How to setup Serverless in Python and Deploy It on Azure

Serverless computing has revolutionized the way we develop and deploy applications. By abstracting server management, it allows developers to focus on writing code without worrying about the underlying infrastructure. In this blog post, we’ll guide you through starting serverless in Python for Azure and deploying your function on Azure. This guide is optimized for SEO and is designed to be easy for anyone to understand.

Why Choose Serverless Computing?

Serverless computing, especially with Azure Functions, offers several advantages:

• Cost Efficiency: Pay only for the compute resources you use, reducing overall costs.
• Scalability: Automatically scale your application in response to demand.
• Simplicity: Focus on writing code without managing servers, making development faster and easier.

Imagine serverless as a valet service for your code: You write your application and hand it over, and Azure takes care of parking (deploying), scaling, and maintenance.

Getting Started with Serverless in Python on Azure

Step 1: Set Up Your Development Environment

Before you start, ensure you have the following prerequisites:

1. Azure Account: Sign up for a free Azure account if you don’t have one.
2. Azure CLI: Install the Azure Command-Line Interface (CLI) from this link.
3. Python: Ensure Python is installed on your machine. You can download it from python.org.

Step 2: Install Azure Functions Core Tools

Azure Functions Core Tools allow you to develop and test your functions locally before deploying them to Azure. Install the tools using npm (Node Package Manager):

npm install -g azure-functions-core-tools@4 --unsafe-perm true

Step 3: Create a New Function App

1. Open your terminal and create a new directory for your project:
   mkdir my-python-function cd my-python-function
2. Initialize a new Function App:
   func init my-python-function-app --python

This command sets up a new Function App with Python as the runtime.

Step 4: Create a New Function

Inside your Function App directory, create a new function:

cd my-python-function-app
func new

Select a template for your function, such as “HTTP trigger,” and give your function a name. This creates a basic HTTP-triggered function.

Step 5: Write Your Function Code

Open the generated __init__.py file in your preferred code editor. Here’s a simple example of an HTTP-triggered function:

import logging
import azure.functions as func

def main(req: func.HttpRequest) -> func.HttpResponse:
    logging.info('Python HTTP trigger function processed a request.')

    name = req.params.get('name')
    if not name:
        try:
            req_body = req.get_json()
        except ValueError:
            pass
        else:
            name = req_body.get('name')

    if name:
        return func.HttpResponse(f"Hello, {name}!")
    else:
        return func.HttpResponse(
            "Please pass a name on the query string or in the request body",
            status_code=400
        )

This function responds with a greeting message based on the input provided in the query string or request body.

Deploying Your Function to Azure

Step 6: Log In to Azure

Use the Azure CLI to log in to your Azure account:

az login

Step 7: Create a Resource Group

A resource group is a container that holds related resources for an Azure solution. Create a new resource group:

az group create --name myResourceGroup --location <your-preferred-location>

Step 8: Create a Function App in Azure

Create a new Function App in your resource group:

az functionapp create --resource-group myResourceGroup --consumption-plan-location <your-preferred-location> --runtime python --runtime-version 3.9 --functions-version 3 --name myFunctionApp --storage-account <your-storage-account-name>

Replace <your-preferred-location> with your desired Azure region and <your-storage-account-name> with a unique name for your storage account.

Step 9: Deploy Your Function

Deploy your Function App to Azure:

func azure functionapp publish myFunctionApp

Benefits of Using Azure Functions for Serverless Python

1. Reduced Operational Complexity: No need to manage servers, allowing you to focus on code development.
2. Automatic Scaling: Azure Functions automatically scale your application to handle varying loads.
3. Integrated Development Environment: Develop, test, and deploy using familiar tools and languages.

Challenges and Considerations

1. Cold Starts: Serverless functions might experience a delay (cold start) when invoked after a period of inactivity.
2. State Management: Serverless architecture is stateless, so managing state between function calls requires additional strategies.
3. Learning Curve: Initial setup and understanding of serverless concepts might be challenging for beginners.

Practical Tips

1. Monitor Performance: Use Azure Monitor to keep an eye on your function’s performance and resource usage.
2. Optimize Cold Starts: Use Azure’s Premium plan to reduce cold start times.
3. Security: Secure your functions with proper authentication and authorization mechanisms.

Conclusion

Starting with serverless in Python for Azure is a powerful way to build scalable, cost-effective applications. By following these steps, you can set up and deploy your first serverless function with ease. Ready to dive into serverless computing? Set up your Azure Function App today and explore the potential of serverless architecture!

Also read about: Creating serverless flask application from scratch.