[Feature flag] Rollout of `namespace_model_default_org`

Summary

This issue is to roll out the feature on production, that is currently behind the namespace_model_default_org feature flag.

Owners

  • Most appropriate Slack channel to reach out to: #g_organizations
  • Best individual to reach out to: @rutgerwessels

Expectations

What are we expecting to happen?

Default value for namespaces.organization_id value will be set by model. The model default attribute value is hard coded to 1, which is the same for the database default value

What can go wrong and how would we detect it?

The database default is still there. If the id is missing, it will use the database default.

Kibana log: errors 500 should not increase (json.meta.feature_category:"groups_and_projects" and json.status:500)

Rollout Steps

Note: Please make sure to run the chatops commands in the Slack channel that gets impacted by the command.

Rollout on non-production environments

  • Verify the MR with the feature flag is merged to master and have been deployed to non-production environments with /chatops run auto_deploy status 1f335ecf6403e681ff7879bcd80514e459d863ff
  • Enable the feature globally on non-production environments with /chatops run feature set namespace_model_default_org true --dev --pre --staging --staging-ref
  • Verify that the feature works as expected. The best environment to validate the feature in is staging-canary as this is the first environment deployed to. Make sure you are configured to use canary.
  • If the feature flag causes end-to-end tests to fail, disable the feature flag on staging to avoid blocking deployments.
    • See #e2e-run-staging Slack channel and look for the following messages:
      • test kicked off: Feature flag namespace_model_default_org has been set to true on **gstg**
      • test result: This pipeline was triggered due to toggling of namespace_model_default_org feature flag

For assistance with end-to-end test failures, please reach out via the #test-platform Slack channel. Note that end-to-end test failures on staging-ref don't block deployments.

Specific rollout on production

For visibility, all /chatops commands that target production should be executed in the #production Slack channel and cross-posted (with the command results) to the responsible team's Slack channel.

  • Ensure that the feature MRs have been deployed to both production and canary with /chatops run auto_deploy status 1f335ecf6403e681ff7879bcd80514e459d863ff

Preparation before global rollout

  • Set a milestone to this rollout issue to signal for enabling and removing the feature flag when it is stable.
  • Check if the feature flag change needs to be accompanied with a change management issue. Cross link the issue here if it does.
  • Ensure that you or a representative in development can be available for at least 2 hours after feature flag updates in production. If a different developer will be covering, or an exception is needed, please inform the oncall SRE by using the @sre-oncall Slack alias.
  • Ensure that documentation exists for the feature, and the version history text has been updated.
  • Leave a comment on the feature issue announcing estimated time when this feature flag will be enabled on GitLab.com.
  • Ensure that any breaking changes have been announced following the release post process to ensure GitLab customers are aware.
  • Notify the #support_gitlab-com Slack channel and your team channel (more guidance when this is necessary in the dev docs).
  • Ensure that the feature flag rollout plan is reviewed by another developer familiar with the domain.

Global rollout on production

For visibility, all /chatops commands that target production should be executed in the #production Slack channel and cross-posted (with the command results) to the responsible team's Slack channel (#g_organizations).

  • Enable the feature globally on production environment:
    • /chatops run feature set namespace_model_default_org --actors 5
    • /chatops run feature set namespace_model_default_org --actors 50
    • /chatops run feature set namespace_model_default_org --actors 100
  • Observe appropriate graphs on https://dashboards.gitlab.net and verify that services are not affected.
  • Leave a comment on the feature issue announcing that the feature has been globally enabled.
  • Wait for at least one day for the verification term.

Release the feature

After the feature has been deemed stable, the clean up should be done as soon as possible to permanently enable the feature and reduce complexity in the codebase.

You can either create a follow-up issue for Feature Flag Cleanup or use the checklist below in this same issue.

  • Create a merge request to remove the namespace_model_default_org feature flag. Ask for review/approval/merge as usual. The MR should include the following changes:
    • Remove all references to the feature flag from the codebase.
    • Remove the YAML definitions for the feature from the repository.
    • Create a changelog entry.
  • Ensure that the cleanup MR has been included in the release package. If the merge request was deployed before the monthly release was tagged, the feature can be officially announced in a release blog post: /chatops run release check <merge-request-url> 17.5
  • Close the feature issue to indicate the feature will be released in the current milestone.
  • Clean up the feature flag from all environments by running these chatops command in #production channel: /chatops run feature delete namespace_model_default_org --dev --pre --staging --staging-ref --production
  • Close this rollout issue.

Rollback Steps

  • This feature can be disabled on production by running the following Chatops command:
/chatops run feature set namespace_model_default_org false
  • Disable the feature flag on non-production environments:
/chatops run feature set namespace_model_default_org false --dev --pre --staging --staging-ref
  • Delete feature flag from all environments:
/chatops run feature delete namespace_model_default_org --dev --pre --staging --staging-ref --production
Edited by Rutger Wessels