apache/beam · error · TypeError

Element type must be compatible with Beam Schemas…

Error message

Element type {element_type} must be compatible with Beam Schemas (https://beam.apache.org/documentation/programming-guide/#schemas) for batch type pa.Table.

What it means

PyarrowBatchConverter.from_typehints requires the element type to be a Beam-schema-compatible row type when batch_type is pa.Table. If RowTypeConstraint.from_user_type cannot derive a schema from the given element type, it raises TypeError pointing to the Beam Schemas docs.

Solutions

Use a schema-annotated type: NamedTuple with type hints or @dataclass fields matching Beam Schemas
Register the type with @beam.typehints.with_output_types(RowTypeConstraint...) or call element_type = RowTypeConstraint.from_user_type(T) yourself and check it's not None
Change element data to conform to Beam Schemas (typed rows) before batching

Example fix

// before
converter = PyarrowBatchConverter.from_typehints(dict, pa.Table)
// after
class Row(typing.NamedTuple):
    x: int
    y: str
converter = PyarrowBatchConverter.from_typehints(Row, pa.Table)

Defensive patterns

Strategy: validation

Validate before calling

from apache_beam.typehints.row_type import RowTypeConstraint
if RowTypeConstraint.from_user_type(element_type) is None:
    raise TypeError(f'{element_type} is not Beam-Schema compatible; use a typed NamedTuple/dataclass')

Type guard

def is_schema_compatible(t) -> bool:
    from apache_beam.typehints.row_type import RowTypeConstraint
    return isinstance(t, RowTypeConstraint) or RowTypeConstraint.from_user_type(t) is not None

Try / catch

try:
    converter = create_pyarrow_batch_converter(element_type, pa.Table)
except TypeError:
    converter = create_pyarrow_batch_converter(schema_annotated_row_type, pa.Table)

Prevention

Define batch element types as typed NamedTuple or @dataclass classes
Call beam.schema_inference to verify types map to Beam Schemas
Avoid plain dicts/untyped classes as batch element types

When it happens

Trigger: Calling create_pyarrow_batch_converter / from_typehints with batch_type=pa.Table and an element_type that isn't a schema-able class (no typed NamedTuple/dataclass annotation, plain dict or arbitrary class) that from_user_type cannot convert.

Common situations: Passing a plain dict or non-annotated class as element_type to a batchable DoFn; a class annotated with types arrow can't map; forgetting @dataclass or NamedTuple typing so schema inference fails.

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/b5d5e3303678d2e2. Report an issue: GitHub.

Appendix: source

Thrown at sdks/python/apache_beam/typehints/arrow_type_compatibility.py:314


class PyarrowBatchConverter(BatchConverter):
  def __init__(self, element_type: RowTypeConstraint):
    super().__init__(pa.Table, element_type)
    self._beam_schema = typing_to_runner_api(element_type).row_type.schema
    arrow_schema = arrow_schema_from_beam_schema(self._beam_schema)

    self._arrow_schema = arrow_schema

  @staticmethod
  def from_typehints(element_type,
                     batch_type) -> Optional['PyarrowBatchConverter']:
    assert batch_type == pa.Table

    if not isinstance(element_type, RowTypeConstraint):
      element_type = RowTypeConstraint.from_user_type(element_type)
      if element_type is None:
        raise TypeError(
            f"Element type {element_type} must be compatible with Beam Schemas "
            "(https://beam.apache.org/documentation/programming-guide/#schemas)"
            " for batch type pa.Table.")

    return PyarrowBatchConverter(element_type)

  def produce_batch(self, elements):
    arrays = [
        pa.array([getattr(el, name) for el in elements],
                 type=self._arrow_schema.field(name).type)
        for name, _ in self._element_type._fields
    ]
    return pa.Table.from_arrays(arrays, schema=self._arrow_schema)

  def explode_batch(self, batch: pa.Table):
    """Convert an instance of B to Generator[E]."""
    for row_values in zip(*batch.columns):
      yield self._element_type.user_type(

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