[Feature flag] Roll out ci_artifact_fast_removal_large_loop_limit

Summary

This issue is to rollout a higher record throughput of the ExpireBuildArtifactsWorker on production, that is currently behind the ci_artifact_fast_removal_large_loop_limit feature flag. This flag is itself behind #347144 (closed), which controls the execution of backlog artifact removal code entirely.

When this flag is disabled, we use SMALL_LOOP_LIMIT = 100, limiting any worker to querying 10,000 rows from ci_job_artifacts.

Turning the flag on switches the Worker to LARGE_LOOP_LIMIT = 500, increasing our theoretical maximum to 50,000 records.

Prior to gitlab-com/gl-infra/production#5952 (closed), we used LOOP_LIMIT = 1000. Now, we're starting at 10% of that cap and this flag lets us flex up to 50%. In either scenario, we should be making fewer updates and creating fewer dead tuples because of the optimizations in !75711 (4afaacf7) as part of !75711 (closed)

Owners

• Team: grouppipeline execution but more specifically the devopsverify Engineering Allocation
• Most appropriate slack channel to reach out to: #verify-reliability-engineering-allocation
• Best individual to reach out to: @drew and/or @mattkasa
• PM: @jheimbuck_gl

Stakeholders

• #g_testing and @shampton (JobArtifact feature owners)
• @sgoldstein & @avielle (managing this particular chunk of the Engineering Allocation)

Expectations

What are we expecting to happen?

We are expecting the ExpireBuildArtifactsWorker to querying 5x the number of records from ci_job_artifacts. Given the optimizations in !75711 (closed), we are hoping that this pace can be sustained at acceptable database performance, but we're starting low and using this flag to throttle up if it looks like we have the capacity to do so.

If for some reason we need to throttle down from 100, we'll instead turn off or reduce #347144 (closed) to a percentage, to reduce the total number of dead tuples living on ci_job_artifacts between autovacuums, which happen roughly once per day.

When is the feature viable?

This flag is viable to turn on after #347144 (closed) is running for 100% of ExpireBuildArtifactsWorker executions and we decide that the overhead is low enough that we want to try removing 5x as many artifacts per 7-minute interval.

What might happen if this goes wrong?

The same as #347144 (closed), since this flag is just an acceleration of that work.

If this goes wrong, query performance on indexes of the ci_job_artifacts table will degrade. Tuple reads will increase sharply, query planning time will increase, and overall throughput will drop. Apdex on gitlab.com will suffer. It will look exactly like gitlab-com/gl-infra/production#5952 (closed).

If this happens, we'll need to both shut the flag off and immediately manually VACUUM the ci_job_artifacts table. An SRE who can do this should be present during the rollout to monitor and possibly perform this mitigation.

What can we check for monitoring production both during and after rollouts?

• Kibana logs reporting the number of updates and removed ci_job_artifact records through each execution path.
• Index tuple reads on ci_job_artifacts in Thanos

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_job_artifacts_large_loop_limit true --dev
  • /chatops run feature set ci_job_artifacts_large_loop_limit 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 ci_job_artifacts_large_loop_limit true
  • /chatops run feature set --project=gitlab-org/gitlab-foss ci_job_artifacts_large_loop_limit true
  • /chatops run feature set --project=gitlab-com/www-gitlab-com ci_job_artifacts_large_loop_limit true
• If you're using group-actor, you must enable the feature on these entries:
  • /chatops run feature set --group=gitlab-org ci_job_artifacts_large_loop_limit true
  • /chatops run feature set --group=gitlab-com ci_job_artifacts_large_loop_limit true
• If you're using user-actor, you must enable the feature on these entries:
  • /chatops run feature set --user=<your-username> ci_job_artifacts_large_loop_limit 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 ci_job_artifacts_large_loop_limit <rollout-percentage> --actors
  • If the feature flag in code does NOT have an actor, perform time-based rollout (random rollout).
    • /chatops run feature set ci_job_artifacts_large_loop_limit <rollout-percentage>
  • Enable the feature globally on production environment.
    • /chatops run feature set ci_job_artifacts_large_loop_limit true
• 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 to true.
  • 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 ci_job_artifacts_large_loop_limit".
  • 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.

• Create a merge request to remove ci_job_artifacts_large_loop_limit 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 ci_job_artifacts_large_loop_limit --dev
  • /chatops run feature delete ci_job_artifacts_large_loop_limit --staging
  • /chatops run feature delete ci_job_artifacts_large_loop_limit
• Close this rollout issue.

Rollback Steps

• This feature can be disabled by running the following Chatops command:

/chatops run feature set ci_job_artifacts_large_loop_limit false

• An SRE should consider running VACUUM on the ci_job_artifacts table if this flag has to be turned off.