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Django file and stream serving performance Gotcha

with 9 comments

Recently I’ve been doing a little bit of work with the Django web framework for Python. Part of this project involves having a bit of reasonable binary file streaming to and from the server. There is currently a patch in trac (#2070) slated for acceptance. So I apply it and try it out and try copying some files in and out through the web server. I have some problems with the particulars of this patch and I intend to amend my complaints, but that’s for another post. What I discovered was an annoying performance gotcha in simply reading back binary files to be served to the user.

The gotcha is simple to expose:

In a Django view, use the documented functionality of passing a file-like object to the response object from the view; preferably a big, binary one. So you do something like this:

return HttpResponse(open('/path/to/big/file.bin'))

And then you surf on over to localhost and try grabbing this file. Your hard drive whirs and you notice your CPU usage is at 100% while serving the file slowly. Most people then rationalize it away saying “well, of course, Python is slow, so it makes sense that it would suck at this. Set up a dedicated static file serving server written in C and use some URL routing incantations.”

The crucial information that I had to dig for is how Django emits bytes to users. Django calls iter() on the input object and then uses calls to .next() to grab more bytes to write out to the stream. Once you factor in that the default iter() behavior for a open file in Python is to read lines you realize that there’s just an enormous amount of time and unnecessarily evil buffering going on just to emit chunks of the file separated by (in the case of binary files) completely arbitrarily spaced newline bytes. The result is lots of heap abuse as well as lots of burned CPU time looking for these needles in the haystack.

The hack to address this is very simple: we write a tiny iterator wrapper that simply uses the read(size) call. It can look something like this:

class FileIterWrapper(object):
  def __init__(self, flo, chunk_size = 1024**2):
    self.flo = flo
    self.chunk_size = chunk_size

  def next(self):
    data =
    if data:
      return data
      raise StopIteration

  def __iter__(self):
    return self

1024 ** 2 in bytes is one megabyte in a chunk. When using this iterator the logic is simple and the result is that Python consumes very little CPU time and memory to rip through a file stream. It can be applied to the previous example like so:

return HttpResponse(FileIterWrapper(open('/path/to/big/file.bin')))

Now everything is fast and happy and running as it should.

So what should Django do about this? It could be just written off as an idiosyncrasy of the framework, but I think that the case is strong that Django should inspect for file-like objects and use more aggressive calls to .read() to prevent such unpredictable behavior. One problem with such large (1MB) read()s is that they may block for too long instead of trickling bytes to the user, so some asynchronous I/O strategy would be better.

There’s no reason why a small to moderate sized site should get hosed performance-wise because several people are downloading binary files from a Django server via modpython or wsgi.

Finally, proper error handling on disposing the file descriptor in the above examples is an exercise to the reader. I suggest the using the “with” statement that can be currently imported from future.


Written by fdr

February 12, 2008 at 1:51 pm

Posted in django, projects, python

Tagged with , ,

The Lisp Before the End of My Lifetime

with 14 comments

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.

Written by fdr

August 4, 2007 at 3:08 am

Posted in languages, lisp, projects

DeckWiki: Proposed Collaborative Presentation Creation

with one comment

This is something I have been turning over in my head for at least six or seven months as I discovered the prevalence of Microsoft PowerPoint™ in business settings. There are a number of shortcomings:

  • Big binary files exchanged by email[0]
  • Relatively weak version control/merging capabilities
  • Out of date borrowed slides[1]
  • Lobotomizing an old set of slides just to get the same style in an integrated Microsoft PowerPoint™ file
  • Having to reprocess slides manually to get them to conform to some new style
  • Weak metadata/commenting capabilities, leading to bad slides
  • Lack of visual support for “off the rails” discussion, leading to unnecessary hand waving
  • Almost no serious collaboration support whatsoever[2]
  • Doesn’t facilitate location of useful, other slides available in the organization

This is kind of a solved problem, if you could get everyone to accept using something like Prosper and a bunch of TeX files on some sort of version control plus a few scripts. But that’s not going to happen. Even I will admit that it would probably be a little painful.

So let’s discuss something viable.

Many people at this point are pretty familiar with the idea of a Wiki…reading one, at least. Instead of some heavy-handed new tool that has to convince everyone its way is the One True Way and takes no prisoners[3], I suggest using a wiki package or writing something employing the wiki model to facilitate authorship of slides for presentations. Most professionals in technology sort of understand the idea of a wiki, even if in practice they never use them or contribute to them, but at least it’s not completely alien and probably not too scary to new users. I think. Let’s start with that premise. Let’s also presume the Wiki has a notion of “Presentations,” or a path through a series of presentations, including the empty presentation (no pages) and automatic singleton presentations (consisting of one page, every slide is a presentation). This is an important recurrence relation, because I will henceforth never organize things in terms of slides except when referring to the current way of doing things: in this model all presentations will be formed by composing presentations.

