shunping commented on issue #33815:
URL: https://github.com/apache/beam/issues/33815#issuecomment-2844087710
I can reproduce this with a simplified pipeline in Python as well.
```python
import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.transforms.periodicsequence import PeriodicImpulse
from apache_beam.utils.timestamp import Timestamp
options = PipelineOptions([
"--streaming",
"--job_server_timeout=600",
"--environment_type=LOOPBACK",
#"--runner=PrismRunner", "--prism_beam_version_override=v2.64.0",
"--runner=PortableRunner",
"--job_endpoint=localhost:8073",
])
class InitCount(beam.DoFn):
def process(self, element):
return [1]
class PlusOne(beam.DoFn):
def process(self, element):
print(element)
return [element + 1]
INT32_MAX_TIMESTAMP = Timestamp(micros=2147483647 * 1000)
duration = 5
## windowing before flatten
with beam.Pipeline(options=options) as p:
unboundedSource = p | "s2" >> PeriodicImpulse(
start_timestamp=INT32_MAX_TIMESTAMP, fire_interval=duration)
c = unboundedSource | "i2" >> beam.ParDo(InitCount())
_ = c | beam.Reshuffle() | beam.ParDo(PlusOne())
```
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