Limits and FAQ for Git integration with Databricks Git folders
Databricks Git folders and Git integration have limits specified in the following sections. For general information, see Databricks limits.
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To learn about Databricks asset types supported in Git folders, see What asset types are supported by Git folders?.
File and repo limits
Databricks doesn’t enforce a limit on the size of a repo. However:
- Working branches are limited to 1 gigabyte (GB).
- Files larger than 10 MB can’t be viewed in the Databricks UI.
- Individual workspace files are subject to a separate size limit. For more details, read Limitations.
Databricks recommends that in a repo:
- The total number of workspace assets and files does not exceed 20,000.
For any Git operation, memory usage is limited to 2 GB and disk writes are limited to 4 GB. Because the limit is per operation, you get a failure if you attempt to clone a Git repo that is 5 GB in size. However, if you clone a Git repo that is 3 GB in size in one operation and then add 2 GB to it later, the next pull operation will succeed.
If your repo exceeds these limits, you might receive an error message. You might also receive a timeout error when cloning the repo, even though the operation might still complete in the background.
To work with a repo larger than the size limits, try sparse checkout.
If you must write temporary files that you do not want to keep after the cluster is shut down, writing the temporary files to $TEMPDIR avoids exceeding branch size limits and yields better performance than writing to the current working directory (CWD) if the CWD is in the workspace filesystem. For more information, see Where should I write temporary files on Databricks?.
Maximum number of Git folders per workspace
You can have a maximum of 2,000 Git folders per workspace. If you require more, contact Databricks support.
Recovering files deleted from Git folders in your workspace
Workspace actions on Git folders vary in file recoverability. Some actions allow recovery through the Trash folder, while others do not. Files previously committed and pushed to a remote branch can be restored using the Git commit history for the remote Git repository. This table outlines each action’s behavior and recoverability:
Action | Is the file recoverable? |
|---|---|
Delete file with workspace browser | Yes, from the Trash folder |
Discard a new file with the Git folder dialog | Yes, from the Trash folder |
Discard a modified file with the Git folder dialog | No, the file is gone |
| No, file modifications are gone |
| No, file modifications are gone |
Switch branches with the Git folder dialog | Yes, from the remote Git repo |
Other Git operations, such as commit or push, from the Git folder dialog | Yes, from the remote Git repo |
| Yes, from the remote Git repo |
Monorepo support
Databricks recommends not creating Git folders backed by monorepos. A monorepo is a large, single-organization Git repository with many thousands of files across many projects.
Frequently Asked Questions: Git folder configuration
Where is Databricks repo content stored?
The contents of a repo are temporarily cloned onto disk in the control plane. Databricks notebook files are stored in the control plane database just like notebooks in the main workspace. Non-notebook files are stored on disk for up to 30 days.
Do Git folders support on-premises or self-hosted Git servers?
Databricks Git folders supports GitHub Enterprise, Bitbucket Server, Azure DevOps Server, and GitLab Self-managed integration, if the server is internet accessible. For details on integrating Git folders with an on-premises Git server, read Git Proxy Server for Git folders.
To integrate with a Bitbucket Server, GitHub Enterprise Server, or a GitLab self-managed subscription instance that is not internet-accessible, contact your Databricks account team.
What Databricks asset types are supported by Git folders?
For details on supported asset types, see What asset types are supported by Git folders?.
Do Git folders support .gitignore files?
Yes. If you add a file to your repo and do not want it to be tracked by Git, create a .gitignore file or use one cloned from your remote repository and add the filename, including the extension.
.gitignore works only for files not already tracked by Git. If you add a file already tracked by Git to a .gitignore file, the file is still tracked by Git.
Do Git folders support Git submodules?
No. You can clone a repo that contains Git submodules, but the submodule is not cloned.
Source management
Why do notebook dashboards disappear when I pull or checkout a different branch?
This is a limitation because Databricks notebook source files don’t store notebook dashboard information.
To preserve dashboards in the Git repository, change the notebook format to .ipynb (the Jupyter notebook format). By default, .ipynb supports dashboard and visualization definitions. To preserve graph data (data points), you must commit the notebook with outputs.
To learn about committing .ipynb notebook outputs, see Manage IPYNB notebook output commits.
Do Git folders support branch merging?
Yes. You can also create a pull request and merge through your Git provider.
Can I delete a branch from a Databricks repo?
