Introduction
Airflow is an open-source platform for developing, scheduling, and monitoring batch-oriented workflows. Airflow's extensible Python framework enables you to build workflows connecting with virtually any technology. A web interface helps manage the state of your workflows. Airflow is deployable in many ways, varying from a single process on your laptop to a distributed setup to support even the biggest workflows.
Workflows in Airflow are defined as Python code, which means they are dynamic, extensible, flexible, and testable. Workflows can be stored in version control, developed by multiple people simultaneously, and parameterized using the Jinja templating engine. Workflows consist of tasks that can run any arbitrary code, such as running a Spark job, moving data between buckets, or sending an email. Tasks can be configured with dependencies, retries, alerts, and more.
download airflow
If you prefer coding over clicking, Airflow is the tool for you. Airflow allows you to automate and orchestrate your data pipelines, tasks, and jobs in a scalable, reliable, and elegant way. In this article, you will learn how to download and install Airflow, how to create and run a simple Airflow DAG (Directed Acyclic Graph), what are the benefits of using Airflow for workflow management, and what are some best practices to optimize your Airflow usage.
Prerequisites
Before installing Airflow, you need to check the prerequisites and supported versions. Airflow requires Python as a dependency. Therefore, the first step would be to check the Python installation on the server where you wish to set up Airflow. It can be easily achieved by logging in to your server and executing the command python --version or python3 --version.
Airflow is tested with Python 3.7, 3.8, 3.9, 3.10, and 3.11. You can use any of these versions to run Airflow. However, we recommend using the latest stable version of Python for better performance and compatibility.
How to download airflow on windows
Download airflow docker image
Download airflow source code from github
Download airflow helm chart for kubernetes
Download airflow providers packages
Download airflow python package from pypi
Download airflow documentation pdf
Download airflow examples dags
Download airflow cli tool
Download airflow webserver ui
Download airflow logs and metrics
Download airflow scheduler and executor
Download airflow plugins and hooks
Download airflow dependencies and requirements
Download airflow configuration file
Download airflow security certificates
Download airflow database schema and migrations
Download airflow testing and debugging tools
Download airflow custom operators and sensors
Download airflow rest api client
Download airflow dag serialization and parsing
Download airflow backport packages for 1.10.x
Download airflow upgrade check script
Download airflow stable release version
Download airflow nightly build version
Download airflow tutorial videos and courses
Download airflow best practices and tips
Download airflow community cookbook and recipes
Download airflow official logo and branding
Download airflow roadmap and vision
Download airflow integration with aws services
Download airflow integration with google cloud platform services
Download airflow integration with azure services
Download airflow integration with apache spark and hadoop
Download airflow integration with snowflake and redshift
Download airflow integration with postgresql and mysql
Download airflow integration with mongodb and cassandra
Download airflow integration with slack and email
Download airflow integration with jira and github
Download airflow integration with looker and tableau
Download airflow integration with salesforce and hubspot
Download airflow integration with stripe and paypal
Download airflow integration with twilio and sendgrid
Download airflow integration with shopify and woocommerce
Download airflow integration with spotify and youtube
Download airflow integration with twitter and facebook
Airflow also requires a database backend to store its metadata and state information. You can use PostgreSQL, MySQL, SQLite, or MSSQL as your database backend. However, SQLite is only used for testing purposes and should not be used in production. PostgreSQL is the most commonly used database backend for Airflow and has the best support and features.
The minimum memory required we recommend Airflow to run with is 4GB, but the actual requirements depend wildly on the deployment options you choose. You should also check out the page on the official Airflow documentation for more details.
Installation
There are different ways to install Airflow depending on your preferences and needs. You can install Airflow from PyPI (Python Package Index), from sources (released by Apache Software Foundation), or using Docker images or Helm charts (for Kubernetes deployments). In this article, we will focus on installing Airflow from PyPI or from sources.
Installing from PyPI
This installation method is useful when you are not familiar with containers and Docker and want to install Apache Airflow on physical or virtual machines using custom deployment mechanisms. You can use pip (Python package manager) to install Airflow from PyPI.
To install Airflow from PyPI, you need to follow these steps:
Create a virtual environment for your Airflow installation using python -m venv <environment name>. For example: python -m venv airflow-envActivate the virtual environment using source <environment name>/bin/activate. For example: source airflow-env/bin/activate
Upgrade pip to the latest version using pip install --upgrade pip
Install Airflow using pip install apache-airflow. You can also specify the version of Airflow you want to install using pip install apache-airflow==<version>. For example: pip install apache-airflow==2.2.3
Optionally, you can also install extra packages or providers for Airflow using pip install apache-airflow[extras]. For example: pip install apache-airflow[postgres,google]. You can check the list of available extras and providers on the page on the official Airflow documentation.
