리뷰 Dataflow: Qwik Start - Python개

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root@faaaa8e4c12b:/# python -m apache_beam.examples.wordcount --project $DEVSHELL_PROJECT_ID --runner DataflowRunner --staging_location $BUCKET/staging --temp_location $BUCKET/temp --output $BUCKET/results/output --region asia-east1 INFO:root:Runner defaulting to pickling library: cloudpickle. WARNING:apache_beam.options.pipeline_options:Bucket specified in temp_location has soft-delete policy enabled. To avoid being billed for unnecessary storage costs, turn off the soft delete feature on buckets that your Dataflow jobs use for temporary and staging storage. For more information, see https://cloud.google.com/storage/docs/use-soft-delete#remove-soft-delete-policy. WARNING:apache_beam.options.pipeline_options:Bucket specified in staging_location has soft-delete policy enabled. To avoid being billed for unnecessary storage costs, turn off the soft delete feature on buckets that your Dataflow jobs use for temporary and staging storage. For more information, see https://cloud.google.com/storage/docs/use-soft-delete#remove-soft-delete-policy. INFO:apache_beam.io.iobase:*** WriteImpl min_shards undef so it's 1, and we write per Bundle WARNING:apache_beam.options.pipeline_options:Bucket specified in temp_location has soft-delete policy enabled. To avoid being billed for unnecessary storage costs, turn off the soft delete feature on buckets that your Dataflow jobs use for temporary and staging storage. For more information, see https://cloud.google.com/storage/docs/use-soft-delete#remove-soft-delete-policy. WARNING:apache_beam.options.pipeline_options:Bucket specified in staging_location has soft-delete policy enabled. To avoid being billed for unnecessary storage costs, turn off the soft delete feature on buckets that your Dataflow jobs use for temporary and staging storage. For more information, see https://cloud.google.com/storage/docs/use-soft-delete#remove-soft-delete-policy. INFO:apache_beam.runners.dataflow.dataflow_runner:Pipeline has additional dependencies to be installed in SDK worker container, consider using the SDK container image pre-building workflow to avoid repetitive installations. Learn more on https://cloud.google.com/dataflow/docs/guides/using-custom-containers#prebuild INFO:apache_beam.runners.dataflow.internal.apiclient:Starting GCS upload to gs://qwiklabs-gcp-02-253f867c18e1-bucket//staging/beamapp-root-0414033720-982235-ls351r2o.1776137840.982432/submission_environment_dependencies.txt... INFO:apache_beam.runners.dataflow.internal.apiclient:Completed GCS upload to gs://qwiklabs-gcp-02-253f867c18e1-bucket//staging/beamapp-root-0414033720-982235-ls351r2o.1776137840.982432/submission_environment_dependencies.txt in 0 seconds. INFO:apache_beam.runners.dataflow.internal.apiclient:Starting GCS upload to gs://qwiklabs-gcp-02-253f867c18e1-bucket//staging/beamapp-root-0414033720-982235-ls351r2o.1776137840.982432/pipeline.pb... INFO:apache_beam.runners.dataflow.internal.apiclient:Completed GCS upload to gs://qwiklabs-gcp-02-253f867c18e1-bucket//staging/beamapp-root-0414033720-982235-ls351r2o.1776137840.982432/pipeline.pb in 0 seconds. INFO:apache_beam.runners.dataflow.internal.apiclient:Create job: <Job clientRequestId: '20260414033720983719-4357' createTime: '2026-04-14T03:37:23.803493Z' currentStateTime: '1970-01-01T00:00:00Z' id: '2026-04-13_20_37_22-1391013827276618636' location: 'asia-east1' name: 'beamapp-root-0414033720-982235-ls351r2o' projectId: 'qwiklabs-gcp-02-253f867c18e1' stageStates: [] startTime: '2026-04-14T03:37:23.803493Z' steps: [] tempFiles: [] type: TypeValueValuesEnum(JOB_TYPE_BATCH, 1)> INFO:apache_beam.runners.dataflow.internal.apiclient:Created job with id: [2026-04-13_20_37_22-1391013827276618636] INFO:apache_beam.runners.dataflow.internal.apiclient:Submitted job: 2026-04-13_20_37_22-1391013827276618636 INFO:apache_beam.runners.dataflow.internal.apiclient:To access the Dataflow monitoring console, please navigate to https://console.cloud.google.com/dataflow/jobs/asia-east1/2026-04-13_20_37_22-1391013827276618636?project=qwiklabs-gcp-02-253f867c18e1 INFO:apache_beam.runners.dataflow.dataflow_runner:Job 2026-04-13_20_37_22-1391013827276618636 is in state JOB_STATE_PENDING INFO:apache_beam.runners.dataflow.dataflow_runner:2026-04-14T03:37:26.453Z: JOB_MESSAGE_BASIC: Auto VM Selection is enabled. Workers will be allocated with flexible resource constraints where possible, but not necessarily for all scenarios. Please refer to #limitations for details. ERROR:apache_beam.runners.dataflow.dataflow_runner:2026-04-14T03:37:27.178Z: JOB_MESSAGE_ERROR: Staged package submission_environment_dependencies.txt at location 'gs://qwiklabs-gcp-02-253f867c18e1-bucket/staging/beamapp-root-0414033720-982235-ls351r2o.1776137840.982432/submission_environment_dependencies.txt' is inaccessible. ERROR:apache_beam.runners.dataflow.dataflow_runner:2026-04-14T03:37:27.193Z: JOB_MESSAGE_ERROR: Workflow failed. Causes: One or more access checks for temp location or staged files failed. Please refer to other error messages for details. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors#staged-package-inaccessible. INFO:apache_beam.runners.dataflow.dataflow_runner:2026-04-14T03:37:27.244Z: JOB_MESSAGE_BASIC: Worker pool stopped. INFO:apache_beam.runners.dataflow.dataflow_runner:Job 2026-04-13_20_37_22-1391013827276618636 is in state JOB_STATE_FAILED ERROR:apache_beam.runners.dataflow.dataflow_runner:Console URL: https://console.cloud.google.com/dataflow/jobs/asia-east1/2026-04-13_20_37_22-1391013827276618636?project=qwiklabs-gcp-02-253f867c18e1 Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/usr/local/lib/python3.12/site-packages/apache_beam/examples/wordcount.py", line 116, in <module> run() File "/usr/local/lib/python3.12/site-packages/apache_beam/examples/wordcount.py", line 110, in run result.wait_until_finish() File "/usr/local/lib/python3.12/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 814, in wait_until_finish raise DataflowRuntimeException( apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error: Workflow failed. Causes: One or more access checks for temp location or staged files failed. Please refer to other error messages for details. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors#staged-package-inaccessible. root@faaaa8e4c12b:/# python -m apache_beam.examples.wordcount --project $DEVSHELL_PROJECT_ID --runner DataflowRunner --staging_location $BUCKET/staging --temp_location $BUCKET/temp --output $BUCKET/results/output --region asia-east1 INFO:root:Runner defaulting to pickling library: cloudpickle. WARNING:apache_beam.options.pipeline_options:Bucket specified in temp_location has soft-delete policy enabled. To avoid being billed for unnecessary storage costs, turn off the soft delete feature on buckets that your Dataflow jobs use for temporary and staging storage. For more information, see https://cloud.google.com/storage/docs/use-soft-delete#remove-soft-delete-policy. WARNING:apache_beam.options.pipeline_options:Bucket specified in staging_location has soft-delete policy enabled. To avoid being billed for unnecessary storage costs, turn off the soft delete feature on buckets that your Dataflow jobs use for temporary and staging storage. For more information, see https://cloud.google.com/storage/docs/use-soft-delete#remove-soft-delete-policy. INFO:apache_beam.io.iobase:*** WriteImpl min_shards undef so it's 1, and we write per Bundle WARNING:apache_beam.options.pipeline_options:Bucket specified in temp_location has soft-delete policy enabled. To avoid being billed for unnecessary storage costs, turn off the soft delete feature on buckets that your Dataflow jobs use for temporary and staging storage. For more information, see https://cloud.google.com/storage/docs/use-soft-delete#remove-soft-delete-policy. WARNING:apache_beam.options.pipeline_options:Bucket specified in staging_location has soft-delete policy enabled. To avoid being billed for unnecessary storage costs, turn off the soft delete feature on buckets that your Dataflow jobs use for temporary and staging storage. For more information, see https://cloud.google.com/storage/docs/use-soft-delete#remove-soft-delete-policy. INFO:apache_beam.runners.dataflow.dataflow_runner:Pipeline has additional dependencies to be installed in SDK worker container, consider using the SDK container image pre-building workflow to avoid repetitive installations. Learn more on https://cloud.google.com/dataflow/docs/guides/using-custom-containers#prebuild INFO:apache_beam.runners.dataflow.internal.apiclient:Starting