Airflow UI Guide – DAGs, Graph View, Code View, Logs, Task Instances
Imagine the Airflow UI as the control room of your data factory. From here, you can monitor every workflow, inspect tasks, debug errors, and track your data pipelines in real-time. Understanding the UI is essential for managing complex workflows efficiently.
1. DAGs View
The DAGs View is the starting point where you can:
- See all available DAGs
- Check DAG status (running, success, failed)
- Trigger DAGs manually
- Pause or unpause DAGs
Example Input:
- DAG: daily_sales
- Status: paused
Example Output:
- Toggle “unpause” → DAG now schedules automatically according to its schedule interval
2. Graph View
The Graph View provides a visual representation of task dependencies in a DAG.
Example Input: DAG with tasks: extract_data >> transform_data >> load_data
Example Output: Graph shows:
extract_data → transform_data → load_data
- Colors indicate task states: green (success), red (failed), yellow (running)
Use Case: Quickly identify failed tasks and dependencies to troubleshoot issues.
3. Code View
The Code View allows you to inspect the Python code defining a DAG directly from the UI.
Example Input: Open daily_sales DAG
Example Output: Full Python DAG code visible for review:
with DAG('daily_sales', start_date=datetime(2025, 12, 15), schedule_interval='@daily') as dag:
task1 >> task2 >> task3
Use Case: Verify DAG logic or share code with teammates.
4. Task Logs
Logs provide detailed execution history of each task instance. You can:
** Debug errors
** Track outputs
** Confirm execution times
Example Input: Click on task transform_data → “View Log”
Example Output:
[2025-12-15 06:01:02] INFO - Transforming sales data...
[2025-12-15 06:01:05] INFO - Total sales calculated: 400
[2025-12-15 06:01:06] INFO - Task succeeded
5. Task Instances
Task Instances represent individual runs of a task for a specific DAG run. You can:
- View status (success, failed, skipped)
- Retry tasks
- Clear failed tasks to rerun them
Example Input: Task load_data for DAG run 2025-12-15 failed
Example Output: Retry → Task executes again and succeeds
Task load_data retried
2025-12-15 06:02:10 - Task succeeded
Inputs and Outputs Table
| UI Component | Input Example | Output Example |
|---|---|---|
| DAGs View | DAG: daily_sales, paused | DAG scheduled and running |
| Graph View | DAG task dependencies | Visual representation of task flow |
| Code View | Select DAG code | Python DAG code displayed |
| Logs | Task: transform_data | Detailed task execution logs |
| Task Instance | Retry failed task | Task executed successfully |
Final Thoughts
The Airflow UI is your workflow command center. By understanding DAGs, Graph and Code Views, Logs, and Task Instances, you can:
- Monitor pipelines in real-time
- Quickly identify and fix issues
- Ensure workflows run smoothly and efficiently
Mastering the UI is as important as understanding DAGs and components—it gives you full visibility and control over your Airflow workflows.
Summary
The Airflow UI enables you to:
- View and manage DAGs
- Understand task dependencies via Graph View
- Inspect DAG code in Code View
- Debug tasks through Logs
- Track and manage Task Instances
By using the UI effectively, you can maintain, troubleshoot, and optimize your workflows with confidence.
Next Up: [Creating Your First DAG in Apache Airflow (Beginner to Pro Guide)]