Metadata-Version: 2.1
Name: airflow-cdk
Version: 0.5.0
Summary: Custom cdk constructs for apache airflow
Home-page: https://github.com/knowsuchagency/airflow-cdk
Author: Stephan Fitzpatrick
Author-email: stephan@knowsuchagency.com
License: UNKNOWN
Description: # airflow-cdk
        
        This project makes it simple to deploy airflow via ECS fargate using the aws cdk in Python.
        
        ## Usage
        
        **There are two main ways that this package can be used.**
        
        ### Standalone Package
        
        For those already familiar with the aws cdk, add this project
        as a dependency i.e. `pip install airflow-cdk`  and/or add to `requirement.txt` and use the `FargateAirflow`
        construct like so.
        
        ```python3
        from aws_cdk import core
        from airflow_cdk import FargateAirflow
        
        
        app = core.App()
        
        FargateAirflow(
            app,
            "airflow-cdk",
            postgres_password="replacethiswithasecretpassword")
        
        app.synth()
        ```
        
        `cdk deploy`
        
        That's it.
        
        ### Cloning
        
        You can also clone this repository and alter the `FargateAirflow` construct
        to your heart's content.
        
        That also provides you an added benefit of utilizing the `tasks.py` tasks
        with [invoke](http://www.pyinvoke.org) to do things like create new dags easily
        i.e. `inv new-dag`
        
        You would then also easily be able to use the existing docker-compose for local development
        with some minor modifications for your setup.
        
        The easiest way to get started would be just a one-line change to the `app.py` example above
        and to the `docker-compose.yml` file.
        
        ```python3
        from aws_cdk import core
        from airflow_cdk import FargateAirflow
        
        
        app = core.App()
        
        FargateAirflow(
            app,
            "airflow-cdk",
            postgres_password="replacethiswithasecretpassword"),
            # this is the only change to make when cloning
            base_image=aws_ecs.ContainerImage.from_asset(".")
        
        app.synth()
        ```
        
        Then, in the `docker-compose.yml` file, simply delete, comment out, or change the image name
        for the `image: knowsuchagency/airflow-cdk` line in `x-airflow`.
        
        Now the same container that would be created by `docker-compose build` will be deployed to ECS for your web, worker, and 
        scheduler images by `cdk deploy`.
        
        
        ## Components
        
        The following aws resources will be deployed as ecs tasks within the same cluster and vpc by default:
        
        * an airflow webserver task
          * and an internet-facing application load-balancer
        * an airflow scheduler task
        * an airflow worker task
          * (note) it will auto-scale based on cpu and memory usage up to a total of 16 instances at a time by default starting from 1
        * a rabbitmq broker
          * an application load balancer that will allow you to log in to
            the rabbitmq management console with the default user/pw guest/guest
        * an rds instance
        * an s3 bucket for logs
        
        ## Why is this awesome?
        
        Apart from the fact that we're able to describe our infrastructure using the same language and codebase we use to author 
        our dags?
        
        Since we're using cloudformation under-the-hood, whenever we change a part of our code or infrastructure, only those
        changes that are different from our last deployment will be deployed.
        
        Meaning, if all we do is alter the code we want to run on our deployment, we simply re-build and publish our docker
        container (which is done for us if we use `aws_ecs.ContainerImage.from_asset(".")`) prior to `cdk deploy`!
        
        Existing users of airflow will know how tricky it can be to manage deployments when you want to distinguish between
        pushing changes to your codebase i.e. dags and actual infrastructure deployments.
        
        We just have to be careful not to deploy while we have some long-running worker task we don't want to interrupt since 
        fargate will replace those worker instances with new ones running our updated code. 
        Now there's basically no distinction.
        
        ## Notes
        
        * Before running `cdk destroy`, you will want to empty the s3 bucket that's created otherwise the command may fail at
          that stage and the bucket can be left in a state that makes it difficult to delete later on
        
        ## TODOs
        
        * create a custom component to deploy airflow to an ec2 cluster
        * improve documentation
        * (possibly) subsume the [airflow stable helm chart](https://hub.helm.sh/charts/stable/airflow) as a cdk8s chart
        
        ## Contributions Welcome!
Keywords: aws,cdk,airflow,k8s
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: JavaScript
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Software Development :: Code Generators
Classifier: Topic :: Utilities
Classifier: Typing :: Typed
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
