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Review Apps

Review Apps are automatically deployed by the pipeline.

How does it work?

CI/CD architecture diagram

graph TD
  A["build-qa-image, compile-production-assets<br/>(canonical default refs only)"];
  B[review-build-cng];
  C[review-deploy];
  D[CNG-mirror];
  E[review-qa-smoke];

  A -->|once the `prepare` stage is done| B
  B -.->|triggers a CNG-mirror pipeline and wait for it to be done| D
  D -.->|polls until completed| B
  B -->|once the `review-build-cng` job is done| C
  C -->|once the `review-deploy` job is done| E

subgraph "1. gitlab `prepare` stage"
  A
  end

subgraph "2. gitlab `review-prepare` stage"
  B
  end

subgraph "3. gitlab `review` stage"
  C["review-deploy<br><br>Helm deploys the Review App using the Cloud<br/>Native images built by the CNG-mirror pipeline.<br><br>Cloud Native images are deployed to the `review-apps`<br>Kubernetes (GKE) cluster, in the GCP `gitlab-review-apps` project."]
  end

subgraph "4. gitlab `qa` stage"
  E[review-qa-smoke<br><br>gitlab-qa runs the smoke suite against the Review App.]
  end

subgraph "CNG-mirror pipeline"
  D>Cloud Native images are built];
  end

Detailed explanation

  1. On every pipeline during the prepare stage, the compile-production-assets job is automatically started.
  2. Once compile-production-assets is done, the review-build-cng job triggers a pipeline in the CNG-mirror project.
    • The review-build-cng job automatically starts only if your MR includes CI or frontend changes. In other cases, the job is manual.
    • The CNG-mirror pipeline creates the Docker images of each component (e.g. gitlab-rails-ee, gitlab-shell, gitaly etc.) based on the commit from the GitLab pipeline and stores them in its registry.
    • We use the CNG-mirror project so that the CNG, (Cloud Native GitLab), project's registry is not overloaded with a lot of transient Docker images.
    • Note that the official CNG images are built by the cloud-native-image job, which runs only for tags, and triggers itself a CNG pipeline.
  3. Once review-build-cng is done, the review-deploy job deploys the Review App using the official GitLab Helm chart to the review-apps Kubernetes cluster on GCP.
  4. Once the review-deploy job succeeds, you should be able to use your Review App thanks to the direct link to it from the MR widget. To log into the Review App, see "Log into my Review App?" below.

Additional notes:

  • If the review-deploy job keep failing (note that we already retry it twice), please post a message in the #g_qe_engineering_productivity channel and/or create a ~"Engineering Productivity" ~"ep::review apps" ~bug issue with a link to your merge request. Note that the deployment failure can reveal an actual problem introduced in your merge request (i.e. this isn't necessarily a transient failure)!
  • If the review-qa-smoke job keeps failing (note that we already retry it twice), please check the job's logs: you could discover an actual problem introduced in your merge request. You can also download the artifacts to see screenshots of the page at the time the failures occurred. If you don't find the cause of the failure or if it seems unrelated to your change, please post a message in the #quality channel and/or create a ~Quality ~bug issue with a link to your merge request.
  • The manual review-stop can be used to stop a Review App manually, and is also started by GitLab once a merge request's branch is deleted after being merged.
  • The Kubernetes cluster is connected to the gitlab projects using GitLab's Kubernetes integration. This basically allows to have a link to the Review App directly from the merge request widget.

Auto-stopping of Review Apps

Review Apps are automatically stopped 2 days after the last deployment thanks to the Environment auto-stop feature.

If you need your Review App to stay up for a longer time, you can pin its environment or retry the review-deploy job to update the "latest deployed at" time.

The review-cleanup job that automatically runs in scheduled pipelines (and is manual in merge request) stops stale Review Apps after 5 days, deletes their environment after 6 days, and cleans up any dangling Helm releases and Kubernetes resources after 7 days.

