3. February 20204. February 2020 by Leif

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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26. January 202026. January 2020 by Leif

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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8. August 20198. August 2019 by Leif

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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29. April 20181. May 2018 by Leif

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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10. March 201810. March 2018 by Leif

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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3. February 201823. February 2018 by Leif

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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22. December 201722. December 2017 by Leif

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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8. October 20178. October 2017 by Leif

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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5. September 20176. September 2017 by Leif

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.

Continue reading →

17. August 201727. August 2017 by Leif

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.

Continue reading →

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.

Example: Extracting names and emails from a CSV file

Let's take an example that might be a bit artificial, but should not be too complex to demonstrate the techniques to eliminate the pitfalls of conditionals.

The function toContactInfoList from the opening example parses the contents of a CSV file into a sequence of ContactInfo.

The Boolean parameters control whether rows containing either an empty name or an empty email should be omitted from the result or not.

An implementation of this function might look like this:

def toContactInfoList(
  csv: Seq[String],
  nameRequired: Boolean,
  emailRequired: Boolean): Seq[ContactInfo] = {
  csv
    .map(_.split(';'))
    .map(tokens => 
      (tokens.headOption.getOrElse(""), tokens.drop(1).headOption.getOrElse("")))
    .flatMap {
      case (name, email) =>
        if ((name == "" && nameRequired) || (email == "" && emailRequired)) {
          None
        } else {
          Some(ContactInfo(name, email))
        }
    }
}

Inversion of control

Boolean parameters can be seen as a serialization protocol.

At the caller site, we serialize our intention into a bit (or other data value), pass it to the function, and then the function deserializes the value into an intention (a chunk of code). -- John A De Goes

The serialization on the call side and the deserialization on the implementation side are spots where things can potentially go wrong.

Therefore the first step to improve the design is to get rid of these Boolean parameters by replacing them with a lambda.

This way the caller no longer has to encode their intentions as Boolean values. Instead the caller can now pass a function, the actual intention, to the API.

Here is a new version of our original function where the Boolean parameters are replaced by a lambda. Out of convenience we will use a type alias for the lambda:

type Converter = (String, String) => Option[ContactInfo]

def toContactInfoList(
  csv: Seq[String],
  convert: Converter): Seq[ContactInfo] = {
  csv
    .map(_.split(';'))
    .map(tokens =>
      (tokens.headOption.getOrElse(""), tokens.drop(1).headOption.getOrElse("")))
    .flatMap { case (name, email) => convert(name, email) }
}

With an implementation like this the caller has to construct the converter parameter themselves, e.g. like this:

def noEmptyNameOrEmail: Converter = {
  case ("", _) | (_, "") =>
    None
  case (name, email) =>
    Some(ContactInfo(name, email))
}

toContactInfoList(csv, noEmptyNameOrEmail)

It can be good to give the caller a greater amount of control like this. On the other hand, this is not very convenient most of the time, and also error prone.

So, we are only half way there.

Exposing available options as combinators

For a better usability the API should expose ready made implementations (in the form of functions) that the caller can choose to pass back into the API method.

In our example these functions have the type Converter.

Let's start with the simplest converter:

def makeContactInfo: Converter = {
  case (name, email) => Some(ContactInfo(name, email))
}

Passing this converter (like so: toContactInfoList(csv, makeContactInfo)) is equivalent to calling the original version like this: toContactInfoList(csv, false, false).

Now, to enable the caller to specify the other options, we will implement combinators. These combinators are higher-order functions that take a Converter as input and return a new Converter, therefore have the type: Converter => Converter. The returned converter combines the behavior of the converter that was given as an input parameter with a new behavior.

A combinator that will omit empty emails looks like this:

def noEmptyEmail: Converter => Converter = {
  converter => {
    case (_, "") =>
      None
    case (name, email) =>
      converter(name, email)
  }
}

The combinator that will omit empty names is very similar:

def noEmptyName: Converter => Converter = {
  converter => {
    case ("", _) =>
      None
    case (name, email) =>
      converter(name, email)
  }
}

Now these functions can be combined like this:

noEmptyEmail(noEmptyName(makeContactInfo))

Enabling a nicer syntax

For better readability and to enable auto-completion which makes it easier for the caller to find available combinators, we will use Scala's implicit classes:

implicit class ConverterSyntax(convert: Converter) {
  def noEmptyName = ContactInfoConverters.noEmptyName(convert)
  def noEmptyEmail = ContactInfoConverters.noEmptyEmail(convert)
}

The syntax is much nicer now:

makeContactInfo.noEmptyEmail.noEmptyName

Conclusion

Finally, a call of the new version of the API method looks like this:

toContactInfoList(csv, makeContactInfo.noEmptyEmail.noEmptyName)

Not only is it much easier than before to reason about what's going on, we also eliminated the potentials for errors related to encoding and decoding of intent. Also the caller can now provide custom converters.

Moreover, with the new design it is possible to extend the API by adding more combinators in the sense of the Open-Closed Principle.

Note that in Scala we don't have the same guaranties for purity and control of side effects as in PureScript or Haskell.

However, I definitely think the benefits described in this post make it worthwhile giving these techniques a try. What do you think? Let me know in the comments!

Leif Battermann

Functional Programming Fu

Identify Side Effects And Refactor Fearlessly

Why avoid side effects?

PureScript Case Study And Guide For Newcomers

Elm And The Algorithm Of Music

From a music data type to performance

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

How To Use Applicatives For Validation In Scala And Save Much Work

Parsers in Scala built upon existing abstractions

Using existing abstractions

Strongly Typed Configuration Access With Code Generation

Error and state handling with monad transformers in Scala

Use lambdas and combinators to improve your API

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

Leif Battermann

Functional Programming Fu

Use lambdas and combinators to improve your API

Example: Extracting names and emails from a CSV file

Inversion of control

Exposing available options as combinators

Enabling a nicer syntax

Conclusion