Hands-on Git aliases

Git is deservedly the most popular version control system among software developers. You can easily find Git extensions and plugins for any IDE of your choice. However, if you like me, working mostly in the command line git aliases could save you a lot of time and your productivity will be even higher. Here I want to share with you some of my Git aliases which you could find useful for your daily work.

Add to staging area and commit in one go

// Aliases
ac = !git add -A && git commit
acm = !git add -A && git commit -m

// Usage 
git ac
git acm "My awesome commit"

I use it to add all modified files to staging area and commit it to local repository. As you can see there is two variations of this alias: with or without commit message.

Show me the deal

When you need to check the log with a nice colored layout:

lg = !git log --graph --pretty=format:'%Cred%h%Creset -%C(yellow)%d%Creset %s %Cgreen(%cr) %C(bold blue)<%an>%Creset' --abbrev-commit

You will see something like this:

Slightly different version:

graph = !git log --oneline --all --graph --decorate

Shorthand notations

Few simple shorthand notations for familiar commands:

// Aliases
co = !git checkout
st = !git status
n = !git checkout -b

// Usage
git co master
git st
git n feature/my_awesome_new_feature

If you want to add changes from your working directory to the last commit in one go:

amend = !git add -A && git commit --amend --no-edit 

If you want to undo changes you haven’t committed yet in staging area:

discard = !git checkout --

In case you played a while with changes and commits but really want to start all over again:

fuckit = !git reset --hard && git clean -df

To do some cleanup:

cleanup = !git remote prune origin && git gc && git clean -dfx && git stash clear


To quickly get the latest updates from remote:

up = !git fetch origin && git rebase origin/$(git rev-parse --abbrev-ref HEAD)

To push all your commits from local repository to remote (remote branch will be created automatically):

p = !git push origin $(git rev-parse --abbrev-ref HEAD)

Other useful

Sometimes you want to see stats on who is working on that repository and what contribution of that user:

who = shortlog -n -s --no-merges

Often there is a need to check which files changed in the specific commit:

list-changes = !git diff-tree --no-commit-id --name-only -r
git list-changes <SHA>

You just need to specify commit SHA.

Sometimes I’m curious in lines of code stats for my project. So I can do it in this way:

loc = !git ls-files '*.cs' | xargs wc -l
Just change the file extension in that alias to the language of your choice.

Usage patterns

Here is some most common usage patterns with these aliases.

Checkout to master and get latest changes from remote

git co master
git up

Create new feature branch, save changes, commit and push:

git n feature/new_large_hadron_collider 
// Create new collider here...
git acm "New collider is finished"
// More changes 
git amend
// It's time to push now
git p

Fork some repository as playground

git clone
// Add proper maybe monad support
git acm "Almost done"
// Add more nice features
git acm "More awesomeness"
// Realize that it doesn't work
git fuckit
git cleanup

The complete list of aliases

	ac = !git add -A && git commit
	acm = !git add -A && git commit -m
	co = !git checkout
	st = !git status
	amend = !git add -A && git commit --amend --no-edit
	discard = !git checkout --
	up = !git fetch origin && git rebase origin/$(git rev-parse --abbrev-ref HEAD)
	p = !git push origin $(git rev-parse --abbrev-ref HEAD)
	n = !git checkout -b
	lg = !git log --graph --pretty=format:'%Cred%h%Creset -%C(yellow)%d%Creset %s %Cgreen(%cr) %C(bold blue)<%an>%Creset' --abbrev-commit
	graph = !git log --oneline --all --graph --decorate
	fuckit = !git reset --hard && git clean -df
	who = shortlog -n -s --no-merges
	cleanup = !git remote prune origin && git gc && git clean -dfx && git stash clear
	loc = !git ls-files '*.cs' | xargs wc -l
	mr = !sh -c 'git fetch $1 merge-requests/$2/head:mr-$1-$2 && git checkout mr-$1-$2' -
	list-changes = !git diff-tree --no-commit-id --name-only -r
F#, Programming, Software design

Power of composition with map and bind

In functional architecture functionalities get composed into workflows. Workflows are essential part of any business behavior modeling. Things get complicated when you need to build bigger systems from small components. Sometimes it is hard to find proper connectors to fit multiple functions having different inputs and outputs. There are various tools to achieve that composition in FP world which you could have heard by the names like functors, monoids or monads. These tools allow you to glue things together by connecting outputs of one functions to the inputs of another functions with proper transformations in between. In practice it is much easier to understand how it works than diving in category theory and trying to figure out the math beneath it.

🔌 Composition basics

When dealing with relatively simple types like strings and numbers connecting inputs and outputs is quite straightforward. Consider this example:

let addOne a = a + 1
let multipleByTwo a = a * 2

Here we defined two functions both of which takes a number as an input and returns number as an output, so their signatures are the same – we expect number on input and the result of operation in output is also a number:

(int -> int)

We can call it in following ways:

multipleByTwo (addOne 2)
// OR
2 |> addOne |> multiplyByTwo
val it : int = 6

We also can create new function which is composition of addOne and multiplyByTwo:

let addOneMultipliedByTwo = addOne >> multiplyByTwo
addOneMultipliedByTwo 2

This way you can build really complex logic from smaller pieces just like with a Lego bricks.

🅰️ ADTs are everywhere

More often, however, you will find yourself writing a bit more complicated things than adding or multiplying numbers. It could be custom types, or types based on other types which are known as algebraic data types (ADTs). It is very common to build up things from abstract types and provide functions which transform other values to that types. One very familiar to you example could be Maybe (a.k.a. Option) type which you could heard as Maybe monad or Maybe functor. In a nutshell it is a container for value or absence of the value. This is extremely effective abstraction to avoid nulls in your code and hence having peace 🧘 and no null reference exceptions everywhere.

In F# it is presented in Option module with set of functions to work with that type. So you have type Option<‘T> with possible value Value: ‘T or no value which is None. You can find tons of functions in the module. They help you building more complex things from smaller and make proper transformations for connecting functions which require that type.

