Azure Functions are the Function-as-a-Service offering from Microsoft Azure cloud. Basically, an Azure Function is a piece of code which gets executed by Azure every time an event of some kind happens. The environment manages deployment, event triggers and scaling for you. This approach is often reffered as Serverless.

In this post I will describe one use case for Azure Functions: we implemented a number of functions as a proxy layer between our operations/monitoring tool and Azure metric APIs.


Automated monitoring and alerting are crucial in order to ensure 24x7 smooth operations of our business-critical applications. We host applications both on-premise and in Azure cloud, and we use a single set of tools for monitoring across this hybrid environment.

Particularly, we use PRTG Network Monitor to collect all kinds of metrics about the health of our systems and produce both real-time alerts and historic trends.

A unit of monitoring in PRTG is called "sensor". Each sensor polls a specific data source to retrieve the current value of a metric. The data source can be a performance counter, a JSON value in HTTP response, a SQL query result and so on.

The problem is that there is no PRTG sensor for Azure metrics out of the box. It might be possible to implement a sensor with custom code, e.g. in PowerShell, but it would be problematic in two ways (at least):

  1. The custom code sensors are cumbersome to develop and maintain.
  2. We would have to put sensitive information like Azure API keys and connection strings to PRTG.

Solution Overview

To overcome these problems we introduced an intermediate layer, as shown on the following picture:

PRTG to HTTP to Azure

We use PRTG HTTP XML/REST sensor type. This sensor polls a given HTTP endpoint, parses the response as JSON and finds a predefined field. This field is then used as the sensor value. It takes 30 seconds to setup such sensor in PRTG.

The HTTP endpoint is hosted inside Azure. It provides a facade for metric data access. All the sensitive information needed to access Azure metrics API is stored inside Azure configuration itself. The implementation knows which Azure API to use to get a specific metric, and it hides those complications from the client code.

Azure Functions

We chose Azure Functions as the technology to implement and host such HTTP facade.

The functions are very easy to create or modify. They are deployed independently from any other code, so we can update them at any cadence. And no need to provision any kind of servers anywhere - Azure will run the code for us.

Here is how the whole setup works:

Retrieval of data from Azure to PRTG

  1. Every X minutes (configured per sensor), PRTG makes an HTTP request to a predefined URL. The request includes an Access Key as a query parameter (the key is stored in sensor URL configuration). Each access key enables access to just one endpoint and is easily revokable.

  2. For each Metric type there is an Azure Function listening for HTTP requests from PRTG. Azure authorizes requests that contain valid access keys.

  3. Based on query parameters of the request, Azure Function retrieves a proper metric value from Azure management API. Depending on the metric type, this is accomplished with Azure .NET SDK or by sending a raw HTTP request to Azure REST API.

  4. Azure Function parses the response from Azure API and converts it to just the value which is requested by PRTG.

  5. The function returns a simple JSON object as HTTP response body. PRTG parses JSON, extracts the numeric value, and saves it into the sensor history.

At the time of writing, we have 13 sensors served by 5 Azure Functions:

Map of PRTG sensors to Functions to Azure services

I describe several functions below.

Service Bus Queue Size

The easiest function to implement is the one which gets the amount of messages in the backlog of a given Azure Service Bus queue. The function.json file configures input and output HTTP bindings, including two parameters to derive from the URL: account (namespace) and queue name:

  "bindings": [
      "authLevel": "function",
      "name": "req",
      "type": "httpTrigger",
      "direction": "in",
      "route": "Queue/{account}/{name}"
      "name": "$return",
      "type": "http",
      "direction": "out"
  "disabled": false

The C# implementation uses standard Service Bus API and a connection string from App Service configuration to retrieve the required data. And then returns a dynamic object, which will be converted to JSON by Function App runtime.

#r "Microsoft.ServiceBus"

using System.Net;
using Microsoft.ServiceBus;

public static object Run(HttpRequestMessage req, string account, string name)
    var connectionString = Environment.GetEnvironmentVariable("sb-" + account);
    var nsmgr = NamespaceManager.CreateFromConnectionString(connectionString);
    var queue = nsmgr.GetQueue(name);
    return new 
        messageCount = queue.MessageCountDetails.ActiveMessageCount,
        dlq = queue.MessageCountDetails.DeadLetterMessageCount

And that is all the code required to start monitoring the queues!

Service Bus Queue Statistics

In addition to queue backlog and dead letter queue size, we wanted to see some queue statistics like amount of incoming and outgoing messages per period of time. The corresponding API exists, but it's not that straightforward, so I described the whole approach in a separate post: Azure Service Bus Entity Metrics .NET APIs.

