Trying out the Azure Front Door Service

Introduction

With the statement “Scalable and secure entry point for fast delivery of your global applications”, Azure Front Door (Service) saw the lights of dawn! The features it promises to bring to the table?

My first thought was… Does this fill the gap where I’m currently leveraging CloudFlare for my personal deployments? So today’s post will be to see what the process of creating the service looks like in real life!

 

Let’s take it for a spin!

After browsing through the marketplace, we select the “Azure Front Door” and press the button to create one. Here I must say that the configuration flow here is a pleasant surprise. First we enter the “basics”…

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Generating a docs website powered by Git & Markdown

Introduction

Did you know I’m a huge fan of the Microsoft / Azure docs? Did you also know that the docs websites are powered by GitHub repositories? Let that one sink in… So you can leverage the same way you collaborate on code, work on publishing documentation?!? How awesome is that!

After a bit of looking around, it appears DocFX is actually powered to do this. I don’t know if this is the tool used behind the docs website. Though there seem to be a lot of similarities. Anyhow, today’s post will be a quick walkthrough on how to setup DocFX with VSTS to publish your GitHub driven repo to an Azure Web App.

 

So what will we be needing?

Ingredients

  • GitHub repository
  • VSTS Account
  • Azure App Service
  • A tool to do the conversion : DocFX
  • Chocolatey to install DocFX

 

Initialize the repository

Be sure to install DocFX on your dev station to initialize the repository. This is done by running “docfx init -q” inside of your repository.

 

Afterwards do your typical Git magic to sync your local version with GitHub (or equivalent). Now you’ll have a dummy skeleton ready for usage, and you can now structure it to your liking! My effort is going into making docs for VMchooser.

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Drupal on Azure – Leveraging the Linux App Service for a Managed Platform Experience

Introduction

WordPress is probably the most popular CMS around. Though when I look at my home country, then I see a lot of Drupal deployments too. This might be due to the fact that the creation is of Belgian origin? Though for the region I live in, Drupal is amongst the most popular CMS systems.

That being said, Drupal is a very resource hungry system. When you enable the WebProfiler (part of the Devel module), then you can see that typical page will execute between 90 and 200 database queries. This puts a lot of stress on the underlying database system, but also on the local file system.

Due to this we see a lot of articles on how to improve the performance of Drupal. Most commonly seen is the implementation of ;

  • Varnish on the front end side, as a web application accelerator / caching HTTP reverse proxy
  • Redis or Memcache, as a way to cache data (in memory instead of hammering the database)

For today’s post, we’ll briefly discuss the various options and afterwards delve into a more advanced scenario where we leverage the Azure Linux App Service’s multi container capability.

 

What options do I have for running Drupal on Azure?

In essence there are various ways to run Drupal on Azure ;

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Azure Active Directory : Group integration for daemon / server applications (aka Service Principals)

Introduction

Today’s blog post will be how you can leverage the authentication scenario of a Daemon, Service User or Server Application when our application/API is using Azure Active Directory for its authentication flows.

“An example of a daemon application is a batch job, or an operating system service running in the background. This type of application requests an access token by using its application identity and presenting its Application ID, credential (password or certificate), and application ID URI to Azure AD. After successful authentication, the daemon receives an access token from Azure AD, which is then used to call the web API.”

In essence, a “daemon application” will do a “clients credentials grant” whilst using an Azure Active Directory Service Principal. The “application id” of the service principal will serve as the “client_id” and a generated “secret” will service as the “client_secret”.

In addition to this, we want our application to grant permissions (authorization & identification) based on the group memberships of Azure Active Directory. Where this is pretty straightforward for our basic user objects. This requires a bit of attention when wanting to achieve the same for our service principal.

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Combining Azure Traffic Manager, CloudFlare & Azure App Service for Geographic Scale!

Introduction

For today’s post, let’s take a look at an architecture example where you want to provide a geographic deployment of your webapp by using a cloudbased WAF (like Cloudflare, or Akamai, …).

 

High Level Setup

So what will we be setting up & testing today?

The user will receive a url that is powered by “Azure Traffic Manager”. That will have three endpoints ; one in Europe, one in the US and one in Asia. These endpoints will be powered Cloudflare and back by an Azure Webapp.  You’re question will probably be ; “Why use that sequence?” Because the Traffic Manager is DNS based and will do a “basic” HTTP check. If you would setup the Traffic Manager behind Cloudflare/Akamai/…, then you would see the source IPs of that service. Thus you would be unable to route the clients to the nearest location.

 

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Azure App Service : Deploying a java webapp (geoserver) to Azure

Introduction

For today’s post, we’ll be deploying a java web-app to Azure. Here we’ll be leveraging our well-known “App Service” as the underlying PaaS component to host our web-app.

 

Sample Workload : Geoserver

For today’s example, we’ll be using “geoserver” as the grounds for our little proof-of-concept. In addition, we’ll even trow in the Azure PostgreSQL Database for the back-end store.

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The anatomy of “vmchooser”… Adding some serverless into the architecture!

Introduction

In an earlier blog post I discussed the decision criteria in selecting a VM. In that post I also showed a tool called “VMchooser“. Today’s post will be on the architecture I used to build this one. As you might have guessed, it’s built on Azure components. Let’s get to it and check the anatomy of this application.

 

High Level Architecture

VMchooser has the following high level architecture ;

  • Web App : The front-end of the application is hosted on an Azure Web App.
  • Azure Functions : The back-end API & batch parser are built with Azure Functions. Which unlocks insane scaling possibilities.
  • Storage Account : The storage account serves as decoupled/central storage component for the batch parsing. And it could also be used for hosting the “database” (flat file).
  • Application Insights : Application insights is used to have the needed insights into the usage & other metrics.
  • Github : All code for this project is open-source and publically hosted. You can run your own VMchooser if you want… 😉 Every change is immediately pushed towards the front-end, back-end & database.
  • API Management : As the back-end API is decoupled from the application, I’ve also linked this api with api management. This would provide me with the option to allow 3th party application integrations via an API subscription plan.

 

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