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[Feature flag] Enable data_consistency_delayed_execution feature flag

Summary

This issue is to rollout delaying worker execution for all workers utilizing LB on production, that is currently behind the data_consistency_delayed_execution feature flag.

Owners

  • Team: Memory team
  • Most appropriate slack channel to reach out to: #g_memory
  • Best individual to reach out to: @nmilojevic1
  • PM: @changzhengliu

Stakeholders

The Rollout Plan

  • Partial Rollout on GitLab.com with testing groups
  • Rollout on GitLab.com for a certain period (How long)
  • Percentage Rollout on GitLab.com
  • Rollout Feature for everyone as soon as it's ready

Testing Groups/Projects/Users

  • gitlab-org/gitlab project
  • gitlab-org/gitlab-com groups
  • ...

Expectations

What are we expecting to happen?

It will delay the execution of workers that are utilizing the LB cababilities. When perform_async is called, it will delay its execution for 1s in order to give the replication process more time to finish.

What might happen if this goes wrong?

It can happen that BuildHooksWorker gets retried more often, since the current delay for this worker is 3s, and this will reduce the delay to 1s.

What can we monitor to detect problems with this?

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 data_consistency_delayed_execution true --dev
    • /chatops run feature set data_consistency_delayed_execution true --staging
  • Verify that the feature works as expected. Posting the QA result in this issue is preferable.

Preparation before production rollout

  • Ensure that the feature MRs have been deployed to both production and canary.
    • /chatops run auto_deploy status <merge-commit-of-your-feature>
  • 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.
  • If the feature flag in code has an actor, enable it on GitLab.com for testing groups/projects.
    • /chatops run feature set --<actor-type>=<actor> <feature-flag-name> true
  • Verify that the feature works as expected. Posting the QA result in this issue is preferable.

Global rollout on production

  • Incrementally roll out the feature.
    • If the feature flag in code has an actor, perform actor-based rollout.
      • /chatops run feature set data_consistency_delayed_execution <rollout-percentage> --actors
    • If the feature flag in code does NOT have an actor, perform time-based rollout (random rollout).
      • /chatops run feature set data_consistency_delayed_execution <rollout-percentage>
    • Enable the feature globally on production environment.
      • /chatops run feature set <feature-flag-name> true
  • Announce on the feature issue that the feature has been globally enabled.
  • Cross-post chatops slack command to #support_gitlab-com. (more guidance when this is necessary in the dev docs) and in your team channel
  • 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.
  • Ensure that the above 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>
  • Close the feature issue to indicate the feature will be released in the current milestone.

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 data_consistency_delayed_execution 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 above 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>
  • 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 data_consistency_delayed_execution --dev
    • /chatops run feature delete data_consistency_delayed_execution --staging
    • /chatops run feature delete data_consistency_delayed_execution
  • Close this rollout issue.

Rollback Steps

  • This feature can be disabled by running the following Chatops command:
/chatops run feature set data_consistency_delayed_execution false
Edited by Nikola Milojevic