Default args and DAG parameters in Apache Airflow - Mini Project: Build & Apply

Default args and DAG parameters

📖 Scenario: You are setting up a simple Airflow DAG to automate a daily data backup task. To keep your code clean and avoid repetition, you want to use default_args to set common parameters for your DAG and tasks.

🎯 Goal: Create an Airflow DAG named daily_backup that runs daily at 2 AM. Use default_args to set the owner to data_engineer and the start date to January 1, 2024. Then, define a simple task that prints 'Backing up data...'.

📋 What You'll Learn

Create a dictionary called default_args with keys 'owner' and 'start_date' with exact values 'data_engineer' and datetime(2024, 1, 1) respectively.

Create a DAG named daily_backup with default_args=default_args and schedule_interval='0 2 * * *'.

Define a PythonOperator task named backup_task that runs a Python function printing 'Backing up data...'.

Print the DAG's dag_id to confirm setup.

💡 Why This Matters

🌍 Real World

Using <code>default_args</code> helps keep Airflow DAGs clean and consistent, especially when many tasks share the same settings like owner, retries, or start date.

💼 Career

Understanding DAG parameters and default arguments is essential for data engineers and DevOps professionals managing automated workflows with Airflow.

1

Create default_args dictionary

Create a dictionary called default_args with these exact entries: 'owner': 'data_engineer' and 'start_date': datetime(2024, 1, 1). Import datetime from the datetime module.

Apache Airflow

from datetime import datetime

# Your code here

Hint

Use default_args = {'owner': 'data_engineer', 'start_date': datetime(2024, 1, 1)}.

2

Create the DAG with default_args

Import DAG from airflow. Create a DAG named daily_backup using default_args=default_args and schedule_interval='0 2 * * *'.

Apache Airflow

from datetime import datetime
from airflow import DAG

default_args = {
    'owner': 'data_engineer',
    'start_date': datetime(2024, 1, 1)
}

# Your code here

Hint

Use daily_backup = DAG('daily_backup', default_args=default_args, schedule_interval='0 2 * * *').

3

Define the backup task

Import PythonOperator from airflow.operators.python. Define a Python function called backup_function that prints 'Backing up data...'. Then create a PythonOperator task named backup_task using task_id='backup_task', python_callable=backup_function, and dag=daily_backup.

Apache Airflow

from datetime import datetime
from airflow import DAG
from airflow.operators.python import PythonOperator

default_args = {
    'owner': 'data_engineer',
    'start_date': datetime(2024, 1, 1)
}

daily_backup = DAG(
    'daily_backup',
    default_args=default_args,
    schedule_interval='0 2 * * *'
)

# Your code here

Hint

Define a function that prints the message, then create a PythonOperator with the given parameters.

4

Print the DAG ID

Write a print statement to display the dag_id of the DAG daily_backup.

Apache Airflow

from datetime import datetime
from airflow import DAG
from airflow.operators.python import PythonOperator

default_args = {
    'owner': 'data_engineer',
    'start_date': datetime(2024, 1, 1)
}

daily_backup = DAG(
    'daily_backup',
    default_args=default_args,
    schedule_interval='0 2 * * *'
)

def backup_function():
    print('Backing up data...')

backup_task = PythonOperator(
    task_id='backup_task',
    python_callable=backup_function,
    dag=daily_backup
)

# Your code here

Hint

Use print(daily_backup.dag_id) to show the DAG ID.

Practice

1. What is the main purpose of default_args in an Airflow DAG?

easy

A. To define the schedule interval of the DAG

B. To specify the Python version used by the DAG

C. To set common parameters like retries and owner for all tasks

D. To list all the tasks in the DAG

Solution

1. Step 1: Understand the role of default_args

   default_args is a dictionary used to set common settings for tasks, such as retries, owner, start date, etc.

2. Step 2: Differentiate from DAG parameters

   DAG parameters like schedule_interval control when the DAG runs, not task defaults.

3. Final Answer:

   To set common parameters like retries and owner for all tasks -> Option C

4. Quick Check:

   default_args = common task settings [OK]

Hint: default_args sets task defaults, not DAG schedule [OK]

Common Mistakes:

• Confusing default_args with DAG schedule_interval
• Thinking default_args lists tasks
• Assuming default_args sets Python version

2. Which of the following is the correct way to define default_args with retries set to 3 and owner as 'airflow'?

easy

A. default_args = {'retries': 3, 'owner': 'airflow'}

B. default_args = ['retries=3', 'owner=airflow']

C. default_args = (retries=3, owner='airflow')

D. default_args = retries:3, owner:'airflow'

Solution

1. Step 1: Recall Python dictionary syntax

   Python dictionaries use curly braces with key:value pairs, keys as strings and values properly quoted if strings.

