claudevdm opened a new issue, #36775:
URL: https://github.com/apache/beam/issues/36775
### What happened?
There is a pattern in core transforms where typevars are used as typehints
for core transforms along with decorator type hints e.g.
```
T = TypeVar('T')
K = TypeVar('K')
V = TypeVar('V')
@with_input_types(tuple[K, V])
@with_output_types(tuple[K, V])
class PerKey(ptransform.PTransform):
"""Compute elements with the latest timestamp for each key
from a keyed PCollection"""
@staticmethod
def add_timestamp(element, timestamp=core.DoFn.TimestampParam):
key, value = element
return [(key, (value, timestamp))]
def expand(self, pcoll):
return (
pcoll
| core.ParDo(self.add_timestamp).with_output_types(
tuple[K, tuple[T, TimestampType]])
| core.CombinePerKey(LatestCombineFn()))
```
The `@with_input_types` suggest that the input type to the root ParDo of
this transform will be tuple[K,V], but the decorator approach does not pass
typehints to root transforms.
As a result when Pipeline._infer_result_type is invoked for the ParDo, this
line returns false
https://github.com/apache/beam/blob/5ffd998ed8028e5db7f9c1dc5b3084e84c0897cf/sdks/python/apache_beam/pipeline.py#L924
and then the resulting element type falls back to Any.
See
https://stackoverflow.com/questions/79797891/apache-beam-2-68-0-throws-using-fallback-deterministic-coder-for-type-warning
for an example where user's coder is not used because type hints are lost via
this mechanism.
There are a couple of potential fixes
#1 - explicitly set with_input_type and with_output_type on all core
transforms in the SDK.
#2 - pass the decorated input type hint to all root transforms in a
composite transform. In the above example decorating
`@with_input_types(tuple[K, V])` means all root transforms should also have
this type hint.
Both of these are breaking changes, so it might be good to audit all of the
core transforms for where typehints are lost and do the breaking change in the
same version?
### Issue Priority
Priority: 2 (default / most bugs should be filed as P2)
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