Multi-Container ASP .NET Core Web App with Docker Compose

Previously, we have seen how we could containerise our ASP .NET Core 6.0 web app and manage it with docker commands. However, docker commands are mainly for only one image/container. If our solution has multiple containers, we need to use docker-compose to manage them instead.

docker-compose makes things easier because it encompasses all our parameters and workflow into a configuration file in YAML. In this article, I will share my first experience with docker-compose to build mutli-container environments as well as to manage them with simple docker-compose commands.

To help my learning, I will create a simple online message board where people can login with their GitHub account and post a message on the app.

PROJECT GITHUB REPOSITORY

The complete source code of this project can be found at https://github.com/goh-chunlin/Lunar.MessageWall.

Create Multi-container App

We will start with a solution in Visual Studio with two projects:

  • WebFrontEnd: A public-facing web application with Razor pages;
  • MessageWebAPI: A web API project.

By default, the web API project will have a simple GET method available, as shown in the Swagger UI below.

Default web API project created in Visual Studio will have this WeatherForecast API method available by default.

Now, we can make use of this method as a starting point. Let’s have the our client, WebFrontEnd, to call the API and output the result returned by the API to the web page.

var request = new System.Net.Http.HttpRequestMessage();
request.RequestUri = new Uri("http://messagewebapi/WeatherForecast");

var response = await client.SendAsync(request);

string output = await response.Content.ReadAsStringAsync();

In both projects, we will add Container Orchestrator Support with Linux as the target OS. Once we have the docker-compose YAML file ready, we can directly run our docker compose application by simply pressing F5 in Visual Studio.

The docker-compose YAML file for our solution.

Now, we shall be able to see the website output some random weather data returned by the web API.

Congratulations, we’re running a docker compose application.

Configure Authentication in Web App

Our next step is to allow users to login to our web app first before they can post a message on the app.

It’s usually a good idea not to build our own identity management module because we need to deal with a lot more than just building a form to allow users to create an account and type their credentials. One example will be managing and protecting our user’s personal data and passwords. Instead, we should rely on Identity-as-a-Service solutions such as Azure Active Directory B2C.

Firstly, we will register our web app in our Azure AD B2C tenant.

Normally for first-timers, we will need to create a Azure AD B2C tenant first. However, there may be an error message saying that our subscription is not registered to use namespace ‘Microsoft.AzureActiveDirectory’. If you encounter this issue, you can refer to Adam Storr’s article on how to solve this with Azure CLI.

Once we have our Azure AD B2C tenant ready (which is Lunar in my example here), we can proceed to register our web app, as shown below. For testing purposes, we set the Redirect URI to https://jwt.ms, a Microsoft-owned web application that displays the decoded contents of a token. We will update this Redirect URL in the next section below when we link our web app with Azure AD B2C.

Registering a new app “Lunar Message Wall” under the Lunar Tenant.

Secondly, once our web app is registered, we need to create a client secret, as shown below, for later use.

Secrets enable our web app to identify itself to the authentication service when receiving tokens. In addition, please take note that although certificate is recommended over client secret, currently certificates cannot be used to authenticate against Azure AD B2C.

Adding a new client secret which will expire after 6 months.

Thirdly, since we want to allow user authentication with GitHub, we need to create a GitHub OAuth app first.

The Homepage URL here is a temporary dummy data.

After we have registered the OAuth app on GitHub, we will be provided a client ID and client secret. These two information are needed when we configure GitHub as the social identity provider (IDP) on our Azure AD B2C, as shown below.

Configuring GitHub as an identity provider on Azure AD B2C.

Fourthly, we need to define how users interact with our web app for processes such as sign-up, sign-in, password reset, profile editing, etc. To keep thing simple, here we will be using the predefined user flows.

For simplicity, we allow only GitHub sign-in in our user flow.

We can also choose the attributes we want to collect from the user during sign-up and the claims we want returned in the token.

User attributes and token claims.

After we have created the user flow, we can proceed to test it.

In our example here, GitHub OAuth app will be displayed.

Since we specify in our user flow that we need to collect the user’s GitHub display name, there is a field here for the user to enter the display name.

The testing login page from running the user flow.

Setup the Authentication in Frontend and Web API Projects

Now, we can proceed to add Azure AD B2C authentication to our two ASP.NET Core projects.

