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Roman Baumer (Freenode: rba #raku or ##raku-infra) / 2021-01-24T21:19:18When working with IO::Path we have to take platform dependent directory separator into account. To alleviate the problem .add was added. Surprisingly there is no candidate that takes a list. So we have to chain method calls as if we would use an inferior language.
'/home/dex'.IO.chain.add('tmp').add('foo.txt').say;
"/home/dex/tmp/foo.txt".IOSince it is rather unlikely that we ever do calculations with IO::Path objects we can repurpose infix:</>. Doing so gives us metaoperators for free.
multi sub infix:</>(IO::Path:D \p is raw, Str:D \s) {
p.add(s).resolve()
}
multi sub infix:</>(IO::Path:D \p is copy, List:D \l) {
for l {
p = p.add: .Str
}
p
}
my $p = '/home/dex/'.IO;
$p /= 'bar';
dd $p;
# OUTPUT: Path $p = IO::Path.new("/home/dex/bar", :SPEC(IO::Spec::Unix), :CWD("/"))Having to chain method calls because the implementer was lazynot overly imaginative is a common theme. Since the default parent class is Any, any method added to that class should show up everywhere.
use MONKEY-TYPING;
augment class Any {
method chain {
class Chainer {
has $.the-object;
method FALLBACK($name, *@a) {
my $o = $.the-object;
for @a -> $e {
$o = $o."$name"($e, |%_);
}
$o
}
}
Chainer.new(the-object => self)
}
}
IO::Path.HOW.compose(IO::Path);
'/home/dex'.IO.chain.add(<tmp foo.txt>).say;
# OUTPUT: "/home/dex/tmp/foo.txt".IOThe method chain actually breaks the chain by returning an instance of a private class. This object knows the original object and will change any method call to a loop over the first positional. Named arguments are forwarded via the implicit argument %_.
You likely spotted both “should” and .HOW.compose. This is a long-standing issue. The MOP does keep a list of parent classes but not for children. So neither the compiler nor we can easily walk all type objects to recompose them. It’s a bit of a shame. There is likely much more that could be done in raku.land with a properly working augment.
I have a script where I’m only interested in the last two lines of its output. That’s easy to handle with Shell::Piping.
px«script.sh ./foo.txt» |» my @a;
my ($second-last-line, $last-line) = @a.tail(2);That works but is wasteful because it stores lines of text in @a and keeps them around until the Array goes out of scope. Also, any temporary variable is a clear indicator of boilerplate. And we don’t do boilerplate in raku.land.
Declarators are quite powerful because they can take a list and immediately hand it over to infix:<=>. We can even skip values by using anonymous scalars.
my ($, $a, $b) = sub-that-returns-a-list();I want to do something quite similar. Looking for the last two elements means to skip over any but the last two elements. In subscripts and signatures we use the Whatever * to indicate multiplicity. (Sometimes I don’t get English. Why is “manyness” not a word? o.O)
px«script.sh ./foo.txt» |» my (*, $second-last, $last);That doesn’t quite work because Rakudo doesn’t expect the whatever star in a declarator list. In fact it doesn’t expect any term in that spot. We can work around that by being explicit. While we are on it we may add an anonymous scalar to the mix.
px«script.sh ./foo.txt» |» my (Whatever, $third-last, $, $last);Declarators return a List of containers and values. We can use introspection to dissect it.
my \l := my (Whatever, $second-last, $, $last);
say l».&{ .WHAT, .VAR.WHAT, .VAR.name };
# OUTPUT: (((Whatever) Whatever anon) ((Any) Any $second-last) ((Any) Any anon) ((Any) Any $last))By checking both for type objects and for type object and names in .VAR we can tell *, $ and normal containers apart.
sub infix:<|»>(\l, \r) {
sub is-whatever($_ is raw) { (.VAR.name eq 'anon' && .WHAT === Whatever) but role :: { method gist { self ?? '*' !! '' } } }
sub is-anon($_ is raw) { (.VAR.name eq 'anon' && .WHAT === Any) but role :: { method gist { self ?? 'anon' !! '' } } }
sub is-scalar($_ is raw) { (.VAR ~~ Scalar && .WHAT !=== Whatever && .VAR.name ne 'anon') but role :: { method gist { self ?? 'Scalar' !! '' } } }
sub is-left-slurpy(\l) { l.head.&is-whatever but role :: { method gist { self ?? 'left-slurpy: yes' !! 'left-slurpy: no' } } }
sub is-right-slurpy(\l) { l.tail.&is-whatever but role :: { method gist { self ?? 'right-slurpy: yes' !! 'left-slurpy: no' } } }
say r».&{ .&is-whatever, .&is-anon, .&is-scalar }
say r.&is-left-slurpy;
say r.&is-right-slurpy;
}
42 |» my (W, $, $c);
42 |» my ($d, $e, W);
# OUTPUT: ((* ) ( anon ) ( Scalar))
left-slurpy: yes
left-slurpy: no
(( Scalar) ( Scalar) (* ))
left-slurpy: no
right-slurpy: yesNow I have everything I need to teach Shell::Piping to skip over values.
That Rakudo doesn’t allow the Whatever star in declarator lists feels like a bug.
my @List = my (42, "", Cool, $, $a);
dd @List;
Array @List = [Mu, Mu, Cool, Any, Any]It is perfectly fine to skip over literals, take type objects and keep containers but for whatever reason it doesn’t like stars. However, what does work, works really well. It does provide us with containers that we can reason about via .VAR. Looks like Santa was onto something.
I don’t usually announce regular releases of my modules. But not this time. I
start this new year with the new v0.1 branch of
Cro::RPC::JSON.
Version 0.1.1 is currently available on CPAN (will likely be replaced with
fez as soon as it is ready). The
release is a result of so extensive changes in the module that I had to bump its
:api version to 2.
Here is what it’s all about.
Cro::RPC::JSONBefore I tell about the changes, let me briefly introduce the module itself.
The primary purpose of Cro::RPC::JSON is to provide a simple way to use the
same API class for both server-side Raku code and for JSON-RPC calls. For
example, if we have a class for accessing an inventory database of some kind
then it should be easy to use the same class to serve our front-end JavaScript
code:
class Inventory {
...
method find-item(Str:D $name) is json-rpc { ... }
...
}
That’s all. The only limitation the module currently imposing on the methods is
accepting simple arguments and returning JSONifiable values, as supported by
JSON::Fast.
Marshalling/unmarshalling of parameters/return values is considered, but I’m not
certain yet as to how exactly to implement it.
Now, all we need to serve JSON-RPC calls is to add this kind of entry into
Cro routes:
# If Inventory is not thread-safe this this line has to be placed inside the
# 'post' block.
my $inventory-actor = Inventory.new;
route {
...
post -> 'api' {
json-rpc $inventory-actor;
}
}
And that’s all.
Cro::RPC::JSON also supports code objects as handlers of the RPC requests. But
let me omit this part and send you directly to the
documentation.
Let’s get to the point instead!
A long-planned feature I never had enough time to get implemented. But when the life itself demands it in a form of an in-house project, where WebSockets are a natural fit, who am I to disobey the command?
The support comes in a form of implementing both JSON-RPC and asynchronous
notifications support by treating socket as a bi-directional stream of JSON
objects. JSON-RPC ones are recognized by either presence of jsonrpc key in an
object; or by treating a JSON array as a JSON-RPC batch. Because the RPC traffic
is prioritized over free-form notifications it means that the latter can only be
represented by JSON objects (and without jsonrpc key in them!). No other
limitations implied. I don’t consider the constraint as a problem because the
primary purpose of non-RPC objects is to support push notifications. For any
other kind of traffic it is always possible to open a new RPC-free socket.
The use of our actor/API class with WebSockets is as simple as:
route {
...
get -> 'api' {
json-rpc :web-socket, $inventory-actor;
}
}
Note that there is no need to change our Inventory class aside of the already
added is json-rpc trait. Same code will work for both HTTP/POST and WebSockets
transports! It actually makes it possible to provide both interfaces on the
server side by simply having two route entries – one for each case. Which one to
use would depend on what kind of optimization developer is willing to achieve. I
haven’t had time to benchmark, but common sense tells me that where WebSockets
provide less latency and is great for single requests affecting your application
response time on user actions; HTTP/POST would serve better on big
parallelizable requests. For example, we can fill in a big table by requesting
each individual line of it from a server in asynchronous manner over HTTP/POST,
allowing the server to process all requests in parallel.
The initial v0.0 branch of the module was strictly about HTTP/POST transport and
thus only supported simple “invoke code - get result” mode. The only alternative
provided to a user was the choice between using a class or a code object (see,
for example, this route
entry
from Cro::RPC::JSON test suite.
Introduction of asynchronous WebSockets consequently introduced the demand for
additional asynchronousity. If we’re about to support push notifications then we
have to somehow react to server-side events too, right? Therefore the module now
provides three different modes of operation: synchronous, asynchronous, and
hybrid. Again, I’d better avoid citing the documentation here. Just single
example for our Inventory class:
method subsribe(Str:D $notification) is json-rpc("rpc.on") { ... }
method unsubscribe(Str:D $notification) is json-rpc("rpc.off") { ... }
method on-inventory-update is json-rpc(:async) {
supply {
whenever $!database.updated -> $update-event {
emit %(
notification => 'ItemUpdate',
params => %(
id => $update-event.item.id,
status => $update-event.status,
),
) if %!subsriptions<ItemUpdate>;
}
}
}
Now, all our client-side JavaScript code needs is to call rpc.on JSON-RPC
method with “ItemUpdate” as the argument and start listening for incoming
events. Any item update in the inventory will now be tracked on the client side
automatically.
Don’t wanna focus on this, mostly technical fixes like support for parameterized roles, fixed inheritance and role consumption.
A note to myself: don’t you want to document all this?
This was perhaps the biggest nightmare. As it is always with asynchronous code.
But, hopefully, I finally got it done right. For synchronous code and class
method calls the module should now do all is needed to properly handle any
exception leaked from the user code. For JSON-RPC method calls this would mean
correct response produced with error key set.
For asynchronous code things are much harder to keep control of. Whereas
HTTP/POST allows to do some guesses and provide some additional error processing
by Cro::RPC::JSON, for WebSockets any unhandled exception in any async code
means socket termination. Being rather new to this technology, I spent a lot of
time trying to figure out how to properly respond to a request until realized
that actually there is no solution to this problem. The reason is as simple as
it only can be: if asynchronous user code thrown then the Supply it provided
is actually terminated. Since the supply is an integral part of the whole
JSON-RPC/WebSocket processing pipeline, its termination means the pipeline is
disrupted.
It’s now time to dive into the muddy waters of TypeScript/JavaScript. I have
already tested Cro::RPC::JSON with Vue framework and rpc-websockets module
and it passed the basic tests of calling methods and processing push
notifications. Will see where this all eventually takes me. After all, this is
gonna be my first production project in Raku.
The course of the Raku Programming Language by Andrew Shitov made it to the top 20 of Hacker News and spurred quite a few comments. The first associated Grant Report was also published. Nice to see so much more exposure!
Yanzhan Yang published two videos in the Raku Riddle series:
Joelle Maslak has written an extensive blogpost about how they created an “On Air” light in Making a Linux-Based “On-Air” Light for My Home Office w/ Camera & Google Calendar Integration (associated Raku module: BusyIndicator).
It looks like the Raku Programming Language made it to the programminglanguages.info website by Ramiro Gómez. The influence network graphic is pretty nice, but what is Self?
HortenseAndI has posted a love declaration about the Raku Programming Language on /r/rakulang, but with a twist: Raku is friggin’ slow. Many people reacted to it, with Ralph Mellor‘s comment standing out (as usual).
TIL that there is a LibHunt Index that monitors social media about mentions of programming languages. The Raku Programming Language is currently at #89. There should be ways to improve that :-).
And again a nice number of interesting blog posts:
Weekly Challenge #96 is available for your perusal.
nqp::spawnprocasync op.sec, asec and sech functions in terms of other NQP trigonomic functions, improving portability of Rakudo implementations on different backends.CachedIterator role to speed up those Iterators that already have a cache (such as when iterating over a reified array), and made hash-slice assignment about 1.7x as fast.Pull Request of the past week:
Please check it out and leave any comments that you may have!
%?RESOURCES key during development? by Richard Hainsworth.say True? by Julio.REPL by Richard Hainsworth.wchar_t‘s from int64 by Paul Procacci.On what a travel agency has dubbed “Blue Monday“, it is indeed hard to not have the feeling of wanting to travel. I guess we will have to make do with a lot of video-conferencing, such as for the Raku Devroom at FOSDEM this year. Meanwhile, it bears repeating, that one should stay safe and stay healthy. Otherwise you wouldn’t be able to read next weeks Rakudo Weekly News!
IRC is a good place to find answers. Often I find the questions found there to be even more enlightening.
12:26 < notandinus> do other languages have this Label thing? apart from Perl and Raku, i don't seem to find it for python, nim
12:28 < gfldex> notandinus: C/C++ does, but with different semantics (read: you can mess up the stack).
12:30 < sortiz> Javascript also, with semantics similar to Raku.
12:32 < El_Che> isn't label extremely commom to break out of the correct loop?
12:34 < El_Che> Besides python, all other languages probably have it
12:34 < sortiz> But only in Raku they are also objects, like almost everything.
12:35 < notandinus> is it being object better?So are objects better? That depends on the needs and the attributes or methods provided. Let’s have a look what Label can do for us.
LABEL: Nil;
say LABEL.^attributes;
# OUTPUT: (Str $!name Str $!file Int $!line Str $!prematch Str $!postmatch)
(new name goto leave Int next redo last gist BUILDALL)So we get the label name, what means we can label things with them. There are also attributes with the line number and file providing a location for human consumption. With .Str and .gist we get some strings.
Label<94051339353632>
Label<LABEL>(at /home/dex/tmp/tmp2.raku:99, ' ~ self.line } });
⏏LABEL: Nil;
say LABEL.^a')We do get the line number and file but in a piece of text that we don’t want to parse. Stringification is not helpful and there are no accessors for $!line and $!file. Not to worry, Lord Hanuman is with us.
use MONKEY-TYPING;
augment class Label {
method Str { $!file ~ ':' ~ $!line }
}
sub documented {
BEWARE: sub hax { 0x1ee7 }
warn „Beware of the leet hax at {BEWARE} !“;
}
documented;
# OUTPUT: Beware of the leet hax at /home/dex/tmp/tmp2.raku:113 !
in sub documented at /home/dex/tmp/tmp2.raku line 115The class Label feels unfinished to me. With better .Str or access to all attributes it could be a nice tool to refer to spots in source code. This in turn could lead to better error messages and allow folk more creative then me to come up with something clever. I doubt it would be a hard change. I shall query Rakudo’s source code the coming week.
The past two weeks, while yours truly was taking a little break, have turned out so many new projects that one can only say this is an excellent beginning of 2021 for the Raku Programming Language. Which will hopefully turn out to be better for everybody than 2020 turned out to be. So let’s see what we have:
Andrew Shitov has published part 1 called “Raku Essentials” of a complete course of the Raku Programming Language in 5 parts (original grant proposal). This course covers all the main aspects that you need to use in your daily practice: it explains the theory and offers many practical assignments. Part one contains 91 topics, 73 quizzes and 65 exercises. A great start!
Tony O’Dell and Nick Logan have published the first version of fez, an alternate way to register and upload Raku modules to the ecosystem. Still very much in development, it is exciting to see what is now materializing from the original grant request.
JJ Atria and James Raspass have unveiled another online Raku ecosystem browser called Raku.land. Still a little rough around the edges, and not complete yet, but good to see something like this being developed in the Raku Programming Language itself (see source on Gitlab and /r/rakulang comments).
Yanzhan Yang has returned with another series of YouTube videos. Such as an introduction to the Raku UI Playground and the Tiny Platformer game. But perhaps more interestingly, a weekly Raku Riddle Contest:
It’s great to see such nice online content being created! And of course, your participation will be much appreciated. A nice companion to the Weekly Challenge!
And a nice number of interesting blog posts in the post-advent season:
Matthew Stuckwisch finally released a completely re-worked and re-imagined version of the Intl::CLDR module, which makes the Unicode CLDR (Common Locale Data Repository) easily accessible in the Raku Programming Language.
The Perl Foundation warns about a phishing attack against CPAN authors. Since this also applies to Raku module developers that upload to PAUSE, yours truly thought it appropriate to mention it in the Rakudo Weekly News as well.
Weekly Challenge #95 is available for your perusal. Weekly Challenge #94 has already passed, but is of course still fun to work on.
is implementation-detail trait on classes and roles, added a Seq.slice method (long discussed), and made slicing arrays between 2x and 10x faster (and more correct with regards to nested indexes and use of adverbs).The new Pull Requests:
log() for negative values.spawnprocasync to use a separate arg for the program name (MoarVM)spawnprocasync to use a separate arg for the program name (NQP)sec, asec and sech in terms of other NQP trigonometric functions.spawnprocasync to use a separate arg for the program namePlease check them out and leave any comments that you may have!
my %h = :1x style syntax? by ganjaptics.sub appends Nil to the end by Lars Malmsteen.gnuplot script by 足立千鳥（工学系Vtuber).== by Nicholas Basila.Bag by Markus Holzer.rakuenv by 八雲アナグラ.wchar_t CArray to something printable by Paul Procacci.. needed by Richard Hainsworth.whenever by Gianni Ceccarelli.Having a week off has done yours truly good. And it was a nice way to build up a good, fat first issue of the Rakudo Weekly News in 2021! Even though the end of the tunnel seems to be teasing us in the distance, it has become even more important now to stay healthy and stay safe. So please do! Looking forward to seeing you next week again with the next Rakudo Weekly News.
Bugs that originate in reliable subsystems are hard to spot because we don’t expect them to fail. A filesystem path usually just works. This has bit me a few times before. While renovating my backup script I found that I provide quite a few status and debug messages that basically call .gist on an IO::Path instance. Since one might want to use a hypothetical path, Raku can’t complain on non-existing paths until they are used. Further, it can be quite useful to know that a path is a symlink. Bash and friends help here with colour coding. Since I have plenty of say-calls, it would be nice to change them all in one go. So wraping it is!
my &RED = sub (*@s) { "\e[31m{@s.join('')}\e[0m" }
my &GREEN = sub (*@s) { "\e[32m{@s.join('')}\e[0m" }
my &CYAN = sub (*@s) { "\e[36m{@s.join('')}\e[0m" }
my &io-path-gist = IO::Path.^can('gist')[0];
&io-path-gist = &io-path-gist.wrap(my method gist (Mu: --> Str ) {
nextsame unless self ~~ IO::Path;
my $s = self.Str;
$s = self.e && self.r ?? $s !! RED($s);
$s = self.l ?? CYAN($s) !! $s;
$s = self.x && !self.d ?? GREEN($s) !! $s;
$s = self.d ?? $s ~ ‚/‘ !! $s;
"⟨$s⟩"
}).&{ -> { .restore } };We get a handle on the proto of .gist because .wrap never operates on multi candidates (this might change, see here). I’m using a method with Mu as the invocant for clarity. A sub with a single positional would do as well. Since the wrapper is called by the proto, we need to redispatch on anything but IO::Path. The returned WrapHandle isn’t really useful and neither is the unwrapped handle to .gist. By storing a closure we can restore the default by calling io-path-gist(). The rest is some colouring and adding a / to mark directories. I like to put a path into fancy quotes to make it easy to spot leading and trailing white spaces.
Declaring class-methods and calling them is a hard thing that is easy because the implementer was sufficiently tormented. Getting hold of methods at runtime is a simple thing that is hard. Huffmanizing applies. We call methods much more often then wrapping them. Sometimes it is good to break a design principle.
I have been able to spend some time on the Physics::Unit module over the holidays and to expunge some of the frustrations that have crept in regarding the compile times of raku.
The basic problem I have been wrestling with is the desire to express physical SI units using the raku custom postfix operator mechanism without having to wait for 10 mins for raku to compile my module.
This is the story of how judicious design, lazy execution, trial & error and the raku power tools got from 10 mins+ to under 13 secs!
Let’s start by looking at the sunlit uplands. Imagine a raku which provides a simple and intuitive tool for scientists and educators to perform calculations that automatically figure out what the physical units are doing.
Something like this:
use Physics::Constants;
use Physics::Measure :ALL;
$Physics::Measure::round-to = 0.01;
my \λ = 2.5nm;
my \ν = c / λ;
my \Ep = ℎ * ν;
say "Wavelength of photon (λ) is " ~λ; #2.5 nm
say "Frequency of photon (ν) is " ~ν.norm; #119.92 petahertz
say "Energy of photon (Ep) is " ~Ep.norm; #79.46 attojouleNow, before you ask, yes – this is real code that works and compiles in a few seconds. It uses the latest version Physics::Measure module which in turn uses the Physics::Units module. Let me point out a few cool things about how raku’s unique combination of features is helping out:
So – how can it be that hard? Well the devil is in the little word all [the SI unit postfix operators]. Consider this table:
So we have 27 units and 20 prefixes – that’s, err, 540 combinations. And you want me to import all of these into my namespace. And you want me to have a library of 540 Physics::Unit types that get loaded when I use the postfix. Have you thought this through!!??
So – by way of sharing the pain of this part of my raku Physics::Journey – here are the lessons I have learned to optimise my module code:
My first instinct was to punt on the issue. The original perl5 Physics::Unit module allows coders to specify a unit type via a string expression – something like this:
my $u2 = GetUnit( 'kg m^2 / s^2' );Anyway I knew I would need unit expressions to cope with textual variants such as ‘miles per hour’ or ‘mph’, or ‘m/s’, ‘ms^-1’, ‘m.s-1’ (the SI derived unit representation) or ‘m⋅s⁻¹’ (the SI recommended string representation, with superscript powers). So a new unit expression parser was built into Physics::Unit from the start with raku Grammars. However, it became apparent that saying:
my $l = Length.new( value => 42, units => 'yards' );Is a pretty long-winded way to enter each measurement. Still, this was a cool way to apply (and for me to learn) raku Grammars and Actions which has resulted in a flexible, human-friendly unit expression slang as a built-in piece of the Physics::Unit toolkit.
