damccorm opened a new issue, #35934:
URL: https://github.com/apache/beam/issues/35934
### What happened?
I was trying to repro an issue with slowly updating side inputs, and I found
that the following code only logs one element:
```
pipeline = beam.Pipeline(options=options)
from apache_beam.transforms import core
start_timestamp = time.time() # start timestamp of the periodic impulse
main_input_fire_interval = 60 # interval in seconds at which the main input
PCollection is emitted.
side_input_fire_interval = 60 # interval in seconds at which the side input
PCollection is emitted.
def add_timestamp(element, timestamp=core.DoFn.TimestampParam):
return [(element, timestamp)]
side_input = (
pipeline
| "SideInputImpulse" >> PeriodicImpulse(
start_timestamp=start_timestamp,
fire_interval=main_input_fire_interval)
| "Window" >> WindowInto(
GlobalWindows(),
# Define the trigger. Since the Global Window never # closes, we
must tell the runner WHEN to emit a result. This trigger
# fires repeatedly for every new element that arrives.
trigger=Repeatedly(AfterCount(1)),
# Define the accumulation mode. DISCARDING tells the # runner to
forget old values after a trigger fires. This prevents # state from growing
indefinitely and ensures Latest.Globally() only
# considers the most recent element.
accumulation_mode=AccumulationMode.DISCARDING,
)
| 'Add timestamp' >> core.ParDo(add_timestamp)
)
side_input | "LocalLatest" >> Latest.Globally() | "show latest" >>
beam.Map(logging.info)
```
However, if I change `Latest.Globally()` to
`Latest.Globally().without_defaults()` it repeatedly yields a stream of
elements as expected based on the trigger. I observed this on Dataflow (this
pattern isn't supported on Prism, and I haven't validated it on other runners).
As best I can tell, it is getting hung up on side inputs.
### Issue Priority
Priority: 2 (default / most bugs should be filed as P2)
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