Skip to content

Latest commit

 

History

History
168 lines (118 loc) · 5.72 KB

Knative-func.md

File metadata and controls

168 lines (118 loc) · 5.72 KB
Raw

Native build with Knative functions

This doc will help you get started developing Spring Boot based functions using Knative Functions project. Knative Functions provides a simple programming model for using functions on Knative, without requiring in-depth knowledge of Knative, Kubernetes, containers, or dockerfiles.

Spring Boot based Knative functions can easily be built as GraalVM native images as we show below.

Installation

  1. install Knative - https://knative.dev/docs/install/
  2. install func CLI - https://knative.dev/docs/functions/install-func/

The first function

Crating the function

Run the following command to create youe first springboot Knative http function:

func create kn-fun -l springboot -t http

We then change to the function directory. Since all func commands will use the current directory as the path and also as the name of the function, this allows us to not repeat this information for each command.

cd kn-fun

Building the function

Knative func supports multiple language packs and we are using the springboot language pack. There are two types of function templates, http and cloudevents. We are staring off with the simpler http one.

Knative func uses Paketo buildpacks for building springboot functions. This works fine on Intel based systems but ARM based systems are currently not supported. Building your function with a buildpack is also a bit slow, so we prefer to build with Jib to speed things up. Jib also works great on ARM based systems.

You can run the following to set the registry to use for the image:

export FUNC_REGISTRY=docker.io/springdeveloper

Then, run this build the function creating a new image in the registry set above:

./mvnw compile com.google.cloud.tools:jib-maven-plugin:3.3.2:build \
  -Dimage=$FUNC_REGISTRY/kn-fun \
  -Djib.container.user=1000

Note: The above command creates an amd64 based image which you can deploy to an Intel based cluster. If you are on an ARM based system and want to use Kind as the deplyment cluster, then you can configure Jib to build multi-arch images. Add the following build plugin to the pom.xml.

        <plugin>
            <groupId>com.google.cloud.tools</groupId>
            <artifactId>jib-maven-plugin</artifactId>
            <version>3.3.2</version>
            <configuration>
                <from>
                    <image>eclipse-temurin:17-jre</image>
                    <platforms>
                        <platform>
                            <architecture>amd64</architecture>
                            <os>linux</os>
                        </platform>
                        <platform>
                            <architecture>arm64</architecture>
                            <os>linux</os>
                        </platform>
                    </platforms>
                </from>
                <container>
                    <user>1000</user>
                </container>
            </configuration>
        </plugin>

Deploying the function

To deploy the function we use the func CLI and specify not to build or push the image since we already did that during the build with Jib:

func deploy --build=false --push=false --image=$FUNC_REGISTRY/kn-fun

Once the function gets deployed we can access it with CURL.

We can set APP_URL env var to the URL that is displayed when deployment completes.

APP_URL=<the-url-for-the-function>

We can also get the URL from tke Knative service status:

APP_URL=$(kubectl get service.serving.knative.dev/kn-fun -ojsonpath='{.status.url}')

Now, we can access the deployed function:

curl $APP_URL -H 'content-type: text/plain' -d SpringOne

Building and deploying as a GraalVM native image

Once you are done developing your function, you most likely want to build it as a GraalVM native image since the startup time is so much quicker. The best way to do that with Knative functions is to push your code to a Git repository and build the image on the cluster.

Prepare the cluster

There are two installation steps documented in the Knative Functions cluster build docs:

  1. Install the Tekton Pipelines following the Prerequisite step.
  2. Prepare the namespace you are using for on-cluster builds following the Enabling a namespace to run Function related Tekton Pipelines step.

Push your function code to a Git repo

You can create a Git repo with your function code using the following.

Note: You can skip this step and just use the already created Git repo with the URL https://github.com/trisberg/kn-fun

Create an empty public Git repository using GitHub UI or any other Git host and then set your Git remote. As an example:

export GIT_REMOTE=git@github.com:trisberg/kn-fun.git

Then initalize your repo:

git init
git branch -M main
git remote add origin $GIT_REMOTE

Next, add your function and push to the Git repo:

git add .
git commit -m "initial commit"
git push --set-upstream origin main

Build and Deploy as native image

Update the func.yaml file changing the following build env:

build:
  buildEnvs:
  - name: BP_NATIVE_IMAGE
    value: "false"

to be "true".

Set the GIT_URL as the https URL for your Git repo:

export GIT_URL=https://github.com/trisberg/kn-fun

Then deloy the function using:

func deploy -v --remote --registry $FUNC_REGISTRY --git-url $GIT_URL