Migrating from AWS Lambda 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 usage. Knative consists of several components:

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

This guide walks through the process of migrating an AWS Lambda 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. Install jq, a lightweight command line JSON processor:

   sudo apt install jq

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 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
   lke242177-380780-1261b5670000   Ready    <none>   49s   v1.31.0
   lke242177-380780-3496ef070000   Ready    <none>   47s   v1.31.0
   lke242177-380780-53e2290c0000   Ready    <none>   51s   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 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.

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

3. 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-extensions/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

4. 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 as well.

5. 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   10.128.48.124   172.235.159.7   80:31938/TCP,443:30800/TCP   4m37s

6. Since Kourier added several deployments, check the updated list to ensure everything is functioning 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           7m36s
   autoscaler               1/1     1            1           7m36s
   controller               1/1     1            1           7m36s
   net-kourier-controller   1/1     1            1           5m7s
   webhook                  1/1     1            1           7m36s

7. This guide uses 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-domain 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 allows developers to create stateless, event-driven functions without requiring in-depth knowledge of containers, Kubernetes, or Knative itself.

The func CLI streamlines the developer experience by providing tools to work with Knative Functions. It allows 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.

1. 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.

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

   func create -l python get-emojis

   This command creates a complete directory structure with multiple files:

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

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

   ls -laGh get-emojis

   total 48K
   drwxr-xr-x 3 USERNAME 4.0K Oct  9 15:57 .
   drwxr-x--- 9 USERNAME 4.0K Oct  9 15:57 ..
   -rwxr-xr-x 1 USERNAME   55 Oct  9 15:57 app.sh
   drwxrwxr-x 2 USERNAME 4.0K Oct  9 15:57 .func
   -rw-r--r-- 1 USERNAME  217 Oct  9 15:57 .funcignore
   -rw-r--r-- 1 USERNAME 1.8K Oct  9 15:57 func.py
   -rw-r--r-- 1 USERNAME   97 Oct  9 15:57 func.yaml
   -rw-r--r-- 1 USERNAME  235 Oct  9 15:57 .gitignore
   -rw-r--r-- 1 USERNAME   28 Oct  9 15:57 Procfile
   -rw-r--r-- 1 USERNAME  862 Oct  9 15:57 README.md
   -rw-r--r-- 1 USERNAME   28 Oct  9 15:57 requirements.txt
   -rw-r--r-- 1 USERNAME  259 Oct  9 15:57 test_func.py

4. While reviewing the purpose of each file is outside the scope of this guide, you should examine the func.py file, the default implementation that Knative generates:

   cat ~/get-emojis/func.py

   File: ~/get-emojis/func.py

   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

   from parliament import Context from flask import Request import json # parse request body, json data or URL query parameters def payload_print(req: Request) -> str: if req.method == "POST": if req.is_json: return json.dumps(req.json) + "\n" else: # MultiDict needs some iteration ret = "{" for key in req.form.keys(): ret += '"' + key + '": "'+ req.form[key] + '", ' return ret[:-2] + "}\n" if len(ret) > 2 else "{}" elif req.method == "GET": # MultiDict needs some iteration ret = "{" for key in req.args.keys(): ret += '"' + key + '": "' + req.args[key] + '", ' return ret[:-2] + "}\n" if len(ret) > 2 else "{}" # pretty print the request to stdout instantaneously def pretty_print(req: Request) -> str: ret = str(req.method) + ' ' + str(req.url) + ' ' + str(req.host) + '\n' for (header, values) in req.headers: ret += " " + str(header) + ": " + values + '\n' if req.method == "POST": ret += "Request body:\n" ret += " " + payload_print(req) + '\n' elif req.method == "GET": ret += "URL Query String:\n" ret += " " + payload_print(req) + '\n' return ret def main(context: Context): """ Function template The context parameter contains the Flask request object and any CloudEvent received with the request. """ # Add your business logic here print("Received request") if 'request' in context.keys(): ret = pretty_print(context.request) print(ret, flush=True) return payload_print(context.request), 200 else: print("Empty request", flush=True) return "{}", 200

   Note that this function acts as a server that returns the query parameters or form fields of incoming requests.

