In this article, I’ll explain how I implemented version based traffic routing between using service mesh. Fn Functions Istio I’ll start by explaining the basics of routing and the way Fn gets deployed and runs on Kubernetes. In the end, I’ll explain how I was able to leverage Istio service mesh and its routing rules to route traffic between two different Fn functions. Istio Be aware that the explanations that follow are very basic and simple — my intent was not to explain the in-depth working of Istio or Fn, instead I wanted to explain enough, so you could understand how to make routing work yourself. Istio routing 101 Let’s spend a little time to explain how routing works. Istio uses a sidecar container ( ) that you inject into your deployments. The injected proxy then hijacks all network traffic going in or out of that pod. The collection of all these proxies in your deployments communicate with other parts of the Istio system to determine how and where to (and bunch of other cool things like , and ) . Istio istio-proxy route the traffic traffic mirroring fault injection circuit breaking To explain how this works, we are going to start with a single Kubernetes service ( ) and two deployments of the app that are version specific ( and ). myapp v1 v2 Service routing to all app versions In the figure above, we have the Kubernetes service that has a selector set to — this means that it will look for all pods that have the label set and it’s going to route the traffic to them. Basically, if you do a you will get a response back either from a pod running the v1 version of the app or from a pod running the v2 version. myapp app=myapp app=myapp curl myapp-service We also have two Kubernetes deployments there — these deployments have the v1 and v2 code running. In addition to the label, each pod also has the label set to either or . myapp app=myapp version v1 v2 Everything in the diagram above is what you get with Kubernetes out of the box. Enter Istio. To be able to do more intelligent and weight-based routing, we need to install and then inject the proxy into each of our pods as shown in another awesome diagram below. Each pod in the diagram below has a container with an Istio proxy (represented by the blue icon) and the container where your app runs. In the diagram above, we only had one container running inside each pod — the app container. Istio Pods with Istio proxy sidecars Note that there’s is much more to Istio than shown in the diagram. I am not showing other Istio pods and services that are also deployed on the Kubernetes cluster — the injected Istio proxies communicate with those pods and service in order to know how to route traffic correctly. For an in-depth explanation of different parts of Istio, see the docs . here If we could curl the service at this point, we would still get the exact same results as we did with the setup in the first diagram — random responses from and pods. The only difference would be in the way the network traffic is flowing from the service and to the pods. In the second case any call to the service ends up in the Istio proxy and then proxy decides (based on any defined routing rules) where to route the traffic to. myapp v1 v2 Just like pretty much everything else today, Istio routing rules are defined using YAML and they look something like this: Route all traffic coming to myapp-service to pods, labeled “v1” The above routing rule takes requests coming to and re-routes them to pods labeled . This is how the diagram with the above routing rule in place would look like: myapp-service version=v1 Routing to v1 pods The big Istio icon at the bottom represents the Istio deployment/services where, amongst other things, the routing rules are being read from. These rules are then used to reconfigure Istio proxy sidecars running inside each pod. With this rule in place, if we curl the service we only get back the responses from the pods labeled (depicted by blue connectors in the diagram). version=v1 Now that we have an idea on how routing works, we can look into , get it deployed an see how it works and if we could use Istio somehow to set up the routing. Fn Fn Functions on Kubernetes We are going to start with a basic diagram of how some pieces of look like on Kubernetes. You can use the to deploy Fn on top of your Kubernetes cluster. Fn Helm chart A simple representation of Fn on Kubernetes The Fn API service at the top of the diagram is the entry point to the Fn and it’s used for managing your functions (creating, deploying, running, etc.) — this is the URL that’s referred to as in the Fn project. FN_API_URL This service, in turn, routes the calls to the Fn load balancer (that is, any pods marked with ). The load balancer then does it’s magic and routes the calls to an instance of the pod. These are deployed as part of a Kubernetes daemon set and you will usually have one instance of the pod per Kubernetes node. role=fn-lb