Workspace manager visual studio extension12/29/2023 ![]() OAuth machine-to-machine (M2M) authenticationįor OAuth user-to-machine (U2M) authentication, skip ahead to Set up OAuth U2M authentication. ![]() Depending on the type of authentication that you want to use, finish your setup by completing the instructions for your target Databricks authentication type.įor the following authentication types, skip ahead to Set up authentication with a configuration profile:ĭatabricks personal access token authentication It enables you to configure Databricks authentication once and then use that configuration across multiple Databricks tools and SDKs without further authentication configuration changes.īefore you can use the Databricks extension for Visual Studio Code, you must set up authentication between the Databricks extension for Visual Studio Code and your Databricks workspace. This approach helps make setting up and automating authentication with Databricks more centralized and predictable. The Databricks extension for Visual Studio Code implements portions of the Databricks client unified authentication standard, a consolidated and consistent architectural and programmatic approach to authentication. The output appears in the Debug Console pane. In the Explorer view, right-click the demo.py file, and then click Upload and Run File on Databricks. In the Configuration pane, next to Sync Destination, click the circled arrows ( Start synchronization) icon. createDataFrame ( data, schema ) customers. getOrCreate () schema = StructType () data =, , ] customers = spark. This code creates and displays the contents of a basic PySpark DataFrame:įrom pyspark.sql import SparkSession from import * spark = SparkSession. Name the file demo.py and save it to the project root.Īdd the following code to the file and then save it. In the Command Palette, click the sync destination name that is randomly generated by the extension.Ĭreate a basic, local Python code file to sync and run: on the sidebar, click the Explorer logo. Set the sync destination: in the Configuration pane, click Sync Destination, and then click the gear ( Configure cluster) icon. Start the cluster, if it is not already started: in the Configuration pane, next to Cluster, click the play ( Start Cluster) icon. Set the Databricks cluster: in the Configuration pane, click Cluster, and then click the gear ( Configure cluster) icon.Ĭlick the entry for the cluster that you want to use. Then press Enter.Ĭlick the entry DEFAULT: Authenticate using the DEFAULT profile. Set the Databricks workspace: in the Command Palette, for Databricks Host, enter your workspace instance URL, for example. Start configuring the extension: in the Configuration pane, click Configure Databricks. Open the extension: on the sidebar, click the Databricks logo. ![]() To complete the installation, follow the on-screen instructions. Install the extension: on the Databricks extension for Visual Studio Code page in the Visual Studio Code Marketplace, click Install. To complete this quickstart, follow these steps: You have already added your Databricks personal access token as a token field along with your workspace instance URL, for example, as a host field to the DEFAULT configuration profile in your local. See Databricks personal access token authentication. You have already generated a Databricks personal access token for your target Databricks workspace. Visual Studio Code is already running and has a local project opened. See Setting up Visual Studio Code and Getting Started with Python in VS Code. You already have Visual Studio Code 1.69.1 or higher installed and configured for Python coding. This section describes how to use the Databricks extension for Visual Studio Code to run a basic Python code file on a Databricks cluster in your remote workspace. ![]()
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