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debian-mirror-gitlab/doc/development/lfs.md
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Create Source Code To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/product/ux/technical-writing/#assignments

Git LFS developer information (FREE)

This page contains developer-centric information for GitLab team members. For the user documentation, see Git Large File Storage.

Deep Dive

In April 2019, Francisco Javier López hosted a Deep Dive (GitLab team members only: https://gitlab.com/gitlab-org/create-stage/issues/1) on the GitLab Git LFS implementation to share domain-specific knowledge with anyone who may work in this part of the codebase in the future. You can find the recording on YouTube, and the slides on Google Slides and in PDF. This deep dive was accurate as of GitLab 11.10, and while specific details may have changed, it should still serve as a good introduction.

Including LFS blobs in project archives

Introduced in GitLab 13.5.

The following diagram illustrates how GitLab resolves LFS files for project archives:

sequenceDiagram
    autonumber
    Client->>+Workhorse: GET /group/project/-/archive/master.zip
    Workhorse->>+Rails: GET /group/project/-/archive/master.zip
    Rails->>+Workhorse: Gitlab-Workhorse-Send-Data git-archive
    Workhorse->>Gitaly: SendArchiveRequest
    Gitaly->>Git: git archive master
    Git->>Smudge: OID 12345
    Smudge->>+Workhorse: GET /internal/api/v4/lfs?oid=12345&gl_repository=project-1234
    Workhorse->>+Rails: GET /internal/api/v4/lfs?oid=12345&gl_repository=project-1234
    Rails->>+Workhorse: Gitlab-Workhorse-Send-Data send-url
    Workhorse->>Smudge: <LFS data>
    Smudge->>Git: <LFS data>
    Git->>Gitaly: <streamed data>
    Gitaly->>Workhorse: <streamed data>
    Workhorse->>Client: master.zip
  1. The user requests the project archive from the UI.
  2. Workhorse forwards this request to Rails.
  3. If the user is authorized to download the archive, Rails replies with an HTTP header of Gitlab-Workhorse-Send-Data with a base64-encoded JSON payload prefaced with git-archive. This payload includes the SendArchiveRequest binary message, which is encoded again in base64.
  4. Workhorse decodes the Gitlab-Workhorse-Send-Data payload. If the archive already exists in the archive cache, Workhorse sends that file. Otherwise, Workhorse sends the SendArchiveRequest to the appropriate Gitaly server.
  5. The Gitaly server calls git archive <ref> to begin generating the Git archive on-the-fly. If the include_lfs_blobs flag is enabled, Gitaly enables a custom LFS smudge filter via the -c filter.lfs.smudge=/path/to/gitaly-lfs-smudge Git option.
  6. When git identifies a possible LFS pointer using the .gitattributes file, git calls gitaly-lfs-smudge and provides the LFS pointer via the standard input. Gitaly provides GL_PROJECT_PATH and GL_INTERNAL_CONFIG as environment variables to enable lookup of the LFS object.
  7. If a valid LFS pointer is decoded, gitaly-lfs-smudge makes an internal API call to Workhorse to download the LFS object from GitLab.
  8. Workhorse forwards this request to Rails. If the LFS object exists and is associated with the project, Rails sends ArchivePath either with a path where the LFS object resides (for local disk) or a pre-signed URL (when object storage is enabled) via the Gitlab-Workhorse-Send-Data HTTP header with a payload prefaced with send-url.
  9. Workhorse retrieves the file and send it to the gitaly-lfs-smudge process, which writes the contents to the standard output.
  10. git reads this output and sends it back to the Gitaly process.
  11. Gitaly sends the data back to Rails.
  12. The archive data is sent back to the client.

In step 7, the gitaly-lfs-smudge filter must talk to Workhorse, not to Rails, or an invalid LFS blob is saved. To support this, GitLab 13.5 changed the default Omnibus configuration to have Gitaly talk to the Workhorse instead of Rails.

One side effect of this change: the correlation ID of the original request is not preserved for the internal API requests made by Gitaly (or gitaly-lfs-smudge), such as the one made in step 8. The correlation IDs for those API requests are random values until this Workhorse issue is resolved.

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