Browser Wars, the game

February 14th, 2013

A monoculture is usually better in the short term. It’s a better allocation of resources (everyone working on the same thing!) If you want to write a rich web app that works today (ie, on the browsers of today), it’s much better.

But the web is a platform. Platforms are different beasts.

Imagine it’s an all-WebKit mobile web. Just follow the incentives to figure out what will happen.

Backwards bug compatibility: There’s a bug — background SVG images with a prime-numbered width disable transparency. A year later, 7328 web sites have popped up that inadvertently depend on the bug. Somebody fixes it. The websites break with dev builds. The fix is backed out, and a warning is logged instead. Nothing breaks, the world’s webkit, nobody cares. The bug is now part of the Web Platform.

Preventing innovation: a gang of hackers makes a new browser that utilizes the 100 cores in 2018-era laptops perfectly evenly, unlike existing browsers that mostly burn one CPU per tab. It’s a ground-up rewrite, and they do heroic work to support 99% of the websites out there. Make that 98%; webkit just shipped a new feature and everybody immediately started using it in production websites (why not?). Whoops, down to 90%; there was a webkit bug that was too gross to work around and would break the threading model. Wtf? 80%? What just happened? Ship it, quick, before it drops more!

The group of hackers gives up and starts a job board/social network site for pet birds, specializing in security exploit developers. They call it “Polly Want a Cracker?”

Inappropriate control: Someone comes up with a synchronization API that allows writing DJ apps that mix multiple remote streams. Apple’s music studio partners freak out, prevent it from landing, and send bogus threatening letters to anyone who adds it into their fork.

Complexity: the standards bodies wither and die from lack of purpose. New features are fine as long as they add a useful new capability. A thousand flowers bloom, some of them right on top of each other. Different web sites use different ones. Some of them are hard to maintain, so only survive if they are depended upon by a company with deep enough pockets. Web developers start playing a guessing game of which feature can be depended upon in the future based on the market cap of the current users.

Confusion: There’s a little quirk in how you have to write your CSS selectors. It’s documented in a ton of tutorials, though, and it’s in the regression test suite. Oh, and if you use it together with the ‘~’ operator, the first clause only applies to elements with classes assigned. You could look it up in the spec, but it hasn’t been updated for a few years because everybody just tries things out to see what works anyway, and the guys who update the spec are working on CSS5 right now. Anyway, documentation is for people who can’t watch tutorials on youtube.

End game: the web is now far more capable than it was way back in 2013. It perfectly supports the features of the Apple hardware released just yesterday! (Better upgrade those ancient ‘pads from last year, though.) There are a dozen ways to do anything you can think of. Some of them even work. On some webkit-based browsers. For now. It’s a little hard to tell what, because even if something doesn’t behave like you expect, the spec doesn’t really go into that much detail and the implementation isn’t guaranteed to match it anyway. You know, the native APIs are fairly well documented and forward-compatible, and it’s not really that hard to rewrite your app a few times, once for each native platform…

Does this have to happen just because everybody standardizes on WebKit? No, no more than it has to happen because we all use silicon or TCP. If something is stable, a monoculture is fine. Well, for the most part — even TCP is showing some cracks. The above concerns only apply to a layer that has multiple viable alternatives, is rapidly advancing, needs to cover unexpected new ground and get used for unpredicted applications, requires multiple disconnected agents to coordinate, and things like that.

What’s your random seed?

April 18th, 2012

Greg Egan is awesome

I’m going back and re-reading Luminous, one of his collections of short stories. I just read the story Transition Dreams, which kinda creeped me out. Partly because I buy into the whole notion that our brains are digitizable — as in, there’s nothing fundamentally unrepresentable about our minds. There’s probably a fancy philosophy term for this, with some dead white guy’s name attached to it (because only a dozen people had thought of it before him and he talked the loudest).

Once you’re willing to accept accurate-enough digitization, the ramifications get pretty crazy. And spooky. I can come up with some, but Egan takes it way farther, and Transition Dreams is a good illustration. But I won’t spoil the story. (By the way, most of Egan’s books are out of print or rare enough to be expensive, but Terrence tells me that they’re all easily available on Kindle. Oddly, although I would be happy to transition my mental workings from meat to bits, I’m still dragging my heels on transitioning my reading from dead trees to bits.)

Transition and Free Will

Now, let’s assume that you’ve converted your brain to live inside a computer (or network of computers, or encoded into the flickers of light on a precisely muddy puddle of water, it really doesn’t matter.) So your thinking is being simulated by all these crazy cascades of computation (only it’s not simulated; it’s the real thing, but that’s irrelevant here.) Your mind is getting a stream of external sensor input, it’s chewing on that and modifying its state, and you’re just… well, being you.

