The Lisp Before the End of My Lifetime
Many wax poetic on the virtues of Lisp, and I would say for good reason: it was a language and philosophy that was (and is) far ahead of its time in principle and oftentimes in practice. But I have to cede the following: the foundations of Common Lisp are becoming somewhat ancient and there are many places that have more modern roots where I would have it borrow heavily to assist in creating my programming nirvana. In talking with yet another friend from Berkeley (and the author of sudo random) we had discussed some of these things and I decided it was worth enumerating some of them and pointing to ongoing work that implements those fragments or something close to it.
The reason this post is titled in such a sober way is because the Lisp I envision is probably many lifetimes of work to accomplish, and as such, I cannot see myself accomplishing everything on my own. Granted, I still have a lot of life ahead of me yet, but that only makes the equation all the more depressing. Implementation could probably span many PhD theses and industrial man-decades. As such, I can only hope that it’s the Lisp that more or less exists before The End Of My Lifetime. I would be glad to one day say that I contributed in some or large part to any one piece of it. This whole post smacks of the “sufficiently smart compiler” daydreaming, so turn away if you must. Alternatively, you can sit back, enjoy, and nit-pick at the details of compiler theory and implementation, some (or many) of which I’m sure have been overlooked by me.
Finally, this is not by any means a list of things that current implementations do not have, just things that I feel would seem most valuable. Some are not even necessarily technical challenges so much as social and design ones. I view this hypothetical Lisp as not only some new features, but a set of idioms that I more programmers generally agree on. “The Zen of Python” is an excellent example of this. There are definitely some lisp-idioms, but they have become somewhat antiquated and are hard to enumerate in some part because of the baroque and aging Common Lisp specification. The hardest idiom to get around is fearlessness and ease of metaprogramming, which in part is great, but also can make standardization difficult socially as it assists in making herding Lisp programmers difficult. Herding lisp programmers is about as tough as herding cats armed with machine guns.
However, I think Lisp’s guiding intentions have lied in flexibility. Common Lisp, for its time, was the kitchen sink. It still is, in large part, but may benefit from new idioms and a fresh slate, as well as deeper and more integrated compiler support for some of the features mentioned below.
1. The Compiler is your Friend.
Leaders in this area: SLIME and its Swank component
Honorable Mentions: DrScheme, IPython
Nowadays modern IDEs seem to do everything up to the semantic analysis step in compilation to give you advanced searching and refactoring capabilities. Oftentimes a lot of compiler work is reimplemented to support the features of the given IDE at hand, and much work is duplicated, sometimes to the point of implementing a whole compiler, as in Eclipse.
SLIME and Swank have a twist on this that I like: Swank is responsible for asking the compiler implementation itself (in my case SBCL) for information on various symbols, their source location, documentation, and so on. It communicates all this information through a socket to a frontend, which comprises the rest of SLIME. In doing so it gains the authoritative answer to queries about the program because the compiler of choice itself is delivering its opinion on the matter, even as it runs.
This allows for an accurate way to track down references that may be created dynamically by asking the figurative question “What would the compiler do?”. From this SLIME gains extremely powerful auto-completion facilities that are robust to techniques are either unavailable in other programming cultures or, if used, would defeat the programmer’s completeness of assessment of the program. Lisp is the only runtime/language I know of where I can eval a string and still be able to access the resulting, say, function definition and documentation strings with full auto-completion and hinting in my editing environment.
Were Lisp more popular, I would bet Swank-compatibility and feature-richness would be a defining feature for Lisp implementations, and frontends using Swank would be prolific. The socket interface was definitely the way to go here.
2. Networked & Concurrent Programming
Leaders in this area: Erlang
Honorable mentions: Termite & Gambit, Rhino, SISC, Parallel Python, Stackless Python, and many others.
