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https://github.com/villasv/aws-airflow-stack

Turbine: the bare metals that gets you Airflow
https://github.com/villasv/aws-airflow-stack

airflow airflow-cluster airflow-cookbook aws aws-cloudformation

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Turbine: the bare metals that gets you Airflow

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> ⚠️ This project is no longer receiving updates. We have moved on from using CloudFormation to manage our infrastructure and recommend others doing the same. As of 2021/2022, we think CDK and Terraform are now the best in class for IaC. Also, Airflow for Kubernetes has gotten more traction and we have moved into EKS for our self-managed Airflow. If you're looking for Open Source Airflow deployment options, we recommend [Astronomer](https://www.astronomer.io/).






# Turbine [![GitHub Release](https://img.shields.io/github/release/villasv/aws-airflow-stack.svg?style=flat-square&logo=github)](https://github.com/villasv/aws-airflow-stack/releases/latest) [![Build Status](https://img.shields.io/github/workflow/status/villasv/aws-airflow-stack/Stack%20Release%20Pipeline?style=flat-square&logo=github&logoColor=white&label=build)](https://github.com/villasv/aws-airflow-stack/actions?query=workflow%3A%22Stack+Release+Pipeline%22+branch%3Amaster) [![CFN Deploy](https://img.shields.io/badge/CFN-deploy-green.svg?style=flat-square&logo=amazon-aws)](#get-it-working)



Turbine is the set of bare metals behind a simple yet complete and efficient

Airflow setup.



The project is intended to be easily deployed, making it great for testing,

demos and showcasing Airflow solutions. It is also expected to be easily

tinkered with, allowing it to be used in real production environments with

little extra effort. Deploy in a few clicks, personalize in a few fields,

configure in a few commands.



## Overview



![stack diagram](/.github/img/stack-diagram.png)



The stack is composed mainly of three services: the Airflow web server, the

Airflow scheduler, and the Airflow worker. Supporting resources include an RDS

to host the Airflow metadata database, an SQS to be used as broker backend, S3

buckets for logs and deployment bundles, an EFS to serve as shared directory,

and a custom CloudWatch metric measured by a timed AWS Lambda. All other

resources are the usual boilerplate to keep the wind blowing.



### Deployment and File Sharing



The deployment process through CodeDeploy is very flexible and can be tailored

for each project structure, the only invariant being the Airflow home directory

at `/airflow`. It ensures that every Airflow process has the same files and can

upgraded gracefully, but most importantly makes deployments really fast and easy

to begin with.



There's also an EFS shared directory mounted at at `/mnt/efs`, which can be

useful for staging files potentially used by workers on different machines and

other synchronization scenarios commonly found in ETL/Big Data applications. It

facilitates migrating legacy workloads not ready for running on distributed

workers.



### Workers and Auto Scaling



The stack includes an estimate of the cluster load average made by analyzing the

amount of failed attempts to retrieve a task from the queue. The metric

objective is to measure if the cluster is correctly sized for the influx of

tasks. Worker instances have lifecycle hooks promoting a graceful shutdown,

waiting for tasks completion when terminating.



The goal of the auto scaling feature is to respond to changes in queue load,

which could mean an idle cluster becoming active or a busy cluster becoming

idle, the start/end of a backfill, many DAGs with similar schedules hitting

their due time, DAGs that branch to many parallel operators. **Scaling in

response to machine resources like facing CPU intensive tasks is not the goal**;

the latter is a very advanced scenario and would be best handled by Celery's own

scaling mechanism or offloading the computation to another system (like Spark or

Kubernetes) and use Airflow only for orchestration.



## Get It Working



### 0. Prerequisites



- Configured AWS CLI for deploying your own files

  [(Guide)](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html)



### 1. Deploy the stack



Create a new stack using the latest template definition at

[`templates/turbine-master.template`](/templates/turbine-master.template). The

following button will deploy the stack available in this project's `master`

branch (defaults to your last used region):



[![Launch](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/images/cloudformation-launch-stack-button.png)](https://console.aws.amazon.com/cloudformation/home#/stacks/new?templateURL=https://turbine-quickstart.s3.amazonaws.com/quickstart-turbine-airflow/templates/turbine-master.template)



The stack resources take around 15 minutes to create, while the airflow

installation and bootstrap another 3 to 5 minutes. After that you can already

access the Airflow UI and deploy your own Airflow DAGs.



### 2. Upstream your files



The only requirement is that you configure the deployment to copy your Airflow

home directory to `/airflow`. After crafting your `appspec.yml`, you can use the

AWS CLI to deploy your project.



For convenience, you can use this [`Makefile`](/examples/project/Makefile) to

handle the packaging, upload and deployment commands. A minimal working example

of an Airflow project to deploy can be found at

[`examples/project/airflow`](/examples/project/airflow).



If you follow this blueprint, a deployment is as simple as:



```bash

make deploy stack-name=yourcoolstackname

```



## Maintenance and Operation



Sometimes the cluster operators will want to perform some additional setup,

debug or just inspect the Airflow services and database. The stack is designed

to minimize this need, but just in case it also offers decent internal tooling

for those scenarios.



### Using Systems Manager Sessions



Instead of the usual SSH procedure, this stack encourages the use of AWS Systems

Manager Sessions for increased security and auditing capabilities. You can still

use the CLI after a bit more configuration and not having to expose your

instances or creating bastion instances is worth the effort. You can read more

about it in the Session Manager

[docs](https://docs.aws.amazon.com/systems-manager/latest/userguide/session-manager.html).



### Running Airflow commands



The environment variables used by the Airflow service are not immediately

available in the shell. Before running Airflow commands, you need to load the

Airflow configuration:



```bash

$ export $(xargs This project aims to be constantly evolving with up to date tooling and newer

>AWS features, as well as improving its design qualities and maintainability.

>Requests for Enhancement should be abundant and anyone is welcome to pick them

>up.

>

>Stacks can get quite opinionated. If you have a divergent fork, you may open a

>Request for Comments and we will index it. Hopefully this will help to build a

>diverse set of possible deployment models for various production needs.



See the [contribution guidelines](/CONTRIBUTING.md) for details.



You may also want to take a look at the [Citizen Code of

Conduct](/CODE_OF_CONDUCT.md).



Did this project help you? Consider buying me a cup of coffee ;-)



[![Buy me a coffee!](https://www.buymeacoffee.com/assets/img/custom_images/white_img.png)](https://www.buymeacoffee.com/villasv)



## Licensing



> MIT License

>

> Copyright (c) 2017 Victor Villas



See the [license file](/LICENSE) for details.