Rachit Sarna

Managing large files such as audio, video and graphics files has always been one of the shortcomings of Git. The general recommendation is to not have Git repositories larger than 1 GB to preserve performance.

Files tracked by Git LFS display an icon to indicate if the file is stored as a blob or an LFS pointer.

Git LFS client communicates with the GitLab server over HTTPS. It uses HTTP Basic Authentication to authorize client requests. After the request is authorized, Git LFS client receives instructions from where to fetch or where to push the large file.

Documentation for GitLab instance administrators is under LFS administration doc.

When you add an LFS object to a repository, GitLab:

When your repository is forked, LFS objects from the upstream project are associated with the fork. When the fork is created, the LFS object storage for the fork is equal to the storage used by the upstream project. If new LFS objects are added to the fork, the total object storage changes for the fork, but not the upstream project.

If you create a merge request from the fork back to the upstream project, any new LFS objects in the fork become associated with the upstream project.

Let’s take a look at the workflow for checking large files into your Git repository with Git LFS. For example, if you want to upload a very large file and check it into your Git repository:

After you mark a file extension for tracking as a LFS object you can use Git as usual without redoing the command to track a file with the same extension:

Make sure that .gitattributes is tracked by Git. Otherwise Git LFS doesn’t work properly for people cloning the project:

Cloning the repository works the same as before. Git automatically detects the LFS-tracked files and clones them via HTTP. If you performed the git clone command with a SSH URL, you have to enter your GitLab credentials for HTTP authentication.

If you already cloned the repository and you want to get the latest LFS object that are on the remote repository, such as for a branch from origin:

Make sure your files aren’t listed in .gitignore, otherwise, they are ignored by Git and are not pushed to the remote repository.

Read the documentation on how to migrate an existing Git repository with Git LFS.

To remove objects from LFS:

See the documentation on File Locking.

Prior to GitLab 13.5, project source downloads would include Git LFS pointers instead of the actual objects. For example, LFS pointers look like the following:

In GitLab version 13.5 and later, these pointers are converted to the uploaded LFS object.

Technical details about how this works can be found in the development documentation for LFS.

This error indicates the files are expected to be tracked by LFS, but the repository is not tracking them as LFS. This issue can be one potential reason for this error: Files not tracked with LFS when uploaded through the web interface

To resolve the problem, migrate the affected file (or files) and push back to the repository:

This error can occur for a few reasons, including:

Check if you have permissions to push to the project or fetch from the project.

LFS object you are trying to push to the project or fetch from the project is not available to the project anymore. Probably the object was removed from the server.

Git LFS logs the failures into a log file. To view this log file, while in project directory:

If the status error 501 is shown, it is because:

If you push an LFS object to a project and receive an error like this, the LFS client is trying to reach GitLab through HTTPS. However, your GitLab instance is being served on HTTP:

This behavior is caused by Git LFS using HTTPS connections by default when a lfsurl is not set in the Git configuration.

To prevent this from happening, set the LFS URL in project Git configuration:

Git LFS authenticates the user with HTTP Basic Authentication on every push for every object, so user HTTPS credentials are required.

By default, Git has support for remembering the credentials for each repository you use. For more information, see the official Git documentation.

For example, you can tell Git to remember the password for a period of time in which you expect to push the objects:

This remembers the credentials for an hour, after which Git operations require re-authentication.

If you are using OS X you can use osxkeychain to store and encrypt your credentials. For Windows, you can use wincred or Microsoft’s Git Credential Manager for Windows.

More details about various methods of storing the user credentials can be found on Git Credential Storage documentation.

GitLab checks files to detect LFS pointers on push. If LFS pointers are detected, GitLab tries to verify that those files already exist in LFS on GitLab.

Verify that LFS is installed locally and consider a manual push with git lfs push --all.

If you are storing LFS files outside of GitLab you can disable LFS on the project by setting lfs_enabled: false with the projects API.

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The negatives you list for IM/FPIM can often be ameliorated by appropriate use of an as clause. from some.package import mymodulewithalongname as mymod can usefully shorten your code and enhance its readability, and if you rename mymodulewithalongname to somethingcompletelydifferent tomorrow, the as clause can be used as a single statement to edit.

Consider your pro-FMIF point 3 (call it R for redirection) vs your pro-FPIM point 2 (call it F for flexibility): R amounts to facilitating the loss of integrity of module boundaries, while F strenghtens it. Multiple functions, classes and variables in a module are often intended to work together: they should not be independently switched to different meanings. For example, consider module random and its functions seed and uniform: if you were to switch the import of just one of them to a different module, then you'd break the normal connection between calls to seed and results of calls to uniform. When a module is well designed, with cohesion and integrity, R's facilitation of breaking down the module's boundaries is actually a negative -- it makes it easier to do something you're better off not doing.

Vice versa, F is what enables coordinated switching of coupled functions, classes, and variables (so, generally, of entities that belong together, by modularity). For example, to make testing repeatable (FPIM pro-point 1), you mock both seed and random in the random module, and if your code follows FPIM, you're all set, coordination guaranteed; but if you have code that has imported the functions directly, you have to hunt down each such module and repeat the mocking over and over and over again. Making tests perfectly repeatable typically also requires "coordinated mocking" of date and time functions -- if you use from datetime import datetime in some modules, you need to find and mock them all (as well as all those doing from time import time, and so forth) to ensure that all the times received when the various parts of the system ask "so what time is it now?" are perfectly consistent (if you use FPIM, you just mock the two relevant modules).

I like FPIM, because there's really not much added value by using a multiply qualified name rather than a singly qualified one (while the difference between barenames and qualified names is huge -- you get so much more control with a qualified name, be it singly or multiply, than you possibly ever can with a barename!).

Ah well, can't devote all of the working day to responding to each and every one of your points -- your question should probably be half a dozen questions;-). I hope this at least addresses "why is F better than R" and some of the mocking/testing issues -- it boils down to preserving and enhancing well-designed modularity (via F) rather than undermining it (via R).

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