Category: Azure

App Service Easy Auth with Auth0 (or any Open ID Connect provider)

So I’m going to prefix this with a warning – I doubt this is officially supported but at a basic level it does seem to work. I would use at your peril and I’m writing this in the hope that it makes for a useful starting point discussion with the App Service team.

I was looking at Easy Auth this week and found myself curious as to if it would work with a generic Open ID Connect identity provider. My first choice provider is Auth0 but that’s not one of the listed providers on the Easy Auth configuration page which, on the face of it, is quite limited:

Azure AD is (as well as many other things) an Open ID Connect Provider so I had a look at its settings in the advanced tab and its asking for two pretty common pieces of information in the identity world: a client ID and an issuer URL. I had an app in Auth0 that I use for general testing so I pasted in its well known configuration endpoint and the ID for my client:

I hit save and it seemed to accept everything. My web app is sat on the URL so on the Auth0 side I added a callback URL to the Easy Auth callback endpoint:

Easy Auth forwards on the contents of common claims in headers such as X-MS-CLIENT-PRINCIPAL-ID (the subject) and X-MS-CLIENT-PRINCIPAL-NAME (the name) so to see if this was working I uploaded a simple ASP.Net Core app that would output the contents of the request headers to a web page. Then I paid it a visit in my browser:

Oh. So that’s hurdle one passed. It does redirect successfully to a none-Azure AD identity provider. What about logging in?

Great. Yes. This works too. And the headers are correct based on the identity I used to login with.

How does this compare to the headers from an Azure AD backed Easy Auth:

Basically the Auth0 login is missing the refresh token (I did later set a client secret and tweak configuration in Auth0) – so there might be some work needed there. But I don’t think that’s essential.

It would be incredibly useful to be able to use Easy Auth in a supported manner with other identity providers – particularly for Azure Functions where dealing with token level authorization is a bit more “low level” than in a fully fledged framework like ASP .Net Core (though my Function Monkey library can help with this) and is only dealt with after a function invocation.

Using Function Monkey with MediatR

There are a lot of improvements coming in v4 of Function Monkey and the beta is currently available on NuGet. As the full release approaches I thought it would make sense to introduce some of these new capabilities here.

In order to simplyify Azure Functions development Function Monkey makes heavy use of commanding via a mediator and ships with my own mediation library. However there’s a lot of existing code out their that makes use of the popular MediatR library which, if Function Monkey supported, could fairly easily be moved into a serverless execution environment.

Happily Function Monkey now supports just this! You can use my existing bundled mediator, bring your own mediator, or add the shiny new FunctionMonkey.MediatR NuGet package. Here we’re going to take a look at using the latter.

First begin by creating a new, empty, Azure Functions project and add three NuGet packages:


At the time of writing be sure to use the prerelease packages version 4.0.39-beta.4 or later.

Next create a folder called Models. Add a class called ToDoItem:

public class ToDoItem
    public Guid Id { get; set; }
    public string Title { get; set; }
    public bool IsComplete { get; set; }

Now add a folder called Services and add an interface called IRepository:

internal interface IRepository
    Task<ToDoItem> Create(string title);

And a memory based implementation of this called Repository:

internal class Repository : IRepository
    private readonly List<ToDoItem> _items = new List<ToDoItem>();
    public Task<ToDoItem> Create(string title)
        ToDoItem newItem = new ToDoItem()
            Title = title,
            Id = Guid.NewGuid(),
            IsComplete = false
        return Task.FromResult(newItem);

Now create a folder called Commands and in here create a class called CreateToDoItemCommand:

public class CreateToDoItemCommand : IRequest<ToDoItem>
    public string Title { get; set; }

If you’re familiar with Function Monkey you’ll notice the difference here – we’d normally implement the ICommand<> interface but here we’re implementing MediatR’s IRequest<> interface instead.

Next create a folder called Handlers and in here create a class called CreateToDoItemCommandHandler as shown below:

internal class CreateToDoItemCommandHandler : IRequestHandler<CreateToDoItemCommand, ToDoItem>
    private readonly IRepository _repository;

    public CreateToDoItemCommandHandler(IRepository repository)
        _repository = repository;
    public Task<ToDoItem> Handle(CreateToDoItemCommand request, CancellationToken cancellationToken)
        return _repository.Create(request.Title);

Again the only real difference here is that rather than implement the ICommandHandler interface we implement the IRequestHandler interface from MediatR.

