Migrating from Google Cloud Run Functions to Knative

Knative is an open source platform that extends Kubernetes to manage serverless workloads. It provides tools to deploy, run, and manage serverless applications and functions, enabling automatic scaling and efficient resource utilization. Knative consists of several components:

• Serving: Deploys and runs serverless containers.
• Eventing: Manages event-driven architectures.
• Functions: Deploys and runs functions locally and on Kubernetes.

This guide walks through the process of migrating a Google Cloud Run function to a Knative function running on the Linode Kubernetes Engine (LKE).

Before You Begin

1. Read our Getting Started with Linode guide, and create a Linode account if you do not already have one.

2. Create a personal access token using the instructions in our Manage personal access tokens guide.

3. Ensure that you have Git installed.

4. Follow the steps in the Install kubectl section of our Getting started with LKE guide to install kubectl.

5. Install the Linode CLI using the instructions in our Install and configure the CLI guide.

6. Ensure that you have Knative’s func CLI installed.

7. Ensure that you have Docker installed and have a Docker Hub account.

8. Ensure that Go is installed on your system:

   sudo apt install golang-go

9. Install jq, a lightweight command line JSON processor:

   sudo apt install jq

10. Install tree, a command line utility that displays directory structures in a tree-like format:

    sudo apt install tree

Provision a Kubernetes Cluster

While there are several ways to create a Kubernetes cluster on Linode, this guide uses the Linode CLI to provision resources.

1. Use the Linode CLI command (linode) to see available Kubernetes versions:

   linode lke versions-list

   ┌──────┐
   │ id   │
   ├──────┤
   │ 1.31 │
   ├──────┤
   │ 1.30 │
   ├──────┤
   │ 1.29 │
   └──────┘

   It’s generally recommended to provision the latest version of Kubernetes unless specific requirements dictate otherwise.

2. Use the following command to list available Linode plans, including plan ID, pricing, and performance details. For more detailed pricing information, see Akamai Connected Cloud: Pricing :

   linode linodes types

3. The examples in this guide use the g6-standard-2 Linode, which features two CPU cores and 4 GB of memory. Run the following command to display detailed information in JSON for this Linode plan:

   linode linodes types --label "Linode 4GB" --json --pretty

   [
     {
       "addons": {
         "backups": {
           "price": {
             "hourly": 0.008,
             "monthly": 5.0
           },
           "region_prices": [
             {
               "hourly": 0.009,
               "id": "id-cgk",
               "monthly": 6.0
             },
             {
               "hourly": 0.01,
               "id": "br-gru",
               "monthly": 7.0
             }
           ]
         }
       },
       "class": "standard",
       "disk": 81920,
       "gpus": 0,
       "id": "g6-standard-2",
       "label": "Linode 4GB",
       "memory": 4096,
       "network_out": 4000,
       "price": {
         "hourly": 0.036,
         "monthly": 24.0
       },
       "region_prices": [
         {
           "hourly": 0.043,
           "id": "id-cgk",
           "monthly": 28.8
         },
         {
           "hourly": 0.05,
           "id": "br-gru",
           "monthly": 33.6
         }
       ],
       "successor": null,
       "transfer": 4000,
       "vcpus": 2
     }
   ]

4. View available regions with the regions list command:

   linode regions list

5. With a Kubernetes version and Linode type selected, use the following command to create a cluster named knative-playground in the us-mia (Miami, FL) region with three nodes and auto-scaling. Replace knative-playground and us-mia with a cluster label and region of your choosing, respectively:

   linode lke cluster-create \
     --label knative-playground \
     --k8s_version 1.31 \
     --region us-mia \
     --node_pools '[{
       "type": "g6-standard-2",
       "count": 3,
       "autoscaler": {
         "enabled": true,
         "min": 3,
         "max": 8
       }
     }]'

   Once your cluster is successfully created, you should see output similar to the following:

   Using default values: {}; use the --no-defaults flag to disable defaults
   ┌────────────────────┬────────┬─────────────┐
   │ label              │ region │ k8s_version │
   ├────────────────────┼────────┼─────────────┤
   │ knative-playground │ us-mia │ 1.31        │
   └────────────────────┴────────┴─────────────┘

Access the Kubernetes Cluster

To access your Kubernetes cluster, fetch the cluster credentials in the form of a kubeconfig file.

