Category: Solaris

If you haven’t seen it, DTrace is now shipping in Mac OS X Leopard. This is very exciting for us here at Sun, but one could be forgiven for asking an obvious question: why? How is having our technology in Leopard (which, if Ars Technica is to be believed, is “perhaps the most significant change in the Leopard kernel”) helping Sun? More bluntly, haven’t we in Sun handed Apple a piece of technology that we could have sold them instead? The answer to these questions — which are very similar in spirit to the questions that were asked over and over again internally as we prepared to open source Solaris — is that they are simply the wrong questions.

The thrust of the business questions around open source should not be “how does this directly help me?” or “can’t I sell them something instead?” but rather “how much does it cost me?” and “does it hurt me?” Why must one shift the bias of the questions? Because open source often helps in much more profound (and unanticipated) ways than just this quarter’s numbers; one must look at open source as long term strategy rather than short term tactics. And as for the intense (and natural) desire to sell a technology instead of giving away the source code, one has to understand that the choice is not between “I give a customer my technology” and “I sell a customer my technology”, but rather between “a customer that I never would have had uses my technology” and “a customer that I never would have had uses someone else’s technology.” When one thinks of open source in this way, the business case becomes much clearer — but this still may be a bit abstract, so let’s apply these questions to the specific case of DTrace in Leopard…

The first question is “how much did it cost Sun to get DTrace on Leopard?” The answer to this first question is that it cost Sun just about nothing. And not metaphorical nothing — I’m talking actual, literal nothing: Adam, Mike and I had essentially one meeting with the Apple folks, answering some questions that we would have answered for anyone anyway. But answering questions doesn’t ship product; how could the presence of our software in another product cost us nothing? This is possible because of that most beautiful property of software: it has no variable cost; the only meaningful costs associated with software are fixed costs, and those costs were all borne by Apple. Indeed, it has cost Sun more money in terms of my time to blog how this didn’t cost anything to Sun than it did in fact cost Sun in the first place…

With that question answered, the second question is “does the presence of DTrace on Leopard hurt Sun?” The answer is that it’s very hard to come up with a situation whereby this hurts Sun: one would have to invent a fictitious customer who is happily buying Sun servers and support — but only because they can’t get DTrace on their beloved Mac OS X. In fact, this mythical customer apparently hates Sun (but paradoxically loves DTrace?) so much that they’re willing to throw out all of their Sun and Solaris investment over a single technology — and one that is present in both systems no less. Even leaving aside that Solaris and Mac OS X are not direct competitors, this just doesn’t add up — or at least, it adds up to such a strange, irrational customer that you’ll find them in the ones and twos, not the thousands or millions.

But haven’t we lost some competitive advantage to Apple? Doesn’t that hurt Sun? The answer, again, is no. If you love DTrace (and again, that must be presupposed in the question — if DTrace means nothing to you, then its presence in Mac OS X also means nothing to you), then you are that much more likely to look at (and embrace) other perhaps less ballyhooed Solaris technologies like SMF, FMA, Zones, least-privilege, etc. That is, the kind of technologist who appreciates DTrace is also likely to appreciate the competitive advantages of Solaris that run far, far beyond merely DTrace — and that appreciation is not likely to be changed by the presence of DTrace in another system.

Okay, so this doesn’t cost Sun anything, and it doesn’t hurt Sun. Once one accepts that, one is open to a much more interesting and meaningful question: namely, does this help Sun? Does it help Sun to have our technology — especially a revolutionary one — present in other systems? The answer is “you bet!” There are of course some general, abstract ways that it helps — it grows our DTrace community, it creates larger markets for our partners and ISVs that wish to offer DTrace-based solutions and services, etc. But there are also more specific, concrete ways: for example, how could it not help Solaris to have Ruby developers (the vast majority of whom develop on Mac OS X) become accustomed to using DTrace to debug their Rails app? Today, Rails apps are generally developed on Mac OS X and deployed on Linux — but one can make a very, very plausible argument that getting Rails developers hooked on DTrace on the development side could well the change the dynamics on the deployment side. (After all, DTrace + Leopard + Ruby-on-Rails is crazy delicious!) This all serves as an object lesson of how unanticipatable the benefits of open source can be: despite extensive war-gaming, no one at Sun anticipated that open sourcing DTrace would allow it to be used to Sun’s advantage on a hot web development platform running on a hip development system, neither of which originated at Sun.

And the DTrace/Leopard/Ruby triumvirate points to a more profound change: the presence of DTrace in other systems assures that it transcends a company or its products — that it moves beyond a mere a feature, and becomes a technological advance. As such, you can be sure that systems that lack DTrace will become increasingly unacceptable over time. DTrace’s shift from product to technological advance — just like the shifts in NFS or Java before it — is decidedly and indisputably in Sun’s interest, and indeed it embodies the value proposition of the open systems vision that started our shop in the first place. So here’s to DTrace on Leopard, long may it reign!

