Scalable computer programming languages
There will always be things we wish to say in our programs that in all known
languages can only be said poorly.
-- Alan Perlis
If you're going to send me email comments about this article, please keep a
few things in mind:
I wrote this article a long time ago (in 2001, if memory serves), and
frankly I'm not that interested in discussing it anymore. These days I'd
rather write programs and teach programming languages than write opinion
The article accurately reflects my opinions as of the time I wrote it,
but may not accurately reflect my current opinions.
I didn't post this article to reddit.com or to any other site. I have
no interest in drumming up traffic to this page.
I've received some surprisingly hostile emails from people who
disagree with me. To those people: please realize that this is a rant, an
opinion piece. If you don't agree with me, feel free to write your own
opinion piece. But name-calling and trolling just makes you look bad and
undercuts your own arguments. I won't respond to trolls, even if they also
have some good points to make. If you can't talk civilly, talk to someone
else. I am continually astonished by how angry comparative discussions of
programming languages can get. Can't we all get along? There are more
important things to get mad about, like cancer, world hunger, nuclear
That said, I welcome constructive comments. In fact, I've received a
number of insightful comments, and I've added some notes at the end
summarizing some of the feedback I've received as well as some of the ways my
views have shifted since I originally wrote this.
Computer programmers often use the word scalable to describe a
desirable feature of a program or algorithm. The idea is that something that
is scalable not only works for very small scale jobs but is equally effective
(or nearly as effective) when the job in question is much larger (or, more
commonly, when the job grows from being small to being large). A trivial
example of poor scalability might be an inefficient algorithm used in a
prototype implementation of a program. It works fine as long as only very
small data sets are being used, but it breaks down when larger data sets are
used because the computation takes so much time that the program effectively
grinds to a halt. This kind of scalability is well understood by any decent
programmer. A different kind of scalability is represented by the
architecture of a computer program. A program that can't easily be extended
to cope with new requirements is called brittle, which is the opposite
of scalable. Since effectively all programs that are successful grow new
parts, a nonscalable program usually will require a total rewrite when the
requirements change, which is very wasteful. The usual reason for this kind
of non-scalability is poor design abstraction; too many fundamental design
decisions have been hard-wired into the code in so many places that it is
difficult to change them all without introducing lots of bugs. Many, many
books have been written about good program design; a classic is Abelson and
Sussman's Structure and
Interpretation of Computer Programs, which covers this and much more
interesting material as well and is highly recommended. But that's not what
I want to talk about here. What I want to talk about is what makes
computer programming languages scalable or not. I want to show that
the notion of scalability is every bit as valid when applied to programming
languages as it is when applied to programs or algorithms. I'll also discuss
several well-known and not so well-known programming languages from this
perspective and give some concrete recommendations, as well as discuss some
of the social factors which hinder progress in this field.
What is a scalable computer language?
Simply put, a scalable computer language is a language that one can write
very large programs in (and extend very large programs that have already been
written) without feeling an undue amount of pain. In a scalable programming
language, the difficulty of managing the complexity of the program goes up
roughly linearly with the size of the program. Conversely, a nonscalable
computer language is one in which increasing the size and scope of a problem
tends to make the program much harder to manage (i.e. the complexity
of the program goes up much more than linearly with its size).
Aspects of a programming language that affect scalability
Garbage collection: very, very good
Garbage collection (GC) means that the computer language (literally, the
computer language's runtime system) automatically manages the reclamation
("freeing") of memory that is no longer being used. This is a huge
win for the programmer, and can dramatically increase the scalability of the
language. The reason is simple. It's usually obvious where one has to
allocate memory. It can be very difficult to know exactly when it's safe to
free it. This is especially true when references to the allocated memory are
passed around, returned from functions, stored in multiple data structures
that are later deleted (or not), etc. etc. The effects of this are very
insidious. Languages without GC implicitly discourage the programmer from
using all but the simplest data structures, because more often than not, the
memory management problem quickly becomes intractable when using more complex
data structures. What usually happens in practice is that the programmer
rolls his/her own (bad) garbage collector (maybe a reference counter), with
performance that is usually worse than what you would get if you used a
language with GC built in.
I saw a rather stark example of this recently. One of the things I do
professionally is teach the C programming language to Caltech undergraduates.
I emphasized how important it was to always free memory that had been
allocated. However, many of my students simply ignored me, and their code
was littered with memory leaks. I got so tired of writing "this code has a
memory leak here" on their assignments that I wrote a very simple memory leak
checker. They are now required to write code that passes through the memory
leak checker without any reported leaks before they submit their
assignments. However, I was somewhat dismayed to find that my own answers to
the assignments had a couple of subtle memory leaks as well! Since I have
more than ten years of C programming experience, and have worked on several
very large projects, this suggests to me that manual memory management is
much harder than I'd previously supposed it to be.
There is a cost to GC, both in time and space efficiency. Well-designed
garbage collectors (especially generational GC) can be extremely efficient
(more efficient, for instance, than naive approaches such as reference
counting). However, in order to do this they tend to have significantly
greater space usages than programs without GC (I've heard estimates on the
order of 50% more total space used). On the other hand, a program that
leaks memory has the greatest space usage of all. I've wasted way too much
of my life hunting down memory leaks in large C programs, and I have no
interest in continuing to do so.
In conclusion, I would say that of all the items I'm discussing here, GC is
the single most important one to ensure that a programming language is
scalable. This is why programmers who move from a language without GC (say
C++) to one of roughly equivalent abstractive power but with GC (say Java)
invariably say how much happier they are now that they don't have to worry
about memory management and can concentrate on the algorithms they're trying
to write. Personally, I'd rather pull my own teeth out than write a large
project in a language without GC.
Direct access to memory and pointer arithmetic: very bad
Some computer languages, notably C and C++, allow the programmer to directly
interact with memory addresses through pointers, as well as allowing pointer
arithmetic (incrementing and decrementing pointer variables). This kind of
low-level programming is sometimes necessary (e.g. when writing device
drivers) and sometimes simply useful (e.g. when micro-optimizing code
that has to run as fast as possible). However, my (imperfect) understanding
of the issue is that programming with pointers makes precise garbage
collection impossible, or nearly so. There is a kind of GC called
"conservative" garbage collection (here is a free
implementation of the Boehm-Demers conservative GC) which can work with
languages like C and C++. It's certainly better than nothing, but there are
no guarantees that all memory will be managed correctly (i.e. memory
leaks are unlikely, but possible). In practice, this supposedly isn't a
problem, but I note with some interest that very little C/C++ code that I've
seen actually uses conservative GC, and those that do are usually in code
that is implementing a language that includes GC.
The scalability cost of pointers goes far beyond just making GC harder.
