DAG concept (Directed Acyclic Graph) 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 import [1]

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

ADAG

BTask

COperator

DScheduler

Attempts:

3 left

💡 Hint

Common Mistakes

Importing Task instead of DAG

Using Scheduler which is a different component

The DAG class is imported from airflow to define workflows.

2fill in blank

medium

Complete the code to set the DAG's start date using datetime.

Apache Airflow

from datetime import datetime
start_date = datetime([1])

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

A2023, 1, 1

B1, 1, 2023

C'2023-01-01'

Ddatetime.now()

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping month and year

Passing a string instead of integers

The datetime constructor requires year, month, day as integers in that order.

3fill in blank

hard

Fix the error in the DAG definition by completing the missing argument.

Apache Airflow

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

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

Atasks

Bstart_date

Cdefault_args

Dschedule

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'schedule' instead of 'start_date'

Passing 'tasks' which is not a DAG argument

The DAG requires the start_date argument to know when to begin scheduling.

4fill in blank

hard

Fill both blanks to create a task using BashOperator with a command and assign it to the DAG.

Apache Airflow

from airflow.operators.bash import BashOperator

bash_task = BashOperator(task_id='print_date', bash_command=[1], dag=[2])

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

A'date'

Bdag

C'ls -l'

Dstart_date

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ls -l' instead of 'date' for this task

Passing start_date instead of dag object

The bash_command should be a string with the shell command, and dag should be the DAG object.

5fill in blank

hard

Fill all three blanks to set task dependencies so task1 runs before task2 and task2 before task3.

Apache Airflow

task1 [1] task2
 task2 [2] task3

# Using Airflow's bitshift operators

[3]

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

A>>

B<<

Ctask1 >> task2 >> task3

Dtask3 >> task2 >> task1

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<<' which reverses the order

Writing the chain backwards

The '>>' operator sets the order: left task runs before right task. The last line shows the full chain.

Practice

1. What is a DAG in Apache Airflow?

easy

A. A tool to store data in a database

B. A programming language for writing tasks

C. A type of server used to run Airflow

D. A Directed Acyclic Graph that defines task order without loops

Solution

1. Step 1: Understand the meaning of DAG

   DAG stands for Directed Acyclic Graph, which means tasks are connected in one direction without cycles or loops.

2. Step 2: Relate DAG to Airflow

   In Airflow, DAGs organize tasks so they run in a specific order safely and predictably.

3. Final Answer:

   A Directed Acyclic Graph that defines task order without loops -> Option D

4. Quick Check:

   DAG = Directed Acyclic Graph [OK]

Hint: Remember DAG means tasks flow one way, no loops [OK]

Common Mistakes:

• Thinking DAG is a database or server
• Confusing DAG with a programming language
• Assuming tasks can loop back in DAG

2. Which of the following is the correct way to define a DAG in Airflow using Python?

easy

A. dag = DAG('my_dag', schedule_interval='@daily')

B. dag = DAG('my_dag', run_every='daily')

C. dag = DAG('my_dag', interval='daily')

D. dag = DAG('my_dag', schedule='daily')

Solution

1. Step 1: Recall Airflow DAG parameters

   The correct parameter to set the schedule is schedule_interval.

2. Step 2: Match the correct syntax

   Only dag = DAG('my_dag', schedule_interval='@daily') uses schedule_interval='@daily', which is valid syntax.

3. Final Answer:

   dag = DAG('my_dag', schedule_interval='@daily') -> Option A

4. Quick Check:

   Use schedule_interval to set DAG schedule [OK]

Hint: Use schedule_interval, not run_every or schedule [OK]

Common Mistakes:

• Using wrong parameter names like run_every
• Forgetting quotes around schedule value
• Using invalid schedule strings

3. Given this DAG snippet, what is the order of task execution?

task1 >> task2 >> task3

medium

A. task3 runs first, then task2, then task1

B. All tasks run at the same time

C. task1 runs first, then task2, then task3

D. task2 runs first, then task1, then task3

Solution

1. Step 1: Understand the '>>' operator in Airflow

   The '>>' operator means 'set downstream', so task1 runs before task2, and task2 before task3.

2. Step 2: Determine execution order

   Tasks run in the order task1, then task2, then task3.

3. Final Answer:

   task1 runs first, then task2, then task3 -> Option C

4. Quick Check:

   task1 >> task2 >> task3 means sequential order [OK]

Hint: Remember '>>' means run left task before right task [OK]

Common Mistakes:

• Thinking '>>' means run tasks in parallel
• Reversing the order of tasks
• Assuming tasks run simultaneously

4. What is wrong with this DAG task dependency code?

task1.set_downstream(task2)
task2.set_downstream(task1)

medium

A. It creates a cycle, which is not allowed in DAGs

B. The syntax for set_downstream is incorrect

C. Tasks must be named differently

D. No error, this is valid

Solution

1. Step 1: Analyze the dependencies

   task1 depends on task2, and task2 depends on task1, creating a loop.

2. Step 2: Understand DAG rules

   DAGs cannot have cycles; tasks must not depend on each other circularly.

3. Final Answer:

   It creates a cycle, which is not allowed in DAGs -> Option A

4. Quick Check:

   DAGs must be acyclic, no loops allowed [OK]

Hint: Check for circular dependencies to avoid cycles [OK]

Common Mistakes:

• Ignoring circular dependencies
• Assuming set_downstream syntax is wrong
• Thinking task names cause errors

5. You want to create a DAG where task3 runs only after both task1 and task2 complete. Which code correctly sets this dependency?

hard

A. task3 >> task1
task3 >> task2

B. task1 >> task3
task2 >> task3

C. task1 << task3
task2 << task3

D. task3.set_downstream([task1, task2])

Solution

1. Step 1: Understand dependency direction

   To run task3 after task1 and task2, task1 and task2 must be upstream of task3.

2. Step 2: Check correct syntax

   Using '>>' from task1 and task2 to task3 sets task3 to run after both complete.

3. Final Answer:

   task1 >> task3 task2 >> task3 -> Option B

4. Quick Check:

   Upstream tasks >> downstream task [OK]

Hint: Use 'task1 >> task3' and 'task2 >> task3' for multiple dependencies [OK]

Common Mistakes:

• Reversing dependency arrows
• Using set_downstream incorrectly with lists
• Assuming task3 runs before task1 and task2