Get Notebook Execution Details
get_notebook_execution_detailsRetrieve execution metadata for notebook runs, including timing, resource usage, and execution state, to monitor performance and verify resource allocation.
Instructions
Get detailed execution information for a notebook run by job instance ID.
Retrieves execution metadata from the Fabric Notebook Livy Sessions API, which provides detailed timing, resource usage, and execution state information.
Use this tool when:
You want to check the status and timing of a completed notebook run
You need to verify resource allocation for a notebook execution
You want to analyze execution performance (queue time, run time)
Note: This method returns execution metadata (timing, state, resource usage).
Cell-level outputs are only available for active sessions. Once a notebook job
completes, individual cell outputs cannot be retrieved via the REST API. To
capture cell outputs, use mssparkutils.notebook.exit() in your notebook and
access the exitValue through Data Pipeline activities.
Parameters: workspace_name: The display name of the workspace containing the notebook. notebook_name: Name of the notebook. job_instance_id: The job instance ID from execute_notebook or run_on_demand_job result.
Returns: Dictionary with: - status: "success" or "error" - message: Description of the result - session: Full Livy session details (state, timing, resources) - execution_summary: Summarized execution information including: - state: Execution state (Success, Failed, Cancelled, etc.) - spark_application_id: Spark application identifier - queued_duration_seconds: Time spent in queue - running_duration_seconds: Actual execution time - total_duration_seconds: Total end-to-end time - driver_memory, driver_cores, executor_memory, etc.
Example: ```python # After executing a notebook exec_result = run_on_demand_job( workspace_name="Analytics", item_name="ETL_Pipeline", item_type="Notebook", job_type="RunNotebook" )
# Get detailed execution information
details = get_notebook_execution_details(
workspace_name="Analytics",
notebook_name="ETL_Pipeline",
job_instance_id=exec_result["job_instance_id"]
)
if details["status"] == "success":
summary = details["execution_summary"]
print(f"State: {summary['state']}")
print(f"Duration: {summary['total_duration_seconds']}s")
print(f"Spark App ID: {summary['spark_application_id']}")
```Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| workspace_name | Yes | ||
| notebook_name | Yes | ||
| job_instance_id | Yes |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||