Pointers (and especially pointer arithmetic) tend to destroy any safety
guarantees you might want to be able to make about a program. It's not hard
to see why. When you have a pointer to (say) an integer, and you can add
1,000,000 to that pointer and dereference some random region of memory which
may or may not be part of your program's run-time image, all hell can break
loose. If you're lucky, you'll just get a core dump and your program will
terminate. If you're not so lucky, some part of the program memory will be
corrupted, leading to mysterious bugs that are extremely difficult to track
down, because they manifest themselves far away from where the original
problem was. This leads to a huge increase in debugging times, which
dramatically hurts programmer productivity. As the program gets larger, the
opportunities for this kind of problem increase, which represents a
significant barrier to scalability.
The usual argument in favor of direct pointer manipulations is that they make
it possible to write faster code. This is often true; I've seen cases where
using pointer arithmetic judiciously increased the speed of a program by a
factor of five. However, the reverse is also often true; many program
optimizations (normally performed automatically by the compiler) are rendered
much more difficult or impossible in code that uses pointers. In other
words, languages that enable micro-optimizations often make
The author of the Eiffel language,
Bertrand Meyer, has said that (I'm paraphrasing) "you can have pointer
arithmetic, or you can have correct programs, but you can't have both". I
agree with him. I think that direct memory access through pointers is the
single biggest barrier to programming language scalability.
None of this is meant to imply that pointers and pointer arithmetic don't
have their place; they are crucial for low-level close-to-the-metal
programming. However, large programs written at a low level are simply not
scalable. The right way to use languages like C is to implement small,
focused low-level components of applications written primarily in
higher-level languages. In fact, this is also the right way to use C++; you
write some low-level classes that use pointers in some of their methods and
then encapsulate these methods so you never have to expose the pointer
manipulations to a user of the class. This would be nearly ideal, except
that the compiler has no way to enforce this; you can always use pointers if
you want to.
By the way, there is an interesting language called Cyclone which is
essentially a "safe" variant of C. It has three different types of pointers,
some of which allow pointer arithmetic and some of which don't. Pointer
arithmetic in cyclone is always checked for safety. This language is thus
much safer than C; the run-time cost varies from negligible to substantial.
However, cyclone doesn't correct the other problems with C (described below),
so I wouldn't describe it as particularly scalable.
Static type checking: mostly very good
A statically typed language is one where each data item has a specific
type which cannot be changed for the duration of the program. In addition,
variables have specific types, and you can't assign a value of a different
type to that variable. There are a number of issues related to static
typing, and there is a lot of confusion about these issues, so I'll try to
summarize the main points here.
Advantages of static type checking
The primary scalability advantage of static type checking is that a great
number of errors are caught at compile time, rather than at run time. Thus,
in a statically typed language with a well-designed type system, most of the
trivial errors will be caught by the compiler, which leaves the programmer
free to concentrate on the more interesting parts of the program. This
increases productivity as well as programmer happiness ;-) Furthermore, if
the type system extends to the module system (see below), then any use of an
imported module will be type-checked as well. This rules out large classes
of errors in the use of external modules written by others just as it does in
the code one writes for oneself. This, in turn, makes it easier to re-use
other people's code with confidence, which makes the language significantly
In addition, static type checking leads to significantly faster code than
dynamic (run-time) type checking. If the compiler knows that 'a' and 'b'
both represent small integers, and it needs to compute 'a + b', then it can
insert the code for addition of small integers right into the program. If
the only thing that's known about 'a' and 'b' is that they represent objects
that might be integers, or floats, or strings, then the decision on what to
do has to be deferred until run time. Type checking at run time is
expensive. Therefore, in addition to the other advantages of static typing,
you also get faster code.
Type declarations vs. type inference
The usual reason why many programmers don't like static type checking is
that type declarations are verbose and detract from the purity of the
algorithm. This is certainly the case in most statically typed languages; a
particularly egregious example is Java, where you can have statements like
Foo foo = new Foo(); // Declare a new object of type Foo.
You have to use the word "foo" three times to get the message across! What
most programmers don't realize is that there is an alternative. Languages
with type inference can figure out the types of almost all variables
from their context, which means that the programmer doesn't have to type in
declarations. A good example of this is the objective CAML (Ocaml) programming language.
This leads to code that is as concise as code written in dynamically typed
languages like Scheme or Python but with all the benefits of static typing.
Unfortunately for scalability, some languages (notably C and C++, again) have
mechanisms for type casting, which is basically an escape hatch that
allows you to tell the compiler "I know I said that this value was of type X,
but from now on you can pretend that it's actually of type Y, and I'll take
the responsibility". This feature is necessary in C because the type system
is extremely weak (it's not nearly as necessary in C++ and is generally
considered bad programming style). It's also possible (and often necessary)
to cast between types in Java (almost always from a superclass to a
subclass), but if the cast fails then an exception is thrown, whereas in C,
the cast never fails (and anything can happen if you cast an object to the
wrong type). In conclusion, unchecked type casting is bad, and languages
that require a lot of it are less scalable than those that don't. Note that
the majority of the type casting in Java (which is checked) is simply to get
around the lack of parameterized types, so even checked type casting may
indicate a weakness in the language.
Static vs. dynamic type checking
The relative virtues of static versus dynamic type checking are one of the
great holy wars amongst computer language researchers and users. In contrast
to static type checking (described above), in dynamic type systems objects,
not variables, have types. A variable in these system is simply a binding to
an object, and it can be bound to (for instance) an integer one moment, a
string another moment, and a list another moment. This means that operations
which require particular types for their arguments (e.g. addition)
have to check these types at run-time instead of at compile time. This leads
to significant performance penalties, as well as a loss of safety; a type
error is not normally caught until run-time. On the other hand, dynamic
typing is incredibly expressive, much more so than any static type system.
This is a big topic that I don't intend to explore here (it would be an essay
all by itself). Suffice it to say that a lot of the flexibility of dynamic
types can be recovered in a statically typed language if the type system is
powerful enough. A good example of a statically typed language with a very
powerful type system is, once again, Ocaml
(get the idea that I like that language yet? ;-)).
Another (weak) argument in favor of dynamic type checking is that dynamically
typed languages tend to be much less verbose than statically typed ones,
which means that the algorithm can often be expressed more clearly and
succinctly. However, statically typed languages with type inference like
(ahem) Ocaml circumvent this problem, as mentioned above.
If static type checking is just too restrictive for your tastes, then
dynamically typed languages like Lisp, Scheme, Python, Ruby or Smalltalk are
quite pleasant to program in, although my view is that the lack of static
type checking hurts the scalability of these languages substantially. What
typically happens in large projects written in these languages is that
extensive unit tests are written to catch type errors as well as logical
errors (the Smalltalk community has been particularly aggressive about
promoting this approach; see Kent Beck's extreme programming site for
much more on this, which is not limited to Smalltalk or to dynamically typed
Another interesting approach is "soft typing", which is a cross between
static type checking and dynamic type checking. Roughly speaking, it allows
the same expressiveness as dynamic typing, but will statically check the
types of everything that it can, and if it can't decide whether some program
element is correctly typed at compile time it will check it at run time.