So far, my Physics::Unit module would happily take a unit string, parse it with the UnitGrammar and create a suitable instance of a Unit object. Something like this:
Unit.new( factor => 0.00016631, offset => 0,
defn => 'furlong / fortnight',
type => Speed, dims => [1,0,-1,0,0,0,0,0],
dmix => ("fortnight"=>-1,"furlong"=>1).MixHash, names => ['ff'] );This user-defined object is generated by iterating over it’s roots (e.g.) 1 fortnight => 2 weeks => 14 days => 336 hours => 2,016 mins => 120,960 secs (thus the factor attribute). More than 270 built in unit and prefix definitions – covering SI, US (feet, inches), Imperial (pints, gallons) and so on. And the .in() method is used for conversions. [There does not seem much point in a Unit library unless it can support common usage such as mph and conversion between this and the formal SI units.]
But, now I come to implement my postfix operators – then I need to pass 540 definitions to the Grammar on first compilation and it needs to build 540 object instances. Welcome to 10 mins+ compile times.
Before I go too far with this critique – I would like to note a couple of very important caveats:
Sooo – the third attempt to combine the desired features and reasonable speed was to pre-generate the 540 units as literals – “Stock” units. So the code could be run in “dump” mode to generate the unit literals using the Grammar and store to a text file, then paste them back into the module source so that in the released version they are just read in via a “fast start” mode.
By reusing the same Grammar for pre-generation and on the fly generation of user-defined Units, this eliminated any potential compatibility issues. I chose not to compromise with any of the MONKEY-SEE-NO-EVAL for module security and code integrity reasons.
Performance improvements were dramatic. By bypassing the Grammar step, first compile times came down to ~340s and precompile start times to under 3s. Nevertheless, I was still unhappy to release a module with such a slow first compile time and searched for a better design.
On the fourth pass, I decided to re-factor the module with lazy Unit instantiation. Thus the unit definitions are initialised as hash data maps, but only a handful of objects are actually created (the base units and prefixes).
Then, when a new object is called for, it is generated “lazily” on demand. Even in an extreme case such as the ‘furlong / fortnight’ example, only O(10) objects are created.
By eliminating the Stock units, this reduced the module source by 2000+ lines (540 x 4 lines per object literal). Performance improved substantially again – this time about 60s first compile and 2.6s precomp start times.
However, the Physics::Measure code still had to embody the postfix operators and to export them to the user program. Thus 540 lines to individually compile. Each postfix declaration like this:
sub postfix:<m> ( Real:D $x ) is export { do-postfix( $x, 'm' ) }Even better, I could learn from the excellent raku documentation again – this time to discover UNIT::EXPORT that gave me a good start to produce a programmatic export all 540 postfixes in just 6 lines of code. Goodbye boilerplate!
my package EXPORT::ALL {
for %affix-by-name.keys -> $u {
OUR::{'&postfix:<' ~ $u ~ '>'} :=
sub (Real:D $x) { do-postfix($x,"$u") };
}
}This had the additional performance boost – the final numbers below…
Finally, a word on selective import. Prior to Attempt 5, I experimented with labelling the less common units (:DEFAULT, :electrical, :mechanical, :universal and :ALL if you are interested). But even by reducing :DEFAULT to only 25% of the total, this did not reduce the first compile time measurably. I interpret this as the compiler needing to process all the lines even if the import labels are not specified by the user program.
But with the package approach, Physics::Measure :ALL will export all of the SI units. Just drop the :ALL label if you want even more speed and plan to go without the postfix operators.
So, the latest speed measurements (on my low spec 1.2GHz/8GB laptop) are:
# use Physics::Measure; ...10s first-, 1.2s pre- compiled
# use Physics::Measure :ALL; ...13s first-, 2.8s pre- compiledYMMV!
~p6steve (pronounced p-sics)
With writing more and more shell scripts in Raku, I realised that I call a MAIN by a MAIN in a very indirect manner. I wondered if I can find a way to reduce the indirection to get rid of the extra process and at least some of the overhead of Proc::Async. First we need a script to call.
#! /usr/bin/env raku
constant \frame = gather while my $frame = callframe($++) {
take $frame
}
sub MAIN {
say frame[0];
.note for lines;
exit 42;
say 'alive';
}This script prints its caller. Then reads blocking indirectly on $*IN via lines. It would print alive if the process would make it past the exit. Please note, that MAIN is the last statement in this compunit. That allows us to slurp the script and EVAL it.
my &main := $path.IO.slurp.&EVAL;And that’s it! We can call a MAIN from another script. There are a few considerations though. We can pipe data to from one script to another because they share $*IN and $*OUT. Also, STDERR might get confusing. The semantics of exit may be wrong. It is likely that we want to keep the outer script running. Capturing the return-value of the inner script is easy, the exitcode is not.
Since we are calling functions lexicals can help with solving most of those problems. In Raku we can’t easily trap c-land exit. But we can prevent it from being called.
my &*EXIT = sub ($exitcode) {
CapturedExitcode.new(:$exitcode).throw;
}Here the exitcode is packaged in an exception so we can extract it from MAIN. This sub might exit with a return so we have to capture that one too.
$out.exitcode = main();
CATCH {
when CapturedExitcode {
$out.exitcode = .exitcode;
}
default {
say .^name, ': ', .message;
put .backtrace».Str
}
}Now we need to deal with $*IN and $*OUT. Since the target script just calles lines and that forwards to $*ARGFILES.lines we can use a Channel. One of the Channels is a good place to store the exitcode.
my $out = Channel.new but role :: { has $.exitcode is rw; };
my $in = Channel.new but role :: { method put(Any:D \value){ self.send: value ~ $?NL } };Since lines requires a Str we provide the familiar put-method. Other methods like say would go into the anonymous role too. When the inner MAIN terminates, we want to close the $out channel. We can do so in a LEAVE block. The whole thing can be wrapped into a sub which provides the outer script with a nice interface.
my ($out, $in) = embed-script('./script.raku');
start {
for ^10 {
$in.put: $++;
}
$in.close;
}
.print for $out.list;
say $out.exitcode;Dealing with a multi sub MAIN is tricky. If the last statement in the script is &MAIN, it will refer to the dispatcher. With any multi candidate at the end, we can only get hold of the proto by descending into nqp-land.
my &main := $path.IO.slurp.&EVAL;
use nqp;
my &disp = &main.multi ?? nqp::getattr(&main, Routine, '$!dispatcher') !! &main;We can then call disp() to dispatch to the correct MAIN candidate. I’m not sure if this is fragile. Every routine that is a multi got a dispatcher. Yet Routine.dispatcher is not exposed by CORE.
The whole example can be found here.
Avoiding Proc::Async does speed things up quite a bit. Since we can use a Channel, we don’t have to stringify and parse output when moving data around. The called MAIN needs to cooperate in this case and thus needs to know it is not called by the runtime. We could introduce a new lexical or check $*IN against Channel. There is also the option to check the callframe for EVAL.
constant \frame = gather while my $frame = callframe($++) {
take $frame
}
say 'indirect' if any frame».gist».starts-with('EVAL');Quite in contrast to Perl 5, for Raku I never used EVAL much. Not because I’m scared — there was little reason for code generation. After all, between the two of us I have always been the more evil twin.
As jnthn pointed out, Routine.dispatcher is exposed. We can therefore keep well clear of nqp with:
my &disp = &main.multi ?? &main.dispatcher() !! &main;As of now, it is unclear if this should be specced and thus documented.
C omputer security has seen its share of mind-boggling news lately. None more mind boggling than the news about how alleged Russian hackers installed a backdoor into the IT monitoring product Solarwind Orion. Through this they got got entrance into the computer systems of several US agencies and departments — ironically even into the systems of a cyber security company (Fireeye) and Microsoft itself . The news made me think of my own history with computer security, and down memory lane I went.
One particular day in late July or early August 1989 my parents, sister and me were driving home from a short summer vacation. At a short stop in a largish city, I had found a newsstand carrying foreign magazines. There I’d bought a copy of PC/Computing’s September issue (to this day I don’t understand why American magazines are on sale a couple of months before the cover date) so that I had something to make time in the backseat pass faster.
Among articles about the relatively new MS-DOS replacement OS/2 (an OS co-developed with IBM that Microsoft would come to orphan the minute they launched Windows 3.0 and understood the magnitude of the success they had on their hands) and networking solutions from Novell (which Windows would kill as well, albeit more indirectly), the magazine brought an excerpt of the book “The Cuckoo’s Egg” by a guy named Clifford Stoll. Although I had bought the magazine for the technical information such as the stuff mentioned above, this excerpt stood out. It was a mesmerising story about how an astronomer-turned-IT-guy stumbled over a hacker, and how he, aided by interest but virtually no support from the FBI, CIA and NSA, almost single handedly traced the hacker’s origins back to a sinister government sponsored organisation in the then communist East Germany.
This is the exact moment I discovered that my passion — computers — could form the basis of a great story.
Coastal Norway where I grew up is probably as far from the author’s native San Francisco as anything; at least our book stores were. So it wasn’t until the advent of Amazon.com some years later that I was able to order a copy of the book. Luckily, the years passed had not diminished the story. Granted, the Internet described by the author Clifford Stoll was a little more clunky than the slightly more modern dial-up internet I ordered the book on. But subtract the World Wide Web from the equation and the difference between his late eighties internet and my mid-nineties modem version weren’t all that big. My Internet was as much a monochrome world of telnet and text-only servers as it was a colourful web. Email, for instance, was something I managed by telnetting into a HP Unix server and using the command line utility pine to read and send messages.
What struck me with the story was that the hacker’s success very often was enabled by sloppy system administration; one could arguably say that naïve or ignorant assumptions by sysadmins all across the US made the hack possible. Why sloppy administration and ignorant assumptions? Well, some of the reason was that the Internet was largely run by academia back then. Academia was (and is) a culture of open research and sharing of ideas and information. As such it’s not strange that sysadmins of that time assumed that users of the computer systems had good intentions too.
But no one had considered that the combination of several (open) sources of information and documents stored on these servers, could end in very comprehensive insight into, say, the Space Shuttle program or military nuclear research. Actually, the main downside to unauthorised usage had to do with cost: processing power was expensive at the time and far from a commodity. Users were billed by for their computer usage. So billing was actually the reason why Stoll started his hacker hunt. There was a few cents worth of computer time that couldn’t be accounted for. Finding out whether this was caused by a bug or something else, was the primary goal of Mr. Stoll’s hunt. What the hacker had spent this time on was — at first — a secondary issue at best.
With that in mind it’s maybe not so strange that one of the most common errors made was not changing default passwords on multi-user computers connected to the internet. One of the systems having a default password was the now virtually extinct VAX/VMS operating system for Digital’s microcomputer series VAX. This was one of the things Mr. Stoll found out by logging each and every interaction the hacker, using Stoll’s compromised system as a gateway, had with other systems (the description of how he logged all this by wiring printers up to physical ports on the microcomputer, rewiring the whole thing every time the hacker logged on through another port, is by itself worth reading the book for). Using the backdoor, the hacker did not only gain access to that computer — they got root privileges as well.
In the 30+ years passed since I read the book I’ve convinced myself about two things: 1) we’ve learned to not use default passwords anymore, and 2) that VMS systems exhibiting this kind of backdoor are long gone.
Well, I believed these things until a few weeks ago. That’s when I stumbled on to a reddit post — now deleted, but there still is a cached version available on Waybackmachine. Here the redditor explained how he’d identified 33 remaining VAX/VMS systems still on the Internet:
About a year ago I read the book “A Cuckoo’s Egg”, written in 1989. It included quite a bit of information pertaining to older systems such as the VAX/VMS. I went to Censys (when their big data was still accessible easily and for free) and downloaded a set of the telnet (port 23) data. A quick grep later and I had isolated all the VAX/VMS targets on the net. Low and behold, of the 33 targets (I know, really scraping the bottom of the barrel here) more than half of them were still susceptible to default password attacks literally listed in a book from almost 3 decades ago. After creating super user accounts I contacted these places to let them know that that they were sporting internet facing machines using default logins from 1989. All but one locked me out. This is 31 years later… The future will be a mess, kids
I applaud the redditor that discovered this. Because isn’t what he found a testament of something breathtakingly incompetent and impressive at the same time? Impressive in the sense that someone’s been able to keep these ancient systems alive on the internet for decades; incompetent because the sysadmins has ignored patching the most well documented security flaw of those systems for well over a quarter century?
So maybe this starts to answer question posed in the title: Did we learn anything from this?
Yes, of course we did. If we look past the VMS enthusiast out there, computer security is very different now than back then. Unencrypted communication is almost not used anywhere anymore. Security is provided by multilayered hardware and software solutions. In addition are not only password policies widely enforced on users, but two-factor and other extra layers of authentication is used as well.
But the answer is also No. While my organisations such as my workplace — which is not in the business of having secrets — has implemented lots of the newest security measures, this autumn we learned that the Norwegian parliament — which is in the business of having secrets — haven’t. They had weak password policies and no two-factor authentication for their email system.
Consequently they recently became an easy target for Russian hackers. I obviously don’t know what was the reasoning behind having weak security implemented. But my guess is that the IT department assessed the digital competence of the parliament members and concluded that it was too low for them to handle strong passwords and managing two-factor authentication.
And this is perhaps the point where the security of yesteryear and security today differs the most: As we’re closing in on 2021, weak security is a conscious choice; but it is the same as leaving the door wide open, and any good sysadmin knows it.
The ignorance exhibited in the case of the Norwegian parliament borders, in my opinion, on criminal ignorance — although I guess no one will ever have to take the consquence. What it does prove, however, is that while systems can be as good as anything, people are still the weakest link in any such system.
In sum I think my answer to the initial question is an uneasy Maybe. We still have some way to go before what Cliff Stoll taught us 32 years ago has become second nature.
Thanks for a great introduction to Puppeteer. I just wanted to add that if your need simply is to generate a PDF file of a web page, you can also do this from the command line without resorting to Javascript.
Simply run...
/path/to/Google\ Chrome --headless --print-to-pdf=./file.pdf https://www.google.com/
(Replace /path/to with the appropriate path to your Chrome or Chromium executable)
What I do like about JavaScript is how elegant many of their solutions to own f*ckups!
Jonathan Worthington describes the road from the first release five years ago of what is now Raku in Reminiscence, Refinement, Revolution. Reminiscing on the torture of core developers, remaking design decisions, but also about the forging of lifetime friendships. An inspiring blog post in these dark times (/r/rakulang comments)!
The other blog posts from the final crop of the Raku Programming Language Advent Posts of 2020!
Wenzel P.P. Peppmeyer and Vadim Belman have started an extensive dialogue about the implementation, meaning and use of the new coercion protocol:
Of course, other people can chime in as well!
Good news from glot.io: it now also properly supports the Raku Programming Language (/r/rakulang comments)!
Weekly Challenge #93 is available for your perusal.
@a[*]) to make it up to about 80x as fast in some scenarios.This week’s new Pull Requests:
procspawnasync to explicitly take the program nameprocspawnasync to explicitly take the program nameis implementation-detail for classesPlease check them out and leave any comments that you may have!
Hash? by JJ Merelo.NativeCall and Inlines by Ralph Mellor.It was special to see that the number of Rakoons on /r/rakulang reached 666 on Christmas. If that isn’t a good sign . Meanwhile, yours truly will take some time off from publishing the Rakudo Weekly News, so there will not be an issue on the 4th of January. So see you in two weeks for the first issue of the Rakudo Weekly News of 2021 on the 11th of January. Please stay safe and healthy. And have a good time nonetheless!
As Wenzel P.P. Peppmeyer continues with his great blog posts about Raku, he touches some very interesting subjects. His last post is about implementing DWIM principle in a module to allow a user to care less about boilerplate code. This alone wouldn’t make me writing this post but Wenzel is raising a matter which I expected to be a source of confusion. And apparently I wasn’t mistaken about it.
Here is the quote from the post:
The new coercion protocol is very useful but got one flaw. It forces me to return the type that contains the COERCE-method. In a role that doesn’t make much sense and it forces me to juggle with IO::Handle.
Basically, the claim winds down to the following snippet:
role Foo {
multi method COERCE(Str:D $s) {
$s.IO.open: :r
}
}
sub foo(Foo() $v) {
say $v.WHICH;
}
foo($?FILE);
Trying to run it one will get:
Impossible coercion from 'Str' into 'Foo': method COERCE returned an instance of IO::Handle
Let’s see why the error is legitimate here.
The short answer would be: coercion, though relaxed in a way, uppermost is a type constraint.
The longer answer is: the user expects Foo in $v in sub foo. I propose to
do a little thought experiment which involves adding a method to the role Foo.
For example, we want to pad text in the file with spaces. For this we implement
method shift-right(Int:D $columns) {...} in role Foo. Then we use the method
in our sub foo:
sub foo(Foo() $handle) {
...
$handle.shift-right(4);
...
}
Do I need to elaborate on what’s gonna happen when $handle is not Foo?
Here is the version of the role as I would do it:
subset Pathish of Any:D where Str | IO::Handle;
role Filish[*%mode] is IO::Handle {
multi method COERCE(IO:D(Pathish) $file) {
self.new(:path($file)).open: |%mode
}
}
sub prep-file( Filish[:r, :!bin]() $h,
Str:D $pfx )
{
$h.lines.map($pfx.fmt('%-10s: ') ~ *)».say;
}
prep-file($?FILE, "Str");
prep-file($?FILE.IO, "IO");
prep-file($?FILE.IO.open(:a), "IO::Handle");
Note the use of coercion to implement coercion. The idea is to take anything,
what could be turned into an IO instance.
Also, I forcibly re-open any IO::Handle because the source could have
different open modes from what I expect to be the result of the coercion. In my
example I’m intentionally passing a handle opened in append mode into a sub
expecting a read handle.
I’d like to finish with a note that here we have a good example of Raku allowing DWIM code semantics without breaking its predictability.
The friendly battle continues with the next post from Wenzel where he considers different cases where strict typechecking doesn’t work. The primary point he makes is that many decisions Raku makes are run-time decisions and this is where static typechecking doesn’t work well. This is true. But this doesn’t change the meaning of my previous post.
First of all, let’s make clear difference between compile time decisions and run-time decisions. Basically, when we write something like:
sub foo(Int:D $x) {...}
semantically it means:
sub foo($x) {
die "oopsie!"
unless $x ~~ Int && $x.defined;
...
}
There is nothing magical in it. The difference between the two snippets is apparently about the performance as compiler can do much more optimizations related to static typing of the first case, than it is able with the second variant. This is pretty much clear. What’s not is that if we declare a parameterized role we often end up with run-time code produced by the compiler.
role R1[::T] {
sub foo(T $x) { ... }
sub bar(Int:D $x) { ... }
}
Here foo will do a lot of extra work at runtime because the compiler doesn’t
know what type $x will have. So, when it comes to:
class C1 does R1[Str(Int:D)] { ... }
Then something like C1.bar(pi) will throw after a simple pi ~~ Int:D check.
But C1.foo(pi) case will result in the signature binding code to do extra
steps to resolve T, and then a few more operations before actually throwing a
bad parameter type exception.
So, eventually, where one would expect things to be done at compile time, they’re not. Hopefully, this is a good example of the dualistic nature of Raku which is balancing between static and dynamic approaches.
Let’s see what it leads us to.
Though in general Raku design tries to stick to Liskov substitution
principle,
submethods are a special case which breaks it intentionally. Anyone utilizing
a submethod must remember this. Moreover, I’d say that a submethod must not be
invoked directly without a really good reason to do so! If one do call a
submethod they must either make sure that call is done on the submethod’s class,
or use .? operator to prevent their code from throwing:
class Foo {
submethod foo { ... }
}
class Bar is Foo { }
sub foo(Foo $v) {
with $v.?foo {
...
}
}
sub bar(Foo $v) {
if $v.WHAT === Foo {
$v.foo;
}
}
To my own point of view, the most useful use case for submethods is iteration
over a class’ MRO order to call submethods on classes where they’re
defined. There is a special method on Mu which implements this behavior –
WALK.
It is not documented yet, unfortunately. But it’s
specced.
Partially, its functionality is implemented with
.+ and
.* operators.
FALLBACKI’d rather skip this case. Except for one note to be made: somehow it reminds me about the ways of TypeScript when it comes to type matching. I.e. we’d need to match an object’s content against our constrains.
Anyway, FALLBACK implementation is so much about runtime processing that I see
no problem here whatsoever. Moreover, I’d rather avoid this kind of design
pattern in a production code, unless it is tightly wrapped in a very small and
perfectly documented module.
This last case is perhaps the most interesting one because here what we can do about it right away:
subset Pathish of Any:D where Str | IO::Handle;
role Filish[*%mode] is IO::Handle {
multi method COERCE(IO:D(Pathish) $file) {
self.new(:path($file)).open: |%mode
}
multi method CALL-ME(Pathish:D $file) {
IO::Handle.new(:path($file)).open: |%mode
}
}
sub prep-file(Filish[:r, :!bin]() $h, Str:D $pfx) {
$h.lines.map($pfx.fmt('%-10s: ') ~ *)».say;
}
sub prep-file2($path, Str:D $pfx) {
my $h = Filish[:r, :!bin]($path);
$h.lines.map($pfx.fmt('%-10s: ') ~ *)».say;
}
prep-file($?FILE, "Str");
prep-file($?FILE.IO, "IO");
prep-file($?FILE.IO.open(:a), "IO::Handle");
prep-file2($?FILE, "Str-2");
This is slightly extended example from the previous post. I have only added
CALL-ME method and prep-file2 sub. Apparently, the only significant
difference with Wenzel’s code snippet is that invocation of Filish has been
moved from the signature into the function body. I think this is perfectly OK
because one way or another it’s a runtime thingie.
LEAVE {}Just to sum up the above written, Wenzel is right when he says that coercion is about static type checking. It indeed is. For this reason it ought to be strict because this is what we expect it to be.
It is also true that there’re cases where only run-time checks make it possible to ensure that the object we work with conforms to our requirements. And this is certainly not where coercion comes into mind. This is a field of dynamic transformations where specialized routines is what we need.
