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https://github.com/tekn0ir/airflow-chart

Helm chart for deploying Apache Airflow in kubernetes
https://github.com/tekn0ir/airflow-chart

Last synced: 15 days ago
JSON representation

Helm chart for deploying Apache Airflow in kubernetes

Host: GitHub
URL: https://github.com/tekn0ir/airflow-chart
Owner: tekn0ir
Created: 2018-11-19T13:35:02.000Z (over 5 years ago)
Default Branch: master
Last Pushed: 2019-08-13T05:33:45.000Z (almost 5 years ago)
Last Synced: 2024-02-29T12:41:50.976Z (4 months ago)
Language: Smarty
Size: 241 KB
Stars: 19
Watchers: 5
Forks: 10
Open Issues: 4
Metadata Files:
- Readme: README.md

Lists

awesome-apache-airflow - KubernetesExecutor Helm Chart - A lean Helm Chart using the KubernetesExecutor for a more k8s native experience and complementary [KubernetesExecutor Docker Image](https://github.com/tekn0ir/airflow-docker). (Airflow deployment solutions)
awesome-apache-airflow-br - KubernetesExecutor Helm Chart - Um Helm Chart mais simples usando o KubernetesExecutor para uma experiencia nativa com o Kubernetes ou k8s e que também serve para complementar (Soluções de deployment do Airflow)

README

# Airflow chart

Helm chart for deploying Apache Airflow on kubernetes.

Read more about Kubernetes Executor and Operator [here](https://airflow.incubator.apache.org/kubernetes.html).

## Guideance

To force you not to end up in performance and/or other issues, this template takes some experience into account.

### Chain of configuration

Any airflow setting can be set by the scheme AIRFLOW__{SECTION}__{KEY} in the config section your values.yaml.
Rollout is performed when running `helm updgrade` on config changes, due to a checksum annotation on the pods.

The chart might seem to have secrets and configmaps that are not used, and worker pods might seem to be missing mounts.
The configuration design of airflow is not straight forward. Webservers and scheduler gets config overloaded with a
configmap, while a secret populate the configration file, that in turn creates environment variables for the workers(tasks).
All of which is configurable from the values.yaml file in one single place.

The chart automatically sets the following variables:
```
AIRFLOW__CORE__SQL_ALCHEMY_CONN,
AIRFLOW__KUBERNETES__AIRFLOW_CONFIGMAP,
AIRFLOW__KUBERNETES__NAMESPACE
```
and:
```
AIRFLOW__KUBERNETES__WORKER_SERVICE_ACCOUNT_NAME
```
if rbac is enabled.

### Database backend

If you want your own DB backend for Airflow, just disable postgresql in the values file and add the sqlAlchemyConn
value in the values file:
```
sqlAlchemyConn: somespec+other://username:password@db-hostname:5432/schema
```

### Provision connections

The backend DB needs to be initialized, but also connections has to be provisioned to Ariflow. There is a provision
job for this. Look at the example in the provisioner section of the values.yaml file for some inspiration.
```
provisioner:
enabled: true
cmds: |-
airflow initdb;
airflow connections --add --conn_id my_rs_connection \
--conn_type jdbc \
--conn_host jdbc:redshift://my-redshift.eu-west-1.redshift.amazonaws.com \
--conn_login my_rs_user \
--conn_password my_secret_password \
--conn_schema my_database \
--conn_port 5439 \
--conn_extra '{"extra__jdbc__drv_path": "/usr/local/ariflow/drivers/RedshiftJDBC42-no-awssdk-1.2.15.1025.jar", "extra__jdbc__drv_clsname": "com.amazon.redshift.jdbc42.Driver"}';
```
_this shows how to provision a AWS Redshift JDBS connection supported by the default docker image_

### Provision users

The provision job can also be used to provision users.
```
provisioner:
enabled: true
cmds: |-
airflow initdb;
airflow create_user \
--role Admin \
--username airflow \
--password airflow \
--firstname Air \
--lastname Flow \
--email [email protected];
```
_this shows how to provision an admin user called `airflow` with password `airflow`_

### Worker logs

Worker(task) logs are not available by default, check debugging section for now to check how to get logs.
You can configure remote logging on AWS S3 for example:
```
AIRFLOW__CORE__REMOTE_LOGGING: "True"
AIRFLOW__CORE__REMOTE_BASE_LOG_FOLDER: s3://eu-production-airflow/logs/
AIRFLOW__CORE__ENCRYPT_S3_LOGS: "False"
```
_in this case we rely on EC2 node instance profile to have access to that bucket_

#### TODO: Figure out a way to serve logs to webservers.

Either add optional persistent volume shared between all pods, requires a ReadWriteMany shared persistant volume,
that is not that common to have. Or somehow use the `airflow serve_logs` functionality.

