Skip to content
GitLab

Cells

This document is a work-in-progress and represents a very early state of the Cells design. Significant aspects are not documented, though we expect to add them in the future.

Cells is a new architecture for our software as a service platform. This architecture is horizontally scalable, resilient, and provides a more consistent user experience. It may also provide additional features in the future, such as data residency control (regions) and federated features.

For more information about Cells, see also:

Work streams

We can't ship the entire Cells architecture in one go - it is too large. Instead, we are defining key work streams required by the project.

Not all objectives need to be fulfilled to reach production readiness. It is expected that some objectives will not be completed for General Availability (GA), but will be enough to run Cells in production.

1. Data access layer

Before Cells can be run in production we need to prepare the codebase to accept the Cells architecture. This preparation involves:

  • Allowing data sharing between Cells.
  • Updating the tooling for discovering cross-Cell data traversal.
  • Defining code practices for cross-Cell data traversal.
  • Analyzing the data model to define the data affinity.

Under this objective the following steps are expected:

  1. Allow to share cluster-wide data with database-level data access layer.

    Cells can connect to a database containing shared data. For example: application settings, users, or routing information.

  2. Evaluate the efficiency of database-level access vs. API-oriented access layer.

    Reconsider the consequences of database-level data access for data migration, resiliency of updates and of interconnected systems when we share only a subset of data.

  3. Cluster-unique identifiers

    Every object has a unique identifier that can be used to access data across the cluster. The IDs for allocated Projects, issues and any other objects are cluster-unique.

  4. Cluster-wide deletions

    If entities deleted in Cell 2 are cross-referenced, they are properly deleted or nullified across clusters. We will likely re-use existing loose foreign keys to extend it with cross-Cells data removal.

  5. Data access layer

    Ensure that a stable data access (versioned) layer is implemented that allows to share cluster-wide data.

  6. Database migration

    Ensure that migrations can be run independently between Cells, and we safely handle migrations of shared data in a way that does not impact other Cells.

2. Essential workflows

To make Cells viable we require to define and support essential workflows before we can consider the Cells to be of Beta quality. Essential workflows are meant to cover the majority of application functionality that makes the product mostly useable, but with some caveats.

The current approach is to define workflows from top to bottom. The order defines the presumed priority of the items. This list is not exhaustive as we would be expecting other teams to help and fix their workflows after the initial phase, in which we fix the fundamental ones.

To consider a project ready for the Beta phase, it is expected that all features defined below are supported by Cells. In the cases listed below, the workflows define a set of tables to be properly attributed to the feature. In some cases, a table with an ambiguous usage has to be broken down. For example: uploads are used to store user avatars, as well as uploaded attachments for comments. It would be expected that uploads is split into uploads (describing Group/Project-level attachments) and global_uploads (describing, for example, user avatars).

Except for the initial 2-3 quarters this work is highly parallel. It is expected that group::tenant scale will help other teams to fix their feature set to work with Cells. The first 2-3 quarters are required to define a general split of data and build the required tooling.

  1. Instance-wide settings are shared across cluster.

    The Admin Area section for the most part is shared across a cluster.

  2. User accounts are shared across cluster.

    The purpose is to make users cluster-wide.

  3. User can create Group.

    The purpose is to perform a targeted decomposition of users and namespaces, because namespaces will be stored locally in the Cell.

  4. User can create Project.

    The purpose is to perform a targeted decomposition of users and projects, because projects will be stored locally in the Cell.

  5. User can change profile avatar that is shared in cluster.

    The purpose is to fix global uploads that are shared in cluster.

  6. User can push to Git repository.

    The purpose is to ensure that essential joins from the Projects table are properly attributed to be Cell-local, and as a result the essential Git workflow is supported.

  7. User can run CI pipeline.

    The purpose is that ci_pipelines (like ci_stages, ci_builds, ci_job_artifacts) and adjacent tables are properly attributed to be Cell-local.

  8. User can create issue, merge request, and merge it after it is green.

    The purpose is to ensure that issues and merge requests are properly attributed to be Cell-local.

  9. User can manage Group and Project members.

    The members table is properly attributed to be either Cell-local or cluster-wide.

