Dapr Bindings

In this quickstart, you'll create two microservices, one with an input binding and another with an output binding. You'll bind to Kafka, but note that there are a myriad of components that Dapr can bind to (see Dapr components).

This quickstart includes two microservices:

Node.js microservice that utilizes an input binding
Python microservice that utilizes an output binding

The bindings connect to Kafka, allowing us to push messages into a Kafka instance (from the Python microservice) and receive message from that instance (from the Node microservice) without having to know where the instance is hosted. Instead, connect through the sidecars using the Dapr API. See architecture diagram to see how the components interconnect locally:

Dapr allows us to deploy the same microservices from the local machines to Kubernetes. Correspondingly, this quickstart has instructions for deploying this project locally or in Kubernetes.

Prerequisites

Prerequisites to Run Locally

Dapr CLI with Dapr initialized
Node.js version 8 or greater
Python 3.4 or greater

Prerequisites to Run in Kubernetes

Dapr enabled Kubernetes cluster

Run Locally

Clone the quickstarts repository

Clone this quickstarts repository to your local machine:

git clone [-b <dapr_version_tag>] https://github.com/dapr/quickstarts.git

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.

Run Kafka Docker Container Locally

In order to run the Kafka bindings quickstart locally, you will run the Kafka broker server in a docker container on your machine.

To run the container locally, run:

docker-compose -f ./docker-compose-single-kafka.yml up -d

To see the container running locally, run:

docker ps

The output should be similar to this:

342d3522ca14        kafka-docker_kafka                      "start-kafka.sh"         14 hours ago        Up About
a minute   0.0.0.0:9092->9092/tcp                               kafka-docker_kafka_1
0cd69dbe5e65        wurstmeister/zookeeper                  "/bin/sh -c '/usr/sb…"   8 days ago          Up About
a minute   22/tcp, 2888/tcp, 3888/tcp, 0.0.0.0:2181->2181/tcp   kafka-docker_zookeeper_1

Run Node Microservice with Input Binding

Now that you have Kafka running locally on your machine, you'll need to run the microservices. You'll start by running the Node microservice that uses input bindings:

Navigate to Node subscriber directory in your CLI:

cd nodeapp

Install dependencies:

npm install

Run Node quickstart app with Dapr:

dapr run --app-id bindings-nodeapp --app-port 3000 node app.js --components-path ../components

Run Python Microservice with Output Binding

Next, run the Python microservice that uses output bindings

Open a new CLI window and navigate to Python subscriber directory in your CLI:

cd pythonapp

Install dependencies:

pip3 install requests

Run Python quickstart app with Dapr:

dapr run --app-id bindings-pythonapp python3 app.py --components-path ../components

Observe Logs

Observe the Python logs, which show a successful output binding with Kafka:

[0m?[94;1m== APP == {'data': {'orderId': 1}}
[0m?[94;1m== APP == <Response [200]>
[0m?[94;1m== APP == {'data': {'orderId': 2}}
[0m?[94;1m== APP == <Response [200]>
[0m?[94;1m== APP == {'data': {'orderId': 3}}
[0m?[94;1m== APP == <Response [200]>

Observe the Node logs, which show a successful input binding with Kafka:

[0m?[94;1m== APP == { orderId: 1 }
[0m?[94;1m== APP == Hello from Kafka!
[0m?[94;1m== APP == { orderId: 2 }
[0m?[94;1m== APP == Hello from Kafka!
[0m?[94;1m== APP == { orderId: 3 }
[0m?[94;1m== APP == Hello from Kafka!

Cleanup

To cleanly stop the dapr microservices, run:

dapr stop --app-id bindings-nodeapp

dapr stop --app-id bindings-pythonapp

Once you're done, you can spin down your local Kafka Docker Container by running:

docker-compose -f ./docker-compose-single-kafka.yml down

Run in Kubernetes

Setting up a Kafka in Kubernetes

Install Kafka via bitnami/kafka

helm repo add bitnami https://charts.bitnami.com/bitnami

helm repo update

kubectl create ns kafka

helm install dapr-kafka bitnami/kafka --wait --namespace kafka -f ./kafka-non-persistence.yaml

Wait until kafka pods are running

kubectl -n kafka get pods -w

NAME                     READY   STATUS    RESTARTS   AGE
dapr-kafka-0             1/1     Running   0          2m7s
dapr-kafka-zookeeper-0   1/1     Running   0          2m57s

Deploy Assets

Now that the Kafka binding is set up, deploy the assets.

In your CLI window, in the bindings directory run:

kubectl apply -f ./deploy

This will deploy bindings-nodeapp and bindings-pythonapp microservices. It will also apply the Kafka bindings component configuration you set up in the last step.

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/bindings-nodeapp

kubectl rollout status deploy/bindings-pythonapp

Run kubectl get pods to see that pods were correctly provisioned.

Observe Logs

Observe the Python app logs, which show a successful output binding with Kafka:

kubectl get pods

The output should look like this:

NAME                                    READY   STATUS        RESTARTS   AGE
bindings-nodeapp-699489b8b6-mqhrj       2/2     Running       0          4s
bindings-pythonapp-644489969b-c8lg5     2/2     Running       0          4m9s

Look at the Python app logs by running:

kubectl logs --selector app=bindingspythonapp -c python --tail=-1

...
{'data': {'orderId': 10}, 'operation': 'create'}
<Response [204]>
{'data': {'orderId': 11}, 'operation': 'create'}
<Response [204]>
...

Observe the Node app logs, which show a successful input bining with Kafka:

kubectl get pods

The output should look like this:

NAME                                    READY   STATUS        RESTARTS   AGE
bindings-nodeapp-699489b8b6-mqhrj       2/2     Running       0          4s
bindings-pythonapp-644489969b-c8lg5     2/2     Running       0          4m9s

Look at the Node app logs by running:

kubectl logs --selector app=bindingsnodeapp -c node --tail=-1

The output should look like this:

Node App listening on port 3000!
...
Hello from Kafka!
{ orderId: 240 }
Hello from Kafka!
{ orderId: 241 }
...

Cleanup

Once you're done, you can spin down your Kubernetes resources by running:

kubectl delete -f ./deploy

This will spin down each resource defined by the .yaml files in the deploy directory, including the kafka component.

Once you delete all quickstart apps, delete Kafka in the cluster.

helm uninstall dapr-kafka --namespace kafka

And finally, you can delete the kafka namespace

kubectl delete ns kafka

How it Works

Now that you've run the quickstart locally and/or in Kubernetes, let's unpack how this all works. The app is broken up into input binding app and output binding app:

Kafka Bindings yaml

Before looking at the application code, let's see the Kafka bindings component yamls(local, and Kubernetes), which specify brokers for Kafka connection, topics and consumerGroup for consumer, and publishTopic for publisher topic.

See the howtos in references for the details on input and output bindings

This configuration yaml creates sample-topic component to set up Kafka input and output bindings through the Kafka sample topic.

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: sample-topic
spec:
  type: bindings.kafka
  version: v1
  metadata:
  # Kafka broker connection setting
  - name: brokers
    value: [kafka broker address]
  # consumer configuration: topic and consumer group
  - name: topics
    value: sample
  - name: consumerGroup
    value: group1
  # publisher configuration: topic
  - name: publishTopic
    value: sample
  - name: authRequired
    value: "false"

Node Input binding app

Navigate to the nodeapp directory and open app.js, the code for the Node.js input bindings sample app. Here you're exposing one API endpoint using express. The API name must be identical to the component name which is specified in Kafka bindings component yaml. Then Dapr runtime will consume the event from sample topic and then send the POST request to Node app with the event payload.

app.post('/sample-topic', (req, res) => {
    console.log("Hello from Kafka!");
    console.log(req.body);
    res.status(200).send();
});

Python Output binding app

Navigate to the pythonapp directory and open app.py, the code for the output bindings sample app. This sends POST request to Dapr http endpoint http://localhost:3500/v1.0/bindings/<output_bindings_name> with the event payload every second. This app uses sample-topic bindings component name as <output_bindings_name>. Then Dapr runtime will send the event to sample topic which is specified in the above Kafka bindings component yaml.

dapr_url = "http://localhost:{}/v1.0/bindings/sample-topic".format(dapr_port)
n = 0
while True:
    n += 1
    payload = { "data": {"orderId": n}, "operation": "create" }
    print(payload, flush=True)
    try:
        response = requests.post(dapr_url, json=payload)
        print(response.text, flush=True)

    except Exception as e:
        print(e)

    time.sleep(1)

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README.md

README.md

Dapr Bindings

Prerequisites

Prerequisites to Run Locally

Prerequisites to Run in Kubernetes

Run Locally

Clone the quickstarts repository

Run Kafka Docker Container Locally

Run Node Microservice with Input Binding

Run Python Microservice with Output Binding

Observe Logs

Cleanup

Run in Kubernetes

Setting up a Kafka in Kubernetes

Deploy Assets

Observe Logs

Cleanup

How it Works

Kafka Bindings yaml

Node Input binding app

Python Output binding app

Related links

Next Steps

Files

README.md

Latest commit

History

README.md

File metadata and controls

Dapr Bindings

Prerequisites

Prerequisites to Run Locally

Prerequisites to Run in Kubernetes

Run Locally

Clone the quickstarts repository

Run Kafka Docker Container Locally

Run Node Microservice with Input Binding

Run Python Microservice with Output Binding

Observe Logs

Cleanup

Run in Kubernetes

Setting up a Kafka in Kubernetes

Deploy Assets

Observe Logs

Cleanup

How it Works

Kafka Bindings yaml

Node Input binding app

Python Output binding app

Related links

Next Steps