Finally we need to add our FunctionAppConfiguration class to the root of the project to wire everything up:

public class FunctionAppConfiguration : IFunctionAppConfiguration
    public void Build(IFunctionHostBuilder builder)
            .Setup(sc => sc
                .AddSingleton<IRepository, Repository>()
            .Functions(functions => functions
                .HttpRoute("todo", route => route

Again this should look familiar however their are two key differences. Firstly in the Setup block we use MediatR’s IServiceCollection extension method AddMediatR – this will wire up the request handlers in the dependency injector. Secondly the .UseMediatR() option instructs Function Monkey to use MediatR for its command mediation.

And really that’s all their is to it! You can use both requests and notifications and you can find a more fleshed out example of this on GitHub.

As always feedback is welcome on Twitter or over on the GitHub issues page for Function Monkey.

Azure Data Factory – Mapping Bug

This is something to be aware of as its effects can be very very subtle.

As part of a project for a client I have an Azure Data Factory that picks up data from various sources and moves it into a reporting database. Their are various types of copy operation going on but a common one is to take a data source and execute it via a stored procedure passing it in as a table parameter. You set up column mappings from the source to the target. I had this table type defined for a parameter to my ingestion stored procedure:

create type SessionType as table(
    [Id]                uniqueidentifier not null primary key nonclustered,
    [ProgrammeId]       UNIQUEIDENTIFIER not null,
    [StartDateTimeUtc]  datetime2        not null,
    [VolunteerId]       uniqueidentifier not null,
    [SessionCancelled]  bit              not null,
    [LastModified]      binary(8)        not null,
    [VolunteerRating]   int              null,
    [VolunteerComments] nvarchar(1025)   null

And a mapping set up from the source to this table as follows:

My ingestion procedure ran ok (it does a merge) but I was getting weird downstream results: data didn’t seem to be correlating as we expected. On back and forthing between the data sources I realised that the VolunteerId and the ProgrammeId were switched – the target VolunteerId was getting the source ReadingProgrammeId and the target ProgrammeId was getting the source VolunteerId.

I’d edited this so wandered if their was some weird caching going on or if the publish hadn’t really published so I made a change to try and force things – plus I’d run out of ideas. I couldn’t see a thing wrong with any of the SQL. I removed the two mappings and added them at the end:

On rerunning my data factory I found I now got an error. An issue trying to insert a datetime2 type into a uniqueidentifier column. The penny dropped. Despite the GUI, despite the tooling, despite the ARM definition the data factory is not using the column names – its merrily ignoring them and using order of the columns in the schema table type definition for targets.

I verified this by setting up a mapping based on the order of columns in the table type:

That fixed things and my downstream systems can now make sense of the data.

Hopefully they’ll get this fixed as unless you get a type clash its pretty dangerous.

Writing and Testing Azure Functions with Function Monkey – Part 3

Part 3 of my series on writing Azure Functions with Function Monkey focuses on writing tests using the newly released testing package – while this is by no means required it does make writing high value acceptance tests that use your applications full runtime easy and quick.

Lessons Learned

It really is amazing how quickly time passes when you’re talking and coding – I really hadn’t realised I’d recorded over an hours footage until I came to edit the video. I thought about splitting it in two but the contents really belonged together so I’ve left it as is.

Bike Reminders – A breakdown of a real Azure application (Part 1)

I’ve been meaning to write about a real cloud based project for some time but the criteria a good candidate project needs to fit are challenging:

  • Significant enough to illustrate numerous design and implementation decisions
  • Not so large that the time investment for a reader to get into it is prohibitive
  • I need to own, or have free access to, the intellectual property
  • It needs to be something I want, or am contracted, to build for reasons beyond writing about it

To expand upon that last point a little – I don’t have the time to build something just for a series of blog posts and if I did I suspect it would be too artificial and essentially would end up a strawman.

The real world and real development is constrained messy, you come across things that you can’t economically solve in an ivory towered fashion. You can’t always predict everything in advance, you get things wrong and don’t always have the time available to start again and so have to do the best that you can with what you have.