This approach is used in the Dylan
language (and also in Common Lisp, to a greater or lesser extent depending on
the compiler) and is being actively developed from both the theoretical and
practical standpoints by the PLT
Scheme team of computer language researchers/implementors. I look
forward to seeing what they come up with.
Exception handling: good
Exception handling is a useful feature for making code more robust and also
cleaner. The basic idea is that some operations can fail for various
reasons, making it impossible to return a desired value. For instance,
reading a line from a text file is impossible if the file pointer is already
at the end of the file. In primitive languages like C, the only solution is
to return an error code and laboriously test for it whenever the function is
called. However, since a C function can only return one value, the real
return value of the function has to be kludged as a writable parameter of the
function, which is very messy and bug-prone (since it requires that the user
manipulate pointers). With exception handling, when an exceptional condition
occurs an exception is thrown, and it unwinds the call stack until it reaches
a suitable exception handler. This feature is so useful that almost all new
languages incorporate it. Interestingly, exceptions also work much better in
the presence of garbage collection; avoiding memory leaks in a language like
C++ that has exception handling but has no GC is quite tricky (see Scott
Meyers' books Effective C++ and More Effective C++ for an
extensive description of this issue). This is yet another argument for
garbage collection (as if we needed one).
Run-time error checking: good
Dynamically typed languages do all their error checking at run time. Most of
this error checking is to ensure that operations are called with
correctly typed arguments. This is costly and somewhat error-prone when
compared to languages that can do all their type checking at compile time.
However, even in statically-typed languages, there arise situations which
can't be handled by the type system alone. More precisely, there arise
situations where any reasonable (decidable, efficient) type system can't know
at compile time that an error is going to happen. In order to deal with this
you need run-time error checking. Here are some typical examples.
Array bounds violations
If an attempt is made to access an array outside of its bounds (e.g.
trying to access the 100th element of a 10-element array), then an array
bounds violation occurs. Scalable languages will always catch this error and
will usually throw an exception so that either the program can deal with it
or the person running the program knows that something bad happened (ideally
a stack trace will be printed in the latter case). Checking for array bounds
violations has a significant cost, however, so ideally the compiler will have
an option that permits the user to disable this check if speed is more
important than safety. Note that array bounds violations are never caught in
C programs; instead, they cause fun problems like core dumps and memory
Another class of errors that can normally only be caught efficiently at run
time are arithmetic errors. The classic example of this is integer division
by zero. Again, scalable languages will throw an exception in this case.
Other integer arithmetic errors such as overflows also typically raise
exceptions in many languages. In contrast, floating point errors such as 1.0
/ 0.0 (= infinity), -1.0 / 0.0 (= -infinity) and 0.0 / 0.0 (= not-a-number or
NaN) do not normally raise exceptions even in otherwise very safe languages
(such as Java or Ocaml). The reason for this is not clear to me (apparently
it's written into the IEEE floating-point arithmetic standards and nobody
wants to challenge it), and frankly, I think that this is a mistake. Like
array bounds checking, it's good to have a compiler option to disable
arithmetic error checking if speed is more important than safety for a given
application. This is a generally useful principle: make the language safe by
default, and give the programmer the option of trading safety for speed,
ideally without requiring him/her to rewrite any code.
Assertions and contracts: very good
Most languages (even C) provide an "assert" feature, which allows the
programmer to state that at a given point in a program a particular
relationship must be true. For instance, in a C program you might say:
assert(i == 100);
meaning "at this point in the program, 'i' should have the value 100, and if
it doesn't something is wrong." This is a kind of built-in sanity checking
for your program. It is possible to disable assertion checks during
compilation as well. Typically, asserts are enabled while developing a
program and disabled after the program is completed in order to speed up the
program. Some languages (notably Eiffel) have a much more elaborate
assertion system known as "Design by Contract" which includes separate kinds
of assertion checks for preconditions of functions, postconditions of
functions, class invariants, loop invariants and variants, and much more, all
incorporated into the object system of the language. If used correctly, this
is a huge win. Bugs will tend to show up much closer to where they occurred,
which makes debugging vastly easier. This in turn makes the language much
Support for abstractions
In general, the more support a language provides for developing abstractions,
the better. Good abstraction capabilities mean you can say more with less
code, and less code means fewer bugs and shorter development times, which
leads to better scalability. There are lots of worthwhile language features
I won't discuss here; instead, I'll single out the ones that have the most
effect on language scalability.
Module systems: very good
Frankly, a language without a good module system is useless for developing
large programs. A module system allows the programmer to develop and test
parts of a program in isolation, and then combine these parts at a later
time. Modules are usually used to implement reusable code libraries
containing data structures and functions representing some interesting aspect
of the problem domain (for instance, operations on linked lists). Once a
module is written and debugged, any other program can then import and use the
code in that module. This is a huge win, since fundamental data structures
and operations don't have to be re-written for every program (which is called
"reinventing the wheel" in programmer jargon). This allows the programmer to
write code much more rapidly and with a much greater confidence of success,
and is thus a vital component of scalable computer languages.
Object-oriented programming: good
Object-oriented programming (OOP) is often presented as the cure to all
programming problems. It is not. However, some kinds of applications
benefit greatly from the object-oriented paradigm. The basic idea in OOP is
that programs are decomposed into "objects" which have "methods". Methods
are functions that have access to the internal state of the object. Only the
methods corresponding to an object can access that internal state, so if the
state changes in an unexpected way, one of the methods must have been
responsible for it. This is called "data hiding" or "encapsulation". Also,
in OOP it is possible to create new classes of objects by inheritance; this
means that the new object class is a version of the old object class, but
with some new methods and/or new data. Finally, and most importantly,
calling object methods is "polymorphic"; this basically means that the
appropriate method to run on an object is chosen at run time rather than at
compile time. Certain kinds of programming tasks (such as simulations) are
well suited to the OO paradigm, while other tasks (such as compilers) don't
really need OO. In general, it's good if a language supports OO, but I
prefer languages that don't force you to use OO for all programs. It is
often claimed that programs written in the OO style are easier to extend and
modify than non-OO programs. I think this is an exaggeration. If you have
to subclass a class every time you want to add a new method, your program
will start to look very kludgy very fast. Nevertheless, OO is a useful
abstraction technique to have available.
