👀 Views: 66 💬 Answers: 1 📅 Created: 2025-06-09

google-cloud dataflow apache-beam Python

I've been struggling with this for a few days now and could really use some help. I am currently developing a data processing pipeline using GCP Dataflow with Apache Beam (version 2.30.0). The job is configured to read data from Pub/Sub, process it, and write the results to BigQuery. However, I am working with an scenario where the Dataflow job fails with the behavior message: `Windowing not supported for the specified trigger`. I have implemented a custom trigger to fire after a specific time window combined with late data handling, but it seems Dataflow does not accept the configuration. Here’s a simplified version of my pipeline code: ```python import apache_beam as beam from apache_beam.options.pipeline_options import PipelineOptions options = PipelineOptions() with beam.Pipeline(options=options) as p: (p | 'Read from PubSub' >> beam.io.ReadFromPubSub(subscription='projects/my-project/subscriptions/my-sub') | 'Process Data' >> beam.Map(lambda x: x) | 'Windowing' >> beam.WindowInto(beam.window.FixedWindows(60), trigger=beam.trigger.AfterProcessingTime(30), allowed_lateness=beam.window.Duration(minutes=5)) | 'Write to BigQuery' >> beam.io.WriteToBigQuery(table='my_dataset.my_table')) ``` I have tried adjusting the trigger settings to `AfterWatermark()` with a specified lateness, but the same behavior continues. I have also examined the windowing strategy and ensured that it aligns with the expected input data types. However, the behavior still arises at runtime, causing my job to unexpected result completely. I’ve searched through the documentation and various forums but haven’t found a solution that addresses this specific use case. Any guidance or insight into how I can resolve this configuration scenario would be greatly appreciated! I'm working on a CLI tool that needs to handle this. Any ideas what could be causing this? What would be the recommended way to handle this?