GCP Dataflow pipeline experiencing 'Out of memory' errors when processing large datasets with Apache Beam
I'm not sure how to approach I'm trying to debug This might be a silly question, but I'm running a Dataflow pipeline that processes large datasets using Apache Beam (version 2.30.0)... Recently, I started working with 'Out of memory' errors during the execution of my job even though I configured the worker instances with enough resources. I have set my pipeline options as follows: ```python from apache_beam.options import PipelineOptions options = PipelineOptions( runner='DataflowRunner', project='my-gcp-project', temp_location='gs://my-bucket/temp', region='us-central1', job_name='my-dataflow-job', max_num_workers=5, worker_machine_type='n1-standard-4', # 4 vCPUs, 15 GB memory autoscaling_algorithm='THROUGHPUT_BASED' ) ``` I'm applying a ParDo transformation to filter and transform records; however, my dataset often exceeds 1 million rows. When I run the job, I see logs indicating that the workers are running out of memory: ``` [WARNING] Worker exited with behavior: java.lang.OutOfMemoryError: Java heap space ``` To mitigate this, I tried increasing the `--worker_machine_type` to `n1-highmem-4`, but the scenario continues. I've also attempted to reduce the size of the data being processed by filtering out unnecessary fields at the source, but this hasn't resolved the memory scenario. Is there a way to optimize my Dataflow pipeline to handle larger datasets and prevent these out of memory errors? Any suggestions for best practices or configurations that might help would be greatly appreciated. I'd really appreciate any guidance on this. This is part of a larger REST API I'm building. What's the correct way to implement this? I've been using Python for about a year now. The stack includes Python and several other technologies. I'm open to any suggestions.