What causes "Either size or error should be set. Received {}." in apache/beam?

Pass exactly one of size or error to ApproximateUnique.

apache/beam · error · ValueError

Either size or error should be set. Received

Error message

Either size or error should be set. Received {}.

What it means

The same mutual-exclusion rule as 3836: parse_input_params raises ApproximateUnique._NO_VALUE_ERR_MSG ('Either size or error should be set. Received {}.') when NEITHER size nor error is provided. At least one parameter must be present to define the estimator.

Solutions

Pass exactly one of size or error to ApproximateUnique.
Validate configuration before building the pipeline so missing fields fail early with a clear message.
Default one parameter explicitly in your wrapper, e.g. error=0.02.

Example fix

// before
beam.ApproximateUnique()
// after
beam.ApproximateUnique(error=0.02)

Defensive patterns

Strategy: validation

Validate before calling

assert size is not None or error is not None, \
    'ApproximateUnique requires size or error'
if size is None and error is None:
    error = 0.02  # default

Type guard

def has_estimation_param(size, error) -> bool:
    return size is not None or error is not 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=0.02)  # sensible default
    else:
        raise

Prevention

Always pass an explicit error or size to ApproximateUnique.
Use keyword arguments so renamed config keys fail validation, not silently arrive as None.
Default the parameter in your pipeline template schema.

When it happens

Trigger: beam.ApproximateUnique() with no arguments; a config/transform spec where the key holding size/error was renamed or dropped so the value arrives as None.

Common situations: Dynamic configuration where the field name changed (e.g. 'sample_size' vs 'size'), leaving both unset; template fills missing optional parameters.

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

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/5a69748f5b408ee0. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/transforms/stats.py:124

  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

    Calculate sample size from estimation error
    """

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