Initialize the database for Airflow using airflow db init. This will create the necessary tables and users for Airflow in your database backend.
Create a user account for accessing the Airflow web interface using airflow users create --username <username> --password <password> --firstname <firstname> --lastname <lastname> --role Admin --email <email>. For example: airflow users create --username admin --password admin123 --firstname John --lastname Doe --role Admin --email john.doe@example.com
Start the Airflow web server using airflow webserver. This will launch the web server on port 8080 by default. You can change the port using the --port option.
Start the Airflow scheduler using airflow scheduler. This will start the scheduler process that monitors and triggers your workflows.
Open your browser and navigate to You should see the Airflow web interface where you can log in with your user account and manage your workflows.
Installing from sources
This installation method is useful when you want to install the latest development version of Airflow or when you want to customize or contribute to the Airflow codebase. You can install Airflow from sources by cloning the GitHub repository and building it locally.
To install Airflow from sources, you need to follow these steps:
Clone the Airflow GitHub repository using git clone
Navigate to the cloned directory using cd airflow
Create a virtual environment for your Airflow installation using python -m venv <environment name>. For example: python -m venv airflow-env
Activate the virtual environment using source <environment name>/bin/activate. For example: source airflow-env/bin/activate
Upgrade pip to the latest version using pip install --upgrade pip
Install all the dependencies for Airflow using pip install -e .[all]. This will install all the extras and providers for Airflow as well as some development tools.
If you want to run tests or use Breeze (a development environment for Airflow), you also need to install some additional dependencies using pip install -e .[devel].
You can now follow the same steps as installing from PyPI to initialize the database, create a user account, start the web server and scheduler, and access the web interface.
Tutorial
In this section, we will show you how to create and run a simple Airflow DAG that prints "Hello, world!" to the console. A DAG is a collection of tasks that define a workflow in Airflow. Each task is an instance of an operator, which is a class that defines what action to perform. Operators can be built-in (such as BashOperator, PythonOperator, etc.) or custom (such as your own Python class).
Creating a DAG file
To create a DAG in Airflow, you need to write a Python script that defines the DAG object and its tasks. The script should be placed in the dags folder under your Airflow home directory (which is usually $AIRFLOW_HOME/dags). The script should have a .py extension and follow some naming conventions. For example, you can name your script hello_world.py.
The basic structure of a DAG file is as follows:
# Import the modules and classes you need from airflow import DAG from airflow.operators.bash import BashOperator from datetime import datetime # Define the default arguments for the DAG default_args = 'owner': 'airflow', 'start_date': datetime(2023, 6, 20), 'retries': 1, 'retry_delay': timedelta(minutes=5), # Instantiate the DAG object dag = DAG( dag_id='hello_world', default_args=default_args, schedule_interval='@daily', ) # Define the tasks for the DAG task_1 = BashOperator( task_id='print_hello', bash_command='echo "Hello, world!"', dag=dag, ) task_2 = BashOperator( task_id='print_goodbye', bash_command='echo "Goodbye, world!"', dag=dag, ) # Define the dependencies for the tasks task_1 >> task_2
Let's break down this code and explain what it does:
The first lines import the modules and classes we need to create the DAG and its tasks. We import the DAG class from airflow, the BashOperator class from airflow.operators.bash, and the datetime and timedelta classes from datetime.
Next, we define the default arguments for the DAG. These are a dictionary of parameters that apply to all the tasks in the DAG. We specify the owner of the DAG, the start date of the DAG, the number of retries for each task, and the delay between retries.
Then, we instantiate the DAG object using the DAG class. We pass in the dag_id, which is a unique identifier for the DAG, the default_args, which are the arguments we defined earlier, and the schedule_interval, which is a cron expression that defines how often the DAG should run. In this case, we use '@daily', which means the DAG will run once a day at midnight.
After that, we define the tasks for the DAG using the BashOperator class. This class allows us to execute a bash command as a task. We pass in the task_id, which is a unique identifier for each task, the bash_command, which is the command we want to run, and the dag, which is the DAG object we created earlier.
Finally, we define the dependencies for the tasks using the >> operator. This operator sets a downstream dependency between two tasks, meaning that the task on the left must complete before the task on the right can start. In this case, we set task_1 as a dependency of task_2, meaning that task_1 must print "Hello, world!" before task_2 can print "Goodbye, world!".
Running a DAG
Once you have created your DAG file and placed it in your dags folder, you can run it using Airflow. There are two ways to run a DAG: manually or automatically.
Running a DAG manually
To run a DAG manually, you can use either the Airflow web interface or the Airflow CLI (command-line interface).
To run a DAG manually using the web interface, you need to follow these steps:
Open your browser and navigate to You should see your hello_world DAG listed on the home page.
Click on your hello_world DAG to open its details page.