Let’s have a quick look on how to use it:

let someValue = Some 10
let noValue = None

someValue |> Option.get // val it : int = 10
someValue |> Option.isSome // val it : bool = true
noValue |> Option.isNone // val it : bool = true
(10, someValue) ||> Option.contains // val it : bool = true
(99, someValue) ||> Option.defaultValue // val it : int = 10
(99, noValue) ||> Option.defaultValue // val it : int = 99

😲 When things go wrong

Now let’s have a small programming exercise. Suppose silly scenario where we have a players (any game you can imagine) and we need to check if the score player collected is good or not. So we come up with something like this:

type Player = { 
    Name: string 
    Score: int

let isGoodScore score = if score >= 70 then true else false

So all we need is to create players and check their scores:

let frank = { Name = "Frank"; Score = 90; }
let jack = { Name = "Jack"; Score = 37; }

frank.Score |> isGoodScore // val it : bool = true
jack.Score |> isGoodScore // val it : bool = false

“Hey, but player could have no score as well”

So how about to support that? Well, piece of a cake. Let’s make few minor changes:

type Player = { 
    Name: string 
    Score: int option
let frank = { Name = "Frank"; Score = Some 90; }
let john = { Name = "John"; Score = None; }
let jack = { Name = "Jack"; Score = Some 37; }

Nice! We’ve wrapped score in Option type just exactly like in requirement we got. How about isGoodScore function, will it still work?

frank.Score |> isGoodScore
error FS0001: Type mismatch. Expecting a
    'int option -> 'a'
but given a
    'int -> bool'
The type 'int option' does not match the type 'int'

Oops, we can’t use optional type with plain type like that:

So we need a ways to glue up monadic types like Option with functions working on plain values. And that’s where two most essential functions get into the big picture: map and bind.

🤝When composition meet ADT

As I mentioned before in the FP toolbox there various tools to help us with transformations. One such tool is map function. There are other names for it like fmap, lift, Select (think of C# LINQ). Each monadic-like type has this function.

Let’s have a look what signature of that function for Option:

(('a -> 'b) -> 'a option -> 'b option)

There 3 arguments: function which transforms input of type ‘a to ‘b, optional ‘a and optional ‘b. So how can we apply map for our use case? Pretty straightforward actually:

frank.Score |> isGoodScore // val it : bool option = Some true
john.Score |> isGoodScore // val it : bool option = None
jack.Score |> isGoodScore // val it : bool option = Some false

You see how return type is changed? We just applied standalone function which works on int to Option type. It lifted result of the function execution back to the Option. If input value is Some int it will be extracted from container (Option type), piped to the function and on the other end lifted up/wrapped back to the Option type. In case if there no value, it will just use None.

In C# IEnumerable with Select method on it works in the same way but applied to collections which means that collections are also ADTs. Here some visuals to help in understanding what’s going on:

👷 Bind it

Another very useful tool is bind function which you may have heard by other names like flatMap, collect, SelectMany. This one allows you to compose monadic functions in a little bit different way. Here the signature of the bind function for Option:

(('a -> 'b option) -> 'a option -> 'b option)

Let’s extend on our previous example and say that now we have an external source (database, file, etc.) from which we need to fetch players to find out score. So we define tryFindPlayer function as follows:

let tryFindPlayer name = 
    [ frank; john; jack ] |> List.tryFind (fun c -> c.Name = name)

List.tryFind is built-in function which returns Some ‘T if satisfies predicate in lambda or None. In our case it will return Some Player or None. Now we would be able to get the score of the player:

tryFindPlayer "Frank"
    |> Option.bind (fun c -> c.Score)

Here the visuals:

As you see, unlike map, bind allows you to compose up things within the same category (Option) but with different underlying types. It is flattening result, so instead of having Option<Option<int>> with bind it skips unnecessary wrapping.

💪The power of composition

There a lot of ADTs in form of data structures, workflows and other concepts which you need to combine to build working software: List<T>, Option<T>, State<T>, Async<T>, etc.

Once you get a grasp on how to use it – it becomes straightforward how to compose things up:

tryFindPlayer "Frank" 
    |> Option.bind (fun c -> c.Score)
    |> isGoodScore
val it : bool option = Some true
.NET, F#, Programming

How to read settings from configuration file in F#

During the work on one of the projects I had to make connection to the SQL server to fetch data. Most of the development time I spent in F# interactive – I create some sort of scratchpad script file (with fsx extension) and run VSCode with Ionide extension. This works like a charm with all features you expect from modern code editor like autocomplete, linting and syntax highlighting. Having built-in REPL allows you to use NuGet packages, load files with F# code, reference managed assemblies and execute selected parts of the code by pressing Alt+Enter directly in editor.

During development you could keep connection string in constant or variable, but at some stage, when you finalize project you want to move everything to config file. There is a problem related to this however. The way how default executable treated depends on the context. In case of F# project the default executable is the current project .config file. In case of F# interactive it is Fsi.exe.config. So solution which works fine for your F# project will fail when you run from F# interactive. I will show you how you can make it work in both contexts.

So, how to read configuration file in your F# project? Well, one great and simple option is just to use AppSettings type provider. It will expose your app.config in a strongly typed way. If you don’t know what type providers are please refer to the documentation. There is no direct analogy in C# to this concept. As author of F# language said:

A design-time component that computes a space of types and methods on-demand…

An adapter between data/services and the .NET type system…

On-demand, scalable compile-time provision of type/module definitions…

Don Syme

However in this post I would like to show you how you can create a simple abstraction to read connection string (or any other section like appSettings) and what caveats are on your way. Assume we have following app.config file in the root folder of our demo app:

<?xml version="1.0" encoding="utf-8" ?>
        <add name="NinjaConnectionString" connectionString="Server=(localdb)\MsSqlLocalDb;Database=NinjaDb;Trusted_Connection=True;"/>

Solution for F# projects

Let’s create Configuration.fs file and start with class definition for our configuration abstraction:

type NinjaConfiguration() = class
    static member ConnectionString = ()