Now, where is free will in this picture? Assuming free will exists in the first place, I mean, and that it existing and not existing are distinguishable. If you start in a particular, fully-described state, and you receive the exact same inputs, will you always behave in exactly the same way? You could build the mind hosting computer either way, you know, and the hosted minds wouldn’t normally be able to tell the difference. But they could tell the difference if they recorded all of their sensory inputs (which is fairly plausible, actually), because they could make a clone of themselves back at the previous state and replay all their sensory input and see if they made the same decisions. (Actually, it’s easier than that; if the reproduction was accurate, they should end up bit-for-bit identical.)

I don’t know about you, but I’d rather not be fully predictable. I don’t want somebody to copy me and my sensor logs, and then when I’m off hanging out in the Gigahertz Ghetto (read: my brain is being hosted on a slow computer), they could try out various different inputs on faster computers to see how “I” reacted and know for 100% certainty how to achieve some particular reaction.

Well, ok, my time in the GHzGhetto might change me enough to make the predictions wrong, so you’d really have to do this while I was fully suspended. Maybe the shipping company that suspends my brain while they shoot me off to a faster hosting facility in a tight orbit around the Sun (those faster computers need the additional solar energy, y’know) is also selling copies on the side to advertisers who want to figure out exactly what ads they can expose me to upon reawakening to achieve a 100% clickthrough rate. Truly, truly targeted advertising.

So, anyway, I’m going to insist on always having access to a strong source of random numbers, and I’ll call that my free will. You can record the output of that random number generator, but that’ll only enable you to accurately reproduce my past, not my future.

The Pain and Joy of Determinism

Or will I? What if that hosting facility gets knocked out by a solar flare? Do I really want to start over from a backup? If it streams out the log of sensor data to a safer location, then it’d be pretty cool to be able to replay as much of the log as still exists, and recover almost all of myself. I’d rather mourn a lost day than a lost decade. But that requires not using an unpredictable random number generator as an input.

So what about a pseudo-random number generator? If it’s a high quality one, then as long as nobody else can access the seed, it’s just as good. But that gives the seed incredible importance. It’s not “you”, it’s just a simple number, but in a way it allows substantial control over you, so it’s private in a more fundamental way than anything we’ve seen before. Who would you trust it to? Not yourself, certainly, since you’ll be copied from computer to computer all the time and each transfer is an opportunity for identity theft. What about your spouse? Or maybe just a secure service that will only release it for authorized replays of your brain?

Without that seed (or those timestamped seeds?), you can never go back. Well, you can go back to your snapshots, but you can’t accurately go forward from there to arbitrary points in time. Admittedly, that’s not necessary for some uses — if you want to know why you did something, you can go back to a snapshot and replay with a different seed. If you do something different, it was a choice made of your own free will. You could use it in court cases, even. If you get the same result, well, it’s trickier, because you might make the same choice for 90% of the possible random seeds or something. “Proof beyond a reasonable confidence interval?” Heh.

bzexport changes released

April 13th, 2012

bzexport –new and hg newbug have landed

My bzexport changes adding a --new flag and an hg newbug command have landed. Ok, they landed months ago. See my previous blog post for details; all of the commands and options described there are still valid in the current version. But please pull from the official repo instead of my testing repo given in the earlier blog post.

Installing bzexport

mkdir -p ~/hg-extensions
cd ~/hg-extensions
hg clone http://hg.mozilla.org/users/tmielczarek_mozilla.com/bzexport

in the [extensions] section of your ~/.hgrc, add:
bzexport = ~/hg-extensions/bzexport/bzexport.py

Note to Windows users: unfortunately, I think the python packaged with MozillaBuild is missing the json.py package that bzexport needs. I think it still works if you use a system Python with json.py installed, but I’m not sure.

Trying it out

For the (understandably) nervous users out there, I’d like you to give it a try and I’ve made it safe to do so. Here are the levels of paranoia available: Read the rest of this entry »

Only pay for the entropy you use

February 22nd, 2012

Log Files Are Boring

Just an idea, based on hearing that build log transfers seem to consume large amounts of bandwidth. (Note that for all I know, this is already being done.)

Logs are pretty dull. In particular, two consecutive log files are usually quite similar. It’d be nice if we could take advantage of this redundancy to reduce the bandwidth/time consumed by log transfers.

rsync likes boring data

The natural thing that springs to mind is rsync. I grabbed two log files that are probably more similar to each than is really fair, but they shouldn’t be horribly unrepresentative. rsyncing one to the other found them to share 32% of their data, based on the |rsync –stat| output lines labeled “Matched data” and “Literal data”, for a speedup of 1.46x.

I suspected that rsync’s default block size is too large, and so most of the commonalities are not found. So I tried setting the block size ridiculously low, to 8 bytes, and it found them to be 98% similar. Which is silly, because it has to retrieve more block hashes at that block size than it saves. The total “speedup” is reported as 0.72x.

