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How to configure your application

In this guide we'll set up an application in Radix. Here's what we need:

  • A GitHub repository for our code (only GitHub is supported at the moment)
  • A 📖 radixconfig.yaml file that defines the running environments. By default, it is in the root directory of our repository.
  • At least one Dockerfile that builds and serves our application. We can have several of these files: one per component, in separate directories (e.g. a "front-end" component and a "back-end" component).

We will go over these points below.

The repository

All of our components must be in the same repository. A component is code that has its own build and deployment process: for instance a "front end" served by Nginx and a "back end" running on Node.js would be two components. Components are built in parallel from the same repository and deployed together into an environment. There is currently no concept of a multi-repository application.

The way we use branches and tags in our repository depends on what type of workflow we use. You can read more about the choices available in the workflows section — but let's continue with setting up for now.

The radixconfig.yaml file

A 📖 radixconfig.yaml file that defines the running environments, which specifies how our application is built and deployed. By default, it is in the root directory of our repository.

tip

Radix only reads radixconfig.yaml from the branch we set as the Config Branch in the application registration/configuration form. If the file is changed in other branches, those changes will be ignored. The Config Branch must be mapped to an environment in radixconfig.yaml

If you are unfamiliar with YAML, it is fine to write the configuration as JSON instead — just keep the same filename.

Here is a simple example of the file:

Click me to view the code
apiVersion: radix.equinor.com/v1
kind: RadixApplication
metadata:
name: myapp
spec:
environments:
- name: dev
build:
from: master
- name: prod
components:
- name: frontend
src: "."
publicPort: http
ports:
- name: http
port: 8080

The same, but as JSON:

Click me to view the code
{
"apiVersion": "radix.equinor.com/v1",
"kind": "RadixApplication",
"metadata": { "name": "myapp" },
"spec": {
"environments": [
{ "name": "dev", "build": { "from": "master" } },
{ "name": "prod" }
],
"components": [
{
"name": "frontend",
"src": ".",
"publicPort": "http",
"ports": [
{ "name": "http", "port": 8080 }
]
}
]
}
}

A breakdown of the configuration above:

  • Our application is called myapp
  • There are two environments, dev and prod, and only one component, frontend
  • Commits to the master branch will trigger a build and deployment of the application to the dev environment. We can use this behavior to build a workflow
  • Radix will look for the Dockerfile for the frontend component in the root directory of the repository
  • Once frontend is built, it will be exposed on the internet on port 8080 on each environment it is deployed to (in dev, for instance, it will have a domain name like frontend-myapp-dev.playground.radix.equinor.com (on the Playground cluster) or frontend-myapp-dev.radix.equinor.com (on the Platform cluster))

The full syntax of radixconfig.yaml is explained in 📖 radixconfig.yaml reference.

A Dockerfile per component

Each component in Radix is built into a Docker image. Images for all components are deployed as containers running in an environment. To do this, Radix requires a Dockerfile for each component.

If we organize our repository with this structure, for instance:

/
├─ fe/
│ ├─ Dockerfile
│ └─ *frontend component code*

├─ be/
│ ├─ Dockerfile
│ └─ *backend component code*

└─ radixconfig.yaml

In radixconfig.yaml we can define the following components:

components:
- name: frontend
src: "./fe"
- name: backend
src: "./be"

Note the src property for each component: this is the path to the directory containing the Dockerfile for that component. Radix will try to build the image within that directory.

The Dockerfile should define - it is strongly recommended, when applicable - a multi-stage build in order to speed up the builds and make the resulting image as small as possible also to avoid running debug versions of the code and servers. Python images usually run as is, but there is a "distroless" image like these - we did not try them though.

This means that we can decouple the build and deployment concerns. Here is an example for a simple Node.js single-page application:

FROM node:carbon-alpine as builder
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
RUN npm run build

FROM nginx:1.20-alpine
WORKDIR /app
COPY --from=builder /app/build /app
COPY nginx.conf /etc/nginx/conf.d/default.conf
USER 101

Note how the first section uses a large image (node) which has the dependencies needed to build the component. In the second stage, the built files are copied into a small image (nginx) to serve them without all the build dependencies.