### Provision dags

Inner workings of airflow seems to work best if you do not share a volume for dags, but rather put your materialized dags
as a layer on your docker image. Not hosting files on a remote filesystem also improves performance a small bit.
It might seem cumbersome to rebuild your docker image each time and you might have dags in many different repos with
different pipelines. I am sorry, the most reliable way is to do it like this.

Problem is that most filesystems used for kubernetes are not ReadWriteMany, and thus not mountable by more than one
pod at the time. And most ReadWriteMany solutions are hideously slow, like AWS EFS.

The alternative way is to use the gitsync function built into Airflow, that still should work in this config.
But it git syncs for each task in an EmptyDir mount, so basically a full clone...

So I made a tiny patch of Airflow in my docker image.
Edit: `worker_container_contains_dags = True` is set by default

### Installing the Chart

To install the chart with the release name `my-airflow` in the `my-airflow` namespace:

```
$ helm repo add - username - password tekn0ir-airflow 'https://raw.githubusercontent.com/tekn0ir/airflow-chart/master/'
$ helm repo update
$ helm upgrade --install my-airflow --namespace=my-airflow tekn0ir-airflow/airflow-chart
```

This chart includes a postgresql chart as a dependency to the Airflow cluster in its `requirement.yaml` by default.
The chart can be customized using the following configurable parameters:

| Parameter
| ------------------------------
| `airflowImage`
| `airflowImageTag`
| `imagePullPolicy`
| `fernetKey`
| `ingress.enabled`
| `ingress.annotations`
| `ingress.labels`
| `ingress.hosts`
| `ingress.tls`
| `service.annotations`
| `webserver.replicas`
| `webserver.annotations`
| `scheduler.annotations`
| `rbac.enabled`
| `serviceAccountName`
| `provisioner.enabled`
| `provisioner.cmds`
| `config`
| `postgresql.enabled` | Description | Default | | -------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------- | | Airflow Container image name | `tekn0ir/airflow-docker` | | Airflow Container image tag | `1.10.1rc2` | | Airflow Container pull policy | `IfNotPresent` | | Airflow fernet key, for encryption of data | `af7CN0q6ag5U3g08IsPsw3K45U7Xa0axgVFhoh-3zB8=` | | Enables Ingress for Drone | `true` | | Ingress annotations | `{}` | | Ingress labels | `{}` | | Ingress accepted hostnames | `[airflow.192.168.99.100.xip.io]` | | Ingress TLS configuration | `[]` | | Service annotations | `{prometheus.io scrape config}` | | Number of webserver replicas | `2` | | Webserver annotations | `{}` | | Scheduler annotations | `{}` | | Enable a service account and role for the cluster to use | `true` | | ServiceAccount namer to use if it cannot be created with RBAC | `` | | Enable the provisioning job to run arbitrary bash commands in the Airflow cluster, example initiate DB and provision connections | `true` | | The provisioning commands... | `...` | | Set any environment variable, mostly used to set any airflow setting by the scheme AIRFLOW__{SECTION}__{KEY} | `...` | | Configure dependency: https://github.com/helm/charts/tree/master/stable/postgresql | `true` |

Specify parameters using `--set key=value[,key=value]` argument to `helm install`

Alternatively a YAML file that specifies the values for the parameters can be provided like this:

```bash
$ git clone https://github.com/tekn0ir/airflow-chart.git
$ cd airflow-chart
$ helm dependency update
$ helm install --name my-airflow -f values.yaml .
```

## Debugging

One simple thing you can do is to set `AIRFLOW__KUBERNETES__DELETE_WORKER_PODS: "False"` in the config section in your
values file. That makes Airflow not remove terminated worker pods so you can check logs and descriptions to see that
all is correctly set.