By Jonathan Worthington
Christmas day, 2015. I woke up in the south of Ukraine – in the very same apartment where I’d lived for a month back in the spring, hacking on the NFG representation of Unicode. NFG was just one of the missing pieces that had to fall into place during 2015 in order for that Christmas – finally – to bring the first official release of the language we now know as Raku.
I sipped a coffee and looked out onto a snowy courtyard. That, at least, was reassuring. Snow around Christmas was relatively common in my childhood. It ceased to be the year after I bought a sledge. I opened my laptop, and took a look at the #perl6-dev IRC channel. Release time would be soon – and I would largely be a spectator.
My contributions to the Rakudo compiler had started eight years prior. I had no idea what I was getting myself into, although if I had known, I’m pretty sure I’d still have done it. The technical challenges were, of course, fascinating for somebody who had developed a keen interest in languages, compilers, and runtimes while at university. Larry designs languages with an eye on what’s possible, not on what’s easy, for the implementer. I learned, and continue to learn, a tremendous amount by virtue of working on Raku implementation. Aside from that, the regular speaking at workshops and conferences opened the door to spending some years as a teacher of software development and architecture, and after that left me with a wealth of knowledge to draw on as I moved on to focus on consultancy on developer tooling. Most precious of all, however, are the friendships forged with some of those sharing the Raku journey – which I can only imagine lasting a lifetime.
Eight years had gone by surprisingly quickly. When one is involved with the day-to-day development, the progress is palpable. This feature got done, this bug got fixed, this design decision got made, this library got written, this design decision got re-made for the third time based on seeing ways early adopters stubbed their toe, this feature got re-implemented for the fourth time because of the design change… From the outside, it all looks rather different; it’s simply taking forever, things keeping getting re-done, and the developers of it “have failed us all” (all seasons have their grinches).
Similarly, while from the outside the Christmas release was “the big moment”, from the inside, it was almost routine. We shipped a Rakudo compiler release, just as we’d been doing every month for years on end. Only this time, we also declared the specification test suite – which constitutes the official specification of the language – as being an official language release. The next month would look much like the previous ones: more bug reports arrive, more things get fixed and improved, more new users show up asking for guidance.
The Christmas release was the end of the beginning. But the beginning is only the start of a story.
It’s been five years since That Christmas. Time continues to fly by. Each week brings its advances – almost always documented in what’s currently known as the Rakudo Weekly, the successor of the Perl 6 Weekly. Some things seem constant: there’s always some new bug reports, there will always be something that’s under-documented, no matter what you make fast there will always be something else that is still slow, no matter how unlikely it seemed someone would depend on an implementation detail they will have anyway, new Unicode versions require at least some effort, and the latest release of MacOS seemingly always requires some kind of tweak. Welcome to the life of those working on a language implementation that’s getting some amount of real-world use.
Among the seemingly unending set of things to improve, it’s easy to lose sight of just how far the Raku Programming Language, its implementation in Rakudo, and the surrounding ecosystem has come over the last five years. Here I’ll touch on just a few areas worthy of mention.
Maturity of a language implementation, its tools, and its libraries, is really, really, hard won. There’s not all that many shortcuts. Experience helps, and whenever a problem can be avoided in the first place, by having somebody about with the background to know to do so, that’s great. Otherwise, it’s largely a case of making sure that when there are problems, they get fixed and something is done to try and avoid a recurrence. It’s OK to make mistakes, but making the same mistake twice is a bit careless.
The most obvious example of this is making sure that all implementation bugs have test coverage before the issue is considered resolved. However, being proactive matters too. Prior to every Rakudo compiler release, the tests of all modules in the ecosystem are run against it. Regressions are noted, and by now can even be automatically bisected to the very commit that caused the regression. Given releases take place every month, and this process can be repeated multiple times a month, there’s a good chance the developer whose work caused the regression will have it relatively fresh in their mind.
A huge number of things have been fixed and made more robust over the last five years, and there are tasks I can comfortably reach to Raku for today that I wouldn’t have five years back. Just as important, the tooling supporting the development and release process has improved too, and continues to do so.
There are around 3.5x as many modules available as there were five years ago, which means there’s a much greater chance of finding a module to do what you need. The improvement in quantity is easy enough to quantify (duh!), but the increase in quality has also had a significant impact, given that many problems we need to solve draw on a relatively small set of data formats, protocols, and so forth.
Just to pick a few examples, 5 years ago we didn’t have:
DB::Pg provides a well-engineered alternative that – at least to me – has an API that feels more natural in Raku.Given how regularly I’ve used many of these in my recent work using Raku, it’s almost hard to imagine that five years ago, none of them existed!
I was somewhat reluctant to put this in as a headline item, given that I’m heavily involved with it, but the Comma IDE for Raku has been a game changer for my own development work using the language. Granted IDEs aren’t for everyone or everything; if I’m at the command line and want to write a short script, I’ll open Vim, not fire up Comma. But for much beyond that, I’m glad of the live analysis of my code to catch errors, quick navigation, auto-complete, effortless renaming of many program elements, and integrated test runner. The timeline view can offer insight into asynchronous programs, especially Cro services, while the grammar live view comes in handy when writing parsers. While installing an IDE just for a REPL is a bit over the top, Comma also provides a REPL with syntax highlighting and auto-completion too.
Five years ago, the answer to “is there an IDE for Raku” was “uhm, well…” Now, it’s an emphatic yes, and for some folks to consider using the language, that’s important.
The Christmas release of Raku was not speedy. While just about everyone would agree there’s more work needed in this area, the situation today is a vast improvement on where we were five years ago. This applies at all levels of the stack: MoarVM’s runtime optimizer and JIT have learned a whole bunch of new tricks, calls into native libraries have become far more efficient (to the benefit of all bindings using this), the CORE setting (Raku’s standard library) has seen an incredible number of optimizations, and some key modules outside of the core have received performance analysis and optimization too. Thanks to all of that, Raku can be considered “fast enough” for a much wider range of tasks than it could five years ago.
Five years ago, Raku wasn’t called Raku. It was called Perl 6. Name change came up now and then, but was a relatively fringe position. By 2019, it had won sufficient support to take place. A year and a bit down the line, I think we can say the rename wasn’t a panacea, but nor could it be considered a bad thing for Raku. While I’ve personally got a lot of positive associations with the name “Perl”, it does carry an amount of historical baggage. One of the more depressing moments for me was when we announced Comma, and then saw many of the comments from outside of the community consist of the same tired old Perl “jokes”. At least in that sense, a fresh brand is a relief. Time will tell what values people will come to attach to it.
With software, it feels like the ideal time to start working on something is after having already done most of the work on it. At that point, the required knowledge and insight is conveniently to hand, and at least a good number of lessons wouldn’t need to be learned the hard way again.
Alas, we don’t have a time machine. But we do have an architecture that gives us a chance of being able to significantly overhaul one part of the stack at a time, so we can use what we’ve learned. A number of such efforts are now underway, and stand to have a significant impact on Raku’s next five years.
The most user-facing one is known as “RakuAST”. It involves a rewrite of the Rakudo compiler frontend that centers around a user-facing AST – that is, a document object model of the program. This opens the door to features like macros and custom compiler passes. These may not sound immediately useful to the everyday Raku user, but will enable quite a few modules to do what they’re already doing in a better way, as well as opening up some new API design possibilities.
Aside from providing a foundation for new features, the compiler frontend rewrite that RakuAST entails is an opportunity to eliminate a number of long-standing fragilities in the current compiler implementation. This quite significant overhaul is made achievable by the things that need not change: the standard library, the object system implementation, the compiler backend, and the virtual machine.
A second ongoing effort is to improve the handling of dispatch in the virtual machine, by introducing a single more general mechanism that will replace a range of feature-specific optimizations today. It should also allow better optimization of some things we presently struggle with. For example, using deferral via callsame, or where clauses in multiple dispatch, comes with a high price today (made to stand out because many other constructs in that space have become much faster in recent years). The goal is to do more with less – or at least, with less low-level machinery in the VM.
It’s not just the compiler and runtime that matter, though. The recently elected Raku Steering Council stands to provide better governance and leadership than has been achieved in the last few years. Meanwhile, efforts are underway to improve the module ecosystem and documentation.
Today, we almost take for granted much of the progress of the last five years. It’s exciting to think what will become the Raku norm in the next five. I look forward to creating some small part of that future, and especially to seeing what others – perhaps you, dear reader – will create too.
In the previous installment of this series of articles, we started with a straightforward script, and we wanted to arrive to a sound object-oriented design using Raku.
Our (re)starting point was this user story:
[US1] As a NPCC dean, given I have a list of classrooms (and their capacity) and a list of courses (and their enrollment), I want to assign classrooms to courses in the best way possible.
And we arrived to this script:
my $courses = Course-List.new( "docs/courses.csv");
my $classes = Classroom-List.new( "docs/classes.csv");
say ($classes.list Z $courses.list )
.map( { $_.map( { .name } ).join( "\t→\t") } )
.join( "\n" );
That does not really cut it, though. Every user story must be solved with a set of tests. But, well, the user story was kinda vague to start with: “in the best way possible” could be anything. So it could be argued that the way we have done is, indeed, the best way, but we can’t really say without the test. So let’s reformulate a bit the US:
[US1] As a NPCC dean, given I have a list of classrooms (and their capacity) and a list of courses (and their enrollment), I want to assign classrooms to courses so that no course is left without a classroom, and all courses fit in a classroom.
This is something we can hold on to. But of course, scripts can’t be tested (well, they can, but that’s another story). So let’s give this script a bit of class.
Actually, there’s something that does not really cut it in the script above. In the original script, you took a couple of lists and zipped it together. Here you need to call the .list method to achieve the same. But the object is still the same, right? Shouldn’t it be possible, and easy, to just zip together the two objects? Also, that begs the client of the class to know the actual implementation. An object should hide its internals as much as possible. Let’s make that an issue to solve
As a programmer, I want the object holding the courses and classrooms to behave as would a list in a “zipping” context.
Santa rubbed his beard thinking about how to pull this off. Course-List objects are, well, that precise kind of objects. They include a list, but, how can they behave as a list? Also, what’s precisely a list “in a zipping context”.
Long story short, he figured out that a “zipping context” actually iterates over every member of the two lists, in turn, putting them together. So we need to make the objects Iterable. Fortunately, that’s something you can definitely do in Raku. By mixing roles, you can make objects behave in some other way, as long as you’ve got the machinery to do so.
unit role Cap-List[::T] does Iterable;
has T @!list;
submethod new( $file where .IO.e ) {
$file.IO.lines
==> map( *.split( /","\s+/) )
==> map( { T.new( @_[0], [email protected]_[1] ) } )
==> sort( { -$_.capacity } )
==> my @list;
self.bless( :@list );
}
submethod BUILD( :@!list ) {}
method list() { @!list }
method iterator() {@!list.iterator}
With respect to the original version, we’ve just mixed in the Iterable role and implemented an iterator method, that returns the iterator on the @!list attribute. That’s not the only thing we need for it to be in “a zipping context”, however. Which begs a small digression on Raku containers and binding.
El cielo esta entablicuadrillado, ¿quién lo desentablicuadrillará? El que lo entablicuadrille, buen entablicuadrillador será. — Spanish tongue twister, loosely translated as “The sky is tablesquarebricked, who will de-trablesquarebrick it? The tablesquarebrickalyer that tablesquaresbricks it, good tablesquarebrickalyer will be.
It’s worth the while to check out this old Advent article, by Zoffix Znet, on what’s binding and what’s assignment in the Raku world. Binding is essentially calling an object by another name. If you bind an object to a variable, that variable will behave exactly the same as the object. And the other way round.
my $courses := Course-List.new( "docs/courses.csv");
We are simply calling the right hand side of this binding by another name, which is shorter and more convenient. We can call any method, and also we can put this “in a zipping context” by calling for on it:
.name.say for $courses;
Will return
Woodworking 101 Toymaking 101 ToyOps 310 Wrapping 210 Ha-ha-haing 401 Reindeer speed driving 130
As you can see, the “zipping context” is exactly the same as the (not-yet-documented) iterable context, which is also invoked (or coerces objects into, whatever you prefer) when used with for. for $courses will actually call $courses.iterator, returning the iterator of the list it contains.
This is not actually a digression, this is totally on topic. I will have to digress, however, to explain what would have happened in the case we would have used normal assignment, as in
my $boxed-courses = Course-List.new( "docs/courses.csv");
Assignment is a nice and peculiar thing in Raku. As the above mentioned article says, it boxes an object into a container. You can’t easily box any kind of thing into a Scalar container, so, Procusto style it needs to fit it into the container in a certain way. But any way you think about it, the fact is that, unlike before, $boxes-courses is not a Course-List object; it’s a Scalar object that has scalarized, or itemized, a Course-List object. What would you need to de-scalarize it? Simply calling the de-cont operator on it, $boxed-courses<>, which unwraps the container and gives you what’s inside.
OK, back to our regular schedule…r.
Again, don’t let’s just try to do things as we see fit. We need to create an issue to fix
Santa is happy to prove such a thing:
use Course-List;
use Classroom-List;
unit class Schedule;
has @!schedule;
submethod new( $courses-file where .IO.e,
$classes-file where .IO.e) {
my $courses := Course-List.new($courses-file);
my $classes := Classroom-List.new($classes-file);
my @schedule = ($classes Z $courses).map({ $_.map({ .name }) });
self.bless(:@schedule);
}
submethod BUILD( :@!schedule ) {}
method schedule() { @!schedule }
method gist {
@!schedule.map( { .join( "\t⇒\t" ) } ).join("\t");
}
Not only it schedules courses, you can simply use it by saying it. It’s also tested, so you know that it’s going to work no matter what. With that, we can close the user story.
But, can we?
Santa was really satisfied with this new application. He only needed to write this small main script:
use Schedule;
sub MAIN( $courses-file where .IO.e = "docs/courses.csv",
$classes-file where .IO.e = "docs/classes.csv") {
say Schedule.new( $courses-file, $classes-file)
}
Which was straight and to the point: here are the files, here’s the schedule. But, besides, it was tested, prepared for the unexpected, and could actually be expanded to take into account unexpected events (what happens if you can’t fit elves into a particular class? What if you need to take into account other constraints, like not filling biggest first, but filling smuggest first? You can just change the algorithm, without even changing this main script. Which you don’t really need:
raku -Ilib -MSchedule -e "say Schedule.new( | @*ARGS )" docs/courses.csv docs/classes.csv
using the command line switches for the library search path (-I) and loading a module ( -M) you can just write a statement that will take the arguments and flatten them to make them into the method’s signature.
Doing this, Santa sat down in his favorite armchair to enjoy a cup of cask-aged eggnog and watch every Santa-themed movie that was being streamed until next NPCC semester started.
Every year by the beginning of the school year, which starts by January 8th in the North Pole, after every version of the Christmas gift-giving spirit has made their rounds, Santa needs to sit down to schedule the classes of the North Pole Community College. These elves need continuous education, and they need to really learn about those newfangled toys, apart from the tools and skills of the trade.
Plus it’s a good thing to have those elves occupied during the whole year in something practical and useful, so that they don’t start to invent practical jokes and play them on each other.
Since there are over one million elves, the NPCC is huge. But there’s also a huge problem assigning courses to classrooms. Once registration for classes is open, they talk to each other about what’s the ultimate blow off class, which one gives you extra credit for winning snowball fights. So you can’t just top up enrollment: every year, you need to check the available classrooms, and then match it to the class that will be the most adequate for it.
Here are the available classrooms:
Kris, 33 Kringle, 150 Santa, 120 Claus, 120 Niklaas, 110 Reyes Magos, 60 Olentzero, 50 Papa Noël, 30
They’re named after gift-giving spirits from all over the world, with the biggest class obviously named Kringle. In any given year, this could be the enrollment after the registration period is over.
Woodworking 101, 130 Toymaking 101, 120 Wrapping 210, 40 Reindeer speed driving 130, 30 ToyOps 310, 45 Ha-ha-haing 401, 33
They love woodworking 101, because it’s introductory, and they get to keep whatever assignment they do during the year. Plus you get all the wood parings for burning in your stove, something immensely useful in a place that’s cold all year long.
So Santa created this script to take care of it, using a bit of point free programming and Perl being Perl, the whippipitude and dwimmability of the two sister languages, Perl and Raku.
sub read-and-sort( $file where .IO.e ) {
$file.IO.lines
==> map( *.split( /","\s+/) )
==> sort( { -$_[1].Int } )
==> map( { Pair.new( |@_ ) } )
}
say (read-and-sort( "classes.csv") Z read-and-sort( "courses.csv"))
.map( { $_.map( { .key } ).join( "\t→\t") } )
.join( "\n" )
The subroutine reads the file given its name, checking that it exists before, splits it by the comma, sorts it in decreasing number, and then creates a pair out of it. The other command uses the Z operator to zip the two lists together in decreasing order of elves, and produce a list just like this one:
Kringle → Woodworking 101 Santa → Toymaking 101 Claus → ToyOps 310 Niklaas → Wrapping 210 Reyes Magos → Ha-ha-haing 401 Olentzero → Reindeer speed driving 130
So the Kringle lecture hall gets woodworking, and it goes down from there. The Kris and Papa Noël classroom get nothing, having been eliminated but kept there to be used for extra-curricular activities such as carol singing and origami.
All this works well while it does: as long as you remember where’re the files, what the script did, nothing changes name or capacity, and the files are not lost. But those are a lot of ifs, and Santa is not getting any younger
As a matter of fact, not getting any older either.
So Santa and its ToyOps team will need a more systematic approach to this scheduling, by creating an object oriented application from requirements. After learning all about TWEAKS and roles, now it’s the time to stand back and put it to work from the very beginning.
The cold that pervades the North Pole makes everything a little less agile. But no worries, we can still be agile when we create something for IT operations there. First thing we need are user stories. Who wants to create a schedule and what is it? So let’s sit down and write them down.
OK, we got something to work on here, so we can apply a tiny bit of domain driven design. We have a couple of entities, classrooms and courses, and a few value objects: single classrooms and single courses. Let’s go and write them. Using Comma, of course.
Using classes for classes is only natural. But looking at the two defined classes, Santa couldn’t say which was which. At the end of the day, something with a name and a capacity is something with a name and a capacity. This begs for a factoring out of the common code, following the DRY (don’t repeat yourself) principle.
Besides, we have a prototype above that pretty much says that whatever we use for classrooms and courses, our life is going to be easier if we can sort it in the same way. So it’s probably best if we spin off a role with the common behavior. Let’s make an issue out of that. Pretty much the same as the US, but the protagonist is going to be a programmer:
Let’s call it Capped, as in having a certain capacity. Since both objects will have the self-same method, capacity, we can call it to sort them out. Our example above shows that we need to create an object out of a couple of elements, so that’s another issue
So finally our role will have all these things:
unit role Cap;
has Str $!name is required;
has Int $!capacity is required;
submethod new( Str $name, Int $capacity ) {
self.bless( :$name, :$capacity )
}
submethod BUILD( :$!name, :$!capacity ) {};
method name() { $!name }
method capacity() { $!capacity }
plus handy accessors for name and capacity, all the while keeping these privates, also implying that they are immutable. Value objects are things that simply get a value, there’s not much business logic to them.
We could already create a function that does the sorting out of a list of classrooms/courses, that is, Caps, but in OO design we should try and put into classes (from which we spawn objects) as many entities from the original problem as possible. These entities will be the ones actually doing the heavy lifting.
It would be great, again, if we could create kinda the same things, because we will able to handle them uniformly. But there’s the conundrum: one of them will contain a list of Courses, another a list of Classrooms. They both do Cap, so in principle we could create a class that hosts lists of Caps. But this controller class will have some business logic: it will create objects of that class; we can’t simply use Roles to create classes that compose them. So we will use a curried Role, a parametrized role that uses, as a parameter, the role we’ll be instantiating it with. This will be Cap-List:
unit role Cap-List[::T];
has T @!list;
submethod new( $file where .IO.e ) {
$file.IO.lines
==> map( *.split( /","\s+/) )
==> map( { T.new( @_[0], [email protected]_[1] ) } )
==> sort( { -$_.capacity } )
==> my @list;
self.bless( :@list );
}
submethod BUILD( :@!list ) {}
method list() { @!list }
This code is familiar and similar to what we’ve done above, except we’re swapping the object creation and sorting list, and we’re use .capacity to sort the list. We create a list and bless it into the object. Out of that, we create a couple of classes:
unit class Classroom-List does Cap-List[Classroom]; unit class Course-List does Cap-List[Course];
We don’t need any more logic; that’s all there is. It’s essentially the same thing, same business logic, but we’re working in a type-safe way. We have also tested the whole thing, so we’ve frozen the API and protected it from future evolution. Which Santa approves.
So we’re almost there. Let’s write the assignment function with this:
my $courses = Course-List.new( "docs/courses.csv");
my $classes = Classroom-List.new( "docs/classes.csv");
say ($classes.list Z $courses.list )
.map( { $_.map( { .name } ).join( "\t→\t") } )
.join( "\n" );
This returns the same thing as we had before. But we’ve hidden all business logic (sorting, and anything else we might want) in the object capsule.
Not actually. Assignment should also be encapsulated in some class, and thoroughly tested. That’s, however, left for another occasion.
This week’s task has an interesting solution in Raku. So, here’s the task:
You are given two strings $A and $B. Write a script to check if the given strings are Isomorphic. Print 1 if they are otherwise 0.
OK, so if the two strings are isomorphic, their characters are mapped: for each character from the first string, the character at the same position in the second string is always the same.
In the stings abc and def, a always corresponds to d, b to e, and c to f. That’s a trivial case. But then for the string abca, the corresponding string must be defd.
The letters do not need to go sequentially, so the strings aeiou and bcdfg are isomorphic too, as well as aeiou and gxypq. But also aaeeiioouu and bbccddffgg, or the pair aeaieoiuo and gxgyxpyqp.
The definition also means that the number of different characters is equal in both strings. But it also means that if we make the pairs of corresponding letters, the number of unique pairs is also the same, right? If a matches x, there cannot be any other pair with the first letter a.
Let’s exploit these observation:
sub is-isomorphic($a, $b) {
+(([==] ($a, $b)>>.chars) &&
([==] ($a.comb, $b.comb, ($a.comb Z~ $b.comb))>>.unique));
}
First of all, the strings must have the same length.
