GCP Dataflow job timing out despite adequate resource allocation and optimal configuration
I've hit a wall trying to I tried several approaches but none seem to work. This might be a silly question, but I'm currently running a Google Cloud Dataflow job that processes large batches of data from a Pub/Sub topic, but I'm working with a timeout scenario. The job often fails with the behavior message `java.util.concurrent.TimeoutException: Input/Output operation timed out`, despite having allocated 8 workers with `n1-standard-4` instances. I've tried increasing the number of workers and adjusting the autoscaling options, but the scenario continues. Here’s the code snippet I’m using to set up the Dataflow pipeline: ```java PipelineOptions options = PipelineOptionsFactory.create(); options.setProject("my-gcp-project"); options.setStagingLocation("gs://my-bucket/staging"); options.setTempLocation("gs://my-bucket/temp"); options.setMaxNumWorkers(8); options.setWorkerMachineType("n1-standard-4"); DataflowPipelineOptions dataflowOptions = options.as(DataflowPipelineOptions.class); Pipeline p = Pipeline.create(dataflowOptions); p.apply("ReadFromPubSub", PubsubIO.read().topic("projects/my-gcp-project/topics/my-topic")) .apply("ProcessData", ParDo.of(new MyProcessFn())) .apply("WriteToBigQuery", BigQueryIO.writeTableRows() .to("my_dataset.my_table") .withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_APPEND)); p.run().waitUntilFinish(); ``` I monitor the job using the Dataflow monitoring interface, and it shows that the workers are active but eventually times out after around 10 minutes. I’ve also checked that the Pub/Sub subscription is confirmed and the messages are accessible. I’m utilizing the latest version of the Dataflow SDK, which is 2.24.0. What could be causing this timeout? Are there specific configurations or best practices I may have overlooked to ensure the Dataflow job runs smoothly? Is there a better approach? Thanks in advance! What are your experiences with this?