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 directory:

   cd ~/get-emojis

2. To build your function, run the following build command while in the ~/get-emojis 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: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|ID'

   REPOSITORY                           TAG       IMAGE ID       CREATED        SIZE
   ghcr.io/knative/builder-jammy-base   0.4.283   204e70721072   44 years ago   1.45GB
   DOCKER_HUB_USERNAME/get-emojis                  latest    IMAGE_ID       44 years ago   293MB

   Note

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

4. 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

5. 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 the 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"}

6. 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.default.IP_ADDRESS.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 repository should also now exist on your Docker Hub account:

2. To invoke your Knative function, curl the function’s public URL, adding any required query parameters. For example:

   curl http://get-emojis.default.IP_ADDRESS.sslip.io/?yeah=it-works!

   The output should display a JSON object containing the query parameters:

   {"yeah": "it-works!"}

With your Knative function running, the next step is migrate an AWS Lambda function to Knative.

Migrate Your AWS Lambda Function to Knative

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

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

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

def handler(event, context): try: logger.info("Received event: %s", event) # The descriptions may arrive as attribute of the event descriptions = event.get("descriptions") if descriptions is None: # Parse the JSON body of the event body = json.loads(event.get("body", "{}")) logger.info("Parsed body: %s", body) descriptions = body.get("descriptions", []) logger.info("Descriptions: %s", descriptions) fuzz_emoji = FuzzEmoji() result = fuzz_emoji.get_emojis(descriptions) response = { 'statusCode': 200, 'body': json.dumps(result) } except Exception as e: response = { 'statusCode': 500, 'body': json.dumps({'error': str(e)}) } return response

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

The code above the highlighted lines extracts emoji descriptions from the event object passed to the handler. The code below the highlighted lines wraps the result in a response with a proper status code for success or failure.

At the time of this writing, this sample Lambda function was deployed and available at the following HTTP endpoint:

curl -s -X POST --header "Content-type:application/json" \
    --data '{"descriptions":["flame","confused"]}' \
    https://64856ijzmi.execute-api.us-west-2.amazonaws.com/default/fuzz-emoji | \
    json_pp

Invoking the function returns the following result:

{
   "confused" : "('confused_face', '😕')",
   "flame" : "('fire', '🔥')"
}

The function successfully returns the fire (🔥) emoji for the description “flame”, and the confused_face emoji (😕) for the description “confused.”

Isolating the AWS Lambda Code from AWS Specifics

To migrate the Lambda function to Knative, the core application logic must be decoupled from AWS-specific dependencies. In this case, the function’s main logic is already isolated. The get_emojis() method only accepts a list of strings as input, which makes it more adaptable for other platforms.

If the get_emojis() method were dependent on the AWS Lambda event object, it would not be compatible with Knative and would require some refactoring, as Knative does not provide an event object.

Migrating a Single-File Function to a Knative Function

The core logic of the function is encapsulated into a single Python module named fuzz_emoji.py, which can be migrated to your Knative function.

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

   nano ~/get-emojis/fuzz_emoji.py

   Give the file the following content:

