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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Template Method Pattern. Can we do better?

Often we encounter algorithms with a certain structure that consist of individual steps that may vary for specific implementations.

To keep things clean and in order to reduce duplicate code we should refactor common parts out.

The object-oriented solution to this is the Template Method design pattern.

But there might be an alternative, better solution to this that uses higher-order functions.

Here is an example

These two methods have a common structure that could be generalized to follow DRY:

public static int Sum(IEnumerable<int> seq)
{
    var sum = 0;
    foreach (var i in seq)
        sum += i;
    return sum;
}

public static int Product(IEnumerable<int> seq)
{
    var product = 1;
    foreach (var i in seq)
        product *= i;
    return product;
}

Generalizing the algorithmic structure

An object-oriented solution to this is to apply the Template Method design pattern.

Let's see how we can refactor the code from above by using the Template Method pattern. Here is an abstract template class that represents the algorithmic structure:

// this implementation is not recommended, it is only for demonstration purposes
public abstract class Aggregator<TSource, TState>
{
    protected TState State;
    protected IEnumerable<TSource> Seq;

    protected Aggregator(IEnumerable<TSource> seq)
    {
        Seq = seq;
    }

    public TState Evaluate()
    {
        SetInitialState();
        foreach (var x in Seq)
            Aggregate(x);
        return State;
    }

    protected abstract void SetInitialState();
    protected abstract void Aggregate(TSource x);
}

Now we can create specific sub-classes that implement Sum and Product:

public class Sum : Aggregator<int, int>
{
    public Sum(IEnumerable<int> seq) : base(seq) { }

    protected override void SetInitialState() { State = 0; }
    protected override void Aggregate(int i) { State += i; }
}

public class Product : Aggregator<int, int>
{
    public Product(IEnumerable<int> seq) : base(seq) { }

    protected override void SetInitialState() { State = 1; }
    protected override void Aggregate(int i) { State *= i; }
}

Here is the usage:

var seq = Enumerable.Range(1, 10);

var sum = new Sum(seq);
var sumResult = sum.Evaluate(); // sumResult = 55

var product = new Product(seq);
var productResult = product.Evaluate(); // productResult = 3628800

Pros

It reduces code duplication.

Cons

There is a ridiculous amount of boiler-plate code involved.

Also, in other more complex scenarios state can be scattered throughout many (sub-) classes and source files that has to be managed and maintained somehow.


(this representation was adopted from Yan Cui from here)

Finally, instantiating objects to be able to call the methods is cumbersome. In general it is just an unwieldy construct.

The light-weight approach

We can achieve the same reduction of code duplication by using higher-order functions. The benefit is that it is much more light-weight.

Higher-order functions ftw

All we have to do is to create a higher-order function that represents the overall structure and that takes the specific parts as arguments. Those specific parts are the same as the abstract methods of the template class that we saw above.

This is the implementation:

public static TState Aggregate<TSource, TState>(this IEnumerable<TSource> seq,
    Func<TSource, TState, TState> aggrgator, TState initial)
{
    var state = initial;
    foreach (var x in seq)
        state = aggrgator(x, state);
    return state;
}

We have to be careful with the parameters' signatures because the abstract methods of the template class have hidden inputs and outputs. This means that we cannot directly use their signatures for the parameters.

A closer look at the signature

Let's take a closer look at the signature of Aggregate and compare it to the Template Method implementation.

SetInitialState() from the template class e.g. takes no arguments and returns no values. But it has a hidden output.

The hidden output is the local field State of type TState within the class Aggregator. Therefore the type of the parameter should be Func<TState>.

We will call this parameter initial. Because initial has only an output, but no input and because it is a pure function without side effects, we can replace it with a constant value of type TState.

The abstract method Aggregate(TSource x) from the template class takes one argument and returns no values. But it has hidden inputs and outputs.

The local field State is both input and output. We have to make this explicit by declaring the signature Func<TSource, TState, TState> to the parameter aggregator.

The specific implementations

Here are the specific implementations that make use of our generalized function Aggregate:

public static int Sum(this IEnumerable<int> seq)
{
    return seq.Aggregate((a, b) => a + b, 0);
}

public static int Product(this IEnumerable<int> seq)
{
    return seq.Aggregate((a, b) => a * b, 1);
}

Now we can easily call Sum and Product as extension methods on lists of integers.

Of course, all of this is already built-in into LINQ.

Pros

It reduces code duplication.

Much more concise and much less boiler-plate code.

Every function has explicit inputs and outputs.

It is easy to use, test and reason about.

Cons

Maybe the function signature of Aggregate looks a little complex. However, I can't think of anything else.

Conclusion

If we have several implementations of an algorithm following the same structure, but with some different individual steps the Template Method design pattern seems to be a good fit.

To keep things clean and in order to reduce duplicate code the Template Method pattern helps to generalize common parts.

We looked at an example and compared the application of the Template Method pattern to an alternative approach adopted from functional programming.

This alternative approach made use of higher-order functions.

Even though this example is kind of simple and bold it demonstrates that the functional way may lead to cleaner and more maintainable code.