GCP Dataflow Job scenarios with 'Out of Memory' scenarios When Processing Large CSV Files
This might be a silly question, but I'm working with an 'Out of Memory' behavior when my Apache Beam Dataflow job processes large CSV files. The job seems to hang for a while, and then I see the following behavior in the logs: ``` FATAL: Out of memory while trying to allocate 102400 bytes. ``` I am using Python 3.8 with the Apache Beam SDK version 2.33.0. My Dataflow job reads data from a Cloud Storage bucket and performs some transformations before writing the output to another Cloud Storage location. Here's a simple version of my pipeline: ```python import apache_beam as beam from apache_beam.options.pipeline_options import PipelineOptions def process_row(row): # processing logic here return row options = PipelineOptions( runner='DataflowRunner', project='my-gcp-project', temp_location='gs://my-bucket/temp', ) with beam.Pipeline(options=options) as p: (p | 'ReadCSV' >> beam.io.ReadFromText('gs://my-bucket/input.csv') | 'ProcessRows' >> beam.Map(process_row) | 'WriteCSV' >> beam.io.WriteToText('gs://my-bucket/output', file_name_suffix='.csv')) ``` I've tried increasing the machine types in the Dataflow job settings, but that hasn't resolved the scenario. I also experimented with using `with_shard` in the `ReadFromText` to read smaller chunks of the file, but it didn't help. I suspect that the way I'm processing each row might be causing memory problems, especially if the transformations require a lot of memory for larger datasets. What are some potential optimizations or configurations I can apply to prevent this behavior? Any guidance would be greatly appreciated! The stack includes Python and several other technologies. What am I doing wrong? My team is using Python for this mobile app. Am I approaching this the right way?