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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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.

Example

Here is a first example. The function generateNext takes a string, tries to generate the next ID by incrementing the numeric part of the string by one and returns the result if it succeeds. Otherwise it will return a failure. It is not really a great implementation but it works.

open Chessie.ErrorHandling
open System

let generateNext (current:string) =
    try
        let (|Int|_|) str =
            match System.Int32.TryParse(str) with
            | (true,int) -> Some(int)
            | _          -> None

        let prefix =
            match current.[0] with
            | a when a = 'A' -> Some a
            | _              -> None

        match current.[1..6], prefix with
        | Int n, Some pf when n >= 0 && n < 999999 -> sprintf "%c%06i" pf (n + 1) |> ok
        | _                                        -> IdGenerationFailure "Ivalid input." |> fail
    with
    | ex -> IdGenerationFailure ex.Message |> fail

Here are some tests for the success cases:

[<TestCase("A000000","A000001")>]
[<TestCase("A009999","A010000")>]
[<TestCase("A999998","A999999")>]
let ``generateNext should succeed if input is valid`` (input, expected) =
   generateNext input |> resultShouldEqual (expected |> ok)

Refactoring

Let's look at the refactored version. It uses a computation expression to build the parser. By using the let! construct parsers can be applied sequentially and if one of them fails the rest of the operation will be bypassed.

open FParsec

let generateNext current =
    let p = parse {
        let! prefix = pstring "A"
        let! n = int32Within 0 999998
        let! _ = eof
        return sprintf "%s%06u" prefix (n + 1) }

    match run p current with
    | Success (v,_,_)       -> Chessie.ErrorHandling.Trial.ok (v)
    | Failure(errorMsg,_,_) -> Chessie.ErrorHandling.Trial.fail (IdGenerationFailure errorMsg)

The result is really nice, compact and well readable. pstring "A" will succeed if the input starts with "A". int32Within will succeed if the prefix is followed by a number within the given interval and eof will verify that there is no more unconsumed input. In the return statement the result is constructed given that all previous parsers succeeded.

For this to work we need a little helper function int32Within which is again built from another helper function sat. Those helper functions are of general purpose and are therefore completely reusable.

open FParsec

let sat parser predicate msg = 
    parser >>= (fun x -> if predicate x then preturn x else fail msg)

let int32Within from ``to`` = 
    sat pint32 (fun n -> n >= from && n <= ``to``) (sprintf "unexpected number.\nexpected a number within [%d, %d]" from ``to``)

Note that the refactored version will not verify the length of the input string. E.g. A123 is a valid input as well as A0000000123. Also the original version will also successfully parse inputs that have trailing characters. In the context of generateNext this is not so important. But in others scenarios one might have to pay close attention to these details.

Conclusion

So far I found that refactoring hand rolled parsing code with the use of FParsec made the code more concise, more readable and eventually better, even in a very simple scenario like in the example above. One reason for that is that the aspect of error handling is completely abstracted away. Off course in the example the result is mapped to the Result type of the domain. But the parsing code itself is not cluttered with try catch statements or any other code that is concerned with handling the failure cases.