Creating a basic DAG file in Apache Airflow - Try It Yourself

Creating a basic DAG file

📖 Scenario: You are working as a data engineer. You need to create a simple workflow to run tasks in Apache Airflow. This workflow is called a DAG (Directed Acyclic Graph). It helps you schedule and organize tasks.

🎯 Goal: Build a basic Airflow DAG file that defines a simple workflow with one task. This task will print a message when it runs.

📋 What You'll Learn

Create a DAG object with a specific dag_id and start_date.

Define a Python function that prints a message.

Create a PythonOperator to run the function.

Set the task in the DAG.

Print the DAG object at the end.

💡 Why This Matters

🌍 Real World

Airflow DAGs are used to automate and schedule workflows in data pipelines and other automated tasks.

💼 Career

Knowing how to create and manage DAGs is essential for data engineers and DevOps professionals working with workflow automation.

1

Create the DAG object

Import DAG from airflow and datetime from datetime. Then create a DAG object called my_dag with dag_id='my_first_dag' and start_date=datetime(2023, 1, 1).

Apache Airflow

# Your code here

Hint

Use DAG to create the workflow object. The start_date tells Airflow when to begin scheduling.

2

Define the Python function

Define a Python function called print_hello that prints the message 'Hello from Airflow!'.

Apache Airflow

from airflow import DAG
from datetime import datetime

my_dag = DAG(dag_id='my_first_dag', start_date=datetime(2023, 1, 1))

# Your code here

Hint

Define a simple function that prints a message. This function will be run by the task.

3

Create the PythonOperator task

Import PythonOperator from airflow.operators.python. Then create a task called hello_task using PythonOperator with task_id='hello_task', python_callable=print_hello, and assign it to the DAG my_dag.

Apache Airflow

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

def print_hello():
    print('Hello from Airflow!')

my_dag = DAG(dag_id='my_first_dag', start_date=datetime(2023, 1, 1))

# Your code here

Hint

Use PythonOperator to run the function as a task in the DAG.

4

Print the DAG object

Write a print statement to display the my_dag object.

Apache Airflow

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

def print_hello():
    print('Hello from Airflow!')

my_dag = DAG(dag_id='my_first_dag', start_date=datetime(2023, 1, 1))

hello_task = PythonOperator(
    task_id='hello_task',
    python_callable=print_hello,
    dag=my_dag
)

# Your code here

Hint

Use print(my_dag) to show the DAG object details.

Practice

1. What is the main purpose of a DAG file in Apache Airflow?

easy

A. To configure the Airflow webserver settings

B. To store logs generated by tasks

C. To install Python packages required by tasks

D. To define tasks and their schedule for workflow automation

Solution

1. Step 1: Understand what a DAG file represents

   A DAG file in Airflow is a Python script that defines a Directed Acyclic Graph, which represents tasks and their dependencies.

2. Step 2: Identify the role of the DAG file

   The DAG file schedules and organizes tasks to automate workflows, not for logging, configuration, or package installation.

3. Final Answer:

   To define tasks and their schedule for workflow automation -> Option D

4. Quick Check:

   DAG file purpose = Define tasks and schedule [OK]

Hint: DAG files define workflows, not configs or logs [OK]

Common Mistakes:

• Confusing DAG file with Airflow config files
• Thinking DAG files store logs
• Assuming DAG files install packages

2. Which of the following is the correct way to import the DAG class in an Airflow DAG file?

easy

A. import DAG from airflow.models

B. import airflow.DAG

C. from airflow.models import DAG

D. from airflow import DAG

Solution

1. Step 1: Recall the correct import syntax in Python

   Python imports use 'from module import class' syntax for specific classes.

2. Step 2: Match with Airflow's module structure

   The recommended import used in official Airflow documentation is 'from airflow.models import DAG'.

3. Final Answer:

   from airflow.models import DAG -> Option C

4. Quick Check:

   Correct import syntax = from airflow.models import DAG [OK]

Hint: Use 'from airflow.models import DAG' to import DAG class [OK]

Common Mistakes:

• Using incorrect import syntax
• Trying to import DAG directly from airflow
• Using invalid Python import statements

3. Given the following DAG snippet, what will be the task_id of the BashOperator task?

from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

default_args = {'start_date': datetime(2024, 1, 1)}
with DAG('example_dag', default_args=default_args, schedule_interval='@daily') as dag:
    task = BashOperator(task_id='print_date', bash_command='date')

medium

A. print_date

B. task

C. example_dag

D. bash_command

Solution

1. Step 1: Identify the task_id parameter in BashOperator

   The BashOperator is created with task_id='print_date', which names the task.

2. Step 2: Understand task_id usage

   task_id is the unique identifier for the task in the DAG, so the task's id is 'print_date'.

3. Final Answer:

   print_date -> Option A

4. Quick Check:

   task_id value = print_date [OK]

Hint: Look for task_id argument in operator constructor [OK]

Common Mistakes:

• Confusing variable name with task_id
• Using DAG id as task_id
• Mistaking bash_command as task_id

4. Identify the error in this DAG file snippet:

from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

with DAG('my_dag', start_date=datetime(2024, 1, 1), schedule_interval='@daily') as dag:
    task1 = BashOperator(task_id='task1', bash_command='echo Hello')
    task2 = BashOperator(task_id='task2', bash_command='echo World')
    task1 >> task2

print('DAG created')

medium

A. No error, DAG is correctly defined

B. Missing default_args dictionary for start_date

C. Incorrect import of BashOperator

D. Using print statement inside DAG context

Solution

1. Step 1: Check imports and DAG definition

   Imports are correct, DAG is created with start_date and schedule_interval directly in constructor.

2. Step 2: Verify task creation and dependencies

   Tasks are created with valid task_ids and bash commands, and dependency is set with 'task1 >> task2'.

3. Step 3: Confirm print statement usage

   Print outside DAG context is allowed and does not affect DAG creation.

4. Final Answer:

   No error, DAG is correctly defined -> Option A

5. Quick Check:

   DAG syntax correct = No error [OK]

Hint: Check if DAG and tasks follow Airflow syntax rules [OK]

Common Mistakes:

• Assuming default_args is mandatory
• Thinking print inside DAG causes error
• Confusing import paths for operators

5. You want to create a DAG that runs a task every Monday at 7 AM starting from March 1, 2024. Which schedule_interval and start_date combination is correct in the DAG definition?

hard

A. schedule_interval='@weekly', start_date=datetime(2024, 3, 4)

B. schedule_interval='0 7 * * 1', start_date=datetime(2024, 3, 1)

C. schedule_interval='@daily', start_date=datetime(2024, 3, 1, 7, 0)

D. schedule_interval='0 7 * * 0', start_date=datetime(2024, 3, 1)

Solution

1. Step 1: Determine the cron expression for Monday 7 AM

   Cron '0 7 * * 1' runs every Monday at 7:00 AM (1=Monday).

2. Step 2: Validate start_date

   March 1, 2024 (Friday) is before the first Monday (March 4), so first run on March 4, 7 AM.

3. Step 3: Eliminate other options

   A: '@weekly'='0 0 * * 0' (Sunday midnight), wrong day/time. C: daily. D: Sunday (0).

4. Final Answer:

   schedule_interval='0 7 * * 1', start_date=datetime(2024, 3, 1) -> Option B

5. Quick Check:

   Monday 7AM cron = '0 7 * * 1' [OK]

Hint: Use cron '0 7 * * 1' for Mon 7AM [OK]

Common Mistakes:

• Setting start_date on wrong weekday
• Confusing cron day numbers (0=Sunday, 1=Monday)
• Using daily schedule for weekly task