Identify Side Effects And Refactor Fearlessly

When we refactor code how can we be confident that we don't break anything?

3 of the most important things that allow us to refactor fearlessly are:

  • Side effect free - or pure - expressions
  • Statically typed expressions
  • Tests

In this article we will solely focus on the aspect of side effects and strictly speaking on how to identify them. Being able to identify side effects in our programs clearly is the precondition for eliminating them.

Why avoid side effects?

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PureScript Case Study And Guide For Newcomers

Have you ever wanted to try out PureScript but were lacking a good way to get started?

If you

  • Have some prior functional programming knowledge - maybe you know Haskell,Elm,F#,or Scala,etc.
  • Want to solve a small task with PureScript
  • And want to get started quickly

This post is for you!

In this post we will walk through setting up and implementing a small exemplary PureScript application from scratch.

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Elm And The Algorithm Of Music

In this article I would like to present a minimal implementation of a music data type and everything that is needed to turn that into audible sound from an Elm application.

We will see how to transcribe an existing composition - an excerpt from Chick Corea's Children's Songs No. 6 - and listen to the result right here,embedded in this article.

From a music data type to performance

My colleague Jonas recently pointed out the presentation Making Algorithmic Music by Donya Quick to me. Donya Quick shows how she uses the Haskell library Euterpea to produce algorithmic music.

It got me really excited about the idea of porting this to Elm and to be able to use this in web applications.

In the following we will see the core data types and algorithms from Euterpea ported to Elm. To focus on the core concepts the implementation is stripped down to the minimum that is required to transcribe and perform an existing polyphonic piece of music (for a single instrument).

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Interactive Command Line Applications In Scala –Well Structured And Purely Functional

This post is about how to implement well structured,and purely functional command line applications in Scala using PureApp.

PureApp originated in an experiment while refactoring out some glue code of an interactive command line application. At the same time it was inspired by the Elm Architecture Pattern,and scalaz's SafeApp,as well as scalm.

To show the really cool things we can do with PureApp,we will implement a self-contained example application from scratch.

This application translates texts from and into different languages. And it provides basic user interactions via the command line.

The complete source code is compiled with tut. Every output (displayed as code comments) is generated by tut.
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How To Use Applicatives For Validation In Scala And Save Much Work

In this post we will see how applicatives can be used for validation in Scala. It is an elegant approach. Especially when compared to an object-oriented way.

Usually when we have operations that can fail,we have them return types like Option or Try. We sequence operations and once there is an error the computation is short circuited and the result is a None or a Failure.

Applicatives allow us to compose independent operations and evaluate each one. Even if an intermediate evaluation fails. This allows us to collect error messages instead of returning only the first error that occurred.

A classic example where this is useful is the validation of user input. We would like to return a list of all invalid inputs rather than aborting the evaluation after the first error.

Scala Cats provides a type that does exactly that. So let's dive into some code and see how it works.

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Parsers in Scala built upon existing abstractions

After some initial struggles,the chapter Functional Parsers from the great book Programming in Haskell by Graham Hutton,where a basic parser library is built from scratch,significantly helped me to finally understand the core ideas of parser combinators and how to apply them to other programming languages other than Haskell as well.

While I recently revisited the material and started to port the examples to Scala I wasn't able to define a proper monad instance for the type Parser[A].

The type Parser[A] alias was defined like this:

type Parser[A] = String =>Option[(A,String)] // defined type alias Parser 

To test the monad laws with discipline I had to provide an instance of Eq[Parser[A]]. Because Parser[A] is a function,equality could only be approximated by showing degrees of function equivalence,which is not a trivial task.

Also the implementation of tailRecM was challenging. (I couldn't figure it out.)

Using existing abstractions

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Strongly Typed Configuration Access With Code Generation

Most config libraries use a stringly typed approach.

