[DEPRECATED] Databricks Labs CI/CD Templates
This repository provides a template for automated Databricks CI/CD pipeline creation and deployment.
NOTE: This repository is deprecated and provided for maintenance purposes only. Please use the dbx init functionality instead.
Table of Contents
- Databricks Labs CI/CD Templates
Sample project structure (with GitHub Actions)
.
โโโ .dbx
โย ย โโโ project.json
โโโ .github
โย ย โโโ workflows
โย ย โโโ onpush.yml
โย ย โโโ onrelease.yml
โโโ .gitignore
โโโ README.md
โโโ conf
โย ย โโโ deployment.json
โย ย โโโ test
โย ย โโโ sample.json
โโโ pytest.ini
โโโ sample_project
โย ย โโโ __init__.py
โย ย โโโ common.py
โย ย โโโ jobs
โย ย โโโ __init__.py
โย ย โโโ sample
โย ย โโโ __init__.py
โย ย โโโ entrypoint.py
โโโ setup.py
โโโ tests
โย ย โโโ integration
โย ย โย ย โโโ sample_test.py
โย ย โโโ unit
โย ย โโโ sample_test.py
โโโ unit-requirements.txt
Some explanations regarding structure:
.dbx
folder is an auxiliary folder, where metadata about environments and execution context is located.sample_project
- Python package with your code (the directory name will follow your project name)tests
- directory with your package testsconf/deployment.json
- deployment configuration file. Please read the following section for a full reference..github/workflows/
- workflow definitions for GitHub Actions
Sample project structure (with Azure DevOps)
.
โโโ .dbx
โย ย โโโ project.json
โโโ .gitignore
โโโ README.md
โโโ azure-pipelines.yml
โโโ conf
โย ย โโโ deployment.json
โย ย โโโ test
โย ย โโโ sample.json
โโโ pytest.ini
โโโ sample_project_azure_dev_ops
โย ย โโโ __init__.py
โย ย โโโ common.py
โย ย โโโ jobs
โย ย โโโ __init__.py
โย ย โโโ sample
โย ย โโโ __init__.py
โย ย โโโ entrypoint.py
โโโ setup.py
โโโ tests
โย ย โโโ integration
โย ย โย ย โโโ sample_test.py
โย ย โโโ unit
โย ย โโโ sample_test.py
โโโ unit-requirements.txt
Some explanations regarding structure:
.dbx
folder is an auxiliary folder, where metadata about environments and execution context is located.sample_project_azure_dev_ops
- Python package with your code (the directory name will follow your project name)tests
- directory with your package testsconf/deployment.json
- deployment configuration file. Please read the following section for a full reference.azure-pipelines.yml
- Azure DevOps Pipelines workflow definition
Sample project structure (with GitLab)
.
โโโ .dbx
โย ย โโโ project.json
โโโ .gitignore
โโโ README.md
โโโ .gitlab-ci.yml
โโโ conf
โย ย โโโ deployment.json
โย ย โโโ test
โย ย โโโ sample.json
โโโ pytest.ini
โโโ sample_project_gitlab
โย ย โโโ __init__.py
โย ย โโโ common.py
โย ย โโโ jobs
โย ย โโโ __init__.py
โย ย โโโ sample
โย ย โโโ __init__.py
โย ย โโโ entrypoint.py
โโโ setup.py
โโโ tests
โย ย โโโ integration
โย ย โย ย โโโ sample_test.py
โย ย โโโ unit
โย ย โโโ sample_test.py
โโโ unit-requirements.txt
Some explanations regarding structure:
.dbx
folder is an auxiliary folder, where metadata about environments and execution context is located.sample_project_gitlab
- Python package with your code (the directory name will follow your project name)tests
- directory with your package testsconf/deployment.json
- deployment configuration file. Please read the following section for a full reference..gitlab-ci.yml
- GitLab CI/CD workflow definition
Note on dbx
NOTE:
dbx is a CLI tool for advanced Databricks jobs management. It can be used separately from cicd-templates, and if you would like to preserve your project structure, please refer to dbx documentation on how to use it with customized project structure.
Quickstart
NOTE:
As a prerequisite, you need to install databricks-cli with a configured profile. In this instruction we're based on Databricks Runtime 7.3 LTS ML. If you don't need to use ML libraries, we still recommend to use ML-based version due to%pip
magic support.
