apache/beam · error · ValueError

Minimum ( ) must not be greater than maximum ( )

Error message

Minimum (%s) must not be greater than maximum (%s)

What it means

Raised in the _BatchSizeEstimator (BatchElements) __init__ when min_batch_size > max_batch_size. The adaptive batching estimator needs a valid size range; a minimum larger than the maximum makes the range empty and cannot produce batches, so construction fails with ValueError.

Solutions

1. Swap or correct the values so min_batch_size <= max_batch_size.
2. Validate at the call site: assert min_batch_size <= max_batch_size before constructing BatchElements.
3. If bounds come from config, clamp them (min = min(min, max); max = max(min, max)) or fail with a clear config-validation message upstream.

Example fix

// before
beam.BatchElements(min_batch_size=500, max_batch_size=100)

// after
beam.BatchElements(min_batch_size=100, max_batch_size=500)

if min_batch_size > max_batch_size:
    raise ValueError(f'min_batch_size ({min_batch_size}) must be <= max_batch_size ({max_batch_size})')

def valid_batch_range(lo, hi):
    return isinstance(lo, int) and isinstance(hi, int) and 0 < lo <= hi

try:
    t = beam.BatchElements(min_batch_size=cfg.min, max_batch_size=cfg.max, target_batch_overhead=0.05)
except ValueError as e:
    logging.error('invalid BatchElements config: %s', e)
    t = beam.BatchElements(target_batch_overhead=0.05)  # safe defaults

When it happens

Trigger: Calling BatchElements(min_batch_size=N, max_batch_size=M) with N > M, e.g. min_batch_size=100, max_batch_size=10.

Common situations: Config typo or swapped arguments; computing the bounds from variables whose order got reversed; tuning batch sizes after a workload change and forgetting to keep min <= max.

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

• bucket_boundaries must be a non-empty sorted list of…
• ignore_first_n_seen_per_batch_size
• target_batch_duration_secs_including_fixed_cost
• target_batch_duration_secs
• target_batch_overhead

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/1fd94567cfd49530. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/transforms/util.py:521

  """Estimates the best size for batches given historical timing.
  """

  _MAX_DATA_POINTS = 100
  _MAX_GROWTH_FACTOR = 2

  def __init__(
      self,
      min_batch_size=1,
      max_batch_size=10000,
      target_batch_overhead=.05,
      target_batch_duration_secs=10,
      target_batch_duration_secs_including_fixed_cost=None,
      variance=0.25,
      clock=time.time,
      ignore_first_n_seen_per_batch_size=0,
      record_metrics=True):
    if min_batch_size > max_batch_size:
      raise ValueError(
          "Minimum (%s) must not be greater than maximum (%s)" %
          (min_batch_size, max_batch_size))
    if target_batch_overhead and not 0 < target_batch_overhead <= 1:
      raise ValueError(
          "target_batch_overhead (%s) must be between 0 and 1" %
          (target_batch_overhead))
    if target_batch_duration_secs and target_batch_duration_secs <= 0:
      raise ValueError(
          "target_batch_duration_secs (%s) must be positive" %
          (target_batch_duration_secs))
    if (target_batch_duration_secs_including_fixed_cost and
        target_batch_duration_secs_including_fixed_cost <= 0):
      raise ValueError(
          "target_batch_duration_secs_including_fixed_cost "
          "(%s) must be positive" %
          (target_batch_duration_secs_including_fixed_cost))
    if not (target_batch_overhead or target_batch_duration_secs or
            target_batch_duration_secs_including_fixed_cost):

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