Airflow UI overview - Step-by-Step Execution

Process Flow - Airflow UI overview

Open Airflow UI in Browser

↓

View DAGs List

↓

Select a DAG

↓

Explore DAG Graph View

↓

Check DAG Tree View

↓

Review Task Instances & Logs

↓

Trigger or Pause DAG

↓

Monitor DAG Runs & Status

The Airflow UI lets you open the web page, see all workflows (DAGs), pick one, explore its tasks visually, check logs, and control runs.

Execution Sample

Apache Airflow

1. Open Airflow UI at http://localhost:8080
2. See list of DAGs
3. Click a DAG name
4. View DAG graph and tree
5. Click task for logs
6. Trigger or pause DAG

This sequence shows how a user interacts step-by-step with the Airflow UI to monitor and control workflows.

Process Table

Step	User Action	UI Response	Resulting State
1	Open Airflow UI in browser	Loads DAGs list page	Shows all available DAGs with status
2	Click on a DAG name	Displays DAG details page	Shows graph and tree views of tasks
3	Select Graph View tab	Renders DAG graph	Visual task dependencies appear
4	Select Tree View tab	Shows task execution tree	Task statuses over time visible
5	Click on a task instance	Opens logs panel	Logs for that task instance displayed
6	Click Trigger DAG button	Starts a new DAG run	DAG run status updates to running
7	Click Pause DAG toggle	Pauses or unpauses DAG	DAG scheduling enabled or disabled
8	Monitor DAG runs	Updates run status in UI	User sees success, failure, or running states
9	Exit	Close browser or navigate away	UI session ends

💡 User finishes monitoring or controlling DAGs and closes the UI

Status Tracker

UI Element	Initial State	After Step 2	After Step 3	After Step 5	After Step 6	Final State
DAGs List	Visible	Hidden	Hidden	Hidden	Hidden	Visible
DAG Graph View	Hidden	Hidden	Visible	Visible	Visible	Hidden
DAG Tree View	Hidden	Hidden	Hidden	Hidden	Hidden	Hidden
Task Logs Panel	Hidden	Hidden	Hidden	Visible	Visible	Hidden
DAG Run Status	Idle	Idle	Idle	Idle	Running	Updated

Key Moments - 3 Insights

Why does the DAG graph view appear only after clicking a DAG name?

Because the UI first shows the list of DAGs. Only after selecting one does it load that DAG's detailed views like the graph (see execution_table step 2 and 3).

What happens when you click a task instance in the tree or graph view?

The UI opens the logs panel for that specific task instance so you can see detailed execution logs (see execution_table step 5).

How does pausing a DAG affect its scheduling?

Pausing disables automatic scheduling of the DAG, so no new runs start until unpaused (see execution_table step 7).

Visual Quiz - 3 Questions

Look at the execution table, what UI element becomes visible at step 3?

A Task Logs Panel

B DAG Graph View

C DAGs List

D DAG Tree View

💡 Hint

Check the 'UI Response' and 'Resulting State' columns at step 3 in the execution_table.

At which step does the DAG run status change to 'Running'?

A Step 4

B Step 7

C Step 6

D Step 5

💡 Hint

Look for the 'DAG Run Status' changes in the execution_table rows.

If you pause a DAG, what happens to its scheduling according to the execution table?

A Scheduling is disabled

B DAG is deleted

C Scheduling continues as normal

D DAG runs immediately

💡 Hint

Refer to step 7 in the execution_table about the pause toggle effect.

Concept Snapshot

Airflow UI lets you see all workflows (DAGs) in a list.
Click a DAG to view its graph and tree of tasks.
Click tasks to see logs.
Use buttons to trigger or pause DAG runs.
Monitor run status live in the UI.

Full Transcript

The Airflow UI is a web interface to manage and monitor workflows called DAGs. When you open the UI, you see a list of all DAGs with their current status. Clicking on a DAG name opens detailed views including a graph showing task dependencies and a tree view showing task execution over time. You can click on individual tasks to view their logs. The UI also provides buttons to trigger a new DAG run or pause the DAG to stop scheduling. The status of DAG runs updates live so you can monitor progress. This visual flow helps users control and understand their workflows easily.

