A recent post compared different ways of approaching the same problem in Scala and Go:
The Go approach is longer than the one in Scala, but every line is dead simple to understand. We might dismiss it as repetitive, boiler plate code rather than abstract, business logic. However, setting break points for debugging or changing the behaviour of the code when the new requirements arrive, six months down the line, is trivial. This can be a mixed blessing, as there’s really nothing to stop code like that ballooning in size as new conditional statements get added to cope with the changing needs of the software. Every 5 kLoC ball of mud that any programmer ever wrestled with started out as something dead simple like that.
The following article investigates how non-programmers approach programming problems and whether programming languages make the task harder because they do not match how an untrained mind thinks.
http://alumni.cs.ucr.edu/~ratana/PaneRatanamahatanaMyers00.pdf
More children developed rules to describe the behaviour of Pacman than instructions for the sprites to follow. People apparently start out thinking in a more declarative style and then develop the habit of thinking in the imperative style.
Is this an argument for using more declarative languages? If declarative programming is more natural, surely it would be easier. However, many programmers start out with imperative code and perhaps only try declarative code later. Is this just a historical or social accident caused by the greater availability of imperative languages compared to declarative ones? It is possible that this is a greater issue for novice programmers than for experienced ones.
Often, we cannot move between languages at whim. Few programmers have the privilege of being able to write part of their system in Go in the morning, then head over to Scala land after lunch. However, .Net has Linq, which affords a C# developer, like your author, greater freedom to move freely back and forth between imperative and declarative styles of programming.
Imagine a book stall in a market. There is a table with books with different numbers of pages with covers that are red or blue.
The types in this world are an enum for the colours and a class for the book:
enum Colour
{
Red,
Blue
}
class Book
{
internal Book(string name, int numberOfPages, Colour colour)
{
Name = name;
NumberOfPages = numberOfPages;
Colour = colour;
}
internal string Name { get; }
internal int NumberOfPages { get; }
internal Colour Colour { get; }
}
We can model our bookstall with a list:
var books = new List
{
new Book ("War and Peace", 1123, Colour.Red),
new Book ("Doctor Zhivago", 803, Colour.Blue),
new Book ("Jeeves and the Feudal Spirit", 154, Colour.Red),
new Book ("Shogun", 1213, Colour.Red)
};
You want to write down the names of the books that are more than 500 pages long and have red covers. Here are three ways to solve this problem.
The imperative approach:
foreach (var book in books)
{
if (book.Colour == Colour.Red && book.NumberOfPages > 500)
{
WriteLine(book.Name);
}
}
This way, we are putting ourselves in the role of the person actually sifting through the books one by one and writing the names of the books that meet our criteria as we go. However, the presentation code (the WriteLine) is mixed up with the filtering logic in the loop.
Linq makes use of the Where extension method to allow the so-called fluent syntax:
var longRedBooks = books.Where(b => b.Colour == Colour.Red && b.NumberOfPages > 500);
foreach (var longRedBook in longRedBooks)
{
WriteLine(longRedBook.Name);
}
Here, we first ask for the books that match our criteria, defined in the lambda expression passed to the Where method. Then, we print the books that matched. The filtering logic and presentation logic are now separate.
Using Linq’s query syntax below, the code looks quite different from the fluent syntax used above, but the meaning is the same. This approach is perhaps more familiar to developers with a background in SQL.
var longRedBooks = from book in books
where book.Colour == Colour.Red && book.NumberOfPages > 500
select book;
foreach (var longRedBook in longRedBooks)
{
WriteLine(longRedBook.Name);
}
The complete source can be found on GitHub:
https://github.com/robert-impey/CodingExperiments/blob/master/C-Sharp/BookFinding/Program.cs
Which is easiest to understand? If the requirements change, let’s say that we now only want one book, which should be the longest book, shorter than 500 pages, with a blue cover, which approach will be easiest to update and understand with the new logic? With the Linq approach, we can take advantage of the type system to see that the query now returns a Book (which may be null) rather than an IEnumerable<Book> and handle the object appropriately. Will the change in meaning be as obvious in the imperative code?
For what it’s worth, I’ve experienced plenty of resistance in code reviews to Linq queries, but almost none to writing the same thing in a for loop. Sometimes the reasoning is to do with performance, but I doubt there’s much in that. Anyway, a read-only code review is a terrible place to make assumptions about relative performance. However, it’s very easy to reason about the performance of the for loop, but more advanced knowledge of lazy evaluation, the iterator pattern and its implementation is needed to understand what gets executed where and how many times for the linq approaches.
I would be very interested to see an analysis of open source projects that compared the frequency on these approaches to see what is the approach that people tend to settle on. Do pull request with Linq get accepted more or less often? Is code generally refactored from one to the other? In which direction?
How should this influence job interviews and hiring? If we want to hire experienced programmers, we could check for signs that their thinking has been moulded to the structures of industrial programming languages. Perhaps we should pity such creatures. Should we look for signs of skills in imperative programming in hands-on programmers and for a declarative approach in tech leads and more senior roles, where coordinating the efforts of several developers is the required skill?
Reversing Entropy 