Actions35
- Genie Actions
- Databricks SQL Actions
- Unity Catalog Actions
- Model Serving Actions
- Files Actions
- Vector Search Actions
Overview
The node provides integration with Databricks API, specifically supporting multiple resources including "Model Serving". For the "Get Serving Endpoint Logs" operation under the "Model Serving" resource, the node retrieves logs related to a specified serving endpoint. This is useful for monitoring and debugging deployed machine learning models served via Databricks, allowing users to inspect request logs, errors, or performance data from the serving endpoint.
Practical examples include:
- Fetching recent logs of a model serving endpoint to diagnose issues.
- Monitoring usage patterns or error rates of a deployed model.
- Integrating logs retrieval into automated workflows for alerting or auditing.
Properties
| Name | Meaning |
|---|---|
| Endpoint Name | Name of the serving endpoint to retrieve logs from. This is a required string input specifying which model serving endpoint's logs should be fetched. |
Output
The node outputs JSON data containing the logs of the specified serving endpoint. The exact structure depends on the Databricks API response but typically includes log entries such as timestamps, log levels, messages, and possibly metadata about requests handled by the endpoint.
If binary data were involved (e.g., log files), it would be indicated here, but based on the provided code and properties, the output is JSON-formatted logs.
Dependencies
- Requires an active Databricks account with API access.
- Needs an API authentication token credential configured in n8n to authorize requests.
- The node uses the base URL and authorization token from the credentials to communicate with the Databricks API.
Troubleshooting
- Missing or incorrect Endpoint Name: Since the endpoint name is required, omitting it or providing an invalid name will likely cause errors. Ensure the endpoint name matches exactly the one configured in Databricks.
- Authentication errors: Invalid or expired API tokens will result in authorization failures. Verify that the API token credential is valid and has sufficient permissions.
- API connectivity issues: Network problems or incorrect host URLs can prevent successful API calls. Confirm the base URL and network accessibility.
- Unexpected API responses: If the Databricks API changes or returns unexpected data, the node might fail to parse logs correctly. Check for updates or changes in the Databricks API documentation.