Pronk like you mean it

Uploaded on

Slides from my invited talk at IFL 2011 (the Symposium On Implementation And Application Of Functional Languages) in Lawrence, Kansas.

Slides from my invited talk at IFL 2011 (the Symposium On Implementation And Application Of Functional Languages) in Lawrence, Kansas.

More in: Technology , Education
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads


Total Views
On Slideshare
From Embeds
Number of Embeds



Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

    No notes for slide


  • 1. Pronk like you mean it! A few years of gadding about in Haskell Bryan O’Sullivan, MailRank, Inc.Monday, October 3, 2011
  • 2. pronk |prô ng k; prä ng k| verb [ intrans. ] (of a springbok or other antelope) leap in the air with an arched back and stiff legs, typically as a form of display or when threatened. ORIGIN late 19th cent.: from Afrikaans, literally ‘show off,’ from Dutch pronken ‘to strut.’Monday, October 3, 2011
  • 3. Pronking as it is practiced in the wild.Monday, October 3, 2011
  • 4. “Someone ought to do something!” • I re-entered the Haskell world in the mid-2000s • At the time, I noticed the lack of “the kind of book I want to read” • After several months of concentrated wishful thinking... still no book! • So... I found some collaborators and wrote the book I wished I had: • Real World Haskell,, October 3, 2011
  • 5. 2.5 years of free online access • Nearing a million visits, and still growing! Mar 31, 2009 - Sep 30, 2011 Visitors Overview Comparing to: Site Visitors 6,000 6,000 3,000 3,000 0 0 Mar 31 - Apr 4 Aug 30 - Sep 5 Jan 31 - Feb 6 Jul 4 - Jul 10 Dec 5 - Dec 11 May 8 - May 14 299,443 people visited this site 940,409 Visits 299,443 Absolute Unique Visitors 1,981,816 Pageviews 2.11 Average Pageviews 00:02:27 Time on SiteMonday, October 3, 2011
  • 6. Reader involvement is a big win • We didn’t pioneer comments from readers • But we were the first to do it well 100 75 50 25 0 2009-W13 2009-W24 2009-W35 2009-W46 2010-W05 2010-W16 2010-W27 2010-W38 2010-W49 2011-W07 2011-W18 2011-W29 2011-W40 comments per weekMonday, October 3, 2011
  • 7. Burnout • “Real World Haskell” was a huge effort • 1,328 commits by 3 people over 15 months • Tons of online comments to read • By the end, I was exhausted • I barely touched a computer for several monthsMonday, October 3, 2011
  • 8. From burnout to fusion • Once I recovered from the RWH burnout effect, I felt a keen irony • Haskell was still not especially “real world” for lots of uses • The most glaring hole (to me): no modern text handling • Coutts and Stewart’s bytestring library was wonderful, but binary-only • They’d since moved on from primitive, fragile fusion to stream fusionMonday, October 3, 2011
  • 9. Stream fusion and text • Harper’s MSc thesis took stream fusion and applied it to text processing • I took his MSc work and turned it into the standard Haskell text library • • Now distributed as part of the Haskell platformMonday, October 3, 2011
  • 10. From thesis to bedrock • Harper’s MSc tarball: • 1,699 LOC • No tests (and yes, numerous bugs) • Today: • 9,532 LOC • 330 QuickCheck tests, coverage above 90% • Only 3 bugs ever reported “in the wild”Monday, October 3, 2011
  • 11. When text isn’t enough • The text API is a small superset of the Haskell list/string API (+10%) • It’s missing a lot of important real-world functionality • So I wrote another package, text-icu, to fill the gaps • Based on idiomatic FFI wrappers around the venerable ICU libraryMonday, October 3, 2011
  • 12. What’s in text-icu? • Unicode normalization (è vs. `+e) • Collation: in some locales, lexicographic ordering differs from simple numeric ordering of code points • Character set support: Big5, Shift-JIS, KOI-8, etc. • Perl-compatible regular expressions (and more besides)Monday, October 3, 2011
  • 13. Two data types for different use cases Strict Lazy • An entire string is a single chunk • A string is a list of 64KB chunks • Good for small strings, whole- • Good for single-pass streaming document manipulation • Chunk boundaries are a prolific source of bugs • Nearly twice as much code to maintainMonday, October 3, 2011
  • 14. Was this enough? • 6 months into the project, the API was nearing completion • I wanted to start benchmarking, to see whether the code was “good” • Looked on Hackage for a decent benchmarking library • Found nothing :-(Monday, October 3, 2011
