apache/beam · error · ValueError

Unconvertable type

Error message

Unconvertable type: {beam_type}

What it means

beam_type_to_avro_type raises this ValueError when the given Beam SchemaJava type matches none of the handled AVRO-mappable categories (primitive, iterable, map, row). It is the fall-through guard of the Beam-to-AVRO schema converter, meaning the type system crossing cannot represent this Beam type in AVRO.

Solutions

  1. Inspect the offending schema field and replace the unsupported type with an AVRO-mappable one (primitive, array, map with string keys, or row).
  2. For logical types, either drop the logical wrapper (use the base representation) or extend the conversion yourself before writing.
  3. Flatten unsupported nested structures into rows/arrays that the converter supports.
  4. Wrap conversion in try/except ValueError to produce a schema-specific error message identifying the field.

Example fix

// before
row = beam.Row(ts=custom_extension_type)
WriteToAvro('out.avro', schema=beam.schema_from(row))
// after
row = beam.Row(ts=str(custom_extension_type))  # use a supported primitive
Defensive patterns

Strategy: try-catch

Validate before calling

def is_avro_mappable(field_type) -> bool:
    return field_type.type_info in {
        'atomic_type', 'iterable_type', 'map_type', 'row_type'}

Try / catch

try:
    avro_schema = beam_schema_to_avro_schema(beam_schema)
except ValueError as e:
    if str(e).startswith('Unconvertable type'):
        logging.error('Unsupported Beam type for AVRO: %s', e)
        ...
    raise

Prevention

When it happens

Trigger: Calling beam_schema_to_avro_type()/beam_schema_to_avro_schema() with a Beam FieldType whose type_info is not one of atomic/iterable/map/row — e.g. logical/EXTENSION types or DATETIME variants outside the handled set reaching the converter.

Common situations: Schemas built from custom LogicalType extensions, or schemas produced by newer Beam versions with type kinds the AVRO converter doesn't cover; piping unusual Beam rows into WriteToAvro with schema= provided.

Understand the failure class

Background: Schema validation failed / invalid input schema: payload rejected because its shape doesn't match the expected schema — this error's family across 28 libraries.

Related errors

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/daa8b70b2f65da69. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/io/avroio.py:748

  elif type_info == "map_type":
    if beam_type.map_type.key_type.atomic_type != schema_pb2.STRING:
      raise TypeError(
          f'Only strings allowed as map keys when converting to AVRO, '
          f'found {beam_type}')
    return {
        'type': 'map',
        'values': unnest_primitive_type(beam_type.map_type.element_type)
    }
  elif type_info == "row_type":
    return {
        'type': 'record',
        'name': beam_type.row_type.schema.id,
        'fields': [{
            'name': field.name, 'type': unnest_primitive_type(field.type)
        } for field in beam_type.row_type.schema.fields],
    }
  else:
    raise ValueError(f"Unconvertable type: {beam_type}")


def beam_row_to_avro_dict(
    avro_schema: _AvroSchemaType, beam_schema: schema_pb2.Schema):
  if isinstance(avro_schema, str):
    return beam_row_to_avro_dict({'type': avro_schema}, beam_schema)
  if avro_schema['type'] == 'record':
    return beam_value_to_avro_value(
        schema_pb2.FieldType(row_type=schema_pb2.RowType(schema=beam_schema)))
  else:
    convert = beam_value_to_avro_value(beam_schema)
    return lambda row: convert(row[0])


def beam_value_to_avro_value(
    beam_type: schema_pb2.FieldType) -> Callable[[Any], Any]:
  type_info = beam_type.WhichOneof("type_info")
  if type_info == "atomic_type":

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