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[Feature flag] Enable code_suggestions_direct_completions

Summary

This issue is to roll out the feature on production, that is currently behind the code_suggestions_direct_completions feature flag.

Important: this flag should not be roll-out globally until #455607 (closed) is finished.

Expected roll-out timeline

Based on https://gitlab.com/gitlab-org/editor-extensions/gitlab-lsp/-/issues/279#note_1967213009

  • 2024-06-24 - roll out on .com for gitlab-org/gitlab-com members
  • 2024-06-26 - roll out to 50% of external users (gradually, e.g. 10%, 25%, 50% in ~2 hours interval)
  • 2024-06-28 - roll out to 75% of external users
  • 2024-07-01 - roll out to 100% of .com users (IOW enable the flag globally on .com)

Important: roll-out of this feature will be impacted with roll-out of [additional context feature](#464767 (closed), for that reason it's important to synchronize roll-out of both features.

Related issues/threads to roll-out:

Owners

  • Most appropriate Slack channel to reach out to: #g_code_creation
  • Best individual to reach out to: jprovaznik. This is cross-team effort other individuals to reach out with specific issues:
    • for auth-related issues: rzwambag/alipniagov from cloud connector team
    • for IDE side issues: viktomas

Expectations

What are we expecting to happen?

We are expecting that average request time on IDE side will decrease (by roughly 100-150ms - this depends on other factors, most importantly user's region).

IDE's average response time can be monitored on this Tableau dasboard. Make sure to select "up to date" time range and filter out requests having advanced context ("had advanced context"=false). "Is direct request" filter is sued to distinguish direct connection requests so if this value is used as "IDE dimension" you should see difference between direct/indirect requests.

What can go wrong and how would we detect it?

Because with this flag enabled, IDE/client will send code completion requests directly to AI GW, most probable scenario if something goes wrong is that we will see increase rate of error responses in AI GW logs.

Kibana logs can be used to monitor requests on AI GW side: https://log.gprd.gitlab.net/app/r/s/ZD977 (direct connection requests will have json.jsonPayload.token_issuer value gitlab-ai-gateway)

If something goes wrong, you can just disable this feature flag - IDE/clients will autmatically fallback to use indirect connections as before.

Rollout Steps

Note: Please make sure to run the chatops commands in the Slack channel that gets impacted by the command.

Rollout on non-production environments

  • Verify the MR with the feature flag is merged to master and have been deployed to non-production environments with /chatops run auto_deploy status <merge-commit-of-your-feature>
  • Deploy the feature flag at a percentage (recommended percentage: 50%) with /chatops run feature set code_suggestions_direct_completions <rollout-percentage> --actors --dev --pre --staging --staging-ref
  • Monitor that the error rates did not increase (repeat with a different percentage as necessary).
  • Enable the feature globally on non-production environments with /chatops run feature set code_suggestions_direct_completions true --dev --pre --staging --staging-ref
  • Verify that the feature works as expected. The best environment to validate the feature in is staging-canary as this is the first environment deployed to. Make sure you are configured to use canary.
  • If the feature flag causes end-to-end tests to fail, disable the feature flag on staging to avoid blocking deployments.
    • See #qa-staging Slack channel and look for the following messages:
      • test kicked off: Feature flag code_suggestions_direct_completions has been set to true on **gstg**
      • test result: This pipeline was triggered due to toggling of code_suggestions_direct_completions feature flag

For assistance with end-to-end test failures, please reach out via the #test-platform Slack channel. Note that end-to-end test failures on staging-ref don't block deployments.

Specific 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.

  • Ensure that the feature MRs have been deployed to both production and canary with /chatops run auto_deploy status <merge-commit-of-your-feature>
  • Depending on the type of actor you are using, pick one of these options:
    • For project-actor: /chatops run feature set --project=gitlab-org/gitlab,gitlab-org/gitlab-foss,gitlab-com/www-gitlab-com code_suggestions_direct_completions true
    • For group-actor: /chatops run feature set --group=gitlab-org,gitlab-com code_suggestions_direct_completions true
    • For user-actor: /chatops run feature set --user=<gitlab-username-of-dri> code_suggestions_direct_completions true
  • Verify that the feature works for the specific actors.

Preparation before global rollout

  • Set a milestone to this 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 exists for the feature, and the version history text has been updated.
  • 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 the #support_gitlab-com Slack channel and your team channel (more guidance when this is necessary in the dev docs).
  • Ensure that the feature flag rollout plan is reviewed by another developer familiar with the domain.

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 (#<slack-channel-of-dri-team>).

  • (Optional) Incrementally roll out the feature on production environment.
    • Between every step wait for at least 15 minutes and monitor the appropriate graphs on https://dashboards.gitlab.net.
    • Perform actor-based rollout: /chatops run feature set code_suggestions_direct_completions <rollout-percentage> --actors
  • Enable the feature globally on production environment: /chatops run feature set code_suggestions_direct_completions true
  • Observe appropriate graphs on https://dashboards.gitlab.net and verify that services are not affected.
  • Leave a comment on [the feature issue][main-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

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.

See instructions if you're sure about enabling the feature globally through the feature flag definition

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.
    • If feature was enabled for various actors, ensure the feature has been enabled globally on production /chatops run feature get code_suggestions_direct_completions. If the feature has not been globally enabled then enable the feature globally using: /chatops run feature set code_suggestions_direct_completions true
    • Set the default_enabled attribute in the feature flag definition to true.
    • Decide which changelog entry is needed.
  • 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 code_suggestions_direct_completions --dev --pre --staging --staging-ref --production
  • Close [the feature issue][main-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 code_suggestions_direct_completions".
    • 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.

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 the code_suggestions_direct_completions feature flag. Ask for review/approval/merge as usual. The MR should include the following changes:
    • 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][main-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 code_suggestions_direct_completions --dev --pre --staging --staging-ref --production
  • notify groupoptimize team that this flag was rolled-out (#456443 (comment 1914342335))
  • Close this rollout issue.

Rollback Steps

  • This feature can be disabled on production by running the following Chatops command:
/chatops run feature set code_suggestions_direct_completions false
  • Disable the feature flag on non-production environments:
/chatops run feature set code_suggestions_direct_completions false --dev --pre --staging --staging-ref
  • Delete feature flag from all environments:
/chatops run feature delete code_suggestions_direct_completions --dev --pre --staging --staging-ref --production
Edited by Jan Provaznik