Then, the strings are split into characters, and the number of unique characters should also be equal. But the collection of the unique pairs from the corresponding letters from both strings should also be of the same size.
Test it:
use Test;
# . . .
is(is-isomorphic('abc', 'def'), 1);
is(is-isomorphic('abb', 'xyy'), 1);
is(is-isomorphic('sum', 'add'), 0);
is(is-isomorphic('ACAB', 'XCXY'), 1);
is(is-isomorphic('AAB', 'XYZ'), 0);
is(is-isomorphic('AAB', 'XXZ'), 1);
is(is-isomorphic('abc', 'abc'), 1);
is(is-isomorphic('abc', 'ab'), 0);
* * *
→ GitHub repository
→ Navigation to the Raku challenges post series
He had taken a new name for most of the usual reasons, and for a few unusual ones as well, not the least of which was the fact that names were important to him.
— Patrick Rothfuss, The Name of the Wind
If you’re a programmer, there’s a good chance that names are important to you, too. Giving variables and functions well chosen names is one of the basic tenets of writing good code, and improving the quality of names is one of the first steps in refactoring low-quality code. And if you both are a programmer and are at all familiar with Raku (renamed from “Perl 6” in 2019), then you are even more likely to appreciate the power and importance of names.
This makes the appeal of pointfree programming – which advocates for removing many of the names in your code – a bit mysterious. Given how helpful good names are, it can be hard to understand why you’d want to eliminate them.
This isn’t necessarily helped by some of the arguments put forward by advocates of pointfree programming (which is also sometimes called tacit programming). For example, one proponent of pointfree programming said:
Sometimes, especially in abstract situations involving higher-order functions, providing names for tangential arguments can cloud the mathematical concepts underlying what you’re doing. In these cases, point-free notation can help remove those distractions from your code.
That’s not wrong, but it’s also not exactly helpful; when reading that, I find myself thinking “sometimes; OK, when? in abstract situations; OK, what sort of situations?” And it seems like I’m not the only one with a similar set of questions, as the top Hacker News comment shows. Given arguments like these, I’m not at all surprised that many programmers dismiss pointfree programming in essentially the same way Wikipedia does: according to Wikipedia, pointfree programming is “of theoretical interest” but can make code “unnecessarily obscure”.
This view – though understandable – is both mistaken and, I believe, deeply unfortunate. Programming in a pointfree style can make code far more readable; done correctly, it makes code less obscure rather than more. In the remainder of this post I’ll explain, as concretely as possible, the advantage of coding with fewer names. To keep myself honest, I’ll also refactor a short program into pointfree style (the code will be in Raku, but both the before and after versions should be approachable to non-Raku programmers). Finally, I’ll close by noting a handful of the ways that Raku’s “there’s more than one way to do it” philosophy makes it easier to write clear, concise, pointfree code (if you want to).
I said before that names are important, and I meant it. My claim is the one that G.K. Chesterton (or his dog) might have made if only he’d cared about writing good code: we should use fewer names not because names are unimportant but precisely because of how important names are.
Let’s back up for just a minute. Why do names help with writing clear code in the first place? Well, most basically, because good names convey information. sub f($a, $b) may show you that you’ve got a function that takes two arguments – but it leaves you totally in the dark about what the function does or what role the arguments play. But everything is much clearer as soon as we add names: sub days-to-birthday($person, $starting-date). Suddenly, we have a much better idea what the function is doing. Not a perfect idea, of course; in particular, we likely have a number of questions of the sort that would be answered by adding types to the code (something Raku supports). But it’s undeniable that the names added information to our code.
So if adding names adds info, it’ll make your code clearer and easier to understand, right? Well, sure … up to a point. But this is the same line of thinking that leads to pages and pages of loan “disclosures”, each of which is designed to give you more information about the loan. Despite these intentions, anyone who has confronted a stack of paperwork the approximate size of the Eiffel Tower can attest that the cumulative effect of this extra info is to confuse readers and obscure the important details. Excessive names in code can fall into the same trap: even if each name technically adds info, the cumulative effect of too many names is confusion rather than clarity.
Here’s the same idea in different words: what names add to your code is not just extra info but also extra emphasis. And the thing about emphasis – whether it comes from bold, all-caps, or naming – is that it loses its power when overused. Giving everything a name is the same sort of error as writing in ALL-CAPS. Basically, don’t be this guy:
<Khassaki>: HI EVERYBODY!!!!!!!!!!
<Judge-Mental>: try pressing the Caps Lock key
<Khassaki>: O THANKS!!! ITS SO MUCH EASIER TO WRITE NOW!!!!!!!
<Judge-Mental>: f**k me
source (expurgation added, mostly to have an excuse to use the word expurgation).
I believe that the fundamental benefit of using pointfree programming techniques to write code with fewer names is that it allows the remaining names to stand out more – which lets them convey more information than a sea of names would do.
Do you understand this line of Raku code?
$fuel += $mass
Let’s imagine how a very literal programmer – we’ll call them Literal Larry – might respond. (Literal Larry is, of course, not intended to refer to Raku founder Larry Wall. That Larry may have been accused of various stylistic flaws over the years, but never of excessive literalness.)
Literal Larry might say, “Of course I understand what that line does! There’s a $fuel variable, and it’s incremented by the value of the $mass variable. Could it be any more obvious?”. But my response to Larry (convenient strawman that he is) would be, “You just told me what that line says, but not what it does. Without knowing more of the context around that line, in fact, we can’t know what that line does. Understanding that single – and admittedly simple! – line requires that we hold the context of other lines in our head. Worse, because it’s changing the value of one variable based on the value of another, understanding it requires us to track mutable state – one of the fastest ways to add complexity to a piece of code.”
And that sets up my second claim about coding in a pointfree style: It often reduces the amount of context/state that you need in your head to understand any given line of code. Pointfree code reduces the reliance on context/state in two ways: first, to the extent that we totally eliminate some named variables, then we obviously no longer need to mentally track the state of those variables. Less obviously (but arguably more importantly), a pointfree style naturally pushes you towards limiting the scope of your variables and reduces the number to keep track of at any one time. (You’ll see this in action as we work through the example below.)
Despite keeping our discussion as practical as possible, I worry that it has drifted a bit away from the realm of the concrete. Let’s remedy that by writing some actual code! I’ll present some code in a standard procedural style, refactor it into a more pointfree style, and discuss what we get out of the change.
But where should we get our before code? It needs to be decently written – my exchange with Literal Larry was probably enough strawmanning for one post, and I don’t want you to think that the refactored version is only an improvement because the original was awful. At the same time, it shouldn’t be great idiomatic Raku code, because that would mean using enough of Raku’s superpowers to reduce the code’s accessibility (I want to explain what’s going on in the after code, but don’t want to get bogged down teaching the before). It should also be just the right length – too short, and we won’t be able to see the advantages of reducing context; too long, and we won’t have space to walk through it in any detail.
Fortunately, the Raku docs provide the perfect before code: the Raku by example 101 code. This simple script is not idiomatic Raku; it’s a program that does real (though minimal) work while using only the very basics of Raku syntax. Here’s how that page describes the script’s task:
Suppose that you host a table tennis tournament. The referees tell you the results of each game in the format Player1 Player2 | 3:2, which means that Player1 won against Player2 by 3 to 2 sets. You need a script that sums up how many matches and sets each player has won to determine the overall winner.
The input data (stored in a file called scores.txt) looks like this:
Beth Ana Charlie Dave
Ana Dave | 3:0
Charlie Beth | 3:1
Ana Beth | 2:3
Dave Charlie | 3:0
Ana Charlie | 3:1
Beth Dave | 0:3The first line is the list of players. Every subsequent line records a result of a match.
I believe that the code should be legible, even to programmers who have not seen any Raku. The one hint I’ll provide for those who truly haven’t looked at Raku (or Perl) is that @ indicates that a variable is array-like, % indicates that it’s hashmap-like, and $ is for all other variables. If any of the other syntax gives you trouble, check out the full walkthrough in the docs.
Here’s the 101 version:
use v6d;
# start by printing out the header.
say "Tournament Results:\n";
my $file = open 'scores.txt'; # get filehandle and...
my @names = $file.get.words; # ... get players.
my %matches;
my %sets;
for $file.lines -> $line {
next unless $line; # ignore any empty lines
my ($pairing, $result) = $line.split(' | ');
my ($p1, $p2) = $pairing.words;
my ($r1, $r2) = $result.split(':');
%sets{$p1} += $r1;
%sets{$p2} += $r2;
if $r1 > $r2 {
%matches{$p1}++;
} else {
%matches{$p2}++;
}
}
my @sorted = @names.sort({ %sets{$_} }).sort({ %matches{$_} }).reverse;
for @sorted -> $n {
my $match-noun = %matches{$n} == 1 ?? 'match' !! 'matches';
my $set-noun = %sets{$n} == 1 ?? 'set' !! 'sets';
say "$n has won %matches{$n} $match-noun and %sets{$n} $set-noun";
}
OK, that was pretty quick. It uses my to declare 13 different variables; let’s see what it would look like if we declare 0. Before I start, though, one note: I said that the code above isn’t idiomatic Raku, and the code below won’t be either. I’ll introduce considerably more of Raku’s syntax where it makes the code more tacit, but I’ll still steer clear of some forms I’d normally use that aren’t related to refactoring the code in a more pointfree style. I also won’t make unrelated changes (e.g., removing mutable state) that I’d normally include. Finally, this code also differs from typical Raku (at least the way I write it) by being extremely narrow. I typically aim for a line length under 100 characters, but because I’d like this to be readable on pretty much any screen, these lines never go above 45.
With that throat-clearing out of the way, let’s get started. Our first step is pretty much the same as in the 101 code; we open our file and iterate through the lines.
open('scores.txt')
==> lines()
You can already see one of the key pieces of syntax we’ll be using to adopt a pointfree style: ==>, the feed operator. This operator takes the result from open('scores.txt') and passes it to lines() as its final argument. (This is similar to, but not exactly the same as, calling a .lines() method on open('scores'). Most significantly, ==> passes a value as the last parameter to the following function; calling a method is closer to passing a value as the first parameter.)
Now we’re dealing with a list of all the lines in our input file – but we don’t actually need all the lines, because some are useless (to us) header lines. We’ll solve this in basically the same way we would on the command line: by using grep to limit the lines to just those we care about. In this case, that means just those that have the ” | ” (space-pipe-space) delimiter that occurs in all valid input lines.
==> grep(/\s '|' \s/)
A few syntax notes in passing: first, Raku obviously has first-class support for regular expressions. Second, and perhaps more surprisingly, note that Raku regexes default to being _in_sensitive to whitespace; /'foo' 'bar'/ matches ‘foobar’, not ‘foo bar’. Finally, Raku regexes require non-alphabetic characters to be enclosed in 's before they match literally.
After using grep to limit ourselves to the lines we care about, we’re dealing with a sequence of lines something like Ana Dave | 3:0. Our next task is to convert these lines into something more machine readable. Since we just went over the regex syntax, let’s stick with that approach.
==> map({
m/ $<players>=( (\w+) \s (\w+) )
\s '|' \s
$<sets-won>=((\d+) ':' (\d+) )/;
[$<players>[0], $<sets-won>[0]],
[$<players>[1], $<sets-won>[1]]
})
This uses the Regex syntax we introduced above and adds a bit on top. Most importantly, we’re now naming our capture groups: we have one capture group named players that captures the two space-separated player names before the | character. (Apparently our tournament only identifies players with one-word names, a limitation that was present in the 101 code as well.) And the sets-won named capture group extracts out the :-delimited set results.
Once we’ve captured the names and scores for that match, we associate the correct scores with the correct names and create a 2×2 matrix/nested array with our results.
Actually, though, we’re not quite done with everything we want to do inside this map – we’ve given meaning to the order of the elements within each row, but the order of the rows themselves is currently meaningless. Let’s fix that by sorting our returned array so that the winner is always at the front:
[$<players>[0], $<sets-won>[0]],
[$<players>[1], $<sets-won>[1]]
==> sort({-.tail})
With this addition, our code so far is:
open('scores.txt')
==> lines()
==> grep(/\s '|' \s/)
==> map({
m/ $<players>=( (\w+) \s (\w+) )
\s '|' \s
$<sets-won>=((\d+) ':' (\d+) )/;
[$<players>[0], $<sets-won>[0]],
[$<players>[1], $<sets-won>[1]]
==> sort({-.tail})
})
At this point, we’ve processed our input lines into arrays; we’ve gone from something like Ana Dave | 3:0 to something a bit like
[ [Ana, 3],
[Dave, 0] ]
Now it’s time to start combining our separate arrays into a data structure that represents the results of the entire tournament. As in most languages these days, Raku does this with reduce (some languages call the same operation fold). We’re going to use reduce to build a single hashmap out of our list of nested arrays. However, before we can do so, we’re going to need to add an appropriate initial value to reduce onto (here, an empty Hash).
Raku gives us a solid half-dozen ways to do so – including specifying an initial value when you call reduce, much like you would in modern JavaScript. I’m going to accomplish the same thing differently, both because it’s more fun and because it lets me introduce you to 5 useful pieces of syntax in just 10 characters, which may be some sort of a record. Here’s the line:
==> {%, |$_}()
OK, there’s a lot packed in there! Let’s step through it. {...} is Raku’s anonymous block (i.e., lambda) syntax. So {...}() would normally create an anonymous block and then call it without any arguments. However, as we said above, ==> automatically passes the return value of its left-hand side as the final argument to its right-hand side. So ==> {...}() calls the block with the value that was fed into the ==>.
Since this block doesn’t specify a signature (more on that very shortly), it doesn’t have any named parameters at all; instead, any values the block is called with are placed in the topic variable – which is accessed with $_. Putting what we have so far together, we can show a complex (but succinct!) way to do nothing: ==> {$_}(). That expression feeds a value into a block, loads the value into the topic variable, and then returns it without doing anything at all.
Our line did something, however – after all, we have 4 more characters and 2 new concepts left in our line! Starting at the left, we have the % character, which you may recognize as the symbol that indicates that a variable is hash-like (Associative, if we’re being technical). On its own like this, it effectively creates an empty hash – which we could also have done with Hash.new, {}, or %(), but I like % best here. And the , operator, which we’ve already used without remarking on, combines its arguments into a list.
Here’s an example using the syntax we’ve covered so far:
[1, 2, 3] ==> {0, $_}()
That would build a list out of 0 and [1, 2, 3]. Specifically, it would build a two-element list; the second element would be the array [1, 2, 3]. That is not quite what we want, because we want to add % onto the front of our existing list instead of creating a new and more nested list.
As you may have guessed, the final character we have left – | – solves this problem for us. This Slip operator is one of my favorite bits of Raku cleverness. (Well, top 20 anyway – there are a lot of contenders!) The | operator transforms a list into a Slip, which is “a kind of List that automatically flattens”, as the docs put it. In practice, this means that Slips merge into lists instead of becoming single elements in them. To return to our earlier example,
[1, 2, 3] ==> {0, |$_}()
produces the four-element list (0, 1, 2, 3) instead of the two-element list (0, [1, 2, 3]) we got without the |.
Putting all this together, we are now in a position to easily understand the ~15 character (!) line of code we’ve been talking about. Recall that we’d just used map to transform our list of lines into a list of 2×2 matrices. If we’d printed them out, we would see something kind of like:
( [ [Ana, 3],
[Dave, 0] ],
...
)
When we feed this array into the {%, |$_}() block, we slip it into a list with the empty hash, and end up with something like:
( {},
[ [Ana, 3],
[Dave, 0] ],
...
)
With that short-but-dense line out of the way, we can proceed on to calling reduce. As in many other languages, we’ll pass in a function for reduce to use to combine our values into a single result value. We’ll do this with the block syntax we just introduced (see, taking our time on that line is already starting to pay off!). So it will look something like this:
==> reduce( {
# Block body goes here
})
Before filling in that body, though, let’s say a word about signatures (I told you it’d come up soon). As we discussed, when you don’t specify a signature for a block, all the arguments passed to the block get loaded into the topic variable $_. We can do anything we need to by manipulating/indexing into the topic variable, but that could get pretty verbose. Fortunately, we can specify the signature for a block by placing parameter names between -> and the opening {. Thus, -> $a, $b { $a + $b } is a block that accepts exactly two arguments and returns the sum of its arguments.
In our case, we know that the first argument to reduce is going to be the hash we’re building up to track the total wins in the tournament and the second will be the 2×2 array that represents the results of the next match. That gives us a signature of
==> reduce(-> %wins, @match-results {
# Block body goes here
})
So, how do we fill in the body? Well, since we previously sorted the array we’re now calling @match-results, we know that the first row contains the person who won the most sets (and therefore the match). More specifically, the first element in the first row contains that person’s name. So we want the first element of the first row – that is, the element that would be at (0, 0) if our array were laid out in 2D. Fortunately, Raku supports directly indexing into multi-dimensional arrays, so accessing this name is as simple as @match-results[0;0]. This means we can update our hash to account for the match winner with
%wins{@match-results[0;0]}<matches>++;
Handling the sets is very similar – the biggest difference is that we iterate through both rows of @match-results instead of indexing into the first row:
for @match-results -> [$name, $sets] {
%wins{$name}<sets> += $sets;
}
Note the -> [$name, $sets] signature above. This shows Raku’s strong support for destructuring assignment, another key tool in avoiding explicit assignment statements. -> [$a, $b] tells Raku that the block accepts a single array with two elements in it and assigns names to each. It’s equivalent to writing -> @array { my $a = @array[0]; my $b = @array[1]; ... }.
(And if the idea of using destructuring assignment to avoid assignment feels like cheating in terms of pointfree style, then hold that thought because we’ll come back to it when we get to the end of this example.)
At the end of our reduce block, we need to return the %wins hash we’ve been building. Putting it all together gives us
==> reduce(-> %wins, @match-results {
%wins{@match-results[0;0]}<matches>++;
for @match-results -> [$name, $sets] {
%wins{$name}<sets> += $sets;
}
%wins
})
At this point, we’ve built a hash-of-hashes that contains all the info we need; we’re done processing our input. Specifically, our hash contains keys for each of the player names in the tournament; the value of each is a hash showing that player’s total match and set wins. It looks a bit like this:
{ Ana => { matches => 2,
sets => 8 }
Dave => ...,
...
}
This contains all the information we need but not necessarily in the easiest shape to work with for generating our output. Specifically, we would like to print results in a particular order (winners first) but we have our data in a hash, which is inherently unordered. Thus – as happens so often – we need to reshape our data from the shape that was the best fit for processing our input data into the shape that is the best fit for generating our output.
Here, that means going from a hash-of-hashes to a list of hashes. We do so by first transforming our hash into a list of key-value pairs and then mapping that list into a list of hashes. In that map, we need to add the player’s name (info that was previously stored in the key of the outer hash) into the inner hash – if we skipped that step, we wouldn’t know which scores went with which players.
Here’s how that looks:
==> kv()
==> map(-> $name, %_ { %{:$name, |%_} })
I’ll note, in passing, that our map uses both destructuring assignment and the | slip operator to build our new hash. After this step, our data looks something like
( { name => "Ana",
matches => 2,
sets => 8 }
...
)
This list isn’t inherently unordered the way a hash is, but we haven’t yet put it in any meaningful order. Let’s do so now.
==> sort({.<matches>, .<sets>, .<name>})
==> reverse()
Note that this preserves the somewhat wackadoodle sort order from the original code: sort by match wins, high to low; break ties in matches by set wins; break ties in set wins by reverse alphabetical order.
At this point, we have all our output data organized properly; all that is left is to format it for printing. When printing our output, we need to use the correct singular/plural affixes – that is, we don’t want to say someone won “1 sets” or “5 set”.
Let’s write a simple helper function to handle this for us. We could obviously write a function that tests whether we need a singular or plural affix, but instead let’s take this chance to look at one more Raku feature that makes it easier to write pointfree code: multi-dispatch functions that perform different actions based on how they’re called.
The function we want should accept a key-value pair and return the singular version of the key when the associated value is 1; otherwise, it should return the plural version of the key. Let’s start by stating what all versions of our function have in common using a proto statement:
proto kv-affix((Str, Int $v) --> Str) {{*}}
A few things to know about that proto statement: This is the first time we’ve added type constraints to our code, and they work just about as you’d expect. kv-affix can only be called with a string as its first argument and an integer as its second (this protects us from calling it with the key and value in the wrong order, for example). It’s also guaranteed to return a string. Additionally, note that we can destructure using a type (Str, here) without needing to declare a variable – handy for situations like this, where we want to match on a type without needing to use the value.
Finally, note that the proto is entirely optional; indeed, I don’t think that I’d necessarily use one here. But I would have felt remiss if we didn’t discuss Raku’s support for type constraints, which is generally quite helpful in writing pointfree code (even if we haven’t really needed it today).
Next, let’s handle the case where we need to return the singular version of the key:
multi kv-affix(($_, 1)) { S/e?s$// }
As you can see, Raku lets us destructure/pattern match with literals – this version of our multi will only be invoked when kv-affix is called with 1 as its second argument. Additionally, notice that we’re destructuring the first parameter into $_, the special topic variable. Setting the topic variable not only lets us use that variable without giving it a name, but it also enables all the tools Raku reserves for the current topic. (If we want these tools without destructuring into the topic variable, we can also set the topic with with or given.)
Setting the topic to the key we’re modifying is helpful here because it lets us use the S/// non-destructive substitution operator. This operator matches a regex against the topic and then returns the string that results from replacing the matched portion of the string. Here, we match 0 or 1 e’s (e?) followed by an ‘s’, followed by the end of the string ($). We then replace that ‘s’ or ‘es’ with nothing, effectively trimming the plural affix from the string.
The final multi candidate is trivial. It just says to return the unaltered plural key when the previous multi candidate didn’t match (that is, when the value isn’t 1).
multi kv-affix(($k, $)) { $k }
(We use $ as a placeholder for a parameter when we don’t need to care about its type or its value.)
With those three lines of code, we now have a little helper function that will give us the correct singular/plural version of our keys. In all honesty, I’m not sure it was actually worth using a multi here. This might be a situation where a simple ternary condition – something like sub kv-affix(($_, $v)) { $v ≠ 1 ?? $_ !! S/e?s$// } – might have done the trick more concisely and just as clearly. But that wouldn’t have given us a reason to talk about multis, and those are just plain fun.
