Skip to content

Debugging CI/CD pipelines

DETAILS: Tier: Free, Premium, Ultimate Offering: GitLab.com, Self-managed, GitLab Dedicated

GitLab provides several tools to help make it easier to debug your CI/CD configuration.

If you are unable to resolve pipeline issues, you can get help from:

If you are having issues with a specific CI/CD feature, see the related troubleshooting section for that feature:

Debugging techniques

Verify syntax

An early source of problems can be incorrect syntax. The pipeline shows a yaml invalid badge and does not start running if any syntax or formatting problems are found.

Edit .gitlab-ci.yml with the pipeline editor

The pipeline editor is the recommended editing experience (rather than the single file editor or the Web IDE). It includes:

  • Code completion suggestions that ensure you are only using accepted keywords.
  • Automatic syntax highlighting and validation.
  • The CI/CD configuration visualization, a graphical representation of your .gitlab-ci.yml file.

Edit .gitlab-ci.yml locally

If you prefer to edit your pipeline configuration locally, you can use the GitLab CI/CD schema in your editor to verify basic syntax issues. Any editor with Schemastore support uses the GitLab CI/CD schema by default.

If you need to link to the schema directly, use this URL:

https://gitlab.com/gitlab-org/gitlab/-/blob/master/app/assets/javascripts/editor/schema/ci.json

To see the full list of custom tags covered by the CI/CD schema, check the latest version of the schema.

Verify syntax with CI Lint tool

You can use the CI Lint tool to verify that the syntax of a CI/CD configuration snippet is correct. Paste in full .gitlab-ci.yml files or individual job configurations, to verify the basic syntax.

When a .gitlab-ci.yml file is present in a project, you can also use the CI Lint tool to simulate the creation of a full pipeline. It does deeper verification of the configuration syntax.

Use pipeline names

Use workflow:name to give names to all your pipeline types, which makes it easier to identify pipelines in the pipelines list. For example:

variables:
  PIPELINE_NAME: "Default pipeline name"

workflow:
  name: '$PIPELINE_NAME'
  rules:
    - if: '$CI_PIPELINE_SOURCE == "merge_request_event"'
      variables:
        PIPELINE_NAME: "Merge request pipeline"
    - if: '$CI_PIPELINE_SOURCE == "schedule" && $PIPELINE_SCHEDULE_TYPE == "hourly_deploy"'
      variables:
        PIPELINE_NAME: "Hourly deployment pipeline"
    - if: '$CI_PIPELINE_SOURCE == "schedule"'
      variables:
        PIPELINE_NAME: "Other scheduled pipeline"
    - if: '$CI_COMMIT_BRANCH == $CI_DEFAULT_BRANCH'
      variables:
        PIPELINE_NAME: "Default branch pipeline"
    - if: '$CI_COMMIT_BRANCH =~ /^\d{1,2}\.\d{1,2}-stable$/'
      variables:
        PIPELINE_NAME: "Stable branch pipeline"

CI/CD variables

Verify variables

A key part of troubleshooting CI/CD is to verify which variables are present in a pipeline, and what their values are. A lot of pipeline configuration is dependent on variables, and verifying them is one of the fastest ways to find the source of a problem.

Export the full list of variables available in each problematic job. Check if the variables you expect are present, and check if their values are what you expect.

Use variables to add flags to CLI commands

You can define CI/CD variables that are not used in standard pipeline runs, but can be used for debugging on demand. If you add a variable like in the following example, you can add it during manual runs of the pipeline or individual job to modify the command's behavior. For example:

my-flaky-job:
  variables:
    DEBUG_VARS: ""
  script:
    - my-test-command $DEBUG_VARS /test-dirs

In this example, DEBUG_VARS is blank by default in standard pipelines. If you need to debug the job's behavior, run the pipeline manually and set DEBUG_VARS to --verbose for additional output.

Dependencies

Dependency-related issues are another common source of unexpected issues in pipelines.

Verify dependency versions

To validate that the correct versions of dependencies are being used in jobs, you can output them before running the main script commands. For example:

job:
  before_script:
    - node --version
    - yarn --version
  script:
    - my-javascript-tests.sh

Pin versions

While you might want to always use the latest version of a dependency or image, an update could include breaking changes unexpectedly. Consider pinning key dependencies and images to avoid surprise changes. For example:

variables:
  ALPINE_VERSION: '3.18.6'

job1:
  image: alpine:$ALPINE_VERSION  # This will never change unexpectedly
  script:
    - my-test-script.sh

job2:
  image: alpine:latest  # This might suddenly change
  script:
    - my-test-script.sh

You should still regularly check the dependency and image updates, as there might be important security updates. Then you can manually update the version as part of a process that verifies the updated image or dependency still works with your pipeline.

