[Feature flag] Roll out 7_prometheus refactor
Summary
This issue is to rollout !81133 (merged) and !81276 (closed) on production,
that is currently behind the prometheus_initializer_refactor
feature flag.
The flag defaults to ON, this being executed on the boot path. In case of incident during deploy, turn it off to restore the previous behavior.
Owners
- Team: groupmemory
- Most appropriate slack channel to reach out to:
#g_memory
- Best individual to reach out to:
@mkaeppler
- PM:
@iroussos
Stakeholders
n/a
Expectations
What are we expecting to happen?
No changes in behavior i.e. no regressions in Puma and Sidekiq metrics.
When is the feature viable?
Immediately after deploying !81133 (merged)
What might happen if this goes wrong?
Puma or Sidekiq metrics start to disappear, or pods of either deployment crash-loop.
What can we monitor to detect problems with this?
- Crash-looping pods should be self-evident during a deploy.
- There should be no or marginal drift in the number of metrics exported for Puma and Sidekiq (there could be drift if there are metrics that only come into existence when certain work loads have run.)
What can we check for monitoring production after rollouts?
-
Check before/after for metric counts
- There are fluctuations between deploys since some metrics are initialized lazily, but outside of these short lasting effects, there should not be a "valley" / lower plateau in the count of metrics emitted
- We can also check a few representative specific metrics for the affected deployments:
- Ruby process startup, emitted by RubySampler: should be > 0
- Puma active connections, emitted by PumaSampler: should be > 0
-
ActionCable active connections: >0 for
websockets
, 0 for all othertype
s. - Rails SLIs such as
sli_aggregations:gitlab_sli:rails_request_apdex:success_total_rate1m{env="gprd", app="webservice", type="web"}
; however, Thanos always times out for me when querying this? These drive ourStage Group Error Budget
dashboards, so this will be another place where this could surface.
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
-
/chatops run feature set <feature-flag-name> true --staging
-
-
Verify that the feature works as expected. Posting the QA result in this issue is preferable.
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 <feature-flag-name> true
-
/chatops run feature set --project=gitlab-org/gitlab-foss <feature-flag-name> true
-
/chatops run feature set --project=gitlab-com/www-gitlab-com <feature-flag-name> true
-
- If you're using group-actor, you must enable the feature on these entries:
-
/chatops run feature set --group=gitlab-org <feature-flag-name> true
-
/chatops run feature set --group=gitlab-com <feature-flag-name> true
-
- If you're using user-actor, you must enable the feature on these entries:
-
/chatops run feature set --user=<your-username> <feature-flag-name> 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. -
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>
-
- 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 deployed to both production and canary. If the merge request was deployed before the code cutoff, the feature can be officially announced in a release blog post. -
/chatops run auto_deploy status <merge-commit-of-default-enabling-mr>
-
-
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 deployed to both production and canary. If the merge request was deployed before the code cutoff, the feature can be officially announced in a release blog post. -
/chatops run auto_deploy status <merge-commit-of-cleanup-mr>
-
-
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 <feature-flag-name> --dev
-
/chatops run feature delete <feature-flag-name> --staging
-
/chatops run feature delete <feature-flag-name>
-
-
Close this rollout issue.
Rollback Steps
-
This feature can be disabled by running the following Chatops command:
/chatops run feature set prometheus_initializer_refactor false