kube-airflow (Celery Executor)

kube-airflow provides a set of tools to run Airflow in a Kubernetes cluster. This is useful when you'd want:

Easy high availability of the Airflow scheduler
- Running multiple schedulers for high availability isn't safe so it isn't the way to go in the first place. Someone in the internet tried to implement a wrapper to implement leader election on top of the scheduler so that only one scheduler executes the tasks at a time. It is possible but can't we just utilize a kind of cluster manager here? This is where Kubernetes comes into play.
Easy parallelism of task executions
- The common way to scale out workers in Airflow is to utilize Celery. However, managing a H/A backend database and Celery workers just for parallelising task executions sounds like a hassle. This is where Kubernetes comes into play, again. If you already had a K8S cluster, just let K8S manage them for you.
- If you have ever considered to avoid Celery for task parallelism, yes, K8S can still help you for a while. Just keep using LocalExecutor instead of CeleryExecutor and delegate actual tasks to Kubernetes by calling e.g. kubectl run --restart=Never ... from your tasks. It will work until the concurrent kubectl run executions(up to the concurrency implied by scheduler's max_threads and LocalExecutor's parallelism. See this SO question for gotchas) consumes all the resources a single airflow-scheduler pod provides, which will be after the pretty long time.

This repository contains:

Dockerfile(.template) of airflow for Docker images published to the public Docker Hub Registry.
airflow.all.yaml for manual creating Kubernetes services and deployments to run Airflow on Kubernetes
Helm Chart in ./airflow for deployments using Helm

Informations

Fork of mumoshu/kube-airflow
Highly inspired by the great work puckel/docker-airflow
Based on Debian Stretch official Image debian:stretch and uses the official Postgres as backend and RabbitMQ as queue
Following the Airflow release from Python Package Index

Manual Installation

Create all the deployments and services to run Airflow on Kubernetes:

kubectl create -f airflow.all.yaml
[CMee] new version can use:create -f airflow.cmee.yaml
minikube version: v1.4.0
kubectl  version: v1.16.1

It will create deployments for:

postgres
rabbitmq
airflow-webserver
airflow-scheduler
airflow-flower
airflow-worker

and services for:

postgres
rabbitmq
airflow-webserver
airflow-flower

Helm Deployment (recommended)

Ensure your helm installation is done, you may need to have TILLER_NAMESPACE set as environment variable.

Deploy to Kubernetes using:

make helm-install NAMESPACE=yournamespace HELM_VALUES=/path/to/you/values.yaml

Helm ingresses

The Chart provides ingress configuration to allow customization the installation by adapting the config.yaml depending on your setup.

Prefix

This Helm chart allows using a "prefix" string that will be added to every Kubernetes names. That allows instantiating several, independent Airflow clusters in the same namespace.

Note:

Do NOT use characters such as " (double quote), ' (simple quote), / (slash) or \ (backslash)
in your passwords and prefix and keep it as small as possible.

DAGs deployment: embedded DAGs or git-sync

This chart provide basically two way of deploying DAGs in your Airflow installation:

embedded DAGs
Git-Sync

This helm chart provide support for Persistant Storage but not for sidecar git-sync pod. If you are willing to contribute, do not hesitate to do a Pull Request !

Using embedded Git-Sync

Git-sync is the easiest way to automatically update your DAGs. It simply checks periodically (by default every minute) a Git project on a given branch and check this new version out when available. Scheduler and worker see changes almost real-time. There is no need to other tool and complex rolling-update procedure.

While it is extremely cool to see its DAG appears on Airflow 60s after merge on this project, you should be aware of some limitations Airflow has with dynamic DAG updates:

If the scheduler reloads a dag in the middle of a dagrun then the dagrun will actually start
using the new version of the dag in the middle of execution.

