Apply labels when creating Dataflow jobs for Ingestion #899
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The job labels are derived from the labels of the FeatureSets that the job is expected to process when the Dataflow job is created. When the Dataflow job is processing multiple FeatureSets and there are 2 FeatureSets with the same label key but different label value, the resulting label value for the job will be the concatentation of each label value from the FeatureSets.
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[APPROVALNOTIFIER] This PR is APPROVED This pull-request has been approved by: davidheryanto The full list of commands accepted by this bot can be found here. The pull request process is described here
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pyalex
reviewed
Jul 28, 2020
pyalex
reviewed
Jul 28, 2020
| @@ -262,6 +286,7 @@ private ImportOptions getPipelineOptions( | |||
| pipelineOptions.setUpdate(update); | |||
| pipelineOptions.setRunner(DataflowRunner.class); | |||
| pipelineOptions.setJobName(jobName); | |||
| pipelineOptions.setLabels(labels); | |||
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Cmiiw, but this overwrites all labels that could be set in application properties. Shouldn't we extend them instead?
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Yes I think we should extend them. Thanks for checking
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Closing because there is going to be change in job lifecycle in #903 |
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What this PR does / why we need it:
This PR sets the
labeloptions for Dataflow jobs created whenFeatureSetsare applied. The labels are useful for organizing Dataflow jobs and breaking down costs for Dataflow jobs in Google Cloud.The labels for the Dataflow jobs are derived from the
FeatureSetsthe Dataflow job is expected to process when the Dataflow job is created/updated. This means that new FeatureSets that the Dataflow job processes after it was first created (Dataflow job listens to the message broker for new FeatureSets after it is created) will not be considered in the labels unless the Dataflow job is restarted. (This is mainly due to the limitation in Dataflow jobs API in Google that only allows the setting of labels at jobs creation/update time)When a Dataflow job is processing 2 FeatureSets with the same label key but different label value, the resulting label value for the Dataflow job will be the concatenation of the label value from both FeatureSets. The label keys and values to be applied to the Dataflow jobs will be validated so it can meet the requirement for Dataflow job labels https://cloud.google.com/resource-manager/docs/creating-managing-labels#requirements. If it does not meet the requirement, those labels will be ignored, so that the Dataflow job creation can still proceed. (This is because labels are good to have but should not cause disruption in job creation)
Which issue(s) this PR fixes:
None
Does this PR introduce a user-facing change?:
None