In any event, now that we have our helper function, formatting each line of our output is fairly trivial. Below, I do so with the venerable C-style sprintf, but Raku offers many other options for formatting textual output if you’d prefer something else.
==> map({
"%s has won %d %s and %d %s".sprintf(
.<name>,
.<matches>, kv-affix(.<matches>:kv),
.<sets>, kv-affix(.<sets>:kv )
)})
And once we’ve formatted each line of our output, the final step is to add the appropriate header, concatenate our output lines, and print the whole thing.
==> join("\n", "Tournament Results:\n")
==> say();
And we’re done.
Let’s take a look at the code as a whole and talk about how it went.
use v6d;
open('scores.txt')
==> lines()
==> grep(/\s '|' \s/)
==> map({
m/ $<players>=( (\w+) \s (\w+) )
\s '|' \s
$<sets-won>=((\d+) ':' (\d+) )/;
[$<players>[0], $<sets-won>[0]],
[$<players>[1], $<sets-won>[1]]
==> sort({-.tail}) })
==> {%, |$_}()
==> reduce(-> %wins, @match-results {
%wins{@match-results[0;0]}<matches>++;
for @match-results -> [$name, $sets] {
%wins{$name}<sets> += $sets;
}
%wins })
==> kv()
==> map(-> $name, %_ { %{:$name, |%_} })
==> sort({.<matches>, .<sets>, .<name>})
==> reverse()
==> map({
"%s has won %d %s and %d %s".sprintf(
.<name>,
.<matches>, kv-affix(.<matches>:kv),
.<sets>, kv-affix(.<sets>:kv) )})
==> join("\n", "Tournament Results:\n")
==> say();
proto kv-affix((Str, Int) --> Str) {{*}}
multi kv-affix(($_, 1)) { S/e?s$// }
multi kv-affix(($k, $)) { $k }
So, what can we say about this code? Well, at 32 lines of code, it’s longer than the 101 version (and, even though these lines are pretty short, it’s longer by character count as well). So this version doesn’t win any prizes for concision. But that was never our goal.
So how does it do on the goal we started out with – reducing assignments? Well, if we channel Literal Larry, we can say that it has zero assignment statements; it never assigns a value to a variable with my $name = 'value' or similar syntax. In contrast, the 101 code used my to assign to a variable over a dozen times. So, from a literal perspective, we succeeded.
But, as we already noted, ignoring destructuring assignment feels very much like cheating. Similarly, using named captures in a regex is essentially a form of assignment/naming. So, if we adopt an inclusive view of assignment, the 101 code has 15 assignments and our refactored code has 6. So a significant drop, but nothing like an order of magnitude difference.
But trying to evaluate our refactor by counting assignment statements is probably a fool’s errand to begin with. What I really care about – and, I suspect, what you care about too – is the clarity of our code. To some degree, that’s inherently subjective and depends on your personal familiarity and preferences – by my lights, ==> {%, |$_}() is extremely clear. Maybe, after we spent 3 paragraphs on that line, you might agree; or you might not – and I doubt anything further I could say would change your mind. So, by my lights, the refactored code looks clearer.
But clarity is not entirely a subjective matter. I argue that the refactored code is objectively clearer – and in exactly the ways the pointfree style is supposed to promote. Back at the beginning of this post, I claimed that writing tacit code has two main benefits: it provides better emphasis in your code, and it reduces the amount of context you need to hold in your head to understand any particular part of the code. Let’s look at each of these in turn.
In terms of emphasis, there’s one question I like to ask: what identifiers are in scope at the global program (or module) scope? Those identifiers receive the most emphasis; in an ideal world, they would be the most important. In the refactored code, there are no variables at all in the global scope and only one item: the kv-affix function. This function is appropriately in the global scope since it is of global applicability (indeed, it could even be a candidate to be factored out into a separate module if this program grew).
Conversely, in the 101 code the global-scope variables are $file, @names, %matches, %sets, and @sorted. At least a majority of those are pure implementation details, undeserving of that level of emphasis. And some (though this bleeds into the “context” point, discussed below) are downright confusing in a global scope. What does @names refer to, globally? How about %matches? (does it change your answer if I tell you that Match is a Raku type?) What about %sets? (also a Raku type). Of course, you could argue that these names are just poorly chosen, and I wouldn’t necessarily disagree. But coming up with good variable names is famously hard, and figuring out names that are clear in a global scope is even harder – there are simply more opportunities for conceptual clash.
To really emphasize this last point, take a look at the final line of the refactored code:
multi kv-affix(($k, $)) { $k }
If the name $k occurred in a global context, it would be downright inscrutable. It could be an iteration variable (old-school programmers tend to start with i, and then move on to j and k). It could stand for degrees Kelvin or, oddly enough, the Coulomb constant. Or it could be anything, really.
But because its scope is more limited, the meaning is clear. The function takes a key-value pair (typically generated in Raku with the .kv method or the :kv adverb) and is named kv-affix. Given those surroundings, it’s no mystery at all that $k stands for “key”. Keeping items out of the global scope both provides better emphasis and provides a less confusing context to evaluate the meaning of different names.
The second large benefit I claimed for pointfree code is that it reduces the amount of context/state you need to hold in your head to understand any given bit of code. Comparing these two scripts also supports this point. Take a look at the last line of the 101 code:
say "$n has won %matches{$n} $match-noun and %sets{$n} $set-noun";
Mentally evaluating this line requires you to know the value of $n (defined 3 lines above), $match-noun (2 lines above), $set-noun (1 line), %sets (24 lines), and %matches (25 lines). Considering how simple this script is, that is a lot of state to track!
In contrast, the equivalent portion of the refactored code is
"%s has won %d %s and %d %s".sprintf(
.<name>,
.<matches>, kv-affix(.<matches>:kv),
.<sets>, kv-affix(.<sets>:kv) )
Evaluating the value of this expression only requires you to know the value of the topic variable (defined one line up) and the pure function kv-affix (defined 3–5 lines below). This is not an anomaly: every variable in the refactored code is defined no more than 5 lines away from where it is last used.
(Of course, writing code in a pointfree style is neither sufficient nor necessary to limit the scope of variables. But as this example illustrates – and my other experience backs up – it certainly helps.)
A true devotee of pointfree programming would likely object to the refactored code on the grounds that its not nearly tacit enough. Despite avoiding explicit assignment statements, it makes fairly extensive use of named function parameters and destructuring assignment; it just isn’t pure.
Nevertheless, the refactored code sits in a pragmatic middle ground that I find highly productive: it’s pointfree enough to gain many of the clarity, context, and emphasis benefits of that style without being afraid to use a name or two when that adds clarity.
And this middle ground is exactly where Raku shines (at least in my opinion! It’s entirely possible to write Raku in a variety of different styles and many of them are not in the least bit pointfree).
Here are some of the Raku features that support pragmatic pointfree programming (most, but not all, of which we saw above):
If you’re already a Raku pro, I hope this list and this post have given you some ideas for some other ways to do it. If you’re new to Raku, I hope this post has gotten you excited to explore some of the ways Raku could expand the way you program. And if you’re totally uninterested in writing Raku code – well, I hope you’ll reconsider, but even if you don’t, I hope that this post gave you something to think about and left you with some ideas to try out in your language of choice.
Alexander Kiryuhin announced the 2020.12 Rakudo Compiler Release of the Raku Programming Language. JJ Merelo made sure there’s an up-to-date Alpine-Linux docker image, as well as a raku-test image. And Claudio Ramirez made sure there are Rakudo compiler packages for many Linux distributions. Kudos to all involved!
In related joyous news, Jonathan Worthington released a new version of the Comma Complete IDE (the paid version for subscribers only). The free Comma Community IDE version will be released in January 2021.
The third crop of this year’s Advent Posts!
If you want to stay up-to-date each day, you can also check out the full list of Raku Advent Blog Posts.
Donald Hunter has also started blogging about their Raku Advent of Code solutions. And Andrew Shitov is also still blogging about that. Meanwhile you may still want to check out Daniel Sockwell‘s leaderboard for Raku Advent Of Code solutions.
Getopt::Long by Leon Timmermans.[}:]>* by Steve Roe (/r/rakulang comments).The Request for Presentations for the DevRoom at FOSDEM 2021 is still open, but not for very long anymore! So please make sure you submit any presentations in time! In related news, the German Workshop 2021 (Leipzig, 24-26 March 2021) is also inviting you as a speaker, or just as an attendee!
Weekly Challenge #92 is available for your perusal.
curcode op inlineable (in order to recoup some of the performance loss incurred by the new coercion protocol), fixed issues with simultaneous compilation of code in multiple threads and fixed an issue with lowering signatures.(^) and made the is DEPRECATED trait introspectable for core functions.This week’s new Pull Requests:
tommath to gmpsub MAINCentOS 7 in binary release linux buildenums return Failure on bad conversion@a[Iterable]:adverbs (3x as fast)Please check them out and leave any comments that you may have!
Z+ fails unless one of the lists is forced by Julio.Scalars that aren’t really scalar? by Mark Reed.Proxy.new by JustThisGuy.AngleCosine for invocant of type Rat by Todd.NativeCall to LibX11 screen and display by Todd.Getopt::Long by Elizabeth Mattijsen.Sparrow plugins by Alexey Melezhik.On the day of the solar solstice, it is good to realize that maybe most of the dark is now behind us. Yours truly wishes everybody familiar with the Raku Programming Language an acceptable way of celebrating these festive days. Even more so, keep up the good work, stay safe and stay healthy! And see you next week for this year’s last Rakudo Weekly News!
The fact that Amiga "lost" to the PC was in my opinion not the "Amiga is a toy!". It was just as much caused by Commodore itself. The Amiga was extraordinarily mis-managed by CBM (just look what happened when they were designing the A600 to be a cheaper alternative to A500, and ended up putting so much stuff in that it launched at a *higher* price point). It was poorly marketed as well.
As for Windows not being released until 1990, that's plain wrong. Windows 3.0 was released in 1990 and was a huge success compared to earlier version. Windows 1.0 launched in 1985. But you're right if you meant to say "usable version" of Windows :-)
I think the real reason the PC won was Compaq and their reverse engineering of the BIOS, laying the groundwork for an open standard. Computing is full of examples that, given enough time, open standards win.
Let’s be serious. After all, we’re grown up people and know the full truth about Santa: he is a showman, and he is a top manager of Santa’s family business. No one knows his exact position, because we must not forget about Mrs.Santa whose share in running the company is at least equal. The position is not relevant to our story anyway. What is important though is that running such a huge venture requires a lot of skills. Not to mention that the venture itself is also a tremendous show on its own, as one can find out from documentaries like The Santa Clause and many other filmed over the last several decades of human history.
What would be the hardest part of running The North Pole Inc.? Logistics? Yeah, but with all the magic of the sleds, and the reindeers, and the Christmas night this task is not that hard to be done. Manufacturing? This task has been delegated to small outsourcing companies like Lego, Nintendo, and dozens others across the globe.
What else remains? The employees. Elves. And, gosh!, have you ever tried to organize them? Don’t even think of trying unless you have a backup in form of a padded room served by polite personnel with reliable supply of pills where you’d be spending your scarce vacation days. It’s an inhumane task because when one puts together thousands, if not millions, as some estimations tell, of ambitious stage stars (no elf would ever consider himself as a second-plane actor!), each charged with energy amount more appropriate to a small nuclear reactor… You know…
How do Santas manage? Sure, they’re open-hearted, all-forgiving beyond an average human understanding. But that’s certainly not enough to build a successful business! So, there must be a secret ingredient, something common to both commercial structures and shows. And I think it’s well done role assignment which turns the brownian motion of elf personalities into a self-organized structure.
In this article I won’t be telling how Raku helps Santas sort things out or ease certain tasks. Instead I’ll try to describe some events happening within The North Pole company with help of the Raku language and specifically its OO capabilities.
Basically, an elf is:
class Elf is FairyCreature {...}
For some reason, many of them don’t like this definition; but who are we to judge them as long, as many humans still don’t consider themselves as a kind of apes? Similarly, some elves consider fairy to be archaic and outdated and not related to them, modern beings.
But I digress…
The above definition is highly oversimplified. Because if we start traversing the subtree of the FairyCreature class we gonna find such diverse species in it like unicorns, goblins, gremlins, etc., etc., etc. Apparently, there must be something else, defining the difference. Something what would provide properties sufficiently specific to each particular kind of creature. If we expand the definition of the Elf class we gonna see lines like these:
class Elf is FairyCreature {
also does OneHead;
also does UpperLimbs[2];
also does LowerLimbs[2];
also does Magic;
...
}
I must make a confession here: I wasn’t allowed to see the full sources. When requested for access to the fairy repository the answer was: “Hey, if we reveal everything it’s not gonna look like magic anymore!” So, some code here is real, and some has been guessed out. I won’t tell you which is which; after all, let’s keep it magic!
Each line is a role defining a property, or a feature, or a behavior intrinsic to a generic elf (don’t mess it up with the spherical cow). So, when we see a line like that we say that: Elf does Magic ; or, in other words: class Elf consumes role Magic.
I apologize for not explaining in details to Raku newcomers what a role is; hopefully the link will be helpful here. For those who knows Java (I’m a dinosaur, I don’t), a role is somewhat similar to interfaces but better. It can define attributes and methods to be injected into a consuming class; it can require certain methods to be defined; and it can specify what other roles the class will consume, and other classes it will inherit from.
As a matter of fact, because of complexity of elf species, the number of roles they do is too high to mention all of them here. Normally, when a class consumes only few of them, it’s OK to write the code in another way:
class DisgustingCreature is FairyCreature does Slimy does Tentacles[13] { ... }
But numerous roles are better be put into class body with the prefix also.
This would probably be incorrect to say that elves are hired by Santas. In fact, as we know it, all of them do work for The North Pole Inc. exclusively. Yet, my thinking is such that at some point in the history the hiring did take place. Let’s try to imagine how it could have happen.
One way or another, there is a special role:
role Employee {...}
And there is a problem too: our class Elf is already composed and immutable. Moreover, each elf is an object of that class! Or, saying the same in Raku language: $pepper-minstix.defined && $pepper-minstix ~~ Elf. Ok, what’s the problem? If one tries to $pepper-minstix.hire(:position("security officer"), :company("The North Pole Inc."), ...) a big boom! will happen because of no such method ‘hire’ for invocant of type ‘Elf’. Surely, the boom! is expected because long, long time ago elves and work were as compatible as Christmas and summer! But then there was Santas. And what they did is called mixing a role into an object:
$pepper-minstix does Employee;
From the outside the operation does adds content of Employee role to the object on its left hand side, making all role attributes and methods available on the objet. Internally it creates a new class for which the original $pepper-minstix.WHAT is the only parent class; and which consumes the role Employee. Eventually, after the does operator, say $pepper-minstix.WHAT will output something like (Elf+{Employee}). This is now the new class of the object held by $pepper-minstix variable.
Such a cardinal change in life made elves much happier! Being joyful creatures anyway, they now got a great chance to be also useful by sharing their joy with all the children, and sometimes not only children. The only thing worried them though. You know, it’s really impossible to find two identical people; the more so there’re no two identical elves. But work? Wouldn’t it level them all down in a way? Santas wouldn’t be the Santas if they didn’t share these worries with their new friends. To understand their solution let’s see what Employee role does for us. Of the most interest to us are the following lines:
has $.productivity; has $.creativity; has $.laziness; has $.position; has $.speciality; has $.department; has $.company;
For simplicity, I don’t use typed attributes in the snippet, though they’re there in the real original code. For example, $.lazyness attribute is a coefficient among other things used in a formula calculating how much time is spent for coffee or eggnog breaks. The core of the formula is something like:
method todays-cofee-breaks-length {
$.company.work-hours * $.laziness * (1 + 0.2.rand)
}
Because they felt their responsibility for the children, elves agreed to limit their maximum laziness level. Therefore the full definition of the attribute is something like:
has Num:D $.laziness where * < 0.3;
If anybody thinks that the maximum is too high then they don’t have the Christmas spirit in their hearts! Santa Claus was happy about it, why wouldn’t we? I personally sure his satisfaction is well understood because his own maximum is somewhere closer to 0.5, but – shh! – let’s keep it a secret!
Having all these characteristics in place, Santas wanted to find a way to set them to as diverse combinations, as possible. And here is what they came up with something similar to this:
role Employee {
...
method my-productivity {...}
method my-creativity {...}
method my-laziness {...}
submethod TWEAK {
$!productivity //= self.my-productivity;
$!creativity //= self.my-creativity;
$!laziness //= self.my-laziness;
}
}
Now it was up to an elf to define their own methods to set the corresponding characteristics. But most of them were OK with a proposed special role for this purpose:
role RandomizedEmployee {
method my-productivity { 1 - 0.3.rand }
method my-creativity { 1 - 0.5.rand }
method my-laziness { 1 - 0.3.rand }
}
The hiring process took the following form now:
$pepper-minstix does Employee, RandomizedEmployee;
But, wait! We have three more attributes left behind! Yes, because these were left up to Santas to fill in. They knew what kind of workers and where they needed most. Therefore the final version of the hiring code was more like:
$pepper-minstix does Employee(
:company($santas-company),
:department($santas-company.department("Security")),
:position(HeadOfDepartment),
:speciality(GuardianOfTheSecrets),
...
),
RandomizedEmployee;
With this line the Raku’s mixin protocol does the following:
TWEAKBecause everybody knew that the whole thing is going to be a one-time venture as elves would never leave their new boss alone, the code was a kind of trade-off between efficiency and speed of coding. Still, there were some interesting tricks used, but discussing them is beyond this story mainline. I think many readers can find their own solutions to the problems mentioned here.
Me, in turn, moves on to a story which took place not long ago…
It was one of those craziest December days, when Mrs.Santa left for an urgent business trip. Already busy with mails and phone calls Mr.Santa got additional duties in the logistics and the packing departments which are usually handled by his wife. There were no way he could skip those or otherwise risks of something going wrong on the Christmas night would be too high. The only way to get everywhere on time was to cut on the phone calls. It meant to tell the elf-receptionist to answer with the “Santa is not available” message.
Santa sighed. He could almost see and hear the elf staring at him with deep regret and asking: “Nicholas, are you asking me to lie?” Oh, no! Of course he wouldn’t ask, but…
But? but! After all, even if the time/space magic of the Christmas is not available on other days of year, Santa can still do other kind of tricks! So, here is what he did:
role FirewallishReceptionist {
has Bool $.santa-is-in-the-office;
has Str $.not-available-message;
method answer-a-call {
if $.santa-is-in-the-office {
self.transfer-call: $.santas-number;
}
else {
self.reply-call: $.not-available-message,
:record-reply,
:with-marry-christmas;
}
}
}
my $strict-receptionist =
$receptionist but FirewallishReceptionist(
:!santa-is-in-the-office,
:not-available-message(
"Unfortunately, Santa is not available at the moment."
~ ... #`{ the actual message is longer than this }
)
);
$company.give-a-day-off: $receptionist;
$company.santa-office-frontdesk.assign: :receptionist($strict-receptionist);
The operator but is similar to does, but instead of altering its left hand side operand its clone is created and then mixes in the right hand side role into the clone.
Just imagine the amazement of the receptionist when he saw his own copy taking his place at his desk! But a day off is a day off, he wasn’t really much against applying his laziness coefficient to the rest of that day…
As to Santa himself… He has never been really proud of what he done that day. Even though it was needed in the name of saving the Christmas. Besides, the existence of a clone created a few awkward situations later, especially when both elves were trying to do the same work while still sharing some data structures. But that’s a separate story on its own…
Have you seen the elves this season? They’re always very strict in sticking to the latest tendencies of Christmas fashion: rich colors, spangles, all fun and joy! Yet this year is really something special!
It all started at the end of the spring. Santa was sitting in his chair, having well-deserved rest of the last Christmas he served. The business did not demand as much attention as it usually does at the end of the autumn. So, he was sitting by the fireplace, drinking chocolate, and reading the news. Though the news was far from being the best part of Santa’s respite (no word about 2020!). Eventually, Santa put away his tablet, made a deep sip from his giant mug, and said aloud: “Time to change their caps!” No idea what pushed him to this conclusion, but from this moment on elves knew that a new fashion is coming!
The idea Santa wanted to implement was to add WiFi connection and LEDs to elvish caps, and to make the LEDs twinkle with patterns available from a local server of The North Pole Inc. Here is what he started with:
role WiFiConnect {
has $.wifi-name is required;
has $.wifi-user is required;
has $.wifi-password is required;
submethod TWEAK {
self.connect-wifi( $!wifi-name, $!wifi-user, $!wifi-password );
}
}
role ShinyLEDs {
submethod TWEAK {
if self.test-cirquits {
self.LED( :on );
}
if self ~~ WiFiConnect {
self.set-LED-pattern: self.fetch( :config-key<LED-pattern> );
}
}
}
class ElfCap2020 is ElfCap does WiFiConnect does ShinyLEDs {...}
Note, please, that I don’t include the body of the class here for it’s too big for this article.
But the attempt to compile the code resulted in:
Method 'TWEAK' must be resolved by class ElfCap2020 because it exists in multiple roles (ShinyLEDs, WiFiConnect)
“Oh, sure thing!” – Santa grumbled to himself. And added a TWEAK submethod to the class:
submethod TWEAK {
self.R1::TWEAK;
self.R2::TWEAK;
}
This made the compiler happy. and ElfCap2020.new came up with a new and astonishingly fun cap instance! “Ho-ho-ho!” – Santa couldn’t help laughing of joy. It was the time to start producing the new caps for all company employees; and this was the moment when it became clear that mass production of the new cap will require coordinated efforts of so many third-party vendors and manufacturers that there were no way to equip everybody with the new toy by the time the Christmas comes.
Does Santa give up? No, he never does! What if we try to modernize the old caps? It would only require so many LEDs and controllers and should be feasible to handle on time!