Ok, now we need a function to read a config file (assuming you have your configuration file in bin folder and named {project-executable}.config). Just add this section to your fsproj to copy app.config from your project’s root to bin on each build:

<Target Name="CopyCustomContent" AfterTargets="AfterBuild">
        <Copy SourceFiles="app.config" DestinationFiles="$(OutDir)\ninja_app.dll.config" />

The function to read connection strings could look like this:

let private tryGetConnectionString (connectionStrings: ConnectionStringSettingsCollection) name =
    seq { for i in 0..connectionStrings.Count - 1 -> connectionStrings.[i] }
    |> Seq.tryFind(fun cfg -> cfg.Name = name)
    |> function
    | Some cs -> Some cs.ConnectionString
    | _ -> None

The signature of the function is

(ConnectionStringSettingsCollection -> string -> string option)

It takes ConnectionStringSettingsCollection and name of the connection element in your app.config and returns option of string with it’s value or None.

On line 2 we create a F# sequence expression to wrap standard .NET collection type. This will allow us to use any idiomatic F# language constructs which applicable to collections (think of all functions in Seq module, pipe operator, etc.).

On line 3 we immediately benefit from it by piping all elements from connection string section to Seq.tryFind and using lambda function to find only setting we need by name parameter. This will iterate over all entries in connection strings and compare it against Name property of ConnectionStringSettings class. If it finds an entry, Some of ConnectionStringSettings will be returned, otherwise None.

Lines 4-6 just extract connection string from it with a simple pattern matching.

Let’s update NinjaConfiguration class:

type NinjaConfiguration() = class
    static member ConnectionString = 
        tryGetConnectionString ConfigurationManager.ConnectionStrings "NinjaConnectionString"

This is already working code, however without error handling it is not complete, so let’s add try-with section to be sure that when file is missing we not bubble up runtime exception in your face:

type NinjaConfiguration() = class
    static member ConnectionString = 
            tryGetConnectionString ConfigurationManager.ConnectionStrings "NinjaConnectionString"
            | Failure (_) -> None

Much better. If there is a problem with finding or opening configuration file we return None. Same for the case when there no connection string with NinjaConnectionString name found. Put it all together we should come up with this code:

module Ninja.Configuration

open System.Configuration

let private tryGetConnectionString (connectionStrings: ConnectionStringSettingsCollection) name =
    seq { for i in 0..connectionStrings.Count - 1 -> connectionStrings.[i] }
    |> Seq.tryFind(fun cfg -> cfg.Name = name)
    |> function
    | Some cs -> Some cs.ConnectionString
    | _ -> None

type NinjaConfiguration() = class
    static member ConnectionString =
            tryGetConnectionString ConfigurationManager.ConnectionStrings "NinjaConnectionString"
           | Failure(_) -> None

Extending solution to work in F# interactive

Previous solution works fine when you run it with F5 in VSCode or Visual Studio IDE or via dotnet run command line. But how to make it work in F# interactive?

Let’s create simple scratchpad.fsx to use NinjaConfiguration in F# interactive:

#r "nuget: System.Configuration.ConfigurationManager" // install NuGet package needed for Configuration.fs
#load "Configuration.fs" // load our NinjaConfiguration class

open Ninja.Configuration // open module so that type will be available for use
let connStr = NinjaConfiguration.ConnectionString

val connStr : string option = None. 

App.config in the same folder where scratchpad.fsx and Configuration.fs. So why result is None? The answer is that default path for lookup will be fsi.exe and since we used ConfigurationManager.ConnectionStrings it will start search config file from global scope (machine.config). So to solve that issue we need to set current directory for F# interactive and map configuration file to that folder. To make it work in both contexts we need to add conditional compiler directive (let’s call it COMPILED). Let’s make final changes to our code in Configuration.fs to the following snippet:

module Ninja.Configuration

open System.Configuration

let [<Literal>] private DbConnectionStringName = "NinjaConnectionString"

let private tryGetConnectionString (connectionStrings: ConnectionStringSettingsCollection) name =
    seq { for i in 0..connectionStrings.Count - 1 -> connectionStrings.[i] }
    |> Seq.tryFind(fun cfg -> cfg.Name = name)
    |> function
    | Some cs -> Some cs.ConnectionString
    | _ -> None

type NinjaConfiguration() = class
    static member ConnectionString =
            // Executes in F# project/solution when provided COMPILED compilation directive
            #if COMPILED 
                tryGetConnectionString ConfigurationManager.ConnectionStrings DbConnectionStringName
            #else // Executes in script environment (fsx file)
                System.IO.Directory.SetCurrentDirectory (__SOURCE_DIRECTORY__)
                let fileMap = ExeConfigurationFileMap()
                fileMap.ExeConfigFilename <- "app.config"
                let config = ConfigurationManager.OpenMappedExeConfiguration(fileMap, ConfigurationUserLevel.None)
                tryGetConnectionString config.ConnectionStrings.ConnectionStrings DbConnectionStringName
           | Failure(_) -> None

After change re-execute following lines in the script:

#load "Configuration.fs" // load our NinjaConfiguration class

open Ninja.Configuration // open module so that type will be available for use
let connStr = NinjaConfiguration.ConnectionString

Now result is:

val connStr: string option = Some "Server=(localdb)\MsSqlLocalDb;Database=NinjaDb;Trusted_Connection=True;"

After adjustments the code in Configuration.fs will work in both cases: as a part of F# project or in F# interactive. Same principle applies to any IO: if you want your code to work in both contexts you need to take this in consideration.

Happy coding!

Cloud, DevOps, Software design

The 12-Factor App

Around 2011-2012 when the cloud computing was not that popular, developers from Heroku presented methodology known as Twelve-Factor App. It describes recipes for building cloud-native, scalable, deployable applications. In my opinion it is a baseline for any team building cloud-native apps.

When you design applications for containers you should keep in mind that container image moves from environment to environment, so the image can’t hold things like production database credentials or any other sensitive information. It all should be supplied to the container by injecting configuration on container start up.