The review-gcp-cleanup job that automatically runs in scheduled pipelines (and is manual in merge request) removes any dangling GCP network resources that were not removed along with the Kubernetes resources.

QA runs

On every pipeline in the qa stage (which comes after the review stage), the review-qa-smoke job is automatically started and it runs the QA smoke suite.

You can also manually start the review-qa-all: it runs the full QA suite.

Performance Metrics

On every pipeline in the qa stage, the review-performance job is automatically started: this job does basic browser performance testing using a Sitespeed.io Container.

Cluster configuration

Node pools

The review-apps cluster is currently set up with the following node pools:

  • e2-highcpu-16 (16 vCPU, 16 GB memory) pre-emptible nodes with autoscaling

Node pool image type must be Container-Optimized OS (cos), not Container-Optimized OS with Containerd (cos_containerd), due to this known issue on GitLab Runner Kubernetes executor

Helm

The Helm version used is defined in the registry.gitlab.com/gitlab-org/gitlab-build-images:gitlab-helm3-kubectl1.14 image used by the review-deploy and review-stop jobs.

How to

Get access to the GCP Review Apps cluster

You need to open an access request (internal link) for the gcp-review-apps-dev GCP group and role.

This will grant you the following permissions for:

Log into my Review App

For GitLab Team Members only. If you want to sign in to the review app, review the GitLab handbook information for the shared 1Password account.

  • The default username is root.
  • The password can be found in the 1Password secure note named gitlab-{ce,ee} Review App's root password.

Enable a feature flag for my Review App

  1. Open your Review App and log in as documented above.
  2. Create a personal access token.
  3. Enable the feature flag using the Feature flag API.

Find my Review App slug

  1. Open the review-deploy job.
  2. Look for Checking for previous deployment of review-*.
  3. For instance for Checking for previous deployment of review-qa-raise-e-12chm0, your Review App slug would be review-qa-raise-e-12chm0 in this case.

Run a Rails console

  1. Make sure you have access to the cluster and the container.pods.exec permission first.
  2. Filter Workloads by your Review App slug, e.g. review-qa-raise-e-12chm0.
  3. Find and open the task-runner Deployment, e.g. review-qa-raise-e-12chm0-task-runner.
  4. Click on the Pod in the "Managed pods" section, e.g. review-qa-raise-e-12chm0-task-runner-d5455cc8-2lsvz.
  5. Click on the KUBECTL dropdown, then Exec -> task-runner.
  6. Replace -c task-runner -- ls with -it -- gitlab-rails console from the default command or
    • Run kubectl exec --namespace review-apps review-qa-raise-e-12chm0-task-runner-d5455cc8-2lsvz -it -- gitlab-rails console and
      • Replace review-qa-raise-e-12chm0-task-runner-d5455cc8-2lsvz with your Pod's name.

Dig into a Pod's logs

  1. Make sure you have access to the cluster and the container.pods.getLogs permission first.
  2. Filter Workloads by your Review App slug, e.g. review-qa-raise-e-12chm0.
  3. Find and open the migrations Deployment, e.g. review-qa-raise-e-12chm0-migrations.1.
  4. Click on the Pod in the "Managed pods" section, e.g. review-qa-raise-e-12chm0-migrations.1-nqwtx.
  5. Click on the Container logs link.

Diagnosing unhealthy Review App releases

If Review App Stability dips this may be a signal that the review-apps-ce/ee cluster is unhealthy. Leading indicators may be health check failures leading to restarts or majority failure for Review App deployments.

The Review Apps Overview dashboard aids in identifying load spikes on the cluster, and if nodes are problematic or the entire cluster is trending towards unhealthy.

Release failed with ImagePullBackOff

Potential cause:

If you see an ImagePullBackoff status, check for a missing Docker image.

Where to look for further debugging:

To check that the Docker images were created, run the following Docker command:

`DOCKER_CLI_EXPERIMENTAL=enabled docker manifest repository:tag`

The output of this command indicates if the Docker image exists. For example:

DOCKER_CLI_EXPERIMENTAL=enabled docker manifest inspect registry.gitlab.com/gitlab-org/build/cng-mirror/gitlab-rails-ee:39467-allow-a-release-s-associated-milestones-to-be-edited-thro

If the Docker image does not exist:

  • Verify the image.repository and image.tag options in the helm upgrade --install command match the repository names used by CNG-mirror pipeline.
  • Look further in the corresponding downstream CNG-mirror pipeline in review-build-cng job.

Node count is always increasing (i.e. never stabilizing or decreasing)

Potential cause:

That could be a sign that the review-cleanup job is failing to cleanup stale Review Apps and Kubernetes resources.

Where to look for further debugging:

Look at the latest review-cleanup job log, and identify look for any unexpected failure.

p99 CPU utilization is at 100% for most of the nodes and/or many components

Potential cause:

This could be a sign that Helm is failing to deploy Review Apps. When Helm has a lot of FAILED releases, it seems that the CPU utilization is increasing, probably due to Helm or Kubernetes trying to recreate the components.

Where to look for further debugging:

Look at a recent review-deploy job log.

Useful commands:

# Identify if node spikes are common or load on specific nodes which may get rebalanced by the Kubernetes scheduler
kubectl top nodes | sort --key 3 --numeric

# Identify pods under heavy CPU load
kubectl top pods | sort --key 2 --numeric

The logging/user/events/FailedMount chart is going up

Potential cause:

This could be a sign that there are too many stale secrets and/or configuration maps.

Where to look for further debugging:

Look at the list of Configurations or kubectl get secret,cm --sort-by='{.metadata.creationTimestamp}' | grep 'review-'.

Any secrets or configuration maps older than 5 days are suspect and should be deleted.

Useful commands:

# List secrets and config maps ordered by created date
kubectl get secret,cm --sort-by='{.metadata.creationTimestamp}' | grep 'review-'

# Delete all secrets that are 5 to 9 days old
kubectl get secret --sort-by='{.metadata.creationTimestamp}' | grep '^review-' | grep '[5-9]d$' | cut -d' ' -f1 | xargs kubectl delete secret

# Delete all secrets that are 10 to 99 days old
kubectl get secret --sort-by='{.metadata.creationTimestamp}' | grep '^review-' | grep '[1-9][0-9]d$' | cut -d' ' -f1 | xargs kubectl delete secret

# Delete all config maps that are 5 to 9 days old
kubectl get cm --sort-by='{.metadata.creationTimestamp}' | grep 'review-' | grep -v 'dns-gitlab-review-app' | grep '[5-9]d$' | cut -d' ' -f1 | xargs kubectl delete cm

# Delete all config maps that are 10 to 99 days old
kubectl get cm --sort-by='{.metadata.creationTimestamp}' | grep 'review-' | grep -v 'dns-gitlab-review-app' | grep '[1-9][0-9]d$' | cut -d' ' -f1 | xargs kubectl delete cm

Using K9s

K9s is a powerful command line dashboard which allows you to filter by labels. This can help identify trends with apps exceeding the review-app resource requests. Kubernetes will schedule pods to nodes based on resource requests and allow for CPU usage up to the limits.

  • In K9s you can sort or add filters by typing the / character
    • -lrelease=<review-app-slug> - filters down to all pods for a release. This aids in determining what is having issues in a single deployment
    • -lapp=<app> - filters down to all pods for a specific app. This aids in determining resource usage by app.
  • You can scroll to a Kubernetes resource and hit d(describe), s(shell), l(logs) for a deeper inspection

K9s

Troubleshoot a pending dns-gitlab-review-app-external-dns Deployment

Finding the problem

In the past, it happened that the dns-gitlab-review-app-external-dns Deployment was in a pending state, effectively preventing all the Review Apps from getting a DNS record assigned, making them unreachable via domain name.

