👀 Views: 40 💬 Answers: 1 📅 Created: 2025-07-15

gcp dataflow apache-beam pubsub bigquery Python

I tried several approaches but none seem to work. I'm currently working with an scenario with a Google Cloud Dataflow job that keeps failing with the behavior: `want to find a suitable worker`. This happens when I try to run my pipeline written using Apache Beam's Python SDK (version 2.29.0). The pipeline processes data from a Pub/Sub topic and writes the results to BigQuery. Despite trying to adjust the worker settings, I still encounter this behavior. Here’s a simplified version of my pipeline code: ```python import apache_beam as beam from apache_beam.options.pipeline_options import PipelineOptions class TransformData(beam.DoFn): def process(self, element): # Transform the data here yield {'key': element['key'], 'value': element['value'] * 2} options = PipelineOptions( project='my-gcp-project', runner='DataflowRunner', temp_location='gs://my-bucket/temp', region='us-central1', max_num_workers=5 ) with beam.Pipeline(options=options) as p: (p | 'Read from Pub/Sub' >> beam.io.ReadFromPubSub(topic='projects/my-gcp-project/topics/my-topic') | 'Transform' >> beam.ParDo(TransformData()) | 'Write to BigQuery' >> beam.io.WriteToBigQuery( table='my-gcp-project:my_dataset.my_table', schema='key:STRING, value:INTEGER', write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND) ) ``` I have configured the Dataflow job to use a maximum of 5 workers, but it seems like it need to find any suitable worker instances. I've also ensured that my IAM roles are correctly set, with the Dataflow service account having permissions to access Pub/Sub and BigQuery. I tried changing the region and scaling options, but the question continues. Additionally, I checked the logs, and there are no clear indications of resource constraints or quota issues. The job simply starts and then fails with the worker behavior. Is there anything specific I might be missing in the configuration, or any common pitfalls to watch out for in Dataflow jobs that could lead to this behavior? For context: I'm using Python on Linux. Has anyone else encountered this? I'd really appreciate any guidance on this.