DAG versioning strategies in Apache Airflow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the DAG version using a variable.

Apache Airflow

dag_version = '[1]'

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

Av1

B1.0

Cversion1

Dlatest

Attempts:

3 left

💡 Hint

Common Mistakes

Using numeric values without quotes causes errors.

Using 'latest' does not specify a fixed version.

The DAG version is often set as a simple string like 'v1' to track versions clearly.

2fill in blank

medium

Complete the code to import the DAG version from a configuration file.

Apache Airflow

from config import [1]

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

Aversion_number

Bversion

Cdag_ver

Ddag_version

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect variable names causes import errors.

Using generic names like 'version' may conflict with other modules.

The variable 'dag_version' is commonly used to hold the DAG version in config files.

3fill in blank

hard

Fix the error in the DAG version check condition.

Apache Airflow

if dag_version [1] 'v2':
    print('Using version 2 DAG')

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

A==

B!=

C=

D=>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of '==' causes syntax errors.

Using '=>' is invalid syntax in Python.

Use '==' to compare values in Python, '=' is for assignment.

4fill in blank

hard

Fill both blanks to create a dictionary mapping DAG versions to file paths.

Apache Airflow

dag_files = {'v1': '[1]', 'v2': '[2]'}

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

A/dags/dag_v1.py

B/dags/dag_latest.py

C/dags/dag_v2.py

D/dags/dag_old.py

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing file paths causes confusion in version management.

Using generic file names like 'dag_latest.py' loses version specificity.

Each DAG version should map to its specific file path for clarity and version control.

5fill in blank

hard

Fill all three blanks to filter DAG versions and print active ones.

Apache Airflow

active_dags = {k: v for k, v in dag_files.items() if k [1] 'v1' and v [2] '.py' and 'latest' not in v[3]

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

A!=

Bendswith

C()

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '!=' includes 'v1' which is not desired.

Forgetting parentheses after 'endswith' causes errors.

The code filters out version 'v1', checks if file ends with '.py', and uses parentheses for method call.

Practice

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

easy

A. To speed up the execution of tasks in a DAG

B. To reduce the number of DAGs in the Airflow UI

C. To give each workflow version a unique name for safe management

D. To automatically fix errors in DAG code

Solution

1. Step 1: Understand DAG versioning concept

   DAG versioning means assigning unique names to different versions of workflows to avoid confusion and conflicts.

2. Step 2: Identify the main benefit

   This unique naming helps run and manage multiple workflow versions safely without overwriting or mixing them.

3. Final Answer:

   To give each workflow version a unique name for safe management -> Option C

4. Quick Check:

   DAG versioning = unique naming for safety [OK]

Hint: Remember: versioning means unique names for workflows [OK]

Common Mistakes:

• Thinking versioning speeds up task execution
• Confusing versioning with error fixing
• Believing versioning reduces DAG count

2. Which of the following is a correct way to name a DAG version in Airflow?

easy

A. my_dag_v1

B. my dag version 1

C. my-dag-version-1!

D. my.dag.version.1

Solution

1. Step 1: Check valid DAG naming rules

   DAG names should be simple strings without spaces or special characters like ! or dots.

2. Step 2: Identify the valid option

   my_dag_v1 uses underscores and no spaces or special characters, making it valid.

3. Final Answer:

   my_dag_v1 -> Option A

4. Quick Check:

   Valid DAG names use underscores, no spaces [OK]

Hint: Use underscores, avoid spaces and special chars [OK]

Common Mistakes:

• Using spaces in DAG names
• Including special characters like !
• Using dots which can cause issues

3. Given this DAG code snippet, what will be the DAG id when versioning is applied as shown?

version = 3
dag_id = f"example_dag_v{version}"
print(dag_id)

medium

A. example_dagv3

B. example_dag_version3

C. example_dag_3

D. example_dag_v3

Solution

1. Step 1: Understand f-string usage

   The f-string inserts the value of 'version' variable into the string where {version} is placed.

2. Step 2: Substitute version value

   Since version = 3, the string becomes 'example_dag_v3'.

3. Final Answer:

   example_dag_v3 -> Option D

4. Quick Check:

   f-string with version=3 = example_dag_v3 [OK]

Hint: f-string inserts variable value directly [OK]

Common Mistakes:

• Confusing variable placement in f-string
• Adding extra words like 'version' manually
• Missing underscores in the name

4. You have two DAG versions named data_pipeline_v1 and data_pipeline_v2. You accidentally overwrite data_pipeline_v1 code with v2 code but keep the same DAG id. What is the main problem here?

medium

A. Airflow will only run the latest code under one DAG id, losing version 1

B. The scheduler will crash due to duplicate DAG ids

C. Airflow will create duplicate DAGs with same id

D. Both versions run simultaneously causing conflicts

Solution

1. Step 1: Understand DAG id uniqueness

   Airflow identifies DAGs by their DAG id. If two versions share the same id, only one is recognized.

2. Step 2: Identify effect of overwriting code

   Overwriting v1 code with v2 under same id means v1 is lost and only v2 runs.

3. Final Answer:

   Airflow will only run the latest code under one DAG id, losing version 1 -> Option A

4. Quick Check:

   Same DAG id means only one version runs [OK]

Hint: Unique DAG ids prevent overwriting versions [OK]

Common Mistakes:

• Thinking both versions run at once
• Expecting duplicate DAGs with same id
• Assuming scheduler crashes on duplicates

5. You want to manage multiple DAG versions without changing DAG ids manually. Which strategy below best helps you achieve this in Airflow?

hard

A. Manually rename DAG files for each version

B. Use Airflow Variables to store the version and dynamically set DAG id

C. Use the same DAG id and overwrite code for each version

D. Disable all DAGs except the latest version

Solution

1. Step 1: Understand dynamic DAG id setting

   Using Airflow Variables allows you to store version info externally and inject it into DAG id dynamically.

2. Step 2: Compare with other options

   Manual renaming is error-prone, overwriting loses old versions, disabling DAGs is manual and not scalable.

3. Final Answer:

   Use Airflow Variables to store the version and dynamically set DAG id -> Option B

4. Quick Check:

   Dynamic DAG id via Variables = best versioning [OK]

Hint: Use Variables to automate version naming [OK]

Common Mistakes:

• Relying on manual file renaming
• Overwriting code without versioning
• Disabling DAGs instead of versioning