quickstarts/tutorials/distributed-calculator

Distributed calculator

This quickstart shows method invocation and state persistent capabilities of Dapr through a distributed calculator where each operation is powered by a different service written in a different language/framework:

• Addition: Go mux application
• Multiplication: Python flask application
• Division: Node Express application
• Subtraction: .NET Core application

The front-end application consists of a server and a client written in React. Kudos to ahfarmer whose React calculator

The following architecture diagram illustrates the components that make up this quickstart:

Prerequisites for running the quickstart

Clone the quickstarts repository

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 locally

1. Install .Net Core SDK 7.0
2. Install Dapr CLI
3. Install Go
4. Install Python3
5. Install Npm
6. Install Node

- Run in Kubernetes environment

1. Dapr-enabled Kubernetes cluster. Follow these instructions to set this up.

Running the quickstart locally

These instructions start the four calculator operator apps (add, subtract, multiply and divide) along with the dapr sidecar locally and then run the front end app which persists the state in a local redis state store.

1. Add App - Open a terminal window and navigate to the go directory and follow the steps below:

• Install the gorilla/mux package: Run:

  go get -u github.com/gorilla/mux
  

• Run dapr using the command:

  dapr run --app-id addapp --app-port 6000 --dapr-http-port 3503 go run app.go
  

2. Subtract App - Open a terminal window and navigate to the csharp directory and follow the steps below:

• Set environment variable to use non-default app port 7001

  #Linux/Mac OS:
  export ASPNETCORE_URLS="http://localhost:7001"
  

  #Windows:
  set ASPNETCORE_URLS=http://localhost:7001
  

• Build the app. Run:

  dotnet build
  

• Navigate to ./bin/Debug/netcoreapp7.0 and start Dapr using command:

  dapr run --app-id subtractapp --app-port 7001 --dapr-http-port 3504 dotnet Subtract.dll
  

3. Divide App - Open a terminal window and navigate to the node directory and follow the steps below:

• Install dependencies by running the command:

  npm install
  

• Start Dapr using the command below

  dapr run --app-id divideapp --app-port 4000 --dapr-http-port 3502 node app.js
  

4. Multiply App - Open a terminal window and navigate to the python directory and follow the steps below:

• Install required packages

  pip3 install wheel python-dotenv flask_cors flask
  

• Set environment variable to use non-default app port 5000

  #Linux/Mac OS:
  export FLASK_RUN_PORT=5001
  
  #Windows:
  set FLASK_RUN_PORT=5001
  

• Start dapr using the command:

  dapr run --app-id multiplyapp --app-port 5001 --dapr-http-port 3501 flask run
  

5. Frontend Calculator app - Open a terminal window and navigate to the react-calculator directory and follow the steps below:

• Install the required modules

  npm install
  npm run buildclient
  

• Start Dapr using command below:

  dapr run --app-id frontendapp --app-port 8080 --dapr-http-port 3507 node server.js
  

6. Open a browser window and go to http://localhost:8080/. From here, you can enter the different operations.

   

7. Open your browser's console window (using F12 key) to see the logs produced as you use the calculator. Note that each time you click a button, you see logs that indicate state persistence and the different apps that are contacted to perform the operation.

8. Optional: Curl Validate

• To make sure all the apps are working, you can run the following curl commands which will test all the operations:

  curl -s http://localhost:8080/calculate/add -H Content-Type:application/json --data @operands.json
  

  curl -s http://localhost:8080/calculate/subtract -H Content-Type:application/json --data @operands.json
  

  curl -s http://localhost:8080/calculate/divide -H Content-Type:application/json --data @operands.json
  

  curl -s http://localhost:8080/calculate/multiply -H Content-Type:application/json --data @operands.json
  

  curl -s http://localhost:8080/persist -H Content-Type:application/json --data @persist.json
  

  curl -s http://localhost:8080/state
  

• You should get the following output:

  86
  18
  1.5294117647058822
  1768
  
  {"operation":null,"total":"54","next":null}
  

9. Cleanup

• Cleanup microservices

  dapr stop --app-id addapp
  

  dapr stop --app-id subtractapp
  

  dapr stop --app-id divideapp
  

  dapr stop --app-id multiplyapp
  

  dapr stop --app-id frontendapp
  

• Uninstall node modules by navigating to the node directory and run:

  npm uninstall
  

Running the quickstart in a Kubernetes environment

1. Navigate to the deploy directory in this quickstart directory: cd deploy

   Note: appconfig.yaml is not used directly for this quickstart but is present for the observability quickstart.

