Databricks Continuous Integration and Delivery Pipelines.

Question

Pulumi · Accepted Answer

Continuous Integration (CI) and Continuous Delivery (CD) are practices that enable developers to integrate code changes more frequently and reliably, and deliver apps to production environments using automated pipelines. In a Databricks ecosystem, setting up CI/CD pipelines allows for efficient testing and deployment of code, notebooks, libraries, and even entire workspaces to Databricks clusters.

To set up a Continuous Integration and Delivery pipeline with Databricks, we will create the following resources using Pulumi:

1. **Databricks Pipeline**: This is a resource representing the CI/CD pipeline in Databricks that automates the deployment of jobs, notebooks, and libraries.

2. **Databricks Repo**: A source control repository resource that can be used to integrate with CI/CD pipelines. This will store the code and configurations that the pipeline will use to run tests and deploy changes to the Databricks workspace.

Below is a Python program using Pulumi that creates a simple Databricks CI/CD pipeline and its associated code repository for integration.

```python
import pulumi
import pulumi_databricks as databricks

# The Databricks repository holds the code that defines your jobs and workflows.
# The `Repo` resource models a source control repository in Databricks that can be used with your CI/CD pipeline.
repo = databricks.Repo("my-repo",
    url="https://github.com/my-org/my-repo.git",  # URL of the source Git repository.
    path="/Repos/my-user/my-repo",                # Path in the Databricks workspace where the repo will be placed.
    branch="main",                                # Git branch to sync the code from.
    git_provider="github"                         # Specifies the Git provider, it could be "github", "bitbucket", etc.
)

# A Databricks pipeline to automate deployment of jobs and workflows.
# Here, the `Pipeline` resource represents the CI/CD pipeline in your Databricks environment.
pipeline = databricks.Pipeline("my-pipeline",
    name="my-pipeline-name",
    target="my-databricks-workspace-url",  # The target workspace URL where the pipelines are deployed.
    storage="dbfs:/pipelines/code/",       # DBFS location to store pipeline work items.
    configuration={
        "key1": "value1",                  # Key-value pair configurations for pipeline environments.
        "key2": "value2",
    },
    continuous=True,                       # If set to True, the pipeline is updated continuously when the source repo changes.
    filters={
        "includes": ["*.py", "*.sql"],     # Glob patterns included during deployment
        "excludes": ["*.md", "tests/*"],   # Glob patterns excluded from deployment
    },
    # Clusters define the execution context for tasks. This shows a simple autoscaling cluster configuration.
    clusters=[{
        "label": "default",
        "autoscale": {
            "min_workers": 2,
            "max_workers": 5
        },
        "nodeTypeId": "r3.xlarge"          # Defines the instance type
    }]
)

# Exports useful outputs.
pulumi.export("repo_url", repo.url)
pulumi.export("pipeline_name", pipeline.name)
pulumi.export("pipeline_storage_location", pipeline.storage)
```

The above program sets up a relatively straightforward pipeline in Databricks. There are, however, many additional configurations that you can include depending on your specific CI/CD requirements.

Let's break down what this program does:
- It uses the `pulumi_databricks` package, which is the Pulumi provider for Databricks.
- It defines a `Repo` resource that models the Git repository you want to synchronize with your Databricks environment.
- It creates a `Pipeline` resource that sets up the CI/CD process. This process will continuously integrate changes from the defined repository. The continuous property is set as true, indicating that the pipeline will automatically update when changes are pushed to the source repository.
- We've defined a simple cluster configuration for executing the jobs with auto-scaling enabled. The actual configuration might be more complex, depending on the nature and needs of your workloads.

You would replace parameters like the Git repository URL (`url`), workspace path (`path`), and workspace URL (`target`) with values specific to your environment. The storage location for code artifacts (`storage`) and the included/excluded file patterns (`filters`) are also customizable.

Make sure you have the appropriate access rights to create these resources in your Databricks workspace, and that you have installed the Pulumi CLI and the `pulumi_databricks` package.

Run this program with Pulumi to provision the specified resources in your Databricks environment. Your CI/CD pipeline will then be ready to receive code changes from your repository, run tests, and deploy artifacts to Databricks automatically.

For more detailed information on configuring Databricks pipelines with Pulumi, you can refer to the Pulumi documentation for [Databricks Pipeline](https://www.pulumi.com/registry/packages/databricks/api-docs/pipeline/) and [Databricks Repo](https://www.pulumi.com/registry/packages/databricks/api-docs/repo/).