As presumably many have seen, NetApp is suing Sun over ZFS. I was particularly disturbed to read Dave Hitz’s account, whereby we supposedly contacted NetApp 18 months ago requesting that they pay us “lots of money” (his words) for infringing our patents. Now, as a Sun patent holder, reading this was quite a shock: I’m not enamored with the US patent system, but I have been willing to contribute to Sun’s patent portfolio because we have always used them defensively. Had we somehow lost our collective way?

Now, I should say that there was something that smelled fishy about Dave’s account from the start: I have always said that a major advantage of working for or doing business with Sun is that we’re too disorganized to be evil. Being disorganized causes lots of problems, but actively doing evil isn’t among them; approaching NetApp to extract gobs of money shows, if nothing else, a level of organization that we as a company are rarely able to summon. So if Dave’s account were true, we had silently effected two changes at once: we had somehow become organized and evil!

Given this, I wasn’t too surprised to learn that Dave’s account isn’t true. As Jonathan explains, NetApp first approached STK through a third party intermediary (boy, are they ever organized!) seeking to buy the STK patents. When we bought STK, we also bought the ongoing negotiations, which obviously, um, broke down.

Beyond clarifying how this went down, I think two other facts merit note: one is that NetApp has filed suit in East Texas, the infamous den of patent trolls. If you’ll excuse my frankness for a moment, WTF? I mean, I’m not a lawyer, but we’re both headquartered in California — let’s duke this out in California like grown-ups.

The second fact is that this is not the first time that NetApp has engaged in this kind of behavior: in 2003, shortly after buying the patents from bankrupt Auspex, NetApp went after BlueArc for infringing three of the patents that they had just bought. Importantly, NetApp lost on all counts — even after an appeal. To me, the misrepresentation of how the suit came to be, coupled with the choice of venue and history show that NetApp — despite their claptrap to the contrary — would prefer to meet their competition in a court of law than in the marketplace.

In the spirit of offering constructive alternatives, how about porting DTrace to ONTAP? As I offered to IBM, we’ll help you do it — and thanks to the CDDL, you could do it without fear of infringing anyone’s patents. After all, everyone deserves DTrace — even patent trolls. 😉

Recently, I gave a Tech Talk at Google on DTrace, the video for which is now online. If you’ve seen me present before and you don’t want to suffer through the same tired anecdotes, arcane jokes, and disturbing rants, jump ahead to about 57:16 to see a demo of DTrace for Python — and in particular John Levon‘s incredible Python ustack helper. You also might want to skip ahead to 1:10:46 for the Q&A — any guesses what the first question was? (Hint: it was such an obvious question, both I and the room erupted with laughter.) Note that my rather candid answer to that question represents my opinion alone, and does not constitute legal advice — and nor does it represent any sort of official position of Sun…

Software is such an abstract domain that it is rare for a photograph to say much. But during his recent trip to OSCON, Adam snapped a photo that actually says quite a bit. For those who are curious, “jdub” is the nom de guerre of Jeff Waugh, who should really know better…

I finished Beautiful Code this week, and have been reflecting on the book and its development. In particular, I have thought back to some of the authors’ discussion, in which some advocated a different title. Many of us were very strongly in favor of the working title of “Beautiful Code”, and I weighed in with my specific views on the matter:

Date: Tue, 19 Sep 2006 16:18:22 -0700
From: Bryan Cantrill
To: [Beautiful Code Co-Authors]
Subject: Re: [Beautifulcode] reminder: outlines due by end of September
Probably pointless to pile on here, but I'm for "Beautiful Code", if
only because it appropriately expresses the nature of our craft.  We
suffer -- tremendously -- from a bias from traditional engineering that
writing code is like digging a ditch:  that it is a mundane activity best
left to day labor -- and certainly beneath the Gentleman Engineer.  This
belief is profoundly wrong because software is not like a dam or a
superhighway or a power plant:  in software, the blueprints _are_ the
thing; the abstraction _is_ the machine.

It is long past time that our discipline stood on its feet, stepped out
from the shadow of sooty 19th century disciplines, and embraced our
unique confluence between mathematics and engineering.  In short,
"Beautiful Code" is long, long overdue.

Now, I don’t disagree with my reasoning from last September (though I think that the Norma Rae-esque tone was probably taking it a bit too far), but having now read the book, I stand by the title for a very different reason: this book is so widely varied — there are so many divergent ideas here — that only the most subjective possible title could encompass them. That is, any term less subjective than “beautiful” would be willfully ignorant of the disagreements (if implicit) among the authors about what constitutes ideal software.

To give an idea of what I’m talking about, here is the breakdown of languages and their representations in chapters:

(I’m only counting each chapter once, so for the very few chapters that included two languages, I took whatever appeared more frequently. Also, note that some chapters were about the implementation of one language feature in a different language — so for example, while there are two additional chapters on Python, both pertain more to the C-based implementation of those features than to their actual design or use in Python.)

Now, one could argue (and it would be an interesting argument) about how much choice of language matters in software or, for that matter, in thought. And indeed, in some (if not many) of these chapters, the language of implementation is completely orthogonal to the idea being discussed. But I believe that language does ultimately affect thought, and it’s hard to imagine how one could have a sense of beauty that is so uncritical as to equally accommodate all of these languages.

