20k Page Views! Thank you!

This blog has been started 4 months ago on the 27th of May, 2018. My first post was post about new machine learning framework from Microsoft called ML.NET (this). It was my first time ever starting any blog! And yesterday this blog has reached a mark of 20,000 page views. It took a 4 months and 7 posts :)

If you are reading this, I want to say you: "Thank you for your visit!".
I hope you have found some of my posts interesting and will get back in the future :)

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How to Build TypeScript App and Deploy it on GitHub Pages

Quick Summary In this post, I will show you how to easily build and deploy a simple TicksToDate time web app like this: https://zubialevich.github.io/ticks-to-datetime .

Pros and cons of different ways of storing Enum values in the database

Lately, I was experimenting with Dapper for the first time. During these experiments, I've found one interesting and unexpected behavior of Dapper for me. I've created a regular model with string and int fields, nothing special. But then I needed to add an enum field in the model. Nothing special here, right? Long story short, after editing my model and saving it to the database what did I found out? By default Dapper stores enums as integer values in the database (MySql in my case, can be different for other databases)! What? It was a surprise for me! (I was using ServiceStack OrmLite for years and this ORM by default set's enums to strings in database) Before I've always stored enum values as a string in my databases! After this story, I decided to analyze all pros and cons I can imagine of these two different ways of storing enums. Let's see if I will be able to find the best option here.

Caching strategies

One of the easiest and most popular ways to increase system performance is to use caching. When we introduce caching, we automatically duplicate our data. It's very important to keep your cache and data source in sync (more or less, depends on the requirements of your system) whenever changes occur in the system. In this article, we will go through the most common cache synchronization strategies, their advantages, and disadvantages, and also popular use cases.

How to maintain Rest API backward compatibility?

All minor changes in Rest API should be backward compatible. A service that is exposing its interface to internal or/and external clients should always be backward compatible between major releases. A release of a new API version is a very rare thing. Usually, a release of a new API version means some global breaking changes with a solid refactoring or change of business logic, models, classes and requests. In most of the cases, changes are not so drastic and should still work for existing clients that haven't yet implemented a new contract. So how to ensure that a Rest API doesn't break backward compatibility?

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