[Feature flag] Rollout of `partition_pruning_dry_run`
Summary
This issue is to rollout the first step of partition pruning for date-partitioned tables on production,
that is currently behind the partition_pruning_dry_run
feature flag.
This feature flag will, when enabled, simply log the list of partitions that we are planning to prune from tables with associated pruning. Since the code for this change touches the code for creating new partitions, it is being released behind a feature flag out of an abudnance of caution.
Once we're satisfied that the list of partitions to detach is correct, this feature flag will be removed and new code (behind a second feature flag) will be added that actually drops these partitions.
Owners
- Team: Database
- Most appropriate slack channel to reach out to:
#g_database
- Best individual to reach out to: @stomlinson
- PM: @fzimmer
The Rollout Plan
- Enable
partition_pruning_dry_run
flag on Gitlab.com - Make sure that the code is logging the correct partitions to detach and not altering the partitions being created.
- Deploy code to actually detach these partitions, and remove this feature flag.
Testing Groups/Projects/Users
This flag is not specific to a group/project/user, so it will be toggled globally only.
Expectations
What are we expecting to happen?
The PartitionDetachWorker
will start logging about partitions it's planning to detach from the webhook logs table.
No other changes will happen, specifically the set of partitions being created by the PartitionCreationWorker will not change.
The log messages will look like Planning to detach web_hook_logs_202003 for table web_hook_logs
,
What might happen if this goes wrong?
If there is a logic bug, the set of partitions being created by the creation worker could change.
Alternatively, the set of partitions being logged to be deleted could be wrong. This would not affect the database, but would need to be fixed before going live with the actual detach code.
Since the partition creator creates partitions 6 months in the future, we will have many months to toggle this flag if there is a problem.
What can we monitor to detect problems with this?
We can monitor the set of partitions being created with this kibana search
We should see no partitions being created until August 1, when partitions for 2022-02 will be created.
We can also monitor the set of partitions that the worker is planning to delete with this kibana search
Once every 6 hours, when the detach worker runs, we should see it logging all the web_hook_logs partitions that are more than 3 months old, and no others. As of July 2021, that's partitions for the month of March 2021 and earlier.
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 partition_pruning_dry_run true --dev
-
/chatops run feature set partition_pruning_dry_run 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 might impact the user experience, notify #support_gitlab-com
and your team channel (more guidance when this is necessary in the dev docs). -
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> partition_pruning_dry_run 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 partition_pruning_dry_run <rollout-percentage> --actors
-
- If the feature flag in code does NOT have an actor, perform time-based rollout (random rollout).
-
/chatops run feature set partition_pruning_dry_run <rollout-percentage>
-
- Enable the feature globally on production environment.
-
/chatops run feature set partition_pruning_dry_run 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 partition_pruning_dry_run
". -
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 partition_pruning_dry_run
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 partition_pruning_dry_run --dev
-
/chatops run feature delete partition_pruning_dry_run --staging
-
/chatops run feature delete partition_pruning_dry_run
-
-
Close this rollout issue.
Rollback Steps
-
This feature can be disabled by running the following Chatops command:
/chatops run feature set partition_pruning_dry_run false