1. Use the following command to retrieve the cluster’s ID:

   CLUSTER_ID=$(linode lke clusters-list --json | \
       jq -r \
         '.[] | select(.label == "knative-playground") | .id')

2. Create a hidden .kube folder in your user’s home directory:

   mkdir ~/.kube

3. Retrieve the kubeconfig file and save it to ~/.kube/lke-config:

   linode lke kubeconfig-view --json "$CLUSTER_ID" | \
       jq -r '.[0].kubeconfig' | \
       base64 --decode > ~/.kube/lke-config

4. Once you have the kubeconfig file saved, access your cluster by using kubectl and specifying the file:

   kubectl get no --kubeconfig ~/.kube/lke-config

   NAME                            STATUS   ROLES    AGE     VERSION
   lke244724-387910-0fef31d70000   Ready    <none>   5m51s   v1.31.0
   lke244724-387910-13ae14340000   Ready    <none>   5m48s   v1.31.0
   lke244724-387910-5f9c3b0e0000   Ready    <none>   5m40s   v1.31.0

   Note

   Optionally, to avoid specifying --kubeconfig ~/.kube/lke-config with every kubectl command, you can set an environment variable for your current terminal window session:

   export KUBECONFIG=~/.kube/lke-config

   Then run:

   kubectl get no

Set Up Knative on LKE

There are multiple ways to install Knative on a Kubernetes cluster . The examples in this guide use the YAML manifests method.

Install Knative

1. Run the following command to install the Knative CRDs:

   RELEASE=releases/download/knative-v1.15.2/serving-crds.yaml
   kubectl apply -f "https://github.com/knative/serving/$RELEASE"

   Upon successful execution, you should see a similar output indicating that the CRDs are configured:

   customresourcedefinition.apiextensions.k8s.io/certificates.networking.internal.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/configurations.serving.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/clusterdomainclaims.networking.internal.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/domainmappings.serving.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/ingresses.networking.internal.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/metrics.autoscaling.internal.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/podautoscalers.autoscaling.internal.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/revisions.serving.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/routes.serving.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/serverlessservices.networking.internal.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/services.serving.knative.dev created
   customresourcedefinition.apiextensions.k8s.io/images.caching.internal.knative.dev created

2. Next, install the Knative Serving component:

   RELEASE=releases/download/knative-v1.15.2/serving-core.yaml
   kubectl apply -f "https://github.com/knative/serving/$RELEASE"

   You should see similar output indicating that various resources are now created:

