Build and run a containerized Python web app locally with MongoDB

Article
12/29/2023

This article is part of a tutorial about how to containerize and deploy a containerized Python web app to Azure App Service. App Service enables you to run containerized web apps and deploy through continuous integration/continuous deployment (CI/CD) capabilities with Docker Hub, Azure Container Registry, and Visual Studio Team Services. In this part of the tutorial, you learn how to build and run the containerized Python web app locally. This step is optional and isn't required to deploy the sample app to Azure.

Running a Docker image locally in your development environment requires setup beyond deployment to Azure. Think of it as an investment that can make future development cycles easier, especially when you move beyond sample apps and you start to create your own web apps. To deploy the sample apps for Django and Flask, you can skip this step and go to the next step in this tutorial. You can always return after deploying to Azure and work through these steps.

The following service diagram highlights the components covered in this article.

Clone the repository:

# Django
git clone https://github.com/Azure-Samples/msdocs-python-django-container-web-app.git

# Flask
git clone https://github.com/Azure-Samples/msdocs-python-flask-container-web-app.git

Then navigate into that folder:

# Django
cd msdocs-python-django-container-web-app

# Flask
cd msdocs-python-flask-container-web-app

2. Build a Docker image

If you're using one of the framework sample apps available for Django and Flask, you're set to go. If you're working with your own sample app, take a look to see how the sample apps are set up, in particular the Dockerfile in the root directory.

VS Code
Docker CLI

These instructions require Visual Studio Code and the Docker extension. Go to the sample folder you cloned or downloaded and open VS Code with the command code ..

Note

The steps in this section require the Docker daemon to be running. In some installations, for example on Windows, you need to open Docker Desktop, which starts the daemon, before proceeding.

Instructions	Screenshot
Open the Docker extension. If the Docker extension reports an error "Failed to connect", make sure Docker is installed and running. If this is your first time working with Docker, you probably won't have any containers, images, or connected registries.
Build the image. In the project Explorer showing the project files, right-click the Dockerfile and select Build Image.... Alternately, you can use the Command Palette (F1 or Ctrl+Shift+P) and type "Docker Images: Build Images" to invoke the command. For more information about Dockerfile syntax, see the Dockerfile reference.
Confirm the image was built. Go to the IMAGES section of the Docker extension. Look for recently built image. The name of the container image is "msdocspythoncontainerwebapp", which is set in the .vscode/tasks.json file.

These instructions require Docker.

Note

The steps in this section require the Docker daemon to be running. In some installations, for example on Windows, you need to open Docker Desktop, which starts the daemon, before proceeding.

Start in the root of the sample app you cloned or downloaded.

Step 1. At a shell prompt, confirm that Docker is accessible.

docker

If after running this command, you see help for the Docker CLI, then continue. Otherwise, make sure Docker is installed or your shell has access to the Docker CLI.

Step 2. Build the image.

The general form of the docker build command is docker build --rm --pull --file "<path-to-project-root>/Dockerfile" --label "com.microsoft.created-by=docker-cli" --tag "<container-name>:latest" "<path-to-project-root>".

For example, if you are at the root of the project directory, you can use the command like this to build an image:

bash
PowerShell terminal

docker build --rm --pull \
  --file "Dockerfile" \
  --label "com.microsoft.create-by=docker-cli" \
  --tag "msdocspythoncontainerwebapp:latest" \
  .

docker build --rm --pull `
  --file "Dockerfile" `
  --label "com.microsoft.create-by=docker-cli" `
  --tag "msdocspythoncontainerwebapp:latest" `
  .

Note the dot (".") at the end of the command referring to the current directly in which the command runs. You can add --no-cache to force a rebuild.

Step 3. Confirm the image was built.

Use the docker images command to return a list of images.

docker images

You should see images listed by REPOSITORY name, TAG, and CREATED date among other image characteristics.

