GCP Dataflow job scenarios with 'ResourceError' when using Apache Beam with Pandas UDFs
I'm not sure how to approach I'm trying to figure out I'm reviewing some code and I'm working with a `ResourceError` when running a Google Cloud Dataflow job that utilizes Apache Beam with Pandas UDFs. My setup involves Dataflow version 2.41.0 and I'm using Python 3.8 to process a large dataset. The job fails during execution with the following behavior message: ``` behavior: ResourceError: Unable to create the worker pool due to resource constraints. ``` I have specifically tried increasing the worker machine type from `n1-standard-1` to `n1-standard-4`, yet the behavior continues. Additionally, I've adjusted the autoscaling parameters, allowing the job to scale out to 10 workers, but it still fails with the same behavior. Here's a snippet of the code that I'm using to define my pipeline: ```python import apache_beam as beam from apache_beam.options.pipeline_options import PipelineOptions import pandas as pd def process_data(data): df = pd.DataFrame(data) # Some processing logic using Pandas return df.to_dict('records') options = PipelineOptions( runner='DataflowRunner', project='my-gcp-project', region='us-central1', temp_location='gs://my-gcs-bucket/temp', staging_location='gs://my-gcs-bucket/staging' ) with beam.Pipeline(options=options) as p: (p | 'ReadFromSource' >> beam.io.ReadFromText('gs://my-gcs-bucket/input/*.csv') | 'ProcessData' >> beam.Map(process_data) | 'WriteToSink' >> beam.io.WriteToBigQuery('my_dataset.my_table')) ``` I've also verified that the GCP project has sufficient quota for CPU and memory resources. Is there something specific I might be missing in the configuration of my Dataflow job or in the use of Pandas UDFs that could lead to this resource behavior? Any insights would be greatly appreciated! My development environment is Windows 11. Am I missing something obvious? My team is using Python for this web app. Thanks, I really appreciate it! I'm using Python 3.10 in this project.