[Feature flag] Rollout of `limited_capacity_seat_refresh_worker` feature flag
Summary
This issue is to rollout the limited capacity worker for GitlabSubscription
seat refreshes on production,
that is currently behind the limited_capacity_seat_refresh_worker_*
feature flags.
There are 4 feature flags to be rolled out:
limited_capacity_seat_refresh_worker_low
limited_capacity_seat_refresh_worker_medium
limited_capacity_seat_refresh_worker_high
disable_update_max_seats_worker
The first 3 flags will determine the number of concurrent max_running_jobs
the limited capacity worker will have.
With all 3 feature flags off, no jobs will be enqueued, we can then use the flags to ramp up the jobs to 2, 4 and 6 accordingly.
The last flag will be used to disable the old worker, UpdateMaxSeatsUsedForGitlabComSubscriptionsWorker
before we remove it entirely.
Note: We may not need to enable all 3 of these flags, if we find that a concurrent job size of 2
is sufficient for processing the subscriptions, we can consider the rollout successful and proceed with removing the flags.
Owners
- Team: Utilization
- Most appropriate slack channel to reach out to:
#g_utilization
- Best individual to reach out to:
@vij
- PM:
@csouthard
Stakeholders
-
groupscalability - There were performance concerns with the original worker (#334903 (closed)) -
@reprazent
is familiar with this effort
Expectations
What are we expecting to happen?
Enabling the feature flag will allow the new limited capacity worker to start performing seat refreshes for subscriptions.
We have not removed the existing job at this point, which means we might end up refreshing a subscription's seats twice in a 24 hour period. This should have no negative consequence.
Once the new job has been verified, the old job can be removed.
- When enabling the
limited_capacity_seat_refresh_worker_low
feature flag, we should begin seeing 2 jobs being queued up at a time to process gitlab subscription seat refreshes - When enabling the
limited_capacity_seat_refresh_worker_medium
feature flag, we should begin seeing 4 jobs being queued up at a time - When enabling the
limited_capacity_seat_refresh_worker_medium
feature flag, we should begin seeing 6 jobs being queued up at a time
When is the feature viable?
What might happen if this goes wrong?
We might see failing jobs for the limited capacity worker and the feature flag can be safely disbled
What can we monitor to detect problems with this?
Staging:
- Scheduling the jobs: https://nonprod-log.gitlab.net/goto/d5565650-ae08-11ed-9af2-6131f0ee4ce6
- Monitoring the jobs: https://nonprod-log.gitlab.net/goto/e344c620-ae08-11ed-9af2-6131f0ee4ce6
Production:
- Scheduling the jobs: https://log.gprd.gitlab.net/goto/11070fc0-ab8c-11ed-9f43-e3784d7fe3ca
- Monitoring the jobs: https://log.gprd.gitlab.net/goto/da8972a0-ab8e-11ed-9f43-e3784d7fe3ca
- Sentry errors: https://sentry.gitlab.net/gitlab/gitlabcom/?query=is%3Aunresolved+RefreshSeatsWorker
- Monitoring
GitlabSubscription
: https://log.gprd.gitlab.net/goto/0fae8e30-ae09-11ed-9f43-e3784d7fe3ca
Consider mentioning checks for 5xx errors or other anomalies like an increase in redirects (302 HTTP response status)
What can we check for monitoring production after rollouts?
Consider adding links to check for Sentry errors, Production logs for 5xx, 302s, etc. As above
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 limited_capacity_seat_refresh_worker_low 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 limited_capacity_seat_refresh_worker_low true
-
- If you're using group-actor, you must enable the feature on these entries:
-
/chatops run feature set --group=gitlab-org,gitlab-com limited_capacity_seat_refresh_worker_low true
-
- If you're using user-actor, you must enable the feature on these entries:
-
/chatops run feature set --user=<your-username> limited_capacity_seat_refresh_worker_low true
-
-
Verify that the feature works on the specific entries. Posting the QA result in this issue is preferable.
Preparation before global rollout
-
Set a milestone to the 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 has been updated (More info). -
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 #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 does NOT have an actor, perform time-based rollout (random rollout).
-
Enable the feature globally on production environment.
-
/chatops run feature set limited_capacity_seat_refresh_worker_low true
-
/chatops run feature set limited_capacity_seat_refresh_worker_medium true
-
/chatops run feature set limited_capacity_seat_refresh_worker_high true
-
/chatops run feature set disable_update_max_seats_worker true
-
-
-
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.
(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 <feature-flag-name> --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 <feature-flag-name>
". -
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 <feature-flag-name>
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 <feature-flag-name> --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 limited_capacity_seat_refresh_worker_low false
/chatops run feature set limited_capacity_seat_refresh_worker_medium false
/chatops run feature set limited_capacity_seat_refresh_worker_high false
/chatops run feature set disable_update_max_seats_worker false