GCP Dataflow job scenarios with 'Java.lang.OutOfMemoryError' on large dataset despite tuning memory settings
I'm experiencing a `Java.lang.OutOfMemoryError` while running a Google Cloud Dataflow job that processes a large dataset using Apache Beam SDK version 2.39.0. I've specified custom worker machine types with 4 vCPUs and 15 GB of memory, but it seems the job is still failing due to memory constraints. Here's a snippet of my Dataflow job setup: ```java PipelineOptions options = PipelineOptionsFactory.create(); options.as(DataflowPipelineOptions.class).setProject("my-gcp-project"); options.as(DataflowPipelineOptions.class).setStagingLocation("gs://my-bucket/staging"); options.as(DataflowPipelineOptions.class).setTempLocation("gs://my-bucket/temp"); options.as(DataflowPipelineOptions.class).setWorkerMachineType("n1-standard-4"); // 4 vCPUs, 15 GB memory Pipeline p = Pipeline.create(options); ``` In my pipeline, I am reading from a Pub/Sub topic and writing to BigQuery. I have tried increasing the worker count and the `maxNumWorkers` setting, but the OutOfMemoryError keeps occurring around the 70% completion mark. I've also attempted to set the `--workerDiskType` to `pd-ssd` for better performance, but that didn’t help either. I've checked the logs, and it seems like the memory usage spikes when processing a specific transformation that involves a groupByKey operation, likely due to excessive data aggregation. How can I effectively manage memory usage in my Dataflow job to avoid this behavior? Are there specific configurations or design patterns that I should consider for processing large datasets?