apache/beam · error · ValueError

'No output transforms detected.'

Error message

'No output transforms detected.'

What it means

Raised by create_test when generating an expected-inputs skeleton: it scans the pipeline's transforms for anything that consumes output (LogForTesting transforms, transforms with explicit outputs, or sinks whose type starts with 'Write') and found none. Without at least one output-consuming transform there is nothing to attach assertions to, so test generation fails.

Solutions

1. Add a sink (e.g. a Write* transform) or a LogForTesting transform to the pipeline so output is consumed
2. Add explicit 'output' to a transform so it is treated as an output transform

When it happens

Trigger: Thrown at sdks/python/apache_beam/yaml/yaml_testing.py:454 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/49611162ccffd75b. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/yaml/yaml_testing.py:454

          _try_row_as_dict(row)
          for row in _first_n(t, options, max_num_inputs, providers)
      ],
  } for t in input_transforms]

  output_transforms = [
      t for t in transform_spec['transforms'] if t['type'] == 'LogForTesting' or
      yaml_transform.empty_if_explicitly_empty(t.get('output', [])) or
      t['type'].startswith('Write')
  ]

  expected_inputs = [{
      'name': get_name(t),
      'elements': [],
  } for t in output_transforms]

  if not expected_inputs:
    # TODO: Optionally take this as a parameter.
    raise ValueError('No output transforms detected.')

  num_inputs = min_num_outputs
  while True:
    test_spec = {
        'mock_outputs': [{
            'name': t['name'],
            'elements': random.sample(
                t['elements'], min(len(t['elements']), num_inputs)),
        } for t in mock_outputs],
        'expected_inputs': expected_inputs,
    }
    fixes = run_test(pipeline_spec, test_spec, options, fix_failures=True)
    if len(fixes) < len(output_transforms):
      actual_output_size = 0
    else:
      actual_output_size = min(len(e) for e in fixes.values())
    if actual_output_size >= min_num_outputs:
      break

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