Airflow DagRuns

This page provides drill-down visibility into DAG execution history.

Pulse enables full-stack observability for Airflow on Kubernetes by combining:

DAG-level monitoring
Task-level drilldowns
Scheduler and executor health tracking
Resource utilization visibility
Performance trend analysis
Operator-level metrics

This allows faster root cause analysis, improved SLA tracking, and proactive workload optimization.

Navigation Steps

In the xDP UI, select Airflow from the left navigation pane.
Select Airflow > DAG Runs.
Select the desired cluster from the cluster selector in the upper-right corner.

The DAG Runs page opens and displays the DAG runs for the selected Airflow cluster.

Time Period

Use the Time Period selector in the upper-left corner of the dashboard to filter metrics and charts for a specific monitoring window.

DAGs

DAG ID — Unique DAG name
Owner — DAG owner
Recent Runs — Status indicators of the latest runs
Schedule — Cron or preset schedule
Paused — Whether DAG is paused
Start Date — Initial activation date

Runs Section

Expand a DAG, and for each DAG run, you can find these details.

Run ID — Unique run identifier
State — Success, Failed, Running, etc.
Run Type — Scheduled, Manual, Backfill
Started At — Execution start time
Queued At — Time entered queue
Ended At — Completion time

DAG Run Details

Provides deep observability for a specific DAG run.

Run Summary

Run Information

Run ID
Run Type
State

Timing Details

Started At
Queued At
Ended At
Duration

DAG Run Trends

Compares:

Elapsed Time
Queued Duration

Across historical runs.

Tasks Table

For each task:

Task Information

Task ID
Job ID
Operator

Execution Status

Previous State
Current State

Timing Details

Queue
Started At
Ended At
Duration

Resource and Scheduling

Pool
Pool Slots
Priority Weight

Task Tries

Shows retry attempts and retry behavior trends.

Landing Times

Displays task scheduling, landing times, and execution distribution.

Execution Timeline

Visual timeline view showing:

Task execution order
Parallelism
Dependencies
Branch behavior

DAG

Displays the workflow execution flow as a Directed Acyclic Graph (DAG), showing task dependencies, execution order, task status, and operator details from workflow initialization through completion.

Features

Search and filter: You can search and filter the records.
Refresh the page to retrieve the latest job metrics.