Git LFS

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 GitLab’s Git LFS implementation to share his domain specific knowledge with anyone who may work in this part of the code base in the future. You can find the recording on YouTube, and the slides on Google Slides and in PDF. Everything covered in this deep dive was accurate as of GitLab 11.10, and while specific details may have changed since then, 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 will call 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 will be 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 will be random values until this Workhorse issue is resolved.