At this point, you have built an image locally. The image you created has the name "msdocspythoncontainerwebapp" and tag "latest". Tags are a way to define version information, intended use, stability, or other information. For more information, see Recommendations for tagging and versioning container images.

Images that are built from VS Code or from using the Docker CLI directly can also be viewed with the Docker Desktop application.

3. Set up MongoDB

For this tutorial, you need a MongoDB database named restaurants_reviews and a collection named restaurants_reviews. The steps in this section show you how to use a local installation of MongoDB or Azure Cosmos DB for MongoDB to create and access the database and collection.

Important

Don't use a MongoDB database you'll use in production. In this tutorial, you'll store the MongoDB connection string in an environment variable. This makes it observable by anyone capable of inspecting your container (for example, using docker inspect).

Local MongoDB
Azure Cosmos DB for MongoDB

Step 1: Install MongoDB if it isn't already.

You can check for the installation of MongoDB by using the MongoDB Shell (mongosh).

The following command enters the shell and gives you the version of both mongosh and mongoDB server installed on your system:
```
mongosh
```
The following command gives you just the version of MongoDB server installed on your system:
```
mongosh --quiet --exec 'db.version()'
```

If these commands don't work, you might need to explicitly install mongosh or connect mongosh to your MongoDB server.

An alternative in some installations is to directly invoke the Mongo daemon.

mongod --version

Step 2: Edit the mongod.cfg file to add your computer's IP address.

The mongod configuration file has a bindIp key that defines hostnames and IP addresses that MongoDB listens for client connections. Add the current IP of your local development computer. The sample app running locally in a Docker container will communicate to the host machine with this address.

For example, part of the configuration file should look like this:

net:
  port: 27017
  bindIp: 127.0.0.1,<local-ip-address>

Restart MongoDB to pick up changes to the configuration file.

Step 3: Create a database and collection in the local MongoDB database.

Set the database name to "restaurants_reviews" and the collection name to "restaurants_reviews". You can create a database and collection with the VS Code MongoDB extension, the MongoDB Shell (mongosh), or any other MondoDB-aware tool.

For the MongoDB shell, here are example commands to create the database and collection:

> help
> use restaurants_reviews
> db.restaurants_reviews.insertOne({})
> show dbs
> exit

At this point, your local MongoDB connection string is "mongodb://127.0.0.1:27017/", the database name is "restaurants_reviews", and the collection name is "restaurants_reviews".

You can use Azure CLI commands to create an Azure Cosmos DB for MongoDB account and then create the required database and collection for this tutorial. If you haven't used the Azure CLI before, see Get started with Azure CLI to learn how to download and install the Azure CLI locally or how to run Azure CLI commands in Azure Cloud Shell.

Before running the following script, replace the location and Azure Cosmos DB for MongoDB account name with appropriate values. You can use the resource group name specified in the script or change it. Either way, we recommend using the same resource group for all the Azure resources created in the different articles of this tutorial. It makes them easier to delete when you're finished with the tutorial. If you've arrived here from part 4. Deploy container App Service, use the resource group name and location that you've already been using for your resources.

The script assumes that you're using a Bash shell. If you want to use a different shell, you'll need to change the variable declaration and substitution syntax. The script might take a few minutes to run.

#!/bin/bash

# LOCATION: The Azure region. Use the "az account list-locations -o table" command to find a region near you.
# RESOURCE_GROUP_NAME: The resource group name. Can contain underscores, hyphens, periods, parenthesis, letters, and numbers.
# ACCOUNT_NAME: The Azure Cosmos DB for MongDB account name. Can contain lowercase letters, hyphens, and numbers.
LOCATION='eastus'
RESOURCE_GROUP_NAME='msdocs-web-app-rg'
ACCOUNT_NAME='<cosmos-db-account-name>'

# Create a resource group
echo "Creating resource group $RESOURCE_GROUP_NAME in $LOCATION..."
az group create --name $RESOURCE_GROUP_NAME --location $LOCATION