But the sweet spot in the middle, with a block size of 192, gives 84% similarity for a speedup of 4.73x.

compression likes boring data too

Take a step back: this only applies to uncompressed files. Simply gzipping the log file before transmitting it gives us a speedup of 14.5x. Oops!

Well, rsync can compress the stuff it sends around too. Adding a -z flag with block size 192 gives a speedup of 16.2x. Hey, we beat basic gzip!

But compression needs decent chunks to work with, so the sweet spot may be different. I tried various block sizes, and managed a speedup of 24.3x with -B 960. An additional 1.7x speedup over simple compression is pretty decent!

To summarize our story so far, let’s say you want to copy over a log file named log123.txt. The proposal is:

  1. Have a vaguely recent benchmark log file, call it log_compare.txt, available on all senders and receivers. (Actually, it’d probably be a different one per build configuration, but whatever.)
  2. On the server, hard link log123.txt to log_compare.txt.
  3. From the client, rsync -z -B 960 log123.txt server:log123.txt

stop repeating what I say!

But it still feels like there ought to be something better. The benchmark log file is re-hashed every time you do this and the hashes are sent back over the wire, costing bandwidth. So let’s eliminate that part. Note that we’ll drop the -z from flag because we may as well compress the data during the transfer instead:

 ssh server 'ln log_compare.txt log123.txt'
 rsync -B 960 log123.txt log_compare.txt --only-write-batch=batch.dat
 ssh -C server 'rsync --read-batch=- argleblargle log132.txt' < batch.dat

Note that “argleblargle” is ignored, since the source file isn’t needed.

So what’s the speedup now? Let’s only consider the bytes transmitted over the network. Assuming the compression from ssh -C has the same effect as gzipping the file locally, I get a speedup of 28.9x, about 2x the speedup of simply compressing the log file in the first place.

But wait. The block size of 960 was based on the cost of retrieving all those hashes from the remote side. We’re not doing that anymore, so a smaller block size should again be more effective. Let’s see… -B 192 gets a total speedup of 139x, which is almost exactly one order of magnitude faster than plain gzipped log files. Now we’re talking!

loose ends

Two things still bug me. One is a minor detail — the above is writing out batch.dat, then reading it back in to send over to the server. This uselessly consumes disk bandwidth. It would be better if rsync could directly read/write compressed batch files to stdin/stdout. (It can read uncompressed batches from stdin, but not write to stdout. You could probably hack it somehow, perhaps with /proc/pidN/fd/…, but it’s not a big deal. And you can just use use /dev/shm/batch.dat for your temporary filename, and remove it right after. It’d still be better if it never had to exist uncompressed anywhere, but whatever.)

The other is that we’re still checksumming that benchmark file locally for every log file we transfer. It doesn’t change the number of bytes spewed over the network, but it slows down the overall procedure. I wonder if librsync would allow avoiding that somehow…? (I think rsync uses two checksums, a fast rolling checksum and a slower precise one, so you’d need to compute both for all offsets. And reading those in would probably cost more than recomputing from the original file. But I haven’t thought too hard about this part.)

not just emacs and debuggers

I sent this writeup to Jim Blandy, who in a typically insightful fashion noticed that (1) this requires some fiddly bookkeeping to ensure that you have a comparison file, and (2) revision control systems already handle all of this. If you have one version of a file checked in and then you check in a modified version of it, the VCS can compute a delta to save storage costs. Then when you transmit the new revision to a remote repository, the VCS will know if the remote already has the baseline revision so it can just send the delta.

Or in other words, you could accomplish all of this by simply checking your log files into a suitable VCS and pushing them to the server. That’s not to say that you’re guaranteed that your VCS will be able to fully optimize this case, just that it’s possible for it to do the “right” thing.

I attempted to try this out with git, but I don’t know enough about how git does things. I checked in my baseline log file, then updated it with the new log file’s contents, then ran git repack to make a pack file containing both. I was hoping to use the increase in size from the original object file to the pack file as an estimate of the incremental cost of the new log file, but the pack file was *smaller* than either original object file. If I make a pack with just the baseline, then I end up with two pack files, but the new one is still smaller.

clients could play too

As a final thought, this idea is not fundamentally restricted to the server. You could do the same thing inside eg tbpl: keep the baseline log(s) in localStorage or IndexedDB. When requesting a log, add a parameter ?I_have_baseline_36fe137a1192. Then, at the server’s discretion, it could compute a delta from that baseline and send it over as a series of “insert this literal data, then copy bytes 3871..17313 from your baseline, then…”. tbpl would reconstruct the resulting log file, the unicorns would do their lewd tap dance, and everyone would profit.

Scenario 1: you have a patch to some bug sitting in our mercurial queue. You want to attach it to a bug, but the bugzilla interface is painful and annoying. What do you do?

