Add a new Redis instance

GitLab can make use of multiple Redis instances. These instances are functionally partitioned so that, for example, we can store CI trace chunks from one Redis instance while storing sessions in another.

From time to time we might want to add a new Redis instance. Typically this will be a functional partition split from one of the existing instances such as the cache or shared state. This document describes an approach for adding a new Redis instance that handles existing data, based on prior examples:

This document does not cover the operational side of preparing and configuring the new Redis instance in detail, but the example epics do contain information on previous approaches to this.

Step 1: Support configuring the new instance

Before we can switch any features to using the new instance, we have to support configuring it and referring to it in the codebase. We must support the main installation types:

Fallback instance

In the application code, we need to define a fallback instance in case the new instance is not configured. For example, if a GitLab instance has already configured a separate shared state Redis, and we are partitioning data from the shared state Redis, our new instance’s configuration should default to that of the shared state Redis when it’s not present. Otherwise we could break instances that don’t configure the new Redis instance as soon as it’s available.

You can define a .config_fallback method in Gitlab::Redis::Wrapper (the base class for all Redis instances) that defines the instance to be used if this one is not configured. If we were adding a Foo instance that should fall back to SharedState, we can do that like this:

module Gitlab
  module Redis
    class Foo < ::Gitlab::Redis::Wrapper
      # The data we store on Foo used to be stored on SharedState.
      def self.config_fallback
        SharedState
      end
    end
  end
end

We should also add specs like those in trace_chunks_spec.rb to ensure that this fallback works correctly.

Step 2: Support writing to and reading from the new instance

When migrating to the new instance, we must account for cases where data is either on:

  • The ‘old’ (original) instance.
  • The new one that we have just added support for.

As a result we may need to support reading from and writing to both instances, depending on some condition.

The exact condition to use varies depending on the data to be migrated. For the trace chunks case above, there was already a database column indicating where the data was stored (as there are other storage options than Redis).

This step may not apply if the data has a very short lifetime (a few minutes at most) and is not critical. In that case, we may decide that it is OK to incur a small amount of data loss and switch over through configuration only.

If there is not a more natural way to mark where the data is stored, using a feature flag may be convenient:

  • It does not require an application restart to take effect.
  • It applies to all application instances (Sidekiq, API, web, etc.) at the same time.
  • It supports incremental rollout - ideally by actor (project, group, user, etc.) - so that we can monitor for errors and roll back easily.

Step 3: Migrate the data

We then need to configure the new instance for GitLab.com’s production and staging environments. Hopefully it will be possible to test this change effectively on staging, to at least make sure that basic usage continues to work.

After that is done, we can roll out the change to production. Ideally this would be in an incremental fashion, following the standard incremental rollout documentation for feature flags.

When we have been using the new instance 100% of the time in production for a while and there are no issues, we can proceed.

Step 4: clean up after the migration

We may choose to keep the migration paths or remove them, depending on whether or not we expect self-managed instances to perform this migration. gitlab-com/gl-infra/scalability#1131 contains a discussion on this topic for the trace chunks feature flag. It may be - as in that case - that we decide that the maintenance costs of supporting the migration code are higher than the benefits of allowing self-managed instances to perform this migration seamlessly, if we expect self-managed instances to cope without this functional partition.

If we decide to keep the migration code:

  • We should document the migration steps.
  • If we used a feature flag, we should ensure it’s an ops type feature flag, as these are long-lived flags.

Otherwise, we can remove the flags and conclude the project.