  • 15. What’s in a benchmarking tool? • A typical benchmarking harness: • Run a function a few times (often configurable) • Print a few statistics (min, max, mean)Monday, October 3, 2011
  • 16. Pitfalls for the unwary • Supposing your benchmark harness does something like this: 1.Record the start time 2.Run the thingumbob 3.Record the end time • Looks fine, right? • So... what can go wrong?Monday, October 3, 2011
  • 17. Clock resolution and cost • On my Mac, getPOSIXTime has a resolution of 2.15μs (±80ns) • Suppose we can tolerate a 1% error ‣ We cannot naïvely measure anything that runs in less than 200μs • On my system, a call to getPOSIXTime costs 60.5ns ‣ Failure to account for this introduces a further 5% of inaccuracy in the limitMonday, October 3, 2011
  • 18. Advice for the 1990s • Longstanding benchmarking advice: • Run on a “quiet” system • This is no longer remotely achievable, so ... forget it?Monday, October 3, 2011
  • 19. The impossibility of silence • All modern CPUs vary their performance in response to demand • Contention from input devices, networking gear, that web browser you forgot to quit, you name it • Virtualization introduces interference from invisible co-tenantsMonday, October 3, 2011
  • 20. That O’Sullivan seems awfully gloomy • Does this mean we should abandon the ideal of a quiet system? • No, but understand that there’s only so much you’ll achieve • What is now very important is to • Measure the perturbationMonday, October 3, 2011
  • 21. (Re)introducing the criterion library • The library I wrote to benchmark the text package • Can measure pure functions (strict and lazy) and IO actions • Automates much of the pain of benchmarking • “How many samples do I need for a good result?” • “Can I trust my numbers?” • “What’s the shape of my distribution?”Monday, October 3, 2011
  • 22. Sampling safely • We measure clock resolution and cost, then compute the number of samples needed to provide a low measurement error • Samples are corrected for clock cost • A warmup run sets code and data up for reproducible measurements • We can force the garbage collector to run between samples for more stable measurements • We measure wall clock time, not “CPU time consumed by this process” • This lets us handle I/O-bound, networked, and multi-process codeMonday, October 3, 2011
  • 23. Outliers and the inflated mean • Suppose you launch Call of Duty 3 while benchmarking • This will eat a lot of CPU and memory, and intermittently slow down the benchmarked code • Slower code will show up as outliers (spikes) in time measurements • Enough outliers, and the sample statistics will be inflated, perhaps drasticallyMonday, October 3, 2011
  • 24. Reporting dodgy measurements • Our goal is to identify outliers, but only when they have a significant effect • Outliers that don’t inflate our measurements are not really a problem • We use the boxplot technique to categorize outliers • We report outliers that are perturbing our measurements, along with the extent of the problem (mild, moderate or severe)Monday, October 3, 2011
  • 25. Trustworthy numbers • It’s exceptionally rare for measurements of performance to resemble an idealized statistical distribution • The bootstrap is a resampling method for estimating parameters of a statistical sample without knowledge of the underlying distribution • Following Boyer, we use the bootstrap to give confidence intervals on our measurements of the mean and standard deviationMonday, October 3, 2011
  • 26. What do measurements look like? • Some sample output from a criterion benchmark of the Builder type: • mean: 4.855 ms (lb 4.846 ms, ub 4.870 ms) • std dev: 57.9 μs (lb 39.6 μs, ub 93.5 μs) • Builder is a type we provide to support efficient concatenation of many strings (for formatting, rendering, and such)Monday, October 3, 2011
  • 27. Resampling revisited • The bootstrap requires repeated pseudo-random resampling with replacement • Resampling: given a number of measurements, choose a subset at random • Replacement: okay to choose the same measurement more than once in a single resample • Since we resample a collection of measurements many times, PRNG performance becomes a bottleneckMonday, October 3, 2011
  • 28. Fast pseudo-random number generation • The venerable random package is not very fast • So I wrote an implementation of Marsaglia’s MWC8222 algorithm • mwc-random is up to 60x faster than random • mwc-random: 19.96ns per 64-bit Int (about 50,000,000 per second) • random: 1227.51ns per 64-bit IntMonday, October 3, 2011
