GCP Dataflow Job scenarios with OutOfMemoryError When Using Windowing with Apache Beam
After trying multiple solutions online, I still can't figure this out. I've hit a wall trying to I've searched everywhere and can't find a clear answer. I'm working on a personal project and I'm currently running a Dataflow job using Apache Beam SDK 2.34.0, and I'm working with an `OutOfMemoryError` intermittently when processing a large stream of data with windowing... My pipeline is set up to apply a sliding window of 10 minutes with a 1-minute slide, and it seems to be causing memory pressure on the worker nodes. I've tried increasing the worker machine type to `n1-standard-4` from `n1-standard-1`, but the scenario continues. 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( runner='DataflowRunner', project='my-gcp-project', region='us-central1', temp_location='gs://my-bucket/temp', staging_location='gs://my-bucket/staging' ) with beam.Pipeline(options=options) as p: (p | 'ReadFromPubSub' >> beam.io.ReadFromPubSub(subscription='projects/my-gcp-project/subscriptions/my-subscription') | 'Windowing' >> beam.WindowInto(beam.window.SlidingWindows(600, 60)) | 'ProcessData' >> beam.Map(lambda x: process_data(x)) | 'WriteToBigQuery' >> beam.io.WriteToBigQuery( table='my_dataset.my_table', write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND) ) ``` The `process_data` function can be quite intensive as it involves complex computations and multiple API calls. I've also tried adding `--max_num_workers=10` and `--autoscaling_algorithm=THROUGHPUT_BASED` to utilize more resources, but the memory scenario still shows up in the logs like this: ``` 2023-10-10T12:34:56.789Z FATAL: java.lang.OutOfMemoryError: Java heap space ``` Is there a way to optimize memory usage in this scenario, or should I consider breaking down the processing into smaller chunks or using an alternative approach? Any insights would be appreciated! I'm working on a CLI tool that needs to handle this. My development environment is macOS. Has anyone else encountered this? This issue appeared after updating to Python stable. I'm coming from a different tech stack and learning Python. Hoping someone can shed some light on this.