   namespace/knative-serving created
   role.rbac.authorization.k8s.io/knative-serving-activator created
   clusterrole.rbac.authorization.k8s.io/knative-serving-activator-cluster created
   clusterrole.rbac.authorization.k8s.io/knative-serving-aggregated-addressable-resolver created
   clusterrole.rbac.authorization.k8s.io/knative-serving-addressable-resolver created
   clusterrole.rbac.authorization.k8s.io/knative-serving-namespaced-admin created
   clusterrole.rbac.authorization.k8s.io/knative-serving-namespaced-edit created
   clusterrole.rbac.authorization.k8s.io/knative-serving-namespaced-view created
   clusterrole.rbac.authorization.k8s.io/knative-serving-core created
   clusterrole.rbac.authorization.k8s.io/knative-serving-podspecable-binding created
   serviceaccount/controller created
   clusterrole.rbac.authorization.k8s.io/knative-serving-admin created
   clusterrolebinding.rbac.authorization.k8s.io/knative-serving-controller-admin created
   clusterrolebinding.rbac.authorization.k8s.io/knative-serving-controller-addressable-resolver created
   serviceaccount/activator created
   rolebinding.rbac.authorization.k8s.io/knative-serving-activator created
   clusterrolebinding.rbac.authorization.k8s.io/knative-serving-activator-cluster created
   customresourcedefinition.apiextensions.k8s.io/images.caching.internal.knative.dev unchanged
   certificate.networking.internal.knative.dev/routing-serving-certs created
   customresourcedefinition.apiextensions.k8s.io/certificates.networking.internal.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/configurations.serving.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/clusterdomainclaims.networking.internal.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/domainmappings.serving.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/ingresses.networking.internal.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/metrics.autoscaling.internal.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/podautoscalers.autoscaling.internal.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/revisions.serving.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/routes.serving.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/serverlessservices.networking.internal.knative.dev unchanged
   customresourcedefinition.apiextensions.k8s.io/services.serving.knative.dev unchanged
   image.caching.internal.knative.dev/queue-proxy created
   configmap/config-autoscaler created
   configmap/config-certmanager created
   configmap/config-defaults created
   configmap/config-deployment created
   configmap/config-domain created
   configmap/config-features created
   configmap/config-gc created
   configmap/config-leader-election created
   configmap/config-logging created
   configmap/config-network created
   configmap/config-observability created
   configmap/config-tracing created
   horizontalpodautoscaler.autoscaling/activator created
   poddisruptionbudget.policy/activator-pdb created
   deployment.apps/activator created
   service/activator-service created
   deployment.apps/autoscaler created
   service/autoscaler created
   deployment.apps/controller created
   service/controller created
   horizontalpodautoscaler.autoscaling/webhook created
   poddisruptionbudget.policy/webhook-pdb created
   deployment.apps/webhook created
   service/webhook created
   validatingwebhookconfiguration.admissionregistration.k8s.io/config.webhook.serving.knative.dev created
   mutatingwebhookconfiguration.admissionregistration.k8s.io/webhook.serving.knative.dev created
   validatingwebhookconfiguration.admissionregistration.k8s.io/validation.webhook.serving.knative.dev created
   secret/webhook-certs created

Install Kourier

1. Knative relies on an underlying networking layer. Kourier is designed specifically for Knative, and the examples in this guide use Kourier for Knative networking . Use the commands below to download and install the latest Kourier release:

   RELEASE=releases/download/knative-v1.15.1/kourier.yaml
   kubectl apply -f "https://github.com/knative/net-kourier/$RELEASE"

   The output should again indicate the creation of multiple new elements:

   namespace/kourier-system created
   configmap/kourier-bootstrap created
   configmap/config-kourier created
   serviceaccount/net-kourier created
   clusterrole.rbac.authorization.k8s.io/net-kourier created
   clusterrolebinding.rbac.authorization.k8s.io/net-kourier created
   deployment.apps/net-kourier-controller created
   service/net-kourier-controller created
   deployment.apps/3scale-kourier-gateway created
   service/kourier created
   service/kourier-internal created
   horizontalpodautoscaler.autoscaling/3scale-kourier-gateway created
   poddisruptionbudget.policy/3scale-kourier-gateway-pdb created

2. The following command configures Knative to use Kourier as the default ingress controller:

   kubectl patch configmap/config-network \
       --namespace knative-serving \
       --type merge \
       --patch \
         '{"data":{"ingress-class":"kourier.ingress.networking.knative.dev"}}'

   configmap/config-network patched

   Note

   If Istio is already installed in your cluster, you may choose to reuse it for Knative .

3. With Kourier configured, the Knative serving installation now has a LoadBalancer service for external access. Use the following command to retrieve the external IP address in case you want to set up your own DNS later:

   kubectl get service kourier -n kourier-system

   The output should display the external IP address of the LoadBalancer:

   NAME      TYPE           CLUSTER-IP       EXTERNAL-IP       PORT(S)                      AGE
   kourier   LoadBalancer   10.128.164.131   172.233.166.148   80:30227/TCP,443:32467/TCP   65s

4. Since Kourier adds several deployments, check the updated list to ensure everything functions correctly:

   kubectl get deploy -n knative-serving

   Use the output to confirm availability of the various components:

   NAME                     READY   UP-TO-DATE   AVAILABLE   AGE
   activator                1/1     1            1           2m29s
   autoscaler               1/1     1            1           2m29s
   controller               1/1     1            1           2m29s
   net-kourier-controller   1/1     1            1           85s
   webhook                  1/1     1            1           2m28s

Configure DNS

This guide use the Magic DNS method to configure DNS , which leverages the sslip.io DNS service. When a request is made to a subdomain of sslip.io containing an embedded IP address, the service resolves that IP address. For example, a request to https://52.0.56.137.sslip.io returns 52.0.56.137 as the IP address. Use the default-domain job to configure Knative Serving to use sslip.io:

MANIFEST=knative-v1.15.2/serving-default-domain.yaml
kubectl apply -f "https://github.com/knative/serving/releases/download/$MANIFEST"

Upon successful execution, you should see output confirming the creation of the default-domian job and service:

job.batch/default-domain created
service/default-domain-service created

With Knative now operational in your cluster, you can begin working with Knative Functions.

