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:
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.
Have you ever wanted to try out PureScript but were lacking a good way to get started?
If you
This post is for you!
In this post we will walk through setting up and implementing a small exemplary PureScript application from scratch.
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.
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).
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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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.
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.)
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.
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:
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.
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.