GCP Dataflow Job scenarios with 'Windowed Data how to Be Read' scenarios When Using GroupByKey
I'm having trouble with I'm writing unit tests and I'm not sure how to approach I've looked through the documentation and I'm still confused about I'm relatively new to this, so bear with me..... I'm running a Dataflow job that processes streaming data from Pub/Sub and I'm working with an unexpected behavior when I try to group elements by key using `GroupByKey`. The behavior message I'm receiving is `Windowed data want to be read from unbounded PCollection` when I attempt to run my job. The code snippet where the question arises looks like this: ```python import apache_beam as beam from apache_beam.options.pipeline_options import PipelineOptions options = PipelineOptions( project='my-gcp-project', runner='DataflowRunner', temp_location='gs://my-bucket/temp', region='us-central1' ) with beam.Pipeline(options=options) as p: messages = (p | 'ReadFromPubSub' >> beam.io.ReadFromPubSub(subscription='projects/my-gcp-project/subscriptions/my-subscription') | 'ParseJson' >> beam.Map(lambda x: json.loads(x)) | 'GroupByKey' >> beam.GroupByKey()) ``` I've confirmed that I'm using Apache Beam version 2.30.0 and my input data is indeed unbounded because it's coming from a Pub/Sub subscription. I thought that `GroupByKey` could be used with unbounded PCollections, but it seems like this isn't the case when working with windowed data. I've tried applying a windowing strategy before the `GroupByKey`, like so: ```python import apache_beam as beam from apache_beam import window with beam.Pipeline(options=options) as p: messages = (p | 'ReadFromPubSub' >> beam.io.ReadFromPubSub(subscription='projects/my-gcp-project/subscriptions/my-subscription') | 'ParseJson' >> beam.Map(lambda x: json.loads(x)) | 'WindowInto' >> beam.WindowInto(window.FixedWindows(60)) | 'GroupByKey' >> beam.GroupByKey()) ``` However, I'm still running into the same behavior. I suspect that I might need to add a `CoGroupByKey` or apply some other transformation to manage the key-value pairs properly. What’s the right approach to resolving this scenario? Is there a specific windowing strategy that I should be using for unbounded data in Dataflow? Thanks for any help you can provide! The stack includes Python and several other technologies. How would you solve this? Has anyone else encountered this?