Configure Databricks In VS Code: A Quick Guide

Nov 8, 2025 by Admin 47 views

Configure Databricks in Visual Studio Code: A Quick Guide

Hey guys! Want to level up your Databricks development game? Configuring Databricks in Visual Studio Code (VS Code) is the way to go! It brings the power of VS Code's excellent editing, debugging, and version control capabilities to your Databricks workflows. Trust me, it's a game-changer. This comprehensive guide walks you through, step-by-step, on how to seamlessly integrate Databricks with VS Code, boosting your productivity and streamlining your data engineering and data science projects. Let's dive in!

Prerequisites

Before we get started, make sure you have the following prerequisites in place:

Databricks Account: You'll need an active Databricks account with appropriate permissions to access clusters and notebooks. If you don't have one, head over to the Databricks website and sign up for a free trial or a paid plan.
Visual Studio Code: Make sure you have VS Code installed on your machine. You can download it from the official VS Code website (https://code.visualstudio.com/).
Python: Databricks often involves Python code, so ensure you have Python installed. It's recommended to use Python 3.6 or later. You can download it from the official Python website (https://www.python.org/downloads/).
Databricks CLI: The Databricks Command Line Interface (CLI) is essential for interacting with your Databricks workspace from VS Code. We'll cover the installation and configuration in the next section.

Step-by-Step Configuration

Alright, let's get down to the nitty-gritty. Follow these steps to configure Databricks in VS Code:

1. Install the Databricks CLI

The Databricks CLI is your gateway to interacting with Databricks from your local machine. It allows you to manage clusters, upload and download files, and execute Databricks jobs. Here's how to install it:

Using pip: Open your terminal or command prompt and run the following command:
```
pip install databricks-cli
```
Verify Installation: After the installation is complete, verify it by running:
```
databricks --version
```
This should display the installed version of the Databricks CLI.

2. Configure the Databricks CLI

Now that you have the Databricks CLI installed, you need to configure it to connect to your Databricks workspace. This involves providing your Databricks host and authentication token. Here's how:

Run the configure command:
```
databricks configure
```
Enter Databricks Host: The CLI will prompt you to enter your Databricks host. This is the URL of your Databricks workspace (e.g., https://your-workspace.cloud.databricks.com). You can find this URL in your browser's address bar when you're logged into your Databricks workspace.
Enter Token: Next, the CLI will ask for your Databricks token. To generate a token, follow these steps:
1. In your Databricks workspace, click on your username in the top right corner and select "User Settings."
2. Go to the "Access Tokens" tab.
3. Click on "Generate New Token."
4. Enter a description for the token and set an optional expiration date.
5. Click "Generate."
6. Important: Copy the generated token and store it in a safe place. You won't be able to see it again after you close the dialog.
Paste the token into the CLI when prompted.

3. Install the Databricks VS Code Extension

To seamlessly integrate Databricks with VS Code, you'll need the Databricks VS Code extension. This extension provides features like syntax highlighting, code completion, and the ability to run Databricks notebooks directly from VS Code. Here's how to install it:

Open VS Code.
Go to the Extensions Marketplace: Click on the Extensions icon in the Activity Bar on the side of VS Code (or press Ctrl+Shift+X or Cmd+Shift+X).
Search for "Databricks.": In the search bar, type "Databricks" and look for the official Databricks extension.
Install the Extension: Click the "Install" button next to the Databricks extension.
Reload VS Code: After the installation is complete, VS Code may prompt you to reload. Click "Reload" to activate the extension.

4. Configure the Databricks VS Code Extension

Now that you have the Databricks extension installed, you need to configure it to connect to your Databricks workspace. This involves specifying your Databricks host and authentication method. Here's how:

Open VS Code Settings: Go to File > Preferences > Settings (or press Ctrl+, or Cmd+,).
Search for "Databricks.": In the Settings search bar, type "Databricks" to filter the settings related to the Databricks extension.
Configure Databricks Host: Find the Databricks: Host setting and enter your Databricks workspace URL (e.g., https://your-workspace.cloud.databricks.com).
Configure Authentication Method: Find the Databricks: Authentication Type setting and select "Databricks CLI." This tells the extension to use the Databricks CLI for authentication, which you configured earlier.