Work with Knative Functions and the func CLI

Knative Functions is a programming model that simplifies writing distributed applications on Kubernetes and Knative. It enables developers to create stateless, event-driven functions without requiring in-depth knowledge of containers, Kubernetes, or Knative itself.

The func CLI provides tools for developers to manage the entire lifecycle of functions (creating, building, deploying, and invoking). This allows for local development and testing of functions without the need for a local Kubernetes cluster.

To get started, run the following command:

func

This displays help information for managing Knative Function resources:

func is the command line interface for managing Knative Function resources

    Create a new Node.js function in the current directory:
    func create --language node myfunction

    Deploy the function using Docker hub to host the image:
    func deploy --registry docker.io/alice

Learn more about Functions:  https://knative.dev/docs/functions/
Learn more about Knative at: https://knative.dev

Primary Commands:
  create      Create a function
  describe    Describe a function
  deploy      Deploy a function
  delete      Undeploy a function
  list        List deployed functions
  subscribe   Subscribe a function to events

Development Commands:
  run         Run the function locally
  invoke      Invoke a local or remote function
  build       Build a function container

System Commands:
  config      Configure a function
  languages   List available function language runtimes
  templates   List available function source templates
  repository  Manage installed template repositories
  environment Display function execution environment information

Other Commands:
  completion  Output functions shell completion code
  version     Function client version information

Use "func <command> --help" for more information about a given command.

Create a Function

1. Use the following command to create an example Golang function (get-emojis-go) that can be invoked via an HTTP endpoint (the default invocation method):

   func create -l go get-emojis-go

   This command creates a complete directory with multiple files:

   Created go function in /home/USERNAME/get-emojis-go

2. Examine the contents of the newly created ~/get-emojis-go directory:

   ls -laGh get-emojis-go

   total 40K
   drwxr-xr-x 3 USERNAME 4.0K Oct 14 15:07 .
   drwxr-x--- 9 USERNAME 4.0K Oct 14 15:07 ..
   drwxrwxr-x 2 USERNAME 4.0K Oct 14 15:07 .func
   -rw-r--r-- 1 USERNAME  217 Oct 14 15:07 .funcignore
   -rw-r--r-- 1 USERNAME   97 Oct 14 15:07 func.yaml
   -rw-r--r-- 1 USERNAME  235 Oct 14 15:07 .gitignore
   -rw-r--r-- 1 USERNAME   25 Oct 14 15:07 go.mod
   -rw-r--r-- 1 USERNAME  483 Oct 14 15:07 handle.go
   -rw-r--r-- 1 USERNAME  506 Oct 14 15:07 handle_test.go
   -rw-r--r-- 1 USERNAME  611 Oct 14 15:07 README.md

3. While reviewing the purpose of each file is outside the scope of this guide, you should examine the handle.go file, the default implementation that Knative generates:

   cat ~/get-emojis-go/handle.go

   File: handle.go

   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

   package function import ( "fmt" "net/http" "net/http/httputil" ) // Handle an HTTP Request. func Handle(w http.ResponseWriter, r *http.Request) { /* * YOUR CODE HERE * * Try running `go test`. Add more test as you code in `handle_test.go`. */ dump, err := httputil.DumpRequest(r, true) if err != nil { http.Error(w, err.Error(), http.StatusInternalServerError) return } fmt.Println("Received request") fmt.Printf("%q\n", dump) fmt.Fprintf(w, "%q", dump) }

   Note that this function works as a server that returns information from the original request.

Build a Function Image

The next step is to create a container image from your function. Since the function is intended to run on a Kubernetes cluster, it must be containerized. Knative Functions facilitates this process for developers, abstracting the complexities of Docker and Dockerfiles.