   File: ~/get-emojis/fuzz_emoji/py

   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

   from typing import List, Mapping, Tuple import emoji import requests class FuzzEmoji: def __init__(self): self.emoji_dict = {} emoji_list = {name: data for name, data in emoji.EMOJI_DATA.items() if 'en' in data} for emoji_char, data in emoji_list.items(): name = data['en'].strip(':') self.emoji_dict[name.lower()] = emoji_char @staticmethod def get_synonyms(word): response = requests.get(f"https://api.datamuse.com/words?rel_syn={word}") if response.status_code == 200: synonyms = [word_data['word'] for word_data in response.json()] return synonyms raise RuntimeError(response.content) def get_emoji(self, description) -> Tuple[str, str]: description = description.lower() # direct match if description in self.emoji_dict: return description, self.emoji_dict[description] # Subset match for name in self.emoji_dict: if description in name: return name, self.emoji_dict[name] synonyms = self.get_synonyms(description) # Synonym match for syn in synonyms: if syn in self.emoji_dict: return syn, self.emoji_dict[syn] return '', '' def get_emojis(self, descriptions: List[str]) -> Mapping[str, str]: return {d: str(self.get_emoji(d)) for d in descriptions}

   When complete, save your changes.

2. Run the ls command:

   ls -laGh ~/get-emojis/

   The folder structure should now look like this:

   total 52K
   drwxr-xr-x 3 USERNAME 4.0K Oct 10 17:32 .
   drwxr-x--- 9 USERNAME 4.0K Oct 10 16:51 ..
   -rwxr-xr-x 1 USERNAME   55 Oct 10 16:51 app.sh
   drwxrwxr-x 3 USERNAME 4.0K Oct 10 17:20 .func
   -rw-r--r-- 1 USERNAME  217 Oct 10 16:51 .funcignore
   -rw-r--r-- 1 USERNAME 1.8K Oct 10 16:51 func.py
   -rw-r--r-- 1 USERNAME  317 Oct 10 17:22 func.yaml
   -rw-rw-r-- 1 USERNAME 1.4K Oct 10 17:32 fuzz_emoji.py
   -rw-r--r-- 1 USERNAME  235 Oct 10 16:51 .gitignore
   -rw-r--r-- 1 USERNAME   28 Oct 10 16:51 Procfile
   -rw-r--r-- 1 USERNAME  862 Oct 10 16:51 README.md
   -rw-r--r-- 1 USERNAME   28 Oct 10 16:51 requirements.txt
   -rw-r--r-- 1 USERNAME  259 Oct 10 16:51 test_func.py

3. Edit your func.py file so that it calls the fuzz_emoji module:

   nano ~/get-emojis/func.py

   Insert or adjust the highlighted lines so that the contents of your fuzz_emoji.py file appear as below. Remember to save your changes:

   File: ~/get-emojis/func.py

   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

   from parliament import Context from flask import Request import json from fuzz_emoji import FuzzEmoji # parse request body, json data or URL query parameters def payload_print(req: Request) -> str: if req.method == "POST": if req.is_json: return json.dumps(req.json) + "\n" else: # MultiDict needs some iteration ret = "{" for key in req.form.keys(): ret += '"' + key + '": "'+ req.form[key] + '", ' return ret[:-2] + "}\n" if len(ret) > 2 else "{}" elif req.method == "GET": # MultiDict needs some iteration ret = "{" for key in req.args.keys(): ret += '"' + key + '": "' + req.args[key] + '", ' return ret[:-2] + "}\n" if len(ret) > 2 else "{}" # pretty print the request to stdout instantaneously def pretty_print(req: Request) -> str: ret = str(req.method) + ' ' + str(req.url) + ' ' + str(req.host) + '\n' for header, values in req.headers.items(): ret += " " + str(header) + ": " + values + '\n' if req.method == "POST": ret += "Request body:\n" ret += " " + payload_print(req) + '\n' elif req.method == "GET": ret += "URL Query String:\n" ret += " " + payload_print(req) + '\n' return ret def main(context: Context): """ Function template The context parameter contains the Flask request object and any CloudEvent received with the request. """ # Add your business logic here print("Received request") if 'request' in context.keys(): ret = pretty_print(context.request) print(ret, flush=True) descriptions = context.request.args.get('descriptions').split(',') fuzz_emoji = FuzzEmoji() result = fuzz_emoji.get_emojis(descriptions) return json.dumps(result, ensure_ascii=False), 200 else: print("Empty request", flush=True) return "{}", 200

   Below is a breakdown of the file code functionality:

   • Imports the built-in json, the Context from parliament (the function invocation framework that Knative uses for Python functions), and the FuzzEmoji class.
   • The main() function accepts the parliament Context as its only parameter, which contains a Flask request property.
   • The first line extracts the emoji descriptions from the Flask request arguments. It expects the descriptions to be a single comma-separated string, which it splits into a list of descriptions.
   • Instantiates a FuzzEmoji object and calls the get_emojis() method.
   • Uses the json module to serialize the response and return it with a 200 status code.