Another thing is to not bake-in any networking inside your image. Container images should not contain hostnames or port numbers. That’s because the setting needs to change dynamically while the container image stays the same. Links between containers are all established by the control plane when starting them up.

Containers are meant to start and stop rapidly. Avoid long startup or initialization sequences. Some production servers take many minutes to load reference data or to warm up caches. These are not suited for containers. Aim for a total startup time of one second.

It’s hard to debug an containerized applications. Just getting access to log files can be a challenge. Such applications need to send their telemetry out to a data collector.

Let’s take a closer look to these 12 factors which you should keep in mind each time developing application which meant to be running in container. The “factors” identify different potential impediments to deployment, with recommended solutions for each:

1 Codebase Track one codebase in revision control. Deploy the same build to every environment
2 Dependencies Explicitly declare and isolate dependencies
3 Config Store config in the environment
4 Backing services Treat backing services as attached resources
5 Build, release, run Strictly separate build and run stages
6 Processes Execute the app as one or more stateless processes
7 Port binding Export services via port binding
8 Concurrency Scale out via the process model
9 Disposability Maximize robustness with fast startup and graceful shutdown
10 Dev/prod parity Keep development, staging, and production as similar as possible
11 Logs Treat logs as event streams
12 Admin processes Run admin/management tasks as one-off processes

ASP.NET Core, C#, Programming

Tips on using Autofac in .NET Core 3.x

.NET Core supports DI (dependency injection) design pattern which is technique for achieving Inversion of Control (IoC) between classes and their dependencies. The native, minimalistic implementation is known as conforming container and it is anti-pattern. You can read more about issues related with it here. There is a promise from Microsoft to make better integration points with 3rd party DI vendors in a new .NET Core releases but it is good enough for most of the pet projects and small production projects. Being a user of DI for quite a long time I used to have more broad support from dependency injection frameworks. For the sake of keeping this article short and focused I would skip the definite list of functionality I miss in native DI implementation for .NET Core. I will mention only few: extended lifetime scopes support, automatic assembly scanning for implementations, aggregate services and multi-tenant support. There plenty of DI frameworks on the market. Back in 2008/2009 when I switched to .NET one of my favorite DI frameworks was StructureMap. Having rich functionality it was one of the standard choice for my projects. Another popular framework was Castle Windsor. For some time I was also a user of a Ninject DI which I found very easy to use.

However, StructureMap was deprecated for some time already, Ninject is still good, but I was looking for some different DI to try with one of my new .NET Core projects. Autofac caught my attention immediately. It is on the market since 2007 and it gets only better with 3400+ stars and 700+ forks on the GitHub. It has exhaustive documentation and feature list. On the moment of writing this post the latest version of Autofac is 6 and the way how you bootstrap it in .NET Core 3.x and 5 changed compared to 5.x branch.

So, enough talks: talk is cheap, show me some code…

Tip 1


public class Program
  public static void Main(string[] args)
    // ASP.NET Core 3.0+:
    // The UseServiceProviderFactory call attaches the
    // Autofac provider to the generic hosting mechanism.
    var host = Host.CreateDefaultBuilder(args)
        .UseServiceProviderFactory(new AutofacServiceProviderFactory())
        .ConfigureWebHostDefaults(webHostBuilder => {

Startup Class

public class Startup
  public Startup(IHostingEnvironment env)
    // In ASP.NET Core 3.0 `env` will be an IWebHostEnvironment, not IHostingEnvironment.
    this.Configuration = new ConfigurationBuilder().Build();
  public IConfigurationRoot Configuration { get; private set; }
  public ILifetimeScope AutofacContainer { get; private set; }
  public void ConfigureServices(IServiceCollection services)

  public void ConfigureContainer(ContainerBuilder builder)
    // Register your own things directly with Autofac here. Don't
    // call builder.Populate(), that happens in AutofacServiceProviderFactory
    // for you.
    builder.RegisterModule(new MyApplicationModule());

  public void Configure(
    IApplicationBuilder app,
    ILoggerFactory loggerFactory)
    // If, for some reason, you need a reference to the built container, you
    // can use the convenience extension method GetAutofacRoot.
    this.AutofacContainer = app.ApplicationServices.GetAutofacRoot();

Tip 2

Scanning assemblies

Autofac can use conventions to find and register components in assemblies.

public void ConfigureContainer(ContainerBuilder builder)

This will register types that are assignable to closed implementations of the open generic type. In that case it will register all implementations of IConfigureOptions<>. See options pattern for more information on how to configure configuration settings with dependency injection.

Tip 3

Use Mvc/Api controllers instantiation with Autofac

Controllers aren’t resolved from the container; just controller constructor parameters. That means controller lifecycles, property injection, and other things aren’t managed by Autofac – they’re managed by ASP.NET Core. You can change that using AddControllersAsServices().

  public void ConfigureServices(IServiceCollection services)
public void ConfigureContainer(ContainerBuilder builder) {
	var controllersTypesInAssembly = typeof(Startup).Assembly.GetExportedTypes().Where(type => typeof(ControllerBase).IsAssignableFrom(type)).ToArray();

Here we register all types that are descendants of ControllerBase type. We also enable property injection capability (line 5). This is useful when you want to have some property in the base controller implementation which could be re-used (e.g. IMediator).

Tip 4

Register EF Core DbContext with Autofac

If you use Entity Framework Core you want your DbContext to be managed by DI container. One important notice is that DbContext should behave as a unit of work and be scoped to request lifetime. In native DI it registered as a scoped service which in Autofac equal to InstancePerLifetimeScope.

public static void AddCustomDbContext(this ContainerBuilder builder, IConfiguration configuration) {
	builder.Register(c => {
		var options = new DbContextOptionsBuilder<ApplicationContext>();
		options.UseSqlServer(configuration["ConnectionStrings:ApplicationDb"], sqlOptions => { sqlOptions.MigrationsAssembly(typeof(Startup).GetTypeInfo().Assembly.GetName().Name);
			sqlOptions.EnableRetryOnFailure(maxRetryCount: 15, maxRetryDelay: TimeSpan.FromSeconds(30), errorNumbersToAdd: null);
		return options.Options;
public void ConfigureContainer(ContainerBuilder builder) {

Tip 5

Use modules for your registrations

public void ConfigureContainer(ContainerBuilder builder) {
	builder.RegisterModule(new MediatorModule());
	builder.RegisterModule(new ApplicationModule());
public class ApplicationModule: Autofac.Module {
	public ApplicationModule() {}
	protected override void Load(ContainerBuilder builder) {

Keeping registrations in modules makes your wire-up code structured and allow deployment-time settings to be injected.