GCS upload to gs://qwiklabs-gcp-02-253f867c18e1-bucket//staging/beamapp-root-0414033747-760772-ubpe99s7.1776137867.760973/submission_environment_dependencies.txt... INFO:apache_beam.runners.dataflow.internal.apiclient:Completed GCS upload to gs://qwiklabs-gcp-02-253f867c18e1-bucket//staging/beamapp-root-0414033747-760772-ubpe99s7.1776137867.760973/submission_environment_dependencies.txt in 0 seconds. INFO:apache_beam.runners.dataflow.internal.apiclient:Starting GCS upload to gs://qwiklabs-gcp-02-253f867c18e1-bucket//staging/beamapp-root-0414033747-760772-ubpe99s7.1776137867.760973/pipeline.pb... INFO:apache_beam.runners.dataflow.internal.apiclient:Completed GCS upload to gs://qwiklabs-gcp-02-253f867c18e1-bucket//staging/beamapp-root-0414033747-760772-ubpe99s7.1776137867.760973/pipeline.pb in 0 seconds. INFO:apache_beam.runners.dataflow.internal.apiclient:Create job: <Job clientRequestId: '20260414033747762098-6991' createTime: '2026-04-14T03:37:50.497129Z' currentStateTime: '1970-01-01T00:00:00Z' id: '2026-04-13_20_37_49-15069291334409287855' location: 'asia-east1' name: 'beamapp-root-0414033747-760772-ubpe99s7' projectId: 'qwiklabs-gcp-02-253f867c18e1' stageStates: [] startTime: '2026-04-14T03:37:50.497129Z' steps: [] tempFiles: [] type: TypeValueValuesEnum(JOB_TYPE_BATCH, 1)> INFO:apache_beam.runners.dataflow.internal.apiclient:Created job with id: [2026-04-13_20_37_49-15069291334409287855] INFO:apache_beam.runners.dataflow.internal.apiclient:Submitted job: 2026-04-13_20_37_49-15069291334409287855 INFO:apache_beam.runners.dataflow.internal.apiclient:To access the Dataflow monitoring console, please navigate to https://console.cloud.google.com/dataflow/jobs/asia-east1/2026-04-13_20_37_49-15069291334409287855?project=qwiklabs-gcp-02-253f867c18e1 INFO:apache_beam.runners.dataflow.dataflow_runner:Job 2026-04-13_20_37_49-15069291334409287855 is in state JOB_STATE_PENDING INFO:apache_beam.runners.dataflow.dataflow_runner:2026-04-14T03:37:54.016Z: JOB_MESSAGE_BASIC: Auto VM Selection is enabled. Workers will be allocated with flexible resource constraints where possible, but not necessarily for all scenarios. Please refer to #limitations for details. ERROR:apache_beam.runners.dataflow.dataflow_runner:2026-04-14T03:37:55.187Z: JOB_MESSAGE_ERROR: Staged package submission_environment_dependencies.txt at location 'gs://qwiklabs-gcp-02-253f867c18e1-bucket/staging/beamapp-root-0414033747-760772-ubpe99s7.1776137867.760973/submission_environment_dependencies.txt' is inaccessible. ERROR:apache_beam.runners.dataflow.dataflow_runner:2026-04-14T03:37:55.218Z: JOB_MESSAGE_ERROR: Workflow failed. Causes: One or more access checks for temp location or staged files failed. Please refer to other error messages for details. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors#staged-package-inaccessible. INFO:apache_beam.runners.dataflow.dataflow_runner:2026-04-14T03:37:55.303Z: JOB_MESSAGE_BASIC: Worker pool stopped. INFO:apache_beam.runners.dataflow.dataflow_runner:Job 2026-04-13_20_37_49-15069291334409287855 is in state JOB_STATE_FAILED ERROR:apache_beam.runners.dataflow.dataflow_runner:Console URL: https://console.cloud.google.com/dataflow/jobs/asia-east1/2026-04-13_20_37_49-15069291334409287855?project=qwiklabs-gcp-02-253f867c18e1 Traceback (most recent call last): File "<frozen runpy>", line 198, in _run_module_as_main File "<frozen runpy>", line 88, in _run_code File "/usr/local/lib/python3.12/site-packages/apache_beam/examples/wordcount.py", line 116, in <module> run() File "/usr/local/lib/python3.12/site-packages/apache_beam/examples/wordcount.py", line 110, in run result.wait_until_finish() File "/usr/local/lib/python3.12/site-packages/apache_beam/runners/dataflow/dataflow_runner.py", line 814, in wait_until_finish raise DataflowRuntimeException( apache_beam.runners.dataflow.dataflow_runner.DataflowRuntimeException: Dataflow pipeline failed. State: FAILED, Error: Workflow failed. Causes: One or more access checks for temp location or staged files failed. Please refer to other error messages for details. For more information, see https://cloud.google.com/dataflow/docs/guides/common-errors#staged-package-inaccessible.

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Sairam T. · 1일 전에 리뷰됨

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