  10. User can manage instance-wide runners.

    The purpose is to scope all CI runners to be Cell-local. Instance-wide runners in fact become Cell-local runners. The expectation is to provide a user interface view and manage all runners per Cell, instead of per cluster.

  11. User is part of Organization and can only see information from the Organization.

    The purpose is to have many Organizations per Cell, but never have a single Organization spanning across many Cells. This is required to ensure that information shown within an Organization is isolated, and does not require fetching information from other Cells.

3. Additional workflows

Some of these additional workflows might need to be supported, depending on the group decision. This list is not exhaustive of work needed to be done.

  1. User can use all Group-level features.
  2. User can use all Project-level features.
  3. User can share Groups with other Groups in an Organization.
  4. User can create system webhook.
  5. User can upload and manage packages.
  6. User can manage security detection features.
  7. User can manage Kubernetes integration.
  8. TBD

4. Routing layer

The routing layer is meant to offer a consistent user experience where all Cells are presented under a single domain (for example, gitlab.com), instead of having to navigate to separate domains.

The user will be able to use https://gitlab.com to access Cell-enabled GitLab. Depending on the URL access, it will be transparently proxied to the correct Cell that can serve this particular information. For example:

  • All requests going to https://gitlab.com/users/sign_in are randomly distributed to all Cells.
  • All requests going to https://gitlab.com/gitlab-org/gitlab/-/tree/master are always directed to Cell 5, for example.
  • All requests going to https://gitlab.com/my-username/my-project are always directed to Cell 1.

  1. Technology.

    We decide what technology the routing service is written in. The choice is dependent on the best performing language, and the expected way and place of deployment of the routing layer. If it is required to make the service multi-cloud it might be required to deploy it to the CDN provider. Then the service needs to be written using a technology compatible with the CDN provider.

  2. Cell discovery.

    The routing service needs to be able to discover and monitor the health of all Cells.

  3. Router endpoints classification.

    The stateless routing service will fetch and cache information about endpoints from one of the Cells. We need to implement a protocol that will allow us to accurately describe the incoming request (its fingerprint), so it can be classified by one of the Cells, and the results of that can be cached. We also need to implement a mechanism for negative cache and cache eviction.

  4. GraphQL and other ambiguous endpoints.

    Most endpoints have a unique sharding key: the Organization, which directly or indirectly (via a Group or Project) can be used to classify endpoints. Some endpoints are ambiguous in their usage (they don't encode the sharding key), or the sharding key is stored deep in the payload. In these cases, we need to decide how to handle endpoints like /api/graphql.

5. Cell deployment

We will run many Cells. To manage them easier, we need to have consistent deployment procedures for Cells, including a way to deploy, manage, migrate, and monitor.

We are very likely to use tooling made for GitLab Dedicated with its control planes.

  1. Extend GitLab Dedicated to support GCP.
  2. TBD

6. Migration

When we reach production and are able to store new Organizations on new Cells, we need to be able to divide big Cells into many smaller ones.

  1. Use GitLab Geo to clone Cells.

    The purpose is to use GitLab Geo to clone Cells.

  2. Split Cells by cloning them.

    Once a Cell is cloned we change the routing information for Organizations. Organizations will encode a cell_id. When we update the cell_id it will automatically make the given Cell authoritative to handle traffic for the given Organization.

  3. Delete redundant data from previous Cells.

    Since the Organization is now stored on many Cells, once we change cell_id we will have to remove data from all other Cells based on organization_id.

Availability of the feature

We are following the Support for Experiment, Beta, and Generally Available features.

1. Experiment

Expectations:

  • We can deploy a Cell on staging or another testing environment by using a separate domain (for example cell2.staging.gitlab.com) using Cell deployment tooling.
  • User can create Organization, Group and Project, and run some of the essential workflows.
  • It is not expected to be able to run a router to serve all requests under a single domain.
  • We expect data loss of data stored on additional Cells.
  • We expect to tear down and create many new Cells to validate tooling.