5. Test the Connection

To ensure that everything is set up correctly, let's test the connection to your Databricks workspace. Here's how:

Create a New Notebook: Create a new file in VS Code with the .py extension (e.g., test.py).
Add Databricks Magic Command: Add the following Databricks magic command at the beginning of the file:
```
# Databricks notebook source
```
Write some Python code: Add some simple Python code to the notebook, such as:
```
print("Hello, Databricks from VS Code!")
```
Run the Notebook: Right-click in the editor and select "Databricks: Run Current File in Databricks."
Select Cluster: The extension will prompt you to select a Databricks cluster to run the notebook on. Choose the cluster you want to use.
View Results: The results of the notebook execution will be displayed in the VS Code output panel. If you see "Hello, Databricks from VS Code!", then congratulations! You've successfully configured Databricks in VS Code.

Advanced Configuration (Optional)

Here are some optional advanced configurations you might find useful:

1. Configure Databricks Environment Variables

Instead of hardcoding your Databricks host and token in the VS Code settings, you can use environment variables. This is a more secure and flexible approach, especially when working with multiple Databricks workspaces or sharing your code with others. Here's how to do it:

Set Environment Variables: Set the following environment variables on your machine:
- DATABRICKS_HOST: Your Databricks workspace URL (e.g., https://your-workspace.cloud.databricks.com).
- DATABRICKS_TOKEN: Your Databricks token.
The way you set environment variables depends on your operating system. For example, on macOS and Linux, you can add the following lines to your .bashrc or .zshrc file:
```
export DATABRICKS_HOST=https://your-workspace.cloud.databricks.com
export DATABRICKS_TOKEN=YOUR_DATABRICKS_TOKEN
```
On Windows, you can set environment variables in the System Properties dialog.
Update VS Code Settings: In the VS Code settings, set the Databricks: Authentication Type to "Databricks CLI" and leave the Databricks: Host setting empty. The extension will automatically use the environment variables for authentication.

2. Configure Remote Debugging

If you need to debug your Databricks code running on a remote cluster, you can use the Databricks VS Code extension to set up remote debugging. This allows you to step through your code, set breakpoints, and inspect variables in real-time.

Install pydevd: Install the pydevd package on your Databricks cluster. You can do this by running the following command in a Databricks notebook:
```
%pip install pydevd-pycharm
```
Add Debugging Code: Add the following code to your Databricks notebook to initiate the remote debugging session:
```
import pydevd_pycharm

pydevd_pycharm.settrace('your_local_ip', port=12345, stdoutToServer=True, stderrToServer=True)
```
Replace 'your_local_ip' with the IP address of your local machine where VS Code is running. Make sure the port (e.g., 12345) is open in your firewall.

Configure VS Code Debugger: In VS Code, create a new debug configuration by going to Run > Add Configuration... and selecting "Python: Remote Attach." Modify the configuration file (launch.json) to match the following:

{
  "version": "0.2.0",
  "configurations": [
    {
      "name": "Databricks Remote Debug",
      "type": "python",
      "request": "attach",
      "connect": {
        "host": "localhost",
        "port": 12345
      },
      "pathMappings": [
        {
          "localRoot": "${workspaceFolder}",
          "remoteRoot": "."
        }
      ]
    }
  ]
}

Start Debugging: Start the Databricks notebook execution. Then, in VS Code, go to Run > Start Debugging (or press F5). VS Code will connect to the remote debugging session on the Databricks cluster, and you can start debugging your code.

Troubleshooting

Sometimes, things don't go as planned. Here are some common issues and their solutions:

Authentication Errors:
- Invalid Token: Double-check that you've copied the correct Databricks token and that it hasn't expired.
- Incorrect Host: Verify that you've entered the correct Databricks workspace URL.
- CLI Configuration: Ensure that the Databricks CLI is correctly configured and that you can authenticate using the databricks configure command.
Connection Errors:
- Network Issues: Check your network connection and make sure you can access your Databricks workspace from your local machine.
- Firewall Issues: Ensure that your firewall isn't blocking the connection between VS Code and your Databricks cluster.
Extension Issues:
- Outdated Extension: Make sure you're using the latest version of the Databricks VS Code extension.
- Extension Conflicts: Try disabling other VS Code extensions to see if there are any conflicts.

Conclusion

And there you have it! You've successfully configured Databricks in Visual Studio Code. This integration will significantly improve your development workflow, allowing you to write, test, and debug your Databricks code more efficiently. So go forth and build awesome data solutions! Happy coding!

By following these steps, you'll unlock a more streamlined and efficient development experience, directly within your familiar VS Code environment. Embrace the power of integration and take your Databricks projects to new heights!