1. Navigate into the ~/get-emojis-go directory:

   cd ~/get-emojis-go

2. To build your function, run the following build command while in the ~/get-emojis-go directory, specifying Docker Hub (docker.io) as the registry along with your DOCKER_HUB_USERNAME:

   func build --registry docker.io/DOCKER_HUB_USERNAME

   This command fetches a base image and builds a Docker image from your function. You should see output similar to the following as the function image is built:

   Building function image
   Still building
   Still building
   Yes, still building
   Don't give up on me
   Still building
   This is taking a while
   🙌 Function built: index.docker.io/DOCKER_HUB_USERNAME/get-emojis-go:latest

3. To verify that the image is successfully created, use the following command to list your Docker images:

   docker images | grep -E 'knative|get-emojis-go|ID'

   REPOSITORY                           TAG       IMAGE ID       CREATED        SIZE
   DOCKER_HUB_USERNAME/get-emojis-go    latest    9a1aa84a2b79   44 years ago   44MB
   ghcr.io/knative/builder-jammy-tiny   0.0.240   0f71b69eedae   44 years ago   770MB

   Note

   While the CREATED timestamp may be incorrect, the image is valid.

Run the Function Locally

1. Use the run command to run the function locally:

   func run

   The terminal should display output indicating that the function now runs on localhost at port 8080:

   function up-to-date. Force rebuild with --build
   Running on host port 8080
   Initializing HTTP function
   listening on http port 8080

2. With your function running, open a second terminal session and enter the following command:

   curl "http://localhost:8080?a=1&b=2"

   By default, this initial implementation returns the URL query parameters as a JSON object. The resulting output should be:

   {"a": "1", "b": "2"}

   Meanwhile, you should see output similar to the following in your original terminal window:

   Received request
   GET http://localhost:8080/?a=1&b=2 localhost:8080
     Host: localhost:8080
     User-Agent: curl/7.81.0
     Accept: */*
   URL Query String:
     {"a": "1", "b": "2"}

3. When done, close the second terminal and stop the function in the original terminal by pressing the CTRL+C keys.

Deploy the Function

1. Use the deploy command to deploy your function to your Kubernetes cluster as a Knative function and push it to the Docker registry:

   func deploy

   function up-to-date. Force rebuild with --build
   Pushing function image to the registry "index.docker.io" using the "DOCKER_HUB_USERNAME" user credentials
   🎯 Creating Triggers on the cluster
   ✅ Function deployed in namespace "default" and exposed at URL:
      http://get-emojis-go.default.172.233.166.148.sslip.io

   Once the function is deployed and the Magic DNS record is established, your Knative function is accessible through this public HTTP endpoint. The new get-emojis-go repository should also now exist on your Docker Hub account:

2. To invoke your Knative function, open a web browser and visit your function’s URL. An example invocation may look like this:

With your Knative function accessible through a public HTTP endpoint, the next step is to migrate a Cloud Run Function to Knative.

Migrate Cloud Run Functions to Knative

This guide examines a sample Cloud Run function and walks through how to migrate it to Knative. Cloud Run functions are similar to Knative functions, as they both have a trigger and extract their input arguments from a context or event.

The main application logic is highlighted in the example Cloud Run function below:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

package google_cloud_function import ( "fmt" "github.com/GoogleCloudPlatform/functions-framework-go/functions" "github.com/the-gigi/get-emojis-google-cloud-function/pkg/fuzz_emoji" "net/http" "strings" ) func init() { functions.HTTP("get-emojis", getEmojis) } func getEmojis(w http.ResponseWriter, r *http.Request) { descriptions := strings.Split(r.URL.Query().Get("descriptions"), ",") fuzzer := fuzz_emoji.NewFuzzEmoji() result := fuzzer.GetEmojis(descriptions) for k, v := range result { _, _ = fmt.Fprintf(w, "%s: {%v}\n", k, v) } }

This example function instantiates a FuzzEmoji object and calls its getEmojis() method, passing a list of emoji descriptions. The emoji descriptions may or may not map to official emoji names like “fire” (🔥) or “sunrise” (🌅). The function performs a “fuzzy” search of the descriptions to find matching emojis.