Use bzexport. It’s great! You can even request review at the same time.

What I really like about bzexport is that while writing and testing a patch, I’m in an editor and the command line. I may not even have a browser running, if I’m constantly re-starting it to test something out. Needing to go to the bugzilla web UI interrupts my flow. With bzexport, I can stay in the shell and move onto something else immediately.

Scenario 2: You have a patch, but haven’t filed a bug yet. Neither has anybody else. But your patch has a pretty good description of what the bug is. (This is common, especially for small things.) Do you really have to go through the obnoxious bug-filing procedure? It sure is tempting just to roll this fix up into some other vaguely related bug, isn’t it? Surely there’s a simple way to do things the right way without bouncing between interfaces?

Well, you’re screwed. Unless you’re willing to test something out for me. If not, please stop reading.
Read the rest of this entry »

patch queue dependencies

January 5th, 2012

A little while back, I was again contemplating a tangled patch queue, considering how to rework it for landing. I thought it’d be nice to see at a very basic level which patches in the queue were going to be problematic, and which I could freely reorder at whim.

So I whipped together a silly little script to do that at a file level only. Example output:

% patchdeps
Note: This is based on filename collisions only, so may overreport conflicts
if patches touch different parts of the same file. (TODO)
                                                                          
A bug-663281-deque                   X   *       *     *   * *     *      
A bug-663281-deque-test              |   :       :     :   : *     :      
A bug-642054-func-setline          X |   *       :     :   : :     :      
A bug-642054-js_MapPCToLineNumber--' |   *       :     :   : :     :      
A bug-642054-rwreentrant             |   : X     :     :   : :     :      
A algorithm--------------------------'   X |     *     *   * *     *      
A system-libunwind                     X | |     :   * : * : *   * :      
A try-libunwind------------------------' | |     :   X : * : *   * :      
A backtrace------------------------------' | X * * * | * : * * * : * * * *
U shell-backtrace                          | | : * : | : : : : : : : : : :
U M-reentr---------------------------------' | : : : | : : : : : : : : : :
U M-backtrace--------------------------------' X : : | : : : : : : : * : :
U activities-----------------------------------' X : | : : : : * * : X * *
U profiler---------------------------------------' X | * : * * X * * | * *
U bug-675096-valgrind-jit--------------------------' | * : * : | : : | : :
U bug-599499-opagent-config--------------------------' X * : * | * : | : :
U bug-599499-opagent-----------------------------------' X X * | : * | : :
U bug-642320-gdb-jit-config------------------------------' | * | * : | : :
U bug-642320-gdb-jit---------------------------------------' X | : * | : :
U import-libunwind                                           | | : : | : :
U libunwind-config-------------------------------------------' | X X | : :
U warnings-fixes-----------------------------------------------' | | | : *
U bug-696965-cfi-autocheck---------------------------------------' | | X :
U mystery-librt-stuff----------------------------------------------' | | :
U bug-637393-eval-lifetime                                           | | :
U register-dwarf-----------------------------------------------------' | :
U bug-652535-JM__JIT_code_performance_counters-------------------------' X
U JSOP_RUNMODE-----------------------------------------------------------'

How to read it: patches that have no conflicts earlier in the stack are shown without a line next to them. They’re free spirits; you can “sink” them anywhere earlier in your queue without getting conflicts. (The script removes their lines to make the grid take up less horizontal space.)

Any other patch gets a horizontal line that then bends up to show the interference pattern with earlier patches. All in all, you have a complete interference matrix showing whether the set of files touched by any patch intersects the set of files for any other patch.

‘X’ marks the first conflict. After that, the marker turns to ‘*’ and the vertical lines get broken. (That’s just because it’s mostly the first one that matters when you’re munging your queue.)

So the patch named “backtrace” conflicts with the earlier “algorithm” patch, as well as the even earlier “bug-642054-js_MapPCToLineNumber” and others. The “M-reentr” patch only touches the same stuff as “bug-642054-rwreentrant” (not surprising, since “M-…” is my notation for a patch that needs to be folded into an earlier patch.) “system-libunwind” doesn’t conflict with anything earlier in the queue, and so can be freely reordered in the series file to anywhere earlier than where it is now — but note that several later patches touch the same stuff as it does. (It happens to be a patch to js/src/configure.in.)

Useful? Not very. But it was kinda fun to write and I find myself running it occasionally just to see what it shows, so I feel the entertainment value was worth the small investment of time. Though now I’m tempted to enhance it by checking for collisions in line ranges, not just in the files…

I suppose I could make a mercurial extension out of it, but that’d require porting it from Perl to Python, which is more trouble than it’s worth. (Yes, I still use Perl as my preferred language for whipping things together. Even though I dislike the syntax for nested data structures, I very much like the feature set, and it’s still the best language I’ve found for these sorts of things. So phbbbttt!)

hg adventure

December 16th, 2011

Inspired by some silliness on #developers:

<jgilbert>	well that was an hg adventure
<dholbert>	$ hg adventure
You are in a twisty maze of passageways, all alike...
<cpeterson>	$ hg look
It is pitch black. You are likely to be eaten by a grue.
<hub>		$ hg doctor
How can I help you?