Some handle runtime failures due to invalid configuration schemas by leveraging data types like Option or Result to represent missing values or errors. This allows us to handle these failures by either providing default values or by providing decent error messages.

This is a good strategy that we should definitely stick to.

However,the problem with default values is that we might not even notice if the configuration is broken. This could potentially fail in production. In any case an error e.g. due to a misspelled config property will be observable at runtime at the earliest.

Wouldn't it be a great user experience (for us developers) if the compiler told us if the configuration schema is invalid? Even better,imagine we could access the configuration data in a strongly typed way like any other data structure,and with autocompletion.

Moreover,what if we didn't have to write any glue code,not even when the configuration schema changes?

This can be done with the costs of an initial setup that won't take more than probably around 5 minutes.

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Error and state handling with monad transformers in Scala

In this post I will look at a practical example where the combined application (through monad transformers) of the state monad and the either monad can be very useful.

I won't go into much theory,but instead demonstrate the problem and then slowly build it up to resolve it.

You don't have to be completely familiar with all the concepts as the examples will be easy to follow. Here is a very brief overview:

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Use lambdas and combinators to improve your API

If your API overflows with Boolean parameters,this is usually a bad smell.

Consider the following function call for example:

toContactInfoList(csv,true,true) 

When looking at this snippet of code it is not very clear what kind of effect the two Boolean parameters will have exactly. In fact,we would probably be without a clue.

We have to inspect the documentation or at least the parameter names of the function declaration to get a better idea. But still,this doesn't solve all of our problems.

The more Boolean parameters there are,the easier it will be for the caller to mix them up. We have to be very careful.

Moreover,functions with Boolean parameters must have conditional logic like if or case statements inside. With a growing number of conditional statements,the number of possible execution paths will grow exponentially. It will become more difficult to reason about the implementation code.

Can we do better?

Sure we can. Lambdas and combinators come to the rescue and I'm going to show this with a simple example,a refactoring of the function from above.

This post is based on a great article by John A De Goes,Destroy All Ifs — A Perspective from Functional Programming.

I'm going to take John's ideas that he backed up with PureScript examples and present how the same thing can be elegantly achieved in Scala.

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Modelling API Responses With sbt-json –Print Current Bitcoin Price

I'm currently working on an sbt plugin that generates Scala case classes at compile time to model JSON API responses for easy deserialization especially with the Scala play-json library.

The plugin makes it possible to access JSON documents in a statically typed way including auto-completion. It takes a sample JSON document as input (either from a file or a URL) and generates Scala types that can be used to read data with the same structure.

Let's look at a basic example,an app that prints the current Bitcoin price to the console.

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Purity in an impure language with the free monad – by example of a Tic-Tac-Toe backend with CQRS and event sourcing

This post is part of the F# Advent Calendar in English 2016. Please also checkout the other posts or the F# Advent Calendar 2016 eBook.

Pure code intermingled with impure code.

This is not a very good separation of concerns and has many other disadvantages.

Here is an example of how many programs look like:

alt

In Haskell e.g. this would not be possible. But how should we deal with this in an impure programming language that does not enforce side effects to be made explicit, like F# e.g.?

There are a few approaches that will be presented in this post, one of which is the free monad pattern.

We will also examine a proof of concept implementation of a Tic-Tac-Toe backend following the command query responsibility segregation pattern (CQRS) together with event sourcing (ES).

See how you can implement a program in F# that is entirely pure and free from any effects and side effects!
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How to parse a Git log with FParsec

In this post we will see how to parse a Git log using F# and FParsec.

FParsec is a parser combinator library for F#. The library provides many simple parser functions that can be combined to create quite complex and powerful parsers.

For an introduction on how this works please refer to Functional Monadic Parsers ported to C# which explains some basic concepts and shows how a parser combinator library is built from scratch. Another good starting point is the FParsec tutorial or this post by Mathias Brandewinder.

In this post, however, we will focus on the usage rather than on how it works.

Complete Gist for this post.

