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README.md

KETTLE -- Kubernetes Extract Tests/Transform/Load Engine

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. See overview for more details.

Results are stored in the kubernetes-public:k8s_infra_kettle BigQuery dataset, which is publicly accessible.

Deploying

Kettle runs as a pod in the kubernetes-public/aaa cluster. To drop into it's context, run <root>$ make -C kettle get-cluster-credentials

If you change:

  • buckets.yaml: do nothing, it's automatically fetched from GitHub
  • deployment.yaml: deploy with make push deploy
  • any code: Run from root deploy with make -C kettle push update, revert with make -C kettle rollback if it fails
    • push builds the continer image and pushes it to the image registry
    • update sets the image of the existing kettle Pod which triggers a restart cycle
    • this will build the image to Google Container Registry
    • See Makefile for details

Note:

  • If you make local changes in the branch prior to make push/update the image will be uploaded with -dirty in the tag. Keep this in mind when seeting the image. If you see a Pod in a ImagePullBackOff loop, there is likely an issue when kubectl image set was run, where the image does not exist in the specified location.

Restarting

Find out when the build started failing

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 Details) and you can see a table showing last date/time the metrics were collected.

Replace pods

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

Verify functionality

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

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

or access log history with the Query: resource.labels.container_name="kettle".

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.

Kettle Staging

Kettle Staging uses a similar deployment to Kettle with the following differences

  • 100G SSD vs 1001G in production
  • Limit option for number of builds to pull from each job bucket (Default 1000 each). Set via BUILD_LIMIT env in deployment-staging.yaml.
  • writes to build.staging table only. This differs from production that writes to three tables build.all, build.day, and build.week.

It can be deployed with make -C kettle deploy-staging. If already deployed, you may just run make -C kettle update-staging.

Adding Fields

To add fields to the BQ table, Visit the kubernetes-public:k8s_infra_kettle BigQuery dataset and Select the table (Ex. Build > All). Schema -> Edit Schema -> Add field. As well as update schema.json

Adding Buckets

To add a new GCS bucket to Kettle, simply update buckets.yaml in master, it will be auto pulled by Kettle on the next cycle.

gs://<bucket path>: #bucket url
  contact: "username" #Git Hub Username
  prefix: "abc:" #the identifier prefixed to jobs from this bucket (ends in :).
  sequential: (bool) #an optional boolean that indicates whether test runs in this
  #                  bucket are numbered sequentially
  exclude_jobs: # list of jobs to explicitly exclude from kettle data collection
    - job_name1
    - job_name2

CI

A postsubmit job runs that pushes Kettle on changes.

PubSub

Kettle stream.py leverages Google Cloud PubSub to alert on GCS changes within the kubernetes-jenkins bucket. These events are tied to the gcs-changes Topic in the kubernetes-jenkins project where Prow job artifacts are collated. Each time an artifact is finalized, a PubSub event is triggered and Kettle collects job information when it sees a resource uploaded called finished.json (indicating the build completed).

Topic Creation can be performed by running gcloud config set project kubernetes-jenkins and gsutil notification create -t gcs-changes -f json gs://kubernetes-jenkins

Subscriptions are in Kuberenetes Jenkins Build - PubSub.

  • kettle
  • kettle-staging

They are split so that the staging instance does not consume events aimed at production.

These can be created via:

gcloud pubsub subscriptions create <subscription name> --topic=gcs-changes --topic-project="kubernetes-jenkins" --message-filter='attributes.eventType = "OBJECT_FINALIZE"'

Auth

For kettle to have permission, kettle's user needs access. When updating or changing a [Subscription] make sure to add kettle@kubernetes-public.iam.gserviceaccount.com as a PubSub Editor.

gcloud pubsub subscriptions add-iam-policy-binding \
  projects/kubernetes-jenkins/subscriptions/kettle-staging \
  --member=serviceAccount:kettle@kubernetes-public.iam.gserviceaccount.com \
  --role=roles/pubsub.editor

Known Issues

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