Click on Trigger Dag button on top of page to trigger an instance of your DAG.
Click on Graph View button on top of page to see your tasks and their dependencies.
Click on each task to see its status and logs.
Running a DAG automatically
To run a DAG automatically, you need to rely on the Airflow scheduler. The scheduler is responsible for triggering your workflows based on their schedule_interval. The scheduler runs as a separate process from your web server and needs to be started separately.
To run a DAG automatically using the scheduler, you need to follow these steps:
Start your Airflow web server using airflow webserver.
Start your Airflow scheduler using airflow scheduler.
Wait for your schedule_interval to elapse. For example, if your schedule_interval is '@daily', wait for midnight to pass.
Check your web interface or CLI to see if your DAG has been triggered and executed by the scheduler.
Benefits
Now that you know how to download and install Airflow, and how to create and run a simple Airflow DAG, you might be wondering what are the benefits of using Airflow for workflow management. Here are some of the main benefits of Airflow:
Code as configuration: Airflow allows you to define your workflows as Python code, which gives you full control and flexibility over your logic, dependencies, parameters, and more. You can also leverage the power of Python libraries and frameworks to enhance your workflows.
Scalability and reliability: Airflow can scale to handle any size and complexity of workflows, from simple scripts to complex data pipelines. Airflow also ensures that your workflows are reliable and resilient, by handling failures, retries, alerts, logging, and monitoring.
Extensibility and integration: Airflow has a rich ecosystem of plugins, providers, hooks, sensors, operators, and executors that enable you to integrate with virtually any technology or service. You can also create your own custom components to suit your specific needs.
Web interface and CLI: Airflow provides a user-friendly web interface that allows you to manage and monitor your workflows. You can also use the CLI to perform various tasks such as triggering, testing, debugging, or inspecting your workflows.
Community and support: Airflow is an open-source project that is supported by a large and active community of developers and users. You can find help and guidance from the official documentation, mailing lists, Slack channels, forums, blogs, podcasts, videos, books, courses, and more.
Best Practices
To make the most out of Airflow, you should follow some best practices that will help you optimize your workflow performance, maintainability, readability, and security. Here are some of the best practices for using Airflow:
Use meaningful names: You should use descriptive and consistent names for your DAGs, tasks, operators, variables, connections, etc. This will make your code easier to read and understand by yourself and others.
Organize your code: You should structure your code in a modular and reusable way. You can use functions, classes, modules, packages, etc. to organize your code. You can also use to parameterize your code.
Document your code: You should add comments and docstrings to your code to explain what it does and why. You can also use the doc_md argument in your DAG and task definitions to add markdown documentation that will be displayed in the web interface.
Test your code: You should test your code before deploying it to production. You can use the airflow tasks test command to test individual tasks or the airflow dags test command to test entire DAGs. You can also use unit testing frameworks such as pytest or unittest to write automated tests for your code.
Avoid hard-coding values: You should avoid hard-coding values such as credentials, paths, URLs, etc. in your code. Instead, you should use , or environment variables to store and access these values securely.
Schedule wisely: You should schedule your workflows according to their frequency and priority. You should avoid overlapping or conflicting schedules that might cause resource contention or data inconsistency. You should also use to handle daylight saving time changes.
Maintain DAG integrity: You should ensure that your DAGs are valid and consistent throughout their lifecycle. You should avoid changing the dag_id or start_date of a DAG after it has been deployed. You should also avoid creating cyclic dependencies or dynamic dependencies in your DAGs.
Migrate gracefully: You should follow the steps when upgrading from one version of Airflow to another. You should also backup your database and test your workflows before upgrading.
Tune performance: You should monitor and optimize the performance of your workflows using various tools and techniques such as such as guide on the official Airflow documentation for more tips and tricks.
Conclusion
In this article, you have learned how to download and install Airflow, how to create and run a simple Airflow DAG, what are the benefits of using Airflow for workflow management, and what are some best practices to optimize your Airflow usage. You have also seen some examples of code and commands that you can use to get started with Airflow.
Airflow is a powerful and flexible platform for developing, scheduling, and monitoring batch-oriented workflows. Airflow's extensible Python framework enables you to build workflows connecting with virtually any technology. A web interface helps manage the state of your workflows. Airflow is deployable in many ways, varying from a single process on your laptop to a distributed setup to support even the biggest workflows.
If you want to learn more about Airflow, you can check out the , which is a vibrant and diverse group of developers and users who are passionate about Airflow and eager to help and share their knowledge and experience.
We hope you enjoyed this article and found it useful. Happy airflowing!
FAQs
Here are some common questions and answers about Airflow:
What is the difference between Airflow and other workflow management tools?
Airflow is different from other workflow management tools in several ways. Some of the main differences are:
Airflow uses Python as its configuration language, which makes it easy to write, read, test, and debug workflows.
