Multi-environment deployment (dev, staging, prod) in Apache Airflow - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the Airflow environment variable for the deployment environment.

Apache Airflow

export AIRFLOW_ENV=[1]

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

Adevelopment

Btesting

Cdebug

Dproduction

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'production' instead of 'development' for dev environment.

Setting AIRFLOW_ENV to development configures Airflow for the dev environment.

2fill in blank

medium

Complete the code to select the correct Airflow configuration value for the staging environment.

Apache Airflow

airflow config get-value core [1]

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

Aairflow.cfg

Bdags_folder

Cexecutor

Dsql_alchemy_conn

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing the config file name with a config key.

The executor setting defines how tasks are run, which can differ in staging.

3fill in blank

hard

Fix the error in the command to deploy Airflow DAGs to the production environment.

Apache Airflow

scp dags/ [1]:/usr/local/airflow/dags/

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

Auser@prod-server

Bprod-server

Cuser@staging-server

Dlocalhost

Attempts:

3 left

💡 Hint

Common Mistakes

Omitting the user in the remote host address.

Using user@prod-server correctly specifies the remote user and host for production deployment.

4fill in blank

hard

Fill both blanks to set the Airflow home directory and the environment variable for staging.

Apache Airflow

export AIRFLOW_HOME=[1]
export AIRFLOW_ENV=[2]

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

A/opt/airflow/staging

Bstaging

C/usr/local/airflow

Dproduction

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing production paths or environment names in staging setup.

Setting AIRFLOW_HOME to the staging directory and AIRFLOW_ENV to 'staging' configures the environment correctly.

5fill in blank

hard

Fill all three blanks to create a dictionary mapping environments to their Airflow executor types.

Apache Airflow

executors = {
    '[1]': '[2]',
    '[3]': 'LocalExecutor'
}

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

Aproduction

BCeleryExecutor

Cdevelopment

DSequentialExecutor

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping executor types between environments.

This dictionary maps 'production' to 'CeleryExecutor' and 'development' to 'LocalExecutor', common Airflow setups.

Practice

1. What is the main reason to use separate environments like dev, staging, and prod in Airflow deployment?

easy

A. To run all tasks faster by using multiple servers

B. To reduce the number of DAGs needed

C. To keep development, testing, and production work isolated and safe

D. To share the same database for all environments

Solution

1. Step 1: Understand environment separation purpose

   Separate environments prevent changes in development or testing from affecting production stability.

2. Step 2: Identify the correct purpose in options

   Only To keep development, testing, and production work isolated and safe mentions isolation and safety, which matches the main reason.

3. Final Answer:

   To keep development, testing, and production work isolated and safe -> Option C

4. Quick Check:

   Environment isolation = Safety [OK]

Hint: Remember: separate environments protect production [OK]

Common Mistakes:

• Thinking all environments share the same database
• Believing environments speed up task execution
• Assuming fewer DAGs are needed with multiple environments

2. Which of the following is the correct way to specify different Airflow configurations for dev and prod environments?

easy

A. Set environment variables like AIRFLOW__CORE__SQL_ALCHEMY_CONN differently per environment

B. Run Airflow without any configuration files

C. Use the same airflow.cfg file for all environments

D. Use a single database for all environments

Solution

1. Step 1: Understand configuration methods in Airflow

   Airflow supports environment variables to override settings per environment.

2. Step 2: Identify the correct method for environment-specific configs

   Set environment variables like AIRFLOW__CORE__SQL_ALCHEMY_CONN differently per environment correctly uses environment variables to set different database connections per environment.

3. Final Answer:

   Set environment variables like AIRFLOW__CORE__SQL_ALCHEMY_CONN differently per environment -> Option A

4. Quick Check:

   Env vars = environment-specific config [OK]

Hint: Use env vars to separate configs per environment [OK]

Common Mistakes:

• Using the same config file for all environments
• Not setting database connections separately
• Ignoring environment variables for configs

3. Given this command to start Airflow in the staging environment:

export AIRFLOW_HOME=/opt/airflow_staging
export AIRFLOW__CORE__SQL_ALCHEMY_CONN=postgresql+psycopg2://user:pass@staging-db:5432/airflow
airflow webserver

What will happen when you run this command?

medium

A. Airflow will start using the staging database and configs in /opt/airflow_staging

B. Airflow will start using the production database and default configs

C. Airflow will fail because environment variables cannot be set this way

D. Airflow will start but ignore the SQL connection string

Solution

1. Step 1: Analyze environment variables set before starting Airflow

   Setting AIRFLOW_HOME changes the config directory; setting AIRFLOW__CORE__SQL_ALCHEMY_CONN changes the database connection.

2. Step 2: Understand Airflow behavior on startup

   Airflow uses these environment variables to load configs and connect to the specified database.

3. Final Answer:

   Airflow will start using the staging database and configs in /opt/airflow_staging -> Option A

4. Quick Check:

   Env vars control config and DB connection [OK]

Hint: Env vars before airflow start set environment [OK]

Common Mistakes:

• Assuming Airflow ignores env vars
• Thinking Airflow uses production DB by default
• Believing env vars cannot be set inline

4. You deployed Airflow for prod but accidentally used the dev database connection string in the config. What is the best way to fix this?

medium

A. Change the airflow.cfg file in the dev environment

B. Delete all DAGs and restart Airflow

C. Run Airflow without any environment variables

D. Change the AIRFLOW__CORE__SQL_ALCHEMY_CONN environment variable to the production database and restart Airflow

Solution

1. Step 1: Identify the incorrect database connection setting

   The problem is the wrong database connection string used in production.

2. Step 2: Correct the environment variable and restart Airflow

   Updating AIRFLOW__CORE__SQL_ALCHEMY_CONN to the correct production DB and restarting applies the fix.

3. Final Answer:

   Change the AIRFLOW__CORE__SQL_ALCHEMY_CONN environment variable to the production database and restart Airflow -> Option D

4. Quick Check:

   Fix DB connection env var and restart [OK]

Hint: Fix DB env var, then restart Airflow [OK]

Common Mistakes:

• Deleting DAGs unnecessarily
• Ignoring environment variables
• Changing dev configs instead of prod

5. You want to deploy Airflow for dev, staging, and prod on the same server. Which approach ensures proper isolation and avoids conflicts?

hard

A. Run one Airflow instance and switch configs manually before each run

B. Run three separate Airflow instances with different AIRFLOW_HOME and database connections

C. Use the same database but different DAG folders for each environment

D. Run Airflow only in production mode and test DAGs there

Solution

1. Step 1: Understand isolation needs for multiple environments on one server

   Each environment needs its own config directory and database to avoid conflicts.

2. Step 2: Identify the correct deployment approach

   Running separate Airflow instances with distinct AIRFLOW_HOME and DB connections ensures isolation.

3. Final Answer:

   Run three separate Airflow instances with different AIRFLOW_HOME and database connections -> Option B

4. Quick Check:

   Separate instances + configs = isolation [OK]

Hint: Use separate AIRFLOW_HOME and DB per instance [OK]

Common Mistakes:

• Switching configs manually risks errors
• Sharing one database causes conflicts
• Running only production ignores testing needs