functional

Static Typing – the dangers of incomplete info

Ok, so I’m going to use this post – Making Illegal States Unrepresentable – and I’ll add my experience to it. For people that don’t know F# (or that don’t want to check all the post to see what’s the point), the idea is that he’s trying to construct a type that will only be valid if a user does at least an e-mail address or a postal contact. Then, he ends with the following type (I’m “inventing” a way to represent this type that’s close to Scala, but easier to read for people that don’t know Scala or Haskell or F#):

type Contact {
  Name: String
  AND Contact: ContactInfo
}

type ContactInfo {
  EmailOnly: EmailInfo
  OR PostOnly: PostalInfo
  OR EmailAndPost: EmailInfo with PostalInfo
}

// Types EmailInfo and PostalInfo have to be defined also

Then, he uses 13 lines to construct a ContactInfo, and another 12 to update a contact info. He ends up concluding that these complicated types are necessary because the logic is complicated. And that’s where we start to disagree.
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By Maurício Szabo, ago

Functional SOLID – Open/Closed Principle

Continuing on the series about SOLID principles on functional programming, the next one is the Open/Closed Principle. The definition from the Wikipedia:

The open/closed principle states “software entities (classes, modules, functions, etc.) should be open for extension, but closed for modification”; that is, such an entity can allow its behavior to be extended without modifying its source code.

This is kinda interesting on its own way: what’s an “extension”? Considering the context when it was written, and future interpretations of the principle, the idea is that any program should not be re-compiled (re-written, modified, etc) to be extended. The idea of this principle is that local changes should not propagate to other parts of the program: make entities as self-contained as possible, write then in a way that extensions would not depend on modifications on these entities, then “close” then to modifications. I can see two cases for the “open-closed principle” violation, and the first one is the most common:

(defn as-int [some-str]
  (when (re-matches #"\d+" some-str)
    (Integer/parseInt some-str)))

This code returns an Integer if it can parse a string as a number, and nil if it can’t. Now, suppose we want to “extend” this functionality by accepting other objects like Double (truncates to integer) or nil (returns 0). The only way to do it is to change the when to a case, but that means that for every new implementation I’ll have to change this function. Now, a better way is to use protocols:
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By Maurício Szabo, ago

Functional SOLID – Single Responsibility Principle

I am a firm believer that we have to learn from the past instead of throwing all away for the future. And this is one of these moments: I have seen multiple talks, presentations, slides telling about SOLID and Design Patterns in functional programmings. Some are serious, some are satires, but most only speaks “you just need functions, really!” on their explanation on the principles, effectively diminishing the usefulness of the principles and also by imagining that, somehow, if we only use functions all our problems on developing software would be solved forever.

So, to counter that, I’m going to re-visit SOLID, but this time I’ll not compare with “OO” – instead, I’ll ask for us to try to grasp the meaning behind each principle. I’m going to use the definitions from Wikipedia, because (1) it’s easier to track changes and (2) it’s condensed from multiple sources, so it’ll probably not reflect a single opinion from an author. I also thought about being one single post, but it became quite complicated, so I’m splitting this post in multiple ones (and probably the one about LSP will be the most extensive of all). So, let’s begin by the first: the Single Responsibility Principle:

The single responsibility principle is a computer programming principle that states that every module, class, or function should have responsibility over a single part of the functionality provided by the software, and that responsibility should be entirely encapsulated by the class, module or function
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By Maurício Szabo, ago

Object-oriented after working with functional programming

Sometimes I need to divide my knowledge in phases: when I started to program, I felt that C/C++ were the best languages to make a software. Then, I’ve began to work with Ruby and learned Test-Driven Development. I divide my knowledge “before TDD” and “after TDD”, because it literally changed the way I could think about software and tests in general. Now, I’ll probably make the same division: “before functional programming” and “after FP”, and I only discovered that things have changed this week: I just discovered I don’t know how to do object oriented programming anymore – and to be honest, it’s not really a bad thing: I also saw in practice how object-oriented programming expects you to write lots of boilerplate in most situations.

This realization came to me when I was working in a peculiar kind of problem – it’s basically a ruby code that needs to connect on messaging system and, when it receives a message, needs to persist it in a relational database. The problem is that this specific system have a strong consistency requirement, so the message could only be updated on DB before a specific event happens: after that, we can’t change it anymore, except for some specific fields. Also, as we’re working with messaging, we can receive the message multiple times, out of order, and all of those strange things that happens on Internet-land. So, to summarize:

  1. The system will receive a message
  2. It’ll UPSERT a record on DB, just with some identification fields (they’ll never change, and if they do, they characterize another message to be persisted – think about an external id)
  3. It’ll BEGIN a transaction and SELECT ... FOR UPDATE my just upserted message
  4. It’ll find, on another table, the rest of the fields on the message
    1. If this record doesn’t exist, we’ll create it (and log)
    2. If it does exist, we’ll check additional info, and decide: or we ignore it (and log), or we upsert (and log), or raise an error

Now, for the implementation. Because consistency is a must have on this system, I don’t want to expose for future programmers (be it myself or some of my friends) some code that’ll induce me to errors: after all, who wants to do with_consistency_check(msg) { Message.upsert!(msg) } when I can just do Message.upsert!(msg), right? So, I don’t want to allow people to be able to modify, find, update, or anything else outside of the with_consistency_check (or whatever name I decide). So, how to do it?
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By Maurício Szabo, ago

The perfect programming language (for me)

Disclaimer: the perfect programming language does not exist. Even if it did, different people want different things, so probably the ideas in this post would not reflect the ideas from different people. With that being said, let’s start with a little background:

I usually prefer dynamic languages. There are also moments when I miss static typing, but most of the time the “code/solution exploration” and a good REPL (and code design) do the job of reasoning about the shape of my data.

Except when it doesn’t. Then, things get ugly. Real fast. There’s always bad code that you need to work with, even one that you wrote about six months ago, and now you’re dumbfounded, looking at the code trying to remember what the hell were you thinking when you wrote the code and why you did think it was a good idea to wrote it that way at all. It happens with everyone. And that’s when static typing can (and will) help: it reasons about your data. You can have a variable named a, but at least you know it have the fields b: String, c: Int, whatever that means. But it helps.

Maybe we could have a language that allows you to turn on/off the typing whenever you wanted? With better REPL support? So, this would be my dream language to work with.
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By Maurício Szabo, ago