fn = window.Sessions(
            YamlProviders.WindowInto._parse_duration(spec.pop('gap'), 'gap'))
      else:
        raise ValueError(f'Unknown window type {window_type}')
      if spec:
        raise ValueError(f'Unknown parameters {spec.keys()}')
      # TODO: Triggering, etc.
      return beam.WindowInto(window_fn)

  @staticmethod
  @beam.ptransform_fn
  @maybe_with_exception_handling_transform_fn
  def log_for_testing(
      pcoll, *, level: Optional[str] = 'INFO', prefix: Optional[str] = ''):
    """Logs each element of its input PCollection.

    The output of this transform is a copy of its input for ease of use in
    chain-style pipelines.

    Args:
      level: one of ERROR, INFO, or DEBUG, mapped to a corresponding
        language-specific logging level
      prefix: an optional identifier that will get prepended to the element
        being logged

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