Functional programming: good
The "dual" to the OOP paradigm, if you will, is the functional programming
(FP) paradigm. Instead of decomposing a program into a group of objects,
functional programs decompose a program into a group of functions. Like OO,
functional programming requires a language that supports certain language
features. The primary feature is that in a functional language, functions
are "first-class", which means that they can be treated as data (i.e.
they can be passed as arguments to a function, returned from a function,
and/or created on the fly inside a function). Another crucial feature is the
ability to define recursive functions efficiently (technically, there has to
be tail-recursion optimization). It turns out that if you have these
capabilities, then you can do without loop statements (e.g.
for and while loops in C), instead using recursion to
implement the loop. In addition, functional programs typically avoid using
mutable data (assignment statements) as much as possible. They also tend to
use a lot of very small functions instead of a few large functions. The
advantages of FP include:
I don't have nearly enough space here to go into this in detail. If you're
interested, you should read Abelson and Sussman's Structure and Interpretation of
Computer Programs, which goes into great detail on this subject. Suffice
it to say that having support for the functional style can make a computer
language much more scalable. Unfortunately, very few languages support this
style (examples include Lisp, Scheme, Ocaml, Standard ML, and Haskell),
apparently because average programmers find the functional style "hard to
understand" and because FP has a (largely but not entirely undeserved)
reputation for inefficiency (in fact, the efficiency of FP depends enormously
on the details of the language implementation).
- Many algorithms can be expressed much more concisely and elegantly.
- Not using mutable data removes a large class of potential bugs.
- Functional programs are much easier to verify for correctness than
- Higher-order functions (functions that take functions as input and/or
return functions as output) can be used to factor out common programming
idioms. This makes it easy to modify one kind of program to do related
Macros: mostly good
By "macros" I mean structural macros like in Lisp, not textual substitution
macros like in C. Structural macros can be a very useful abstraction device;
they allow the programmer to encapsulate programming idioms that are often
repeated and which cannot be expressed as higher-order functions. A good
example of this would be macros to implement control constructs. In most
languages, you are stuck with the control constructs (if,
for, while) that are provided by the language, but in Lisp
you can define your own using macros. Macros are very interesting (see Paul
Graham's book On Lisp for
much more on them). They have some problems as well:
Nonetheless, when used correctly macros can increase the abstraction level of
a program dramatically, which is good for scalability.
- They can make debugging substantially more complicated.
- They can in some cases make code harder to understand, because macros
(at least in Lisp) look like functions but behave in a totally different
- Writing effective macros is non-trivial.
"Components" is a vague word in the context of computer languages, but here
I'm using it to represent a single unit of functionality that can be composed
with other parts of a program. It's sort of like a library, but more
self-contained. There are a number of component architectures around (Java
beans, CORBA, Microsoft COM); some, like Java beans, only support one
language, while others support a whole range of languages. One big
attraction of components is that they can be language-independent and even
location-independent. However, if a language specifically supports one (or
many) component architectures it's a big win for scalability because then you
can re-use components written by another developer (and maybe in another
language). This is usually just a library issue, but some languages (C#
being a notable example) are designed specifically to make component
development easier. This is a good thing, but I don't have the space to go
into it here.
Syntax and readability
Syntax is not the most interesting aspect of computer languages; in fact,
it's probably the least interesting aspect, which is odd considering that it
seems to be the only aspect that most programmers ever learn ;-) Syntax does
make a difference to scalability, however. In my opinion, it's not that
important that a syntax be "familiar" (which in practice means "like C
syntax"); rather, the important issue is how consistent it is. Very baroque
and inconsistent syntaxes with dozens of strange exceptions to general rules
(C++ and (especially) Perl are the worst offenders here) make programs very
hard to understand and maintain, simply because nobody ever remembers all the
special cases. At the other extreme, very minimalistic and simple syntaxes
like Lisp syntax are difficult for many people to get used to (though
personally, I quite like Lisp's syntax). Python is a good example of a
language that has a very friendly syntax both for new programmers and
The scalability of different programming languages
In this section I'll make more specific comments about how some programming
languages stack up in terms of their scalability. I can't cover every
programming language, but I'll try to cover a representative sample.
C combines all the power of assembly language with all the ease of use of
I'd just like to take this moment to point out that C has all the
expressive power of two dixie cups and a string.
-- Jamie Zawinski, in the source code for xkeycaps
There is a point in your life when you realize that you have written enough
destructors, and have spent enough time tracking down a memory leak, and you
have spend enough time tracking down memory corruption, and you have spent
enough time using low-level insecure functions, and you have implemented way
too many linked lists.
-- Miguel de Icaza
Greenspun's Tenth Rule of Programming: "Any sufficiently complicated C or
Fortran program contains an ad-hoc, informally-specified bug-ridden
slow implementation of half of Common Lisp."
-- Philip Greenspun
Your superior intellect is no match for our puny weapons.
-- unknown, via Aaron Stern
I'll be blunt: C is a horrible language for developing large projects
in. C contains almost all of the bad language features described above and
almost none of the good ones. Furthermore, some of the good features it does
have (like static type checking) are so corrupted by type casting that they
are much less useful than they would otherwise be. C also offers essentially
no abstraction capabilities whatsoever except for arrays (which are really
just pointers in disguise) and structs (ditto).
Does that mean that C is a useless language? Far from it. It has its place,
and its place is a vital one. C is an excellent language for writing code
that has to interface directly with the machine ("bare-metal" programming).
It is also a good language for implementing better languages in ;-) The Ocaml
runtime engine, for instance, is written in C (with some help from assembly
language). C is useful for writing the 1% of your application that
absolutely, positively, has to run as fast as possible. However, if you're
trying to write a large application entirely in C you are in for a miserable
time. Instead, you're better off picking a good language that has a C
foreign-function interface (FFI), so you can "drop down" into C when you
really need to (which hopefully won't be that often).
Some people (particularly novice programmers who have no idea what they're
talking about) are under the illusion that because C offers such direct
control over the machine, it's the only "real" programming language for "true
hackers". If that were true, all "true hackers" (whatever that means) would
be writing code in assembly language, which is much closer to the machine
than C is. I hope some of the quotes above, some of which are from
world-famous open-source hackers, will help to dispel this myth. If not,
then I recommend that you try to write a really large program (> 100,000
lines of code) in C, and tell me what you think at the end of that
experience. I think you'll find my arguments much more persuasive.
C++ adds object-oriented and generic programming features to C, along with a
host of other features. This can significantly improve the abstraction level
of C++ programs relative to C programs, and this should make the language
more scalable. Unfortunately, the presence of pointers and the absence of
GC, in my opinion, undoes all the benefits of the useful features and makes
C++ non-scalable (for instance, NONE of the standard template library (STL)
container classes use GC). In addition, C++ is so mind-bogglingly complex,
with so many odd features that interact in peculiar ways, that it's almost
impossible to master. I will readily admit that I'd rather write a large
application in C++ than in C, but that's like saying I'd rather eat rotting
meat than swallow sulfuric acid ;-)
C++ : an octopus made by nailing extra legs onto a dog.
-- off smalltalk.org
Think of C++ as an object-oriented assembly language.
