Databricks Connect for Scala

  • Article

Note

This article covers Databricks Connect for Databricks Runtime 13.3 LTS and above.

This article demonstrates how to quickly get started with Databricks Connect by using Scala with IntelliJ IDEA and the Scala plugin.

Databricks Connect enables you to connect popular IDEs such as IntelliJ IDEA, notebook servers, and other custom applications to Azure Databricks clusters. See What is Databricks Connect?.

Tutorial

To skip this tutorial and use a different IDE instead, see Next steps.

Requirements

To complete this tutorial, you must meet the following requirements:

  • Your target Azure Databricks workspace and cluster must meet the requirements for Cluster configuration for Databricks Connect.

  • You must have your cluster ID available. To get your cluster ID, in your workspace, click Compute on the sidebar, and then click your cluster's name. In your web browser's address bar, copy the string of characters between clusters and configuration in the URL.

  • You have the Java Development Kit (JDK) installed on your development machine. Databricks recommends that the version of your JDK installation that you use matches the JDK version on your Azure Databricks cluster. The following table shows the JDK version for each supported Databricks Runtime.

    Databricks Runtime version JDK version
    13.3 LTS - 15.0,
    13.3 ML LTS - 15.0 ML    		 JDK 8

    Note

    If you do not have a JDK installed, or if you have multiple JDK installs on your development machine, you can install or choose a specific JDK later in Step 1. Choosing a JDK install that is below or above the JDK version on your cluster might produce unexpected results, or your code might not run at all.

  • You have IntelliJ IDEA installed. This tutorial was tested with IntelliJ IDEA Community Edition 2023.3.6. If you use a different version or edition of IntelliJ IDEA, the following instructions might vary.

  • You have the Scala plugin for IntelliJ IDEA installed.

Step 1: Configure Azure Databricks authentication

This tutorial uses an Azure Databricks configuration profile for authenticating with your Azure Databricks workspace. To use a different authentication type instead, see Configure connection properties.

Step 2: Create the project

  1. Start IntelliJ IDEA.

  2. On the main menu, click File > New > Project.

  3. Give your project some meaningful Name.

  4. For Location, click the folder icon, and complete the on-screen directions to specify the path to your new Scala project.

  5. For Language, click Scala.

  6. For Build system, click sbt.

  7. In the JDK drop-down list, select an existing installation of the JDK on your development machine that matches the JDK version on your cluster, or select Download JDK and follow the on-screen instructions to download a JDK that matches the JDK version on your cluster.

    Note

    Choosing a JDK install that is above or below the JDK version on your cluster might produce unexpected results, or your code might not run at all.

  8. In the sbt drop-down list, select the latest version.

  9. In the Scala drop-down list, select the version of Scala that matches the Scala version on your cluster. The following table shows the Scala version for each supported Databricks Runtime:

    Databricks Runtime version Scala version
    13.3 LTS - 15.0,
    13.3 ML LTS - 15.0 ML    		 2.12.15

    Note

    Choosing a Scala version that is below or above the Scala version on your cluster might produce unexpected results, or your code might not run at all.

  10. Make sure the Download sources box next to Scala is checked.

  11. For Package prefix, enter some package prefix value for your project's sources, for example org.example.application.

  12. Make sure the Add sample code box is checked.

  13. Click Create.

Create the IntelliJ IDEA project

Step 3: Add the Databricks Connect package

  1. With your new Scala project open, in your Project tool window (View > Tool Windows > Project), open the file named build.sbt, in project-name > target.

  2. Add the following code to the end of the build.sbt file, which declares your project's dependency on a specific version of the Databricks Connect library for Scala:

    libraryDependencies += "com.databricks" % "databricks-connect" % "14.3.1"

    Replace 14.3.1 with the version of the Databricks Connect library that matches the Databricks Runtime version on your cluster. You can find the Databricks Connect library version numbers in the Maven central repository.

  3. Click the Load sbt changes notification icon to update your Scala project with the new library location and dependency.

  4. Wait until the sbt progress indicator at the bottom of the IDE disappears. The sbt load process might take a few minutes to complete.

Install the Databricks Connect package

Step 4: Add code

  1. In your Project tool window, open the file named Main.scala, in project-name > src > main > scala.

  2. Replace any existing code in the file with the following code and then save the file, depending on the name of your configuration profile.

    If your configuration profile from Step 1 is named DEFAULT, replace any existing code in the file with the following code, and then save the file:

    package org.example.application

import com.databricks.connect.DatabricksSession
import org.apache.spark.sql.SparkSession

object Main {
  def main(args: Array[String]): Unit = {
    val spark = DatabricksSession.builder().remote().getOrCreate()
    val df = spark.read.table("samples.nyctaxi.trips")
    df.limit(5).show()
  }
}

    If your configuration profile from Step 1 is not named DEFAULT, replace any existing code in the file with the following code instead. Replace the placeholder <profile-name> with the name of your configuration profile from Step 1, and then save the file:

    package org.example.application

import com.databricks.connect.DatabricksSession
import com.databricks.sdk.core.DatabricksConfig
import org.apache.spark.sql.SparkSession

object Main {
  def main(args: Array[String]): Unit = {
    val config = new DatabricksConfig().setProfile("<profile-name>")
    val spark = DatabricksSession.builder().sdkConfig(config).getOrCreate()
    val df = spark.read.table("samples.nyctaxi.trips")
    df.limit(5).show()
  }
}

Step 5: Run the code

  1. Start the target cluster in your remote Azure Databricks workspace.
  2. After the cluster has started, on the main menu, click Run > Run 'Main'.
  3. In the Run tool window (View > Tool Windows > Run), on the Main tab, the first 5 rows of the samples.nyctaxi.trips table appear.

Step 6: Debug the code

  1. With the target cluster still running, in the preceding code, click the gutter next to df.limit(5).show() to set a breakpoint.
  2. On the main menu, click Run > Debug 'Main'.
  3. In the Debug tool window (View > Tool Windows > Debug), on the Console tab, click the calculator (Evaluate Expression) icon.
  4. Enter the expression df.schema and click Evaluate to show the DataFrame's schema.
  5. In the Debug tool window's sidebar, click the green arrow (Resume Program) icon.
  6. In the Console pane, the first 5 rows of the samples.nyctaxi.trips table appear.

Debug the IntelliJ IDEA project

Next steps

To learn more about Databricks Connect, see articles such as the following: