Transform data by running a Jar activity in Azure Databricks

APPLIES TO: Azure Data Factory Azure Synapse Analytics

Tip

Try out Data Factory in Microsoft Fabric, an all-in-one analytics solution for enterprises. Microsoft Fabric covers everything from data movement to data science, real-time analytics, business intelligence, and reporting. Learn how to start a new trial for free!

The Azure Databricks Jar Activity in a pipeline runs a Spark Jar in your Azure Databricks cluster. This article builds on the data transformation activities article, which presents a general overview of data transformation and the supported transformation activities. Azure Databricks is a managed platform for running Apache Spark.

For an eleven-minute introduction and demonstration of this feature, watch the following video:

Add a Jar activity for Azure Databricks to a pipeline with UI

To use a Jar activity for Azure Databricks in a pipeline, complete the following steps:

1. Search for Jar in the pipeline Activities pane, and drag a Jar activity to the pipeline canvas.

2. Select the new Jar activity on the canvas if it is not already selected.

3. Select the Azure Databricks tab to select or create a new Azure Databricks linked service that will execute the Jar activity.

   

4. Select the Settings tab and specify a class name to be executed on Azure Databricks, optional parameters to be passed to the Jar, and libraries to be installed on the cluster to execute the job.

   

Databricks Jar activity definition

Here's the sample JSON definition of a Databricks Jar Activity:

{
    "name": "SparkJarActivity",
    "type": "DatabricksSparkJar",
    "linkedServiceName": {
        "referenceName": "AzureDatabricks",
        "type": "LinkedServiceReference"
    },
    "typeProperties": {
        "mainClassName": "org.apache.spark.examples.SparkPi",
        "parameters": [ "10" ],
        "libraries": [
            {
                "jar": "dbfs:/docs/sparkpi.jar"
            }
        ]
    }
}

Databricks Jar activity properties

The following table describes the JSON properties used in the JSON definition:

Property	Description	Required
name	Name of the activity in the pipeline.	Yes
description	Text describing what the activity does.	No
type	For Databricks Jar Activity, the activity type is DatabricksSparkJar.	Yes
linkedServiceName	Name of the Databricks Linked Service on which the Jar activity runs. To learn about this linked service, see Compute linked services article.	Yes
mainClassName	The full name of the class containing the main method to be executed. This class must be contained in a JAR provided as a library. A JAR file can contain multiple classes. Each of the classes can contain a main method.	Yes
parameters	Parameters that will be passed to the main method. This property is an array of strings.	No
libraries	A list of libraries to be installed on the cluster that will execute the job. It can be an array of <string, object>	Yes (at least one containing the mainClassName method)

Note

Known Issue - When using the same Interactive cluster for running concurrent Databricks Jar activities (without cluster restart), there is a known issue in Databricks where in parameters of the 1st activity will be used by following activities as well. Hence resulting to incorrect parameters being passed to the subsequent jobs. To mitigate this use a Job cluster instead.

Supported libraries for databricks activities

In the previous Databricks activity definition, you specified these library types: jar, egg, maven, pypi, cran.

{
    "libraries": [
        {
            "jar": "dbfs:/mnt/libraries/library.jar"
        },
        {
            "egg": "dbfs:/mnt/libraries/library.egg"
        },
        {
            "maven": {
                "coordinates": "org.jsoup:jsoup:1.7.2",
                "exclusions": [ "slf4j:slf4j" ]
            }
        },
        {
            "pypi": {
                "package": "simplejson",
                "repo": "http://my-pypi-mirror.com"
            }
        },
        {
            "cran": {
                "package": "ada",
                "repo": "https://cran.us.r-project.org"
            }
        }
    ]
}

For more information, see the Databricks documentation for library types.

How to upload a library in Databricks

You can use the Workspace UI:

1. Use the Databricks workspace UI

2. To obtain the dbfs path of the library added using UI, you can use Databricks CLI.

   Typically the Jar libraries are stored under dbfs:/FileStore/jars while using the UI. You can list all through the CLI: databricks fs ls dbfs:/FileStore/job-jars

Or you can use the Databricks CLI:

1. Follow Copy the library using Databricks CLI

2. Use Databricks CLI (installation steps)

   As an example, to copy a JAR to dbfs: dbfs cp SparkPi-assembly-0.1.jar dbfs:/docs/sparkpi.jar

Related content

For an eleven-minute introduction and demonstration of this feature, watch the video.