Configure and edit Databricks Jobs

This article focuses on instructions for creating, configuring, and editing jobs using the Workflows workspace UI. Azure Databricks has other entry points and tools for configuration, including the following:

• To learn about using the Databricks CLI to create and run jobs, see What is the Databricks CLI?.
• To learn about using the Jobs API to create and run jobs, see Jobs in the REST API reference.
• To learn how to run and schedule jobs directly in a Databricks notebook, see Create and manage scheduled notebook jobs.

Create a new job

This section describes the minimum configuration needed to create a new job to schedule a notebook task with the workspace UI.

Jobs contain one or more tasks. You create a new job by configuring the first task for that job.

1. Click Workflows in the sidebar and click .
2. Enter a Task name.
3. Select a notebook for the Path field.
4. Click Create task.

If your workspace is not enabled for serverless compute for jobs, you must select a Compute option. Databricks recommends always using jobs compute when configuring tasks.

A new job appears in the workspace jobs list with the default name New Job <date> <time>.

Select a job to edit in the workspace

To edit an existing job with the workspace UI, do the following:

1. Click Workflows in the sidebar.
2. In the Name column, click the job name.

Use the jobs UI to do the following:

• Edit job settings
• Rename, clone, or delete a job
• Add new tasks to an existing job
• Edit task settings

Edit job settings

The side panel contains the Job details. You can change the job trigger, compute configuration, notifications, the maximum number of concurrent runs, configure duration thresholds, and add or change tags. You can also edit job permissions if job access control is enabled.

Add parameters for all job tasks

Parameters configured at the job level are passed to the job’s tasks that accept key-value parameters, including Python wheel files configured to accept keyword arguments. See Parameterize jobs.

Add tags to a job

To add labels or key-value attributes to your job, you can add tags when you edit the job. You can use tags to filter jobs in the Jobs list. For example, you can use a department tag to filter all jobs that belong to a specific department.

Tags also propagate to job clusters created when a job is run, allowing you to use tags with your existing cluster monitoring.

Click + Tag in the Job details side panel to add or edit tags. You can add the tag as a label or key-value pair. To add a label, enter the label in the Key field and leave the Value field empty.

Rename, clone, or delete a job

To rename a job, go the jobs UI and click the job name.

You can quickly create a new job by cloning an existing job. Cloning a job creates an identical copy of the job except for the job ID. To clone a job, do the following:

1. Go to jobs UI for the job.
2. Click next to the Run now button.
3. Select Clone job from the drop-down menu.
4. Enter a name for the cloned job.
5. Click Clone.

Delete a job

To delete a job, go to the job page, click next to the job name, and select Delete job from the drop-down menu.

Use Git with jobs

If your job contains any tasks that support using a remote Git provider, the jobs UI contains a Git field and the option to add or edit Git settings.

You can configure the following task types to use a remote Git repository:

• Notebooks
• Python scripts
• SQL files
• dbt

All tasks in a job must reference the same commit in the remote repository. You must specify only one of the following for a job that uses a remote repository:

• branch: The name of the branch, for example, main.
• tag: The tag's name, for example, release-1.0.0.
• commit: The hash of a specific commit, for example, e0056d01.

When a job run begins, Databricks takes a snapshot commit of the remote repository to ensure that the entire job runs against the same version of code.

When you view the run history of a task that runs code stored in a remote Git repository, the Task run details panel includes Git details, including the commit SHA associated with the run. See View task run history.

Configure a Git provider

The jobs UI has a dialog to configure a remote Git repository. This dialog is accessible from the Job details panel under the Git heading or in any task configured to use a Git provider.

The options displayed to access the dialog vary based on task type and whether or not a git reference has already been configured for the job. Buttons to launch the dialog include Add Git settings, Edit, or Add a git reference.

In the Git Information dialog (just labelled Git if access by the Job details panel), enter the following details:

• The Git repository URL.
• Select your Git provider from the dropdown list.
• In the Git reference field, enter the identifier for a branch, tag, or commit that corresponds to the version of the source code you want to run.
• Select branch, tag, or commit from the dropdown.

Configure an expected completion time or a timeout for a job

You can configure optional duration thresholds for a job, including an expected and maximum completion time. To configure duration thresholds, click Set duration thresholds under Duration thresholds in the Job details panel.

Enter a duration in the Warning field to configure the job's expected completion time. If the job exceeds this threshold, an event is triggered. You can use this event to notify when a job is running slowly. See Configure notifications for slow running or late jobs.

To configure a maximum completion time for a job, enter the maximum duration in the Timeout field. If the job does not complete in this time, Azure Databricks sets its status to "Timed Out".

You can optionally specify duration thresholds for tasks. See Configure an expected completion time or a timeout for a task.