This in turn prevented other components of the Review App to properly start (e.g. gitlab-runner).

After some digging, we found that new mounts were failing, when being performed with transient scopes (e.g. pods) of systemd-mount:

MountVolume.SetUp failed for volume "dns-gitlab-review-app-external-dns-token-sj5jm" : mount failed: exit status 1
Mounting command: systemd-run
Mounting arguments: --description=Kubernetes transient mount for /var/lib/kubelet/pods/06add1c3-87b4-11e9-80a9-42010a800107/volumes/kubernetes.io~secret/dns-gitlab-review-app-external-dns-token-sj5jm --scope -- mount -t tmpfs tmpfs /var/lib/kubelet/pods/06add1c3-87b4-11e9-80a9-42010a800107/volumes/kubernetes.io~secret/dns-gitlab-review-app-external-dns-token-sj5jm
Output: Failed to start transient scope unit: Connection timed out

This probably happened because the GitLab chart creates 67 resources, leading to a lot of mount points being created on the underlying GCP node.

The underlying issue seems to be a systemd bug that was fixed in systemd v237. Unfortunately, our GCP nodes are currently using v232.

For the record, the debugging steps to find out this issue were:

  1. Switch kubectl context to review-apps-ce (we recommend using kubectx)
  2. kubectl get pods | grep dns
  3. kubectl describe pod <pod name> & confirm exact error message
  4. Web search for exact error message, following rabbit hole to a relevant Kubernetes bug report
  5. Access the node over SSH via the GCP console (Computer Engine > VM instances then click the "SSH" button for the node where the dns-gitlab-review-app-external-dns pod runs)
  6. In the node: systemctl --version => systemd 232
  7. Gather some more information:
    • mount | grep kube | wc -l => e.g. 290
    • systemctl list-units --all | grep -i var-lib-kube | wc -l => e.g. 142
  8. Check how many pods are in a bad state:
    • Get all pods running a given node: kubectl get pods --field-selector=spec.nodeName=NODE_NAME
    • Get all the Running pods on a given node: kubectl get pods --field-selector=spec.nodeName=NODE_NAME | grep Running
    • Get all the pods in a bad state on a given node: kubectl get pods --field-selector=spec.nodeName=NODE_NAME | grep -v 'Running' | grep -v 'Completed'

Solving the problem

To resolve the problem, we needed to (forcibly) drain some nodes:

  1. Try a normal drain on the node where the dns-gitlab-review-app-external-dns pod runs so that Kubernetes automatically move it to another node: kubectl drain NODE_NAME
  2. If that doesn't work, you can also perform a forcible "drain" the node by removing all pods: kubectl delete pods --field-selector=spec.nodeName=NODE_NAME
  3. In the node:
    • Perform systemctl daemon-reload to remove the dead/inactive units
    • If that doesn't solve the problem, perform a hard reboot: sudo systemctl reboot
  4. Uncordon any cordoned nodes: kubectl uncordon NODE_NAME

In parallel, since most Review Apps were in a broken state, we deleted them to clean up the list of non-Running pods. Following is a command to delete Review Apps based on their last deployment date (current date was June 6th at the time) with

helm ls -d | grep "Jun  4" | cut -f1 | xargs helm delete --purge

Mitigation steps taken to avoid this problem in the future

We've created a new node pool with smaller machines so that it's less likely that a machine will hit the "too many mount points" problem in the future.

Frequently Asked Questions

Isn't it too much to trigger CNG image builds on every test run? This creates thousands of unused Docker images.

We have to start somewhere and improve later. Also, we're using the CNG-mirror project to store these Docker images so that we can just wipe out the registry at some point, and use a new fresh, empty one.

How do we secure this from abuse? Apps are open to the world so we need to find a way to limit it to only us.

This isn't enabled for forks.

Other resources

Helpful command line tools

  • K9s - enables CLI dashboard across pods and enabling filtering by labels
  • Stern - enables cross pod log tailing based on label/field selectors

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