The remainder of the code focuses on extracting emoji descriptions from the query parameters in the request and writing the result to the response object.

At the time of this writing, this example Cloud Run function was deployed and available at the following HTTP endpoint:

curl https://us-east1-playground-161404.cloudfunctions.net/get-emojis?descriptions=flame,dawn

Invoking the function returns the following result:

flame: {fire, 🔥}
dawn: {sunrise, 🌅}

The function successfully returns the fire (🔥) emoji for the description “flame” and the sunrise emoji (🌅) for the description “dawn”.

Isolating the Cloud Run Function Code from GCP Specifics

To migrate the Google Cloud Run function to Knative, the core application logic must be decoupled from Google Cloud Platform (GCP)-specific dependencies. In this example, this is already done since the interface for the getEmojis() method accepts a Golang slice of strings as descriptions.

If the getEmojis() method accessed Google Cloud Storage to fetch synonyms instead of by importing the fuzz_emoji package from GitHub, it would not be compatible with Knative and would require refactoring.

Migrating a Single-File Function to a Knative Function

The core logic of the function is encapsulated into a single Golang file called fuzz_emoji.go, which can be migrated to your Knative function.

1. Create the pkg directory and fuzz_emoji subdirectory within the get-emojis-go directory:

   mkdir -p ~/get-emojis-go/pkg/fuzz_emoji

2. Using a text editor of your choice, create the fuzz_emoji.go file in the get-emojis-go directory:

   nano ~/get-emojis-go/pkg/fuzz_emoji/fuzz_emoji.go

   Give the file the following content:

   File: fuzz_emoji.go

   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106

   package fuzz_emoji import ( "encoding/json" "fmt" "io" "net/http" "strings" "github.com/enescakir/emoji" ) type FuzzEmoji struct { emojiDict map[string]string } func NewFuzzEmoji() *FuzzEmoji { f := &FuzzEmoji{ emojiDict: make(map[string]string), } for name, e := range emoji.Map() { name := strings.Trim(name, ":") f.emojiDict[strings.ToLower(name)] = e } return f } func (f *FuzzEmoji) getSynonyms(word string) ([]string, error) { url := fmt.Sprintf("https://api.datamuse.com/words?rel_syn=%s", word) resp, err := http.Get(url) if err != nil { return nil, err } defer resp.Body.Close() if resp.StatusCode != http.StatusOK { return nil, fmt.Errorf("failed to fetch synonyms: %s", resp.Status) } body, err := io.ReadAll(resp.Body) if err != nil { return nil, err } var words []struct { Word string `json:"word"` } if err := json.Unmarshal(body, &words); err != nil { return nil, err } synonyms := make([]string, len(words)) for i, wordData := range words { synonyms[i] = wordData.Word } return synonyms, nil } func (f *FuzzEmoji) getEmoji(description string) (string, string) { description = strings.ToLower(description) // direct match if emojiChar, exists := f.emojiDict[description]; exists { return description, emojiChar } // Subset match for name, emojiChar := range f.emojiDict { if strings.Contains(name, description) { return name, emojiChar } } synonyms, err := f.getSynonyms(description) if err != nil { return "", "" } // Synonym match for _, syn := range synonyms { if emojiChar, exists := f.emojiDict[syn]; exists { return syn, emojiChar } } // Subset match for name, emojiChar := range f.emojiDict { for _, syn := range synonyms { if strings.Contains(name, syn) { return syn, emojiChar } } } return "", "" } func (f *FuzzEmoji) GetEmojis(descriptions []string) map[string]string { result := make(map[string]string) for _, d := range descriptions { name, emojiChar := f.getEmoji(d) result[d] = fmt.Sprintf("%s, %s", name, emojiChar) } return result }

   When complete, save your changes.