Practice

1. What is the primary purpose of the Airflow UI?

easy

A. To store data files for workflows

B. To write Python code for tasks

C. To monitor and manage workflows visually

D. To configure network settings

Solution

1. Step 1: Understand Airflow UI role

   The Airflow UI is designed as a web interface to help users see and control workflows easily.

2. Step 2: Identify main function

   It shows task status, logs, and workflow structure, making monitoring and management simple.

3. Final Answer:

   To monitor and manage workflows visually -> Option C

4. Quick Check:

   Airflow UI = Monitor & Manage Workflows [OK]

Hint: Airflow UI is for watching workflows, not coding [OK]

Common Mistakes:

• Confusing UI with coding environment
• Thinking UI stores data files
• Assuming UI manages network

2. Which section in the Airflow UI shows the status of each task in a workflow?

easy

A. Admin panel

B. DAGs tab

C. Logs tab

D. Graph view

Solution

1. Step 1: Identify UI sections

   The Graph view in Airflow UI visually displays tasks and their statuses in a workflow.

2. Step 2: Compare other options

   DAGs tab lists workflows, Logs tab shows task logs, Admin panel manages users/settings.

3. Final Answer:

   Graph view -> Option D

4. Quick Check:

   Task status = Graph view [OK]

Hint: Task status is shown in Graph view, not Logs [OK]

Common Mistakes:

• Confusing Logs tab with task status
• Thinking DAGs tab shows task details
• Assuming Admin panel shows task status

3. If a task in Airflow UI shows a red color in the Graph view, what does it mean?

medium

A. Task failed

B. Task is running

C. Task succeeded

D. Task is skipped

Solution

1. Step 1: Understand color codes in Graph view

   In Airflow UI, red color indicates a failed task.

2. Step 2: Recall other colors meaning

   Green means success, yellow means running, grey means skipped.

3. Final Answer:

   Task failed -> Option A

4. Quick Check:

   Red = Failed task [OK]

Hint: Red means failure in Airflow UI Graph view [OK]

Common Mistakes:

• Mixing red with running or success
• Confusing skipped with failed
• Ignoring color legend in UI

4. You notice that the Logs tab in Airflow UI shows no logs for a task that ran. What is the likely cause?

medium

A. Task never started or failed before logging

B. Logs tab is broken and needs UI restart

C. Task succeeded without logs

D. Logs are stored in a different UI section

Solution

1. Step 1: Analyze missing logs scenario

   If no logs appear, usually the task did not start or failed before producing logs.

2. Step 2: Eliminate other options

   Logs tab usually works; success tasks still have logs; logs are not in other UI sections.

3. Final Answer:

   Task never started or failed before logging -> Option A

4. Quick Check:

   No logs = Task not started or early failure [OK]

Hint: No logs usually means task didn't start properly [OK]

Common Mistakes:

• Assuming UI bug without checking task status
• Thinking success tasks have no logs
• Looking for logs in wrong UI tabs

5. You want to quickly check the order of tasks and their dependencies in a DAG using the Airflow UI. Which view should you use and why?

hard

A. Tree view, because it shows tasks in a timeline with dependencies

B. Graph view, because it visually displays tasks and their dependencies

C. Calendar view, because it shows task schedules

D. Code view, because it shows the Python code defining tasks

Solution

1. Step 1: Identify views for task order and dependencies

   The Graph view visually shows tasks as nodes connected by arrows representing dependencies.

2. Step 2: Compare other views

   Tree view shows task runs over time, Code view shows code but not visual dependencies, Calendar shows schedules.

3. Final Answer:

   Graph view, because it visually displays tasks and their dependencies -> Option B

4. Quick Check:

   Task order & dependencies = Graph view [OK]

Hint: Use Graph view to see task dependencies clearly [OK]

Common Mistakes:

• Confusing Tree view with dependency graph
• Looking at code instead of visual graph
• Using Calendar view for dependencies