DAG performance tracking in Apache Airflow - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

Identify DAG run duration from Airflow CLI output

You run the command airflow dags list-runs -d example_dag and get the following output snippet:

run_id          | state   | execution_date       | start_date          | end_date            
--------------- | ------- | --------------------| ------------------- | ------------------- 
manual__2024-06-01T00:00:00+00:00 | success | 2024-06-01 00:00:00 | 2024-06-01 00:01:00 | 2024-06-01 00:05:00

What is the duration of this DAG run?

A 4 minutes

B 5 minutes

C 1 minute

D 6 minutes

💡 Hint

Subtract the start_date from the end_date to find the duration.

The DAG started at 00:01 and ended at 00:05, so the duration is 4 minutes.

🧠 Conceptual

intermediate

Understanding Airflow task duration metrics

Which Airflow metric best helps you track how long individual tasks take to complete within a DAG run?

A Scheduler heartbeat

B Task duration

C DAG concurrency

D Pool slots

💡 Hint

Think about what measures time spent per task.

Task duration measures the time taken by each task to run, which helps track performance.

🔀 Workflow

advanced

Steps to enable DAG run duration logging in Airflow

You want to track DAG run durations by logging start and end times automatically. Which sequence of steps is correct?

A 3,1,2,4

B 1,3,2,4

C 1,2,3,4

D 2,1,3,4

💡 Hint

Start with enabling logging, then configure callbacks, then write functions.

First enable logging, then configure callbacks in the DAG, write the callback functions, and finally deploy and monitor.

❓ Troubleshoot

advanced

Diagnosing missing DAG run duration data in Airflow UI

You notice that DAG run durations are not showing in the Airflow UI. Which of the following is the most likely cause?

A The Airflow webserver is down

B The Airflow scheduler is running normally

C The DAG has completed successfully with recorded start and end times

D The DAG runs are stuck in queued state and never start

💡 Hint

If runs never start, duration cannot be calculated.

If DAG runs are stuck queued, they have no start or end time, so duration is missing.

✅ Best Practice

expert

Best practice for long-term DAG performance tracking in Airflow

What is the best practice to maintain historical DAG performance data over months without impacting Airflow database performance?

A Export DAG run and task instance metrics regularly to an external monitoring system

B Keep all DAG run data indefinitely in Airflow's metadata database

C Disable DAG run logging to reduce database size

D Manually delete DAG run records weekly

💡 Hint

Think about separating monitoring data from the main Airflow database.

Exporting metrics to external systems like Prometheus or ELK allows long-term tracking without slowing Airflow.

Practice

1. What is the main purpose of tracking DAG performance in Airflow?

easy

A. To measure how long workflows and tasks take to run

B. To change the order of tasks in a DAG

C. To create new DAGs automatically

D. To delete old task logs

Solution

1. Step 1: Understand DAG performance tracking

   DAG performance tracking focuses on measuring execution times of workflows and tasks.

2. Step 2: Identify the main goal

   The goal is to find how long tasks and DAG runs take to help improve pipeline speed.

3. Final Answer:

   To measure how long workflows and tasks take to run -> Option A

4. Quick Check:

   DAG performance tracking = measuring run times [OK]

Hint: Think: performance means measuring speed or time [OK]

Common Mistakes:

• Confusing performance tracking with task ordering
• Thinking it creates or deletes DAGs
• Mixing logs management with performance tracking

2. Which Airflow model stores information about individual task execution times?

easy

A. DagRun

B. Variable

C. TaskInstance

D. Connection

Solution

1. Step 1: Identify models related to execution times

   DagRun stores info about the whole DAG run, TaskInstance stores info about each task run.

2. Step 2: Match model to task execution time

   TaskInstance tracks start and end times for each task, so it holds execution time data.

3. Final Answer:

   TaskInstance -> Option C

4. Quick Check:

   TaskInstance = task execution info [OK]

Hint: TaskInstance = single task run details [OK]

Common Mistakes:

• Choosing DagRun which tracks whole DAG, not tasks
• Confusing Variables or Connections with timing data
• Assuming all info is in one model

3. Given this code snippet, what will be printed?

from airflow.models import TaskInstance
from datetime import datetime, timedelta

start = datetime(2024, 6, 1, 10, 0, 0)
end = datetime(2024, 6, 1, 10, 5, 30)
ti = TaskInstance(dag_id='example_dag', task_id='task1', execution_date=start)
ti.start_date = start
ti.end_date = end
print((ti.end_date - ti.start_date).total_seconds())

medium

A. 5.5

B. 330

C. SyntaxError

D. 330.0

Solution

1. Step 1: Identify variable name mismatch

   The code assigns TaskInstance to 'ti' but prints using 'ti', which is correct. However, the code snippet uses 'ti' consistently, so no mismatch here.

2. Step 2: Check for syntax errors

   The code is syntactically correct, but the TaskInstance constructor requires a DAG object or context in recent Airflow versions, so this code may raise a runtime error.

3. Step 3: Consider the actual output

   Assuming the code runs, the output would be 330.0 seconds.

4. Final Answer:

   330.0 -> Option D

5. Quick Check:

   5 min 30 sec = 330.0 seconds [OK]

Hint: Subtract datetimes, use total_seconds() for float seconds [OK]

Common Mistakes:

• Confusing minutes with seconds
• Expecting integer instead of float
• Syntax errors from missing imports (not here)

4. You wrote this code to get task duration but it raises an error:

ti = TaskInstance(dag_id='example_dag', task_id='task1', execution_date='2024-06-01T10:00:00')
ti.start_date = datetime(2024, 6, 1, 10, 0, 0)
ti.end_date = datetime(2024, 6, 1, 10, 5, 0)
duration = (ti.end_date - ti.start_date).total_seconds()
print(duration)

What is the likely cause of the error?

medium

A. execution_date should be a datetime, not a string

B. start_date and end_date must be strings

C. TaskInstance requires a DAG object on creation

D. total_seconds() is not a valid method

Solution

1. Step 1: Check execution_date type

   execution_date must be a datetime object, not a string, to avoid errors.

2. Step 2: Confirm other parts are correct

   start_date and end_date are datetime objects, total_seconds() is valid method.

3. Final Answer:

   execution_date should be a datetime, not a string -> Option A

4. Quick Check:

   execution_date type error = execution_date should be a datetime, not a string [OK]

Hint: Pass datetime objects, not strings, for execution_date [OK]

Common Mistakes:

• Passing string instead of datetime for execution_date
• Thinking start_date/end_date must be strings
• Believing total_seconds() is invalid

5. You want to track the average duration of all tasks in a DAG run using Airflow's ORM models. Which approach is best?

hard

A. Calculate average duration by counting TaskInstance rows only

B. Query TaskInstance for all tasks of the DAG run, calculate average of (end_date - start_date)

C. Use Airflow Variables to store durations manually

D. Query DagRun for duration and divide by number of tasks

Solution

1. Step 1: Understand data sources for task durations

   TaskInstance has start_date and end_date for each task, so durations come from there.

2. Step 2: Calculate average duration correctly

   Query all TaskInstances for the DAG run, compute (end_date - start_date) for each, then average.

3. Final Answer:

   Query TaskInstance for all tasks of the DAG run, calculate average of (end_date - start_date) -> Option B

4. Quick Check:

   Average task duration = average of TaskInstance times [OK]

Hint: Average durations come from TaskInstance times, not DagRun [OK]

Common Mistakes:

• Using DagRun duration divided by task count (wrong average)
• Storing durations manually in Variables (extra work)
• Counting rows without timing info