Azure Functions Docker Microservice

Introduction

This tutorial guides you through deploying an Azure Function as a Docker microservice. This approach offers greater flexibility, allows you to use custom runtimes, and provides a consistent development and deployment environment.

By containerizing your Azure Function, you can leverage the power of Docker to manage dependencies, ensure portability, and integrate seamlessly with other containerized services.

Prerequisites

• An Azure subscription.
• Docker installed and running on your local machine.
• Azure CLI installed.
• An Azure Container Registry (ACR) instance. If you don't have one, you can create it using the Azure CLI:

  az acr create --resource-group  --name  --sku Basic --admin-enabled true

Step 1: Create a Dockerfile

For this tutorial, we'll assume you have a simple Azure Function (e.g., an HTTP Trigger). You need to create a Dockerfile in the root of your function project. This file instructs Docker on how to build your image.

Here's a sample Dockerfile for a Python Azure Function:

# Use an official Python runtime as a parent image
FROM mcr.microsoft.com/azure-functions/python:3.9

# Set the working directory in the container
WORKDIR /app

# Copy requirements.txt first to leverage Docker cache
COPY requirements.txt ./

# Install any needed packages specified in requirements.txt
RUN pip install --no-cache-dir -r requirements.txt

# Copy the rest of the application code
COPY . .

# Expose the port the function listens on (default for Azure Functions)
EXPOSE 80

# Set the command to run the Azure Functions host
CMD ["/azure-functions-core-tools/bin/func", "start"]

Remember to replace the base image (e.g., mcr.microsoft.com/azure-functions/python:3.9) with the appropriate one for your function's language and runtime.

Step 2: Build the Docker Image

Navigate to your function project directory in your terminal and build the Docker image using the following command:

docker build -t .azurecr.io/:latest .

Replace <your-acr-name> with the name of your Azure Container Registry and <your-function-name> with the desired name for your function image.

Step 3: Deploy to Azure Container Registry

First, log in to your Azure Container Registry:

az acr login --name 

Then, push the Docker image you just built to your ACR:

docker push .azurecr.io/:latest

Step 4: Create an Azure Container Instance

Now, you can deploy your Docker image to Azure Container Instances (ACI). This is a simple way to run a container in Azure without managing virtual machines.

You can create an ACI using the Azure CLI. Create a file named aci-deployment.json with the following content:

{
    "location": "",
    "name": "",
    "properties": {
        "containers": [
            {
                "name": "",
                "properties": {
                    "image": ".azurecr.io/:latest",
                    "ports": [
                        {
                            "port": 80,
                            "protocol": "TCP"
                        }
                    ],
                    "environmentVariables": []
                }
            }
        ],
        "osType": "Linux",
        "ipAddress": {
            "type": "Public",
            "ports": [
                {
                    "port": 80,
                    "protocol": "TCP"
                }
            ]
        }
    },
    "tags": {},
    "type": "Microsoft.ContainerInstance/containerGroups"
}

Replace placeholders like <your-azure-region>, <your-aci-name>, <your-function-name>, and <your-acr-name>.

Deploy the container instance:

az container create --resource-group  --file aci-deployment.json

Step 5: Configure Azure Function (if deploying as an App Service)

If you prefer to deploy your containerized function to Azure App Service for more robust features (like custom domains, SSL, etc.), you can do so. When creating your App Service, select the "Docker Container" option and point it to your image in ACR.

For ACI, this step is implicitly handled by the container creation.

Step 6: Test the Microservice

Once your Azure Container Instance is deployed, you can get its public IP address:

az container show --resource-group  --name  --query ipAddress.ip --output tsv

You can then send a request to your function's endpoint using this IP address. For example, if your function is an HTTP trigger named HttpTrigger1 and it expects a name parameter:

curl http://<aci-public-ip>/api/HttpTrigger1?name=DockerUser

You should receive the response from your Azure Function.

Conclusion

Congratulations! You've successfully deployed an Azure Function as a Docker microservice using Azure Container Instances. This pattern provides a scalable and manageable way to run your serverless code in a containerized environment.

You can extend this by integrating with other Azure services, setting up continuous integration/continuous deployment (CI/CD) pipelines, and exploring more advanced container orchestration options like Azure Kubernetes Service (AKS) for complex microservice deployments.