apache/beam · error · TypeError

@yields_elements must be applied to a process or process_bat

Error message

@yields_elements must be applied to a process or process_batch method, got {fn!r}.

What it means

The @yields_elements decorator (core.py:672) marks a method as producing individual elements from a batch. It validates that the decorated method is named `process` or `process_batch`, since those are the only methods whose batching semantics Beam understands; applying it anywhere else is a programming mistake.

Source

Thrown at sdks/python/apache_beam/transforms/core.py:672

    """A decorator on process fn specifying that the fn performs an unbounded
    amount of work per input element."""
    def wrapper(process_fn):
      process_fn.unbounded_per_element = True
      return process_fn

    return wrapper

  @staticmethod
  def yields_elements(fn):
    """A decorator to apply to ``process_batch`` indicating it yields elements.

    By default ``process_batch`` is assumed to both consume and produce
    "batches", which are collections of multiple logical Beam elements. This
    decorator indicates that ``process_batch`` **produces** individual elements
    at a time. ``process_batch`` is always expected to consume batches.
    """
    if not fn.__name__ in ('process', 'process_batch'):
      raise TypeError(
          "@yields_elements must be applied to a process or "
          f"process_batch method, got {fn!r}.")

    fn._beam_yields_elements = True
    return fn

  @staticmethod
  def yields_batches(fn):
    """A decorator to apply to ``process`` indicating it yields batches.

    By default ``process`` is assumed to both consume and produce
    individual elements at a time. This decorator indicates that ``process``
    **produces** "batches", which are collections of multiple logical Beam
    elements.
    """
    if not fn.__name__ in ('process', 'process_batch'):
      raise TypeError(
          "@yields_elements must be applied to a process or "

View on GitHub (pinned to 12126d8942)

Solutions

Rename the decorated method to `process` or `process_batch`.
Remove @yields_elements from methods that are not `process`/`process_batch`.
If you need element-level yields from a differently-named method, move the logic into `process` and call the helper from there.

Example fix

# before
class MyDoFn(DoFn):
    @yields_elements
    def expand(self, batch):
        yield from batch

# after
class MyDoFn(DoFn):
    @yields_elements
    def process_batch(self, batch):
        yield from batch

Defensive patterns

Strategy: validation

Validate before calling

def check_yields_elements(fn):
    if fn.__name__ not in ('process', 'process_batch'):
        raise TypeError(f'@yields_elements must decorate process/process_batch, got {fn.__name__}')
    return fn

Type guard

def is_batch_method(fn) -> bool:
    return callable(fn) and getattr(fn, '__name__', None) in ('process', 'process_batch')

Try / catch

try:
    yields_elements(my_method)
except TypeError as e:
    if 'process or process_batch' in str(e):
        raise ValueError(f'Rename {my_method.__name__} to process or process_batch') from e
    raise

Prevention

Only apply @yields_elements directly above a method literally named process or process_batch
Run type checks / doctest imports at module load to catch decorator misuse before pipeline launch
Avoid stacking batching decorators on helper methods

When it happens

Trigger: Decorating a method with any other name, e.g. @yields_elements def expand(...) or @yields_elements def run(...), inside a DoFn.

Common situations: Typo in the method name (e.g. `proces`), applying the decorator to a helper or classmethod, copy-pasting the decorator above the wrong method.

Understand the failure class

Background: "Must be a positive integer", "Invalid value", "Unsupported": the invalid-argument-value error family, when a library rejects the value you pass — this error's family across 35 libraries.

Related errors

AI-assisted analysis of apache/beam@12126d8942 (2026-09-13). Data as JSON: /api/errors/c3241da1c1a54674. Report an issue: GitHub.