3. Run the tree command on the ~/get-emojis-go directory to confirm the new folder structure:

   tree ~/get-emojis-go/

   The folder structure should now look like this:

   get-emojis-go/
   ├── func.yaml
   ├── go.mod
   ├── handle.go
   ├── handle_test.go
   ├── pkg
   │   └── fuzz_emoji
   │       └── fuzz_emoji.go
   └── README.md
   
   2 directories, 6 files

4. Edit your handle.go file so that it uses the fuzz_emoji package:

   nano ~/get-emojis-go/handle.go

   Replace the existing content with the following. Remember to save your changes:

   File: hande.go

   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

   package function import ( "context" "fmt" "net/http" "strings" "function/pkg/fuzz_emoji" ) func Handle(ctx context.Context, res http.ResponseWriter, req *http.Request) { descriptions := strings.Split(req.URL.Query().Get("descriptions"), ",") fuzzer := fuzz_emoji.NewFuzzEmoji() result := fuzzer.GetEmojis(descriptions) for k, v := range result { _, _ = fmt.Fprintf(res, "%s: {%v}\n", k, v) } }

   Below is a breakdown of the file code functionality:

   • Imports standard Go packages for handling HTTP requests, strings, and output.
   • Imports the fuzz_emoji package, which contains the core emoji-matching logic.
   • The Handle() function takes a context (unused), a response, and a request.
   • Extracts the emoji descriptions from the query parameters of the URL. The function expects the descriptions to be a single comma-separated string, which it splits to get a list called descriptions.
   • Calls NewFuzzEmoji to instantiate a FuzzEmoji object.
   • Calls the getEmojis() method, passing the list of descriptions that were extracted.
   • Iterates over the result map, printing the items to the response object.

5. Next, edit the go.mod file to use the emoji package from github.com/enescakir/emoji in the Docker image:

   nano ~/get-emojis-go/go.mod

   Append the highlighted line to the end of the file, and save your changes:

   File: go.mod

   1 2 3 4 5

   module function go 1.21 require github.com/enescakir/emoji v1.0.0

6. Re-build and re-deploy the container:

   func build --registry docker.io/DOCKER_HUB_USERNAME
   func deploy

7. Test your function using the public URL:

   curl http://get-emojis-go-default.IP_ADDRESS.sslip.io/?descriptions=flame,high

   The descriptions provided as a query parameter are echoed back, along with a corresponding emoji name and emoji for each description:

   flame: {fire, 🔥}
   high: {high_speed_train, 🚄}

   This confirms that the Knative function works as expected.

Migrating a Multi-File Function to a Knative Function

In the previous example, the entire application logic was contained in a single file called fuzz_emoji.go. For larger workloads, your function may involve multiple files or multiple directories and packages.

Migrating such a setup to Knative follows a similar process:

1. Copy all relevant files and directories into the pkg subfolder of your Knative function folder.

2. Import any required packages in handle.go.

3. Update the go.mod file to include all of the dependencies used across any of the packages.

Migrating External Dependencies

When migrating a Cloud Run function, it may depend on various GCP services such as Google Cloud Storage, Cloud SQL, Cloud Data Store, or Cloud Pub/Sub. It’s important to evaluate each dependency to determine the best option to suit your situation.

There are typically three options to consider:

1. Keep it as-is: Continue using the Knative function to interact with the GCP service.

2. Replace the service: For example, you might switch from a GCP service like Cloud Data Store to an alternative key-value store in the Kubernetes cluster.

3. Drop the functionality: Eliminate certain GCP-specific functionalities, such as no longer writing messages to Cloud Pub/Sub.

Namespace and Service Account

The Knative function eventually runs as a pod in the Kubernetes cluster. This means it runs in a namespace and has a Kubernetes service account associated with it. These are determined when you run the func deploy command. You can specify them using the -n (or --namespace) and --service-account arguments.

If these options are not specified, the function deploys in the currently configured namespace and uses the default service account of the namespace.

If your Knative function needs to access any Kubernetes resources, it’s recommended to explicitly specify a dedicated namespace and create a dedicated service account. This is the preferred approach since it avoids granting excessive permissions to the default service account.

Configuration and Secrets

If your Cloud Run function uses Runtime Configurator and Secret Manager for configuration and sensitive information, these details should not be embedded directly in the function’s image. For example, if your function needs to access GCP services, it would require GCP credentials to authenticate.

