Static websites are back in the mainstream these days. Website generators like Jekyll, Hugo, or Gatsby, make it fairly easy to combine templates and markdown pages to produce static HTML files. Static assets are the simplest thing to serve and cache, so the whole setup ends up being fast and cost-efficient.

Many platforms offer services to host such static websites. This post explains the steps to create the infrastructure to do so on Microsoft Azure.

Setting up the infrastructure to serve a static website doesn’t sound like it would be all that difficult, but when you consider HTTPS certificates, content…


I wrote a lot about cold starts of serverless functions. The articles are full of charts and numbers which are hopefully useful but might be hard to internalize. I decided to come up with a way to represent colds starts visually.

I created HTTP functions that serve geographic maps (map credit Open Street Map). The map is a combination of small square tiles; each tile is 256 by 256 pixels. My selected map view consists of 12 tiles, so 12 requests are made to the serverless function to load a single view.

During each experiment, I load the map and…


The rise of managed cloud services, cloud-native, and serverless applications brings both new possibilities and challenges. More and more practices from software development processes like version control, code review, continuous integration, and automated testing are applied to cloud infrastructure automation.

Most existing tools suggest defining infrastructure in text-based markup formats, YAML being the favorite. In this article, I’m making a case for using real programming languages like TypeScript instead. Such a change makes even more software development practices applicable to the infrastructure realm.

Sample Application

It’s easier to make a case given a specific example. For this article, we’ll build a URL…


In the past 6 months I published several blog posts under the same theme of comparing the serverless services of the top cloud providers in terms of their performance and scalability properties:

I received some very positive feedback on those posts (readers are fantastic!). On top of that, people always had great suggestions about improving and extending the contents of those articles. I’ve been thinking a lot about them.

Limitations of a blog post

However, the format of a blog post is limited in…


The post is a part of F# Advent Calendar 2018. It’s Christmas time!

This summer I was hired by the office of Santa Claus. Santa is not just a fairy tale character on his own — he leads a large organization that supplies gifts and happiness to millions of children around the globe. Like any large organization, Santa’s office employs an impressive number of IT systems.

As part of its IT modernization effort, North Pole HQ restructured the whole supply chain of Christmas gifts. Many legacy components were moved from a self-managed data center at the North Pole — although…


Stateful Workflows on top of Stateless Serverless Cloud Functions — this is the essence of the Azure Durable Functions library. That’s a lot of fancy words in one sentence, and they might be hard for the majority of readers to understand.

Please join me on the journey where I’ll try to explain how those buzzwords fit together. I will do this in 3 steps:

  • Describe the context of modern cloud applications relying on serverless architecture;
  • Identify the limitations of basic approaches to composing applications out of the simple building blocks;
  • Explain the solutions that Durable Functions offer for those problems.

Microservices


Azure Functions major version 2.0 was released into GA a few days back during Microsoft Ignite. The runtime is now based on .NET Core and thus is cross-platform and more interoperable. It has a nice extensibility story too.

In theory, .NET Core runtime is more lean and performant. But last time I checked back in April, the preview version of Azure Functions V2 had some serious issues with cold start durations.

I decided to give the new and shiny version another try and ran several benchmarks. All tests were conducted on Consumption plan.

TL;DR: it’s not perfect just yet.

Cold Starts

Cold…


Serverless cloud services are hot. Except when they are not :)

AWS Lambda, Azure Functions, Google Cloud Functions are all similar in their attempt to enable rapid development of cloud-native serverless applications.

Auto-provisioning and auto-scalability are the killer features of those Function-as-a-Service cloud offerings. No management required, cloud providers will deliver infrastructure for the user based on the actual incoming load.

One drawback of such dynamic provisioning is a phenomenon called “cold start”. Basically, applications that haven’t been used for a while take longer to startup and to handle the first request.

Cloud providers keep a bunch of generic unspecialized…


I love functional programming for the simplicity that it brings.

But at the same time, I realize that learning functional programming is a challenging process. FP comes with a baggage of unfamiliar vocabulary that can be daunting for somebody coming from an object-oriented language like C#.

Some of functional lingo

“Monad” is probably the most infamous term from the list above. Monads have reputation of being something very abstract and very confusing.

The Fallacy of Monad Tutorials

Numerous attempts were made to explain monads in simple definitions; and monad tutorials have become a genre of its own. And yet, times and times again, they fail to enlighten the readers.

Mikhail Shilkov

Cloud, Azure Functions, Serverless, F#, Functional Programming. Microsoft Azure MVP.

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