This tutorial will get you up and running with Dapr in a Kubernetes cluster. You will be deploying the same applications from Hello World. To recap, the Python App generates messages and the Node app consumes and persists them. The following architecture diagram illustrates the components that make up this quickstart:
This quickstart requires you to have the following installed on your machine:
Also, unless you have already done so, clone the repository with the quickstarts and cd
into the right directory:
git clone [-b <dapr_version_tag>] https://github.com/dapr/quickstarts.git
cd quickstarts/tutorials/hello-kubernetes
Note: See https://github.com/dapr/quickstarts#supported-dapr-runtime-version for supported tags. Use
git clone https://github.com/dapr/quickstarts.git
when using the edge version of dapr runtime.
The first thing you need is an RBAC enabled Kubernetes cluster. This could be running on your machine using Minikube, or it could be a fully-fledged cluster in Azure using AKS.
Once you have a cluster, follow the steps below to deploy Dapr to it. For more details, see Deploy Dapr on a Kubernetes cluster.
Please note, the CLI will install to the dapr-system namespace by default. If this namespace does not exist, the CLI will create it. If you need to deploy to a different namespace, you can use
-n mynamespace
.
dapr init --kubernetes --wait
Sample output:
⌛ Making the jump to hyperspace...
Note: To install Dapr using Helm, see here: https://docs.dapr.io/getting-started/install-dapr-kubernetes/#install-with-helm-advanced
✅ Deploying the Dapr control plane to your cluster...
✅ Success! Dapr has been installed to namespace dapr-system. To verify, run `dapr status -k' in your terminal. To get started, go here: https://aka.ms/dapr-getting-started
Without the
--wait
flag the Dapr CLI will exit as soon as the kubernetes deployments are created. Kubernetes deployments are asyncronous by default, so we use--wait
here to make sure the dapr control plane is completely deployed and running before continuing.
dapr status -k
You will see output like the following. All services should show True
in the HEALTHY column and Running
in the STATUS column before you continue.
NAME NAMESPACE HEALTHY STATUS REPLICAS VERSION AGE CREATED
dapr-operator dapr-system True Running 1 1.0.1 13s 2021-03-08 11:00.21
dapr-placement-server dapr-system True Running 1 1.0.1 13s 2021-03-08 11:00.21
dapr-dashboard dapr-system True Running 1 0.6.0 13s 2021-03-08 11:00.21
dapr-sentry dapr-system True Running 1 1.0.1 13s 2021-03-08 11:00.21
dapr-sidecar-injector dapr-system True Running 1 1.0.1 13s 2021-03-08 11:00.21
Dapr can use a number of different state stores (Redis, CosmosDB, DynamoDB, Cassandra, etc) to persist and retrieve state. This demo will use Redis.
- Follow these steps to create a Redis store.
- Once your store is created, add the keys to the
redis.yaml
file in thedeploy
directory.Note: the
redis.yaml
file provided in this quickstart will work securely out-of-the-box with a Redis installed withhelm install bitnami/redis
. If you have your own Redis setup, replace theredisHost
value with your own Redis master address, and the redisPassword with your own Secret. You can learn more here. - Apply the
redis.yaml
file and observe that your state store was successfully configured!
kubectl apply -f ./deploy/redis.yaml
component.dapr.io/statestore created
kubectl apply -f ./deploy/node.yaml
Kubernetes deployments are asyncronous. This means you'll need to wait for the deployment to complete before moving on to the next steps. You can do so with the following command:
kubectl rollout status deploy/nodeapp
This will deploy the Node.js app to Kubernetes. The Dapr control plane will automatically inject the Dapr sidecar to the Pod. If you take a look at the node.yaml
file, you will see how Dapr is enabled for that deployment:
dapr.io/enabled: true
- this tells the Dapr control plane to inject a sidecar to this deployment.
dapr.io/app-id: nodeapp
- this assigns a unique id or name to the Dapr application, so it can be sent messages to and communicated with by other Dapr apps.
dapr.io/enable-api-logging: "true"
- this is added to node.yaml file by default to see the API logs.
You'll also see the container image that you're deploying. If you want to update the code and deploy a new image, see Next Steps section.
There are several different ways to access a Kubernetes service depending on which platform you are using. Port forwarding is one consistent way to access a service, whether it is hosted locally or on a cloud Kubernetes provider like AKS.
kubectl port-forward service/nodeapp 8080:80
This will make your service available on http://localhost:8080.