2. Follow these instructions to create and configure a Redis store
3. Deploy all of your resources:

kubectl apply -f .

Note: If you had previously installed, dapr using the dapr init -k --dev command, the appconfig previously installed, would have changed with the above command to point to a different zipkin collector. To point it back to the zipkin collector installed during dapr init -k --dev, replace the endpointAddress in appconfig.yaml with the following: endpointAddress: "http://dapr-dev-zipkin.default.svc.cluster.local:9411/api/v2/spans" and run the following command:

kubectl apply -f appconfig.yaml

Note: Services could also be deployed one-by-one by specifying the .yaml file: kubectl apply -f go-adder.yaml.

Each of the services will spin up a pod with two containers: one for your service and one for the Dapr sidecar. It will also configure a service for each sidecar and an external IP for the front-end, which allows us to connect to it externally.

4. 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 commands:

kubectl rollout status deploy/addapp

kubectl rollout status deploy/subtractapp

kubectl rollout status deploy/divideapp

kubectl rollout status deploy/multiplyapp

kubectl rollout status deploy/calculator-front-end

You can view the status of the running pods with:

kubectl get pods

When everything is running properly, you'll see output like this:

NAME                                    READY   STATUS    RESTARTS   AGE
addapp-5ff9586df6-5bpll                 2/2     Running   0          16s
calculator-front-end-56dc959b58-bb8vw   2/2     Running   0          16s
divideapp-c64f744d6-wljcc               2/2     Running   0          16s
multiplyapp-6989454d77-tkd6c            2/2     Running   0          16s
subtractapp-869b74f676-9mw94            2/2     Running   0          16s

5. Next, setup access to your service

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/calculator-front-end 8000:80

This will make your service available on http://localhost:8000. Navigate to this address with your browser and voilà! You have a working distributed calculator!

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

NAME                          TYPE           CLUSTER-IP     EXTERNAL-IP     PORT(S)            AGE
dapr-api                      ClusterIP      10.103.71.22   <none>          80/TCP             135m
dapr-placement                ClusterIP      10.103.53.127  <none>          80/TCP             135m
dapr-sidecar-injector         ClusterIP      10.104.220.35  <none>          443/TCP            135m
addapp-dapr                   ClusterIP      10.0.1.170     <none>          80/TCP,50001/TCP   2m
calculator-front-end          LoadBalancer   10.0.155.131   40.80.152.125   80:32633/TCP       3m
calculator-front-end-dapr     ClusterIP      10.0.230.219   <none>          80/TCP,50001/TCP   3m
divideapp-dapr                ClusterIP      10.0.240.3     <none>          80/TCP,50001/TCP   1m
kubernetes                    ClusterIP      10.0.0.1       <none>          443/TCP            33d
multiplyapp-dapr              ClusterIP      10.0.217.211   <none>          80/TCP,50001/TCP   1m
subtractapp-dapr              ClusterIP      10.0.146.253   <none>          80/TCP,50001/TCP   2m

Each service ending in "-dapr" represents your services respective sidecars, while the calculator-front-end service represents the external load balancer for the React calculator front-end.

6. Open your browser's console window (using F12 key) to see the logs produced as you use the calculator. Note that each time you click a button, you see logs that indicate state persistence:

Persisting State:
{total: "21", next: "2", operation: "x"}

total, next, and operation reflect the three pieces of state a calculator needs to operate. The app persists these to a Redis store (see Simplified State Management section below). By persisting these, you can refresh the page or take down the front-end pod and still jump right back where you were. Try it! Enter something into the calculator and refresh the page. The calculator should have retained the state, and the console should read:

Rehydrating State:
{total: "21", next: "2", operation: "x"}

Also note that each time you enter a full equation (e.g. "126 ÷ 3 =") the logs indicate that a call is made to the service:

Calling divide service

The client code calls to an Express server, which routes the calls through Dapr to the back-end services. In this case the divide endpoint is called on the nodejs application.

7. Optional: If your environment doesn't have easy access to a browser, or you just like using curl

Then you can use the following curl commands to make sure each one of the microservies is working:

curl -s http://localhost:8000/calculate/add -H Content-Type:application/json --data @operands.json

curl -s http://localhost:8000/calculate/subtract -H Content-Type:application/json --data @operands.json

curl -s http://localhost:8000/calculate/divide -H Content-Type:application/json --data @operands.json

curl -s http://localhost:8000/calculate/multiply -H Content-Type:application/json --data @operands.json

curl -s http://localhost:8000/persist -H Content-Type:application/json --data @persist.json

curl -s http://localhost:8000/state

You should get the following output:

86
18
1.5294117647058822
1768

{"operation":null,"total":"54","next":null}

Cleanup

Kubernetes environment cleanup

• 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.

The Role of Dapr

This quickstart demonstrates how to use Dapr as a programming model for simplifying the development of distributed systems. In this quickstart, Dapr is enabling polyglot programming, service discovery and simplified state management.

Polyglot programming

Each service in this quickstart is written in a different programming language, but they're used together in the same larger application. Dapr itself is language agnostic - none of the services have to include any dependency in order to work with Dapr. This empowers developers to build each service however they want, using the best language for the job or for a particular dev team.

Service invocation

When the front-end server calls the respective operation services (see server.js code below), it doesn't need to know what IP address they live at or how they were built. Instead it calls their local dapr side-car by name, which knows how to invoke the method on the service, taking advantage of the platform’s service discovery mechanism, in this case Kubernetes DNS resolution.

The code below shows calls to the "add" and "subtract" services via the Dapr URLs:

const daprUrl = `http://localhost:${daprPort}/v1.0/invoke`;

app.post('/calculate/add', async (req, res) => {
  const appResponse = await axios.post(`${daprUrl}/addapp/method/add`, req.body);
  return res.send(`${appResponse.data}`);
});


app.post('/calculate/subtract', async (req, res) => {
  const appResponse = await axios.post(`${daprUrl}/subtractapp/method/subtract`, req.body);
  return res.send(`${appResponse.data}`);
});
...

Microservice applications are dynamic with scaling, updates and failures causing services to change their network endpoints. Dapr enables you to call service endpoints with a consistent URL syntax, utilizing the hosting platform’s service discovery capabilities to resolve the endpoint location.

Learn more about Dapr service invocation.

Simplified state management

Dapr sidecars provide state management. In this quickstart, the calculator's state is persisted each time a new button is clicked. This means a user can refresh the page, close the page or even take down the calculator-front-end pod, and still retain the same state when they next open it. Dapr adds a layer of indirection so that the app doesn't need to know where it's persisting state. It doesn't have to keep track of keys, handle retry logic or worry about state provider specific configuration. All it has to do is GET or POST against its Dapr sidecar's state endpoint: http://localhost:3500/v1.0/state/${stateStoreName}.

Take a look at server.js in the react-calculator directory. Note that it exposes two state endpoints for the React client to get and set state: the GET /state endpoint and the POST /persist endpoint. Both forward client calls to the Dapr state endpoint:

const stateUrl = `http://localhost:${daprPort}/v1.0/state/${stateStoreName}`;

Our client persists state by simply POSTing JSON key-value pairs (see react-calculator/client/src/component/App.js):

    const state = [{
      key: "calculatorState",
      value
    }];

    fetch("/persist", {
      method: "POST",
      body: JSON.stringify(state),
      headers: {
        "Content-Type": "application/json"
      }
    });

[Optional Steps] VS Code Debugging

If you are using Visual Studio Code, you can debug this application using the preconfigured launch.json and task.json files in the .vscode folder. The .vscode folder has already been modified in the project to allow users to launch a compound configuration called "Full Dapr App" which will run all applications and allow you to debug in VS Code.

For more information on how to configure the files visit How-To: Debug multiple Dapr applications

Note: You still need to edit your environment variables for Flask and ASPNETCORE_URLS Note: Dapr offers a preview Dapr Visual Studio Code extension for local development which enables users a variety of features related to better managing their Dapr applications and debugging of your Dapr applications for all supported Dapr languages which are .NET, Go, PHP, Python and Java.

Next Steps

• Explore additional quickstarts.