realaravinth/debian-mirror-gitlab
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity
debian-mirror-gitlab/doc/architecture/blueprints/cells/index.md
Pirate Praveen 271ccb9124 New upstream version 16.0.7+ds1
2023-07-09 08:55:56 +05:30

16 KiB
Raw Blame History

status creation-date authors coach approvers owning-stage participating-stages
accepted 2022-09-07
@ayufan
@fzimmer
@DylanGriffith
@ayufan
@fzimmer
~devops::enablement

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 that allows to share cluster-wide data is implemented.

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

  1. Instance-wide settings are shared across cluster.

    The Admin Area section for 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 the 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 the 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 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 ambigious 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 Cell is cloned we change routing information for organizations. Organization will encode cell_id. When we update cell_id it will automatically make the given Cell to be authoritative to handle the 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 (ex. 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 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 rather be 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

    • 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

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

  3. Iteration 3 - FY24Q3

    • Essential workflows: User can create project.
    • Essential workflows: User can push to Git repository.
    • Cell deployment: Extend GitLab Dedicated to support GCP
    • Routing: Technology.

  4. Iteration 4 - FY24Q4

    • Essential workflows: User can run CI pipeline.
    • Essential workflows: User can create issue, merge request, and merge it after it is green.
    • Data access layer: Evaluate the efficiency of database-level access vs. API-oriented access layer
    • Data access layer: Cluster-unique identifiers.
    • Routing: Cell discovery.
    • Routing: Router endpoints classification.

  5. Iteration 5 - FY25Q1

    • TBD

Technical Proposals

The Cells architecture do have 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. This is the list of known affected features with the proposed solutions.

Decision log

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

Links