Tip 6

Follow best practices and recommendations


Recommended reads and videos of 2020

2020 hit hard on us and brought a lot of unexpected surprises: COVID-19 and burden on healthcare system, hard times for private business, especially for small-to-medium companies, restrictions, lockdowns and much more… On the other hand, a lot of companies and individuals changed the way how they work: in short terms remote work become as natural as it was going to the office in pre-Corona times. We got more time after all. We could dedicate it to our families and things we like to do most. From professional perspective, we could dedicate freed up time for online courses and readings. As for me, this year I started filling some gaps in certain areas like DevOps and Kubernetes, I discovered brilliant resources on Software Architecture and of course it was a year of deep dive to functional programming for me. Also I finally started my own blog, which you, my dear comrade are reading now. I would like to share with you my list of courses and readings which made up my year. Hope you will find it useful as well.

Righting Software by Juval Löwy

It doesn’t matter if you are experienced architect or just interested in Software Architecture, you definitely need to read that brilliant work. Author offers an idea of volatility based decomposition and compelling arguments to avoid functional and domain decompositions. The book is made up of two sections: one which describes The Method for System Design and one which describes the Project Design. System Design is all about technical implementation of volatility-based decomposition. It teaches you how to plan and split complex system into parts and create design which is flexible and maintainable for years of operation. Second part of the book gives you exhaustive knowledge on Project Design: how to plan project from the beginning to the final delivery. It covers staffing, cost planning, estimations and much more. What I found nice in the book is that author operates with very concrete examples and metrics, graphs and formulas. Everything in this book is concise, concrete and beautiful, however I should admit it is not something which is easy to read, you should have some background and experience with building software.

My verdict 9/10

The Pragmatic Programmer: 20th Anniversary Edition, 2nd Edition: Your Journey to Mastery by David Thomas and Andrew Hunt

20 years old classic revisited. All the main concepts described back in 1999 still valid in our field by current days. Authors made corrections to the current realities we live in like cloud computing and spread of microservices, but more important, they give invaluable advises on how to take responsibility on your own actions, how to develop your career, how to behave like Professional. This book reminds me another classic – The Clean Coder by Robert Martin, but I think it covers a bit wider range of aspects and from different angle. In my opinion it is a must read for every software engineer.

I have pleasure to listen to the audiobook version. Audiobook is organized as a series of sections, each containing a series of topics. It is read by Anna Katarina; Dave and Andy (and a few other folks) jump in every now and then to give their take on things.

My verdict 10/10

Release It!: Design and Deploy Production-Ready Software 2nd Edition by Michael Nygard

This book was published in the late 2018. I recommend this book for everyone somehow related to releasing software in production. It will save you years of try and learn in a hard way. Author already had all that experience and kindly shares it with us. You will understand typical problems with distributed systems. It touches a variety of different aspects like robustness of a system, security, versioning and much more. Very engaging book. As soon as you pick up this book, you will not put it down until you read it to the end. Also author had a great sense of humor, so the book is fun to read.

My verdict 9/10

F# From the Ground Up

F Sharp (programming language) - Wikipedia

Great course on Udemy by Kit Eason. Full of examples and quite interactive. Very good for beginners who wants to get grasp on functional programming and get practical experience.

The Art Of Code by Dylan Beattie

Glorious and hilarious talk on code from the perspective of an art. Dylan Beattie the author of the Rockstar programming language. The ending of the video is really epic 🙂

Where We’re Going, We Don’t Need Servers! by Sam Newman

Interesting talk by the author of Building Microservices books. Thoughts on where we are going and trends in cloud computing.

That’s it. Wish you all Merry Xmas and a Happy New Year 🎄🎅

Programming, Software design

Continuous refactoring

A son asked his father (a programmer) why the sun rises in the east, and sets in the west. His response? It works, don’t touch!

Fisrt rule of programming

How often you see software systems where adding new, relatively simple features takes ages, where it is hard to find the core logic or in which part of the system to apply changes? Does reading the code gives you a headache? Does any changes you need to introduce in that software makes you sick and you want to escape from that sinking ship? Well, unfortunately, you became a victim of a software rot.

Ivar Jacobson describes a software entropy as follows:

The second law of thermodynamics, in principle, states that a closed system‘s disorder cannot be reduced, it can only remain unchanged or increase. A measure of this disorder is entropy. This law also seems plausible for software systems; as a system is modified, its disorder, or entropy, tends to increase. This is known as software entropy

Ivar Jacobson

The Problem

In our daily work we too busy with adding new features as fast as possible and closing our tickets. Everyone is happy: the business sells features to customers, and we got paid, and if lucky, we even get some bonuses for that incredible performance. With time, however, software gets more complicated. You’re going to find out that adding more features takes longer and produces more bugs. This tends to grow very quickly and ends in a system which extremely hard and expensive to maintain.

Perhaps you heard of a term Technical Debt? It describes collective debt you owe to your software by doing some shortcuts when implementing some feature because you didn’t have time to do it properly. It could be on any level from overall software design to the low-level implementation. But it is a very optimistic term – it assumes that this debt will be paid off which we all know almost never happen. I prefer to think about it as a Software Rot actually and the whole practice which leads to that as a Shortcut-Driven-Development.