In the case of this project I hadn’t really thought about it as a candidate for writing about until I neared the end of building the MVP and so it comes, rather handily, with warts and all. For sure I’ve refactored things but no more than you’d expect to on any time and budget constrained project.

My intention is, over the course of a series of posts, to explore this application in an end to end fashion: the requirements, the architecture, the code, testing, deployment – pretty much its end to end lifecycle. Hopefully this will contain useful nuggets of information that can be applied on other projects and help those new to Azure get up and running.

About the project

So what does the project do?

If you’re a keen cyclist you’ll know that you need to check various components on your bike at regular intervals. You’ll also know that some of the components last just long enough that you’ll forget about them – my personal nemesis is chain wear, more than once I’ve taken that to the point where it is likely to start damaging the rear cassette having completely forgotten about it.

I’m fortunate enough to have a rather nice bike and so there is nothing cheap about replacing anything so really not a mistake you want to be making. Many bikes also now contain components that need charging – Di2 and eTap are increasingly common and though I’ve yet to get caught out on a ride I’ve definitely run it closer than I realised.

After the last time I made this mistake I decided to do something about it and thus was born Bike Reminders: a website that links up with Strava to send you reminders as you accrue mileage on each of your bikes. While not a substitute for regularly checking your bike I’m hopeful it will at least give me a prod over chain wear! I contemplated going direct to Garmin but they seem to want circa $5000 for API access and thats a lot of component damage before I break even – ouch.

In terms of an MVP that distilled out into a handful of high level requirements:

  • Authenticate with Strava
  • Access a users bikes in Strava
  • Allow a mileage based maintenance schedule to be set up against a bike
  • Allow email reminders to be dismissed / reset
  • Allow email reminders to be snoozed
  • Update the progress towards each reminder based on rider activity in Strava

There were also some requirements I wanted to keep in mind for the future:

  • Time based reminders
  • “First ride of the week” type reminders
  • Allow reminders to be sent via push notifications
  • Predictive information – based on a riders history when is a reminder likely to be triggered, this is useful if you’re going away on a training camp for example and want to get maintenance done before you go

Setting off on the project I set a number of overarching goals / none functional requirements for it:

  • Keep it small enough that it could be built alongside a two (expanded to three!) week cycling training block in Mallorca
  • To have a very low cost to run both in terms of minimum footprint (cost to run 1 user) and per user cost as the system scales up
  • To require little to no maintenance and a fully automated delivery mechanism
  • To support multiple client types (initially web but to be followed up with a Flutter app)
  • Keep personal data out of it as far as possible
  • As far as possible spin out any work that isn’t specific to the problem domain as open source (I’m fairly likely to reuse it myself if nothing else)

And although I try not to jump ahead of myself that mapped nicely onto using Azure as a cloud provider with Azure Functions for compute and Azure DevOps and Application Insights for the operational side of things.


The next step was to figure out what I’d need to build – initially I worked this through on a “mental beermat” while out cycling but I like to use the C4 Model to describe software systems. It gives a basic structure and just enough tools to think about and describe systems at different levels of architecture without disappearing up its own backside in complexity and becoming an end in and of itself.

System Context

For this fairly simple and greenfield system establishing the big picture was fairly straight forward. It’s initially going to comprise of a website accessed by cyclists with their Strava logins, connecting to Strava for tracking mileage, and sending emails for which I chose SendGrid due to existing familiarity with it.


Breaking this down into more detail forced me to start making some additional decisions. If I was going to build an interactive website / app I’d need some kind of API for which I decided to use Azure Functions. I’ve done a lot of work with them, have a pretty good library for building REST APIs with them (Function Monkey) and they come with a generous free usage allowance which would help me meet my low cost to operate criterion. The event based programming model would also lend itself to handling things like processing queues which is how I envisaged sending emails (hence a message broker – the Azure Service Bus).

For storage I wanted something simple – although at an early stage it seemed to me that I’d be able to store all the key details about cyclists, their bikes and reminders in a JSON document keyed off the cyclists ID. And if something more complex emerged I reasoned it would be easy to convert this kind of format into another. Again cost was a factor and as I couldn’t see, based on my simple requirements, any need for complex queries I decided to at least start with plain old Azure Storage Blob Containers and a filename based on the ID. This would have the advantage of being really simple and really cheap!

