Scheduled Jobs¶
Domino allows you to schedule Jobs in advance, and set them to execute on a regular cadence. These can be useful when you have a data source that is updated regularly.
To schedule a Job, or manage existing scheduled Jobs, click Scheduled Jobs from the project menu.
Job Definition
Scheduled Job Name
Enter the name of the job. Each job will have this name on the Jobs Dashboard.
File Name
Enter the name of the file you’d like to execute. Include any optional arguments you wish to pass to your file.
Hardware tier
This drop-down list enables you to set the hardware tier used by the Job.
Environment
This drop-down list enables you to set the compute environment used by the Job.
Datasets
This expanding panel lists the Datasets configuration used by the Job.
Spark Cluster
Attach Domino Managed Spark Cluster
This option will allow you to provision and attach an on-demand Spark cluster to the Job.
The remainder of the configurations are explained in Spark Cluster Settings.
Schedule
Repeat every
Here you can set the frequency at which you want the Job to repeat.
Use custom expression
Here you can enter a custom Quartz CronTrigger expression, if the desire scheduling option is not available in the “Repeat every” selector.
Run sequentially
Setting a Job to Run sequentially will cause the scheduler to always wait for the last Job it started to complete before starting the next one. For example, if you set up a scheduled Job to run once per hour, and one of the Jobs launched by the scheduler takes 90 minutes to complete, the next hourly Job will not start until the previous one has finished. Otherwise, multiple Jobs from this scheduler will be allowed to run simultaneously. The scheduler will not wait for the previous Job to finish if it’s still running. This mode should be used when your Job doesn’t depend on output from the previous Job.
Actions
Emails to notify
In this field, add the email addresses of everyone who should be notified when the Job completes.
Update Model API
If a Model API has been publishing from the Project, the selected Model API will be republished after the Job has completed. This is useful for re-training and updating a Model API on a regular basis.