I think many companies just don’t realize what are the costs of maintenance of a poorly built software. But hey, wouldn’t it be nice if we can show and prove this mathematically? So that you will have strong arguments in your discussions with product owners and managers regarding the need of refactoring. Once upon a time, while I was reading Code Simplicity: The Fundamentals of Software , I reached chapter which describes the equitation of software design and it has brilliant math representation of that equation:

D = (Pv * Vi) / (Ei + Em)

Or in English:

The Desirability of Implementation is directly proportional to the Probability of Value and the Potential Value of Implementation, and inversely proportional to the total effort, consisting of the Effort of Implementation plus the Effort of Maintenance.

However, there is a critical factor missing from the simple form of this equation: time. What we actually want to know is the limit of this equation as time approaches infinity, and that gives us the true Desirability of Implementation. So let’s look at this from a logical standpoint:

The Effort of Implementation is a one-time cost, and never changes, so is mostly unaffected by time.

The Value of Implementation may increase or decrease over time, depending on the feature. It’s not predictable, and so we can assume for the sake of this equation that it is a static value that does not change with time (though if that’s not the case in your situation, keep this factor in mind as well). One could even consider that the Effort of Maintenance is actually “the effort required to maintain this exact level of Value,” so that the Value would indeed remain totally fixed over time.

The Probability of Value, being a probability, approaches 1 (100%) as time goes to infinity.

The Effort of Maintenance, being based on time, approaches infinity as time goes to infinity. What this equation actually tells us is that the most important factors to balance, in terms of time, are probability of value vs. effort of maintenance. If the probability of value is high and the effort of maintenance is low, the desirability is then dependent only upon the Potential Value of Implementation vs. the Effort of Implementation–a decision that a product manager can easily make. If the probability of value is low and the effort of maintenance is high, the only justification for implementation would be a near-infinite Potential Value of Implementation.

The Solution

Let’s take a look at definition of refactoring given by Martin Fowler:

Refactoring is a disciplined technique for restructuring an existing body of code, altering its internal structure without changing its external behavior.

Its heart is a series of small behavior preserving transformations. Each transformation (called a “refactoring”) does little, but a sequence of these transformations can produce a significant restructuring. Since each refactoring is small, it’s less likely to go wrong. The system is kept fully working after each refactoring, reducing the chances that a system can get seriously broken during the restructuring.

Keeping software systems in good order requires developer teams to follow strong practices. But first of all it is your personal responsibility and should be in your culture. Continuous Refactoring is a key factor in that sense. You should refactor and improve your code on a day-to-day basis. Plan this work if you need to, explain the importance of it to the managers and product owners. You should become refactoring machines. This is not only tremendously reduce the denominator part of equation (Em), but also will force you to protect your code from breaking by implementing all kind of testing , one of which is regression testing. Each bit of refactoring will make your code better and easier to extend with new functionality. Gradually, you can switch from Shortcut-Driven-Development to Software Engineering and enjoy from what you are doing and helping business to survive in the long run.

The Mantra

Mantra for TDD followers

.NET, F#, Programming

Having fun with F# operators

F# is very exciting and fun language to learn. It contains pipe and composition operators which allows you to write less code with better conciseness. In addition to familiar prefix and postfix operators it also comes with the “infix” operator. The beauty of it is that you can define your own infix operators and succinctly express business logic in your F# code.

Prefix, infix and postfix 👾

As an example of prefix operators we can define any regular function:

let times n x = x * n 

and call this function with a prefix notation:

times 3 3 // val it : int = 9

In F# vast majority of primitives are functions, just like in pure OOP language everything is an object. So you can also call multiplication operator as a function:

(*) 3 3 // val it : int = 9

which gives the same result as in the previous code snippet.

Postfix operators is not something you often use and mostly comes with built-in keywords:

type maybeAString = string option // built-in postfix keyword
type maybeAString2 = Option<string> // effectively same as this
// Usage
let s:maybeAString = Some "Ninja in the bushes!"
let s2:maybeAString2 = None

But most interesting one is infix operator. As you already could guess, infix operator should be placed between two operands. Everyone did some math in school and wrote something similar to:

3 * 3 // val it : int = 9

Not surprisingly it is something you use without even thinking. Now, let’s define few custom functions:

let (^+^) x y = x ** 2. + y ** 2. // sum of the square of two numbers
let (^^) x y = x ** y // returns x to the power of y

And use it with an infix operator:

3. ^+^ 3. // val it : float = 18.0
3. ^^ 3. // val it : float = 27.0

Note that we can also use it with a prefix notation just as a regular functions:

(^+^) 3. 3. // val it : float = 18.0
(^^) 3. 3. // val it : float = 27.0

Of course infix syntax looks much more succinct in that case.

Pipe, compose and mix 🔌

The king among F# operators is a pipe operator (|>). It allows you to express function composition in a readable way. Function application is left associative, meaning that evaluating x y z is the same as evaluating (x y) z. If you would like to have right associativity you can use explicit parentheses or pipe operator:

let fun x y z = x (y z)
let fun x y z = y z |> x // forward pipe operator
let fun x y z = x <| y z // backward pipe operator

Okay. As you see there two flavors of pipe operators: forward and backward. Here the definition of forward pipe operator:

let (|>) x f = f x

Just as simple as that: feeding the argument from the left side (x) to function (f). The definition of the backward pipe operator is:

let (<|) x f = x f

You may wonder why it is needed and what is benefit of using it? You will see example later in this post.

So how we can apply pipe operators in practice? Here examples:

let listOfIntegers = [5;6;4;3;1;2]
listOfIntegers |> List.sortBy (fun el -> abs el) // val it : int list = [1; 2; 3; 4; 5; 6]
// Same as
List.sortBy (fun el -> abs el) listOfIntegers

It shines when you have long list of functions you need to compose together:

text.Split([|'.'; ' '; '\r'|], StringSplitOptions.RemoveEmptyEntries)
      |> (fun w -> w.Trim())
      |> Array.filter (fun w -> w.Length > 2)
      |> Array.iter (fun w -> ...