Verify job output

Make output verbose

If you use --silent to reduce the amount of output in a job log, it can make it difficult to identify what went wrong in a job. Additionally, consider using --verbose when possible, for additional details.

job1:
  script:
    - my-test-tool --silent         # If this fails, it might be impossible to identify the issue.
    - my-other-test-tool --verbose  # This command will likely be easier to debug.

Save output and reports as artifacts

Some tools might generate files that are only needed while the job is running, but the content of these files could be used for debugging. You can save them for later analysis with artifacts:

job1:
  script:
    - my-tool --json-output my-output.json
  artifacts:
    paths:
      - my-output.json

Reports configured with artifacts:reports are not available for download by default, but could also contain information to help with debugging. Use the same technique to make these reports available for inspection:

job1:
  script:
    - rspec --format RspecJunitFormatter --out rspec.xml
  artifacts:
    reports:
      junit: rspec.xml
    paths:
      - rspec.xmp

WARNING: Do not save tokens, passwords, or other sensitive information in artifacts, as they could be viewed by any user with access to the pipelines.

Run the job's commands locally

You can use a tool like Rancher Desktop or similar alternatives to run the job's container image on your local machine. Then, run the job's script commands in the container and verify the behavior.

Troubleshoot a failed job with Root Cause Analysis

You can use GitLab Duo Root Cause Analysis in GitLab Duo Chat to troubleshoot failed CI/CD jobs.

Job configuration issues

A lot of common pipeline issues can be fixed by analyzing the behavior of the rules or only/except configuration used to control when jobs are added to a pipeline. You shouldn't use these two configurations in the same pipeline, as they behave differently. It's hard to predict how a pipeline runs with this mixed behavior. rules is the preferred choice for controlling jobs, as only and except are no longer being actively developed.

If your rules or only/except configuration makes use of predefined variables like CI_PIPELINE_SOURCE, CI_MERGE_REQUEST_ID, you should verify them as the first troubleshooting step.

Jobs or pipelines don't run when expected

The rules or only/except keywords are what determine whether or not a job is added to a pipeline. If a pipeline runs, but a job is not added to the pipeline, it's usually due to rules or only/except configuration issues.

If a pipeline does not seem to run at all, with no error message, it may also be due to rules or only/except configuration, or the workflow: rules keyword.

If you are converting from only/except to the rules keyword, you should check the rules configuration details carefully. The behavior of only/except and rules is different and can cause unexpected behavior when migrating between the two.

The common if clauses for rules can be very helpful for examples of how to write rules that behave the way you expect.

If a pipeline contains only jobs in the .pre or .post stages, it does not run. There must be at least one other job in a different stage.

Unexpected behavior when .gitlab-ci.yml file contains a byte order mark (BOM)

A UTF-8 Byte-Order Mark (BOM) in the .gitlab-ci.yml file or other included configuration files can lead to incorrect pipeline behavior. The byte order mark affects parsing of the file, causing some configuration to be ignored - jobs might be missing, and variables could have the wrong values. Some text editors could insert a BOM character if configured to do so.

If your pipeline has confusing behavior, you can check for the presence of BOM characters with a tool capable of displaying them. The pipeline editor cannot display the characters, so you must use an external tool. See issue 354026 for more details.

A job with the changes keyword runs unexpectedly

A common reason a job is added to a pipeline unexpectedly is because the changes keyword always evaluates to true in certain cases. For example, changes is always true in certain pipeline types, including scheduled pipelines and pipelines for tags.

The changes keyword is used in combination with only/except or rules. It's recommended to only use changes with if sections in rules or only/except configuration that ensures the job is only added to branch pipelines or merge request pipelines.

Two pipelines run at the same time

Two pipelines can run when pushing a commit to a branch that has an open merge request associated with it. Usually one pipeline is a merge request pipeline, and the other is a branch pipeline.

This situation is usually caused by the rules configuration, and there are several ways to prevent duplicate pipelines.

No pipeline or the wrong type of pipeline runs

Before a pipeline can run, GitLab evaluates all the jobs in the configuration and tries to add them to all available pipeline types. A pipeline does not run if no jobs are added to it at the end of the evaluation.

If a pipeline did not run, it's likely that all the jobs had rules or only/except that blocked them from being added to the pipeline.

If the wrong pipeline type ran, then the rules or only/except configuration should be checked to make sure the jobs are added to the correct pipeline type. For example, if a merge request pipeline did not run, the jobs may have been added to a branch pipeline instead.

It's also possible that your workflow: rules configuration blocked the pipeline, or allowed the wrong pipeline type.

Pipeline with many jobs fails to start

A Pipeline that has more jobs than the instance's defined CI/CD limits fails to start.

To reduce the number of jobs in a single pipeline, you can split your .gitlab-ci.yml configuration into more independent parent-child pipelines.

Pipeline warnings

Pipeline configuration warnings are shown when you:

Job may allow multiple pipelines to run for a single action warning

When you use rules with a when clause without an if clause, multiple pipelines may run. Usually this occurs when you push a commit to a branch that has an open merge request associated with it.