2. Step 2: Check each option's syntax

   default_args = {'retries': 3, 'owner': 'airflow'} uses correct dictionary syntax. Options A, B, and D use invalid Python syntax for dictionaries.

3. Final Answer:

   default_args = {'retries': 3, 'owner': 'airflow'} -> Option A

4. Quick Check:

   Python dict = curly braces with key:value [OK]

Hint: default_args is a Python dict with keys and values [OK]

Common Mistakes:

• Using list or tuple instead of dict
• Missing quotes around string keys or values
• Using colon instead of equal sign inside dict

3. Given this DAG snippet:

default_args = {'owner': 'admin', 'retries': 2}
with DAG('example_dag', default_args=default_args, schedule_interval='@daily') as dag:
    task1 = BashOperator(task_id='task1', bash_command='echo Hello')
    task2 = BashOperator(task_id='task2', bash_command='echo World', retries=5)

What is the number of retries for task2?

medium

A. 5

B. 2

C. 0

D. Undefined, causes error

Solution

1. Step 1: Understand default_args and task-level override

   default_args sets retries=2 for all tasks unless overridden.

2. Step 2: Check task2 retries parameter

   task2 explicitly sets retries=5, which overrides the default.

3. Final Answer:

   5 -> Option A

4. Quick Check:

   Task-level retries override default_args [OK]

Hint: Task args override default_args if set [OK]

Common Mistakes:

• Assuming default_args always applies
• Thinking task2 inherits retries=2
• Confusing retries with schedule_interval

4. You wrote this DAG code:

default_args = {'owner': 'user', 'start_date': '2023-01-01'}
dag = DAG('my_dag', default_args=default_args, schedule_interval='@hourly')

But Airflow throws an error about start_date. What is the likely problem?

medium

A. owner cannot be 'user', must be 'airflow'

B. schedule_interval '@hourly' is invalid syntax

C. default_args must be passed as a list, not dict

D. start_date must be a datetime object, not a string

Solution

1. Step 1: Check start_date type requirement

   Airflow requires start_date to be a datetime object, not a string.

2. Step 2: Validate other parameters

   schedule_interval='@hourly' is valid, default_args is a dict, and owner can be any string.

3. Final Answer:

   start_date must be a datetime object, not a string -> Option D

4. Quick Check:

   start_date = datetime, not string [OK]

Hint: Use datetime objects for start_date in default_args [OK]

Common Mistakes:

• Passing start_date as string instead of datetime
• Misunderstanding schedule_interval format
• Thinking owner must be 'airflow'

5. You want a DAG to run every Monday at 9 AM starting from March 1, 2024, with tasks retrying twice on failure and owned by 'data_team'. Which default_args and DAG parameters are correct?

hard

A. default_args = {'owner': 'data_team', 'retries': 2, 'start_date': '2024-03-01 09:00'}\ndag = DAG('weekly_dag', default_args=default_args, schedule_interval='0 9 * * 1')

B. default_args = {'owner': 'data_team', 'retries': 2, 'start_date': datetime(2024, 3, 1)}\ndag = DAG('weekly_dag', default_args=default_args, schedule_interval='0 9 * * 1')

C. default_args = {'owner': 'data_team', 'retries': 2, 'start_date': datetime(2024, 3, 1, 9, 0)}\ndag = DAG('weekly_dag', default_args=default_args, schedule_interval='@daily')

D. default_args = {'owner': 'data_team', 'retries': 2, 'start_date': '2024-03-01 09:00'}\ndag = DAG('weekly_dag', default_args=default_args, schedule_interval='@weekly')

Solution

1. Step 1: Set start_date with datetime object

   Start date must be a datetime object; time can be set to midnight if not specified.

2. Step 2: Use correct cron for Monday 9 AM

   Cron '0 9 * * 1' means 9 AM every Monday, matching requirement.

3. Step 3: Check retries and owner in default_args

   Retries=2 and owner='data_team' are correctly set.

4. Final Answer:

   default_args with datetime(2024, 3, 1) and schedule_interval='0 9 * * 1' -> Option B

5. Quick Check:

   start_date datetime + cron for Monday 9 AM [OK]

Hint: Use cron '0 9 * * 1' for Monday 9 AM schedule [OK]

Common Mistakes:

• Using string for start_date
• Using '@weekly' which runs Sunday midnight
• Setting schedule_interval to '@daily' instead of Monday