The backward pipe operator could be useful in some cases to make your code looks more English-like:

let myList = []
myList |> List.isEmpty |> not
// Same as above but looks prettier
myList |> (not << List.isEmpty)

Composition operator could also be forward (>>) and backward (<<) and it also used for composing functions. Unlike pipe operator, result of execution compose will be a new function.

Definition of composition operators:

let (>>) f g x = g ( f(x) )
let (<<) f g x = f ( g(x) )

For example:

let add1 x = x + 1
let times2 x = x * 2
let add1Times2 = (>>) add1 times2
add1Times2 3 // val it : int = 8

Which we could re-write like this:

let add x y = x + y
let times n x = x * n
let add1Times2 = add 1 >> times 2
add1Times2 3 // val it : int = 8

In both examples it relies on core concept of partial application, that is when one argument baked-in in functions add1 and times2 but left second argument free so that it will be passed on function invocation by the user.

As long as input and outputs of functions involved in composition match, any kind of value could be used

Same example with backward composition operator gives different result because functions composed in the opposite order:

let add x y = x + y
let times n x = x * n
let times2Add1 = add 1 << times 2
times2Add1 3 // val it : int = 7

Have fun 😝

Now a small exercise for you. What will be outcome of all these expressions? 🤔 :

3 * 3
(*) 3 3
3 |> (*) 3
3 |> (*) <| 3

What about that one:

let ninjaBy3 = 3 * 3 |> (+)
ninjaBy3 5

Try it yourself. Leave comments and have fun!

.NET, C#, Programming

C# 8.0 pattern matching in action

Let’s revisit definition of the pattern matching from the Wiki:

In computer sciencepattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern. In contrast to pattern recognition, the match usually has to be exact: “either it will or will not be a match.” The patterns generally have the form of either sequences or tree structures. Uses of pattern matching include outputting the locations (if any) of a pattern within a token sequence, to output some component of the matched pattern, and to substitute the matching pattern with some other token sequence

Pattern matching

Putting it into the human language means that instead of comparing expressions by exact values (think of if/else/switch statements), you literally match by patterns or shape of the data. Pattern could be constant value, tuple, variable, etc. For full definition please refer to the documentation. Initially, pattern matching was introduced in C# 7. It was shipped with basic capabilities to recognize const, type and var patterns. The language was extended with is and when keywords. One of the last pieces to make it work was introduction of discards for deconstruction of tuples and objects. Combining all these together you was able to use pattern matching in if and switch expressions:

if (o is null) Console.WriteLine("o is null");
if (o is string s && s.Trim() != string.Empty)
        Console.WriteLine("whoah, o is not null");
switch (o)
    case double n when n == 0.0: return 42;
    case string s when s == string.Empty: return 42;
    case int n when n == 0: return 42;
    case bool _: return 42;
    default: return -1;

C# 8 extended pattern matching with switch expressions and three new ways of expressing a pattern: positional, property and tuple. Again, I will refer to full documentation for the details.

In this post I would like to show a real-world example of using pattern matching power. Let’s say you want to build an SQL-query based on list of filters you receive as an input from HTTP request. For example we would like to get a list of all shurikens filtered by shape and material:[shape]='star,stick'&filters[material]='kugi-gata'

We need a model to which we can map this request with list of filters:

public class ShurikenQuery
    [BindProperty(Name = "filters", SupportsGet = true)]
    public IDictionary<string, string> ShurikenFilters { get; set; }

Now, let’s write a function which builds SQL-query string based on provided filters:

private static string BuildFilterExpression(ShurikenQuery query)
    if (query is null)
        throw new ArgumentNullException(nameof(query));

    const char Delimiter = ',';

    var expression = query.ShurikenFilters?.Aggregate(new StringBuilder(), (acc, ms) =>
        var key = ms.Key;
        var value = ms.Value;
        var exp = (key, value) switch
            (_, null) => $"[{key}] = ''",
            (_, var val) when val.Contains(Delimiter) =>
                @$"[{key}] IN ({string.Join(',', val
                    .Replace("'", string.Empty)
                    .Replace("\"", string.Empty)
                    .Split(Delimiter).Select(x => $"'{x}'"))})",
            (_, _) => $"[{key}] = '{value}'"
        return exp != null ? acc.AppendLine($" AND {exp}") : acc;
    return expression?.ToString() ?? string.Empty;

Let’s break this code down. On line 8 we use LINQ Aggregate function which do the main work of building a filter string. We want to iterate over each KeyValuePair in dictionary and based on the data shape in it create a string which represents expression which could be provided for SQL WHERE clause.

On line 10 and 11 we extract key and value for KeyValuePair in own variables just for convenience.

Lines between 12-21 are of main interest to us – that’s where all magic happens. On line 12 we wrapped key and value in a tuple and use switch expression to start pattern match. On line 14 we check if value variable is null (_ in key position means discard – it’s when we don’t care what actual value is). If that’s a case we produce string like [Shape]=”. On line 15-19 again, we not interested what in key position, but now we assign value to a dedicated variable val we can work with. Next, we check if this value contains filter with multiple values (like in case filters[shape]=’star,stick’) and split it in separate values, removing ” and ‘ on the way we go. We want to translate this into SQL IN operator, so string after processing this pattern looks like this: [Shape] IN (‘star’, ‘stick’). Last pattern matches for remaining cases of single value filters (like filters[material]=’kugi-gata’) which produce following string: [Material] = ‘kugi-gata’. Line 23 applies AND to string we built and accumulating result in a StringBuilder variable we provided in initial run of Aggregate function on line 8.

If we would put (_, _) as a first line in a switch expression other patterns will be not evaluated, because (_, _) will catch all values

Be aware that in pattern matching the order is everything

Finally, the resulting string we return looks like this:

 AND [Shape] IN ('star','stick') AND [Material] = 'kugi-gata'

And that’s a valid string for SQL WHERE clause. As you can see pattern matching is a real deal and could be used in a lot of cases for parsing expressions. With C# 9 pattern matching will be extended even more with relational and logical patterns.

Hope you enjoyed. Stay tuned and happy coding.

