Rollout suggested_reviewers_control feature flag
- To remove from environments
/chatops run feature delete suggested_reviewers_control --dev --staging --staging-ref --production
- To add individual projects
/chatops run feature set --project=gitlab-org/modelops/applied-ml/review-recommender/recommender-bot-service suggested_reviewers_control true
Summary
This issue is to rollout the Suggested Reviewers on production, which is currently behind the suggested_reviewers_control
feature flag.
Owners
- Team: ~"group::applied ml"
- Most appropriate slack channel to reach out to:
#g_applied_ml
- Best individual to reach out to: @tle_gitlab, @a_akgun, @mray2020
- PM: @tmccaslin
Stakeholders
- Name of a PM: @phikai
- Team: groupcode review
Expectations
What are we expecting to happen?
Users will see the suggested reviewers dropdown on the side bar when viewing a merge request.
For projects where the Suggested Reviewer bot was present, we will be disabling this once we confirm rollout to that project. For a small period you may still get bot suggestions and dropdown reviewer suggestions as we process the MR to remove the bot from the project.
When is the feature viable?
This feature is only available on gitlab.com and GitLab Ultimate. No application setting is needed at this point but will be implemented in future iteration as part of the onboarding experience.
What might happen if this goes wrong?
Users won't see the suggested reviewers dropdown or no results in the dropdown.
What can we monitor to detect problems with this?
Track suggestion metrics and detect sudden drop in usage.
What can we check for monitoring production after rollouts?
TBA
Rollout Steps
Rollout on non-production environments
- Ensure that the feature MRs have been deployed to non-production environments.
-
/chatops run auto_deploy status <merge-commit-of-your-feature>
-
-
Enable the feature globally on non-production environments. -
/chatops run feature set <feature-flag-name> true --dev --staging --staging-ref
-
-
Verify that the feature works as expected. Posting the QA result in this issue is preferable. The best environment to validate the feature in is staging-canary as this is the first environment deployed to. Note you will need to make sure you are configured to use canary as outlined here when accessing the staging environment in order to make sure you are testing appropriately.
Specific rollout on production
- Ensure that the feature MRs have been deployed to both production and canary.
-
/chatops run auto_deploy status <merge-commit-of-your-feature>
-
- If you're using project-actor, you must enable the feature on these entries:
-
/chatops run feature set --project=gitlab-org/gitlab,gitlab-org/gitlab-foss,gitlab-com/www-gitlab-com suggested_reviewers_control true
-
- If you're using group-actor, you must enable the feature on these entries:
-
/chatops run feature set --group=gitlab-org,gitlab-com suggested_reviewers_control true
-
- If you're using user-actor, you must enable the feature on these entries:
-
/chatops run feature set --user=<your-username> suggested_reviewers_control true
-
-
Verify that the feature works on the specific entries. Posting the QA result in this issue is preferable.
Preparation before global rollout
-
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 has been updated (More info). -
Announce on the feature issue an estimated time this 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 #support_gitlab-com
and your team channel (more guidance when this is necessary in the dev docs).
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_TEAM_NAME
).
-
Incrementally roll out the feature. - If the feature flag in code has an actor, perform actor-based rollout.
-
/chatops run feature set <feature-flag-name> <rollout-percentage> --actors
-
- If the feature flag in code does NOT have an actor, perform time-based rollout (random rollout).
-
/chatops run feature set <feature-flag-name> <rollout-percentage> --random
-
- Enable the feature globally on production environment.
-
/chatops run feature set <feature-flag-name> true
-
- If the feature flag in code has an actor, perform actor-based rollout.
-
Announce on the feature issue that the feature has been globally enabled. -
Wait for at least one day for the verification term.
(Optional) Release the feature with the feature flag
If you're still unsure whether the feature is deemed stable but want to release it in the current milestone, you can change the default state of the feature flag to be enabled. To do so, follow these steps:
-
Create a merge request with the following changes. Ask for review and merge it. -
Set the default_enabled
attribute in the feature flag definition totrue
. -
Create a changelog entry.
-
-
Ensure that the default-enabling 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> <milestone>
-
-
Consider cleaning up the feature flag from all environments by running these chatops command in #production
channel. Otherwise, these settings may override the default enabled.-
/chatops run feature delete suggested_reviewers_control --dev --staging --staging-ref --production
-
-
Close the feature issue to indicate the feature will be released in the current milestone. -
Set the next milestone to this rollout issue for scheduling the flag removal. -
(Optional) You can create a separate issue for scheduling the steps below to Release the feature. -
Set the title to "[Feature flag] Cleanup suggested_reviewers_control
". -
Execute the /copy_metadata <this-rollout-issue-link>
quick action to copy the labels from this rollout issue. -
Link this rollout issue as a related issue. -
Close this rollout issue.
-
WARNING: This approach has the downside that it makes it difficult for us to clean up the flag. For example, on-premise users could disable the feature on their GitLab instance. But when you remove the flag at some point, they suddenly see the feature as enabled and they can't roll it back to the previous behavior. To avoid this potential breaking change, use this approach only for urgent matters.
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 suggested_reviewers_control
feature flag. Ask for review and merge it.-
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> <milestone>
-
-
Close the feature issue to indicate the feature will be released in the current milestone. -
If not already done, clean up the feature flag from all environments by running these chatops command in #production
channel:-
/chatops run feature delete suggested_reviewers_control --dev --staging --staging-ref --production
-
-
Close this rollout issue.
Rollback Steps
-
This feature can be disabled by running the following Chatops command:
/chatops run feature set suggested_reviewers_control false