Suit the action to the world! With a good design it should be no harder than to:
$old-cap does (WiFiConnect(:$wifi-name, :$wifi-user, :$wifi-password), ShinyLEDs);
And… Boom! Method 'TWEAK' must be resolved by class ElfCap+{WiFiConnect,ShinyLEDs} because it exists in multiple roles (ShinyLEDs, WiFiConnect)
Santa sighed. No doubt, this was expected. Because does creates an implicit empty class the two submethods from both roles clash when compiler tries to install them into the class. A deadend? No way! Happy endings is what Santa loves! And he knows what to do. He knows that there is a new version of the Raku language is in development. It is not released yet, but is available for testing with Rakudo compiler if requested with use v6.e.PREVIEW at the very start of a compilation unit which is normally is a file.
Also, Santa knows that one of the changes the new language version brings in is that it keeps submethods where they were declared, no matter what. It means that where previously a submethod was copied over from a role into the class consuming it, it will now remain be the sole property of the role. And the language itself now takes care of walking over all elements of a class inheritance hierarchy, including roles, and invoking their constructor and/or destructor submethods if there’re any.
Not sure what it means? Check out the following example:
use v6.e.PREVIEW;
role R1 {
submethod TWEAK { say ::?ROLE.^name, "::TWEAK" }
}
role R2 {
submethod TWEAK { say ::?ROLE.^name, "::TWEAK" }
}
class C { };
my $obj = C.new;
$obj does (R1, R2);
# R1::TWEAK
# R2::TWEAK
Apparently, adding use v6.e.PREVIEW at the beginning of the modernization script makes the $old-cap does (WiFiConnection, ShinyLEDs); line work as expected!
Moreover, switching to Raku 6.e also makes submethod TWEAK unnecessary for ElfCap2020 class too if its only function is to dispatch to role TWEAKs. Though, to be frank, Santa kept it anyway as he needed a few adjustments to be done at the construction time. But the good thing is that it wasn’t necessary for him to worry that much about minor details of combining all the class components together.
And so the task was solved. At the first stage all the old caps were modernized and made ready before the season started and Christmas preparations took all remaining spare time of The North Pole Inc. company. The new caps will now be produced without extra fuss and be ready by the 2021 season. The time spared Santa used to adapt WiFiConnection and ShinyLEDs to use them with his sleds too. When told by The Security Department that additional illumination makes sled’s camouflaging much harder if ever possible Santa only shrugged and replied: “You’ll manage, I have my trust in you!” And they did, but that’d be one more story…
When it comes to The North Pole it’s always hard to tell the truth from fairy tales, and to separate magic from the science. But, after all, a law of nature tells it that any sufficiently advanced technology is indistinguishable from magic. With Raku we try to bring a little bit of good magic into this life. It is so astonishing to know that Raku is supported by nobody else but the Santa family themselves!
Merry Christmas and Happy New Year!
Today there’s a chance to demonstrate powerful features of Raku on the solution of Day 18 of this year’s Advent of Code.
The task is to print the sum of a list of expressions with +, *, and parentheses, but the precedence of the operations is equal in the first part of the problem, and is opposite to the standard precedence in the second part.
In other words, 3 + 4 * 5 + 6 is (((3 + 4) * 5) + 6) in the first case and (3 + 4) * (5 + 6) in the second.
Here is the solution. I hope you are impressed too.
use MONKEY-SEE-NO-EVAL;
sub infix:<m>($a, $b) { $a * $b }
say [+] ('input.txt'.IO.lines.race.map: *.trans('*' => 'm')).map: {EVAL($_)}
The lines with the expressions come from the file input.txt. For each line, I am replacing * with m, which I earlier made an infix operator that actually does multiplication.
For the second part, we need our m to have lower precedence than +. There’s nothing simpler:
sub infix:<m>($a, $b) is looser<+> { $a * $b }
Parsing and evaluation are done using EVAL.
* * *
→ Browse the code on GitHub
→ See all blog posts on Advent of Code 2020
Santa has been fretting about the most concise way to use his personal emoticon [}:]>* programatically in a raku one-liner.
The best he can do is…
raku -e 'sub santa($x is copy){$x~~s/ <[}:]>* /claus/; $x.say}; santa(":")'
#OUTPUT claus
Can you help him? If so – please send your version via the Comments field below.
The rules are:
(i) to use raku (the language formerly known as perl6), perl5 and other languages will be considered too
(ii) to use the character sequence [}:]>* (or reversed, no spaces)
(iii) these characters must have semantic meaning in the code (ie. to just as a comment)
(iv) it should be a one-liner that uses raku -e on the command line
The objective criteria is: shortest count of chars between the ”
Kudos will also be given to the overall Christmassy-ness … in the example, a call to ‘santa’ creates output ‘claus’
Merry Christmas to one and all (and any)
PS. Code golf is very, very naughty, so be sure not to do this in your day job!
Just another day before Christmas and one more great Raku Advent Calendar article: Day 14: Writing Faster Raku code, Part I. Nothing foreshadowed a surprise until I stumbled over a statement:
But objects in are slow, so I use a nested array.
However surprising it might be to the most of my readers, but the statement is wrong. Though not this alone but also the fact that in the article where the author, Wim Vanderbauwhede, meticulously tests nearly every alternative, this particular one remains unproved. I couldn’t pass by this time and had to bust the myth. And here it goes.
First, let’s define the challenge. The statement says that a tree represented as
a Raku array outperforms an object representation. In turn I state that: a
properly defined and used class outperforms an Array with the current
implementation of Rakudo compiler.
The last part is an important constraint because the situation may change any
time with more optimizations implemented. But the current state of things can be
explained with some help of my article Everything Is An
Object. Arrays are no
exception. Every time we write something like @a[0] we use a shortcut to
@a.AT-POS(0). Or, it would be more correct to say that we invoke a sub
implementing postcircumfix operator [] and the code behind the scene is
&postcircumfix:<[ ]>(@a, 0). It is the &postcircumfix:<[ ]> sub which
eventually calls AT-POS for us.
One may ask: why? The answer is the abstraction level which allows us to do things like:
class Foo {
method AT-POS($i) { "**$i**" }
}
say Foo.new[42]; # **42**
While this example on its own is rather primitive, the real life use allows to simplify a lot of use cases by hiding boilerplate code behind simple operations. In turn, this leads to less error-prone code. After all, it’s a trade-off we sometimes make.
BTW, I still remember the times when for real high performance one used to inline assembler code into their C program!
Besides, it could be one of you, who would eventually implement the optimizations which will skyrocket the speeds of arrays and hashes. Once set your foot into the land of the core sources and there will likely be no return…
Anyhow, I made my statement. It is now time to prove it.
There was an easy path. I could simply define a tree manually, create two different representations of it… Nah! Boring, boring, boring… Besides, the original article was about parsing Fortran sources. I haven’t written a Raku grammar for too long and felt like needing some refreshment in the area.
Eventually, my task list got the following shape:
The grammar took the most of my time. No, I wasn’t writing a Fortran parser. Not even close. Not even close to a fully functional expression parser. Just something sufficient to build an AST tree. And yet I was doing all kinds of mistakes and errors. Anyway, it was fun! Raku grammars are something special.
The I prepared three action classes: one to form an array tree representation,
one based on ExprNode class, and one dummy which has all the statements, the
two other actions has, but builds nothing. The latter serves as the baseline to
measure the time it takes the compiler to handle array- and object-based data
structures.
The last part is to actually do the benchmarking. Sounds simple: take the start time, run the code, measure the duration. But the thing I learned with my laptop is about throttling: don’t trust the measurements taken on two consequent runs. Apparently, the most common case is when a busy benchmark causes CPU overheating. Consequently, the second and any following benchmark is getting less computing power due to the lowered CPU frequency.
Even on systems where throttling or other techniques of manipulating CPU/bus frequencies are not used outcomes of sequentially ran benchmarks might be affected by parallel processes or any other events taking place in a concurrent environment.
For quite some time now I usually run different versions of code in parallel simultaneously to equalize the conditions they’re coping with. Roughly, it means doing something like:
my $test-num = 0;
for @tests -> &test-code {
++$test-num;
start {
my $st = now;
&test-code;
say "duration #$test-num: ", now - $st;
}
}
As usual, there is a catch: neither OS nor Raku is giving no warranty as to how
soon a new thread will start. Reporting $st for each test may reveal delays
sufficient for the throttling or anything else to step in and cause biased
timings. Solution? Basically, any race give it to us: a starting pistol! Or a
kind of.
The common scenario is for supervisor code to make sure that all
contesters are ready and give them a signal to start. In Raku my favourite tool
to implement this is Promise:
my @ready;
my @done;
my $supervisor = Promise.new;
for @tests -> &test-code {
my $test-ready = Promise.new;
@ready.push: $test-ready;
@done.push: start {
$test-ready.keep;
await $supervisor;
... # Benchmarking happens here
}
}
await @ready;
$supervisor.keep;
await @done;
This way we make sure that all parallel benchmarks start at the same time, give or take. Apparently, this doesn’t ensure 100% synchronicity, but it’s as close to the goal as possible.
Because I want to do two benchmarks - one for parse, one for traverse, - the script will need two synchronizations of the kind.
Here is what I came up with. Don’t use it as a sample of a good coding style as it’s more of a sample of “gimme the result, now!” style.
grammar SimpleExpr {
rule TOP {
''
| <expr>
}
rule expr {
$<lhs>=<term> [ <infix> $<rhs>=<expr> ]?
}
token term {
| <value>
| '(' ~ ')' <expr>
| <prefix>? <term>
}
token value {
\d+
}
proto token prefix {*}
token prefix:sym<-> { <.sym> }
token prefix:sym<+> { <.sym> }
proto token infix {*}
token infix:sym<+> { <.sym> }
token infix:sym<-> { <.sym> }
token infix:sym<*> { <.sym> }
token infix:sym</> { <.sym> }
}
constant EMPTY = 0;
constant VAL = 1;
constant PFX_NEG = 2;
constant PFX_NOP = 3;
constant IFX_ADD = 4;
constant IFX_MIN = 5;
constant IFX_MUL = 6;
constant IFX_DIV = 7;
class ArrayTree {
method TOP($/) {
make $/<expr> ?? $/<expr>.ast !! [EMPTY]
}
method term($/) {
if $<expr> {
make $<expr>.ast;
}
elsif $<prefix> {
make [$<prefix>.ast, $<term>.ast];
}
else {
make [VAL, $<value>.ast]
}
}
method value($/) {
make $/.Int
}
method prefix:sym<+>($/) { make PFX_NOP }
method prefix:sym<->($/) { make PFX_NEG }
method infix:sym<+>($/) { make IFX_ADD }
method infix:sym<->($/) { make IFX_MIN }
method infix:sym<*>($/) { make IFX_MUL }
method infix:sym</>($/) { make IFX_DIV }
method expr($/) {
if $<infix> {
make [$<infix>.ast, $<lhs>.ast, $<rhs>.ast]
}
else {
make $<lhs>.ast
}
}
}
class ExprNode {
has $.code;
has $.lhs;
has $.rhs;
method ops {
with $!rhs {
$!lhs, $!rhs
}
else {
$!lhs,
}
}
}
class NodeTree {
method TOP($/) {
make $/<expr> ?? $/<expr>.ast !! ExprNode.new(:code(EMPTY))
}
method term($/) {
if $<expr> {
make $<expr>.ast;
}
elsif $<prefix> {
make ExprNode.new(:code($<prefix>.ast), :lhs($<term>.ast));
}
else {
make ExprNode.new(:code(VAL), :lhs($<value>.ast));
}
}
method value($/) {
make $/.Int
}
method prefix:sym<+>($/) { make PFX_NOP }
method prefix:sym<->($/) { make PFX_NEG }
method infix:sym<+>($/) { make IFX_ADD }
method infix:sym<->($/) { make IFX_MIN }
method infix:sym<*>($/) { make IFX_MUL }
method infix:sym</>($/) { make IFX_DIV }
method expr($/) {
if $<infix> {
make ExprNode.new(:code($<infix>.ast), :lhs($<lhs>.ast), :rhs($<rhs>.ast));
}
else {
make $<lhs>.ast
}
}
}
class DummyTree {
method TOP($/) {
make EMPTY
}
method term($/) {
if $<expr> {
make $<expr>.ast;
}
elsif $<prefix> {
make PFX_NOP;
}
else {
make VAL;
}
}
method value($/) {
make $/.Int
}
method prefix:sym<+>($/) { make PFX_NOP }
method prefix:sym<->($/) { make PFX_NEG }
method infix:sym<+>($/) { make IFX_ADD }
method infix:sym<->($/) { make IFX_MIN }
method infix:sym<*>($/) { make IFX_MUL }
method infix:sym</>($/) { make IFX_DIV }
method expr($/) {
if $<infix> {
make $<infix>.ast;
}
else {
make $<lhs>.ast
}
}
}
my $bench_expr = '(-3*((10+12)/13))*((100-11*(3+(4/-2)))/(123-+23))';
sub parser(Mu $actions is raw, $repeats) {
my $ast;
my $st = now;
for ^$repeats {
$ast = SimpleExpr.parse($bench_expr, :$actions).ast;
}
my $et = now;
($et - $st), $ast
}
proto traverse($,$) {*}
multi traverse(Positional:D $ast, $repeats) {
my sub _trvs(@node) {
my $count = 1;
for 1,2 -> $idx {
with @node[$idx] {
$count += _trvs($_) if $_ ~~ Positional && $_[0] != VAL;
}
}
$count
}
my $st = now;
for ^$repeats {
my $count = _trvs($ast);
}
now - $st
}
multi traverse(ExprNode:D $ast, $repeats) {
my sub _trvs($node) {
my $count = 1;
for $node.ops -> $op {
$count += _trvs($op) if $op ~~ ExprNode && $op.code != VAL;
}
$count
}
my $st = now;
for ^$repeats {
my $count = _trvs($ast);
}
now - $st
}
my @stage1_ready;
my @stage2_ready;
my @bench_done;
my $supervisor_stage1 = Promise.new;
my $supervisor_stage2 = Promise.new;
for ArrayTree, NodeTree, DummyTree -> $actions is raw {
my $ready1 = Promise.new;
my $ready2 = Promise.new;
@stage1_ready.push: $ready1;
@stage2_ready.push: $ready2;
@bench_done.push: start {
$ready1.keep;
await $supervisor_stage1;
my ($duration, $ast) = parser($actions, 10000);
say $actions.^name, " parse: ", $duration;
$ready2.keep;
await $supervisor_stage2;
unless $ast ~~ Int {
$duration = traverse($ast, 100000);
say $actions.^name, " traverse: ", $duration;
}
}
}
await @stage1_ready;
$supervisor_stage1.keep;
await @stage2_ready;
$supervisor_stage2.keep;
await @bench_done;
I tried to reproduce a structure akin to what Wim is using for his task:
The grammar parses expressions without any respect paid to operator priorities,
except for prefix ops - and + which are tightly bound to the term they
precede.
The expression used in the script is totally random and meaningless. Something I just typed in to make a long one.
Code in action classes is intentionally being kept as identical as it’s only
possible to make the time spent on code unrelated to the data manipulations as
identical as possible. For this reason DummyTree still uses make even though
it’s meaningless in the context of this action class. It serves both the purpose
of maintaining the probability of code inlining at the same level as for
ArrayTree and NodeTree; and for the purpose of including the time it takes to
call make into the final duration.
There is no traversal for DummyTree case because there nothing to traverse.
For this reason traversal benchmarks of ArrayTree and NodeTree can only be
compared one to another without attempt to calculate the baseline timing.
Traversal code simply counts a number of nodes it encounters. This is to make it possible to check if both algorithms do walk the whole tree by comparing the counts.
I did several runs of the script. A typical outcome looks like this:
DummyTree parse: 11.1933962
NodeTree parse: 11.6332386
ArrayTree parse: 11.8440641
NodeTree traverse: 4.54844838
ArrayTree traverse: 6.26943534
I saw a couple of results where parsing into an array was even a little tiny bit
faster than parsing into the class. But overall tendency is class approach
beating the array by approximately 2% on parsing by measuring the overall code
run time. This is equivalent to pure data processing time needed to handle the
ExprNode objects 20-50% less then time spent on handling arrays.
Yet, though there is no clear way to measure the time spent on non-data
processing code for traversal, the results here are even more spectacular as the
sub traversing the ExprNode tree is about 30% faster than array traversal. And
this number is more or less consistent over the runs.
Aside from being faster, use of a node class also allows us to define some nice
well-incapsulated API to work with tree nodes. For example, while debugging I
wanted nice-looking output of the tree structure. So, I came up with a
gist method to print a subtree under a node:
method gist {
given $!code {
when EMPTY { '' }
when VAL { ~$!lhs }
when PFX_NOP { $!lhs.gist }
when PFX_NEG {
"[" ~ $_ ~ "]"
~ "-" ~ ($!lhs ~~ ExprNode
?? "\n" ~ $!lhs.gist.indent(2)
!! $!lhs)
}
when IFX_ADD..IFX_DIV {
"[" ~ $_ ~ "]"
~ <+ - * />[$_ - IFX_ADD]
~ "\n" ~ $!lhs.gist.indent(2)
~ "\n" ~ $!rhs.gist.indent(2)
}
}
}
And here is what it gives me with say $ast for the expression I used for benchmarking:
[6]*
[6]*
[2]-
3
[7]/
[4]+
10
12
13
[7]/
[5]-
100
[6]*
11
[4]+
3
[7]/
4
[2]-
2
[5]-
123
23
For a real parsing task an AST node object would provide the best means to implement functional API around it.
Not much to say here. Just don’t let the urban legends to drive you and choose your tools carefully.
When I started covid.observer about seven months ago, I thought there would be no need to update it after about 3-4 months. In reality, we are approaching to the end of the year, and I will have to fix the graphs which display data per week, as the week numbers will very soon make a loop.
All this time, more data arrived, and I also made it even more by adding a separate statistics for the regions of Russia, with its 85 subdivisions, which brought the total count of countries and regions up to almost 400.
mysql> select count(distinct cc) from totals; +--------------------+ | count(distinct cc) | +--------------------+ | 392 | +--------------------+ 1 row in set (0.00 sec)
Due to frequent updates that changes data in the past, it is not that easy to make incremental update of statistics, and again, I did not expect that I’ll run the site for so long.
mysql> select count(distinct date) from daily_totals; +----------------------+ | count(distinct date) | +----------------------+ | 329 | +----------------------+ 1 row in set (0.00 sec)
The bottom line is that daily generation became clumsy and not smooth. Before summer, the whole website could be regenerated in less than 15 minutes, but now it turned to 40-50 minutes. And I tend to do it twice a day, as a fresh portion of today’s Russian data arrives a few hours after we’ve got a daily update by the Johns Hopkins University (for yesterday’s stats).
But the most scary signals began after the program started crashing with quite unpleasant errors.
Latest JHU data on 12/12/20 Latest RU data on 12/13/20 Generating impact timeline... Generating world data... MoarVM panic: Unable to initialize event loop
Failed to open file /Users/ash/Projects/covid.observer/COVID-19/csse_covid_19_data/csse_covid_19_daily_reports_us/12-10-2020.csv: Too many open files
Generating impact timeline... Generating world data... Not enough positional arguments; needed at least 4 in sub per-capita-data at /Users/ash/Projects/covid.observer/lib/CovidObserver/Statistics.rakumod (CovidObserver::Statistics) line 1906
The errors were not consistent, and I managed to re-run the program by pieces to get the update. But none of the errors were easily explainable.
MoarVM panic gives no explanation, but it completely disappears if I run the program in two parts:
$ ./covid.raku fetch $ ./covid.raku generate
instead of a combined run that both fetches the data and generates the statistics:
$ ./covid.raku update
The Too many open files is a strange one as while I process the files in loops, I do not intentionally keep them open. But that error seems to be solved by changing system settings:
$ ulimit -n 10000
The final error, Not enough positional arguments; needed at least 4, is the weirdest. Such thing happens when you call a function that expects a different number of arguments. That never occurred for months after all bugs were found and fixed. It can only be explained by the new piece of data. Indeed, it may happen that some data is missing, but I believe I already found all the cases where I need to provide the function calls with default zero values.
Having all that, and the fact that the program run takes dozens of minutes before you can catch an error, it was quite frustrating.
And here comes Liz!
She proposed to look into the things and actually spent the whole day by first installing the code and all its requirements and then by actually doing that job to run, debug, and re-run. By the end of the day she created a pull request, which made the program twice as fast!
Let’s look at the changes. There are three of them (but no, they do not directly answer the above-mentioned three error messages).
The first two changes introduce parallel processing of countries (remember, there are about 400 of what is considered a unique $cc in the program).
my %country-stats = get-known-countries<>.race(:1batch,:8degree).map: -> $cc {
$cc => generate-country-stats($cc, %CO, :%mortality, :%crude, :$skip-excel)
}
Calling .race on the result of get-known-countries() function improves the previously sequential processing of countries. Indeed, their stats are computed independently, so there’s no reason for one country to wait for another. The parameters of race, the batch size and the number of workers, can probably be tuned to fit your hardware.
The second change is similar, but for another part of the code where the continents are processed in a loop:
for %continents.keys.race(:1batch,:8degree) -> $cont {
generate-continent-stats($cont, %CO, :$skip-excel);
}
Finally, the third change is to make some counters native integers instead of Raku Ints:
my int $c = $confirmed[$index] // 0; my int $f = $failed[$index] // 0; my int $r = $recovered[$index] // 0; my int $a = $active[$index] // 0;
I understand that this reduces both the memory and the processing time of these variables, but for some reason it also eliminated the error in counting function parameters.
And finally, I want to mention the <> thing that you may have noticed in the first code change. This is the so-called decontainerization operator. What it does is illustrated by this example from the documentation:
use JSON::Tiny;
my $config = from-json('{ "files": 3, "path": "/home/some-user/raku.pod6" }');
say $config.raku;
# OUTPUT: «${:files(3), :path("/home/some-user/raku.pod6")}»
my %config-hash = $config<>;
say %config-hash.raku;
# OUTPUT: «{:files(3), :path("/home/some-user/raku.pod6")}»
The $config variable is a scalar variable that keeps a hash. To work with it as with a hash, the variable is decontainerized as $config<>. This gives us a proper hash %config-hash.
