Various validations are ran to check for correctness. There is a reusable workflow that repositories may call with input parameters defining which validations to use, with each input parameter corresponding to a subcommand under the
ddev validate command group.
ddev validate agent-reqs
This validates that each integration version is in sync with the
requirements-agent-release.txt file. It is uncommon for this to fail because the release process is automated.
ddev validate ci
ddev validate ci --sync to resolve most errors.
ddev validate codeowners
This validation is only enabled for integrations-extras.
Default configuration files¶
ddev validate config
This verifies that the config specs for all integrations are valid by enforcing our configuration spec schema. The most common failure is some version of
File <INTEGRATION_SPEC> needs to be synced. To resolve this issue, you can run
ddev validate config --sync
If you see failures regarding formatting or missing parameters, see our config spec documentation for more details on how to construct configuration specs.
Dashboard definition files¶
ddev validate dashboards
This validates that dashboards are formatted correctly. This means that they need to be proper JSON and generated from Datadog's
If you see a failure regarding use of the screen endpoint, consider using our dashboard utility command to generate your dashboard payload.
ddev validate dep
- Verifies the uniqueness of dependency versions across all checks.
- Verifies all the dependencies are pinned.
- Verifies the embedded Python environment defined in the base check and requirements listed in every integration are compatible.
This validation only applies if your work introduces new external dependencies.
ddev validate manifest
This validates that the manifest files contain required fields, are formatted correctly, and don't contain common errors. See the Datadog docs for more detailed constraints.
ddev validate metadata
This checks that every
metadata.csv file is formatted correctly. See the Datadog docs for more detailed constraints.
ddev validate readmes
This ensures that every integration's README.md file is formatted correctly. The main purpose of this validation is to ensure that any image linked in the readme exists and that all images are located in an integration's
Saved views data¶
ddev validate saved-views
This validates that saved views for an integration are formatted correctly and contain required fields, such as "type".
View example saved views for inspiration and guidance.
Service check data¶
ddev validate service-checks
This checks that every service check file is formatted correctly. See the Datadog docs for more specific constraints.
ddev validate imports
This verifies that all integrations import the base package in the correct way, such as:
from datadog_checks.base.foo import bar
See the New Integration Instructions for more examples of how to use the base package.