Atomic operations in pipelines in Apache Airflow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a task that runs a Python function atomically in Airflow.

Apache Airflow

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

def my_task():
    print('Task running')

dag = DAG('my_dag', start_date=datetime(2024, 1, 1))

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

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

Amy_task

Bprint

CDAG

Ddatetime

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'print' instead of the function name.

Passing a function call like 'my_task()' instead of the function itself.

The PythonOperator requires the function name to run as the python_callable argument. Here, 'my_task' is the function defined to run.

2fill in blank

medium

Complete the code to set the task to run only after the previous task finishes successfully.

Apache Airflow

task1 >> [1]

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

Atask1

Bdag

Ctask2

Dtask3

Attempts:

3 left

💡 Hint

Common Mistakes

Using the same task on both sides.

Using the DAG object instead of a task.

The '>>' operator sets task dependencies. 'task1 >> task2' means task2 runs after task1 completes successfully.

3fill in blank

hard

Fix the error in the code to ensure the task retries 3 times on failure.

Apache Airflow

task = PythonOperator(task_id='retry_task', python_callable=my_task, retries=[1], dag=dag)

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

ANone

B'3'

CTrue

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Passing retries as a string like '3'.

Passing a boolean value instead of a number.

The retries parameter expects an integer number of retries, not a string or boolean.

4fill in blank

hard

Fill both blanks to create a DAG that runs daily and starts on January 1, 2024.

Apache Airflow

dag = DAG('daily_dag', schedule_interval=[1], start_date=[2])

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

A'@daily'

Bdatetime(2024, 1, 1)

C'@hourly'

Ddatetime(2023, 12, 31)

Attempts:

3 left

💡 Hint

Common Mistakes

Using '@hourly' instead of '@daily'.

Using a string instead of a datetime object for start_date.

The schedule_interval '@daily' runs the DAG daily. The start_date must be a datetime object for January 1, 2024.

5fill in blank

hard

Fill all three blanks to create a task that runs a Python function, retries twice, and has a retry delay of 5 minutes.

Apache Airflow

from datetime import timedelta

task = PythonOperator(task_id='retry_task', python_callable=[1], retries=[2], retry_delay=timedelta(minutes=[3]), dag=dag)

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

Amy_task

B2

C5

Dprint

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'print' instead of the function name.

Passing retries as a string.

Using a wrong value for retry_delay minutes.

The python_callable is 'my_task', retries is 2, and retry_delay is set to 5 minutes using timedelta.

Practice

1. What does it mean for a task in an Airflow pipeline to be atomic?

easy

A. The task can be skipped without affecting the pipeline

B. The task runs multiple times in parallel

C. The task either completes fully or does not change anything at all

D. The task runs only once per day

Solution

1. Step 1: Understand atomicity in pipelines

   Atomic means an operation is indivisible: it either fully happens or not at all.

2. Step 2: Apply this to Airflow tasks

   In Airflow, atomic tasks ensure no partial changes if they fail.

3. Final Answer:

   The task either completes fully or does not change anything at all -> Option C

4. Quick Check:

   Atomic = all or nothing [OK]

Hint: Atomic means all or nothing for task completion [OK]

Common Mistakes:

• Thinking atomic means running multiple times
• Confusing atomic with task scheduling frequency
• Assuming atomic means task can be skipped

2. Which of the following is the correct way to define a PythonOperator task in Airflow to ensure atomicity?

easy

A. task = PythonOperator(task_id='task1', python_callable=my_func, retry=3)

B. task = PythonOperator(task_id='task1', python_callable=my_func, retries='three')

C. task = PythonOperator(task_id='task1', python_callable=my_func, retry_count=3)

D. task = PythonOperator(task_id='task1', python_callable=my_func, retries=3)

Solution

1. Step 1: Check PythonOperator parameters

   The correct parameter for retry attempts is retries with an integer value.

2. Step 2: Validate each option

   task = PythonOperator(task_id='task1', python_callable=my_func, retries=3) uses retries=3 correctly. Options A, B, and D use wrong parameter names or types.

3. Final Answer:

   task = PythonOperator(task_id='task1', python_callable=my_func, retries=3) -> Option D

4. Quick Check:

   Correct retries param = task = PythonOperator(task_id='task1', python_callable=my_func, retries=3) [OK]

Hint: Use 'retries' with integer for retry count [OK]

Common Mistakes:

• Using string instead of integer for retries
• Using incorrect parameter names like retry or retry_count
• Missing retries parameter for retry logic

3. Given this Airflow task code snippet, what will be the output if my_task fails on first try but succeeds on retry?

task = PythonOperator(
    task_id='my_task',
    python_callable=my_func,
    retries=1,
    retry_delay=timedelta(seconds=5)
)

medium

A. Task fails and pipeline stops immediately

B. Task retries once after 5 seconds and then succeeds

C. Task retries infinitely until success

D. Task skips retry and marks success

Solution

1. Step 1: Understand retries and retry_delay

   Retries=1 means one retry attempt after failure, retry_delay=5 seconds means wait 5 seconds before retry.

2. Step 2: Apply to task behavior

   If first try fails, Airflow waits 5 seconds and retries once. If retry succeeds, task is marked success.

3. Final Answer:

   Task retries once after 5 seconds and then succeeds -> Option B

4. Quick Check:

   Retries=1 means one retry attempt [OK]

Hint: Retries=1 means one retry after delay [OK]

Common Mistakes:

• Assuming no retry happens
• Thinking retries means infinite retries
• Ignoring retry_delay effect

4. You wrote a task that updates a database but sometimes leaves partial updates if it fails. What is the best way to fix this atomicity issue?

medium

A. Add database transactions inside the task to commit or rollback changes

B. Increase the task timeout to avoid failures

C. Remove retries to prevent repeated partial updates

D. Split the task into multiple smaller tasks without transactions

Solution

1. Step 1: Identify cause of partial updates

   Partial updates happen if database changes are not atomic, meaning no transaction control.

2. Step 2: Apply database transactions

   Using transactions ensures all changes commit together or rollback on failure, fixing atomicity.

3. Final Answer:

   Add database transactions inside the task to commit or rollback changes -> Option A

4. Quick Check:

   Transactions ensure atomic DB updates [OK]

Hint: Use DB transactions to ensure all-or-nothing updates [OK]

Common Mistakes:

• Thinking increasing timeout fixes atomicity
• Removing retries causes no atomicity improvement
• Splitting tasks without transactions still risks partial updates

5. You want to ensure a pipeline task that writes files to cloud storage is atomic. Which approach best guarantees no partial files remain if the task fails?

hard

A. Write files to a temporary location first, then move them to final location after success

B. Write files directly to final location and delete on failure manually

C. Write files without retries to avoid duplicates

D. Write files in parallel to speed up the task

Solution

1. Step 1: Understand atomic file writes

   Writing directly risks partial files if failure occurs mid-write.

2. Step 2: Use temporary location and move

   Writing to temp location then moving is atomic because move is usually instant and atomic, avoiding partial files in final location.

3. Final Answer:

   Write files to a temporary location first, then move them to final location after success -> Option A

4. Quick Check:

   Temp write + move = atomic file update [OK]

Hint: Write temp files then move to final location atomically [OK]

Common Mistakes:

• Writing directly risks partial files
• Deleting on failure is error-prone
• Skipping retries does not guarantee atomicity
• Parallel writes do not ensure atomic single file