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SameGame with F# and Fable: Functional programming meets JavaScript

Fable is a compiler for F# to JavaScript.

It brings all the good parts of functional-first programming in F# to JavaScript development.

Even without a lot of knowledge of Node.js and the JavaScript ecosystem, it is fairly easy to get started with and use functional-first programming for client-side browser applications. Fable can also be used for client-server, Node, mobile or desktop applications.

After reading the very nice introductory tutorial Getting started with Fable and Webpack I was ready to make an implementation of SameGame.

It is integrated in this post and you can play it right here in the browser.

This sample was also published on Fable's official sample site where you can find more information on the implementation details.

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Functional error handling in F# by example

Exceptions are bad.

Not only do we have to remember to catch them everywhere, they also provide a second implicit exit strategy for functions, similar to the goto statement.

However, there is an alternative more explicit approach.

In this post we will go through an example of how to implement decent functional error handling in F# without using NULL or exceptions.

We will do this by extending the application from the last post and make it even more reliable and robust.

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Writing efficient and reliable code with F# Type Providers

F# type providers are just awesome because they help to write very efficient and reliable code.

In this post I will show this by implementing a simple, but real-world-like scenario with some F# type providers.

Type providers provide the types, properties and methods to get access to external data sources of various kinds without having to write a lot of boiler-plate code. This makes coding very efficient.

Additionally they offer static types that represent external data and that the compiler will check at compile time. This makes coding very reliable.

So let's look at the scenario that we are going to implement...
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Domain Design, data- or function-centric?

There are two great articles by Scott Wlaschin on how functional programming can be used for the domain design of real-world applications by implementing a Tic-Tac-Toe game. He demonstrates how business rules can be modeled with types, how data hiding, encapsulation, logging and capability-based security can be achieved with functional programming and more.

What I found remarkable is that in the second article he completely re-engineered the first design. Even though to me the original implementation appeared to be very appealing.

I liked that the data-centric domain model was concise, totally clear and very close to the natural notion of the game. Eventually it couldn't meet high security standards since there were ways for malicious users of the API to manipulate the data.

The second function-centric implementation introduced the concept of capability-based security. The design smells of the previous version could be resolved. But I argue that the API of the second version is not as intuitive as the first one anymore. Technically it is also quite simple. However, the recursive structure doesn't come totally natural to me. Also an indicator that the second version is more complex is that logging becomes trickier.
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Refactoring to FParsec

I have been playing around with FParsec a little bit lately, inspired by the chapter 8 "Functional Parsers" of the book Programming in Haskell by Graham Hutton. FParsec is an F# parser combinator library for building quite powerful parsers by combining primitive parsers and functions provided by the FParsec library.

If you haven't been exposed to the concept of functional monadic parsers then this can be a very different experience. I am still totally fascinated by the power and the simplicity of this concept. Here is a brief introduction in C#.

As an exercise and to learn the usage of FParsec I have been looking for code that could be refactored to FParsec.

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XPlot ‘hello world’ – Using Google Charts in Web Applications

In this post I will describe the very basics, in other words the 'hello world example', of how to create cool looking Google Charts using XPlot and integrate them into Web Applications.

The F# library XPlot is part of the FsLab data science package, which you can do very neat things with. But it is also possible to reference an XPlot stand-alone package to just use the Google Charts library.

What is XPlot?

XPlot is a cross-platform data visualization package for the F# programming language powered by popular JavaScript charting libraries Google Charts and Plotly. The library provides a complete mapping for the configuration options of the underlying libraries and so you get a nice F# interface that gives you access to the full power of Google Charts and Plotly. The XPlot library can be used interactively from F# Interactive, but charts can equally easy be embedded in F# applications and in HTML reports. --From XPlot Home Page

Creating the chart

There are only a few easy steps necessary to generate the HTML for let's say a simple bar chart.

First we need an F# library project that references the XPlot.GoogleCharts package via NuGet.