  • 29. Truth in advertising • The benchmark for understanding performance measurements is the histogram • “Do I have a unimodal distribution?” • “What are those outliers doing!?” • Histograms are finicky beasts • Choose a good bin size by hand, or else the data will mislead • I know of no good tools for quickly and efficiently fiddling with histogramsMonday, October 3, 2011
  • 30. Is there something better we can do? • Kernel density estimation is a convolution-based method that gives histogram-like output without the need for hand-tuning • KDEs provide a non-parametric way to estimate the probability density function of a sample • We convolve over a range of points from the sample vector • The size of the convolution window is called the bandwidthMonday, October 3, 2011
  • 31. What does a KDE look like?Monday, October 3, 2011
  • 32. No hand tuning? • There are long-established methods for automatic choice of bandwidth that will give a quality KDE • Unfortunately, the best known methods smooth multimodal samples too aggressively • But wait, didn’t we just see a KDE with 3+ modes (peaks)? • Soon to come: an implementation of Raykar & Duraiswami’s Fast optimal bandwidth selection for kernel density estimation • Much more robust in the face of non-unimodal empirical distributions; doesn’t oversmoothMonday, October 3, 2011
  • 33. For want of a nail • To answer the question of “is the text library fast?”, I built... • ...a benchmarking package, which needed... • ...a statistics library, which needed... • ...a PRNG • After disappearing down that long tunnel, was the library fast? • Not especially - at firstMonday, October 3, 2011
  • 34. Stream fusion - how did it work out? • Didn’t perform well until SimonPJ rewrote the GHC inliner for 7.0 • Performance is now pretty good • But the model seems to force too much heap allocation • Hand-written code still beats stream fusion • One fair-sized win comes with reusability • We can often share code between the two text representations • The programming model is somewhat awkwardMonday, October 3, 2011
  • 35. General-purpose statistics wrangling • Since I needed to write other statistical code while working on criterion, I ended up developing the statistics package • Provides a bunch of useful capabilities: • Working with widely used discrete and continuous probability distributions • Computing with sample data: quantile & KDE estimation, bootstrap methods, significance testing, autocorrelation analysis, ... • Random variate generation under several different distributions • Common statistical tests for significant differences between samplesMonday, October 3, 2011
  • 36. Numerical pitfalls • There are plenty of traps for the unwary in a statistics library • Catastrophic cancellation of small values • Ballooning error margins outside a small range • PRNGs that exhibit unexpected autocorrelation • Example: the popular ziggurat algorithm for normally distributed Double values has subtle autocorrelation problemsMonday, October 3, 2011
  • 37. What does criterion focus on? • Ease of use: writing and running a benchmark must be as easy as possible • Automation: figure out good run times and sample sizes that lead to quality results without human intervention • Understanding: KDE gives an at-a-glance view of performance without manual histogram tweaking • Trust: criterion inspects its own measurements, and warns you if they’re dubiousMonday, October 3, 2011
  • 38. What has criterion made possible? • In just a few projects of mine: • At least 28 commits to the text library since Sep 2009 consist of speed improvements measured with criterion • 10 commits to statistics and mwc-random yield measured performance improvements (i.e. using criterion to help speed itself!) • Most importantly to me, the text library now smokes both bytestring and built-in lists at almost everything :-)Monday, October 3, 2011
  • 39. Putting the “real” into “real world” • In December of 2010, I started a small company in San Francisco, MailRank • We use machine learning techniques to help people deal with email overload • “Show me my email that matters.” • We put our money where my mouth is: • Our cloud services are written in HaskellMonday, October 3, 2011
  • 40. Haskell in the real world • The Haskell community is very lucky to have a fantastic central repository of code in the form of Hackage • It’s a bit of a victim of its own success by now, mind • For commercial users, our community’s widespread use of BSD licensing is very reassuring • Our core library alone depends on 25 open source Haskell libraries • Of these, we developed and open sourced about a dozenMonday, October 3, 2011