I thought I’d stick to actual hg commands, and came up with:

You see a small hole leading to a dark passageway.
820:21d40b86ae37$ echo "enter passageway" > action
820:21d40b86ae37$ hg commit
It is pitch black. You are likely to be eaten by a grue.
821:0121fb347e18$ echo "look" > action
821:0121fb347e18$ hg commit
** You have been eaten by a grue **
822:b09217a7bbc1$ hg backout 822
It is pitch black. You are likely to be eaten by a grue.
821:0121fb347e18$ hg backout 821
You see a small hole leading to a dark passageway.
820:21d40b86ae37$ echo "turn on flashlight" > action
820:21d40b86ae37$ hg commit
Your flashlight is now on.
824:44a4e4bf5f0e$ hg merge 821
Your light reveals a forking passageway leading north and south.

Kinda makes you think, huh? Time reversal games became popular semi-recently (eg Braid). Maybe the fad is over now; I’m *way* out of date.

But did any of them allow you to branch and merge? Push and pull from your friends’ distributed repos? Bisect to find the point where you unknowingly did something that prevented ever winning the game and either continue from there, merge a backout of that action, or create a new branch by splicing that action out?

It’s a whole new genre! It’ll be… um… fun.

(I’ll go back to work now)

Patch reordering

November 3rd, 2011

I have a patch queue that looks roughly like:

  initial-API
  consumer-1
  consumer-2
  unrelated
  consumer-3-plus-API-changes-and-consumer-1-and-2-updates-for-new-API

(So my base repo has a patch ‘initial-API-changes’ applied to it, followed by a patch ‘consumer-1′, etc.)

The idea is that I am working on a new API of some sort, and have a couple of independent consumers of that API. The first two are “done”, but when working on the 3rd, I realize that I need to make changes to or clean up the API that they’re all using. So I hack away, and end up with a patch that contains both consumer 3 plus some API changes, and to get it to compile I also update consumers 1 and 2 to accommodate the new changes. All of that is rolled up into a big hairball of a patch.

Now, what I want is:

  final-API
  consumer-1 (new API)
  consumer-2 (new API)
  unrelated
  consumer-3 (new API)

But how do I do that (using mq patches)? I can use qcrefresh+qnew to fairly easily get to:

  initial-API
  consumer-1 (old API)
  consumer-2 (old API)
  unrelated
  consumer-3 (new API)
  API-changes-plus-API-changes-for-consumers-1-and-2

or I could split out the consumer 1 & 2 API changes:

  initial-API
  consumer-1 (old API)
  consumer-2 (old API)
  unrelated
  consumer-3 (new API)
  API-changes
  consumer-2-API-changes
  consumer-1-API-changes

which theoretically I could qfold the consumer 1 and consumer 2 patches:

  initial-API
  consumer-1 (new API)
  consumer-2 (new API)
  unrelated
  consumer-3 (new API)
  API-changes

Unfortunately, consumer-1-API-changes collides with API-changes, so the fold will fail. It shouldn’t collide, really, but it does because part of the code to “register” consumer-1 with the new API happens to sit right alongside the API itself. Even worse, how do I “sink” the ‘API-changes’ patch down so I can fold it into initial-API to produce final-API? (Apologies for displaying my stacks upside-down from my terminology!) A naive qfold will only work if the API-changes stuff is separate from all the consumer-* patches.

My manual solution is to start with the initial queue:

  initial-API
  consumer-1 (old API)
  consumer-2 (old API)
  unrelated
  consumer-3-plus-API-changes-and-consumer-1-and-2-updates-for-new-API

and then use qcrefresh to rip the API changes and their effects on consumers 1 & 2 back out, leaving:

  initial-API
  consumer-1 (old API)
  consumer-2 (old API)
  unrelated
  API-changes-and-consumer-1-and-2-updates-for-new-API
  (in working directory) consumer-3 (new API)

I qrename/qmv the current patch to ‘api-change’ and qnew ‘consumer-3′ (its original name), cursing about how my commit messages are now on the wrong patch. Now I have

  initial-API
  consumer-1 (old API)
  consumer-2 (old API)
  unrelated
  api-change (API changes and consumer 1 and 2 updates for new API)
  consumer-3 (new API)

Now I know that ‘unrelated’ doesn’t touch any of the same files, so I can qgoto consumer-2 and qfold api-change safely, producing:

  initial-API
  consumer-1 (old API)
  consumer-2 (new API, but also with API change and consumer 1 updates)
  unrelated
  consumer-3 (new API)

