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Azure Functions scenarios

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We often build systems to react to a series of critical events. Whether you're building a web API, responding to database changes, processing event streams or messages, Azure Functions can be used to implement them.

In many cases, a function integrates with an array of cloud services to provide feature-rich implementations. The following are a common (but by no means exhaustive) set of scenarios for Azure Functions.

Select your development language at the top of the article.

Process file uploads

There are several ways to use functions to process files into or out of a blob storage container. To learn more about options for triggering on a blob container, see Working with blobs in the best practices documentation.

For example, in a retail solution, a partner system can submit product catalog information as files into blob storage. You can use a blob triggered function to validate, transform, and process the files into the main system as they're uploaded.

Diagram of a file upload process using Azure Functions.

The following tutorials use a Blob trigger (Event Grid based) to process files in a blob container:

For example, using the blob trigger with an event subscription on blob containers:

[FunctionName("ProcessCatalogData")]
public static async Task Run([BlobTrigger("catalog-uploads/{name}", Source = BlobTriggerSource.EventGrid, Connection = "<NAMED_STORAGE_CONNECTION>")]Stream myCatalogData, string name, ILogger log)
{
    log.LogInformation($"C# Blob trigger function Processed blob\n Name:{name} \n Size: {myCatalogData.Length} Bytes");

    using (var reader = new StreamReader(myCatalogData))
    {
        var catalogEntry = await reader.ReadLineAsync();
        while(catalogEntry !=null)
        {
            // Process the catalog entry
            // ...

            catalogEntry = await reader.ReadLineAsync();
        }
    }
}

Real-time stream and event processing

So much telemetry is generated and collected from cloud applications, IoT devices, and networking devices. Azure Functions can process that data in near real-time as the hot path, then store it in Azure Cosmos DB for use in an analytics dashboard.

Your functions can also use low-latency event triggers, like Event Grid, and real-time outputs like SignalR to process data in near-real-time.

Diagram of a real-time stream process using Azure Functions.

For example, using the event hubs trigger to read from an event hub and the output binding to write to an event hub after debatching and transforming the events:

[FunctionName("ProcessorFunction")]
public static async Task Run(
    [EventHubTrigger(
        "%Input_EH_Name%",
        Connection = "InputEventHubConnectionString",
        ConsumerGroup = "%Input_EH_ConsumerGroup%")] EventData[] inputMessages,
    [EventHub(
        "%Output_EH_Name%",
        Connection = "OutputEventHubConnectionString")] IAsyncCollector<SensorDataRecord> outputMessages,
    PartitionContext partitionContext,
    ILogger log)
{
    var debatcher = new Debatcher(log);
    var debatchedMessages = await debatcher.Debatch(inputMessages, partitionContext.PartitionId);

    var xformer = new Transformer(log);
    await xformer.Transform(debatchedMessages, partitionContext.PartitionId, outputMessages);
}

Machine learning and AI

Besides data processing, Azure Functions can be used to infer on models. The Azure OpenAI binding extension lets easily integrate features and behaviors of the Azure OpenAI service into your function code executions.

Functions can connect to an OpenAI resources to enable text and chat completions, use assistants, and leverage embeddings and semantic search.

A function might also call a TensorFlow model or Azure AI services to process and classify a stream of images.

Diagram of a machine learning and AI process using Azure Functions.

Run scheduled tasks

Functions enables you to run your code based on a cron schedule that you define.

Check out how to Create a function in the Azure portal that runs on a schedule.

A financial services customer database, for example, might be analyzed for duplicate entries every 15 minutes to avoid multiple communications going out to the same customer.

Diagram of a scheduled task where a function cleans a database every 15 minutes deduplicating entries based on business logic. 

[FunctionName("TimerTriggerCSharp")]
public static void Run([TimerTrigger("0 */15 * * * *")]TimerInfo myTimer, ILogger log)
{
    if (myTimer.IsPastDue)
    {
        log.LogInformation("Timer is running late!");
    }
    log.LogInformation($"C# Timer trigger function executed at: {DateTime.Now}");

    // Perform the database deduplication
}

Build a scalable web API

An HTTP triggered function defines an HTTP endpoint. These endpoints run function code that can connect to other services directly or by using binding extensions. You can compose the endpoints into a web-based API.

You can also use an HTTP triggered function endpoint as a webhook integration, such as GitHub webhooks. In this way, you can create functions that process data from GitHub events. To learn more, see Monitor GitHub events by using a webhook with Azure Functions.

Diagram of processing an HTTP request using Azure Functions.

For examples, see the following:

[FunctionName("InsertName")]
public static async Task<IActionResult> Run(
    [HttpTrigger(AuthorizationLevel.Function, "post")] HttpRequest req,
    [CosmosDB(
        databaseName: "my-database",
        collectionName: "my-container",
        ConnectionStringSetting = "CosmosDbConnectionString")]IAsyncCollector<dynamic> documentsOut,
    ILogger log)
{
    string requestBody = await new StreamReader(req.Body).ReadToEndAsync();
    dynamic data = JsonConvert.DeserializeObject(requestBody);
    string name = data?.name;

    if (name == null)
    {
        return new BadRequestObjectResult("Please pass a name in the request body json");
    }

    // Add a JSON document to the output container.
    await documentsOut.AddAsync(new
    {
        // create a random ID
        id = System.Guid.NewGuid().ToString(), 
        name = name
    });

    return new OkResult();
}

Build a serverless workflow

Functions is often the compute component in a serverless workflow topology, such as a Logic Apps workflow. You can also create long-running orchestrations using the Durable Functions extension. For more information, see Durable Functions overview.

A combination diagram of a series of specific serverless workflows using Azure Functions.

Respond to database changes

There are processes where you might need to log, audit, or perform some other operation when stored data changes. Functions triggers provide a good way to get notified of data changes to initial such an operation.

Diagram of a function being used to respond to database changes.

Consider the following examples:

Create reliable message systems

You can use Functions with Azure messaging services to create advanced event-driven messaging solutions.

For example, you can use triggers on Azure Storage queues as a way to chain together a series of function executions. Or use service bus queues and triggers for an online ordering system.

Diagram of Azure Functions in a reliable message system.

The following article shows how to write output to a storage queue.

And these articles show how to trigger from an Azure Service Bus queue or topic.

Next steps

Getting started with Azure Functions