Airflow has a rich ecosystem of plugins, providers, hooks, sensors, operators, and executors that enable it to integrate with virtually any technology or service.
Airflow has a user-friendly web interface that allows you to manage and monitor your workflows.
Airflow is open-source and supported by a large and active community of developers and users.
How can I troubleshoot Airflow issues?
If you encounter any issues or errors while using Airflow, you can use the following methods to troubleshoot them:
Check the logs of your web server, scheduler, workers, tasks, etc. You can find the logs in the $AIRFLOW_HOME/logs directory by default.
Use the web interface or CLI to inspect the status and details of your DAGs and tasks.
Use the airflow tasks test or airflow dags test commands to test your tasks or DAGs locally.
Use the airflow info command to get information about your Airflow installation and configuration.
Use the airflow doctor command to diagnose common problems with your Airflow setup.
Search for similar issues or questions on the , or other online forums.
Ask for help from the Airflow community on the , or other online forums.
How can I contribute to Airflow?
If you want to contribute to Airflow, you are very welcome to do so. There are many ways you can contribute to Airflow, such as:
Report bugs or suggest features on the .
Submit pull requests for bug fixes or new features on the .
Review pull requests from other contributors on the .
Write or update documentation for Airflow on the .
Create or improve plugins, providers, hooks, sensors, operators, or executors for Airflow on the or your own repositories.
Share your knowledge and experience with Airflow on blogs, podcasts, videos, books, courses, etc.
Help other Airflow users on the , or other online forums.
To get started with contributing to Airflow, you should read the guide on the official Airflow documentation. It contains detailed information on how to set up your development environment, how to follow the coding standards and guidelines, how to submit and review pull requests, how to write and run tests, how to write and update documentation, and more.
How can I learn more about Airflow?
If you want to learn more about Airflow, there are many resources available online that can help you. Some of the resources are:
The , which contains detailed information on all the features, components, concepts, and APIs of Airflow.
The , which contains the source code, issues, pull requests, and releases of Airflow.
The , which is a forum for discussing Airflow-related topics and announcements.
The , which is a platform for asking and answering Airflow-related questions.
The , which is a chat room for interacting with other Airflow users and developers.
The list, which is a curated list of awesome resources related to Airflow.
The , which contains videos of Airflow-related talks, webinars, tutorials, and demos.
The , which features interviews with Airflow experts and practitioners.
The , which contains articles and stories about Airflow use cases, best practices, tips and tricks, and more.
The , which is a comprehensive guide to learning and mastering Airflow.
The , which is an online course that teaches you how to use Airflow from scratch.
What are some alternatives to Airflow?
Airflow is not the only workflow management tool available in the market. There are some alternatives to Airflow that you might want to consider depending on your needs and preferences. Some of the alternatives are:
Luigi: Luigi is an open-source Python framework for building complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and more. Luigi is similar to Airflow in many ways but has some differences in terms of design and features. For example, Luigi does not have a web interface or a scheduler by default but relies on external tools such as cron or Kubernetes. Luigi also does not have as many integrations or plugins as Airflow but has a simpler and more lightweight core.
Prefect: Prefect is an open-source Python framework for building data pipelines that are robust, scalable, and elegant. Prefect handles orchestration, scheduling, logging, monitoring, retries , alerts, and more. Prefect is inspired by Airflow but has some differences in terms of design and features. For example, Prefect separates the logic of your workflows from the execution of your workflows, allowing you to run your workflows on any platform or environment. Prefect also has a cloud service that provides a web interface, a scheduler, a database, and other features for managing your workflows.
Dagster: Dagster is an open-source Python framework for building data applications that are testable, reliable, and scalable. Dagster handles orchestration, configuration, type checking, logging, monitoring, testing, and more. Dagster is different from Airflow in many ways but has some similarities in terms of design and features. For example, Dagster also uses Python code to define workflows and tasks, but has a more expressive and type-safe syntax. Dagster also has a web interface and a scheduler, but also supports other modes of execution such as notebooks or scripts.
AWS Step Functions: AWS Step Functions is a cloud service that provides a fully managed service for orchestrating serverless workflows. AWS Step Functions handles coordination, state management, error handling, retries, parallelization, and more. AWS Step Functions is different from Airflow in many ways but has some similarities in terms of design and features. For example, AWS Step Functions also uses a DAG-like structure to define workflows and tasks, but uses JSON or YAML instead of Python code. AWS Step Functions also has a web interface and a scheduler, but also integrates with other AWS services such as Lambda, S3, DynamoDB, etc.
This is the end of the article. Thank you for reading and I hope you found it useful. If you have any feedback or questions, please feel free to leave a comment below or contact me directly. 44f88ac181
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