To prevent duplicate pipelines, use workflow: rules or rewrite your rules to control which pipelines can run.

Pipeline errors

A CI/CD pipeline must run and be successful before merge message

This message is shown if the Pipelines must succeed setting is enabled in the project and a pipeline has not yet run successfully. This also applies if the pipeline has not been created yet, or if you are waiting for an external CI service.

If you don't use pipelines for your project, then you should disable Pipelines must succeed so you can accept merge requests.

Checking ability to merge automatically message

If your merge request is stuck with a Checking ability to merge automatically message that does not disappear after a few minutes, you can try one of these workarounds:

  • Refresh the merge request page.
  • Close & Re-open the merge request.
  • Rebase the merge request with the /rebase quick action.
  • If you have already confirmed the merge request is ready to be merged, you can merge it with the /merge quick action.

This issue is resolved in GitLab 15.5.

Checking pipeline status message

This message displays with a spinning status icon ({spinner}) when the merge request does not yet have a pipeline associated with the latest commit. This might be because:

  • GitLab hasn't finished creating the pipeline yet.
  • You are using an external CI service and GitLab hasn't heard back from the service yet.
  • You are not using CI/CD pipelines in your project.
  • You are using CI/CD pipelines in your project, but your configuration prevented a pipeline from running on the source branch for your merge request.
  • The latest pipeline was deleted (this is a known issue).
  • The source branch of the merge request is on a private fork.

After the pipeline is created, the message updates with the pipeline status.

In some of these cases, the message might get stuck with the icon spinning endlessly if the Pipelines must succeed setting is enabled. See issue 334281 for more details.

Project <group/project> not found or access denied message

This message is shown if configuration is added with include and either:

  • The configuration refers to a project that can't be found.
  • The user that is running the pipeline is unable to access any included projects.

To resolve this, check that:

  • The path of the project is in the format my-group/my-project and does not include any folders in the repository.
  • The user running the pipeline is a member of the projects that contain the included files. Users must also have the permission to run CI/CD jobs in the same projects.

The parsed YAML is too big message

This message displays when the YAML configuration is too large or nested too deeply. YAML files with a large number of includes, and thousands of lines overall, are more likely to hit this memory limit. For example, a YAML file that is 200 kb is likely to hit the default memory limit.

To reduce the configuration size, you can:

  • Check the length of the expanded CI/CD configuration in the pipeline editor's Full configuration tab. Look for duplicated configuration that can be removed or simplified.
  • Move long or repeated script sections into standalone scripts in the project.
  • Use parent and child pipelines to move some work to jobs in an independent child pipeline.

On a self-managed instance, you can increase the size limits.

500 error when editing the .gitlab-ci.yml file

A loop of included configuration files can cause a 500 error when editing the .gitlab-ci.yml file with the web editor.

Ensure that included configuration files do not create a loop of references to each other.

Failed to pull image messages

A runner might return a Failed to pull image message when trying to pull a container image in a CI/CD job.

The runner authenticates with a CI/CD job token when fetching a container image defined with image from another project's container registry.

If the job token settings prevent access to the other project's container registry, the runner returns an error message.

For example:

  • WARNING: Failed to pull image with policy "always": Error response from daemon: pull access denied for registry.example.com/path/to/project, repository does not exist or may require 'docker login': denied: requested access to the resource is denied
  • WARNING: Failed to pull image with policy "": image pull failed: rpc error: code = Unknown desc = failed to pull and unpack image "registry.example.com/path/to/project/image:v1.2.3": failed to resolve reference "registry.example.com/path/to/project/image:v1.2.3": pull access denied, repository does not exist or may require authorization: server message: insufficient_scope: authorization failed

These errors can happen if the following are both true:

  • The Limit access to this project option is enabled in the private project hosting the image.
  • The job attempting to fetch the image is running in a project that is not listed in the private project's allowlist.

To resolve this issue, add any projects with CI/CD jobs that fetch images from the container registry to the target project's job token allowlist.

These errors might also happen when trying to use a project access token to access images in another project. Project access tokens are scoped to one project, and therefore cannot access images in other projects. You must use a different token type with wider scope.

Something went wrong on our end message or 500 error when running a pipeline

You might receive the following pipeline errors:

  • A Something went wrong on our end message when pushing or creating merge requests.
  • A 500 error when using the API to trigger a pipeline.

These errors can happen if records of internal IDs become out of sync after a project is imported.

To resolve this, see the workaround in issue 352382.

config should be an array of hashes error message

You might see an error similar to the following when using !reference tags with the parallel:matrix keyword:

This GitLab CI configuration is invalid: jobs:my_job_name:parallel:matrix config should be an array of hashes.

The parallel:matrix keyword does not support multiple !reference tags at the same time. Try using YAML anchors instead.

Issue 439828 proposes improving !reference tag support in parallel:matrix.