Skip to main content

Use Azure App Configuration To Store Hierarchical Data

.NET Core has first class support for parsing hierarchical configuration data. If you have a JSON configuration like this, 

{
"rootObject": [
{
"level1": "l1v1",
"level2": "l1v2"
},
{
"level1": "l2v1",
"level2": "l2v2"
}
]
}

then you can easily target individual values with "rootObject:[0]:level1". However to store this JSON, you have to either use a File System or some kind of database. The problem with this approach is that you have to modify the entire document when you want to change a value of any field. If you are not careful, you may end up corrupting the JSON and thus breaking your system.

This is where Azure App Configuration comes in the picture. Azure App Configuration is nothing but a key-value store. We can use that to store the hierarchical values like this:


Once you inject this Azure App Configuration in your .NET Core project and call Configuration.Bind() on your strongly typed representation of the JSON configuration, you get the fully populated .NET Object without writing a single line of code!


Hope this post helps you in making a decision on where and how to store your hierarchical configuration data for .NET apps.
Labels:

Comments

Post a Comment

As far as possible, please refrain from posting Anonymous comments. I would really love to know who is interested in my blog! Also check out the FAQs section for the comment policy followed on this site.

Popular posts from this blog

Creating a Smart Playlist

A few days earlier I was thinking that wouldn't it be nice if I had something which will automatically generate a playlist for me with no artists repeated. Also, it would be nice if I could block those artists which I really hate (like Himesh Reshammiya!). Since I couldn't find anything already available, I decided to code it myself. Here is the outcome -  This application is created entirely in .NET Framework 4/WPF and uses Windows Media Player Library as its source of information. So you have to keep your Windows Media Player Library updated for this to work. It is tested only on Windows 7/Vista. You can download it from here . UPDATE : You can download the Windows XP version of the application here . Please provide your feedback!
2 comments
Continue Reading >>

Integrating React with SonarQube using Azure DevOps Pipelines

In the world of automation, code quality is of paramount importance. SonarQube and Azure DevOps are two tools which solve this problem in a continuous and automated way. They play well for a majority of languages and frameworks. However, to make the integration work for React applications still remains a challenge. In this post we will explore how we can integrate a React application to SonarQube using Azure DevOps pipelines to continuously build and assess code quality. Creating the React Application Let's start at the beginning. We will use npx to create a Typescript based React app. Why Typescript? I find it easier to work and more maintainable owing to its strongly-typed behavior. You can very well follow this guide for jsx based applications too. We will use the fantastic Create-React-App (CRA) tool to create a React application called ' sonar-azuredevops-app '. > npx create-react-app sonar-azuredevops-app --template typescript Once the project creation is done, we
1 comment
Continue Reading >>

Serverless Generative AI: How to Query Meta’s Llama 2 Model with Microsoft’s Semantic Kernel and AWS Services

Generative AI is a type of artificial intelligence that can create new content such as text, images, music, etc. in response to prompts. Generative AI models learn the patterns and structure of their input training data by applying neural network machine learning techniques, and then generate new data that has similar characteristics. They are all the rage these days. 😀 Some types of generative AI include: Foundation models , which are complex machine learning systems trained on vast quantities of data (text, images, audio or a mix of data types) on a massive scale. Foundation models can be adapted quickly for a wide range of downstream tasks without needing task-specific training. Examples of foundation models are GPT, LaMDA and Llama . Generative adversarial networks (GANs) , which are composed of two competing neural networks: a generator that creates fake data and a discriminator that tries to distinguish between real and fake data. The generator improves its ability to fool the d
2 comments
Continue Reading >>