This collects test results scattered across a variety of GCS buckets, stores them in a local SQLite database, and outputs newline-delimited JSON files for import into BigQuery.

Results are stored in the k8s-gubernator:build BigQuery dataset, which is publicly accessible.

Use pip install -r requirements.txt to install dependencies.

Kettle runs as a pod in the k8s-gubernator/g8r cluster

If you change:

• buckets.yaml: do nothing, it's automatically fetched from GitHub
• deployment.yaml: deploy with make push deploy
• any code: deploy with make push update, revert with make rollback if it fails

eg: by looking at the logs

make get-cluster-credentials
kubectl logs -l app=kettle

# ...

==== 2018-07-06 08:19:05 PDT ========================================
PULLED 174
ACK irrelevant 172
EXTEND-ACK  2
gs://kubernetes-jenkins/pr-logs/pull/kubeflow_kubeflow/1136/kubeflow-presubmit/2385 True True 2018-07-06 07:51:49 PDT FAILED
gs://kubernetes-jenkins/logs/ci-cri-containerd-e2e-ubuntu-gce/5742 True True 2018-07-06 07:44:17 PDT FAILURE
ACK "finished.json" 2
Downloading JUnit artifacts.

Alternatively, navigate to Gubernator BigQuery page (click on “all” on the left and “Details”) and you can see a table showing last date/time the metrics were collected.

kubectl delete pod -l app=kettle
kubectl rollout status deployment/kettle # monitor pod restart status
kubectl get pod -l app=kettle # should show a new pod name

You can watch the pod startup and collect data from various GCS buckets by looking at its logs

kubectl logs -f $(kubectl get pod -l app=kettle -oname)

It might take a couple of hours to be fully functional and start updating BigQuery. You can always go back to the Gubernator BigQuery page and check to see if data collection has resumed. Backfill should happen automatically.

• Occasionally data from Kettle stops updating, we suspect this is due to a transient hang when contacting GCS (#8800). If this happens, restart kettle