What is a function app ?

 A function app is a container for one or more Azure Functions that allows you to group related functions together as a logical unit for deployment, management, and sharing of resources. It provides an environment for developing, testing, and running functions, and can be scaled automatically based on demand.

How do you create an Azure Function?

 You can create an Azure Function using the following steps:

1. Open the Azure portal and sign in to your account.
2. Click on the Create a Resource button in the left-hand pane and search for "Function App".
3. Click on the Function App option and then click on the Create button.
4. Fill in the required information, including the subscription, resource group, and function app name.
5. Choose the operating system, either Windows or Linux, and the hosting plan, either Consumption or App Service Plan.
6. Choose the runtime stack and version, such as Node.js, Python, .NET Core, or Java.
7. Choose the region where you want to deploy the function app.
8. Click on the Create button to create the function app.

Once the function app is created, you can create a new function by following these steps:

1. In the function app blade, click on the Functions option in the left-hand pane.
2. Click on the + button to create a new function.
3. Choose a template or create a custom function.
4. Choose a trigger type for the function.
5. Fill in the required information for the trigger and any input bindings.
6. Write the function code in your preferred programming language.
7. Test the function using the Test tab or the function URL.
8. Save and publish the function.

What programming languages can you use to develop Azure Functions?

 Azure Functions supports several programming languages for developing functions, including:

1. C#
2. Java
3. JavaScript (Node.js)
4. Python
5. PowerShell

Developers can choose the programming language based on their preference and experience. Each language has its own unique set of tools and libraries that can be used to build Azure Functions. Developers can also use integrated development environments (IDEs) such as Visual Studio and Visual Studio Code to build and debug Azure Functions written in these languages.

What are the different types of Azure Functions?

 Azure Functions provides several different types of triggers that can be used to invoke functions. Here are some of the common types of Azure Functions:

1. HTTP Trigger: Invokes a function when an HTTP request is made to a specified URL.
2. Timer Trigger: Invokes a function on a schedule.
3. Blob Trigger: Invokes a function when a new blob is added to an Azure Storage container.
4. Cosmos DB Trigger: Invokes a function when a new or updated document is added to an Azure Cosmos DB database.
5. Event Grid Trigger: Invokes a function when an event is published to an Azure Event Grid topic.
6. Event Hub Trigger: Invokes a function when a new message is added to an Azure Event Hub.
7. Service Bus Queue Trigger: Invokes a function when a new message is added to an Azure Service Bus queue.
8. Service Bus Topic Trigger: Invokes a function when a new message is added to an Azure Service Bus topic.

Developers can also create custom triggers for Azure Functions using the Azure Event Grid or the Azure Service Bus.

What is an Azure Function?

 An Azure Function is a serverless computing service provided by Microsoft Azure that enables developers to build event-driven applications that can be executed without the need for provisioning and managing servers. With Azure Functions, developers can write small, single-purpose functions that respond to events such as HTTP requests, changes to data in Azure Storage or Azure Cosmos DB, or messages from Azure Service Bus or Azure Event Hubs. These functions can be written in several programming languages including C#, Java, JavaScript, Python, and PowerShell. Azure Functions scales automatically, from just a few instances up to thousands of instances, depending on the demand of the application.

Creating Custom Triggers for Azure Functions with Azure Event Hubs and Azure Service Bus

Azure Functions is a serverless compute service that allows you to run your code on-demand without having to manage infrastructure. With Azure Functions, you can build scalable, event-driven applications that can respond to changes in real-time. One way to achieve this is by creating custom triggers that respond to events from Azure Event Hubs and Azure Service Bus. In this tutorial, we'll show you how to create custom triggers for Azure Functions using these two services.

Prerequisites

Before we get started, you'll need to have the following:

1. An Azure account

2. Visual Studio Code

3. Azure Functions extension for Visual Studio Code

Creating an Azure Event Hub 

The first step is to create an Azure Event Hub. In the Azure portal, select "Create a resource" and search for "Event Hubs". Choose "Event Hubs" and follow the prompts to create a new Event Hub.

Once your Event Hub is created, you can send events to it using any compatible client library. In this tutorial, we'll use the Azure Functions extension for Visual Studio Code to create a custom trigger that responds to events from our Event Hub.

Creating an Azure Service Bus

The next step is to create an Azure Service Bus. In the Azure portal, select "Create a resource" and search for "Service Bus". Choose "Service Bus" and follow the prompts to create a new Service Bus.

Once your Service Bus is created, you can send messages to it using any compatible client library. We'll use the Azure Functions extension for Visual Studio Code to create a custom trigger that responds to messages from our Service Bus.

Creating Custom Triggers for Azure Functions

Now that our Event Hub and Service Bus are set up, we can create custom triggers for Azure Functions that respond to events and messages from these services.

