get_run_task_metrics
Retrieve detailed task execution metrics for Databricks job runs, including setup, execution, and cleanup times to monitor performance and identify bottlenecks.
Instructions
Get job run task execution time details
Args: run_id: Job Run ID
Returns: Task setup/execute/cleanup times (time in local timezone)
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| run_id | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Implementation Reference
- tools/metrics.py:161-220 (handler)The implementation of the get_run_task_metrics tool, which retrieves and formats execution metrics for tasks within a specific job run.
def get_run_task_metrics(ctx: Context, run_id: int) -> Dict[str, Any]: """ Get job run task execution time details Args: run_id: Job Run ID Returns: Task setup/execute/cleanup times (time in local timezone) """ w = get_workspace_client() ctx.info(f"Querying run {run_id} task metrics...") run = w.jobs.get_run(run_id=run_id) run_dict = run.as_dict() def ms_to_local(ms): if not ms: return None local_time = datetime.utcfromtimestamp(ms / 1000) + timedelta(hours=8) return local_time.strftime("%Y-%m-%d %H:%M:%S") tasks = [] total_duration = 0 if run_dict.get("tasks"): for t in run_dict["tasks"]: setup_ms = t.get("setup_duration", 0) or 0 exec_ms = t.get("execution_duration", 0) or 0 cleanup_ms = t.get("cleanup_duration", 0) or 0 total_ms = setup_ms + exec_ms + cleanup_ms total_duration += total_ms tasks.append({ "task_key": t.get("task_key"), "state": t.get("state", {}).get("result_state") or t.get("state", {}).get("life_cycle_state"), "setup_sec": round(setup_ms / 1000, 1), "execution_sec": round(exec_ms / 1000, 1), "cleanup_sec": round(cleanup_ms / 1000, 1), "total_sec": round(total_ms / 1000, 1), "cluster_id": t.get("existing_cluster_id") or t.get("cluster_instance", {}).get("cluster_id") }) tasks.sort(key=lambda x: x["execution_sec"], reverse=True) start_ms = run_dict.get("start_time") end_ms = run_dict.get("end_time") duration_min = round((end_ms - start_ms) / 1000 / 60, 2) if start_ms and end_ms else None return { "run_id": run_id, "job_id": run_dict.get("job_id"), "state": run_dict.get("state", {}).get("result_state"), "start_time_local": ms_to_local(start_ms), "end_time_local": ms_to_local(end_ms), "duration_min": duration_min, "total_task_duration_sec": round(total_duration / 1000, 1), "tasks": tasks, "slowest_task": tasks[0]["task_key"] if tasks else None } - tools/metrics.py:160-160 (registration)Registration of the get_run_task_metrics tool using the @mcp.tool decorator.
@mcp.tool