-- off the guile Scheme mailing list
C makes it easy to shoot yourself in the foot. C++ makes it harder, but
when you do, you blow your whole leg off.
-- Bjarne Stroustrup
Programming in C++ is premature optimization.
-- off comp.lang.python
To be fair, I have to point out that there is some really neat stuff possible
in C++ using templates (typically lumped under the name "template
metaprogramming"; see the book Modern C++ Design by Andrei
Alexandrescu for more information.). This is very much wizard-level
programming (which is OK), but to my mind it doesn't even come close to
compensating for the lack of GC. What would make C++ a more scalable
language is (a) including a garbage collector as part of the standard
library, and (b) having some way to restrict the use of pointers to
particular modules that need direct pointer access (such as very low-level
data structures). I'm not very optimistic that this will ever happen, but I
hope it does.
Java and C#
Java and C# are more recent languages which have learned from the mistakes of
the past. They both have GC, neither have pointers, and they both support
object-oriented programming. This makes programming in these languages
comparatively non-painful, and they scale well. Both also have interesting
mechanisms for achieving platform independence (which is beyond the scope of
the present discussion). C# is part of the .NET framework, which (in theory)
permits a considerable amount of inter-language interaction; this is in my
opinion a huge win, but is also beyond the scope of the present discussion.
Both languages tend to be quite verbose, which is off-putting to many people
(including me), but that's not that big an issue. C# also contains an
interesting mechanism for using unsafe code (i.e. code which uses
pointers and doesn't use GC) but encapsulated in modules specifically marked
"unsafe" (an idea borrowed from the Modula-3 language, which is now sadly all
but defunct). If you feel you can't live without the ability to program with
pointers, but you don't need them for most of your code, that's the right way
The abstraction level of both C# and Java is mediocre; it's much better than
C, somewhat weaker (!) than C++, and not nearly as good as languages that
support both object-oriented and functional programming (such as Lisp and
Ocaml). Therefore, I find programming in these languages to be pretty boring
and tedious. Many of the scalability features in these languages are not,
strictly speaking, part of the languages at all but of the environment(s)
built up around the languages. For instance, the support for components,
versioning, packaging and documentation generation are all features of the
environments. I hope we will soon start to see these kinds of meta-features
in better languages than Java or C#.
Eiffel is a very cleanly-designed object-oriented language that has been
specifically designed to be scalable. It has a number of interesting
There are many other features as well. Eiffel's author, Bertrand Meyer, has
written a number of books about the language and the ideas behind it, of
which the most comprehensive is Object-oriented
- There is a very strong assertional system implementing "design by
contract" (see above), which is totally incorporated into the object-oriented
framework, so subclasses cannot break assertions of their superclasses
(though there are theoretical arguments about whether this was done the right
- Multiple inheritance is handled in what I consider a very simple and
effective way; any naming conflicts must be resolved by renaming the
- Inheritance can be controlled at a finer granularity than the
private/protected/public modifiers seen in C++, Java, and C#.
Eiffel has a few deficiencies. The syntax is very verbose (there are
something like 70 keywords) and also quite unfamiliar. The theory behind
Eiffel rules out some language features that we have come to expect, like
multiple exit points from loops. Many familiar features are found in a
completely unfamiliar form. Classes are the only module units, which I think
is abusing the notion of class (and which leads to the excessive use of
multiple inheritance in situations in which it isn't really needed). These
deficiencies are not show-stoppers, however.
More significantly, functional programming isn't supported (although the last
time I checked something analogous to function pointers was in the process of
being added to Eiffel; I'm not sure what the current situation is). Also,
the type system has some holes in it that require link-time analysis to make
sure that a program is correctly typed. This is a problem if you want to
create shared libraries (although I admit I don't know all the details).
In conclusion, I would say that Eiffel is quite a scalable language,
certainly more so than C++, Java, or C#. I personally find the lack of
support for functional programming quite stifling, but I'd rather program in
Eiffel than in Java, C++, or C# any day.
Python and Smalltalk
I'm lumping together Python and Smalltalk, two quite different languages,
because they are both dynamically typed languages which support
object-oriented programming. One difference is that it's possible to program
in a non-OO manner in Python, whereas Smalltalk is nothing but objects, all
the way down. From a scalability perspective, both languages are safe,
excellent for prototyping, and generally enjoyable to work in. On the other
hand, the lack of static type checking hurts their scalability (not to
mention their efficiency), and to compensate for this most serious Python and
Smalltalk programmers spend a lot of time writing test suites (which is a
good idea, but the test suites can be much smaller in statically typed
languages since you get type safety for free). I have much more experience
with Python than with Smalltalk, and I can say unequivocally that it's a
great language for writing small programs, but when the program source code
is longer than about a thousand lines I start wishing I had a type checker.
I am not a fan of Perl. Basically, I think that Perl is simply Python with
an incredibly obfuscated syntax and a few extra features that nobody really
needs. Perl is incredibly non-scalable; I dare you to try to understand any
Perl program of more than a hundred lines or so. The fault is not just the
syntax; the semantics of the language are full of little oddities
(e.g. overloading on the return type of a function), and frankly, I
recommend that you just stay away from Perl. Maybe Perl 6 will not be as
painful; but then, maybe it won't. I'll check again when Perl 6 actually
Python: executable pseudocode. Perl: executable line noise.
-- off comp.lang.python
Parenthetically, one cool thing that has come out (actually, that is
in the process of coming out) of the Perl 6 effort is an amazingly cool
project called Parrot, which is a
virtual machine targeted at dynamic languages. The goal is to have a common
virtual machine for running Perl, Python, Ruby, Scheme etc. I like this
project very much, so please check it out.
Common Lisp and Scheme
Lisp is, quite simply, a brilliant computer language. There are two forms of
Lisp that are still alive today (three if you count Dylan, but I discuss that
below): Common Lisp and Scheme. Their differences are mostly irrelevant for
the discussion here, so I'll lump them together for the most part.
Most people are just too damn dumb to recognise how great Lisp is.
-- off slashdot
[Lisp] is the only computer language that is beautiful.
-- Neal Stephenson
The journey of a thousand miles begins with an open parenthesis.
-- Rainer Joswig
Will write code that writes code that writes code for food.
-- Martin Rodgers
Those who do not understand lisp are doomed to re-implement it.
-- attributed to Erik Naggum, off comp.lang.lisp
Here are some of Lisp's interesting features.
One of the consequences of all these features is that it is extremely easy to
implement new programming paradigms within the Lisp language. For instance,
you can implement a full object-oriented system (or several different
incompatible OO systems) within the Lisp language itself. For this reason,
Lisp is sometimes referred to as a "language laboratory". Because of this,
Lisp programmers often write their programs bottom-up by effectively writing
a new program-specific language for the application and then writing the
application in that language. Paul Graham's book On Lisp discusses this
approach in great detail. Lisp is also an incredibly dynamic and flexible
language; code can be compiled on the fly and modules can be re-loaded as
needed. Despite this, Lisp compilers exist that are very efficient and can
routinely produce code that is only (say) 1.5 times slower than optimized C
- It has an incredibly simple and unambiguous syntax (which is admittedly
a little peculiar when you first encounter it).