Sun Microsystems, despite its beleaguered business, had at least one thing very, very right: “The network is the computer.” The ability to talk on multiple computers on a network is increasingly important in our era, and making it convenient can lead to extraordinarily powerful, robust applications. Erlang definitely leads the pack in this area: an industrial strength, reasonably efficient compiler that can do I/O pumping using efficient kernel-assisted event polling as well as automatically distributing computation across multiple processors. It also can support sending of most higher-order objects – such as closures – across the network parts of messages, as well as a powerful pattern-matching syntax that allows for relatively easy handling of binary (and other) protocols.
With processors increasing the number of cores and computers continually falling in price the ability to (mostly) correctly use multiple machines and multiple processors on each machine will become a dominant influence for writing programs that require high performance. Erlang has been demonstrated to be excellent at managing network I/O switching and handling, which is not surprising considering that is its main application as a tool. It could, however, stand to improve upon sequential execution performance: let’s just say I won’t be rewriting my numeric codes in Erlang just yet, despite the potential for mass distribution of computation. I also miss some of my amenities I’ve gotten used to in Lisp, but Erlang excels in its area for sure and has many lessons to teach.
3. First Class Environments
Leaders in this area: T, MIT-Scheme — mostly academia
Honorable Mentions: Python, Common Lisp
First class environments are the beginning and end of many problems, but I feel that having this facility would be useful for debugging and implementing creative namespacing and many other important features. Opaque environments can sometimes still be handled with mostly reasonable performance, but as far as I know nice, transparent environments — i.e., things that look like property or assoc lists straight out of the SICP — are just an absolute killer for performance and make compiler optimizations nigh near impossible. But that’s OK…because there’s nothing more annoying that shying away from using thunks or currying when these techniques are the most simple and expressive solution because you are afraid that it will become a chore to poke at the environment to debug these anonymous function instances later. By contrast, “locals()” in Python, for example, can be a godsend for special tasks and quick debugging, even if it only returns the local (and generally most useful) environment.
First class environments also help in “fixing” tricky issues that crop up and are cause for Scheme’s motivation for hygienic macros, famous for being hellishly picky to get right (Lisp-2 fans always seem to harp on this point, although what I’m suggesting may be something more like dynamic-lisp-N). I still feel that the quasiquote, despite its sometimes-ugliness, is the right primitive model to follow. And, in fact, since there seem to be hygienic macro packages build on top of the primitive variants, one could get those almost for free. Perhaps hygienic macros could also be idiomatic, I know not.
In conclusion, the goal is to break down some of the final barriers between code and data and allow for some interesting if unorthodox transformations and redefinitions at run-time and compile-time. It’s also important to have this functionality if one wants to dynamically redistribute computations across machines or perform run-time metaprogramming, which may be a great way to introduce new compiler features that can be toggled on and off.
4. An External Native-Code Generator
Leaders in this area: LLVM, JVM
Honorable Mentions: Parrot (if only because of relative vaporwareness), Mono, C–, Bit-C
More important than the individual merits of any of these specific VMs is that they are maintained separately by Other People™. It is high time to stop re-inventing architecture-specific code generators and local optimizers over and over. With the JVM catching up or passing up language-specific native code generators (it’s now more or less tied with OCaml on the Alioth compiler shootout with Java and doing well with Scala) and LLVM recently showing on-par and sometimes better performance than vanilla versions of GCC for some C code, I am buoyed with hope that one can generate relatively high-level (or at least architecture-independent-ish) bytecode and still get respectable or even good performance. JIT, ironically enough, may be more well suited to the lispy world than the Java one (although its instrumental in the Java world for sure) considering that it’s pretty common to go in and rebind definitions in Lisp while a system is running. One might argue that changing declaim/proclaim statements and evaluating code is in fact better than JIT, and I could see there being a case for that, but it just seems that lots of work is being poured into run-time code generators that could be leveraged.
One interesting idea is compiling to Bit-C, which has support for low-level manipulations and type-verification, yet also is a lisp.
