GCP Dataflow Job scenarios with OutOfMemoryError during Batch Processing of Large Avro Files
I'm having trouble with I'm writing unit tests and I'm working on a personal project and I'm running a Dataflow job on Google Cloud Platform that processes large Avro files (around 5GB each) using Apache Beam (2.39.0). The job reads from a Cloud Storage bucket and writes to BigQuery. I've set the runner to Dataflow and configured the necessary parameters, but I'm working with an `OutOfMemoryError` after processing a few files. Here's the relevant snippet of my pipeline: ```python import apache_beam as beam from apache_beam.options.pipeline_options import PipelineOptions, GoogleCloudOptions, StandardOptions class ProcessAvroDoFn(beam.DoFn): def process(self, element): # Processing logic here return [processed_element] options = PipelineOptions() options.view_as(GoogleCloudOptions).project = 'my-gcp-project' options.view_as(StandardOptions).runner = 'Dataflow' with beam.Pipeline(options=options) as p: (p | 'ReadAvro' >> beam.io.ReadFromAvro('gs://my_bucket/my_avro_files/*.avro') | 'ProcessAvro' >> beam.ParDo(ProcessAvroDoFn()) | 'WriteToBQ' >> beam.io.WriteToBigQuery('my_dataset.my_table')) ``` I’ve tried increasing the `workerMachineType` to `n1-standard-4` and set `maxNumWorkers` to `10`, but the job still fails at the same point with the following behavior message: ``` Exception in thread "main" java.lang.OutOfMemoryError: Java heap space ``` I suspect it might have something to do with how the files are being processed in memory. I also considered that I might not be using windowing correctly or that I might need to enable autoscaling. Can anyone provide insights on how to resolve this memory scenario, or suggest best practices for handling large Avro files in Dataflow? My development environment is macOS. Has anyone dealt with something similar? I'm developing on Ubuntu 22.04 with Python. Any examples would be super helpful.