- It fully supports functional programming (and was the first language to
- It has garbage collection (and was the first language to have it).
- There are no pointers in Lisp code.
- There are no type declarations.
- Structural macros are a fundamental part of the language, and because of
the simple syntax are easier to write than in any other language.
The only serious drawback that Lisp has is that it relies mainly on dynamic
typing. This is great for interactive exploration, but becomes an obstacle
as the size of a program gets larger. Common Lisp at least allows type
declarations, which the compiler is free to check for consistency (or to
ignore). However, it's not easy to get the kind of absolute type safety in a
Lisp program that you'd get in (say) an Ocaml program. Still, I really like
Lisp and I think that every serious programmer needs to learn it. Also, at
least one implementation of Scheme (the PLT Scheme implementation) promises to
provide a usable type checker in the near future, which I look forward to.
Dylan is a dialect of Lisp that uses a more conventional infix syntax and
that has a number of features that improve the language's scalability
relative to Lisp. Most notably, it allows (and encourages) the use of type
declarations in code. If these type declarations can be checked at compile
time, they are; otherwise they're checked at run time. This is a real win
for the programmer. Unfortunately, dylan hasn't yet caught on, but there is
a team of open source hackers at Gwydion Dylan that are hard at work
trying to bring Dylan back from obscurity. I hope they succeed, because
Dylan has the potential to be a very scalable language, and is perhaps the
only language that combines the best features of scripting languages and
Objective CAML (Ocaml) and Standard ML
I've saved the best for last ;-) As you've probably gathered from my comments
above, I am a big fan of the Objective
CAML language (usually referred to as Ocaml). Ocaml is being developed
by a team of researchers at INRIA, a computer science research institute in
France. The curious acronym CAML stands for "Categorical Abstract Machine
Language" and is basically a historical artifact; I won't discuss it further.
Ocaml is closely related to another language called Standard ML. However,
since Ocaml provides almost all of the features of Standard ML and many more,
I won't discuss Standard ML further either.
There is a joke about American engineers and French engineers. The American
team brings a prototype to the French team. The French team's response is:
"Well, it works fine in practice; but how will it hold up in theory?"
Perl, C, FORTRAN, Pascal and AWK are purely procedural (not even OO)
languages. Functional languages are to large reflecting telescopes what these
languages are to binoculars.
-- off slashdot
The best and the brightest in the programming languages field are working
on functional programming languages.
-- Bjarne Stroustrup (paraphrased from an interview)
What does Ocaml provide that makes it so great, and in particular makes it
such a scalable language?
I don't expect you to understand all of these features, but I also don't have
the patience to explain them all here; go to the web site for more
information. The point is that Ocaml has almost all of the typical features
that I've identified as giving a computer language more scalability, and
several other features that are found almost nowhere else that also enhance
the scalability. It's also incredibly efficient for such a sophisticated
language; a lot of Ocaml code is quite competitive with C code that does the
same task (sometimes the Ocaml code is even faster). For this reason, Ocaml
is by far my favorite computer language and the one I consider first when
contemplating writing a new program.
- Garbage collection.
- No pointers.
- Extremely fast compilation to bytecode.
- Compilation to extremely efficient native code (often faster than
C++, often comparable to C speeds).
- Static type checking with type inference (one of the few languages that
has this; you can't imagine how cool this is until you use it).
- Full support for functional programming.
- Full support for imperative and object-oriented programming.
- A great module system.
- Polymorphically-typed functions (functions that are generic over entire
classes of types).
- Algebraic data types, which make creating complex data structures
- Functors (functions which create customized data types parameterized on an
input type or set of types).
- A programmable preprocessor (camlp4) that can be used to change the
language syntax (!).
Having said that, I have to add that Ocaml is a long way from perfect. The
syntax is a little weird in many places. The language has a fairly long
learning curve. There is not enough good documentation and far too few
books. The standard library is much smaller than what you'd find in (say)
Java. On the other hand, none of these problems are show-stoppers, and I
predict that Ocaml is going to become a major force in the world of
programming languages in the next ten years, especially among very high-level
Given all the comments I've made above, the obvious question is: why do
people continue to use lousy non-scalable programming languages when there
are good alternatives that will make their lives much easier? In this
section I'd like to look into the factors that prevent programmers from
learning and using scalable programming languages.
Most programmers, even professional programmers, know next to nothing about
programming languages. Most programmers have probably never even heard of
any languages other than C, C++, Java, C#, Visual Basic, Fortran, and (maybe)
Perl. That's too bad, because there isn't a great language in that entire
list. What this shows is that people who develop and use good languages have
got to get better at getting the word out. For my part, I would recommend
that anyone reading this go out and learn Ocaml (especially!), Lisp and/or
Scheme, Python, Smalltalk, and Eiffel, especially if they think that "all
languages are the same".
The learning curve
As somebody who loves to learn new programming languages and paradigms, I
hate to admit this, but one of the biggest reasons bad languages persist is
that most people hate learning new programming languages. They would
rather stick to a shitty language that they more-or-less understand than take
the one month or so to become familiar with a new language. This is an
example of a very, very general phenomenon both in computing and elsewhere,
which is that nobody ever wants to learn anything new. I think this is
because learning is very painful for most people (I say I think so
because I've always really enjoyed learning and so I can't relate to this at
all). This would make sense if learning a new language wasn't worthwhile
(let's face it, it's a lot of work), but as I've tried to show above, there
are huge differences between good languages and bad ones, and if the only
language you know is a bad one, then learning a good one is most definitely
In addition, the learning problem becomes much, much worse when the new
language embodies a different programming paradigm (e.g. functional
programming), even when the language also supports more familiar paradigms.
I see this even in Caltech undergrads, who supposedly have the highest
average SAT scores of any group of students in the world. Most of them
already know how to program in C when they get here, and we teach them Scheme
in a mostly-functional style that is totally unfamiliar to them. It's
remarkable how much difficulty they have with this, big SAT scores
Finally, it's true (but absolutely amazing to me) that small differences in
syntax between computer languages can make it incredibly difficult for
someone who has mastered one language to learn another. This is why Java and
C# (correctly) adopted a C-like syntax; it's not that it's better, it's just
more familiar and thus is less likely to be rejected. Programmers are an odd
lot; they will often reject a language based on the most trivial criteria
(e.g. whether or not the language uses semicolons to separate
There are a lot of stupid misconceptions about programming languages that
make people not want to learn new ones. The most common ones are:
I've also heard some truly incredible howlers by people who should know
better, such as "in Lisp, lists are the only data type" (this was written in
the book Generative
Programming, which was published in 2000, and is completely, utterly
- All programming languages are basically the same.
