Pronk like you mean it

Pronk like you mean it!

A few years of gadding about in Haskell

Bryan O’Sullivan, MailRank, Inc.

Monday, October 3, 2011

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.’


Pronking as it is practiced in the wild.


“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, http://realworldhaskell.org/


2.5 years of free online access

• Nearing a million visits, and still growing!
book.realworldhaskell.org 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

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00:02:27 Time on Site

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

realworldhaskell.org comments per week


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 months


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 fusion


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

• http://hackage.haskell.org/package/text

• Now distributed as part of the Haskell platform


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”


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 library


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)


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
maintain


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 :-(


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)


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?


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 limit


Advice for the 1990s

• Longstanding benchmarking advice:

• Run on a “quiet” system

• This is no longer remotely achievable, so ... forget it?


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-tenants


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 perturbation


(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?”


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 code


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 drastically


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)


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 deviation


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)


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 bottleneck


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 Int


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 histograms


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 bandwidth


What does a KDE look like?


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 oversmooth


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 ﬁrst


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 awkward


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 samples


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 problems


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
dubious


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 :-)


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 Haskell


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 dozen


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
structures


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 reports


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+ minutes


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
annotations


“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 debugging


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 code


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
linking


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 unique


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!


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 easy


Work in progress

• My HTTP load tester is named “pronk”

• github.com/mailrank/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!


Thank you!


Pronk like you mean it

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