Optional: If you are using a public cloud provider, you can substitue your EXTERNAL-IP address instead of port forwarding. You can find it with:
kubectl get svc nodeapp
To call the service that you set up port forwarding to, from a command prompt run:
curl http://localhost:8080/ports
Expected output:
{"DAPR_HTTP_PORT":"3500","DAPR_GRPC_PORT":"50001"}
Next submit an order to the app
curl --request POST --data "@sample.json" --header Content-Type:application/json http://localhost:8080/neworder
Expected output: Empty reply from server
Confirm the order was persisted by requesting it from the app
curl http://localhost:8080/order
Expected output:
{ "orderId": "42" }
Optional: Now it would be a good time to get acquainted with the Dapr dashboard. Which is a convenient interface to check status, information and logs of applications running on Dapr. The following command will make it available on http://localhost:9999/.
dapr dashboard -k -p 9999
Next, take a quick look at the Python app. Navigate to the Python app in the kubernetes quickstart: cd quickstarts/tutorials/hello-kubernetes/python
and open app.py
.
At a quick glance, this is a basic Python app that posts JSON messages to localhost:3500
, which is the default listening port for Dapr. You can invoke the Node.js application's neworder
endpoint by posting to v1.0/invoke/nodeapp/method/neworder
. The message contains some data
with an orderId that increments once per second:
n = 0
while True:
n += 1
message = {"data": {"orderId": n}}
try:
response = requests.post(dapr_url, json=message)
except Exception as e:
print(e)
time.sleep(1)
Deploy the Python app to your Kubernetes cluster:
kubectl apply -f ./deploy/python.yaml
As with above, the following command will wait for the deployment to complete:
kubectl rollout status deploy/pythonapp
Now that the Node.js and Python applications are deployed, watch messages come through:
Get the logs of the Node.js app:
kubectl logs --selector=app=node -c node --tail=-1
If all went well, you should see logs like this:
Got a new order! Order ID: 1
Successfully persisted state
Got a new order! Order ID: 2
Successfully persisted state
Got a new order! Order ID: 3
Successfully persisted state
Now that the Node.js and Python applications are deployed, watch API call logs come through:
Get the API call logs of the node app:
kubectl logs --selector=app=node -c daprd --tail=-1
When save state API calls are made, you should see logs similar to this:
time="2022-04-25T22:46:09.82121774Z" level=info msg="HTTP API Called: POST /v1.0/state/statestore" app_id=nodeapp instance=nodeapp-7dd6648dd4-7hpmh scope=dapr.runtime.http-info type=log ver=1.7.2
time="2022-04-25T22:46:10.828764787Z" level=info msg="HTTP API Called: POST /v1.0/state/statestore" app_id=nodeapp instance=nodeapp-7dd6648dd4-7hpmh scope=dapr.runtime.http-info type=log ver=1.7.2
Get the API call logs of the Python app:
kubectl logs --selector=app=python -c daprd --tail=-1
time="2022-04-27T02:47:49.972688145Z" level=info msg="HTTP API Called: POST /neworder" app_id=pythonapp instance=pythonapp-545df48d55-jvj52 scope=dapr.runtime.http-info type=log ver=1.7.2
time="2022-04-27T02:47:50.984994545Z" level=info msg="HTTP API Called: POST /neworder" app_id=pythonapp instance=pythonapp-545df48d55-jvj52 scope=dapr.runtime.http-info type=log ver=1.7.2
Call the Node.js app's order endpoint to get the latest order. Grab the external IP address that you saved before and, append "/order" and perform a GET request against it (enter it into your browser, use Postman, or curl it!):
curl $NODE_APP/order
{"orderID":"42"}
You should see the latest JSON in response!
Once you're done, you can spin down your Kubernetes resources by navigating to the ./deploy
directory and running:
kubectl delete -f .
This will spin down each resource defined by the .yaml files in the deploy
directory, including the state component.
Now that you're successfully working with Dapr, you probably want to update the code to fit your scenario. The Node.js and Python apps that make up this quickstart are deployed from container images hosted on a private Azure Container Registry. To create new images with updated code, you'll first need to install docker on your machine. Next, follow these steps:
- Update Node or Python code as you see fit!
- Navigate to the directory of the app you want to build a new image for.
- Run
docker build -t <YOUR_IMAGE_NAME> .
. You can name your image whatever you like. If you're planning on hosting it on docker hub, then it should start with<YOUR_DOCKERHUB_USERNAME>/
. - Once your image has built you can see it on your machines by running
docker images
. - To publish your docker image to docker hub (or another registry), first login:
docker login
. Then rundocker push <YOUR IMAGE NAME>
. - Update your .yaml file to reflect the new image name.
- Deploy your updated Dapr enabled app:
kubectl apply -f <YOUR APP NAME>.yaml
.
- Explore additional quickstarts and deploy them locally or on Kubernetes.