Starting a Terminated Cluster in Databricks
To start a terminated cluster in Databricks, you can follow these steps:
- Access the Compute Section: Navigate to the Compute section in your Databricks workspace.
- Find the Terminated Cluster: Locate the terminated cluster you wish to restart.
- Restart the Cluster: Click on the cluster to go to its details page and select the option to restart it. Alternatively, you can restart it from the compute list or via a notebook.
- Programmatic Restart: You can also use the Clusters API to restart a terminated cluster programmatically.
When you restart a terminated cluster, Databricks re-creates it with the same ID, automatically installs all previously installed libraries, and reattaches any notebooks.
Frequently Asked Questions
- Q: What happens to libraries when a cluster is restarted?
A: All previously installed libraries are automatically reinstalled when a cluster is restarted.
- Q: Can I undo deleting a cluster?
A: No, deleting a cluster is a permanent action and cannot be undone.
- Q: How do I keep a terminated cluster from being permanently deleted?
A: You can pin a terminated cluster to prevent it from being deleted after 30 days. Up to 100 clusters can be pinned.
- Q: What is autostart for jobs and JDBC/ODBC queries?
A: Autostart allows a terminated cluster to automatically restart when a job is scheduled to run or when connecting via JDBC/ODBC.
- Q: Can I restart a cluster if my trial has expired?
A: No, you cannot restart a cluster if your trial has expired.
- Q: How do I view detailed information about Spark jobs?
A: You can view detailed information about Spark jobs by selecting the Spark UI tab on the cluster details page.
- Q: Why should I regularly restart long-running clusters?
A: Regular restarts ensure that your clusters use the latest images for compute resource containers and VM hosts, which is important for maintaining security and performance.
Bottom Line
Restarting a terminated cluster in Databricks is straightforward and can be done manually or programmatically. It’s essential to manage clusters effectively to ensure they remain up-to-date and perform optimally.