No. To delete a branch, you must work in your Git provider.
If a library is installed on a cluster, and a library with the same name is included in a folder in a repo, which library is imported?
The library in the repo is imported. For more information about library precedence in Python, see Python library precedence.
Can I pull the latest version of a repository from Git before running a job without relying on an external orchestration tool?
No. Typically, you can integrate this as a pre-commit hook on the Git server so that every push to a branch (main/prod) updates the Production repo.
Can I export a repo?
You can export notebooks, folders, or an entire repo. You cannot export non-notebook files. If you export an entire repo, non-notebook files are not included. To export, use the workspace export command in the Databricks CLI or use the Workspace API.
Security, authentication, and tokens
Are the contents of Databricks Git folders encrypted?
The contents of Databricks Git folders are encrypted by Databricks using a default key. Encryption using customer-managed keys is not supported except when encrypting your Git credentials.
How and where are the GitHub tokens stored in Databricks? Who would have access from Databricks?
- The authentication tokens are stored in the Databricks control plane, and a Databricks employee can only gain access through a temporary credential that is audited.
- Databricks logs the creation and deletion of these tokens but not their usage. Databricks has logging that tracks Git operations that can be used to audit the usage of the tokens by the Databricks application.
- GitHub enterprise audits token usage. Other Git services might also have Git server auditing.
Does Git folders support GPG signing of commits?
No.
Does Git folders support Git operations using SSH?
No, only the HTTPS protocol is supported.
CI/CD and MLOps
Incoming changes clear the notebook state
Git operations that alter the notebook source code result in the loss of the notebook state, including cell outputs, comments, version history, and widgets. For example, git pull can change the source code of a notebook. In this case, Databricks Git folders must overwrite the existing notebook to import the changes. git commit and push or creating a new branch does not affect the notebook source code, so the notebook state is preserved in these operations.
MLflow experiments don’t work in Git folders with DBR 14.x or lower versions.
Can I create an MLflow experiment in a Git folder?
There are two types of MLflow experiments: workspace and notebook. For details on the two types of MLflow experiments, see Organize training runs with MLflow experiments.
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Workspace experiments: You cannot create workspace MLflow experiments in Git folders. Instead, log MLflow runs to an MLflow experiment created in a regular workspace folder. If multiple users use separate Git folders to collaborate on the same code, log MLflow runs to an MLflow experiment created in a shared workspace folder.
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Notebook experiments: You can create notebook experiments in a Databricks Git folder. If you check your notebook into source control as an
.ipynbfile, you can log MLflow runs to an automatically created and associated MLflow experiment. However, the experiment and the associated runs are not checked into source control. To learn more, see creating notebook experiments.
Prevent data loss in MLflow experiments
Notebook MLflow experiments created using Databricks Jobs with source code in a remote repository are stored in a temporary storage location. These experiments persist initially after workflow execution but are at risk of deletion later during scheduled removal of files in temporary storage. Databricks recommends using workspace MLflow experiments with Jobs and remote Git sources.
Any time you switch to a branch that does not contain the notebook, you risk losing the associated MLflow experiment data. This loss becomes permanent if the prior branch is not accessed within 30 days.
To recover missing experiment data before the 30-day expiry, change the notebook name to the original name, open the notebook, and click 
on the right side pane, which triggers a call to the mlflow.get_experiment_by_name() function. When the function returns, you can see the recovered experiment and runs. After 30 days, any orphaned MLflow experiments will be purged to meet GDPR compliance policies.
To prevent this situation, Databricks recommends not renaming notebooks in a repo. If you do rename a notebook, click the “experiment” icon on the right side pane immediately after renaming the notebook.
What happens if a notebook job is running in a workspace while a Git operation is in progress?
At any point that a Git operation is in progress, some notebooks in the repo might have been updated while others have not. This can cause unpredictable behavior.
For example, suppose notebook A calls notebook Z using a %run command. If a job running during a Git operation starts the most recent version of notebook A, but notebook Z has not yet been updated, the %run command in notebook A might start the older version of notebook Z. During the Git operation, the notebook states are not predictable, and the job might fail or run notebook A and notebook Z from different commits.
To avoid this situation, configure your job tasks to use your Git provider as the source and not a workspace path. To learn more, see Use Git with jobs.
Resources
For details on Databricks workspace files, see What are workspace files?.