[Feature flag] ci_limit_complete_hierarchy_size
Summary
This issue is to rollout <code data-sourcepos="3:28-3:94">Add logic to enforce the Ci::Pipeline#complete_hierachy_count limit</code> on production,
that is currently behind the ci_limit_complete_hierarchy_size
feature flag.
Important Note: As this feature flag is built to be operated on an instance-wide level, it takes no actors and does not lend itself to progressive rollout. Global rollout on SaaS will happen under close monitoring and with advance notice to #support_gitlab-com
.
Owners
- Team: grouppipeline execution
- Most appropriate slack channel to reach out to:
#g_pipeline_execution
- Best individual to reach out to: @drew
- PM: @jheimbuck_gl
Stakeholders
As a largely reliability concern, rather than a user facing feature, it's mostly us. But @marknuzzo
and grouppipeline authoring are read up on this.
Expectations
What are we expecting to happen?
On SaaS, we're not expecting anything to change. We've had hierarchy size monitoring in place for a week and the highest count we've seen is below 400.
I can't speak to self-hosted hierarchy sizes because we don't have the visibility. But the plan is to roll this out with a feature flag, and replace the feature flag with a configurable limit so self-hosted customers can always opt out.
When is the feature viable?
Feature.enable(:ci_limit_complete_hierarchy_size)
What might happen if this goes wrong?
- If the queries to check hierarchy sizes (should never be more than 1000) end up timing out, we should see the
Ci::CreateDownstreamPipelineWorker
dashboards go a little nuts. Query rate should drop off, database time should shoot up, queue length and queuing time should increase, execution rate should decrease. A bunch of alarm bells downstream from spending 15s in the database at a time. This sort of problem would have SaaS-wide performance implications. - If this accidentally triggers when it's not supposed to (that is, hierarchy counts are under 1000 but the bridges are failing anyway), we'll see a big decrease in downstream pipelines created. This shouldn't have SaaS-wide performance implications.
- If anything goes wrong, we should simply switch off the flag. Recovery in individual worker performance should happen in within 15s of the flag change, but overall queueing duration and CPU work might persist if there's a built up backlog of downstream pipelines to create. But it should start showing improvement within a few minutes.
What can we check for monitoring production after rollouts?
The Pipeline Hierarchy dashboard
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 ci_limit_complete_hierarchy_size 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 <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,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 https://gitlab.com/gitlab-org/gitlab/-/issues/358075 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
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_pipeline_execution
).
-
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 https://gitlab.com/gitlab-org/gitlab/-/issues/358075 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 <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 https://gitlab.com/gitlab-org/gitlab/-/issues/358075 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 ci_limit_complete_hierarchy_size --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 ci_limit_complete_hierarchy_size false