More specifically: read say, R. Kent Dybvig’s chapter on the implementation of syntax-case in Scheme and William Otte and Douglas Schmidt’s chapter on implementing a distributed logging service using an object-oriented C++ framework. It seems unlikely to me that one person will come away saying that both are beautiful to them. (And I’m not talking new-agey “beautiful to someone” kind of beautiful — I’m talking the “I want to write code like that” kind of beautiful.) This is not meant to be a value judgement on either of these chapters — just the observation that their definitions of beauty are (in my opinion, anyway) so wildly divergent as to be nearly mutually exclusive. And that’s why the title is perfect: both of these chapters are beautiful to their authors, and we can come away saying “Hey, if it’s beautiful to you, then great.”

So I continue to strongly recommend Beautiful Code, but perhaps not in the way that O’Reilly might intend: you should read this book not because it’s cover-to-cover perfection, but rather to hone your own sense of beauty. To that end, this is a book best read concurrently with one’s peers: discussing (and arguing about) what is beautiful, what isn’t beautiful, and why will help you discover and refine your own voice in your code. And doing this will enable you to write the most important code of all: code that is, if nothing else, beautiful to you.

So my copy of Beautiful Code showed up last week. Although I am one of the (many) authors and I have thus had access to the entire book online for some time, I do all of my pleasure reading in venues that need the printed page (e.g. the J Church) and have therefore waited for the printed copy to start reading.

Although I have only read the first twelve chapters or so, it’s already clear (and perhaps not at all surprising) that there are starkly different definitions of beauty here: the book’s greatest strength — and, frankly, its greatest weakness — is that the chapters are so incredibly varied. For one chapter, beauty is a small and titilating act of recursion; for the next, it’s that a massive and complicated integrated system could be delivered quickly and cheaply. (I might add that the definition of beauty in my own chapter draws something from both of these poles: that in software, the smallest and most devilish details can affect the system at the largest and most basic levels.)

If one can deal with the fact that the chapters are widely divergent, and that there is not even a token attempt to weave them together into a larger tapestry, this book (at least so far, anyway) is (if nothing else) exceptionally thought provoking; if Oprah were a code cranking propeller head, this would be the ideal choice for her book club.

Now in terms of some of my specific thoughts that have been provoked: as I mentioned, quite a few of my coauthors are enamored with the elegance of recursion. While I confess that I like writing a neatly recursive routine, I also find that I frequently end up having to unroll the recursion when I discover that I must deal with data structures that are bigger than I anticipated — and that my beautiful code is resulting (or can result) in a stack overflow. (Indeed, I spent several unpleasant days last week doing exactly this when I discovered that pathologically bad input could cause blown stacks in some software that I’m working on.)

To take a concrete example, Brian Kernighan has a great chapter in Beautiful Code about some tight, crisp code written by Rob Pike to perform basic globbing. And the code is indeed beautiful. But it’s also (at least in a way) busted: it overflows the stack on some categories of bad input. Admittedly, one is talking about very bad input here — strings that consist of hundreds of thousands of stars in this case — but this highlights exactly the problem I have with recursion: it leaves you with edge conditions that on the one hand really are edge conditions (deeply
pathological input), but with a failure mode (a stack overflow) that’s just too nasty to ignore.

Now, there are ways to deal with this. If one can stomach it, the simplest way to deal with this is to setup a sigaltstack and then siglongjmp out of a SIGSEGV/SIGBUS signal handler. You have to be very careful about doing this: the signal handler should look at the si_addr field in the siginfo and comparing it to the stack bounds to confirm that it’s a stack overflow, lest it end up siglongjmp‘ing out of a non-recursion induced SIGSEGV (which, needless to say, would make a bad problem much worse). While an alternative signal stack solution may sound hideous to some, at least the recursion doesn’t have to go under the knife in this approach. If having a SIGSEGV handler to catch this condition feels uncomfortably brittle (as well it might), or if one’s state cannot be neatly unwound after an arbitrary siglongjmp (as well it might not), the code will have to change: either a depth counter will have to be passed down and failure propagated when depth exceeds a reasonable maximum, or the recursion will have to be unrolled into iteration. For most aesthetic senses, none of these options is going to make the code more beautiful — but they will make it indisputably more correct.

I was actually curious about where exactly the Pike/Kernighan code would blow up, so I threw together a little program that uses sigaltstack along with sigsetjmp/siglongjmp to binary search to find the shortest input that induces the failure. My program, which (naturally) includes the Pike/Kernighan code, is here.

Here are the results of running my program on a variety of Solaris platforms, with each number denoting the maximum string length that can be processed by the Pike/Kernighan code without the possibility of stack overflow.

As can be seen, there is a tremendous range here, even across just two different ISAs, two different data models and two different compilers: from 38,821 on 64-bit SPARC using Sun cc without optimization to 582,489 on 32-bit x86 using gcc with optimization — an order of magnitude difference. So while recursion is a beautiful technique, it is one that ends up with the ugliest of implicit dependencies: on the CPU architecture, on the data model and on the compiler. And while recursion is still beautiful to me personally, it will always be a beauty that is more superficial than profound…