.NET, async, LINQ, Programming

Make LINQ Aggregate asynchronous

I often use LINQ in my code. Well, put it in another way: I can’t live without using LINQ in my daily work. One of the my favorite methods is Aggregate. Applying it wisely could save you from having explicit loops, naturally chain into other LINQ methods and at the same time keep your code readable and well-structured. Aggregate is similar to reduce and fold functions which is hammer and anvil of functional programming tooling.

When you use Entity Framework it provides you with async extensions methods like ToListAsync(), ToArrayAsync(), SingleAsync(). But what if you want to achieve asynchronous behavior using LINQ Aggregate method? You will not find async extension in existing framework (on the moment of writing this article I’m using .NET Core 3.1 and C# 8.0). But let me give you a real-world example of the case when you could find this really useful.

Let’s say you need to fetch from database all distinct values for multiple columns in order to build multi-selection filter like this:

Let’s also assume you use SQL Server as it is most common one. For keeping it simple I will show you example with using Dapper micro-ORM.

The function could look like this:

public List<MultiSelectionModel> GetMultiSelectionFilterValues(string[] dataFields) {
  var results = new List<MultiSelectionModel>();

  var query = dataFields.Aggregate(new StringBuilder(), (acc, field) =>{
    return acc.AppendLine($ "SELECT [{field}] FROM Table GROUP BY [{field}];");

  using var connection = new SqlConnection(this.connectionString);

  using(var multi = connection.QueryMultiple(query.ToString())) {
     new List<MultiSelectionModel>(), (acc, field) =>{
      acc.Add(new MultiSelectionModel {
        DataField = field,
        Values = multi.Read(),

      return acc;

  return results;

The function receives as input parameter array of data fields (columns) for which we need to fetch distinct values for multi-selection filter and returns a list of multi-selection model which is just simple data structure defined as:

public class MultiSelectionModel
    public string DataField { get; set; }
    public IEnumerable<dynamic> Values { get; set; }

On lines 4-6 you see how Aggregate method applied for building a SELECT query for fetching distinct values for provided columns. I uses GROUP BY in this example, but you can use DISTINCT with same effect, although there difference in performance between distinct and group by for more complex queries which is excellently explained in this article. Lines 13-21 highlights the main logic of the function where we actually querying database with multi.Read() and assign results with distinct values for each data field in resulting model. In both cases following Aggregate extension used:

public static TAccumulate Aggregate<TSource, TAccumulate>(
	this IEnumerable<TSource> source,
	TAccumulate seed,
	Func<TAccumulate, TSource, TAccumulate> func

In first case as a seed parameter we provided StringBuilder. Second parameter is a function which receives accumulator and element from the source and returns accumulator which is StringBuilder in our case. In second case, as a seed we used List<MultiSelectionModel> which is resulting collection, so that final list is accumulated in that collection.

So that works. You can stop reading now and go for a couple of 🍺 with fellows…

Oh, you still here 😏. You know, curiosity killed the cat. But we different animals, so let’s move on. Well, as you can notice, in the first example we used what is known in Dapper as multi-result result. It executes multiple queries within the same command and map results. The good news is that it also has async version. The bad news is that our Aggregate does not have async version. Should we go back to old good for-each loop for mapping results from query execution then? No way!

So how could we implement all the way down async version of GetMultiSelectionFilterValues? Well, let’s re-write it how we would like to see it:

public async Task<List<MultiSelectionModel>> GetMultiSelectionFilterValuesAsync(string[] dataFields) {
  var results = new List<MultiSelectionModel>();

  var query = dataFields.Aggregate(new StringBuilder(), (acc, field) =>{
    return acc.AppendLine($ "SELECT [{field}] FROM Table GROUP BY [{field}];");

  using var connection = new SqlConnection(this.connectionString);

  using(var multi = await connection.QueryMultipleAsync(query.ToString())) {
    results.AddRange(await dataFields.AggregateAsync(
     new List<MultiSelectionModel>(), async (acc, field) =>{
      acc.Add(new MultiSelectionModel {
        DataField = field,
        Values = await multi.ReadAsync(),

      return acc;

  return results;

Much better now, isn’t it? I’ve highlighted the changes. This is fully asynchronous Aggregate method now. Of course you wish to know where did I get this async extension 😀? Here the extension methods I come up with to make it work:

public static class AsyncExtensions {
	public static Task<TSource> AggregateAsync<TSource>(
	this IEnumerable<TSource> source, Func<TSource, TSource, Task<TSource>> func) {
		if (source == null) {
			throw new ArgumentNullException(nameof(source));

		if (func == null) {
			throw new ArgumentNullException(nameof(func));

		return source.AggregateInternalAsync(func);

	public static Task<TAccumulate> AggregateAsync<TSource,
	this IEnumerable<TSource> source, TAccumulate seed, Func<TAccumulate, TSource, Task<TAccumulate>> func) {
		if (source == null) {
			throw new ArgumentNullException(nameof(source));

		if (func == null) {
			throw new ArgumentNullException(nameof(func));

		return source.AggregateInternalAsync(seed, func);

	private static async Task<TSource> AggregateInternalAsync <TSource> (
	this IEnumerable <TSource> source, Func<TSource, TSource, Task<TSource>> func) {
		var e = source.GetEnumerator();

		if (!e.MoveNext()) {
			throw new InvalidOperationException("Sequence contains no elements");

		var result = e.Current;
		while (e.MoveNext()) {
			result = await func(result, e.Current).ConfigureAwait(false);

		return result;

	private static async Task<TAccumulate> AggregateInternalAsync<TSource,	TAccumulate>(
	this IEnumerable<TSource> source, TAccumulate seed, Func<TAccumulate, TSource, Task<TAccumulate>> func) {
		var result = seed;
		foreach(var element in source) {
			result = await func(result, element);

		return result;

I did it for two of three existing Aggregate overloads. The last one you can implement yourself if you need it. It will be good exercise for you to understand how aggregate works behind the scenes.

Stay tuned and have fun.