Simon Emms

Software Engineer, Technical Leader, Solutions Designer

Building a RESTful API With Functions


There is an accompanying GitHub registry with a working demo

Serverless functions are a great way of building a scalable application. They're small, independent, fast-to-build and infinitely scalable. If you need to build an application with just a couple of endpoints, they will work great.

When it comes to building a big application or one that you want to expose as a public service, serverless functions can end up being just a bit cumbersome to develop. As most serverless frameworks focus on single functions, there can be a lot of repetition in getting a CRUD application set up.

What is a RESTful application

For more information, check out the Wikipedia article.

REST (Representational state transfer) is a way of representing data between machines. It has a several key features:

  • no session state is maintained between calls
  • the HTTP verb decides how the request is handled
  • it returns the data model

What tech are we using

Whilst there's plenty of open-source tech out there that can help, this is my favoured way of achieving the end goal:

Getting started

As I like to keep things simple, all you need to do to get the development cluster up and running is to run make. This will run a series of commands to check you have the correct dependencies, create your k3d cluster and provision your cluster. Once you've done that, you can access your cluster on localhost:9999.

Once you've got the cluster up and running, you can just use make serve to reload the Kubernetes objects.

Your first function

In the example repo, look at the product function as the first function

To create your first function, run the command FN_NAME=product make new. This will create a new OpenFaaS function in the /components directory.

The important file is schema.js which is a standard Mongoose schema. In this example, we're just defining a modelName of Product - in a more complex example, we can add in both synchronous and asynchronous validation, but a simple name property will do for now.

module.exports = ({ model }, BaseSchema) => {
  const modelName = 'Product';

  const ProductSchema = new BaseSchema({
    name: {
      type: String,
      required: true,

  return model(modelName, ProductSchema);

Next, add this to the functions Helm chart in /chart/functions/values.yaml.

      tag: latest
      LOGGER_LEVEL: info
      MONGODB_URL: "mongodb://openfaas-mongodb.openfaas.svc.cluster.local:27017/openfaas"

Finally, add the image to the artifactOverrides section in the skaffold.yaml file.


If you visit the OpenFaaS dashboard, you will see the product function has been deployed to OpeenFaaS.

To get the login credentials, run make openfaas

Making it into an endpoint

Now we have an OpenFaaS function running, we need to configure Kong so that it acts as a gateway. OpenFaaS functions are exposed on /function/:name, which is what we're going to redirect to - in this template, the CRUD endpoints live under /crud.

First, in /charts/openfaas/values.yaml, add a product-gateway service:

  - name: product-gateway
    annotations: /function/product/crud

Finally, in the /charts/openfaas/template/ingress.yaml, tell the Kong ingress about the product-gateway service:

kind: Ingress
  name: openfaas
  annotations: "true"
  ingressClassName: kong
    - http:
          - path: /api/v1/product
            pathType: Prefix
                name: product-gateway
                  name: http

Now you'll find this exposed on localhost:9999/api/v1/product.

Your second function and nested endpoints

In the example, this is the product-size function

So far, we've done a fairly simple example only. Next, we're going to up the complexity by adding a nested endpoint. A root endpoint is fairly straightforward because it just sends everything to the function and returns whatever that returns.

Conversely, a nested endpoint is locked to a product ID. Fortunately, Kong makes this fairly straightforward for us with its plugin system.

Firstly, repeat all the above steps to create a product-size function. In the schema.js, add a productId parameter - this will store the _id from the product function.

This time, when creating the /charts/openfaas/template/ingress.yaml, we're going to add a named path parameter in the path.

- path: /api/v1/product/(?<productId>[\w-]+)/size
  pathType: Prefix
      name: product-size-gateway
        name: http

This searches for any alphanumeric character between /product/ and /size in the URL and assigns it the name productId.

Next, we have to tell it what to do with it. In the /charts/openfaas/values.yaml, add this to the services array:

- name: product-size-gateway
  annotations: /function/productsize/crud product-request-transformer

Notice how this refers to a product-request-transformer plugin. So, let's define it in the same values.yaml file.

  - name: product-request-transformer
    plugin: request-transformer
          - productId
          - productId:$(uri_captures["productId"])
          - filter:productId||$eq||$(uri_captures["productId"])

This does a few things. Firstly, it removes any productId from the body and then adds in the product ID in the URL. It also appends a query string filter=productId||$eq||<productId> to the URL. This ensures that the product-size function behaves like a nested API endpoint.

Importantly, this won't return an HTTP 404 result if the product doesn't exist. That's beyond the scope of this demo, although you could achieve this by adding a middleware.js file to the function. This can be either a function or an array of functions and it follows the same basic interface as an Express middleware function.


With this simple demo, you can see that it's very possible to create a fully RESTful API from a collection of serverless functions. It's important to remember that all these functions are entirely isolated from each other from a coding point of view (so you could even do something interesting like writing them in different languages). This makes them very powerful and almost infinitely scalable.


Photo by Volodymyr Hryshchenko

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