To create a custom trigger for Azure Functions, you'll need to define a function that takes in the event or message as input. This function can then process the event or message and perform any necessary actions.

Custom Trigger for Azure Event Hubs

Here's an example of a custom trigger for Azure Event Hubs:

module.exports = async function(context, eventHubMessages) {

    context.log(`Event hub trigger function called for message array: ${eventHubMessages}`);

    eventHubMessages.forEach(message => {

        // Process message here

    });

};

This function takes in the eventHubMessages array as input and processes each message in the array. You can add your own processing logic to this function, such as sending notifications or updating a database.

To connect this function to your Event Hub, you'll need to add a new function to your Azure Functions app using the Event Hub trigger template. Follow the prompts to specify the Event Hub connection string and configure the function.

Custom Trigger for Azure Service Bus

Here's an example of a custom trigger for Azure Service Bus:

module.exports = async function(context, mySbMsg) {

    context.log(`Service bus trigger function called for message: ${mySbMsg}`);

    // Process message here

};

This function takes in the mySbMsg object as input and processes the message. You can add your own processing logic to this function, such as sending notifications or updating a database.

To connect this function to your Service Bus, you'll need to add a new function to your Azure Functions app using the Service Bus trigger template. Follow the prompts to specify the Service Bus connection string and configure the function.

Real-time Image Processing with Azure Functions and Azure Blob Storage

 

Image processing is a critical component of many applications, from social media to healthcare. However, processing large volumes of image data can be time-consuming and resource-intensive. In this tutorial, we'll show you how to use Azure Functions and Azure Blob Storage to create a real-time image processing pipeline that can handle large volumes of data with scalability and flexibility.

 

Prerequisites

Before we get started, you'll need to have the following:

 

1.       An Azure account

2.       Visual Studio Code

3.       Azure Functions extension for Visual Studio Code

4.       Azure Blob Storage extension for Visual Studio Code

Creating the Azure Functions App

The first step is to create an Azure Functions app. In Visual Studio Code, select the Azure Functions extension and choose "Create New Project". Follow the prompts to choose your programming language and runtime.

 

Once your project is created, you can create a new function by selecting the "Create Function" button in the Azure Functions Explorer. Choose the Blob trigger template to create a function that responds to new files added to Azure Blob Storage.

 

In this example, we'll create a function that recognizes objects in images using Azure Cognitive Services. We'll use the Cognitive Services extension for Visual Studio Code to connect to our Cognitive Services account.

 

Creating the Azure Blob Storage Account

Next, we'll create an Azure Blob Storage account to store our image data. In the Azure portal, select "Create a resource" and search for "Blob Storage". Choose "Storage account" and follow the prompts to create a new account.

 

Once your account is created, select "Containers" to create a new container for your image data. Choose a container name and access level, and select "Create". You can now add images to your container through the Azure portal or through your Azure Functions app.

 

Connecting the Azure Functions App to Azure Cognitive Services

To connect your Azure Functions app to Azure Cognitive Services, you'll need to add the Cognitive Services extension to your project. In Visual Studio Code, select the Extensions icon and search for "Azure Cognitive Services". Install the extension and reload Visual Studio Code.

 

Next, open your function code and add the following code to your function:

const { ComputerVisionClient } = require("@azure/cognitiveservices-computervision");
const { BlobServiceClient } = require("@azure/storage-blob");

module.exports = async function (context, myBlob) {
    const endpoint = process.env["ComputerVisionEndpoint"];
    const key = process.env["ComputerVisionKey"];
    const client = new ComputerVisionClient({ endpoint, key });
    
    const blobEndpoint = process.env["BlobEndpoint"];
    const blobKey = process.env["BlobKey"];
    const blobServiceClient = BlobServiceClient.fromConnectionString(`BlobEndpoint=${blobEndpoint};BlobAccessKey=${blobKey}`);
    const containerClient = blobServiceClient.getContainerClient("mycontainer");
    
    const buffer = myBlob;
    
    const result = await client.analyzeImageInStream(buffer, { visualFeatures: ["Objects"] });
    
    const blobName = context.bindingData.name;
    const blobClient = containerClient.getBlockBlobClient(blobName);
    const metadata = { tags: result.objects.map(obj => obj.objectProperty) };
    await blobClient.setMetadata(metadata);
}

This code connects to your Azure Cognitive Services account and creates a new ComputerVisionClient object. It also connects to your Blob Storage account and retrieves the image data from the blob trigger.

 

The code then uses the Computer Vision API to analyze the image and extract any objects it detects. It adds these object tags to the image metadata and saves the updated metadata to Blob Storage.

 

Testing the Image Processing Pipeline

Now that our image processing pipeline is set up, we can test it by uploading an image to our Blob Storage container. The function should automatically trigger and process the image, adding object tags to the metadata.

 

To view the updated metadata, select the image in the Azure portal and choose "Properties". You should see a list of object tags extracted from the image.