damccorm opened a new issue, #21224: URL: https://github.com/apache/beam/issues/21224
I have a classic Dataflow template written using the Apache Beam Java SDK v2.32.0. The template simply consumes messages from a Pub/Sub subscription and writes them to Google Cloud Storage. The template can successfully be used to run jobs with [Dataflow Prime](https://cloud.google.com/dataflow/docs/guides/enable-dataflow-prime) experimental features enabled through `\-\-additional-experiments enable_prime` and providing a pipeline level resource hint using `\-\-parameters=resourceHints=min_ram=8GiB`: ``` gcloud dataflow jobs run my-job-name \ --additional-experiments enable_prime \ --disable-public-ips \ --gcs-location gs://bucket/path/to/template \ --num-workers 1 \ --max-workers 16 \ --parameters=resourceHints=min_ram=8GiB,other_pipeline_options=true \ --project my-project \ --region us-central1 \ --service-account-email [email protected] \ --staging-location gs://bucket/path/to/staging --subnetwork https://www.googleapis.com/compute/v1/projects/my-project/regions/us-central1/subnetworks/my-subnet ``` In an attempt to use Dataflow Prime's [Right Fitting](https://cloud.google.com/dataflow/docs/guides/right-fitting) capability, I change the pipeline code to include a resource hint on the FileIO transform: ``` class WriteGcsFileTransform extends PTransform<PCollection<Input>, WriteFilesResult<Destination>> { private static final long serialVersionUID = 1L; @Override public WriteFilesResult<Destination> expand(PCollection<Input> input) { return input.apply( FileIO.<Destination, Input>writeDynamic() .by(myDynamicDestinationFunction) .withDestinationCoder(Destination.coder()) .withNumShards(8) .withNaming(myDestinationFileNamingFunction) .withTempDirectory("gs://bucket/path/to/temp") .withCompression(Compression.GZIP) .setResourceHints(ResourceHints.create().withMinRam("32GiB")) ); } ``` Attempting to run jobs from a template based on the new code results in a continuous crash loop with the job never successfully running. The lone repeated error log entry is: ``` { "insertId": "s=97e1ecd30e0243609d555685318325b4;i=4e1;b=6c7f5d65f3994eada5f20672dab1daf1;m=912f16c;t=5d024689cb030;x=b36751718b3d80c1", "jsonPayload": { "line": "pod_workers.go:191", "message": "Error syncing pod 4cf7cbf98df4b5e2d054abce7da1262b (\"df-df-hvm-my-job-name-11061310-qn51-harness-jb9f_default(4cf7c6bf982df4b5eb2d054abce7da12)\"), skipping: failed to \"StartContainer\" for \"artifact\" with CrashLoopBackOff: \"back-off 40s restarting failed container=artifact pod=df-df-hvm-my-job-name-11061310-qn51-harness-jb9f_default(4cf7c6bf982df4b5eb2d054abce7da12)\"", "thread": "807" }, "resource": { "type": "dataflow_step", "labels": { "project_id": "my-project", "region": "us-central1", "step_id": "", "job_id": "2021-11-06_12_10_27-510057810808146686", "job_name": "my-job-name" } }, "timestamp": "2021-11-06T20:14:36.052491Z", "severity": "ERROR", "labels": { "compute.googleapis.com/resource_type": "instance", "dataflow.googleapis.com/log_type": "system", "compute.googleapis.com/resource_id": "4695846446965678007", "dataflow.googleapis.com/job_name": "my-job-name", "dataflow.googleapis.com/job_id": "2021-11-06_12_10_27-510057810808146686", "dataflow.googleapis.com/region": "us-central1", "dataflow.googleapis.com/service_option": "prime", "compute.googleapis.com/resource_name": "df-hvm-my-job-name-11061310-qn51-harness-jb9f" }, "logName": "projects/my-project/logs/dataflow.googleapis.com%2Fkubelet", "receiveTimestamp": "2021-11-06T20:14:46.471285909Z" } ``` If the pipeline level resources hints and step level resources hint are both set to 8GiB, the pipeline fails with the same repetitive error. Imported from Jira [BEAM-13225](https://issues.apache.org/jira/browse/BEAM-13225). Original Jira may contain additional context. Reported by: brentworden. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