This is a known issue with airflow and it means it's unsafe in general to use a git-sync like solution with airflow without:

using explicit locking, ie never pull down a new dag if a dagrun is in progress
make dags immutable, never modify your dag always make a new one

Also keep in mind using git-sync may not be scalable at all in production if you have lot of DAGs. The best way to deploy you DAG is to build a new docker image containing all the DAG and their dependencies. To do so, fork this project

Airflow.cfg as ConfigMap

By default, we use the configuration file airflow.cfg hardcoded in the docker image. This file uses a custom templating system to apply some environmnet variable and feed the airflow processes with (basically it is just some sed).

If you want to use your own airflow.cfg file without having to rebuild a complete docker image, for example when testing new settings, there is a way to define this file in a Kubernetes configuration map:

you need to define your own Value file you will feed to helm with helm install -f myvalue.yaml
you need to enable init the node airflow.airflow_cfg.enable: true
you need to store the content of your airflow.cfg in the node airflow.airflow_cfg.data You can see at airflow/myvalue-with-airflowcfg-configmap.yaml for an example on how to set it in your config.yaml file
note it is important to keep the custom templating in your airflow.cfg (ex: {{ POSTGRES_CREDS }}) or at least keep it aligned with the configuration applyied in your Kubernetes Cluster.

Worker Statefulset

As you can see, Celery workers uses StatefulSet instead of deployment. It is used to freeze their DNS using a Kubernetes Headless Service, and allow the webserver to requests the logs from each workers individually. This requires to expose a port (8793) and ensure the pod DNS is accessible to the web server pod, which is why StatefulSet is for.

Embedded DAGs

If you want more control on the way you deploy your DAGs, you can use embedded DAGs, where DAGs are burned inside the Docker container deployed as Scheduler and Workers.

Be aware this requirement more heavy tooling than using git-sync, especially if you use CI/CD:

your CI/CD should be able to build a new docker image each time your DAGs are updated.
your CI/CD should be able to control the deployment of this new image in your kubernetes cluster

Example of procedure:

Fork this project
Place your DAG inside the dags folder of this project, update requirements-dags.txt to install new dependencies if needed (see bellow)
Add build script connected to your CI that will build the new docker image
Deploy on your Kubernetes cluster

You can avoid forking this project by:

keep a git-project dedicated to storing only your DAGs + dedicated requirements.txt
you can gate any change to DAGs in your CI (unittest, pip install -r requirements-dags.txt,.. )

have your CI/CD makes a new docker image after each successful merge using

DAG_PATH=$PWD
cd /path/to/kube-aiflow
make ENBEDDED_DAGS_LOCATION=$DAG_PATH

trigger the deployment on this new image on your Kubernetes infrastructure

Python dependencies

If you want to add specific python dependencies to use in your DAGs, you simply declare them inside the requirements/dags.txt file. They will be automatically installed inside the container during build, so you can directly use these library in your DAGs.

To use another file, call:

make REQUIREMENTS_TXT_LOCATION=/path/to/you/dags/requirements.txt

Please note this requires you set up the same tooling environment in your CI/CD that when using Embedded DAGs.

Helm configuration customization

Helm allow to overload the configuration to adapt to your environment. You probably want to specify your own ingress configuration for instance.

Build Docker image

git clone this repository and then just run:

make build

Run with minikube

You can browse the Airflow dashboard via running:

minikube start
make browse-web

the Flower dashboard via running:

make browse-flower

If you want to use Ad hoc query, make sure you've configured connections: Go to Admin -> Connections and Edit "mysql_default" set this values (equivalent to values in config/airflow.cfg) :

Host : mysql
Schema : airflow
Login : airflow
Password : airflow

Check Airflow Documentation

Run the test "tutorial"

    kubectl exec web-<id> --namespace airflow-dev airflow backfill tutorial -s 2015-05-01 -e 2015-06-01

Scale the number of workers

For now, update the value for the replicas field of the deployment you want to scale and then:

    make apply

Wanna help?

Fork, improve and PR. ;-)

cmee/kube-airflow