DAG performance tracking in Apache Airflow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the Airflow DAG class.

Apache Airflow

from airflow.[1] import DAG

Drag options to blanks, or click blank then click option'

Ahooks

Boperators

Cmodels

Dutils

Attempts:

3 left

💡 Hint

Common Mistakes

Importing DAG from operators or hooks instead of models.

Using incorrect module names.

The DAG class is imported from the airflow.models module.

2fill in blank

medium

Complete the code to set the start date for a DAG.

Apache Airflow

from datetime import datetime

dag = DAG('example_dag', start_date=[1](2024, 1, 1))

Drag options to blanks, or click blank then click option'

Adate

Btime

Ctimedelta

Ddatetime

Attempts:

3 left

💡 Hint

Common Mistakes

Using date instead of datetime which lacks time info.

Using timedelta which is for durations, not points in time.

The start_date requires a datetime object to specify when the DAG should start running.

3fill in blank

hard

Fix the error in the code to correctly set the DAG schedule interval.

Apache Airflow

dag = DAG('example_dag', schedule_interval=[1])

Drag options to blanks, or click blank then click option'

A'@daily'

Bdaily

Ctimedelta(days=1)

D'daily'

Attempts:

3 left

💡 Hint

Common Mistakes

Using unquoted words like daily which cause syntax errors.

Using timedelta objects directly which is not accepted here.

The schedule_interval accepts cron presets as strings like '@daily'. The option daily without quotes is invalid.

4fill in blank

hard

Fill both blanks to create a task that tracks execution time using the PythonOperator.

Apache Airflow

from airflow.operators.python import PythonOperator

def track_time():
    print('Tracking execution time')

task = PythonOperator(task_id=[1], python_callable=[2], dag=dag)

Drag options to blanks, or click blank then click option'

A'track_time_task'

Btrack_time

C'execution_time'

Dprint_time

Attempts:

3 left

💡 Hint

Common Mistakes

Using a function name as task_id without quotes.

Passing a string as python_callable instead of a function reference.

The task_id must be a string identifier, here 'track_time_task'. The python_callable is the function track_time that runs when the task executes.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that tracks task durations longer than 5 minutes.

Apache Airflow

durations = {task_id: duration for task_id, duration in task_durations.items() if duration [1] [2] and task_id [3] 'task_1'}

Drag options to blanks, or click blank then click option'

A>

B300

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causing wrong filtering.

Using '==' instead of '!=' to exclude the task.

This comprehension filters tasks with duration greater than 300 seconds (5 minutes) and excludes the task with ID 'task_1'.

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