Troubleshooting Auto DevOps

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The information in this documentation page describes common errors when using Auto DevOps, and any available workarounds.

Trace Helm commands

Set the CI/CD variable TRACE to any value to make Helm commands produce verbose output. You can use this output to diagnose Auto DevOps deployment problems.

You can resolve some problems with Auto DevOps deployment by changing advanced Auto DevOps configuration variables. Read more about customizing Auto DevOps CI/CD variables.

Unable to select a buildpack

Auto Test may fail to detect your language or framework with the following error: 

Step 5/11 : RUN /bin/herokuish buildpack build
 ---> Running in eb468cd46085
    -----> Unable to select a buildpack
The command '/bin/sh -c /bin/herokuish buildpack build' returned a non-zero code: 1

The following are possible reasons:

  • Your application may be missing the key files the buildpack is looking for. Ruby applications require a Gemfile to be properly detected, even though it’s possible to write a Ruby app without a Gemfile.
  • No buildpack may exist for your application. Try specifying a custom buildpack.

Builder sunset error

Because of this Heroku update, legacy shimmed heroku/buildpacks:20 and heroku/builder-classic:22 images now generate errors instead of warnings.

To resolve this issue, you should to migrate to the heroku/builder:* builder images. As a temporary workaround, you can also set an environment variable to skip errors.

Migrating to heroku/builder:*

Before you migrate, you should read the release notes for the each spec release to determine potential breaking changes. In this case, the relevant buildpack API versions are 0.6 and 0.7. These breaking changes are especially relevant to buildpack maintainers.

For more information about the changes, you can also diff the spec itself.

Skipping errors

As a temporary workaround, you can skip the errors by setting and forwarding the ALLOW_EOL_SHIMMED_BUILDER environment variable: 

  variables:
    ALLOW_EOL_SHIMMED_BUILDER: "1"
    AUTO_DEVOPS_BUILD_IMAGE_FORWARDED_CI_VARIABLES: ALLOW_EOL_SHIMMED_BUILDER

Pipeline that extends Auto DevOps with only / except fails

If your pipeline fails with the following message: 

Unable to create pipeline

  jobs:test config key may not be used with `rules`: only

This error appears when the included job’s rules configuration has been overridden with the only or except syntax. To fix this issue, you must either:

Failure to create a Kubernetes namespace

Auto Deploy fails if GitLab can’t create a Kubernetes namespace and service account for your project. For help debugging this issue, see Troubleshooting failed deployment jobs.

Detected an existing PostgreSQL database

After upgrading to GitLab 13.0, you may encounter this message when deploying with Auto DevOps: 

Detected an existing PostgreSQL database installed on the
deprecated channel 1, but the current channel is set to 2. The default
channel changed to 2 in GitLab 13.0.
[...]

Auto DevOps, by default, installs an in-cluster PostgreSQL database alongside your application. The default installation method changed in GitLab 13.0, and upgrading existing databases requires user involvement. The two installation methods are:

  • channel 1 (deprecated): Pulls in the database as a dependency of the associated Helm chart. Only supports Kubernetes versions up to version 1.15.
  • channel 2 (current): Installs the database as an independent Helm chart. Required for using the in-cluster database feature with Kubernetes versions 1.16 and greater.

If you receive this error, you can do one of the following actions:

  • You can safely ignore the warning and continue using the channel 1 PostgreSQL database by setting AUTO_DEVOPS_POSTGRES_CHANNEL to 1 and redeploying.

  • You can delete the channel 1 PostgreSQL database and install a fresh channel 2 database by setting AUTO_DEVOPS_POSTGRES_DELETE_V1 to a non-empty value and redeploying.

    caution
    Deleting the channel 1 PostgreSQL database permanently deletes the existing channel 1 database and all its data. See Upgrading PostgreSQL for more information on backing up and upgrading your database.

  • If you are not using the in-cluster database, you can set POSTGRES_ENABLED to false and re-deploy. This option is especially relevant to users of custom charts without the in-chart PostgreSQL dependency. Database auto-detection is based on the postgresql.enabled Helm value for your release. This value is set based on the POSTGRES_ENABLED CI/CD variable and persisted by Helm, regardless of whether or not your chart uses the variable.

caution
Setting POSTGRES_ENABLED to false permanently deletes any existing channel 1 database for your environment.

Error: unable to recognize "": no matches for kind "Deployment" in version "extensions/v1beta1"

After upgrading your Kubernetes cluster to v1.16+, you may encounter this message when deploying with Auto DevOps: 

UPGRADE FAILED
Error: failed decoding reader into objects: unable to recognize "": no matches for kind "Deployment" in version "extensions/v1beta1"

This can occur if your current deployments on the environment namespace were deployed with a deprecated/removed API that doesn’t exist in Kubernetes v1.16+. For example, if your in-cluster PostgreSQL was installed in a legacy way, the resource was created via the extensions/v1beta1 API. However, the deployment resource was moved to the app/v1 API in v1.16.