# Create a Cosmos account for MongoDB API
echo "Creating $ACCOUNT_NAME. This command may take a while to complete."
az cosmosdb create --name $ACCOUNT_NAME --resource-group $RESOURCE_GROUP_NAME --kind MongoDB

# Create a MongoDB API database
echo "Creating database restaurants_reviews"
az cosmosdb mongodb database create --account-name $ACCOUNT_NAME --resource-group $RESOURCE_GROUP_NAME --name restaurants_reviews

# Create a MongoDB API collection
echo "Creating collection restaraunts_reviews"
az cosmosdb mongodb collection create --account-name $ACCOUNT_NAME --resource-group $RESOURCE_GROUP_NAME --database-name restaurants_reviews --name restaurants_reviews

# Get the connection string for the MongoDB database
echo "Get the connection string for the MongoDB account"
az cosmosdb keys list --name $ACCOUNT_NAME --resource-group $RESOURCE_GROUP_NAME --type connection-strings

echo "Copy the Primary MongoDB Connection String from the list above"

When the script completes, copy the Primary MongoDB Connection String from the output of the last command.

{
  "connectionStrings": [
    {
      "connectionString": ""mongodb://msdocs-cosmos-db:pnaMGVtGIRAZHUjsg4GJBCZMBJ0trV4eg2IcZf1TqV...5oONz0WX14Ph0Ha5IeYACDbuVrBPA==@msdocs-cosmos-db.mongo.cosmos.azure.com:10255/?ssl=true&replicaSet=globaldb&retrywrites=false&maxIdleTimeMS=120000&appName=@msdocs-cosmos-db@"",
      "description": "Primary MongoDB Connection String",
      "keyKind": "Primary",
      "type": "MongoDB"
    },

    ...
  ]
}

At this point, you should have an Azure Cosmos DB for MongoDB connection string of the form mongodb://<server-name>:<password>@<server-name>.mongo.cosmos.azure.com:10255/?ssl=true&<other-parameters>, a database named restaurants_reviews, and a collection named restaurants_reviews.

For more detail about using the Azure CLI to create a Cosmos DB for MongoDB account and to create databases and collections, see Create a database and collection for MongoDB for Azure Cosmos DB using Azure CLI. You can also use PowerShell, the VS Code Azure Databases extension, and Azure portal.

Tip

In the VS Code Azure Databases extension, you can right-click on the MongoDB server and get the connection string.

4. Run the image locally in a container

With information on how to connect to a MongoDB, you're ready to run the container locally. The sample app expects MongoDB connection information to be passed in environment variables. There are several ways to get environment variables passed to container locally. Each has advantages and disadvantages in terms of security. You should avoid checking in any sensitive information or leaving sensitive information in code in the container.

Note

When deployed to Azure, the web app will get connection info from environment values set as App Service configuration settings and none of the modifications for the local development environment scenario apply.