Let’s consider how we can address the issues raised above:

  1. Big binary files exchanged by email
    If the presentations are held in a wiki, each presentation can be maintained independently and there will be a common place to view and download presentations. Hopefully this will prevent attaching big files and sending them around all the time. By exploiting page versions it is possible to make sure that a given presentation will remain with the same content for all-time.
  2. Relatively weak version control/merging capabilities
    Since each presentation is tracked and worked on independently, one would only need to be concerned about clashing of the revision of single atom of content. The definition of an “atom” is most obviously at least on the per-page-level, but could be as fine as the word/paragraph level by using diffing/patching, although this gets more complicated. This also doesn’t seem to present a humongous problem on Wikipedia, even on the busiest pages. They still seem to get contributed to and updated.
  3. Out of date borrowed slides
    One can easily obtain a list of updated sub-presentations for any or all presentations and accept or reject changes. The result is a new, unique presentation; old presentations are never lost, so reverting is easy.
  4. Lobotomizing an old set of slides just to get the same style in an integrated Microsoft PowerPoint™ file
    Styles should only be loosely connected to a presentation. One should be able to paint a new style over any presentation unit, so absolute conformity is an option.
  5. Having to reprocess slides manually to get them to conform to some new style
    Simply apply a new style to the presentation
  6. Weak metadata/commenting capabilities, leading to bad slides
    Right now presentation slides are meant as much to be distributed and read as much as they are meant to be shown in person. The results are slides with entirely too much text and visual noise, distracting the viewers during presentations. Cutting out some detail upsets some stakeholders because then the slides no longer communicate everything that was said during the presentation in person. The relatively wimpy commenting facilities seen in most presentation packages doesn’t seem to please anyone, but an up-to-date nicely-formatted cross-referenced wiki-page associated with each presentation may be better. This idea is not new at all[4], although its proper implementation may be tricky.
  7. Lack of visual support for “off the rails” discussion, leading to unnecessary hand waving
    If a presentation goes off the rails — and this is not always a bad thing — one is often left without visual aids must do blackboarding and waving of hands. It’d be better to have a big list of short presentations that one is at least moderately familiar with so that if discussion wavers to another engaging topic that deserves more through discussion one can pull up more appropriate presentations and documentation. Another way to use this is to include “see-also” sub-presentations that one can visit and then jump back from the aside back to the main presentation. In this way a presentation flow resembles a NDFA.
  8. Almost no serious collaboration support whatsoever
    All this version tracking buys us a nice, fluid system that allows for synchronized updating of artifacts with a number of authors that far exceed two (as is, in my experience, the limit with more traditional methods). Wikipedia is an empirical example of this model working.
  9. Doesn’t facilitate location of useful, other slides available in the organization
    This is an exciting one. With all this graph connectivity information searching and finding more information may be a lot easier. One can also tell roughly how much a presentation is being used elsewhere. There are many potential uses for this.

Here’s a conceptual sketch of a more formal treatment[5]:

presentation -> {id: Id, presentation: [From], presentation: [To], page: P, 

This slideshow could not be started. Try refreshing the page or viewing it in another browser.

: Ancestry} presentation -> Nil id -> UniqueIdentifier (probably 64 bit integer) page -> {Version, Data} (A version and some payload)

A presentation from Nil to Nil is the empty presentation. If From is Nil, then this presentation is the head of a presentation, if the To is Nil, then it’s the terminating point of a presentation. The Ancestry variable allows for tracking of the evolution of presentations over time by showing what presentations derived the current one[6].

Another interesting idea is to break free of stack-based thought and use continuation-style thought in order to model presentation traversal, but I suspect that will break the minds of many people. That way you may not jump back to the datum you started at, but somewhere else entirely…possibly never to return, or perhaps carrying all your exit continuations along with you for possible use. In any case, the ‘presentation’ datatype can support this since it has an ‘environment’ (the page and version) and a ‘label’ (the From and To nodes). In fact, all standard linear presentations are similar to invoking a continuation that never calls a return. This is another way to think about this. It’s certainly all within reach if presentations are simply recursively connected to presentations and should you define a ‘page’ datatype an adequate ‘environment’ and the presentation datatype (which includes the page) the ‘code.’ In any case, thinking of it of a NDFA (as mentioned above) is probably easier to understand and a less-stretched analogy, but the idea of carrying multiple exits is an intriguing one that deserves at least cursory attention. An NDFA analogy would also suggest a simple and well known form of visualization.

I have changed my type definitions somewhat to give more indication of the many-entrance many-exit nature of presentations by listifying To and From, although the same thing could have been accomplished by a large number of presentation type instances. I think this is more like what an actual implementation might look like. It may make ancestry tracking less hairy, too. My original goal was to define as little as possible to get things done, but then I decided this was silly and I should be spending a few more characters to more adequately carry the idea.


[0]: Now slightly less-binary with the new Microsoft Office™ 2007 XML format. If anyone can hope to actually understand it in a reasonable amount of time.

[1]: Common scenario: “HR says we’re at X employees…oh, that’s old, we’re actually at X + N, let’s move on…” In isolation this is really not so bad except N is sometimes wrong due to misremembering and with enough such errata it is distracting. There’s no obvious reason why it has to be so hard to stay up to date.