2. Beta

Expectations:

  • We can run many Cells under a single domain (ex. staging.gitlab.com).
  • All features defined in essential workflows are supported.
  • Not all aspects of the routing layer are finalized.
  • We expect additional Cells to be stable with minimal data loss.

3. GA

Expectations:

4. Post GA

Expectations:

Iteration plan

The delivered iterations will focus on solving particular steps of a given key work stream. It is expected that initial iterations will be rather slow, because they require substantially more changes to prepare the codebase for data split.

One iteration describes one quarter's worth of work.

  1. Iteration 1 - FY24Q1 - Complete

    • Data access layer: Initial Admin Area settings are shared across cluster.
    • Essential workflows: Allow to share cluster-wide data with database-level data access layer

  2. Iteration 2 - FY24Q2 - In progress

    • Essential workflows: User accounts are shared across cluster.
    • Essential workflows: User can create Group.

  3. Iteration 3 - FY24Q3 - Planned

    • Essential workflows: User can create Project.
    • Routing: Technology.
    • Data access layer: Evaluate the efficiency of database-level access vs. API-oriented access layer

  4. Iteration 4 - FY24Q4

    • Essential workflows: User can push to Git repository.
    • Essential workflows: User can create issue, merge request, and merge it after it is green.
    • Data access layer: Cluster-unique identifiers.
    • Routing: Cell discovery.
    • Routing: Router endpoints classification.
    • Cell deployment: Extend GitLab Dedicated to support GCP

  5. Iteration 5 - FY25Q1

    • Essential workflows: User can run CI pipeline.
    • Essential workflows: Instance-wide settings are shared across cluster.
    • Essential workflows: User can change profile avatar that is shared in cluster.
    • Essential workflows: User can create issue, merge request, and merge it after it is green.
    • Essential workflows: User can manage Group and Project members.
    • Essential workflows: User can manage instance-wide runners.
    • Essential workflows: User is part of Organization and can only see information from the Organization.
    • Routing: GraphQL and other ambiguous endpoints.
    • Data access layer: Allow to share cluster-wide data with database-level data access layer.
    • Data access layer: Cluster-wide deletions.
    • Data access layer: Data access layer.
    • Data access layer: Database migrations.

  6. Iteration 6 - FY25Q2

    • TBD

  7. Iteration 7 - FY25Q3

    • TBD

  8. Iteration 8 - FY25Q4

    • TBD

Technical Proposals

The Cells architecture has long lasting implications to data processing, location, scalability and the GitLab architecture. This section links all different technical proposals that are being evaluated.

Impacted features

The Cells architecture will impact many features requiring some of them to be rewritten, or changed significantly. Below is a list of known affected features with preliminary proposed solutions.

Impacted features: Placeholders

The following list of impacted features only represents placeholders that still require work to estimate the impact of Cells and develop solution proposals.

Frequently Asked Questions

What's the difference between Cells architecture and GitLab Dedicated?

The new Cells architecture is meant to scale GitLab.com. The way to achieve this is by moving Organizations into Cells, but different Organizations can still share server resources, even if the application provides isolation from other Organizations. But all of them still operate under the existing GitLab SaaS domain name gitlab.com. Also, Cells still share some common data, like users, and routing information of Groups and Projects. For example, no two users can have the same username even if they belong to different Organizations that exist on different Cells.

With the aforementioned differences, GitLab Dedicated is still offered at higher costs due to the fact that it's provisioned via dedicated server resources for each customer, while Cells use shared resources. This makes GitLab Dedicated more suited for bigger customers, and GitLab Cells more suitable for small to mid-size companies that are starting on GitLab.com.

On the other hand, GitLab Dedicated is meant to provide a completely isolated GitLab instance for any Organization. This instance is running on its own custom domain name, and is totally isolated from any other GitLab instance, including GitLab SaaS. For example, users on GitLab Dedicated don't have to have a different and unique username that was already taken on GitLab.com.

Can different Cells communicate with each other?

Up until iteration 3, Cells communicate with each other only via a shared database that contains common data. In iteration 4 we are going to evaluate the option of Cells calling each other via API to provide more isolation and reliability.

Decision log

  • 2022-03-15: Google Cloud as the cloud service. For details, see issue 396641.

Links