Here is a function that will use XPlot to create the HTML and JavaScript code for a simple bar chart:

namespace Charts

module BarCharts =
    open XPlot.GoogleCharts

    let statusCount () =

        let series = [("Open",23); ("In Progress",5); ("Resolved", 58); ("Closed",5)]

        let options =
            Options(
                title = "Status", 
                orientation = "horizontal")

        if series |> Seq.isEmpty then "no data" else
            let chart = 
                series
                |> Chart.Bar
                |> Chart.WithOptions options
                |> Chart.WithLabels ["Count"]

            chart.InlineHtml

The result can be used in any web application if the Google Charts libraries are loaded.

series can be any list of tuples of type (string * int). Off course, in a real project this list would be either passed to the function as an argument, or the function would have a data access dependency injected.

Here is the output of the statusCount() function:

<script type="text/javascript">
  google.charts.setOnLoadCallback(drawChart);
  function drawChart() {
    var data = new google.visualization.DataTable({"cols": [{"type": "string" ,"id": "Column 1" ,"label": "Column 1" }, {"type": "number" ,"id": "Count" ,"label": "Count" }], "rows" : [{"c" : [{"v": "Open"}, {"v": 23}]}, {"c" : [{"v": "In Progress"}, {"v": 5}]}, {"c" : [{"v": "Resolved"}, {"v": 58}]}, {"c" : [{"v": "Closed"}, {"v": 5}]}]});

    var options = {"legend":{"position":"right"},"orientation":"horizontal","title":"Status"} 

    var chart = new google.visualization.BarChart(document.getElementById('9af3e989-c37b-4257-b71f-5053b52f3b48'));
    chart.draw(data, options);
        }
</script>
<div id="9af3e989-c37b-4257-b71f-5053b52f3b48" style="width: 900px; height: 500px;"></div>

Displaying the chart in an ASP.NET MVC application

Now, as an example, we will see how to integrate this into an ASP.NET MVC application.

Loading the Google Charts Library

Load the Google Charts library by adding the following lines to the <head> section of the page e.g. in _Layout.cshtml. (Also see
Load the Libraries
)

<script type="text/javascript" src="https://www.gstatic.com/charts/loader.js"></script>
<script type="text/javascript">
  google.charts.load('current', {packages: ['corechart']});
  google.charts.setOnLoadCallback(drawChart);
</script>

Initializing the View Model

Add a reference to the F# project that was created earlier and build the F# project.

Next add a reference to FSharp.Core.

Then initialize a View Model with the chart and pass it to the View:

public ActionResult Index()
{
    var vm = new BarChartVm
    {
        Chart = Charts.BarCharts.statusCount()
    };

    return View(vm);
}

Add the following code to the page at the position where the chart should be displayed:

<div>
    @Html.Raw(@Model.Chart)
</div>

When running the application, the chart should be displayed like this:

Here is the source code from this post.

SQL Type Providers and Continuous Integration with FAKE

If you want to access a relational database from an F# (or C#) application, SQL F# type providers are commonly used. SQL type providers will provide all the types, properties and methods needed to access and interact with the tables of a SQL database, without having to write any extra boilerplate code. Everything is type checked and if the actual database schema gets out of synch with the database related code, compilation will fail. This is very valuable because it gives you high confidence in the application's data access code.

So at compile time the database has to be up to date and accessible. But how does this work in a continuous integration environment? Off course an option is to have the connection string of the type provider point to a development database on the network. Another solution would be to manually create or update a database on the build server before the build. But I don't really like this because I think the build server should be independent and self-sufficient. A solution to this scenario, that worked for me, is to deploy the database during the build process using Visual Studio database projects and FAKE - F# Make.

There might be other and maybe better solutions that I haven't come up with. I'd be curious to find out. Actually this might be one. The approach that I used, however, works out nicely, so I will give a quick walkthrough on how to set things up.

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