The user interface was a simple decision: I’ve done a lot of work with React and I saw no reason it wouldn’t work for this project. Over the last few months I’ve been experimenting with TypeScript and I’ve found it of help with the maintainability of JavaScript projects and so decided to use that from the start on this project.

Finally I needed to figure out how I’d most likely interact with the Strava API to track changes in mileage. They do have a push API that is available by email request but I wanted to start quickly (and this was Christmas and I had no idea how soon I’d hear back from them) and I’d probably have to do some buffering around the ingestion – when you upload a route its not necessarily associated with the right bike (for example my Zwift rides always end up on my main road bike, not my turbo trainer mounted bike) to prevent confusing short term adjustments.

So to begin with I decided to poll Strava once a day for updates which would require some form of scheduling. While I wasn’t expecting huge amounts of overnight for the website Strava do rate limit APIs and so I couldn’t use a timer function with Azure as that would run the risk of overloading the API quite easily. Instead I figured I could use enqueue visibility on the Service Bus and spread out athletes so that the API would never be overloaded. I’ve faced a similar issue before and so I figured this might also make for a useful piece of open source (it did).

All this is summarised in the diagram below:

Azure Topology

Mapped (largely) onto Azure I expected the system to look something like the below:

The notable exception is the introduction of Netlify for my static site hosting. While you can host static sites on Azure it is inelegant at best (and the Azure Storage SPA support is useless as you can’t use SSL and a custom domain) and so a few months back I went searching for an alternative and came across Netlify. It makes building, deploying and hosting sites ridiculously easy and so I’ve been gradually switching my work over to here.

I also, currently, don’t have API Management in front of the Azure Functions that present the REST API – the provisioned approach is simply too expensive for this system at the moment and the consumption model, at least at the time of writing, has a horrific cold start time. I do plan to revisit this.

Next Steps

In the next part we’ll break out the code and begin by taking a look at how I structured the Azure Function app.

SPA Hosting on Azure / Can We Have More Boring Stuff Please

Microsoft announced the long awaited SPA hosting support for Azure yesterday. Before rushing in and adopting it make sure to read the small print – there is no support for custom domain names and SSL. Which given SSL is becoming almost mandatory (and about to become super-prominent as a warning in Chrome) makes it pretty useless for public facing websites in 2018.

Its based on Azure Storage which has outstanding user voice requests from 2012 asking for SSL support with custom domains so it may also not be something that appears soon. I would love to be wrong on that.

The options for resolving it are much like they were before this feature was introduced: proxies of one form or another (CDN, Functions, CloudFlare etc.).

If you’re happy with an Azure storage based domain name, so probably working on an internal facing system I guess, it may still work for you.

As someone who does a lot of work with SPAs I must admit I’m really disappointed in this – it seems pretty ridiculous to me that in 2018 amidst all the “big” Azure announcements (machine learning on the edge, globally distributed planet scale data etc.) that hosting  a single page app, essentially static files, cheaply and easily is still awkward on the platform and requires multiple services to deliver.

I’d love to see the Azure teams really focus on, and really finish, some of the “boring stuff” like this – it might not get marketing column inches, and might not make for a snazzy new “you can do this in 5 minutes” video to impress purchasing CTOs but it would save time and effort amongst hundreds of thousands of engineers and development shops and would deliver real value to those people. I’m often reminded of the Saving Lives story that Andy Hertzfeld tells.

Using ReactJS with Azure AD B2C

Azure AD B2C is Microsoft’s identity provider for social and enterprise logins allowing you to, for example, unify the login process across Twitter, Facebook, and Azure AD / Office 365. It comes with a generous free tier and following that pricing is reasonable particularly compared to the pricing for “enterprise” logins with some of the competition.

However the downside is the documentation for B2C and integration with specific technologies isn’t that clear – there’s nothing particularly strange about B2C, ultimately its just an OpenID Connect identity provider, but there is some nuance in it.