I again qcrefresh,qmv,qnew to pull a reduced version of the api-change patch, giving:

  initial-API
  consumer-1 (old API)
  api-change (with API change and consumer 1 updates)
  consumer-2 (new API)
  unrelated
  consumer-3 (new API)

Repeat. I’m basically taking a combined patch and sinking it down towards its destination, carving off pieces to incorporate into patches as I pass them by. Now I have:

  initial-API
  api-change (with *only* the API change!)
  consumer-1 (new API)
  consumer-2 (new API)
  unrelated
  consumer-3 (new API)

and finally I can qfold api-change into initial-API, rename it to final-API, and have my desired result.

What a pain in the ass! Though the qcrefresh/qmv/qnew step is a lot better than what I’ve been doing up until now. Without qcrefresh, it would be

 % hg qrefresh -X .
 % hg qcrecord api-change
 % hg qnew consumer-n
 % hg qpop
 % hg qpop
 % hg qpop
 % hg qpush --move api-change
 % hg qpush --move consumer-n
 % hg qfold old-consumer-n

which admittedly preserves the change message from old-consumer-n, which is an advantage over my qcrefresh version.
Or alternatively: fold all of the patches together, and qcrecord until you have your desired final result. In this particular case, the ‘unrelated’ patch was a whole series of patches, and they weren’t unrelated enough to just trivially reorder them out of the way.

Without qcrecord, this is intensely painful, and probably involves hand-editing patch files.

My dream workflow would be to have qfold do the legwork: first scan through all intervening patches and grab out the portions of the folded patch that only modify nonconflicting files. Then try to get clever and do the same thing for the portions of the conflicted files that are independent. (The cleverness isn’t strictly necessary, but I’ve found that I end up selecting the same portions of my sinking patch over and over again, which gets old.) Then sink the patch as far as it will go before hitting a still-conflicting file, and open up the crecord UI to pull out just the parts that belong to the patch being folded (aka sunk). Repeat this for every intervening conflicting patch until the patch has sunk to its destination, then fold it in. If things get too hairy, then at any point abort the operation, leaving behind a half-sunk patch sitting next to the unmodified patch it conflicted with. (Alternatively, undo the entire operation, but since I keep my mq repo revision-controlled, I don’t care all that much.)

I originally wanted something that would do 3-way merges instead of the crecord UI invocations, but merges really want to move you “forward” to the final result of merging separate patches/lines of development. Here, I want to go backwards to a patch that, if merged, would produce the result I already have. So merge(base,base+A,base+B) -> base+AB which is the same as base+BA. From that, I could infer a B’ such that base+A+B’ is my merged base+AB, but that doesn’t do me any good.

In my case, I have base+A+B and want B” and A” such that base+B”+A” == base+A+B.

To anyone who made it this far: is there already an easy way to go about this? Is there something wrong with my development style that I get into these sorts of situations? In my case, I had already landed ‘initial-API'; please don’t tell me that the answer is that I always have to get the API right in the first place. Does anyone else get into this mess? (I can’t say I’ve run into this all that often, but it’s happened more than once or twice.)

I suppose if I had landed consumers 1 and 2, I would’ve just had to modify their uses of the API afterwards. So I could do that here, too. But reviews could tangle things up pretty easily — if a reviewer of consumer 1 or 2 notices the API uglinesses that I fixed for consumer 3, then landing the earlier consumers becomes dependent on landing consumer 3, which sucks. But also, none of this is really ready to land, and I’d like to iterate the API in my queue for a while with all the different consumers as test users, *without* lumping everything together into one massive patch.

distcc, ccache, and bacon

October 7th, 2011

This was initially a response to JGriffin’s GoFaster analysis post but grew out of control. Read that first.

Rampant speculation

tl;dr: hey, we could use ccache and distcc on our build system!

Just speculating (as usual), but…

The note about retiring slow slaves, combined with the performance gap between full and incremental builds, suggests something.

Why does additional hardware (the slow slaves) slow things down? Because load is unevenly distributed. Ignoring communication costs, the fastest way to build with a fast machine and a slow one that takes 2x longer would be to compile 2/3 of the files with the fast machine and 1/3 with the slow one. How? Remove all slow slaves from the build pool and convert them to distcc servers.

What about the clobber builds? Well, if you’ve already built a particular file before with the same compiler and options, it would be nice to not have to build it again. That’s what ccache is for. But a ccache per slave means you have to have built the same thing on the same slave. For try builds (which is where most of the clobbers are), that’s not going to happen all the time.