fn-service With these simple basics out of the way, let’s create and deploy some functions and think about how to do traffic routing. Create and deploy functions If you want to follow along, make sure you have (I am using Docker for Mac) and installed and run the following to create the app and a couple of functions: Fn deployed to your Kubernetes cluster Fn CLI # Create the app foldermkdir hello-app && cd hello-appecho "name: hello-app" > app.yaml # Create a V1 functionmkdir v1cd v1fn init --name v1 --runtime gocd .. # Create a V2 functionmkdir v2cd v2fn init --name v2 --runtime gocd .. Using the above commands, you’ve created a root folder for the app, called . In this folder we create two folders with a single function each — and a Boilerplate Go functions are created using the with Go specified as a runtime — this is how the folder structure looks like: hello-app v1 v2. fn init .├── app.yaml├── v1│   ├── Gopkg.toml│   ├── func.go│   ├── func.yaml│   └── test.json└── v2├── Gopkg.toml├── func.go├── func.yaml└── test.json Open the in both folders and update the message that gets returned to include the version number — the only reason we are doing this is so we can quickly distinguish which function is being called. Here’s how v1 should look like ( ): func.go func.go Hello V1 Hello V1 Once you made these changes, you can deploy the functions to the Fn service running on Kubernetes. To do so, you have to set the environment variable to point to your Docker registry username. FN_REGISTRY Because we are running Fn on Kubernetes cluster, we can’t use locally built images — they need to be pushed to a Docker registry that’s accessible by the Kubernetes cluster. Now we can use the to deploy the functions: Fn CLI FN_API_URL=http://localhost:80 fn deploy --all Above command assumes the Fn API service is exposed on localhost:80 (this is by default if you’re using Kubernetes support in Docker for Mac). If using a different cluster, you can replace the FN_API_URL with the external IP address of the fn-api service. After Docker build and push is completed, our functions are deployed to the Fn service and we can try to invoke them. Any function deployed to the Fn service has a unique URL that includes the app name and the route name. With our app name and routes, we can access the deployed functions at . So, if we want to call the route, we could do: http://$(FN_API_URL)/r/hello-app/v1 v1 $ curl {"message":"Hello V1"} http://localhost/r/hello-app/v1 Similarly, calling the route returns the Hello V2 message. v2 But where does the function run? If you look at the pods that are being created/deleted while you’re calling the functions, you’ll notice that nothing really changes — i.e. no pods get created nor deleted. The reason is that Fn doesn’t create functions as Kubernetes pods as that would be too slow. Instead, all Fn function deployment and invocation magic happens inside the fn-service pods. The Fn load balancer is then responsible for placing and routing to those pods to deploy/execute the functions in the most optimized way. So, we don’t get Kubernetes pods/services for functions, but Istio requires us to have services and pods we can route to… what do we do and how can we use Istio in this case? The idea Let’s take the functions out of the picture for a second and think about what we need for Istio routing to work: Kubernetes service — an entry point to our hello-app Kubernetes deployment for v1 hello-app Kubernetes deployment for v2 hello-app As explained at the beginning of the article in Istio Routing 101, we’d also have to add a label that represents the version and the label to both of our deployments. Selector on the service would have the label only — the version specific labels would then be added by the Istio routing rules. app=hello-app app=hello-app For this to work, each version specific deployment would need to end up calling the Fn load balancer at the correct route (e.g. ). Since everything runs in Kubernetes and we know the name of the Fn load balancer service we could make this happen. /r/hello-app/v1 So we need a container inside our deployments that, when called, forwards calls to the Fn load balancer at a specific path. Here’s the above idea represented in a diagram: Version specific deployments calling the Fn API service at exact path We have a service that represents our app and two deployments that are version specific and route directly to the functions running in the Fn service. Simple proxy To implement this we need some sort of a proxy that will take any incoming calls and forward them to the Fn service. Here’s a simple Nginx configuration that does exactly that: events {worker_connections  4096;} http {upstream fn-server {server ;} my-fn-api.default server {listen 80; location / {proxy_pass ;proxy_set_header X-Real-IP  $remote_addr;proxy_set_header X-Forwarded-For $remote_addr;proxy_set_header Host $host;}}} http://fn-server/ r/hello-app/v1 To explain the configuration: we are saying whenever something comes in to /, pass that call to where (defined as an upstream), is resolved to — this is the Kubernetes service name for fn-api that runs in the namespace. [http://fn-server/r/hello-app/v1](http://fn-server/r/hello-app/v1,) , fn-server my-fn-api.default default The parts in bold are the only things we need to change in order to do the same for . v2 I’ve created a Docker image with a script that generates the Nginx configuration based on the upstream and route values you pass in.The image is available on and you can look at the source . Docker hub here Deploying to Kubernetes Now we can create the Kubernetes YAML files for the service, deployments and the ingress we will use to access the functions. Here’s an excerpt from the deployment file to show how we set up the environment variable for and and the labels. UPSTREAM ROUTE The and environment variables are read by the simple-proxy container and an Nginx configuration gets generated based on those values. UPSTREAM ROUTE The service YAML file is nothing special either — we just set the selector to : app: hello-app Service definition Final part is the Istio ingress where we set the rule to route all incoming traffic to the backend service: Ingress To deploy these, you can use for ingress and the service and for the deployments in order to inject the Istio proxy. kubectl istioctl kube-inject With everything deployed, you should end up with the following Kubernetes resources: hello-app-deployment-v1 (deployment with simple-proxy image that points to v1 route) hello-app-deployment-v2 (deployment with simple-proxy image that points to v2 route) hello-app-service (service that targets v1 and v2 pods in hello-app deployments) ingress that points to the hello-app-service and is annotated with “istio” ingress class annotation Now if we call the hello-app-service or if we call the ingress we should be getting random responses back from v1 and v2 functions. Here’s a sample output of calls made to the ingress: $ while true; do sleep 1; curl {“message”:”Hello V1"}{“message”:”Hello V1"}{“message”:”Hello V1"}{“message”:”Hello V1"}{“message”:”Hello V2"}{“message”:”Hello V1"}{“message”:”Hello V2"}{“message”:”Hello V1"}{“message”:”Hello V2"}{“message”:”Hello V1"}{“message”:”Hello V1"}{“message”:”Hello V1"}{“message”:”Hello V2"} http://localhost:8082;done You’ll notice that we randomly get back responses from V1 and from V2 — this is the exactly what we want at this point! Istio rules! With our service and deployments up and running (and working) we can create Istio route rules for Fn functions. Let’s start with a simple v1 rule that will route all calls ( ) to the to pods that are labeled : weight: 100 hello-app-service v1 Route all calls to v1 label You can apply this rule by running The best way to see the routing in action is to run a loop that continuously calls the endpoint — that way you can see the responses go from mixed (v1/v2) and all v1. kubectl apply -f v1-rule.yaml Just like we did rule with 100 weight, we can similarly define a rule that routes everything to or a rule that routes 50% of traffic to and a 50% of traffic as shown in the demo below. v1 v2 v1 v2 Once I’ve proved that this works with simple curl commands, I stopped :) Luckily, took it a bit further in his article about the importance of DevOps to Serverless (spoiler alert: DevOps is not going away). He used Spinnaker to do a weighted blue-green deployment of Fn functions that were running on an actual Kubernetes cluster. Check out the video of his demo below: Chad Arimura Spinnaker — Fn Project — Istio — Kubernetes in action Conclusion Everyone could agree that service mesh is and will be important in the functions world. There’s a lot of neat benefits one can get if using a service mesh — such as routing, traffic mirroring, fault injects and a bunch of other stuff. The biggest challenge I see is the lack of developer-centric tools that would allow developers to leverage all these nice and cool features. Setting up this project and demo to run it a couple of times wasn’t too complicated. But this was 2 functions that pretty much return a string and do nothing else. It was a simple demo. Just think about running hundreds or thousands of functions and setting up different routing rules between them. Then think about managing all that. Or rolling out new versions and monitoring for failures. I think there are great opportunities (and challenges) ahead in making functions management, service mesh management, routing, [insert other cool features] work in a way so it’s intuitive for everyone involved. Thanks for Reading! Any feedback on this article is more than welcome! You can also follow me on and . If you liked this and want to get notified when I write more stuff, you should subscribe to ! Twitter GitHub my newsletter

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