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Optimizing GitLab for large repositories

Large repositories consisting of more than 50k files in a worktree often require special consideration because of the time required to clone and check out.

GitLab and GitLab Runner handle this scenario well but require optimized configuration to efficiently perform its set of operations.

The general guidelines for handling big repositories are simple. Each guideline is described in more detail in the sections below:

  • Always fetch incrementally. Do not clone in a way that results in recreating all of the worktree.
  • Always use shallow clone to reduce data transfer. Be aware that this puts more burden on GitLab instance due to higher CPU impact.
  • Control the clone directory if you heavily use a fork-based workflow.
  • Optimize git clean flags to ensure that you remove or keep data that might affect or speed-up your build.

Shallow cloning

Introduced in GitLab Runner 8.9.

GitLab and GitLab Runner perform a shallow clone by default.

Ideally, you should always use GIT_DEPTH with a small number like 10. This instructs GitLab Runner to perform shallow clones. Shallow clones make Git request only the latest set of changes for a given branch, up to desired number of commits as defined by the GIT_DEPTH variable.

This significantly speeds up fetching of changes from Git repositories, especially if the repository has a very long backlog consisting of number of big files as we effectively reduce amount of data transfer.

The following example makes the runner shallow clone to fetch only a given branch; it does not fetch any other branches nor tags.

variables:
  GIT_DEPTH: 10

test:
  script:
    - ls -al

Git strategy

Introduced in GitLab Runner 8.9.

By default, GitLab is configured to use the fetch Git strategy, which is recommended for large repositories. This strategy reduces the amount of data to transfer and does not really impact the operations that you might do on a repository from CI.

Git clone path

Introduced in GitLab Runner 11.10.

GIT_CLONE_PATH allows you to control where you clone your sources. This can have implications if you heavily use big repositories with fork workflow.

Fork workflow from GitLab Runner's perspective is stored as a separate repository with separate worktree. That means that GitLab Runner cannot optimize the usage of worktrees and you might have to instruct GitLab Runner to use that.

In such cases, ideally you want to make the GitLab Runner executor be used only for the given project and not shared across different projects to make this process more efficient.

The GIT_CLONE_PATH has to be within the $CI_BUILDS_DIR. Currently, it is impossible to pick any path from disk.

Git clean flags

Introduced in GitLab Runner 11.10.

GIT_CLEAN_FLAGS allows you to control whether or not you require the git clean command to be executed for each CI job. By default, GitLab ensures that you have your worktree on the given SHA, and that your repository is clean.

GIT_CLEAN_FLAGS is disabled when set to none. On very big repositories, this might be desired because git clean is disk I/O intensive. Controlling that with GIT_CLEAN_FLAGS: -ffdx -e .build/ (for example) allows you to control and disable removal of some directories within the worktree between subsequent runs, which can speed-up the incremental builds. This has the biggest effect if you re-use existing machines and have an existing worktree that you can re-use for builds.

For exact parameters accepted by GIT_CLEAN_FLAGS, see the documentation for git clean. The available parameters are dependent on Git version.

Git fetch extra flags

Introduced in GitLab Runner 13.1.

GIT_FETCH_EXTRA_FLAGS allows you to modify git fetch behavior by passing extra flags.

For example, if your project contains a large number of tags that your CI jobs don't rely on, you could add --no-tags to the extra flags to make your fetches faster and more compact.

See the GIT_FETCH_EXTRA_FLAGS documentation for more information.

Fork-based workflow

Introduced in GitLab Runner 11.10.

Following the guidelines above, let's imagine that we want to:

  • Optimize for a big project (more than 50k files in directory).
  • Use forks-based workflow for contributing.
  • Reuse existing worktrees. Have preconfigured runners that are pre-cloned with repositories.
  • Runner assigned only to project and all forks.

Let's consider the following two examples, one using shell executor and other using docker executor.

shell executor example

Let's assume that you have the following config.toml.

concurrent = 4

[[runners]]
  url = "GITLAB_URL"
  token = "TOKEN"
  executor = "shell"
  builds_dir = "/builds"
  cache_dir = "/cache"

  [runners.custom_build_dir]
    enabled = true

This config.toml:

  • Uses the shell executor,
  • Specifies a custom /builds directory where all clones are stored.
  • Enables the ability to specify GIT_CLONE_PATH,
  • Runs at most 4 jobs at once.

docker executor example

Let's assume that you have the following config.toml.

concurrent = 4

[[runners]]
  url = "GITLAB_URL"
  token = "TOKEN"
  executor = "docker"
  builds_dir = "/builds"
  cache_dir = "/cache"

  [runners.docker]
    volumes = ["/builds:/builds", "/cache:/cache"]

This config.toml:

  • Uses the docker executor,
  • Specifies a custom /builds directory on disk where all clones are stored. We host mount the /builds directory to make it reusable between subsequent runs and be allowed to override the cloning strategy.
  • Doesn't enable the ability to specify GIT_CLONE_PATH as it is enabled by default.
  • Runs at most 4 jobs at once.

Our .gitlab-ci.yml

Once we have the executor configured, we need to fine tune our .gitlab-ci.yml.

Our pipeline is most performant if we use the following .gitlab-ci.yml:

variables:
  GIT_DEPTH: 10
  GIT_CLONE_PATH: $CI_BUILDS_DIR/$CI_CONCURRENT_ID/$CI_PROJECT_NAME

build:
  script: ls -al

The above configures a:

  • Shallow clone of 10, to speed up subsequent git fetch commands.
  • Custom clone path to make it possible to re-use worktrees between parent project and all forks because we use the same clone path for all forks.

Why use $CI_CONCURRENT_ID? The main reason is to ensure that worktrees used are not conflicting between projects. The $CI_CONCURRENT_ID represents a unique identifier within the given executor. When we use it to construct the path, this directory does not conflict with other concurrent jobs running.

Store custom clone options in config.toml

Ideally, all job-related configuration should be stored in .gitlab-ci.yml. However, sometimes it is desirable to make these schemes part of the runner's configuration.

In the above example of Forks, making this configuration discoverable for users may be preferred, but this brings administrative overhead as the .gitlab-ci.yml needs to be updated for each branch. In such cases, it might be desirable to keep the .gitlab-ci.yml clone path agnostic, but make it a configuration of the runner.

We can extend our config.toml with the following specification that is used by the runner if .gitlab-ci.yml does not override it:

concurrent = 4

[[runners]]
  url = "GITLAB_URL"
  token = "TOKEN"
  executor = "docker"
  builds_dir = "/builds"
  cache_dir = "/cache"

  environment = [
    "GIT_DEPTH=10",
    "GIT_CLONE_PATH=$CI_BUILDS_DIR/$CI_CONCURRENT_ID/$CI_PROJECT_NAME"
  ]

  [runners.docker]
    volumes = ["/builds:/builds", "/cache:/cache"]

This makes the cloning configuration to be part of the given runner and does not require us to update each .gitlab-ci.yml.

Pre-clone step

For very active repositories with a large number of references and files, you can also optimize your CI jobs by seeding repository data with GitLab Runner's pre_clone_script.

See our development documentation for an overview of how we implemented this approach on GitLab.com for the main GitLab repository.