Empower Your Airflow Production Deployment with Vital Environment Variables

Introduction: Deploying Apache Airflow in a production environment requires careful consideration of various configuration parameters to ensure reliability, security, and scalability. One powerful approach to managing configurations is through environment variables. In this blog post, we’ll explore the key environment variables that are crucial for deploying Apache Airflow in a production setting, acknowledging that specific variables may vary across different Airflow versions.

1. Airflow Configuration: Discuss key Airflow-specific environment variables that control its behavior, such as:

• 1	<strong>AIRFLOW_HOME</strong>

  : Specifies the directory where Airflow stores its configuration files and logs.

• 1	<strong>AIRFLOW__DATABASE__SQL_ALCHEMY_CONN</strong>

  : Defines the connection string for the metadata database.

• 1	<strong>AIRFLOW__CORE__EXECUTOR</strong>

  : Determines the execution mode (e.g., SequentialExecutor, LocalExecutor, CeleryExecutor).

• 1	AIRFLOW__CORE__DAGS_FOLDER

  : The folder where your airflow pipelines live, most likely a subfolder in a code repository. This path must be absolute

2. Security Considerations: Address security-related environment variables to protect sensitive data, such as:

• 1	<strong>AIRFLOW__CORE__FERNET_KEY</strong>

  : Sets the encryption key for sensitive Airflow data.

• 1	<strong>AIRFLOW__CORE__SECRET_KEY</strong>

  : Specifies the secret key used for CSRF protection and session signing.

• 1	<strong>AIRFLOW__WEBSERVER__SECRET_KEY</strong>

  : Defines the secret key for securing the web server.

3. Resource Management: Discuss environment variables for managing resource utilization and scalability, such as:

• 1	<strong>AIRFLOW__CORE__MAX_ACTIVE_TASKS_PER_DAG</strong>

  : The maximum number of task instances allowed to run concurrently in each DAG. To calculate the number of tasks that is running concurrently for a DAG, add up the number of running tasks for all DAG runs of the DAG. This is configurable at the DAG level with 

  1	max_active_tasks

  .

• 1	<strong>AIRFLOW__CORE__PARALLELISM</strong>

  : Defines the maximum number of task instances that can run concurrently.

• 1	<strong>AIRFLOW__CORE__MAX_ACTIVE_RUNS_PER_DAG</strong>

  : The maximum number of active DAG runs per DAG. The scheduler will not create more DAG runs if it reaches the limit. This is configurable at the DAG level with 

  1	max_active_runs

  .

• 1	AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION

  : Determines whether newly created DAGs are paused by default upon creation.

4. Logging and Monitoring: Highlight environment variables for configuring logging and monitoring capabilities, including:

• 1	<strong>AIRFLOW__LOGGING__LOGGING_LEVEL</strong>

  : Sets the logging level for Airflow components.

• 1	<strong>AIRFLOW__SCHEDULER__SCHEDULE_AFTER_TASK_EXECUTION</strong>

  : Enables scheduling DAGs after successful task execution for improved monitoring.

• 1	<strong>AIRFLOW__SCHEDULER__CHILD_PROCESS_LOG_DIRECTORY</strong>

  : Specifies the directory for storing scheduler logs.

Conclusion: Summarize the importance of carefully configuring environment variables for deploying Apache Airflow to production environments. Emphasize the role of environment variables in ensuring security, scalability, and reliability while orchestrating complex workflows with Airflow.

References:

https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html