I think that’s it for now. The main advantage of the above changes is that the program now needs less than 25 minutes to re-generate the whole site and it does not fail.
Well, but it became a bit louder too as Rakudo uses more cores 
Thanks, Liz!
Ogniloud has added a number of Raku sections on Wikipedia, specifically to the Iterator and Iterator pattern lemmas. What a great idea! Yours truly is hoping to see more of these high quality additions to Wikipedia highlighting Raku features (/r/rakulang comments)!
The second crop of this year’s Advent Posts!
NativeCall by Ramiro Encinas (/r/rakulang comments).TWEAKs with a Class by Tom Browder (/r/rakulang comments).If you want to stay up-to-date each day, you can also check out the full list of Raku Advent Blog Posts.
And you might want to check out Daniel Sockwell‘s leaderboard for Raku Advent Of Code solutions. And Andrew Shitov is blogging about their Raku Advent Of Code solutions (/r/rakulang comments).
return by Wenzel P.P. Peppmeyer.Sequence by 0racle.
by alatennaub.The Request for Presentations for the DevRoom at FOSDEM 2021 is still open.
Weekly Challenge #91 is available for your perusal.
gather / take with next / redo in loops on the JVM backend.@a[*-1]) about 60% faster.This week’s new Pull Requests:
clang static analyzer found.contains and .index.of works on type objects alwaysexec instead of system and then exiting with the return codePlease check them out and leave any comments that you may have!
$x.^ by Ali.invmod by Kang-min Liu.async keyword? by Ralph Mellor..cache needs to go by Endorphion.In just about the darkest week on the northern side of the planet, it is good to see so many inspirational developments around the Raku Programming Language. Keep up the good work, stay safe and stay healthy! And see you next week!
Hi, here’s my Raku breakfast with the solutions of Week 91 of The Weekly Challenge.
You are given a positive number $N. Write a script to count the number of digits and display as you read it.
Example:
Input: $N = 1122234
Output: 21321314
as we read "two 1 three 2 one 3 one 4"OK, so we actually don’t have to spell the number of repetitions as I originally thought (and solved ) but we just need to print the number as a digit (yes, I think we are OK to not go beyond 9 repetitions of a digit).
And so, here’s the whole program:
unit sub MAIN(Int $n = 1122234); print .Str.chars ~ .[0] for $n ~~ m:g/ (\d) $0* /; say '';
Maybe we even can gain the line by replacing $n with @*ARGS[0], but you also may think of how to get rid of the last line that makes the output cleaner.
In the regex, I match a single digit (\d) and then try to find more copies of it: $0*. As there’s a :g adverb, the regex will be applied as many times as needed to cover the whole input.
Inside the main loop, there’s an interesting case: we have a Match object as the topic variable, and I am calling .Str.chars and .[0] on it. This is probably the best illustration of when you explicitly call subscripting brackets as a method.
Here is the output for a couple of inputs:
$ raku ch-1.raku 21321314 $ raku ch-1.raku 12333345556 111243143516
After I looked at Stuart Little’s solution, I realised that I forgot about map existence again. So, here’s an updated solution that eliminates the problem with printing the final newline:
unit sub MAIN(Int $n = 1122234);
($n ~~ m:g/ (\d) $0* /).map({.Str.chars ~ .[0]}).join.say;
You are given an array of positive numbers @N, where value at each index determines how far you are allowed to jump further. Write a script to decide if you can jump to the last index. Print 1 if you are able to reach the last index otherwise 0.
Example:
Input: @N = (1, 2, 1, 2)
Output: 1
as we jump one place from index 0 and then twoe places from index 1 to reach the last index.Right, so the input array is actually a list of instructions that moves us over the same instruction list :-).
So, here’s my solution:
unit sub MAIN(*@a); my $pos = 0; $pos += @a[$pos] while @a[$pos] && $pos < @a.end; say $pos == @a.end ?? 1 !! 0;
In the loop, I am moving by the given number of steps towards the end of the data. An important thing is that you have to control not only that the position does not go outside of the array, but also that the current value is not zero (otherwise, you enter an infinite loop).
A couple of test runs for your pleasure:
$ raku ch-2.raku 1 2 1 2 1 $ raku ch-2.raku 2 1 1 0 2 0
* * *
→ GitHub repository
→ Navigation to the Raku challenges post series
Here is my solution of the first part of Day 12 of this year’s Advent of Code. The point in this solution is that I use multi functions a lot. Well, actually, the whole logic of the program is based on the dispatching rules of the variants of a single multi function.
We start with a ship at point (0, 0) on a 2D map and we’ve got a list of instructions such as:
F10 N3 F7 R90 F11
The letters N, S, W, and E mean that you have to move the position the given distance North, South, West, or East. The commands L or R mean that you have to rotate the ship itself anticlockwise or clockwise by 90, 180, or 270 degrees. Initially, the ship looked East.
The task is to tell how far from the beginning your ship finds itself after it followed all the instructions.
Here is my solution.
my ($x, $y) = 0, 0;
my @dirs = <E N W S>;
my $dir = 0; # E
for 'input.txt'.IO.lines -> $line {
$line ~~ /(\w) (\d+)/;
move($/[0].Str, $/[1].Int);
}
say $x.abs + $y.abs;
multi sub move('L', $deg) { $dir = ($dir + $deg / 90) % 4 }
multi sub move('R', $deg) { $dir = ($dir - $deg / 90) % 4 }
multi sub move('E', $dist) { $x += $dist }
multi sub move('N', $dist) { $y += $dist }
multi sub move('W', $dist) { $x -= $dist }
multi sub move('S', $dist) { $y -= $dist }
multi sub move('F', $dist where $dir == 0) { $x += $dist }
multi sub move('F', $dist where $dir == 1) { $y += $dist }
multi sub move('F', $dist where $dir == 2) { $x -= $dist }
multi sub move('F', $dist where $dir == 3) { $y -= $dist }
The file with instructions is read line by line, and then a call to move() happens. I have a bunch of such functions for each possible case.
Notice that the variants treat the second argument differently. In the first case, it means degrees, in all other cases, it is distance. But in any case, Raku can distinguish the functions by its first argument.
With F, the things are a bit different. Both the first and the second arguments of the four variants of move('F', $dist) are the same, but there’s also the where clause, which—and that’s the point!—uses the current value of a global variable. So, it does not look at the arguments the function receives, but it still dispatches its behaviour based on a variable.
* * *
→ Browse the code on GitHub
→ See all blog posts on Advent of Code 2020
Recently I have read a lot of books on swimming — which, if you knew me, would seem unexpected. Having a fear of water after a near-drowning accident as a child, I never became a swimmer. Not even a so-so swimmer: I managed to learn what we in Norway call “Grandma swimming”, a sort of laborious and slow breast swimming with the head as high above water as humanly possible and the feet similarly low beneath.
But many years later, as an adult and a father, this slowly changed when my oldest son started attending swim practice. Even before taking up swimming as a sport, he had surpassed my abilities by a decent margin. After he became serious about training he almost instantly dwarfed me and my abilities.
As parents of swimmers know, being a swim parent involves lots of driving to-and-from and perhaps even more waiting. Sometimes I killed time waiting for him outside the pool area, looking in through the large glass windows that separated spectators —aka annoying parents — from swimmers. From a distance I was amazed by the progress he made month by month.
One summer day a year into his training I stood on a lake’s edge watching him swim happily towards the opposite side. When he passed the middle a couple of hundred feet out, I was struck by an uncomfortable thought: If anything happened to him now, I wouldn’t be able to help. And had I tried, I would probably need help myself.
In that very moment I decided to something about it. I immediately signed up for a beginner’s swim course for adults. But ten weeks and ten lessons in, hanging from the pool side panting uncontrollably, I was struck by a second thought: The progress I’d seen in the children was impossible to match for us, the slightly overweigh 40+ year old newbies. It would take time and patience to become even a so-so swimmer. And, as it turned out, it would take a lot of patience: A few years have passed and only recently have I started to feel that I master freestyle slightly — if swimming 50 meters freestyle without passing out constitutes mastering, that is. My technique is still laughable, breath continues to be an issue, and I haven’t begun to tumble turn or back stroke yet.
So now I know: This takes time.
But to boost my motivation I turned to books — like I do every time I start becoming interested in a new subject. These are not instructional or teach-yourself books, but inspirational books about the topic I’m interested in. In this case about swimmers that does unimaginable feats and/or about the history and cultural impact of swimming.
As they work as inspiration for me, maybe they will for you too. That’s why I give you quick reviews of five swimming related books I’ve read the last months. They are: Swimming to Antarctica by Lynne Cox, The art of resilience by Ross Edgley, Why we swim by Bonnie Tsui, Open Water by Mikael Rosén, and Grayson, also by Lynne Cox.
Lynne Cox: Swimming to Antarctica
This is the autobiography of the accomplished open water swimmer Lynne Cox. It starts in the seventies when Lynne’s family moves from the US east coast to California, so that the children can maximise their swim training. It’s here Lynne discovers that she’s a better long distance swimmer than a sprint swimmer and gradually switches to open water swimming.
Soon she participates in her first long distance swim —a 20 mile swim from Catalina island to mainland Los Angeles— and discovers that she has the potential for record-breaking pace. It seems like she’s a natural at the discipline. Later she will learn that her body is unique in preserving body heat in cold water conditions.
Next up are more feats, such as swimming the English channel. That one earns her an invitation to Cairo to swim the Nile, etc. While all this is happening, she starts to form an idea of becoming the first to swim from the US to the Soviet Union. Lynne sees this as a way to establish bonds and reduce tension between the people of the two countries. Alas, hardly anyone shares her enthusiasm, so large parts of the book covers the quest of getting the necessary permits to cross between two island on the Alaskan and Siberian side of the Bering strait.
That process took maybe ten years and is, to me, the book’s heart and soul. Swimming aside, this tenaciousness is a testament of her ability to persevere not only in open water but also the intricacy and bureaucracy that is international politics. As such I think Swimming through the Iron Curtain would be a more fitting title than Swimming to Antarctica. But the book is written chronologically and ends with a swim to the Antarctica, so I guess that’s why they chose the book’s title.
A weakness with the book is that it’s unusually light, almost coy, is when it comes to Lynne’s relation to other humans. Her parents, which must have been an important part of her support, is peculiarly described as almost faceless entities in her vicinity. As for romantic relations, she occasionally alludes rather vaguely to how she enjoys the company of a certain individual or how she admires the muscular body of a fellow swimmer, etc. But relations are never described deeper than that, and never more than with a few sentences. That means that this autobiography is unusually auto: Her book is a story about herself and her inner journey powered by external journeys — swims that most people can only dream of.
But no matter what the story is called or what weaknesses it may have, it’s a great read about an extraordinary human. That’s why I recommend this book.
Ross Edgley: The art of resilience
I don’t remember how I stumbled across Ross Edgeley and his “Great British Swim”, but I guess Google’s impenetrable algorithms had something to do with it. Regardless of how — when I did discover him (2018) he had just started his Red Bull sponsored swim around Great Britain, and posted weekly videos about his progress on YouTube. He synchronised his efforts to the tides for the duration of the swim: For 157 days he swam with the currents for six hours and rested the following six hours aboard his support boat. Non stop. For the entirety of the journey he never once set foot on land.
When he started the journey the farewell was rather low key, as the turnout consisted of family and friends. When he finished he’d become a household name and was welcomed by hundreds of other open water swimmer as well as large crowds on the beach. And that was well earned, if you ask me: 157 days — initially they thought they use half of that time — and 2884 kilometers (1792 miles) later, he (and his crew) had completed a feat that I think will stay uncontested for a long time.
In short, Ross’s story is an exciting one and he writes really well about it. That part of the book is impeccable. Strangely, the weakest parts are where Ross’s background as a sports scientist comes in. He’s eager to share theories about how to train, explain how endurance vs strength works, suggest workouts, etc. Every chapter ends with these science based musings. But they’re not integrated well into the storyline — yes, these too are filled with Ross’s enthusiasm, but all they do for me is punctuate and slow down an otherwise engaging story.
What I find peculiar, however, is that if you never watched the youtube videos, in the book it seems as if he got the idea and that everything fell into place by itself. In real life a Red Bull sponsorship was what made the swim possible. They kept him fed and enabled him to keep a boat and a four person crew with him at all times during the 157 days (but he’s gratuitous towards the boat’s captain and attributes much of the success to him).
It’s also interesting that this book is the opposite of Lynne Cox’s memoirs in the sense that what Ross is mostly concerned with is the external journey itself. There are some hints of musings about how the swim influenced his personal development, but they are few and far between.
In the grand scheme of things, though, these are small flaws. If you manage to fight through the sports science this is a great read!
Lynne Cox: Grayson
This book covers one very special day in Lynne Cox’s life — a day that weren’t covered in her autobiography. One early morning around daybreak, the seventeen year old Lynne is midway through a solitary open water swim practice. Suddenly she experiences unusual disturbances in the water, only to discover that it’s caused by a baby gray whale.
What seems to be a fun encounter quickly turns into a more serious matter: Communicating with an experienced elderly man on shore, she realises that the baby whale has been separated from his mother. If she swims ashore the infant will follow her, strand and die. The story details how Lynne slowly coaches the whale out to deeper water in the hope that they’ll by chance will find his mother.
Where Swimming to Antartica was a book as much about Lynne’s inner journeys as her outer, Grayson is even more of an inner journey. The book’s style reflects this. Grayson has a far more lyrical, introspective and even pensive form than her first book.
That’s not only positive. As mentioned it covers the events of this one morning only. The only perspective is Lynne’s told in the present tense. To stretch a small story about one morning from one person’s perspective to the necessary 150 pages, a lot of the text is inner monologue. In my opinion that slows the narrative down, and not in a good way. The inner monologues become fillers that doesn’t drive story.
What’s worse is that much of the inner monologue doesn’t seem entirely believable. The amount of depth and detail that Lynne allegedly remembers events and thoughts with, is more than anyone — with the possible expection of Marilu Henner — can recall some 30 odd years later. In addition many of the thoughts and reflections the 17 year old Lynne supposedly has, are astonishingly mature and filled with knowledge she possibly couldn’t have had at the moment . Scientific facts about gray whales, for instance. These are the thoughts and retrospections of a person in their late forties. There’s nothing wrong with thoughts and retrospections from late forty-somethings — after all I’m one myself. And had they been presented as such, as present day reflections of that extraordinary morning in her teens, this would probably not feel alien to the story at all. But the the choice to attribute the thoughts of a soon-to-be 50 year old to a 17-year old ends — to me —up as a significant stylistic crash.
With that in mind, I can’t help but think that if her editor had cut most of this, they’d end up with a tight and great story driven book for adolescents/young adults. As it is now, it’s not. But if you’re a less critical reader than me you’ll get a reasonably engaging book about the inner and outer journey of an almost superhuman swimmer. Should you only want to read only one book by Lynne Cox, however, Swimming to Antartica is the better choice.
Mikael Rosén: Open Water
Swedish author Mikael Rosén’s Open Water is not only about swimming itself, but also about swimming’s history, technique, science, cultural implications, racial issues, and more. Although you’d imagine that a mashup of all that would end up… well… mashy, the book is surprisingly clear and interesting despite juggling many sprawling subject. As such the book really delivers on its subtitle The History and Technique of Swimming.
Although this book talks about specific swimmers such as the pre-WW2 olympian Johnny Weissmuller, the first man to swim across the English channel, captain Matthew Webb, or modern athletes like Michael Phelphs, this is really not a book about individuals. These people are used to illustrate topics such as improvement in sports science (Weissmuller vs the Japanese swimmers that followed) or the history of open water swimming (captain Webb). Consistently interesting throughout, the most interesting part may be the second of the total eight chapter. That section explores prejudice — how female swimmers started to appear on the scene and suddenly break records previously held by men, or how a white, racist population’s negative reaction to black swimmers at public pools contributed to the establishment and strengthening of segregation laws in the southern states of the USA.
This tour de force of interesting and surprising facts reads a little like if Bill Bryson had written a book about swimming, though less humorous. But still, it’s almost on that level. Most books are not perfect, however, and this book is no exception: Written three years before Ross Edgeley’s The Art of Resilience, it shares the latter’s insistence of closing each chapter with a little sports science, training programs, suggestions of drills, etc. They don’t bother me as much in this book — as opposed to Ross’s — as this book is not a chronological story driven narrative. Therefore the training parts fits a little better into the whole. But the book wouldn’t suffer if they’d been edited out.
All in all that’s minor critisism, so I recommend this book wholeheartedly.
Bonnie Tsui: Why we swim
It’s not a coincidence that this book comes last. The reason is that this book is best described in the context of the previously mentioned books.
Why we swim is in a way an amalgamation of the science/history aspects of Mikael Rosén’s Open Water and the introspection of Lynne Cox’s books. But where the latter describes her personal growth in retrospect, Bonnie Tsui documents her quest for personal growth through swimming more or less as it happens — as a part of the process of writing the book itself it seems.
Bonnie Tsui kicks her book off with the story of Guðlaugur Friðþórsson, a fisherman that was the sole survivor after a fishing vessel sank in the frigid winter waters off Iceland. Together with two mates he started to swim towards land, but not long after he was the only one still swimming. Against all odds Friðþórsson survived a six hour swim in six degree celcius water.
For Tsui this becomes the entry point to the history of swimming. Her book is structured around five main topics, going from Survival, Well-Being, Community, Competition and ultimately to the more metaphysical and meditative subject of Flow. She takes on a tour of swimming history, starting in the stone age and the first documentation we have of humans swimming, ending with personal musings about not why we swim, but why she swims.
And this inclusion of a very visible I throughout the book — the chapter about Friðþórsson is not only about Friðþórsson but also about her meeting him and her participation in a swim honoring him—means that you can’t separate her personal journey from her exploration of the history, culture and science of swimming. Granted, Open Water is more hard core when it comes to facts, but the unique interspersion of the author personal story and the overarching topics of the book, makes this the most beautifully written of the five books I’ve mentioned here. Read it!
So… do you become a better swimmer by reading? Of course not. Only practice can improve swimming (although you may pick up some valuable hints to how you can improve through pure instructional books, such as Terry Laughlin’s Total Immersion).
But this being November 2020, the year of Covid-19, all swimming pools are closed and I’m unable to practice and improve for a while. This may be the case for you too. But while you wait for the pools to open again or the summer to heat up the sea to a more welcoming temperature, spending some time on one or more of these books wouldn’t be the worst thing to do.
Who knows? Maybe you’ll come back to the water more inspired than before.
I’d like to thank everyone who voted for me in the recent Raku Steering Council elections. By this point, I’ve been working on the language for well over a decade, first to help turn a language design I found fascinating into a working implementation, and since the Christmas release to make that implementation more robust and performant. Overall, it’s been as fun as it has been challenging – in a large part because I’ve found myself sharing the journey with a lot of really great people. I’ve also tried to do my bit to keep the community around the language kind and considerate. Receiving a vote from around 90% of those who participated in the Steering Council elections was humbling.
Alas, I’ve today submitted my resignation to the Steering Council, on personal health grounds. For the same reason, I’ll be taking a step back from Raku core development (Raku, MoarVM, language design, etc.) Please don’t worry too much; I’ll almost certainly be fine. It may be I’m ready to continue working on Raku things in a month or two. It may also be longer. Either way, I think Raku will be better off with a fully sized Steering Council in place, and I’ll be better off without the anxiety that I’m holding a role that I’m not in a place to fulfill.
In the machine world, we have Int and Num. A High Level Language such as Raku is a abstraction to make it easier for humans. Humans expect decimal math 0.1 + 0.2 = 0.3 to work.
Neither Int nor Num can do this! Huh? How can that be?
Well Int is base-2 and decimals are base-10. And Num (IEEE 754 Floating Point) is inherently imprecise.
Luckily we have Rat. Raku automatically represent decimals and other rational numbers as a ratio of two integers. Taking into account all the various data shuffling any program creates, the performance is practically as fast as Int. Precision, accuracy and performance are maintained for the entire realm of practical algebraic calculations.
And when we get to the limit of Rat (at around 3.614007241618348e-20), Raku automatically converts to a Num. After this, precision cannot be recovered – but accuracy and dynamic range are maintained. Modern Floating Point Units (FPUs) make Num nearly as fast as Int so performance is kept fast.
Here, we have smoothly transitioned to the world of physical measurements and irrational numbers such as square root and logarithms. It’s a one way trip. (Hint: If you want a Num in the first place, just use ‘e’ in the literal.)
Good call, Raku!
Footnote: FatRat and BigInt are not important for most use cases. They incur a performance penalty for the average program and rightly belong in library extensions, not the core language.
I have been unsatisfied with the installation process for MoarPerf for a little while now. You have to either compile the javascript (and css) yourself using npm (or equivalent) which takes a little while, or you have to rely on me releasing an "everything built for you" version to my github repo.
The last few days I've repeatedly bonked my metaphorical pickaxe against the stone wall that is unpleasantly long build times and an endless stream of small mistakes in order to bring you MoarPerf in an AppImage.
AppImage is a format for linux programs that allows programs to be distributed as a single executable file without a separate install process.
The AppImage for MoarPerf includes a full Rakudo along with the dependencies of MoarPerf, the built javascript, and the Raku code. This way you don't even have to have a working Rakudo installation on the machine you want to use to analyze the profiler results. Yours truly tends to put changes in MoarVM or nqp or Rakudo that sometimes prevent things from working fine, and resetting the three repos back to a clean state and rebuilding can be a bit of an annoyance.
With the MoarPerf AppImage I don't have to worry about this at all any more! That's pretty nice.
With an AppImage created for MoarPerf it was not too much work to make an AppImage for Rakudo without a built-in application.
The next step is, of course, to pack everything up nicely to create a one-or-two-click solution to build AppImages for any script that you may be interested in running.
There has also already been a module that creates a windows installer for a Raku program by installing a custom MoarVM/nqp/Rakudo into a pre-determined path (a limitation from back when Rakudo wasn't relocatable yet), and maybe I should offer an installer like this for windows users, too? The AppImage works much like this, too, except it already makes use of the work that made Rakudo relocatable, so it doesn't need to run in a pre-defined path.
If you want to give building AppImages a try as well, feel free to steal everything from the rakudo-appimage repository, and have a look at the .travis.yml and the appimage folder in the moarperf repo!