4. Next, edit the requirements.txt file to include the dependencies of fuzz_emoji.py (the requests and emoji packages) in the Docker image:

   nano ~/get-emojis/requirements.txt

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

   File: ~/get-emojis/requirements.txt

   1 2 3

   parliament-functions==0.1.0 emoji==2.12.1 requests==2.32.3

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

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

6. Test your function using the public URL:

   curl http://get-emojis.default.IP_ADDRESS.sslip.io/?descriptions=cold,plane,fam

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

   {"cold": "('cold_face', '🥶')", "plane": "('airplane', '✈')", "fam": "('family', '👪')"}

   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.py. 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 same get-emojis directory.

2. Import any required modules in func.py.

3. Update the requirements.txt file to include all of the dependencies used across any of the modules.

Migrating External Dependencies

When migrating an AWS Lambda function, it may depend on various AWS services such as S3, DynamoDB, or SQS. 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 AWS service.

2. Replace the service: For example, you might switch from an AWS service like DynamoDB to an alternative key-value store in the Kubernetes cluster.

3. Drop the functionality: Eliminate certain AWS-specific functionalities, such as no longer writing messages to AWS SQS.

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 AWS Lambda function uses ParameterStore and SecretsManager 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 AWS services, it would require AWS 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 Lambda 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 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 AWS Lambda function. In AWS Lambda, output is automatically logged to CloudWatch. 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 and monitor 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 Python Knative function, Knative generates a skeleton for a unit test called test_func.py. At the time of this writing, the generated test is invalid and requires some modifications to work correctly. See this GitHub issue for details.

1. Open the test_func.py file in the get-emojis directory:

   nano ~/get-emojis/test_func.py

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

   File: ~/get-emojis/test_func.py

   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

   import unittest from parliament import Context func = __import__("func") class DummyRequest: def __init__(self, descriptions): self.descriptions = descriptions @property def args(self): return dict(descriptions=self.descriptions) @property def method(self): return 'GET' @property def url(self): return 'http://localhost/' @property def host(self): return 'localhost' @property def headers(self): return {'Content-Type': 'application/json'} class TestFunc(unittest.TestCase): # noinspection PyTypeChecker def test_func(self): result, code = func.main(Context(DummyRequest('flame,confused'))) expected = """{"flame": "('fire', '🔥')", "confused": "('confused_face', '😕')"}""" self.assertEqual(expected, result) self.assertEqual(code, 200) if __name__ == "__main__": unittest.main()

2. Use pip3 to install the dependencies listed in the requirements.txt file:

   pip3 install -r ~/get-emojis/requirements.txt

3. Use the python3 command to run the test_func.py file and test the invocation of your function:

   python3 ~/get-emojis/test_func.py

   A successful test should produce the following output:

   Received request
   GET http://localhost/ localhost
     Content-Type: application/json
   URL Query String:
     {"descriptions": "flame,confused"}
   
   
   .
   ----------------------------------------------------------------------
   Ran 1 test in 0.395s
   
   OK

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.

See More Information below for resources to help you get started with migrating AWS Lambda 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
• Migrating from AWS Lambda to Knative Functions
• GitHub: boson-project/parliament
• Logging and Metrics with Amazon CloudWatch
• Prometheus
• Grafana Labs - Loki, Grafana, Tempo, Mimir
• OpenTelemetry
• Sample AWS Lambda function
• Sample Knative function (Python)