But combine that with the above distcc idea: you could run ccache under distcc on the distcc servers. Now you have a ccache/distcc sandwich: local ccache first, then distcc, then remote ccache, then finally some bacon. Because everything’s better with bacon.

ts;wm: (too short; want more)

You know, in terms of data sources, the above picture is wrong. It’s really local ccache, then remote ccache (via distcc), then remote compile, and only then bacon. But the configuration-centric ccache/distcc/ccache description makes for better visuals. Or would if I put the bacon on the inside, anyway.

Let’s walk through a clobber build. The stuff the local slave has built before gets pulled from local distcc. Some of the remaining stuff gets built locally. The rest gets sent over to various machines in the distcc pool. We can break those things down into 3 categories: (1) stuff that’s never been built anywhere, (2) stuff that’s been built on a different distcc host, and (3) stuff that’s been built on the same distcc host. #3 is a win. #1 is unavoidable, it’s the basic cost of doing business. (Actually, there’s another dimension, which is whether something has been built before on a non-distcc host. I’ll ignore that for now. Conceptually, you can make it go away by making every slave a distcc server.)

#2 is waste. But it’s less waste than we have now, if the distcc pool is smaller than the whole build pool, because you’re doing one redundant build per distcc host rather than one per builder. And it’s self-limiting: a distcc host that has a build cached returns it immediately, meaning it’s more likely to get stuck with something it needs to build, which sucks but at least it populates its ccache so it won’t have to do it again.

Now, I am assuming here that compile costs are greater than communication + ccache lookup costs, which is an insanely flawed assumption. But it’s very very true for my personal builds — I have my own distcc server, and my clobber builds (actually, *all* my builds) feel way way faster when I’m using it. So I don’t think the question is so much “would this work?” as it is “what would we need to do to make this work?”

For starters, do no harm: it would be great if we could partition the network so that distcc servers are separate from the current communication channels. Every build host would sit on two VLANs, say: the regular one and the distcc one. That would reduce chances of infrastructure meltdown through excessive distcc traffic. (I am not a network engineer, nor do I play one on TV, and this may require separate physical networks and possibly Pringles cans.)

On a related note, it might be wise to start out by restricting the slaves from doing too many distcc jobs at a time, to prevent the distcc jobs from getting bogged down through congestion. I do this for my own builds through a ~/.distcc/hosts file containing: “localhost/4 192.168.1.99/7″. That means you can use -j666, and it’ll still only do 4 jobs on localhost and 7 jobs on 192.168.1.99 simultaneously. (Actually, that’s my home ~/.distcc/hosts file. My server at work is beefier, and there I allow the remote to do 12 jobs at once. I have a cron job that checks every 5 minutes to see what network I’m on and sets a ~/.distcc/hosts symlink accordingly. But I digress.)

More worrying is the reason behind all that clobbering. If a slave turns to the dark side, runs amok, gets hit by a cosmic ray, or is just having a bad day, do we really want to use its ccached builds? More to the point, when something goes wrong, what do we need to clobber? Right now everything is local to a slave, so it’s straightforward to pull a slave from the pool, take it out behind the garage, and beat the crap out of it with a stick. With distcc and ccache, it’s harder to tell which server to blame.

Still, how often does this happen? (I have no idea. I’m just a troublemaking developer, dammit.) We can always wipe the ccache on the whole distcc pool. It’d be nice to be able to track problems to their source, though. Maybe we could use the distcc pool redundancy to our advantage: have them cross-check the checksums of their builds with each other. Same input, same output. But that’s even more speculative.

It’s not all bad, though — I’m guessing that most clobbers result from the build system not being able to handle various types of change. If the ccache/distcc/ccache sandwich makes clobbers substantially cheaper, we can be a lot freer with them. Someone accidentally cancelled an m-c build partway through? Clobber the world! Let’s make bacon!!

wtf;yai;bdb: (what the f#@; you’re an idiot; been done before)

Reality check
  • We use local ccache already – see bug 488412
  • distcc has been proposed a number of times, but for the life of me I cannot find the bug. There are most likely some very valid reasons not to use it. Such as making a complete interdependent hairball out of our build system where one machine can kill everything.
  • Given the results in bug 488412, it’s very plausible that remote ccaches would be of no benefit or a net loss. (Though those numbers were using NFS to retrieve remote ccache results, and I deeply distrust NFS.)

Screw Reality. What has it ever done for me?

Hey, if we really needed to conceal network latency and redundant rebuilds across different hosts, we could stream out ccache results before they were even needed! But that’s crazy talk.