VS Code
Docker CLI

Instructions	Screenshot
In the .vscode folder of the sample app, the settings.json file defines what happens when you use the Docker extension and select Run or Run Interactive from the context menu of a Tag. The settings.json file contains two templates each for the `(MongoDB local)` and `(MongoDB Azure)` scenarios. If you're using a local MongoDB database: Replace both instances of `<YOUR_IP_ADDRESS>` with your IP address. Replace both instances of `<CONNECTION_STRING>` with the connection string for your MongoDB database. If you're using an Azure Cosmos DB for MongoDB database: Replace both instances of `<CONNECTION_STRING>` with the Azure Cosmos DB for MongoDB connection string. Set the `docker.dockerPath` configuration setting used by the templates. To set `docker.dockerPath`, open the VS Code Command Palette (Ctrl+Shift+P), enter "Preferences: Open Workspace Settings", then enter "docker.dockerPath" in the Search settings box. Enter "docker" (without the quotes) for the value of the setting. Note Both the database name and collection name are assumed to be `restaurants_reviews`.
Run the image. In the IMAGES section of the Docker extension, find the built image. Expand the image to find the latest tag, right-click and select Run Interactive. You'll be prompted to select the task appropriate for your scenario, either "Interactive run configuration (MongoDB local)" or "Interactive run configuration (MongoDB Azure)". With interactive run, you'll see any print statements in the code, which can be useful for debugging. You can also select Run which is non-interactive and doesn't keep standard input open. Important This step fails if the default terminal profile is set to (Windows) Command Prompt. To change the default profile, open the VS Code Command Palette (Ctrl+Shift+P), enter "Terminal: Select Default Profile", and then select a different profile from the dropdown menu; for example Git Bash or PowerShell.
Confirm that the container is running. In the CONTAINERS section of the Docker extension, find the container. Expand the Individual Containers node and confirm that "msdocspythoncontainerwebapp" is running. You should see a green triangle symbol next to the container name if it's running.
Test the web app by right-clicking the container name and selecting Open in Browser. The browser will open into your default browser as "http://127.0.0.1:8000" for Django or "http://127.0.0.1:5000/" for Flask.
Stop the container. In the CONTAINERS section of the Docker extension, find the running container. Right click the container and select Stop.

Tip

You can also run the container selecting a run or debug configuration. The Docker extension tasks in tasks.json are called when you run or debug. The task called depends on what launch configuration you select. For the task "Docker: Python (MongoDB local)", specify <YOUR-IP-ADDRESS>. For the task "Docker: Python (MongoDB Azure)", specify <CONNECTION-STRING>.

Step 1. Run the latest version of the image.

Local MongoDB
Azure Cosmos DB MongoDB

# PORT=8000 for Django and 5000 for Flask
export PORT=<port-number>
export YOUR_IP_ADDRESS=<your-machine-ip-address>

docker run --rm -it \
  --publish $PORT:$PORT --publish 27017:27017 \
  --add-host mongoservice:$YOUR_IP_ADDRESS \
  --env CONNECTION_STRING=mongodb://mongoservice:27017 --env DB_NAME=restaurants_reviews --env COLLECTION_NAME=restaurants_reviews \
  msdocspythoncontainerwebapp:latest

The command above is formatted for Bash shell. If you use PowerShell, Command Prompt, or another shell, you might need to adjust the line continuation and environment variable format accordingly.

# PORT=8000 for Django and 5000 for Flask
export PORT=<port-number>
export CONNECTION_STRING="<connection-string>"

docker run --rm -it \
  --publish $PORT:$PORT/tcp \
  --env CONNECTION_STRING=$CONNECTION_STRING --env DB_NAME=restaurants_reviews --env COLLECTION_NAME=restaurants_reviews \
  msdocspythoncontainerwebapp:latest

The command above is formatted for Bash shell. If you use PowerShell, Command Prompt, or another shell, you might need to adjust the line continuation and environment variable format accordingly.

Passing in sensitive information as shown here is for demonstration purposes. The connection string information can be viewed by inspecting the container with the command docker container inspect. Another way to handle secrets is to use the BuildKit functionality of Docker.

Step 2. Confirm that the container is running.

Open a second shell and run the docker container ls command.

docker container ls

You should see your container "msdocspythoncontainerwebapp:latest:latest" in the list. Note the NAMES column of the output and the PORTS column. You can use the name to stop the container.

Step 3. Test the web app.

Go to "http://127.0.0.1:8000" for Django and "http://127.0.0.1:5000/" for Flask when running with local MongoDB.

Step 4. Shut down the container

docker container stop <container-name>

You can also start a container from an image and stop it with the Docker Desktop application.

Next step

Build a container image in Azure

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Build and run a containerized Python web app locally with MongoDB

1. Clone or download the sample app

2. Build a Docker image

3. Set up MongoDB

4. Run the image locally in a container

Next step

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