We will be using the Microsoft Identity Web library, a set of ASP.NET Core libraries that simplify adding Azure AD B2C authentication and authorization support to our web apps.

dotnet add package Microsoft.Identity.Web

The library configures the authentication pipeline with cookie-based authentication. It takes care of sending and receiving HTTP authentication messages, token validation, claims extraction, etc.

For the frontend project, we will be using the following package to add GUI for the sign-in and an associated controller for web app.

dotnet add package Microsoft.Identity.Web.UI

After this, we need to add the configuration to sign in user with Azure AD B2C in our appsettings.json in both projects (The ClientSecret is not needed for the Web API project).

"AzureAdB2C": {
    "Instance": "https://lunarchunlin.b2clogin.com",
    "ClientId": "...",
    "ClientSecret": "...",
    "Domain": "lunarchunlin.onmicrosoft.com",
    "SignedOutCallbackPath": "/signout/B2C_1_LunarMessageWallSignupSignin",
    "SignUpSignInPolicyId": "B2C_1_LunarMessageWallSignupSignin"
}

We will use the configuration above to add the authentication service in Program.cs of both projects.

With the help of the Microsoft.Identity.Web.UI library, we can also easily build a sign-in button with the following code. Full code of it can be seen at _LoginPartial.cshtml.

<a class="nav-link text-dark" asp-area="MicrosoftIdentity" asp-controller="Account" asp-action="SignIn">Sign in</a>

Now, it is time to update the Redirect URI to the localhost. Thus, we need to make sure our WebFrontEnd container has a permanent host port. To do so, we first specify the ports we want to use in the launchsettings.json of the WebFrontEnd project.

"Docker": {
    ...
    "environmentVariables": {
      "ASPNETCORE_URLS": "https://+:443;http://+:80",
      "ASPNETCORE_HTTPS_PORT": "44360"
    },
    "httpPort": 51803,
    "sslPort": 44360
}

Then in the docker-compose, we will specify the same ports too.

services:
  webfrontend:
    image: ${DOCKER_REGISTRY-}webfrontend
    build:
      context: .
      dockerfile: WebFrontEnd/Dockerfile
    ports:
      - "51803:80"
      - "44360:443"

Finally, we will update the Redirect URI in Azure AD B2C according, as shown below.

Updated the Redirect URI to point to our WebFrontEnd container.

Now, right after we click on the Sign In button on our web app, we will be brought to a GitHub sign-in page, as shown below.

The GitHub sign-in page.

Currently, our Web API has only two methods which have different required scopes declared, as shown below.

[Authorize]
public class UserMessageController : ControllerBase
{
    ...
    [HttpGet]
    [RequiredScope("messages.read")]
    public async Task<IEnumerable<UserMessage>> GetAsync()
    {
        ...
    }

    [HttpPost]
    [RequiredScope("messages.write")]
    public async Task<IEnumerable<UserMessage>> PostAsync(...)
    {
        ...
    }
}

Hence, when the frontend needs to send the GET request to retrieve messages, we will first need to get a valid access token with the correct scope.

string accessToken = await _tokenAcquisition.GetAccessTokenForUserAsync(new[] { "https://lunarchunlin.onmicrosoft.com/message-api/messages.read" });

client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", accessToken);

client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));

Database

Since we need to store the messages submitted by the users, we will need a database. Here, we use PostgresSQL, an open-source, standards-compliant, and object-relational database.

To run the PostgresSQL with docker-compose we will update our docker-compose.yml file with the following contents.

services:
  ...
  messagewebapi:
    ...
    depends_on:
     - db

  db:
    container_name: 'postgres'
    image: postgres
    environment:
      POSTGRES_PASSWORD: ...

In our case, only the Web API will interact with the database. Hence, we need to make sure that the db service is started before the messagewebapi. In order to specify this relationship, we will use the depends_on option.

User’s messages can now be stored and listed on the web page.

Next Step

This is just the very beginning of my learning journey of dockerising ASP .NET Core solution. In the future, I shall learn more in this area.

References

[KOSD] Dockerise an ASP .NET Core 6.0 Web App

Let’s assume now we want to dockerise a new ASP .NET Core 6.0 web app project that we have locally.

Now, when we build and run the project, we should be able to view it on localhost as shown below.

The default homepage of a new ASP .NET Core web app.

Before adding the .NET app to the Docker image, first it must be published with the following command.

dotnet publish --configuration Release
Build, run, and publish our ASP .NET Core web app.

