Simon Online


Durable Azure Functions vs. NServiceBus Sagas

I’ve been on a bit of an Azure Functions kick over the last little while. I’ve blogged a bunch on Durable Functions and deployed a bunch more. When you’re as old as me then you tend to draw comparisons between new technologies and existing ones. For instance I’m constantly telling people about how web pages are a lot like the cave paintings I use to do in my youth.

The Twitter Exchange

The technology that draws the closest comparison I’ve seen to Durable Functions are NServiceBus Sagas. A few weeks ago I tweeted out wondering if any body had done a comparison. The good folks at Particular stepped up and answered.

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Accessing B2C Claims in an Azure Function

In a previous article I talked about how to authenticate your function application against Azure Active Directory Business to Consumer (which we’re going to call B2C for the sake of my fingers). Chances are in your function you’re going to want to get some of the information which is available as a claim from the bearer token. Here is how to do it.

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Running a single instance of a durable function

I have a durable functions project which orchestrates several thousand function calls the purpose of which is to scrape and load a bunch of data. It is scheduled to run once a day but one of my concerns was that I didn’t want to accidentally run to functions at the same time. They would duplicate a bunch of the data loading and, at least until the function ran again, chaos would reign. I’m not a huge fan of chaos reigning so I set out to find a way around this.

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Azure Functions and Azure B2C Authentication

I had a pretty good struggle setting up Azure Functions and Azure B2C to work together. There are a few guides out there but I wanted to put my own together because I had a terrible time finding these posts initially. The scenario here is that we want a single page application written in React to talk to an API hosted entirely in Azure Functions such that the functions are authenticated.

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Linked lists in C#

C# Advent

This post is one among many which is part of 2018’s C# Advent. There are a ton of really great posts this year and some new bloggers to discover. I’d strongly encourage you to check it out.

One of the things you’ll learn a lot about if you pursue any sort of formal education in computing science is data structures. However, once I hit the real world then it seemed like for most problems the speed differential between a linked list and a System.Collections.Generic.List wasn’t meaningful. List quickly became my go-to and it was very unusual that any other data structure would even cross my mind. This sort of lazy thinking can have a serious effect as the size of input grows. So harkening back to my school days I wanted to talk about the rudimentary data structure that is a linked list.

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Creating Storage Queues in Azure DevOps

Storage Queues are one of the original pieces of Azure dating back about a decade now. They are great for deferring work to later or spreading it out over a bunch of consumers. If you’re following best practices for DevOps you’ll know that the creation of your queues should be done in code. In some cases you can create the queues on application startup but in serverless scenarios there often is no startup code so the responsibility of creating queues falls to your deployment process. Let’s look at how to do that on Azure DevOps

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Durable Functions Logging Best Practices

Durable Functions provide a way to run a series of functions in an orchestrated concert*. The product is relatively new and it is difficult to find a whole lot of guidance on how to do things with it. I found logging to be an area where some guidance was needed. So I’m writing some.

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Setting the version of Azure functions from ARM

Need to set the version of Azure functions runtime from an ARM template? Weirdly it is actually an app setting and not something on the site. The variable is called FUNCTIONS_EXTENSION_VERSION and can take on the values ~1 or ~2. In an ARM template it looks like

    "type": "Microsoft.Web/sites",
    "kind": "functionapp",
    "name": "[variables('webSiteName')]",
    "tags": {
        "displayName": "Site"
    "apiVersion": "2016-08-01",
    "location": "[resourceGroup().location]",
    "scale": null,
    "resources": [
        "name": "appsettings",
        "type": "config",
        "apiVersion": "2015-08-01",
        "dependsOn": [ "[concat('Microsoft.Web/sites/', variables('webSiteName'))]" ],
        "tags": {
        "displayName": "App settings"
        "properties": {

Azure Data Factory - a rapid introduction

Azure is huge. There are probably a dozen ways to host a website, a similar number of different data storage technologies, tools for identity, scaling, DDoS protection - you name it Azure has it. With that many services it isn’t unusual for me to find some service I didn’t even know existed. Today that service is Data Factory. Data factory is a batch based Extract, Transform and Load(ETL) service which means that it moves data between locations. I mention that it is batch to distinguish it from services which are online and process events as they come in. Data Factory might be used to move data between a production database and the test system or between two data sources.

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Checking in packages

If there is one thing that we developers are good at it is holy wars. Vi vs. Emacs, tabs vs. spaces, Python vs. R, the list goes on. I’m usually smart enough to not get involved in such low brow exchanges… haha, who am I kidding? (vi, spaces and R, BTW) Recently I’ve been tilting at the windmill that is checking in package files. I don’t mean the files that tell what version of files to check in but the actual library files.

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