Complexity in a programming language isn't created equal, there is good and bad one. So by itself it not necessary a bad thing. (There is also the complexity of the implementation, that is a different matter and has a different set of advantages/disadvantages. I don't discuss about it here).
For example D is a language much more complex than C, but in practice I am able to write correct programs in *much* less time in D than in C. My D programs contain less bugs too. How to tell the bad complexity from the good one? The good complexity is of features that allow to reduce code, avoid potential bugs, remove often the need of twiddling with pointers, to express a programmer meaning in a clear and standard way, etc. Adding to the language keywords that have a single and clean meaning seems an example of good complexity, while having keywords with multiple meanings looks like a bad form of complexity. Special cases are bad complexity. Adding simple standard ways to perform a common purpose may become good complexity if it's chosen wisely. Here are few examples, mostly from D: 1) C doesn't have an array slicing syntax, so C is less complex. But D slicing syntax is handy, easy to read and understand, and equally efficient (or more, because such slices are lazy), so they are a good complexity. I like slices, but them being lazy has produced several bugs in my code, so I think I may like slices to be copying by default and lazy when required, that is the opposite of the current situation (so [a..b].dup becomes the standard default meaning and [a..b] becomes the special syntax. So it may become: [a..b] an eager copy, and [a..b].lazy the light slicing. This may reduce bugs). 2) D built-in dynamic arrays allow to manage matrixes and tensor is a way WAY simpler than C, so while they introduce complexity in the language (and its implementation), I think it's a good complexity. On the other hand I often find this syntax very confusing: auto m = new int[][](n, m); that is I confuse rows with columns, etc. In Python I use Numpy or a syntax with the native lists that can't be confusing to Python programmers: m = [[None] * n for _ in xrange(m)] So for me that's a little example of bad complexity in D. 3) Python has lazy and eager ways to define lists (lazy ones just define an iterable sequence), D lacks them, so Python is more complex here. But in practice they give: - a short syntax that reduces code clutter, so they avoig bugs too - avoid the usage of lambdas most of the times - avoid the usage of many map() and filter() and related functions present in my libs and in std.algorithm of D2, this reduces complexity a lot and at the same time leads to faster (delegate-free) code as Andrei likes. This shows why using map() and filter() and related functions is bad, and I'd like a better solution, instead of seeing them added to the D stdlib. 4) Adding few associative arrays methods like a 'clear', to return a lazy view of its keys or values or pairs, and so on, increase its complexity, but in practice allow to write less code and to write smarter code, so they are good comlexity. The same may be true for a built-in set data structure, and part of the things that can be found in my dlibs. 5) The D module system is more complex than the basix import semantics of C, but in practice it leads to a simpler management of the parts of the programs, so it's a good complexity. The current module system has some holes, flling them may be a Good Thing [TM] (and probably it doesn't add much extra complexity). 6) Static if and static asserts seem a little more complex that the ways to do similar things in C, but they seem a good complexity. My list can go on :-) I think that most of the complexity of D is good, but there are many corners/things can be improved still, reducing the bad complexity of the language, adding more good complexity, reducing some corner cases, making D code both safer and faster to write and faster to run. Bye, bearophile