- Code written in anything but C is going to be incredibly slow.
- Code written in any language with garbage collection is going to be
- "Real programmers" only write code in C.
I hope I've helped to dispel some of these misconceptions in the material
I've written above.
Sacrificing everything for speed
One serious obstacle to the adoption of good programming languages is the
notion that everything has to be sacrificed for speed. In computer languages
as in life, speed kills. It's not that I like slow programs, but the notion
that speed is the only important attribute of a program (as opposed to
correctness, maintainability, extensibility, readability etc.) is simply
wrong. In fact, most programs only have a small core (maybe 20%) that
absolutely needs to be fast surrounded by a much larger infrastructure of
code that doesn't need to run all that quickly. If you're going to write
your entire application in a non-scalable language like C because only 20%
of the program requires it, then that's a real shame. It's especially ironic
because most good languages have a foreign function interface (FFI) into C
which allows you to write that 20% in C and use the better language for the
rest of the code. However, most programmers can't be bothered learning an
FFI any more than they can be bothered learning a new computer language. In
addition, most programmers who are obsessed with speed never even bother to
profile their code to find out where the bottlenecks are, presumably because
they can't be bothered to learn how to use a profiler either. Do you detect
a pattern here?
Fortunately, the rise of Java and scripting languages, and the success of
projects that have used those languages, have made the speed obsession much
less of a factor than it used to be.
Sacrificing everything for bit-level control
Another factor hindering the adoption of scalable programming languages is
the idea that a programming language must give you intimate, bit-level
control over every data structure or it isn't any good. If you believe this,
think back on all the programs you've ever written. How many of them
actually needed this kind of bit-level control? In my case, the answer is
zero, and unless you spend a lot of time writing device drivers, your answer
is probably pretty low too. Nevertheless, bit-level control is a nice
feature, and that's precisely what C is good at. This is why any good
language worth its salt has a C FFI.
Sacrificing everything for the lowest common denominator
Another major factor hindering the acceptance of new computer languages is
the lowest common denominator. Programmers overwhelmingly prefer to learn a
language that everybody else knows. There are some good reasons for this:
However, this is a chicken-and-egg situation. If nobody ever learns a new
language, then no progress is going to be made in the field. In fact, there
are only two successful models of language adoption that I know of:
- It makes it much easier to find a job.
- A popular language usually has a lot more libraries available.
- It's much easier to find new programmers to work on a project if
it's written in a language that a lot of people know.
Since I can hardly expect large corporations to espouse genuinely good
technology, I think that the only way for scalable programming languages to
catch on is through the grass-roots method. I believe that Ocaml is starting
to gain market share this way. And, truth be told, Java and C# aren't
that bad even though they are corporate-sponsored. They are
reasonably scalable languages with a mediocre abstraction capability. But we
can do much better.
- The language is massively promoted by a company with a lot of money
(e.g. Java and Sun, C# and Microsoft).
- The language spreads in a "grass-roots" fashion (C, C++, Perl, Python).
Conclusions and recommendations
I've tried to show that there are many aspects of computer languages that can
have large effects on their scalability, and I've discussed how different
languages compare in this regard. To recap, a scalable language would
- garbage collection
- no pointers or pointer arithmetic
- a foreign function interface to the C language
- static type checking with type inference
- support for exception handling
- run-time error checking for errors that can't be caught at
compile time, like array bounds violations and division by zero
- support for assertions and design by contract
- a powerful, statically checked module system
- support for object-oriented programming
- support for functional programming
- structural macros
- support for components
- a simple, consistent and readable syntax
If you've read this far and agree with many of my observations, then my
recommendation to you is simple: learn some new languages. In particular,
learn all of the languages that I've discussed above, even the ones I put
down. Write sizable programs in all of these languages. This will not only
be fun, but it will teach more about what makes a computer language scalable
than you will ever learn by reading. And if you only want to learn
one programming language out of all the ones I mentioned, please do
yourself a favor and learn Ocaml. It's not perfect, but it's the best
language I've ever used. If you have the time to learn two languages,
the other language should be Common Lisp or Scheme.
Epilogue: May 2003
Since writing the original version of this article, I've had some experiences
which have modified some of my views. Most importantly, I am not as high on
Ocaml as I was then. While I still think Ocaml is an outstanding language in
many respects, there are significant things I don't like about it. I wanted
to use Ocaml as the language for a large project I wanted to work on, but
found very quickly that I couldn't use it the way I wanted. The object
system doesn't support multimethods (which is no surprise; very few languages
do), and there were certain features I wanted that called for multimethods.
In C++ or Java, you can fake multimethods because you have run-time type
identification (RTTI). But the Ocaml developers refuse to add this feature
to Ocaml on quasi-religious grounds; the idea of having programs which cannot
be proven to be type-safe at compile time goes against everything they
believe in. No matter if the type violations give rise to a safe exception
just like dividing by zero does; they won't hear of it. So I'm not using
Ocaml for that project.
I've also had more experience with both Java and C++ since writing the first
version of this article. I haven't changed my opinion of Java much; the best
thing about it is that it comes with a huge variety of useful libraries. The
upcoming "Tiger" release of Java will add several new language features which
will make programming in Java much less painful (notably generics and
autoboxing), which I welcome. I think Java is a fine language for many types
of applications, but it doesn't have the qualities of coolness that make me
fall in love with a language, and the abstraction level is still less than
many other languages, including C++. Speaking of C++, I now understand
better why C++ is the way it is. Even though C++ is monstrously complex, the
ability to write code that moves smoothly from a bit-level of abstraction to
a fairly high level of abstraction is extremely valuable for many projects,
especially ones where efficiency is paramount. I intend to do more C++
programming and also to use the Boehm-Demers conservative GC to see how
effective it is in combination with C++'s other features.
Martin Rodgers (author of one of the quotes above) sent me an email where he
said that his main criterion for scalability is that a language not put a
glass ceiling on abstraction. This is the classic Lisp programmer's
viewpoint, and I am very sympathetic to it. One of the big challenges in
programming language design is to get languages with the flexibility of Lisp
but with more static guarantees. The ML family of languages are a compromise
between static type safety and Lisp-like flexibility. I look forward to
future developments in this area.
Epilogue 2: July 2006
This article got posted to reddit.com and probably some other places, so
I've been getting a lot of comments on it. Here is my response to some of
the ones I found most interesting.
Julian Morrison sent me this email:
There's a very important feature you missed, and it's the real explanation
for the success of Java: separable, atomic, pre-packaged, versioned
functionality. Jarballs. Those, more than anything else, make reusability
real. Java programming is about plugging together ready-built parts. Nothing
else comes close.