Create the Dockerfile

Now, we will create a file named Dockerfile in directory containing the .csproj and open it in VS Code. The content of the Dockerfile is as follows.

FROM mcr.microsoft.com/dotnet/aspnet:6.0

COPY bin/Release/net6.0/publish/ App/
WORKDIR /App
ENTRYPOINT ["dotnet", "Lunar.Dashboard.dll"]

A Dockerfile must begin with a FROM instruction. It specifies the parent image from which we are building. Here, we are using mcr.microsoft.com/dotnet/aspnet:6.0, an image contains the ASP.NET Core 6.0 and .NET 6.0 runtimes for running ASP.NET Core web apps.

The COPY command tells Docker to copy the publish folder from our computer to the App folder in the container. Then the current directory inside of the container is changed to App with the WORKDIR command.

Finally, the ENTRYPOINT command tells Docker to configure the container to run as an executable.

Docker Build

Now that we have the Dockerfile, we can build an image from it.

In order to perform docker build, we first need to navigate the our project root folder and issue the docker build command, as shown below.

docker build -t lunar-dashboard -f Dockerfile .

We assign a tag lunar-dashboard to the image name using -t. We then specify the name of the Dockerfile using -f. The . in the command tells Docker to use the current folder, i.e. our project root folder, as the context.

Once the build is successful, we can locate the newly created image with the docker images command, as highlighted in the screenshot below.

The default docker images command will show all top level images.

Create a Container

Now that we have an image lunar-dashboard that contains our ASP .NET Core web app, we can create a container with the docker run command. 

docker run -d -p 8080:80 --name lunar-dashboard-app lunar-dashboard

When we start a container, we must decide if it should be run in a detached mode, i.e. background mode, or in a foreground mode. By default the container will be running in foreground.

In the foreground mode, the console that we are using to execute docker run will be attached to standard input, output and error. This is not what we want. What we want is after we start up the container, we can still use the console for other commands. Hence, the container needs to be in detached mode. To do so, we use the -d option which will start the container in detached mode.

We then publish a port of the container to the host with -p 8080:80, where 8080 is the host port and 80 is the container port.

Finally, we name our container lunar-dashboard-app with the --name option. If we do not assign a container name with the --name option, then the daemon will generate a random string name for us. Most of the time, the auto-generated name is quite hard to remember, so it’s better for us to give a meaningful name to the container so we can easily refer the container later.

After we run the docker run command, we should be able to find our newly created container lunar-dashboard with the docker ps command, as shown in the following screenshot. The option -a is to show all containers because by default docker ps will show only containers which are running.

Our container lunar-dashboard is now running.

Now, if we visit the localhost at port 8080, we shall be able to see our web app running smoothly.

This web app is hosting on our Docker container.

Running Docker on Windows 11 Home

You may notice that I am using WSL (Windows Subsystem for Linux). This is because to install Docker Desktop, which includes the Docker Engine that builds and containerise our apps, on Windows, we need to have Hyper-V feature enabled. However, Hyper-V is available only on Windows 10/11 Pro, Enterprise, and Education.

Hence, I have no choice but to use WSL, which runs a Linux kernel inside of a lightweight utility VM. WSL provides a mechanism for running Docker (with Linux containers) on the Windows machine.

For those who are interested to read more about this, please refer to a very detailed online tutorial about how to run Docker with WSL, written by Padok SRE Baptiste Guerin.

References

KOSD, or Kopi-O Siew Dai, is a type of Singapore coffee that I enjoy. It is basically a cup of coffee with a little bit of sugar. This series is meant to blog about technical knowledge that I gained while having a small cup of Kopi-O Siew Dai.

[KOSD Series] Running MS SQL Server 2019 on macOS

Few days ago, my teammate would like to learn how to use MS SQL Server. However, he only has a Macbook and MS SQL Server doesn’t run on macOS. Hence, I decided to write him a quick setup guide on how to do that with the help of container.

Starting from March 2016, besides Windows, SQL Sever 2019 also runs on Linux. So, we can easily spin up a Linux container and host SQL Server on it.

🎨 Microsoft introduced SQL Server on Linux in 2016. 🎨

Docker

We need to run Docker on our Mac machine. Since my teammate is new to Docker, he can simply choose a rather straight-forward path for this, which is to use Docker Desktop on Mac. Kindly take note of the system requirement before proceed to install it.

Once the Docker is up and running, we can proceed to pull the image of SQL Server 2019 from the Docker Hub.

SQL Server 2019 Developer Edition

In 2019, continuing with the approach to delivering a consistent and trustworthy acquisition experience for Microsoft container images, Microsoft Container Registry (MCR) is announced.

We can run the following command in Terminal window to start the database server. Here we are using 1501 as the port. Take note that, we need to replace the password with our password which meets the following guideline:

  • at least 8 characters;
  • including uppercase, lowercase letters, base-10 digits and/or non-alphanumeric symbols.
$ docker run -e 'ACCEPT_EULA=Y' -e 'SA_PASSWORD=yourStrong(!)Password' -p 1501:1433 -d mcr.microsoft.com/mssql/server:2019-latest

In the command above, there are two environment variables.

Firstly, it is the environment variable “ACCEPT_EULA”. Setting it to Y means that we accept the End-User Licensing Agreement of the product. So far I still couldn’t find the EULA of the Microsoft SQL Server 2019. If you know, please drop me a message in the comment section. Thanks!

Secondly, it is the “SA_PASSWORD” which is used to set the password that we will later use to connect to the SQL server later as the database admin (userid = “sa”).

Actually, there is another environment variable which is not set here. It is the MSSQL_PID, i.e. the product ID of the SQL Server. By default, it is the Developer edition. If we would like to use Express or Enterprise edition, we can specify it here.

The reason we chose the Developer edition is because it is the edition that it is licensed for use as a development and test system, not as a production server. In addition, despite being Developer edition, it includes all the functionality of Enterprise edition. Hence, SQL Server Developer is an ideal choice for developers like us to build and test applications.

🎨 Docker Hub page of Microsoft SQL Server. 🎨

There are more information about the MS SQL Server image on the Docker Hub page. Hence I will not repeat them here.

Azure Data Studio

To visualise and manage our data in the databases, we need to use tools such as SQL Server Management Studio (SSMS). However, SSMS is only for Windows (AMD or Intel). So, on macOS, we have to choose another cross-platform alternative, which is Azure Data Studio. Azure Data Studio is usable on Windows and Linux too.

Interestingly, Azure Data Studio was previously called SQL Operations Studio. Hence, please only use the latest one, which is the Azure Data Studio.

Now we can connect to the SQL Server from Azure Data Studio as shown below. Take note that the Server is “localhost,1501” and it is using comma, not dot, between the word localhost and the port number.

🎨 Connecting to the Microsoft SQL Server from Azure Data Studio. 🎨

If the connection is successful, we shall be able to see the Server Dashboard as shown below.

🎨 Server Dashboard in Azure Data Studio. 🎨

That’s all. Now we can have MS SQL Server running on our Mac machine for local testing and development.

References

KOSD, or Kopi-O Siew Dai, is a type of Singapore coffee that I enjoy. It is basically a cup of coffee with a little bit of sugar. This series is meant to blog about technical knowledge that I gained while having a small cup of Kopi-O Siew Dai.

Containerize Golang Code and Deploy to Azure Web App

Continue from the previous topic

Learning about containers is essentially a huge topic but for beginners, there needs to be something small to help them get started. Hence, in this article, we will focus only on the key concepts of containers and the steps to containerize the program and deploy it to Azure Web App.

In 2017, Azure introduced Web Apps for Containers. Before that, the Azure Web Apps actually ran on Windows VMs managed by Microsoft. So now with this new feature, we can build a custom Docker image containing all the binaries and files and then run a Docker container based on the image on Azure Web Apps. Hence, we can now bring our own Docker container images supporting Golang to Azure with its PaaS option.

As explained in the book “How to Containerize Your Go Code“, containers isolates an application so that container thinks it’s running on its own private machine. So, a container is similar to a VM but it uses the OS kernel on the host rather than having its own.

Firstly, we need to prepare the Dockerfile, a file having the instructions telling the Docker how to build the images automatically. So, a Dockerfile is simply a text file containing all the commands a user could call on the command line to assemble an image. Traditionally, the Dockerfile is named Dockerfile and located in the root of the context.

The Dockerfile we have for our project is as follows.

FROM scratch

EXPOSE 80

COPY GoLab /
COPY public public
COPY templates templates

ENV APPINSIGHTS_INSTRUMENTATIONKEY '' \
CONNECTION_STRING '' \
OAUTH_CLIENT_ID '' \
OAUTH_CLIENT_SECRET '' \
COOKIE_STORE_SECRET '' \
OAUTH2_CALLBACK ''

CMD [ "/GoLab" ]

The Dockerfile starts with a FROM command that specifies the starting point for the image to build. For our project, we don’t have any dependencies, so we can start from scratch. So what is scratch? Scratch is basically a special Docker image that is empty (0B). That means there will be nothing else in our container later aside from what we put in with the rest of the Dockerfile.

The reason why we build from scratch is because not only we can have a smaller image to build later, but also our container will have smaller attack surface. This is because the less code there is within our container, the less likely it is to include a vulnerability.

The EXPOSE 80 command is telling Docker that we need to open the port 80 because the web server is listening on port 80. Hence, in order to access our program from outside the container through HTTP, we need to define it in the Dockerfile that we need the port 80 to be always opened.

The next three COPY commands are basically copying firstly the GoLab executable into the root directory of the container and secondly the two directories, public and templates into the container. Without the HTML, CSS, and JavaScript, our web app will not work.

Now you may wonder why the first COPY command says GoLab instead of GoLab.exe. We shall discuss it later in this article.

After that, we use ENV command to set the environment variables that we will be using in the app.

Finally we have the line CMD [“/GoLab”] to directs the container as to which command to execute when the container is run.

Since the container is not a Windows container, the code that runs inside the container thus needs to be a Linux binary. Fortunately, this is really simple to obtain with the cross-compilation support in Go using the following command.

$ $env:GOOS = "linux"
$ go build -o GoLab .

Thus, in the Dockerfile, we use GoLab file instead of GoLab.exe.

We can now proceed to build the container image with the following command (Take note of the dot in the end of line).

$ docker image build -t chunlindocker/golab:v1 .

The -t flag is for us to specify the name and tag of the container. In this case, I call it chunlindocker/golab:v1 where chunlindocker is the Docker ID of my Docker Hub. Naming in such a way later helps me to push it to a registry, i.e. the Docker Hub.

My Docker Hub profile.

If we want to build the image with another dockerfile, for example Dockerfile.development, we can do it as follows.

$ docker image build -t chunlindocker/golab:v1 -f Dockerfile.development .

Once the docker image is built, we can see it listen when we perform the list command as shown in the screenshot below.

Created docker images.

Now the container image is “deployable”. That means we can run it anywhere with a running docker engine. Since our laptop has Docker installed, so we can proceed to run it locally with the following command.

$ docker container run -P chunlindocker/golab:v1

If you run the command above in the Terminal window inside VS Code, you will see that the command line is “stuck”. This is because the container is already running on local machine. So what we need to do is just open another terminal window and view all the running containers.

The docker ps command by default only shows running containers.

To help humans, Docker auto generates a random name with two words and assigns it to the container. We can see that the container we created is given a random name “nifty_elgama”, lol. So now our container has a “human” name to call. If you want to remove the container later, you not only need to Ctrl+C to stop it, but to totally remove it, you need to use the rm command as follows.

$ docker container rm nifty_elgama

The PORTS column shown in the screenshot is important because it tells us how ports exposed on the container can be accessed from the host. So to test it locally, we shall visit http://localhost:32768.

So our next step is to upload it to a container registry so that later it can be pulled onto any machines, including Azure Web Apps, that will run it. To do so, we do push the image we built above to Docker Hub with the following command.

$ docker push chunlindocker/golab:v1
Successfully push our new container image to Docker Hub.

So, now how do we deploy the container to Azure?

Firstly, we need to create a Web App for Containers on the Azure Portal, as shown in the screenshot below.

Creating Web App for Containers.

The last item in the configuration is the “Configure Container”. Clicking on that, we will be brought to the following screen where we can then specify the container image we want to use and pull it from Docker Hub.

We will be deploying single container called chunlindocker/golab:v4 from Docker Hub.

You can of course deploy a private container from Docker Hub by choosing “Private” as Repository Access. Then Azure Portal will prompt you for Docker Hub login credential for it to pull image from Docker Hub.

Once the App Service is created, we can proceed to read the Logs under “Container Settings”. Then we can see the container initializing process.

Logs about the container in App Service.

After that we can proceed to fill up the Application Settings with the environment variables we have in the web application and then we are good to go.

The website is up and running on Azure Web App for Containers.

References