Complete the Databricks connection configuration in the Spark Configuration tab of the Run view of your Job. This configuration is effective on a per-Job basis.
Before you begin
- When running a Spark Streaming Job, only one Job is allowed to run on the same Databricks cluster per time.
- When running a Spark Batch Job, only if you have selected the Do not restart the cluster when submitting check box, you can send more than one Job to run in parallel on the same Databricks cluster; otherwise, since each run automatically restarts the cluster, the Jobs that are launched in parallel interrupt each other and thus cause execution failure.
- From the Cloud provider drop-down list, select Azure.
Enter the basic connection information to Databricks.
Use pool: you can select this check box to leverage a Databricks pool. If you do, you must indicate the pool ID instead of the cluster ID in the Spark Configuration. You must also select the Use transient cluster check box.
In the Endpoint field, enter the URL address of your Azure Databricks workspace. This URL can be found in the Overview blade of your Databricks workspace page on your Azure portal. For example, this URL could look like https://adb-$workspaceId.$random.azuredatabricks.net.
In the Cluster ID field, enter the ID of the Databricks cluster to be used. This ID is the value of the spark.databricks.clusterUsageTags.clusterId property of your Spark cluster. You can find this property on the properties list in the Environment tab in the Spark UI view of your cluster.
You can also easily find this ID from the URL of your Databricks cluster. It is present immediately after cluster/ in this URL.
If you selected the Use pool option, in the Pool ID field, enter the ID of the Databricks pool to be used. This ID is the value of the DatabricksInstancePoolId key of your pool. You can find this key under Tags in the Configuration tab of your pool. It is also available in the tags of the clusters that are using the pool.
You can also easily find this ID from the URL of your Databricks pool. It is present immediately after cluster/instance-pools/view/ in this URL.
Click the [...] button next to the Token field to enter the authentication token generated for your Databricks user account. You can generate or find this token on the User settings page of your Databricks workspace. For further information, see Token management from the Azure documentation.
In the DBFS dependencies folder field, enter the directory that is used to store your Job related dependencies on Databricks Filesystem at runtime, putting a slash (/) at the end of this directory. For example, enter /jars/ to store the dependencies in a folder named jars. This folder is created on the fly if it does not exist then.
Poll interval when retrieving Job status (in ms): enter, without the quotation marks, the time interval (in milliseconds) at the end of which you want the Studio to ask Spark for the status of your Job. For example, this status could be Pending or Running.
The default value is 300000, meaning 30 seconds. This interval is recommended by Databricks to correctly retrieve the Job status.
Use transient cluster: you can select this check box to leverage the transient Databricks clusters.
The custom properties you defined in the Advanced properties table are automatically taken into account by the transient clusters at runtime.
- Autoscale: select or clear this check box to define
the number of workers to be used by your transient cluster.
- If you select this check box,
autoscaling is enabled. Then define the minimum number
of workers in Min
workers and the maximum number of
worders in Max
workers. Your transient cluster is
scaled up and down within this scope based on its
According to the Databricks documentation, autoscaling works best with Databricks runtime versions 3.0 or onwards.
- If you clear this check box, autoscaling is deactivated. Then define the number of workers a transient cluster is expected to have. This number does not include the Spark driver node.
- If you select this check box, autoscaling is enabled. Then define the minimum number of workers in Min workers and the maximum number of worders in Max workers. Your transient cluster is scaled up and down within this scope based on its workload.
- Node type
and Driver node type:
select the node types for the workers and the Spark driver node.
These types determine the capacity of your nodes and their
pricing by Databricks.
For details about these node types and the Databricks Units they use, see Supported Instance Types from the Databricks documentation.
disk: select this check box to enable your
transient cluster to automatically scale up its disk space when
its Spark workers are running low on disk space.
For more details about this elastic disk feature, search for the section about autoscaling local storage from your Databricks documentation.
- SSH public
key: if an SSH access has been set up for your
cluster, enter the public key of the generated SSH key pair.
This public key is automatically added to each node of your
transient cluster. If no SSH access has been set up, ignore this
For further information about SSH access to your cluster, see SSH access to clusters from the Databricks documentation.
- Configure cluster log: select this check box to define where to store your Spark logs for a long term. This storage system could be S3 or DBFS.
- Autoscale: select or clear this check box to define the number of workers to be used by your transient cluster.
- Do not restart the cluster when submitting: select this check box to prevent the Studio restarting the cluster when the Studio is submitting your Jobs. However, if you make changes in your Jobs, clear this check box so that the Studio resarts your cluster to take these changes into account.
If you need the Job to be resilient to failure, select the Activate checkpointing check box to enable the Spark checkpointing operation. In the field that is displayed, enter the directory in which Spark stores, in the file system of the cluster, the context data of the computations such as the metadata and the generated RDDs of this computation.
For further information about the Spark checkpointing operation, see http://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing .