3.distributed-calculator

Distributed Calculator

This sample 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 sample:

Prerequisites for running the sample

Clone the sample repository

git clone https://github.com/dapr/samples.git

- Run locally

1. Install Docker
2. Install .Net Core SDK 3.1
3. Install Dapr CLI
4. Install [Go] https://golang.org/doc/install
5. Install [Python3] https://www.python.org/downloads/
6. Install [Npm] https://www.npmjs.com/get-npm
7. Install [Node] https://nodejs.org/en/download/

- Run in Kubernetes environment

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

Running the sample 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
     

   • Build the app. Run:

     go build app.go
     

   • Run dapr using the command:

     dapr run --app-id addapp --app-port 6000 --port 3503 ./app
     

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 7000

     Linux/Mac OS:
     export ASPNETCORE_URLS="http://localhost:7000"
     
     Windows:
     set ASPNETCORE_URLS="http://localhost:7000"
     

   • Build the app. Run:

     dotnet build
     

   • Navigate to ./bin/Debug/netcoreapp3.1 and start Dapr using command:

     dapr run --app-id subtractapp --app-port 7000 --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 --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
     pip3 install python-dotenv
     

   • Set environment variable to use non-default app port 8000

     Linux/Mac OS:
     export FLASK_RUN_PORT=8000
     
     Windows:
     set FLASK_RUN_PORT=5000
     

   • Start dapr using the command:

     dapr run --app-id multiplyapp --app-port 8000 --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 5000 --port 3500 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 we use the calculator. Note that each time we click a button, we see logs that indicate state persistence and the different apps that are contacted to perform the operation.

Running the sample Kubernetes environment

1. Navigate to the deploy directory in this sample directory: cd deploy
2. Follow these instructions to create and configure a Redis store
3. Deploy all of your resources: kubectl apply -f ..

   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 our front-end, which allows us to connect to it externally.

4. Wait until your pods are in a running state: kubectl get pods -w

NAME                                    READY     STATUS    RESTARTS   AGE
dapr-operator-775c97497c-p92mf          1/1       Running   0          134m
dapr-placement-58c7d5f9cf-l9wcv         1/1       Running   0          134m
dapr-sidecar-injector-5879986bdc-nwdps  1/1       Running   0          134m
calculator-front-end-7c549cc84d-m24cb   2/2       Running   0          3m
divideapp-6d85b88cb4-vh7nz              2/2       Running   0          1m
multiplyapp-746588586f-kxpx4            2/2       Running   0          1m
subtractapp-7bbdfd5649-r4pxk            2/2       Running   0          2m

5. Next, let's take a look at our services and wait until we have an external IP configured for our front-end: kubectl get svc -w

   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.

   Note: Minikube users cannot see the external IP. Instead, you can use minikube service [service_name] to access loadbalancer without external IP.

6. Take the external IP address for calculator-front-end and drop it in your browser and voilà! You have a working distributed calculator!

   For Minikube users, execute the below command to open calculator on your browser

   $ minikube service calculator-front-end
   

7. Open your browser's console window (using F12 key) to see the logs produced as we use the calculator. Note that each time we click a button, we 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. Our app persists these to a Redis store (see Simplified State Management section below). By persisting these, we can refresh the page or take down the front-end pod and still jump right back where we were. Let's try it! Enter something into the calculator and refresh the page. The calculator should have retained the state, and your console should read:

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

Also note that each time we enter a full equation (e.g. "126 ÷ 3 =") our logs indicate that we're calling our to a service:

Calling divide service

Our client code calls to an Express server, which routes our calls through Dapr to our back-end services. In this case we're calling the divide endpoint on our nodejs application.

Cleanup

Local setup cleanup

• Stop all running applications (dapr stop --app-id {application id})
• Uninstall node modules by navigating to the node directory and run:

  npm uninstall
  

Kubernetes environment cleanup

• Once you're done using the sample, 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 sample demonstrates how we use Dapr as a programming model for simplifying the development of distributed systems. In this sample, Dapr is enabling polyglot programming, service discovery and simplified state management.

Polyglot Programming

Each service in this sample is written in a different programming language, but they're used together in the same larger application. Dapr itself is language agnostic - none of our 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 our 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 addUrl = `${daprUrl}/addapp/method/add`;
  req.pipe(request(addUrl)).pipe(res);
});

app.post('/calculate/subtract', async (req, res) => {
  const subtractUrl = `${daprUrl}/subtractapp/method/subtract`;
  req.pipe(request(subtractUrl)).pipe(res);
});
...

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.

Simplified State Management

Dapr sidecars provide state management. In this sample, we persist our calculator's state each time we click a new button. This means we can refresh the page, close the page or even take down our calculator-front-end pod, and still retain the same state when we next open it. Dapr adds a layer of indirection so that our 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 our 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"
      }
    });