5. Optionally Exposed Type Inference and Static Typing
Leaders in this area: Epigram, Qi, Haskell, the ML family
Honorable Mentions: CMUCL and descendant SBCL
Inferred static typing and type inference is all the rage these days, with claims for increased program execution and correctness. And I’m all for that, and Qi is an excellent example of the ability to do considerable amount with standard Common Lisp facilities. Qi has the extremely sensible goal of remaining in Common Lisp, and thus ensuring that it has measurable chance of having traction in my lifetime.
Although I’m not sure that the interface to typing I would expose is necessarily (or necessarily not) Qi’s, but I do want my compiler to tell me what it thinks about various tokens littered throughout my code, allowing my editor to do things like red-flagging unsafe operations and type disagreements or have a mode to show expensive dynamic operations or inferred types when I’m seeking optimization. Ultimately, not all of my code will fit neatly into the pure-functional paradigm and may be better served by the occasional side-effect or global state, and I would like type rigor to extend as far as possible, but not become a burden. Sometimes I just want a heterogeneous hash table of elements without any baggage. I think it makes sense to rigorously type nuggets of code, but the Lisp in question should not be fascist about maintaining ‘perfect’ consistency throughout an entire program. Epigram and Qi have this model exactly right: pay as you go. Flexibilty when you need it, but not to the point where it is fascist. In the future, it’d be nice to see some efficiency benefits from compiler-awareness of carefully statically typed nuggets of code that otherwise would not be possible, such as eliminating some bounds checking.
Finally, CMUCL and SBCL already do quite a bit of type-inference, it’s just not exposed to the user so nicely in SLIME except through warnings blown out of stdout. Even then, they can be very useful. Ideally I could simply ask SLIME to access the type of a given symbol and (CMU|SB)CL could tell me what it thinks.
6. Pattern Matching
Leaders in this area: Many. MLs, Haskell, Erlang, lisp macros for Scheme and CL.
This is an amenity that should become standard part of the lisper’s idiom for convenience if nothing else. It’s just that there are a number of pattern matchers and none of them that I am aware of has become the idiomatic one.
7. Continuations and Dynamic-Wind
Leaders in this area: Scheme, almost exclusively, and an implementation: STALIN
Scheme is probably the canonical continuation and dynamic-wind implementation. Implementation is subtle and performance impacts can be significant, but give pleasant generality to schemers when designing new control constructs. Combined with first-class environments one could do quite a few interesting things, such as save the entire program state as an environment-continuation pair. Unfortunately, implementation is incredibly painful. Yet, it has been done, and with a pay-as-you-go model it may not need to hurt most code’s performance very much (few people ought to be writing code riddled with continuations). See the STALIN compiler, which does all sorts of rather insane things, along with the insane compilation time. It’s mostly intended for numerical codes, though.
8. Pretty and easy (but optional) Laziness
Leaders in this area: Haskell, Python, Ruby, Common Lisp Iterate package, many others
Honorable Mentions: Anything with closures, Screamer. Scheme for call/cc allowing even more strange general flow control.
I like Python’s yield operator that transforms a normal function into a lazy one that is expressed as an iterator. In particular, I like to avoid, when possible, specifying representation formats for a sequence or set of things when they aren’t strictly necessary. With continuations one can get very nice looking implementations of generator-type functions although this may have an undesirable performance impact. As such, most languages that have laziness or generators implement special restricted-case behavior to get good performance, and that should probably sit pretty high up on the optimization list for this Lisp.
9. Convenient and Pervasive Tail Recursion
Leaders in this area: Erlang, Scheme
Honorable mentions: anything with tail recursion elimination and optional arguments
I like using tail recursion to express loops, as I find them more flexible, easier to debug, and understandable than loops and mutation. Combined with pattern matching it’s fiendishly convenient at times and can in some circumstances greatly assist a compiler if assignments go unused. I don’t meant to say this should be the only means, but I would like to see it be idiomatic and terse to write. The Scheme named-let and Erlang’s pattern matching both assist this process. One of the main priorities that is key is making it easy to hide the extra arguments often required in a tail-recursive function to hold state from the outside world, and Scheme’s named-let, I think, handles this rather beautifully for common cases.