JS Probes

September 21st, 2011

Have you ever had your browser mysteriously stall periodically and wondered “what the f#@$! is it doing?!!” Or perhaps you’re working on something, say the garbage collector, and you’d like to see what effect your changes are having. Or maybe even write a little analysis that postprocesses some sort of trace of what is going on, and figures out what the optimal pattern of actions would be. (“If I’d thrown this big chunk of data out of the cache here, then I would’ve had room for all of these little things that got evicted instead, and would have had way fewer misses…”)

The usual way to do things like this is to manually add some instrumentation code (probably just logging a bunch of events) and postprocess the results. This works fine, but it has a few drawbacks: (1) you have to figure out where to insert your instrumentation, often in unfamiliar code; (2) you’ll need to recompile, possibly several times; (3) the logs can get very large very quickly; and (4) you’ll probably end up writing a very special-purpose postprocessor that (5) dumps stuff to a text file that only you know how to interpret, and even you will only remember what it all means for a week or two. The next time you need to do something similar, you’ll find that all of your instrumentation code is severely bitrotted and misses some paths that have been added in the meantime, so you’ll start everything over from scratch.

Well, tough luck. Sometimes those are just facts of life and you’ll need to suck it up. Quit whining, dammit.

But many times, the events of interest (or more precisely, “probe points”) are of general interest. If you can manage to slip them into the code and so get other developers to maintain them for you as they make changes, then everyone can rely on those probes being in roughly the right place permanently. That’s #1 above, and depending on how they’re implemented there’s a good chance you won’t even need to recompile, so that’s #2.

I’ve done an implementation of these sorts of probes in the SpiderMonkey Javascript engine. There are probe points like “a GC is starting (and it’s local to one compartment)”, “the heap has been resized”, and “javascript function F is being called/is returning.” Some of these are straightforward to place into the code — the start of a GC wasn’t hard to figure out, for example. Some weren’t so straightforward, such as JS function calls (they might seem simple, but what if you’re running JITted? Which JIT? Are you still running JITted by the time you return from the function?) I’ve delivered the probe information to various backends — anything from Windows’ ETW (blog post forthcoming whenever I manage to implement the start/stop functionality), to dtrace/systemtap (another blog post, probably coming sooner since I recently scraped together a demo), to a simple callback mechanism (see JS_SetFunctionCallback on MDN) and other special-purpose ones that only care about a small subset of probes.

#3 (log it all vs online handling) ventures into religious territory. It is easiest to mindlessly log everything of interest and postprocess it. But what if you want realtime updates? Or if you want to track different information depending on what you learn from other probe points? Or what if the volume of your log writing interferes with whatever you’re trying to measure (eg disk I/O)? Or maybe you need to track some sort of state in order to give the probes meaning. (GC when idle => good. Avoidable GC when the user is waiting => bad.)

Those arguments are what led to the creation of tools like DTrace and Systemtap. Both give you a scripting environment that can aggregate information from probes as they fire, control exactly what information gets tracked as things are happening, and can be attached/detached at any time. They’re pretty cool, and invaluable once you get familiar with them. They’re also extremely system-dependent and generally require root access or special builds or kernel debuginfo or something, which ends up meaning that you often can’t just hand off analysis scripts to other people and have those people get some use out of them. And even you may not be able to take them to another environment.

Still, they deal pretty well with #4 (avoiding one-use, special-purpose processors), at least for environments matching the one they were written for. And if they can draw from statically-inserted probe points (the type I was talking about above), they can actually be pretty general. #5 is still a killer, though — at least the way I write systemtap scripts, they all end up with idiosyncratic ways of dumping out the results of some particular analysis, and nobody else is going to get much enlightenment without studying the script for a while first.

What if we could do better? What if we could insert these static probes, but rather than feeding the information to some niche tool that is usable by only a handful of people, we make the data available to a plain old Firefox addon? You could collect, aggregate, summarize, mutilate, fold, spindle, or crush the data directly in JS code. Then we could let addon authors go crazy with visualizations and analysis libraries. That’d be cool, right?

Graph GC behavior. Warn the user when slow or suspicious stuff is happening. Figure out what’s going on during long event handlers. Graph the percentage of time spent in different subsystems. Correlate performance/trace data with user-meaningful actions. Make a flight-recording of various metrics and let the user walk through history. Your ideas here.

Ok, so I tricked you. I’m not going to tell you how to do any of that. This blog post is a tease, an advertisement for the work that Brian Burg did this summer during his Mozilla internship. If you’re interested, he’ll be giving his internship final presentation tomorrow (today when you’re reading this, or perhaps yesterday or last month for those of you who have fallen behind on your Planet reading.) That’s 1:30PM PDT on Thursday, September 22 at the Mountain View Mozilla headquarters, and I’m 97.2% sure it will be broadcast over Air Mozilla as well. And taped, I think? (Sadly, I can’t find where those are archived. Somebody please tell me and I’ll update this post.) There will be a demo. With pretty pictures! And he’ll be writing it up on his own blog Real Soon Now. I’m not going to say any more for now — I’d get it wrong anyway.

Update: Argh! I got the date wrong! It’s not Wednesday, September 21 as I originally wrote. It’s today, Thursday, September 22. Sorry for the confusion!