Kubernetes offers the ConfigMap and Secret resources for this purpose. The migration process involves the following steps:

1. Identify all the parameters and secrets the Cloud Run function uses.

2. Create corresponding ConfigMap and Secret resources in the namespace for your Knative function.

3. Grant the service account for your Knative function permissions to read the ConfigMap and Secret.

Roles and Permissions

Your Knative function may need to interact with various Kubernetes resources and services during migration, such as data stores, ConfigMaps, and Secrets. To enable this, create a dedicated role with the necessary permissions and bind it to the function’s service account.

If your architecture includes multiple Knative functions, it is considered a best practice to share the same service account, role, and role bindings between all the Knative functions.

Logging, Metrics, and Distributed Tracing

The logging experience in Knative is similar to printing something in your Cloud Run function. With GCP, output is automatically logged to Google Cloud Logging. In Knative, that same print statement automatically sends log messages to your container’s logs. If you have centralized logging, these messages are automatically recorded in your log system.

LKE provides the native Kubernetes dashboard by default. It runs on the control plane, so it doesn’t take resources from your workloads. You can use the dashboard to explore your entire cluster:

For production systems, consider using a centralized logging system like ELK/EFK, Loki, or Graylog, along with an observability solution consisting of Prometheus and Grafana. You can also supplement your observability by leveraging a telemetry data-oriented solution such as OpenTelemetry. These tools can enhance your ability to monitor, troubleshoot, and optimize application performance while ensuring reliability and scalability.

Knative also has built-in support for distributed tracing, which can be configured globally. This means your Knative function automatically participates in tracing without requiring additional changes.

The Debugging Experience

Knative offers debugging at multiple levels:

• Unit test your core logic
• Unit test your Knative function
• Invoke your function locally

When you create a Go Knative function, Knative generates a skeleton for a unit test called handle_test.go.

1. Open the handle_test.go file in the get-emojis-go directory:

   nano ~/get-emojis-go/handle_test.go

   Replace its contents with the test code below, and save your changes. This code is updated for testing the fuzzy emoji search functionality:

   File: handle_test.go

   1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

   package function import ( "context" "io" "net/http" "net/http/httptest" "testing" ) func TestHandle(t *testing.T) { var ( w = httptest.NewRecorder() req = httptest.NewRequest( "GET", "http://example.com/test?descriptions=flame,dog", nil) res *http.Response ) Handle(context.Background(), w, req) res = w.Result() defer res.Body.Close() data := make([]byte, 512) n, err := res.Body.Read(data) if err != nil && err != io.EOF { t.Fatal(err) } expected := "flame: {fire, 🔥}\ndog: {dog, 🐶}\n" result := string(data[:n]) if expected != result { t.Fatalf("Failed to return the fire emoji") } if res.StatusCode != 200 { t.Fatalf("unexpected response code: %v", res.StatusCode) } }

2. Download the fuzz_emoji package and any other required dependencies:

   go get function/pkg/fuzz_emoji

3. Use the go command to run the handle_test.go file and test the invocation of your function:

   go test

   A successful test should produce output similar to the following:

   PASS
   ok  	function	0.275s

Once the code behaves as expected, you can test the function locally by packaging it in a Docker container using func invoke to run it. This approach is handled completely through Docker, without the need for a local Kubernetes cluster.

After local testing, you may want to optimize the function’s image size by removing any redundant dependencies to improve resource utilization. Deploy your function to a staging environment (a Kubernetes cluster with Knative installed) using func deploy. In the staging environment, you can conduct integration, regression, and stress testing.

If your function interacts with external services or the Kubernetes API server, you should “mock” these dependencies. Mocking, or simulating external services or components that a function interacts with, allows you to isolate a specific function or piece of code to ensure it behaves correctly.

See More Information below for resources to help you get started with migrating Google Cloud Run functions to Knative functions on the Linode Kubernetes Engine (LKE).

More Information

You may wish to consult the following resources for additional information on this topic. While these are provided in the hope that they will be useful, please note that we cannot vouch for the accuracy or timeliness of externally hosted materials.

• Knative
• Knative Functions
• Knative Functions - Deep Dive (Video)
• Accessing request traces - Knative
• Google Cloud Run Functions overview
• Prometheus
• Grafana Labs - Loki, Grafana, Tempo, Mimir
• OpenTelemetry
• Google Cloud Run Function
• Knative function (Golang)