Local steps
Perform the following actions in your development environment:
- Create new conda environment and activate it:
conda create -n <your-environment-name> python=3.7.5
conda activate <your-environment-name>
- If you would like to be able to run local unit tests, you'll need JDK. If you don't have one, It can be installed via:
conda install -c anaconda "openjdk=8.0.152"
- Install cookiecutter and path:
pip install cookiecutter path
- Create new project using cookiecutter template:
cookiecutter https://github.com/databrickslabs/cicd-templates
- Install development dependencies:
pip install -r unit-requirements.txt
- Install generated package in development mode:
pip install -e .
- In the generated directory you'll have a sample job with testing and launch configurations around it.
- Launch and debug your code on an interactive cluster via the following command. Job name could be found in
conf/deployment.json
:
dbx execute --cluster-name=<my-cluster> --job=<job-name>
- Make your first deployment from the local machine:
dbx deploy
- Launch your first pipeline as a new separate job, and trace the job status. Job name could be found in
conf/deployment.json
:
dbx launch --job <your-job-name> --trace
- For an in-depth local development and unit testing guidance, please refer to a generated
README.md
in the root of the project.
Setting up CI/CD pipeline on GitHub Actions
- Create a new repository on GitHub
- Configure
DATABRICKS_HOST
andDATABRICKS_TOKEN
secrets for your project in GitHub UI - Add a remote origin to the local repo
- Push the code
- Open the GitHub Actions for your project to verify the state of the deployment pipeline
Setting up CI/CD pipeline on Azure DevOps
- Create a new repository on GitHub
- Connect the repository to Azure DevOps
- Configure
DATABRICKS_HOST
andDATABRICKS_TOKEN
secrets for your project in Azure DevOps. Note that secret variables must be mapped to env as mentioned here using the syntaxenv:
for example:
variables:
- group: Databricks-environment
stages:
...
...
- script: |
dbx deploy
env:
DATABRICKS_TOKEN: $(DATABRICKS_TOKEN)
- Add a remote origin to the local repo
- Push the code
- Open the Azure DevOps UI to check the deployment status
Setting up CI/CD pipeline on Gitlab
- Create a new repository on Gitlab
- Configure
DATABRICKS_HOST
andDATABRICKS_TOKEN
secrets for your project in GitLab UI - Add a remote origin to the local repo
- Push the code
- Open the GitLab CI/CD UI to check the deployment status
Deployment file structure
A sample deployment file could be found in a generated project.
General file structure could look like this:
{
"<environment-name>": {
"jobs": [
{
"name": "sample_project-sample",
"existing_cluster_id": "some-cluster-id",
"libraries": [],
"max_retries": 0,
"spark_python_task": {
"python_file": "sample_project/jobs/sample/entrypoint.py",
"parameters": [
"--conf-file",
"conf/test/sample.json"
]
}
}
]
}
}
Per each environment you could describe any amount of jobs. Job description should follow the Databricks Jobs API.
However, there is some advanced behaviour for a dbx deploy
.
When you run dbx deploy
with a given deployment file (by default it takes the deployment file from conf/deployment.json
), the following actions will be performed:
- Find the deployment configuration in
--deployment-file
(default:conf/deployment.json
) - Build .whl package in a given project directory (could be disabled via
--no-rebuild
option) - Add this .whl package to a job definition
- Add all requirements from
--requirements-file
(default:requirements.txt
). Step will be skipped if requirements file is non-existent. - Create a new job or adjust existing job if the given job name exists. Job will be found by it's name.
Important thing about referencing is that you can also reference arbitrary local files. This is very handy for python_file
section.
In the example above, the entrypoint file and the job configuration will be added to the job definition and uploaded to dbfs
automatically. No explicit file upload is needed.
Different deployment types
Databricks Jobs API provides two methods for launching a particular workload:
Main logical difference between these methods is that Run Submit API allows to submit a workload directly without creating a job. Therefore, we have two deployment types - one for Run Submit API, and one for Run Now API.
Deployment for Run Submit API
To deploy only the files and not to override the job definitions, do the following:
dbx deploy --files-only
To launch the file-based deployment:
dbx launch --as-run-submit --trace
This type of deployment is handy for working in different branches, and it won't affect the job definition.
Deployment for Run Now API
To deploy files and update the job definitions:
dbx deploy
To launch the file-based deployment:
dbx launch --job=<job-name>
This type of deployment shall be mainly used in automated way during new release.
dbx deploy
will change the job definition (unless --files-only
option is provided).
Troubleshooting
Q: When running dbx deploy
I'm getting the following exception json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
and stack trace:
...
File ".../lib/python3.7/site-packages/dbx/utils/common.py", line 215, in prepare_environment
experiment = mlflow.get_experiment_by_name(environment_data["workspace_dir"])
...
json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
What could be causing it and what is the potential fix?
A:
We've seen this exception when in the profile the host=https://{domain}/?o={orgid}
format is used for Azure. It is valid for the databricks cli, but not for the API. If that's the cause, once the "?o={orgid}" suffix is removed, the problem should be gone.
FAQ
Q: I'm using poetry for package management. Is it possible to use poetry together with this template?
A:
Yes, it's also possible, but the library management during cluster execution should be performed via libraries
section of job description.
You also might need to disable the automatic rebuild for dbx deploy
and dbx execute
via --no-rebuild
option. Finally, the built package should be in wheel format and located in /dist/
directory.
Q: How can I add my Databricks Notebook to the deployment.json
, so I can create a job out of it?
A:
Please follow this documentation section and add a notebook task definition into the deployment file.
Q: Is it possible to use dbx
for non-Python based projects, for example Scala-based projects?
A:
Yes, it's possible, but the interactive mode dbx execute
is not yet supported. However, you can just take the dbx
wheel to your Scala-based project and reference your jar files in the deployment file, so the dbx deploy
and dbx launch
commands be available for you.
Q: I have a lot of interdependent jobs, and using solely JSON seems like a giant code duplication. What could solve this problem?
A:
You can implement any configuration logic and simply write the output into a custom deployment.json
file and then pass it via --deployment-file
option.
As an example, you can generate your configuration using Python script, or Jsonnet.
Q: How can I secure the project environment?
A:
From the state serialization perspective, your code and deployments are stored in two separate storages:
- workspace directory -this directory is stored in your workspace, described per-environment and defined in
.dbx/project.json
, inworkspace_dir
field. To control access to this directory, please use Workspace ACLs. - artifact location - this location is stored in DBFS, described per-environment and defined in
.dbx/project.json
, inartifact_location
field. To control access this location, please use credentials passthrough (docs for ADLS and for S3).
Q: I would like to use self-hosted (private) pypi repository. How can I configure my deployment and CI/CD pipeline?
A:
To set up this scenario, there are some settings to be applied:
- Databricks driver should have network access to your pypi repository
- Additional step to deploy your package to pypi repo should be configured in CI/CD pipeline
- Package re-build and generation should be disabled via
--no-rebuild --no-package
arguments fordbx execute
- Package reference should be configured in job description
Here is a sample for dbx deploy
command:
dbx deploy --no-rebuild --no-package
Sample section to libraries
configuration:
{
"pypi": {"package": "my-package-name==1.0.0", "repo": "my-repo.com"}
}
Q: What is the purpose of init_adapter
method in SampleJob?
A:
This method should be primarily used for adapting configuration for dbx execute
based run.
By using this method, you can provide an initial configuration in case if --conf-file
option is not provided.
Q: I don't like the idea of hosting the host and token variables into ~/.databrickscfg
file inside the CI pipeline. How can I make this setup more secure?
A:
dbx
now supports environment variables, provided via DATABRICKS_HOST
and DATABRICKS_TOKEN
.
If these variables are defined in env, no ~/.databrickscfg
file needed.
Legal Information
This software is provided as-is and is not officially supported by Databricks through customer technical support channels. Support, questions, and feature requests can be communicated through the Issues page of this repo. Please see the legal agreement and understand that issues with the use of this code will not be answered or investigated by Databricks Support.
Feedback
Issues with template? Found a bug? Have a great idea for an addition? Feel free to file an issue.
Contributing
Have a great idea that you want to add? Fork the repo and submit a PR!
Kudos
- Project based on the cookiecutter datascience project
- README.md ToC generated via gh-md-toc