apache/beam · error · TypeError

: table must be of type string; got ValueProvider instead

Error message

%s: table must be of type string; got ValueProvider instead

What it means

ValidateDataflow.create/validate in bigquery.py raises ValueError when both a table and a query are supplied, because BigQuery reads in EXPORT mode must originate from exactly one source — a table export or a query result — and the two are mutually exclusive.

Solutions

Remove the table argument and keep only query
Remove the query argument and keep only table
If the intent was to read a query over a table, express the table inside the query's FROM clause only

Example fix

// before
ReadFromBigQuery(table='proj:ds.tbl', query='SELECT * FROM proj.ds.tbl')
// after
ReadFromBigQuery(query='SELECT * FROM proj.ds.tbl')

Defensive patterns

Strategy: validation

Validate before calling

if isinstance(table, ValueProvider) and output_type == 'BEAM_ROW':
    raise ValueError('BEAM_ROW requires a static string table')

Type guard

def supports_beam_row(table):
    return isinstance(table, str)

Try / catch

try:
    t = ReadFromBigQuery(table=table, output_type='BEAM_ROW')
except TypeError as e:
    if 'ValueProvider' in str(e):
        t = ReadFromBigQuery(table=table, output_type='PYTHON_DICT')

Prevention

Use BEAM_ROW only with static tables
Default templated pipelines to PYTHON_DICT
Resolve table names from options before building the pipeline when possible

When it happens

Trigger: ReadFromBigQuery(table='proj:ds.tbl', query='SELECT ...') — the internal validate() at bigquery.py:3253 fires when both self.table and self.query are non-None.

Common situations: Switching a pipeline from table reads to query reads and forgetting to remove the table argument; config files where both keys are populated; copy-pasted examples merged together.

Related errors

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/630168675e1a4a72. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/io/gcp/bigquery.py:3119

          'The method to read from BigQuery must be either EXPORT '
          'or DIRECT_READ.')
    return self._expand_output_type(output_pcollection)

  def _expand_output_type(self, output_pcollection):
    if self.output_type == 'PYTHON_DICT' or self.output_type is None:
      return output_pcollection
    elif self.output_type == 'BEAM_ROW':
      if self._kwargs.get('query', None) is not None:
        user_schema = bigquery_tools.get_dict_table_schema(
            self.query_output_schema)
        return output_pcollection | bigquery_schema_tools.convert_to_usertype(
            user_schema, self._kwargs.get('selected_fields', None))
      table_details = bigquery_tools.parse_table_reference(
          table=self._kwargs.get("table", None),
          dataset=self._kwargs.get("dataset", None),
          project=self._kwargs.get("project", None))
      if isinstance(self._kwargs['table'], ValueProvider):
        raise TypeError(
            '%s: table must be of type string'
            '; got ValueProvider instead' % self.__class__.__name__)
      elif callable(self._kwargs['table']):
        raise TypeError(
            '%s: table must be of type string'
            '; got a callable instead' % self.__class__.__name__)
      return output_pcollection | bigquery_schema_tools.convert_to_usertype(
          bigquery_tools.BigQueryWrapper().get_table(
              project_id=table_details.projectId,
              dataset_id=table_details.datasetId,
              table_id=table_details.tableId).schema,
          self._kwargs.get('selected_fields', None))
    else:
      raise ValueError(
          'The output type from BigQuery must be either PYTHON_DICT '
          'or BEAM_ROW.')

  def _expand_export(self, pcoll):

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