There’s no average, dumb Java Joe!

Wow, it’s been quite a long time since i’ve blogged the last time (that’s for the one or other reason that would only bother you, so in essence – please excuse) . But now i can’t keep calm any longer, since there’s an increasing number of articles, claiming that e.g. Functional Programming – in particular using Scala – is too hard to get for the average programmer.

First of all,  there isn’t any ‘Java Joe’  (as a synonym for all those so called ‘dumb’ Programmers) out there. Well ok, if it is, then i’m the ideal protoype of that Joe! And as such  i can tell you, if you’re willing to learn some new ideas and try to improve your programming skills, than Scala may provide you with a bunch of whole new concepts, not only from the functional world (and i’m not even talking about type systems here).

In fact, i don’t care if it’s Scala, Haskell or any other language, as soon as it may widen my horizon. But i would answer to all those who think Scala is to hard to grasp, that it’s only a matter of how you would pick up those persons. Granted, it may be difficult to meet those ‘newcomers’ at their common ground – there are some complaints out there that some Scala community members might behave too elitist when it comes to blaming Scala as being too difficult (like ‘it’s not Scala which is too difficult, it’s only you who don’t understand it’).

Frankly, i can follow those complaints. If you’re willing to learn and may have some problems to grasp some blog posts about Scala (or any other language) or even some code snippets, it is by way no solution to retreat to a position, saying your just to dumb (or lazy or whatsoever) to get it. Spitting out some code snippets or short articles everey now and then is easy – especially if they come without any context or any larger explanation. That’s really the easy part! The hard part is meeting that so called average Java Joe at his level and escort him intellectually!

So as a community of Scala fellows (or in any other way), we need to stop  judge over those large herd of programmers as being too dumb. That’s simply not true! There may be some individuals too sluggish to learn and excel. But for all those others, who may have a hard job and having no chance to deal and become proficient with Scala at day (and therefore have only some hours at night) , we need to provide a way of guidance. And that’s best done to face that before mentioned reality. It’s not the people who are dumb. We as a community are dumb if we don’t react to that reality and welcome those people on their ground!

Appendix

In order to walk the talk, i decided to resume my work on writing about functional Scala! Hopefully some may still find it a useful intro to get into the ideas of functional programming in Scala.

Functional Scala: Quiz with Lists – common list functions, handcraftet

Welcome to another episode of Functional Scala!

As promised within the last episode, we’re going to take another detailed look at some more commonly used list functions. As we wanna become well acquainted with the functional side of Scala, we’re again going to implement those list functions one by one. Along the way, we’ll get a better and better understanding on how to apply some of the typical tools within the world of functional programming, like pattern matching, recursion or function composition.

As it turned out, most of us learn best in a synthetical way (that is not by dissecting the frog, but to build one). For that, we’re gonna change the style of the following sections. For every useful function, we’re first looking at their intended behaviour, maybe along with some exemplary showcases. Only when we grasp the intention, we’re trying to come up with a sensible idea for their realization and finally with an implementation. You might consider the whole episode like a little quiz and try to come up with your own solutions before you’ll take a closer look at the presented ones.

Functional Scala: Essential list functions

In the last two episodes we came up with the basic idea of persistent (read immutable) list-like data structures and how to construct and deconstruct (read pattern match) them using a sugarized notational form. We also talked big about their non-destructive nature when it comes to e.g. updating a given list or removing some elements. In this episode, we’ll finally inspect some of those commonly used list functions. While we’re going to reimplement the most essential ones,  you’ll hopefully get a better feeling for the  basic principles and characteristics which build the foundation for operating on persistent data structures in a non-destructive way (preserving the structure of given list values).

Functional Scala: Tinkerbell, Frogs and Lists

Welcome to another episode of Functional Scala!

What do you know about Frogs? Well, i mean beyond the most common facts you learn from books. One way to learn more about Frogs might be to dissect one, as you may have done back in school. That would be the analytic way. But there’s a better way: if you really want to learn about the nature of a frog, you should build one! By building a being that has froglike characteristics, you’re going to learn what makes a frog a frog and how frogs are adapted to their particular environment. It’s a perfect example of learning by synthesis!

Well, as this isn’t a series about biology but about Functional Programming with Scala, we’re going to focus on another research object, which will be list-like data structures in a functional environment. We’re going to construct a simple, functional list type and explore its characteristics along the way. So let’s start and play Tinkerbell …

Functional Scala: Expressions, Extensions and Extractors

Welcome back to another episode of Functional Scala.

This one is the sequel to our extended sample on representing and resolving arithmetic expressions using algebraic datatypes and pattern matching. In this second part i try to answer a legitimate question: what are the advantages and disadvantages of algebraic datatypes (consisting of pure data, no behaviour), espacially if we wanna expand our expression language  – and what’s that all to do with the visitor pattern?

Functional Scala: a little expression language with algebraic datatypes and pattern matching

Welcome to another episode of Functional Scala!

In this episode we’ll going to recap and employ everything we’ve learned so far about algebraic datatypes and pattern matching for implementing a more extensive example. We’ll put all those little pieces together which we’ve discovered so far and see how they work in cooperation for building a little language which allows us to write, pretty print, step-wise reduce and evaluate arbitrary arithmetic expressions (considering addition, subtraction and multiplication on integer values). And as the cherry on top, we’ll discover some new flavour of algebraic datatypes and pattern matching.