To recover such outdated resources, you must convert the current deployments by mapping legacy APIs to newer APIs. There is a helper tool called mapkubeapis that works for this problem. Follow these steps to use the tool in Auto DevOps:

  1. Modify your .gitlab-ci.yml with: 

    include:
  - template: Auto-DevOps.gitlab-ci.yml
  - remote: https://gitlab.com/shinya.maeda/ci-templates/-/raw/master/map-deprecated-api.gitlab-ci.yml

variables:
  HELM_VERSION_FOR_MAPKUBEAPIS: "v2" # If you're using auto-depoy-image v2 or above, please specify "v3".

  2. Run the job <environment-name>:map-deprecated-api. Ensure that this job succeeds before moving to the next step. You should see something like the following output: 

    2020/10/06 07:20:49 Found deprecated or removed Kubernetes API:
"apiVersion: extensions/v1beta1
kind: Deployment"
Supported API equivalent:
"apiVersion: apps/v1
kind: Deployment"

  3. Revert your .gitlab-ci.yml to the previous version. You no longer need to include the supplemental template map-deprecated-api.

  4. Continue the deployments as usual.

Error: not a valid chart repository or cannot be reached

As announced in the official CNCF blog post, the stable Helm chart repository was deprecated and removed on November 13th, 2020. You may encounter this error after that date: 

Error: error initializing: Looks like "https://kubernetes-charts.storage.googleapis.com"
is not a valid chart repository or cannot be reached

Some GitLab features had dependencies on the stable chart. To mitigate the impact, we changed them to use new official repositories or the Helm Stable Archive repository maintained by GitLab. Auto Deploy contains an example fix.

In Auto Deploy, v1.0.6+ of auto-deploy-image no longer adds the deprecated stable repository to the helm command. If you use a custom chart and it relies on the deprecated stable repository, specify an older auto-deploy-image like this example: 

include:
  - template: Auto-DevOps.gitlab-ci.yml

.auto-deploy:
  image: "registry.gitlab.com/gitlab-org/cluster-integration/auto-deploy-image:v1.0.5"

Keep in mind that this approach stops working when the stable repository is removed, so you must eventually fix your custom chart.

To fix your custom chart:

  1. In your chart directory, update the repository value in your requirements.yaml file from : 

    repository: "https://kubernetes-charts.storage.googleapis.com/"

    to: 

    repository: "https://charts.helm.sh/stable"
  2. In your chart directory, run helm dep update . using the same Helm major version as Auto DevOps.
  3. Commit the changes for the requirements.yaml file.
  4. If you previously had a requirements.lock file, commit the changes to the file. If you did not previously have a requirements.lock file in your chart, you do not need to commit the new one. This file is optional, but when present, it’s used to verify the integrity of the downloaded dependencies.

You can find more information in issue #263778, “Migrate PostgreSQL from stable Helm repository”.

Error: release .... failed: timed out waiting for the condition

When getting started with Auto DevOps, you may encounter this error when first deploying your application: 

INSTALL FAILED
PURGING CHART
Error: release staging failed: timed out waiting for the condition

This is most likely caused by a failed liveness (or readiness) probe attempted during the deployment process. By default, these probes are run against the root page of the deployed application on port 5000. If your application isn’t configured to serve anything at the root page, or is configured to run on a specific port other than 5000, this check fails.

If it fails, you should see these failures in the events for the relevant Kubernetes namespace. These events look like the following example: 

LAST SEEN   TYPE      REASON                   OBJECT                                            MESSAGE
3m20s       Warning   Unhealthy                pod/staging-85db88dcb6-rxd6g                      Readiness probe failed: Get http://10.192.0.6:5000/: dial tcp 10.192.0.6:5000: connect: connection refused
3m32s       Warning   Unhealthy                pod/staging-85db88dcb6-rxd6g                      Liveness probe failed: Get http://10.192.0.6:5000/: dial tcp 10.192.0.6:5000: connect: connection refused

To change the port used for the liveness checks, pass custom values to the Helm chart used by Auto DevOps:

  1. Create a directory and file at the root of your repository named .gitlab/auto-deploy-values.yaml.

  2. Populate the file with the following content, replacing the port values with the actual port number your application is configured to use: 

    service:
  internalPort: <port_value>
  externalPort: <port_value>

  3. Commit your changes.

After committing your changes, subsequent probes should use the newly-defined ports. The page that’s probed can also be changed by overriding the livenessProbe.path and readinessProbe.path values (shown in the default values.yaml file) in the same fashion.