  • 41. Third party libraries I love • The Snap team’s snap web framework: fast and elegant • The yesod web framework deserves a shout-out for its awesomeness too • Snoyman’s http-enumerator: a HTTP client done right • Tibell’s unordered-containers: blazingly fast hash maps • Van der Jeugt and Meier’s blaze-builder: fast network buffer construction • Hinze and Paterson’s fingertee: the Swiss army knife of purely functional data structuresMonday, October 3, 2011
  • 42. A few other libraries I’ve written • attoparsec: incremental parsing of bytestrings • aeson: handling of JSON data • mysql-simple: a pleasant client library for MySQL • configurator: app configuration for the harried ops engineer • I tend to focus on ease of use and good performance • By open sourcing, I get a stream of improvements and bug reportsMonday, October 3, 2011
  • 43. Performance: the inliner • The performance of modern Haskell code is a marvel • But we have become reliant on inlining to achieve much of this • e.g. stream fusion depends critically on inlining • Widespread inlining is troubling • Makes reading Core (to grok performance) vastly harder • Slows GHC down enormously - building just a few fusion-heavy packages can take 20+ minutesMonday, October 3, 2011
  • 44. Achieving good performance isn’t always easy • e.g. my attoparsec parsing library is CPS-heavy and GHC generates worse code for it than I’d like... but I don’t know why • Core is not a very friendly language to read, but it’s gotten scary lately with so many type annotations — we need -ddump-hacker-core • Outside of a smallish core of people, lazy and strict evaluation, and their respective advantages and pitfalls, are not well understood • We’ve all seen code splattered with panicky uses of seq and strictness annotationsMonday, October 3, 2011
  • 45. “Well-typed programs can’t be blamed”? Uh huh? • Let me misappropriate Wadler’s nice turn of phrase • I often can’t figure out where to blame my well-typed program because all I see upon a fatal error is this: *** Exception: Prelude.head: empty list • This is a disaster for debuggingMonday, October 3, 2011
  • 46. Our biggest weakness • The fact that it’s almost impossible to get automated assistance to debug a Haskell program, after 20 years of effort, remains painful • No post-mortem crash dump analysis • No equivalent to a stack trace, to tell us “this is the context in which we were executing when the Bad Thing happened” • This is truly a grievous problem; it’s the only thing that keeps me awake at night when I think about deploying production Haskell codeMonday, October 3, 2011
  • 47. What’s worked well for MailRank? • Number of service crashes in 2+ months of closed beta: zero • The server component accepts a pummeling under load without breaking a sweat • Our batch number crunching code is fast and cheap • Builds and deployments are easy thanks to Cabal, native codegen, and static linkingMonday, October 3, 2011
  • 48. A little bit about education • In spite of recent curriculum changes, FP in general is still getting short shrift for teaching • David Mazières and I have started using Haskell as a language for teaching systems programming at Stanford (tradionally not an FP place) • Instead of teaching just Haskell, we’re teaching both Haskell and systems • As far as I can tell, our emphases on practicality and performance are uniqueMonday, October 3, 2011
  • 49. There’s demand for this stuff! • We’re targeting upper division undergrads and grad students • So far, our class is standing room only • We have several outsiders auditing the class • If you’re in a position to teach this stuff, and to do so with a practical focus, now’s a good time to be doing it!Monday, October 3, 2011
  • 50. What’s next? • I’m taking the analytics from criterion and applying them to HTTP load testing • Existing tools (apachebench, httperf) are limited • Difficult to use • Limited SSL support • Little statistical oomph • Thanks to GHC’s scalable I/O manager and http-enumerator, the equivalent in Haskell is easyMonday, October 3, 2011
  • 51. Work in progress • My HTTP load tester is named “pronk” • • It’s still under development, but already pretty good • Because it’s open source, I’m already getting bug reports on the unreleased code!Monday, October 3, 2011
  • 52. Thank you!Monday, October 3, 2011