GCP Dataflow Job scenarios with 'Resource Exceeded' scenarios During Windowing with Large Batches
I can't seem to get I'm optimizing some code but I'm working with an scenario with my Google Cloud Dataflow job that processes a large stream of data... The job uses windowing to handle batches of data, but I'm getting an behavior message saying 'Resource exceeded: No instance available to run the job' when the input data size spikes. I suspect this has to do with the way I'm configuring my windowing strategy or the autoscaling of workers. Here's a snippet of my pipeline code: ```python import apache_beam as beam from apache_beam.options.pipeline_options import PipelineOptions options = PipelineOptions( runner='DataflowRunner', project='my-gcp-project', temp_location='gs://my-bucket/temp', region='us-central1' ) with beam.Pipeline(options=options) as pipeline: (pipeline | 'ReadFromPubSub' >> beam.io.ReadFromPubSub(topic='projects/my-gcp-project/topics/my-topic') | 'WindowIntoFixedIntervals' >> beam.WindowInto(beam.window.FixedWindows(60)) | 'ProcessData' >> beam.Map(lambda x: process_function(x)) | 'WriteToBigQuery' >> beam.io.WriteToBigQuery( 'my-gcp-project:my_dataset.my_table', write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND) ) ``` I've tried increasing the number of workers and setting the autoscaling to 'on', but that doesn't seem to help, as my job still fails during peak loads. I've also checked the worker configuration, and I am using a `n1-standard-1` machine type which seems insufficient for the data volume. Would changing to a more powerful machine type or adjusting the windowing strategy help? Any suggestions on how to optimize this pipeline to handle larger batches more effectively? My team is using Python for this desktop app. Any ideas what could be causing this? I'm on Windows 11 using the latest version of Python. What are your experiences with this?