In parallel Microsoft provide MSAL (MicroSoft Authentication Library) for handling authentication from JavaScript clients and here documentation is clearer but still a little incomplete and it can be difficult to figure out the implementation required for a particular scenario – not helped by the library reference having no content other than to repeat method definitions.

I’m currently working with a handful of projects based around React JS, Azure AD B2C, and a combination of ASP.Net Core MVC and Azure Functions and found myself grappling with this. What I was doing seemed eminently reusable (and I hope useful) and so I set some time aside to take what I’d learned and create a B2C specific npm package – react-azure-adb2c.

To install it if you’re using npm:

npm install react-azure-adb2c --save

Or if you’re using yarn:

yarn add react-azure-adb2c

Before continuing you’ll need to set up Azure AD B2C for API access and the three tutorials here are a reasonably easy to follow guide on how to do that. At the end of that process you should have a tenant name, a sign in and/or up policy, an application ID, and one or more scopes.

The first change to make in your app to use the package is to initialize it with your B2C details:

import authentication from 'react-azure-adb2c';

    // your B2C tenant
    tenant: '',
    // the policy to use to sign in, can also be a sign up or sign in policy
    signInPolicy: 'mysigninpolicy',
    // the the B2C application you want to authenticate with
    applicationId: '75ee2b43-ad2c-4366-9b8f-84b7d19d776e',
    // where MSAL will store state - localStorage or sessionStorage
    cacheLocation: 'sessionStorage',
    // the scopes you want included in the access token
    scopes: [''],
    // optional, the URI to redirect to after logout
    postLogoutRedirectUri: ''

There are then two main ways you can use the library. You can either protect the entire application (for example if you have a React app that is launched from another landing area) or specific components. To protect the entire application simply wrap the app startup code in index.js as shown below: => {
  ReactDOM.render(<App />, document.getElementById('root'));

To require authentication for specific components the react-azure-adb2c library provides a function that will wrap a component in a higher order component as shown in the example below:

import React, { Component } from 'react';
import authentication from 'react-azure-adb2c'
import { BrowserRouter as Router, Route, Switch } from "react-router-dom";
import HomePage from './Homepage'
import MembersArea from './MembersArea'

class App extends Component {
  render() {
    return (
      <Router basename={process.env.PUBLIC_URL}>
          <Route exact path="/" component={HomePage} />
          <Route exact path="/membersArea" component={authentication.required(MembersArea)}>

And finally to get the access token to use with API calls:

import authentication from 'react-azure-adb2c'

// ...

const token = authentication.getAccessToken();

If you find any issues please let me know over on GitHub.

Hopefully that’s useful and takes some of the pain out of using ReactJS with Azure AD B2C and as ever I can be reached on Twitter for discussion.



Azure Functions – Microsoft Feedback on HTTP Trigger Scaling

Since I published this piece Microsoft have made significant improvements to HTTP scaling on Azure Functions and the below is out of date. Please see this post for a revised comparison.

Following the analysis I published on Azure Functions and the latency in scaling HTTP triggered functions the Microsoft development team got in touch to discuss my findings and provide some information about the future which they were happy for me to share.

Essentially the team are already at work making improvements in this area. Understandably they were unable to commit to timescales or make specific claims as to how significant those improvements but my sense is we’re looking at a handful of months and so, hopefully, half one of this year. They are going to get in touch with me once something is available and I’ll rerun my tests.

I must admit I’m slightly sceptical as to if they’ll be able to match the scaling capability of AWS Lambda (and to be clear they did not make any such claim), which is what I’d like to see, as that looks to me as if it would require a radical uprooting of the Functions runtime model rather than an evolution but ultimately I’m just a random, slightly informed, punter. Hopefully they can at least get close enough that Azure Functions can be used in more latency critical and spiky scenarios.

I’d like to thank @jeffhollan and the team for the call – as a predominantly Azure and .NET developer it’s both helpful and encouraging to be able to have these kinds of dialogues around the platform so critical to our success.

In the interim I’m still finding I can use HTTP functions – I just have to be mindful of their current limitations – and have some upcoming blog posts on patterns that make use of them.


  • If you're looking for help with C#, .NET, Azure, Architecture, or would simply value an independent opinion then please get in touch here or over on Twitter.

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