apache/beam · error · ValueError

Either size or error should be set. Received

Error message

Either size or error should be set. Received {size = %s, error = %s}.

What it means

ApproximateUnique.parse_input_params requires exactly one of 'size' (sample size) or 'error' (estimation error) to be given. Passing BOTH is rejected with _MULTI_VALUE_ERR_MSG ('Either size or error should be set. Received {size = %s, error = %s}.'). The two parameters are mutually exclusive constructors of the estimator.

Solutions

Provide only one parameter: delete either size or error.
If you know the desired sample size, pass only size (int >= 16); if you know the tolerance, pass only error.
Centralize construction so config merging cannot fill both fields.

Example fix

// before
beam.ApproximateUnique(size=1000, error=0.02)
// after
beam.ApproximateUnique(error=0.02)

Defensive patterns

Strategy: validation

Validate before calling

assert not (size is not None and error is not None), \
    'ApproximateUnique accepts only one of size or error'
params = {k: v for k, v in [('size', size), ('error', error)] if v is not None}
assert len(params) == 1

Type guard

def exactly_one_of(size, error) -> bool:
    return (size is None) != (error is None)

Try / catch

try:
    t = beam.ApproximateUnique(size=size, error=error)
except ValueError as e:
    if 'Either size or error' in str(e):
        t = beam.ApproximateUnique(error=error if error is not None else 0.02)
    else:
        raise

Prevention

Never pass both size and error; pick one estimation strategy.
Wrap ApproximateUnique in a helper that takes exactly one parameter.
Audit merged configs for both fields being populated.

When it happens

Trigger: beam.ApproximateUnique(size=100, error=0.02) — both supplied; Parse from a transform spec where both keys were set in pipeline options or a YAML payload.

Common situations: Copy-pasted code where a default size stayed while an error value was added; CLI/config merging that set both fields.

Related errors

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/ae6d8996e5215b25. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/transforms/stats.py:122

  @staticmethod
  def parse_input_params(size=None, error=None):
    """
    Check if input params are valid and return sample size.

    :param size: an int not smaller than 16, which we would use to estimate
      number of unique values.
    :param error: max estimation error, which is a float between 0.01 and 0.50.
      If error is given, sample size will be calculated from error with
      _get_sample_size_from_est_error function.
    :return: sample size
    :raises:
      ValueError: If both size and error are given, or neither is given, or
      values are out of range.
    """

    if None not in (size, error):
      raise ValueError(ApproximateUnique._MULTI_VALUE_ERR_MSG % (size, error))
    elif size is None and error is None:
      raise ValueError(ApproximateUnique._NO_VALUE_ERR_MSG)
    elif size is not None:
      if not isinstance(size, int) or size < 16:
        raise ValueError(ApproximateUnique._INPUT_SIZE_ERR_MSG % (size))
      else:
        return size
    else:
      if error < 0.01 or error > 0.5:
        raise ValueError(ApproximateUnique._INPUT_ERROR_ERR_MSG % (error))
      else:
        return ApproximateUnique._get_sample_size_from_est_error(error)

  @staticmethod
  def _get_sample_size_from_est_error(est_err):
    """
    :return: sample size

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