In my Azure Function I'm using the NuGet package that I mentioned in the post. This is accomplished by adding a project.json file:

  "frameworks": {
      "dependencies": {
        "MikhailIo.ServiceBusEntityMetrics": "0.1.2"

The function.json file is similar to the previous one, but with one added parameter called metric. I won't repeat the whole file here.

The Function implementation loads a certificate from the store, calls metric API and returns the last metric value available:

using System.Linq;
using System.Security.Cryptography.X509Certificates;
using MikhailIo.ServiceBusEntityMetrics;

public static DataPoint Run(HttpRequestMessage req, string account, string name, string metric)
    var subscription = Environment.GetEnvironmentVariable("SubscriptionID");
    var thumbprint = Environment.GetEnvironmentVariable("WEBSITE_LOAD_CERTIFICATES");

    X509Store certStore = new X509Store(StoreName.My, StoreLocation.CurrentUser);

    X509Certificate2Collection certCollection = certStore.Certificates.Find(

    var client = new QueueStatistics(certCollection[0], subscription, account, name);
    var metrics = client.GetMetricSince(metric, DateTime.UtcNow.AddMinutes(-30));
    return metrics.LastOrDefault();

Don't forget to set WEBSITE_LOAD_CERTIFICATES setting to your certificate thumbprint, otherwise Function App won't load it.

Web App Instance Count

We are using Azure Web Jobs to run background data processing, e.g. for all queue message handlers. The jobs are hosted in Web Apps, and have auto-scaling enabled. When the load on the system grows, Azure spins up additional instances to increase the overall throughput.

So, the next metric to be monitored is the amount of Web App instances running.

There is a REST endpoint to retrieve this information, but this time authentication and authorization are implemented with Active Directory. I created a helper class to wrap the authentication logic:

public static class RestClient
    public static async Task<T> Query<T>(string url)
        var token = await GetAuthorizationHeader();
        var client = new HttpClient();
        client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", token);

        var response = await client.GetAsync(url);
        var content = await response.Content.ReadAsStringAsync();
        return JsonConvert.DeserializeObject<T>(content);

    private static async Task<string> GetAuthorizationHeader()
        var activeDirectoryID = Environment.GetEnvironmentVariable("ActiveDirectoryID");
        var applicationID = Environment.GetEnvironmentVariable("ActiveDirectoryApplicationID");
        var secret = Environment.GetEnvironmentVariable("ActiveDirectorySecret");

        var context = new AuthenticationContext($"{activeDirectoryID}");
        var credential = new ClientCredential(applicationID, secret);
        AuthenticationResult result = 
            await context.AcquireTokenAsync("", credential);
        return result.AccessToken;

The function then uses this REST client to query Web App management API, converts JSON to strongly typed C# objects and extracts the amount of instances into HTTP response:

public class Instance
    public string id { get; set; }
    public string name { get; set; }

public class Response
    public Instance[] value { get; set; }

public static async Task<HttpResponseMessage> Run(HttpRequestMessage req)
    var subscription = Environment.GetEnvironmentVariable("SubscriptionID");
    var resourceGroup = Environment.GetEnvironmentVariable("ResourceGroup");
    var appService = Environment.GetEnvironmentVariable("AppService");

    var url = $"{subscription}/resourceGroups/{resourceGroup}" +
    var response = await RestClient.Query<Response>(url);

    return req.CreateResponse(HttpStatusCode.OK, new
        instanceCount = response.value.Length

Users Online

The last example I want to share is related to Application Insights data. For instance, we inject a small tracking snippet on our front-end page and then Application Insights track all the page views and other user activity.

We use the amount of users currently online as another metric for the monitoring solution. The Application Insights API is currently in preview, but at least it is nicely described at Be sure to check out API Explorer too.

The following sample function returns the amount of users online:

public class UsersCount
    public long unique { get; set; }

public class Value
    public UsersCount UsersCount { get; set; }

public class Response
    public Value value { get; set; }

public static async Task<HttpResponseMessage> Run(HttpRequestMessage req)
    var appID = Environment.GetEnvironmentVariable("ApplicationInsightsID");
    var key = Environment.GetEnvironmentVariable("ApplicationInsightsKey");

    var client = new HttpClient();
    client.DefaultRequestHeaders.Add("x-api-key", key);
    var url = $"{appID}/metrics/users/count";

    var response = await client.GetAsync(url);
    var content = await response.Content.ReadAsStringAsync();
    var r = JsonConvert.DeserializeObject<Response>(content);

    return req.CreateResponse(HttpStatusCode.OK, new
        usersCount = r.value.UsersCount.unique


It seems that monitoring metrics retrieval is an ideal scenario to start using Azure Functions. The Functions are very easy to create and modify, they abstract away the details of hosting Web API endpoints, and at the same time give you the full power of C# (or F#) and Azure.

And because we only call those functions about 1 time per minute, they are free to run!