DAG versioning strategies in Apache Airflow - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a DAG in Airflow?

A DAG (Directed Acyclic Graph) in Airflow is a collection of tasks organized in a way that reflects their relationships and dependencies, defining the order in which tasks run.

beginner

Why is versioning DAGs important in Airflow?

Versioning DAGs helps track changes, avoid conflicts, and safely deploy updates without breaking running workflows.

beginner

Name one common strategy for DAG versioning.

Using Git branches or tags to manage different versions of DAG code is a common strategy.

intermediate

How can you keep multiple versions of a DAG running simultaneously?

By including version numbers in the DAG ID, you can deploy multiple DAG versions side by side in Airflow.

intermediate

What is a risk of not versioning DAGs properly?

Without proper versioning, changes can break workflows, cause data loss, or make it hard to track what code is running.

What does DAG stand for in Airflow?

A Directed Acyclic Graph

B Data Analysis Group

C Dynamic Application Gateway

D Distributed Async Generator

DAG stands for Directed Acyclic Graph, which is a structure that defines task dependencies in Airflow.

Which method helps run multiple DAG versions simultaneously?

A Deleting old DAGs before deploying new ones

B Using different DAG IDs with version numbers

C Running DAGs on separate Airflow servers

D Changing task names inside the DAG

Including version numbers in DAG IDs allows multiple versions to coexist in Airflow.

What tool is commonly used to manage DAG code versions?

A Jenkins

B Docker

C Kubernetes

D Git

Git is widely used to track and manage changes in DAG code.

What is a key benefit of DAG versioning?

A Safe deployment of updates

B Faster task execution

C Automatic error fixing

D Unlimited task retries

Versioning helps deploy updates safely without breaking existing workflows.

What happens if you don’t version DAGs properly?

A Tasks will run faster

B Airflow will automatically fix errors

C Workflows may break or behave unexpectedly

D DAGs will run in parallel

Improper versioning can cause workflow failures and unpredictable behavior.

Explain why and how you would implement DAG versioning in Airflow.

Think about how software versioning helps manage changes safely.

Describe a strategy to run multiple versions of the same DAG without conflicts.

Consider how naming helps Airflow distinguish DAGs.

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