[2]: Especially with over two people. The track edit feature is useful, but did you ever try giving slides out to five people and merging the changes?

[3]: Sound familiar? It’s Lisp vs UNIX again

[4]: Possibly a variant of Knuth’s notion of Literate Programming

[5]: This is hand-wavy amalgam of syntax/semantics borrowed from Prolog, ML, Erlang, Lisp (for Nil only, really) and/or Haskell. If anyone complains I’ll provide something more rigorous. My variables start with capital letters, my types start with lower case and when describing a variable use a colon, and lists are denoted with […], so

This slideshow could not be started. Try refreshing the page or viewing it in another browser.

is a list filled with “presentation”-typed things. -> is my reduction symbol. Braces denote tuples. Comments are in parentheses

[6]: One of the interesting problems presented here is that Wikis generally attempt to converge on an authoritative page that everyone sees as opposed to branching and derivative works, which itself can create a mess of cross-generational merging. The problem can be seen as the same one that plagues the distributed vs centralized version control camps. As merging is still a problem that seems to not have been solved to everyone’s satisfaction, implementors would do well to pay attention to the subtle issues engaged by both camps in that community in an attempt to make an informed decision.

Written by fdr

June 29, 2007 at 12:05 am

Posted in projects

KPMake: A Proposed Dependency Resolution Tool

with 3 comments

One of the insane ideas I have floating around is to write yet another Make replacement. My rationale is as follows:

  • Make is simple, but not very powerful
  • Absurd macro languages and generation facilities grant enough power for “real work,” but are painful and ugly.
  • People widely use ant, probably a step down from Make if not for the incredible dedication to tool support to make it convenient for Java somewhat.

“But wait!” you say, “haven’t you ever heard of SCons or Rake, you imbecile?”

Why yes, yes I have. The nice thing about these approaches is that they recognize that most non-trivial build-systems are going to demand the power of a “real” programming language; certainly not the looseness of shell scripting nor the inflexibility of ant buildfiles. (Quote a colleague: “it’s like Lisp, without any of the advantages of Lisp!” ) The downside is they lose out on clarity. A hand-written Makefile tends to stay very simple whereas any convoluted thing can happen in a SConstruct file (the rough equivalent to Make’s “Makefile”), leaving the user doing some head scratching as to why something-or-other happened or why-or-not something built and how or why. On the plus side, the machinery of SCons also gets you things like a “clean” target for free. Another downside is agnosticism: Makefiles, thanks to their simple nature, can be easily written to control complex toolchains that do things other than compiling programs[0]. The barrier for entry in writing a builder in SCons is considerably higher than simple rules as dictated by a Makefile such as

foo : bar baz
	cat bar baz > foo

(Yes, I realize you can use automatic variables like ‘$@’ and ‘$^’, but let’s keep it less cryptic for now)

I have been working on and off on a project that uses a Makefile to orchestrate around a dozen individual tools. Not only is it robust, but after almost a year of inactivity it’s possible to read the Makefile and recall the roles of all the tiny moving parts and how they relate to one another. I then went back and wrote documentation. But now I want to improve on Make by allowing for complex behaviors while retaining succinct, declarative syntax and keeping the system easy to debug. How do I portend to do this?

KPMake, or “Knowledge Powered Make” is one possible solution. In a nutshell: I want to leverage state of the art symbolic AI techniques to solve a relatively mundane problem of dependency resolution while including modern explanation generation facilities to increase user understanding of why any particular actions were taken and to help debug these actions. I will probably use the KM knowledge base, partially because of being exposed to it at work and partially because I know it has been successfully been used in large projects such as Vulcan Inc’s ambitious Project HALO. I also happen to know it’s available as a single lisp file and has practically no set-up or portability problems. The base package for KPMake should simply be a distribution of KM, some basic models to handle basic generic dependency resolution, and macro/procedure support to make writing KPMakefiles easier. Knowledge models to build for say, particular languages (such as a gcc or a javac model) will be distributed and maintained separately and ought to have an eye towards making things even more convenient.

A neat feature that is enabled that I didn’t include in this writing to begin with is using situation calculus to allow programmers to load, save, and debug situational traces as well as perform advanced error recovery. KM supports situational reasoning and simulations that can be used to test build scripts (and write fuzz testing!) to make sure they respond in at least a reasonable way to unexpected events as well as increasing visibility of every step of the build process: right now one generally has to do a lot of manual logging to get a trace of what’s going on or, if one is lazy, set a breakpoint and only be able to view a few slices in the process. You can also have your build engineers in India and send your broken build “core dump” abroad and mostly avoid the “well it works on my machine” scenario.

[0]: I owe my appreciation of this technique to Professor Strain. Imagine a Makefile to build the program, run the tests, spit out any pictures, compile the TeX sources, and finally deliver you a postscript file to send off to the journal. I’m sure if submissions were something that had to be done often there would be a “mail” target that’d also deliver the resultant postscript to the relevant journal(s).

Written by fdr

June 27, 2007 at 9:10 pm

Posted in lisp, projects