I have to agree with him on this, and it's a major omission from the
discussion above (the component stuff is sort of related to this). I don't
find Java to be a very inspiring language, but I like the Java infrastructure
a lot (the same comment applies to C#). With Java, you can download packages
and have pretty good confidence that everything will work as it should (with
some caveats that I'll mention below). There are a bunch of features of the
Java infrastructure that make this possible: bytecode, having the same
virtual platform on every real platform, versioning, metadata, etc. but they
all result in a chunk of code which (ideally) "just works". In fact, it
doesn't always "just work" but it "just works" more often than in most other
languages I've used. To give an example, I tried to install a podcasting
program which was written in Python, but which used Python interfaces to a
number of libraries written in C or C++. I couldn't get it to work (and I've
been doing this for a long time) because of all the version skew
problems. This wasn't Python's fault as a language; it was the fact that the
Python packages didn't track the C/C++ versions that were being used well
enough. This is much less likely to happen in Java, for the reasons
mentioned above and also because using code written in C/C++ as part of a
Java program (i.e. through JNI) is less necessary (here's where
JIT-compiling pays off big time).
On the other hand, my colleague Donnie Pinkston, who has forgotten more
about Java than I'll ever know, has pointed out that it's very easy to get
into classloader hell for projects that define their own classloaders (which
apparently is often necessary), so it's not all a bed of roses. But I think
there is a point to be made that Java gets some of the large-scale issues
less wrong than most languages. These issues tend to be boring non-sexy
things that don't excite my computer language sense, but they are absolutely
essential in practice. If nobody can install your program, nobody can use
it. And yes, I think I've spent as much time typing
configure;make;make install" as anyone, and I don't think
that's good enough. Material for another rant.
One person mentioned that Common Lisp has multimethods, which is true
(it's part of CLOS, the Common Lisp Object System). Multimethods are very
cool (some languages, like Goo, build them in right from
the start); my impression, though, is that they're hard to implement
efficiently. One day I'll build a language with multimethods to find out for
Someone argued that I'd been too hard on Ocaml for claiming that it's not
able to fake multimethods. All I can say is: try it, and see how easy you
think it is. I still think Ocaml is a great language, by the way.
One person argued for Lisp's scalability on a number of levels, which
essentially comes down to Paul Graham's argument that in Lisp, you extend the
language towards the problem instead of coding the problem down to the
language (which I mention above). Two key features that enable this are a
uniform syntax and syntactic macros. I'm definitely a fan of uniform syntax
(for one thing, it lessens the conceptual load of learning the language) and
of syntactic macros. However, macros have interesting and complex
interactions with module systems, and many people feel that Common Lisp
didn't get this right. Matthew Flatt's paper Compilable and
Composable Macros goes into the PLT Scheme macro/module system, which is
an interesting new approach to this problem that tries to be the best of both
worlds. Also, there has been a lot of recent work on "multi-stage
programming", which generalizes the notion of compile-time computation to an
arbitrary number of stages (compile-time, run-time, link-time, whatever) and
also can incorporate static type checking. This seems extremely promising to
me. Tim Sheard has a number
of papers on this and related subjects, as well as links to other work.
Julian Noble (an ex-Caltecher!) asked me about Forth, a language that he
is intimately familiar with (he wrote a book called Scientific
Forth on (you guessed it) scientific computing in Forth). Forth was my
first love among programming languages (I read Leo Brodie's Starting
Forth a long time ago), and I've had kind of a love/hate relationship
with it ever since. You can do cool things in Forth that are nearly
impossible to do in other languages (like change the lexical syntax), but it
always seems to me that things that are fairly easy to do in other languages
are hard to do in Forth. I could write a very long article explaining this
in detail, and I probably will one day. Also, Forth has the "write-only"
quality that I dislike about Perl, only even more so (though mitigated
somewhat by the very uniform syntactic and semantic model). I've written two
experimental languages to try to get at the essence of what I like about
Forth without the things I don't like; I haven't gotten there yet. (By the
way, writing Forth-like languages seems to be a rite of passage for computer
language geeks.) I agree with Julian that Forth is a very easy language to
debug, and also that it's a language that everyone with a serious interest in
computer languages should study.
I pretty much stand by my statements on C++ that I expressed in the first
epilogue. C++ is a terrifically powerful language, but it's extremely
complex and there are so many ways to shoot yourself in the foot that it's
hard to use. I've started to think of C++ as a giant macro system around C,
which makes a lot of the design decisions easier to understand. Stan
Lippman's book Inside
the C++ Object Model is essential reading if you want to understand C++
better, though the book is somewhat out of date.
Since writing the last epilogue, I've gotten very enamored of Haskell, which I've been interested in for
a long time. It was actually a couple of my students (Brandon Moore and
Aaron Plattner) who turned me (back) on to Haskell. Haskell has come a long
way in the last few years, and is now starting to be used for serious
applications. I think Haskell has the potential to be the most scalable
language ever, but the learning curve is monstrous. The great thing about
Haskell is that its purely-functional nature allows you to combine components
arbitrarily, and they always work the way you expect! Al Barr once described
this to me as the "snap-together" quality of a language, and Haskell code
snaps together very nicely. However, there's a cost; dealing with I/O and
mutable state requires more work than in most languages (though the type
system does give you nice guarantees about which functions can do I/O or
state manipulation and which ones can't). Another interesting thing: the
"point-free" style of programming in Haskell reminds me a lot of Forth! In
Forth you create new functions by concatenating old functions; in Haskell you
do the same thing, but with a function composition operator between the
functions (of course, it's not _quite_ that simple, but it's close). So
maybe I've come full circle: Haskell is Forth, lambda calculus is SKI
combinators, and I need to up my medication ;-)
- Structure and
Interpretation of Computer Programs, by Hal Abelson, Gerry Sussman and
Julie Sussman (the full text is online, but you really should buy a copy
- The Boehm-Demers
conservative garbage collector (Hans Boehm's page).
- The Eiffel programming language.
- The Cyclone
- The objective CAML (Ocaml)
- The extreme programming
- The Gwydion Dylan home page.
GD is an open-source implementation of the Dylan language.
- The PLT Scheme home page. PLT
Scheme is an outstanding implementation of the Scheme programming
language, with many innovative features.
- Scott Meyers, Effective C++, 2nd Ed. and More Effective C++.
Addison-Wesley, 1997 and 1995 (respectively).
- Andrei Alexandrescu, Modern C++ Design. Addison-Wesley, 2001.
- Paul Graham, On
- Bertrand Meyer, Object-oriented
Software Construction. Prentice-Hall, 2000.
- The Gwydion Dylan
implementation of the Dylan language.
- The Standard ML of
New Jersey (SML/NJ) implementation of the Standard ML language.
- The Haskell home page.
to my home page.
||Last updated April 11, 2017