In any case, I would love to hear from people, whether the AppImages for Rakudo and MoarPerf work on their machines, and what modules/apps they would like to have in an AppImage. Feel free to message me on twitter, write to the Raku users mailing list, or find me on freenode as timotimo.
Thanks for reading, stay safe, and see y'all later!
Good, that's the click-baity title out of the way. Sorry for taking such a long time to write again! There really has been everything going on.
To get back into blogging, I've decided to quickly write about a change I made some time ago already.
This change was for the "instrumented profiler", i.e. the one that will at run-time change all the code of the user's program, in order to measure execution times and count up calls and allocations.
In order to get everything right, the instrumented profiler keeps an entire call graph in memory. If you haven't seen something like it yet, imagine taking stack traces at every point in your program's life, and all these stack traces put together make all the paths in the tree that point at the root.
This means, among other things, that the same function can come up multiple times. With recursion, the same function can in fact come up a few hundred times "in a row". In general, if your call tree can become both deep and wide, you can end up with a whole truckload of nodes in your tree.
Is it a bad thing to have many nodes? Of course, it uses up memory. Only a single path on the tree is ever interesting at any one moment, though. Memory that's not read from or written to is not quite as "expensive". It never has to go into the CPU cache, and is even free to be swapped out to disk and/or compressed. But hold on, is this really actually the case?
It turns out that when you're compiling the Core Setting, which is a code file almost 2½ megabytes big with about 71½ thousand lines, and you're profiling during the parsing process, the tree gets enormous. At the same time, the parsing process slows to a crawl. What on earth is wrong here?
Well, looking at what MoarVM spends most of its time doing while the profiler runs gives you a good hint: It's spending almost all of its time going through the entirety of the tree for garbage collection purposes. Why would it do that, you ask? Well, in order to count allocated objects at every node, you have to match the count with the type you're allocating, and that means you need to hold on to a pointer to the type, and that in turn has to be kept up to date if anything moves (which the GC does to recently-born things) and to make sure types aren't considered unused and thrown out.
That's bad, right? Isn't there anything we can do? Well, we have to know at every node which counter belongs to which type, and we need to give all the types we have to the garbage collector to manage. But nothing forces us to have the types right next to the counter. And that's already the solution to the problem:
Holding on to all types is now the job of a little array kept just once per tree, and next to every counter there's just a little number that tells you where in the array to look.
This increases the cost of recording an allocation, as you'll now have to go to a separate memory location to match types to counters. On the other hand, the "little number" can be much smaller than before, and that saves memory in every node of the tree.
More importantly, the time cost of going through the profiler data is now independent of how big the tree is, since the individual nodes don't have to be looked at at all.
With a task as big as parsing the core setting, which is where almost every type, exception, operator, or sub lives, the difference is a factor of at least a thousand. Well, to be honest I didn't actually calculate the difference, but I'm sure it's somewhere between 100x faster and 10000x faster, and going from "ten microseconds per tree node" to "ten microseconds per tree" isn't a matter of a single factor increase, it's a complexity improvement from O(n) to O(1). As long as you can find a bigger tree, you can come up with a higher improvement factor. Very useful for writing that blog post you've always wanted to put at the center of a heated discussion about dishonest article titles!
Anyway, on testing my patch, esteemed colleague MasterDuke had this to say on IRC:
timotimo: hot damn, what did you do?!?! stage parse only took almost twice as long (i.e., 60s instead of the normal 37s) instead of the 930s last time i did the profile
(psst, don't check what 930 divided by 60 is, or else you'll expose my blog post title for the fraud that it is!)
Well, that's already all I had for this post. Thanks for your attention, stay safe, wear a mask (if by the time you're reading this the covid19 pandemic is still A Thing, or maybe something new has come up), and stay safe!
It was an emotional moment to see the keynote talk at TPRCiC from Sawyer X announcing that perl 7.00 === 5.32. Elation because of the ability of the hardcore perl community to finally break free of the frustrating perl6 roadblock. Pleasure in seeing how the risky decision to rename perl6 to raku has paid off and hopefully is beginning to defuse the tensions between the two rival communities. And Fear that improvements to perl7 will undermine the reasons for many to try out raku and may cannibalise raku usage. (Kudos to Sawyer to recognising that usage is an important design goal).
Then the left side of my brain kicked in. Raku took 15 years of total commitment of genius linguists to ingest 361 RFCs and then synthesise a new kind of programming language. If perl7 seeks the same level of completeness and perfection as raku, surely that will take the same amount of effort. And I do not see the perl community going for the same level of breaking changes that raku did. (OK maybe they could steal some stuff from raku to speed things up…)
And that brought me to Sadness. To reflect that perl Osborned sometime around 2005. That broke the community in two – let’s say the visionaries and the practical-cats. And it drove a mass emigration to Python. Ancient history.
So now we have two sister languages, and each will find a niche in the programming ecosystem via a process of Darwinism. They both inherit the traits (https://en.wikipedia.org/wiki/Perl#Design) that made perl great in the first place….
The design of Perl can be understood as a response to three broad trends in the computer industry: falling hardware costs, rising labor costs, and improvements in compiler technology. Many earlier computer languages, such as Fortran and C, aimed to make efficient use of expensive computer hardware. In contrast, Perl was designed so that computer programmers could write programs more quickly and easily.
Perl has many features that ease the task of the programmer at the expense of greater CPU and memory requirements. These include automatic memory management; dynamic typing; strings, lists, and hashes; regular expressions; introspection; and an eval() function. Perl follows the theory of “no built-in limits,” an idea similar to the Zero One Infinity rule.
Wall was trained as a linguist, and the design of Perl is very much informed by linguistic principles. Examples include Huffman coding(common constructions should be short), good end-weighting (the important information should come first), and a large collection of language primitives. Perl favours language constructs that are concise and natural for humans to write.
Perl’s syntax reflects the idea that “things that are different should look different.” For example, scalars, arrays, and hashes have different leading sigils. Array indices and hash keys use different kinds of braces. Strings and regular expressions have different standard delimiters. This approach can be contrasted with a language such as Lisp, where the same basic syntax, composed of simple and universal symbolic expressions, is used for all purposes.
Perl does not enforce any particular programming paradigm (procedural, object-oriented, functional, or others) or even require the programmer to choose among them.
But perl7 and raku serve distinct interests & needs:
| Thing… | perl7 | raku |
| compilation | static parser | one pass compiler |
| compile speed | super fast | relies on pre-c0mp |
| execution | interpreted | virtual machine |
| execution speed | super fast | relies on jit |
| module library | CPAN native | CPAN import |
| closures | yes | yes |
| OO philosophy | Cor not module | pervasive |
| OO inheritance | Roles + Is | Roles + Is + multiple |
| method invocation | -> | . |
| type checking | no | gradual |
| sigils | idiosyncratic | consistent |
| references | manual | automatic |
| unicode | feature guard | core |
| signatures | feature guard | core |
| lazy execution | nope | core |
| Junctions | nope | core |
| Rat math | nope | core |
| Sets & Mixes | nope | core |
| Complex math | nope | core |
| Grammars | nope | core |
| mutability | nope | core |
| concurrency | nope | core |
| variable scope | “notched” | cleaner |
| operators | C-like | cleaner (e.g. for ->) |
| switch | no | gather/when |
| regexen | classic | cleaner |
| eval | yes | shell |
| AST macros | huh? | … |
A long list and perhaps a little harsh on perl since many things may be got from CPAN – but when you use raku in anger, you do see the benefit if having a large core language. Only when I made this table, did I truly realise just what a comprehensive language raku is, and that perl will genuinely be the lean and mean option.
And, lest we forget our strengths:
When I first saw Python code, I thought that using indents to define the scope seemed like a good idea. However, there’s a huge downside. Deep nesting is permitted, but lines can get so wide that they wrap lines in the text editor. Long functions and long conditional actions may make it hard to match the start to the end. And I pity anyone who miscounts spaces and accidentally puts in three spaces instead of four somewhere — this can take hours to debug and track down. [Source: here]
Coming from Python, the Raku object model is recognizable, but brings a tad more structure:
What works for me, as a convenience seeker, is:
These are the things you want if you are writing in a more procedural or functional style and using class as a means to define a record type.
Here’s the rub…
When we describe OO, terms like “encapsulation” and “data hiding” often come up. The key idea here is that the state model inside the object – that is, the way it chooses to represent the data it needs in order to implement its behaviours (the methods) – is free to evolve, for example to handle new requirements. The more complex the object, the more liberating this becomes.
However, getters and setters are methods that have an implicit connection with the state. While we might claim we’re achieving data hiding because we’re calling a method, not accessing state directly, my experience is that we quickly end up at a place where outside code is making sequences of setter calls to achieve an operation – which is a form of the feature envy anti-pattern. And if we’re doing that, it’s pretty certain we’ll end up with logic outside of the object that does a mix of getter and setter operations to achieve an operation. Really, these operations should have been exposed as methods with a names that describes what is being achieved. This becomes even more important if we’re in a concurrent setting; a well-designed object is often fairly easy to protect at the method boundary.
(source jnthn https://stackoverflow.com/questions/59671027/mixing-private-and-public-attributes-and-accessors-in-raku)
Let’s fix that:
Now, I had to do a bit more lifting, but here’s what I got:
And, in contrast to Chapter 1:
I also discovered Larry’s First Law of Language Redesign: Everyone wants the colon
Apocalypse 1: The Ugly, the Bad, and the Good https://www.perl.com/pub/2001/04/02/wall.html/
I conclude that Larry’s decision was to confer the colon on the method syntax, subtly tilting the balance towards the strict model: by preferring $p.y: 3 over $p.y = 2.
By the way, you could replace … * with Inf or the unicode infinity symbol ∞ to make it more readable, i.e.
my @a = 1, 1, * + * … ∞;
— — or — —
my @a = 1, 1, * + * … Inf;
Hello everyone! In the last report I said that just a little bit of work on the heap snapshot portion of the UI should result in a useful tool.
Photo by Sticker Mule / Unsplash
Here's my report for the first useful pieces of the Heap Snapshot UI!
Last time you already saw the graphs showing how the number of instances of a given type or frame grow and shrink over the course of multiple snapshots, and how new snapshots can be requested from the UI.
The latter now looks a little bit different:
Each snapshot now has a little button for itself, they are in one line instead of each snapshot having its own line, and the progress bar has been replaced with a percentage and a little "spinner".
There are multiple ways to get started navigating the heap snapshot. Everything is reachable from the "Root" object (this is the norm for reachability-based garbage collection schemes). You can just click through from there and see what you can find.
Another way is to look at the Type & Frame Lists, which show every type or frame along with the number of instances that exist in the heap snapshot, and the total size taken up by those objects.
Clicking on a type, or the name or filename of a frame leads you to a list of all objects of that type, all frames with the given name, or all frames from the given file. They are grouped by size, and each object shows up as a little button with the ID:
Clicking any of these buttons leads you to the Explorer.
Here's a screenshot of the explorer to give you an idea of how the parts go together that I explain next:
The explorer is split into two identical panels, which allows you to compare two objects, or to explore in multiple directions from one given object.
There's an "Arrow to the Right" button on the left pane and an "Arrow to the Left" button on the right pane. These buttons make the other pane show the same object that the one pane currently shows.
On the left of each pane there's a "Path" display. Clicking the "Path" button in the explorer will calculate the shortest path to reach the object from the root. This is useful when you've got an object that you would expect to have already been deleted by the garbage collector, but for some reason is still around. The path can give the critical hint to figure out why it's still around. Maybe one phase of the program has ended, but something is still holding on to a cache that was put in as an optimization, and that still has your object in it? That cache in question would be on the path for your object.
The other half of each panel shows information about the object: Displayed at the very top is whether it is an object, a type object, an STable, or a frame.
Below that there is an input field where you can enter any ID belonging to a Collectable (the general term encompassing types, type objects, stables, and frames) to have a look.
The "Kind" field needs to have the number values replaced with human-readable text, but it's not the most interesting thing anyway.
The "Size" of the Collectable is split into two parts. One is the fixed size that every instance of the given type has. The other is any extra data an instance of this type may have attached to it, that's not a Collectable itself. This would be the case for arrays and hashes, as well as buffers and many "internal" objects.
Finally, the "References" field shows how many Collectables are referred to by the Collectable in question (outgoing references) and how many Collectables reference this object in question.
Below that there are two buttons, Path and Network. The former was explained further above, and the latter will get its own little section in this blog post.
Finally, the bottom of the panel is dedicated to a list of all references - outgoing or incoming - grouped by what the reference means, and what type it references.
In this example you see that the frame of the function display from elementary2d.p6 on line 87 references a couple of variables ($_, $tv, &inv), the frame that called this frame (step), an outer frame (MAIN), and a code object. The right pane shows the incoming references. For incoming references, the name of the reference isn't available (yet), but you can see that 7 different objects are holding a reference to this frame.
The newest part of the heap snapshot UI is the Network View. It allows the user to get a "bird's eye" view of many objects and their relations to each other.
Here's a screenshot of the network view in action:
The network view is split into two panes. The pane on the left lists all types present in the network currently. It allows you to give every type a different symbol, a different color, or optionally make it invisible. In addition, it shows how many of each type are currently in the network display.
The right pane shows the objects, sorted by how far they are from the root (object 0, the one in Layer 0, with the frog icon).
Each object has one three-piece button. On the left of the button is the icon representing the type, in the middle is the object ID for this particular object, and on the right is an icon for the "relation" this object has to the "selected" object:
This view was generated for object 46011 (in layer 4, with a hamburger as the icon). This object gets the little "map marker pin" icon to show that it's the "center" of the network. In layers for distances 3, 2, and 1 there is one object each with a little icon showing two map marker pins connected with a squiggly line. This means that the object is part of the shortest path to the root. The third kind of icon is an arrow pointing from the left into a square that's on the right. Those are objects that refer to the selected object.
There is also an icon that's the same but the arrow goes outwards from the square instead of inwards. Those are objects that are referenced by the selected object. However, there is currently no button to have every object referenced by the selected object put into the network view. This is one of the next steps I'll be working on.
Customizing the colors and visibility of different types can give you a view like this:
And here's a view with more objects in it:
Interesting observations from this image:
You have no doubt noticed that the buttons for collectables are very different between the network view and the type/frame lists and the explorer. The reason for that is that I only just started with the network view and wanted to display more info for each collectable (namely the icons to the left and right) and wanted them to look nicer. In the explorer there are sometimes thousands of objects in the reference list, and having big buttons like in the network view could be difficult to work with. There'll probably have to be a solution for that, or maybe it'll just work out fine in real-world use cases.
On the other hand, I want the colors and icons for types to be available everywhere, so that it's easier to spot common patterns across different views and to mark things you're interested in so they stand out in lists of many objects. I was also thinking of a "bookmark this object" feature for similar purposes.
Before most of that, the network viewer will have to become "navigable", i.e. clicking on an object should put it in the center, grab the path to the root, grab incoming references, etc.
There also need to be ways to handle references you're not (or no longer) interested in, especially when you come across an object that has thousands of them.
But until then, all of this should already be very useful!
Here's the section about the heap snapshot profiler from the original grant proposal:
Looking at the list, it seems like the majority of intended features are already available or will be very soon!
Until now the user had to download nodejs and npm along with a whole load of javascript libraries in order to compile and bundle the javascript code that powers the frontend of moarperf.
Fortunately, it was relatively easy to get travis-ci to do the work automatically and upload a package with the finished javascript code and the backend code to github.
You can now visit the releases page on github to grab a tarball with all the files you need! Just install all backend dependencies with zef install --deps-only . and run service.p6!
And with that I'm already done for this report!
It looks like the heap snapshot portion of the grant is quite a bit smaller than the profiler part, although a lot of work happened in moarvm rather than the UI. I'm glad to see rapid progress on this.
I hope you enjoyed this quick look at the newest pieces of moarperf!
- Timo
It has been a while since the last progress report, hasn't it?
Over the last few months I've been focusing on the MoarVM Heap Snapshot Profiler. The new format that I explained in the last post, "Intermediate Progress Report: Heap Snapshots", is available in the master branch of MoarVM, and it has learned a few new tricks, too.
The first thing I usually did when opening a Heap Snapshot in the heapanalyzer (the older command-line based one) was to select a Snapshot, ask for the summary, and then for the top objects by size, top objects by count, top frames by size, and/or top frames by count to see if anything immediately catches my eye. In order to make more sense of the results, I would repeat those commands for one or more other Snapshots.
Snapshot Heap Size Objects Type Objects STables Frames References
======== ================= ======= ============ ======= ====== ==========
0 46,229,818 bytes 331,212 686 687 1,285 1,146,426
25 63,471,658 bytes 475,587 995 996 2,832 1,889,612
50 82,407,275 bytes 625,958 1,320 1,321 6,176 2,741,066
75 97,860,712 bytes 754,075 1,415 1,416 6,967 3,436,141
100 113,398,840 bytes 883,405 1,507 1,508 7,837 4,187,184
Snapshot Heap Size Objects Type Objects STables Frames References
======== ================= ========= ============ ======= ====== ==========
125 130,799,241 bytes 1,028,928 1,631 1,632 9,254 5,036,284
150 145,781,617 bytes 1,155,887 1,684 1,685 9,774 5,809,084
175 162,018,588 bytes 1,293,439 1,791 1,792 10,887 6,602,449
Realizing that the most common use case should be simple to achieve, I first implemented a command summary all and later a command summary every 10 to get the heapanalyzer to give the summaries of multiple Snapshots at once, and to be able to get summaries (relatively) quickly even if there's multiple hundreds of snapshots in one file.
Sadly, this still requires the parser to go through the entire file to do the counting and adding up. That's obviously not optimal, even though this is an Embarrassingly Parallel task, and it can use every CPU core in the machine you have, it's still a whole lot of work just for the summary.
For this reason I decided to shift the responsibility for this task to MoarVM itself, to be done while the snapshot is taken. In order to record everything that goes into the Snapshot, MoarVM already differentiates between Object, Type Object, STable, and Frame, and it stores all references anyway. I figured it shouldn't have a performance impact to just add up the numbers and make them available in the file.
The result is that the summary table as shown further above is available only milliseconds after loading the heap snapshot file, rather than after an explicit request and sometimes a lengthy wait period.
The next step was to see if top objects by size and friends could be made faster in a similar way.
I decided that adding an optional "statistics collection" feature inside of MoarVM's heap snapshot profiler would be worthwhile. If it turns out that the performance impact of summing up sizes and counts on a per-type and per-frame basis makes capturing a snapshot too slow, it could be turned off.
> snapshot 50
Loading that snapshot. Carry on...
> top frames by size
Wait a moment, while I finish loading the snapshot...
Name Total Bytes
==================================== =============
finish_code_object (World.nqp:2532) 201,960 bytes
moarop_mapper (QAST.nqp:1764) 136,512 bytes
!protoregex (QRegex.nqp:1625) 71,760 bytes
new_type (Metamodel.nqp:1345) 40,704 bytes
statement (Perl6-Grammar.nqp:951) 35,640 bytes
termish (Perl6-Grammar.nqp:3641) 34,720 bytes
<anon> (Perl6-BOOTSTRAP.c.nqp:1382) 29,960 bytes
EXPR (Perl6-Grammar.nqp:3677) 27,200 bytes
<mainline> (Perl6-BOOTSTRAP.c.nqp:1) 26,496 bytes
<mainline> (NQPCORE.setting:1) 25,896 bytes
EXPR (NQPHLL.nqp:1186) 25,760 bytes
<anon> (<null>:1) 25,272 bytes
declarator (Perl6-Grammar.nqp:2189) 23,520 bytes
<anon> (<null>:1) 22,464 bytes
<anon> (<null>:1) 22,464 bytes
Showing the top objects or frame for a single snapshot is fairly straight-forward in the commandline based UI, but how would you display how a type or frame develops its value across many snapshots?
Instead of figuring out the best way to display this data in the commandline, I switched focus to the Moarperf Web Frontend. The most obvious way to display data like this is a Line Graph, I believe. So that's what we have now!
And of course you also get to see the data from each snapshot's Summary in graph format:
And now for the reason behind this blog post's Title.
Using Jonathan's module Concurrent::Progress (with a slight modification) I sprinkled the code to parse a snapshot with matching calls of .increment-target and .increment. The resulting progress reports (throttled to at most one per second) are then forwarded to the browser via the WebSocket connection that already delivers many other bits of data.
The result can be seen in this tiny screencast:
The recording is rather choppy because the heapanalyzer code was using every last drop of performance out of my CPU while it was trying to capture my screen.
There's obviously a lot still missing from the heap snapshot analyzer frontend GUI, but I feel like this is a good start, and even provides useful features already. The graphs for the summary data are nicer to read than the table in the commandline UI, and it's only in this UI that you can get a graphical representation of the "highscore" lists.
I think a lot of the remaining features will already be useful after just the initial pieces are in place, so a little work should go a long way.
I didn't spend the whole time between the last progress report and now to work directly on the features shown here. Apart from Life Intervening™, I worked on fixing many frustrating bugs related to both of the profilers in MoarVM. I added a small subsystem I call VMEvents that allows user code to be notified when GC runs happen and other interesting bits from inside MoarVM itself. And of course I've been assisting other developers by answering questions and looking over their contributions. And of course there's the occasional video-game-development related experiment, for example with the GTK Live Coding Tool.
Finally, here's a nice little screencap of that same tool displaying a hilbert curve:
That's already everything I have for this time. A lot has (had to) happen behind the scenes to get to this point, but now there was finally something to look at (and touch, if you grab the source code and go through the needlessly complicated build process yourself).
Thank you for reading and I hope to see you in the next one!
- Timo
This is slightly tangential to the Rakudo Perl 6 Performance Analysis Tooling grant from The Perl Foundation, but it does interact closely with it, so this is more or less a progress report.
The other day, Jonathan Worthington of Edument approached me with a little paid Job: Profiling very big codebases can be tedious, especially if you're interested in only one specific fraction of the program. That's why I got the assignment to make the profiler "configurable". On top of that, the Comma IDE will get native access to this functionality.
The actual request was just to allow specifying whether individual routines should be included in the profile via a configuration file. That would have been possible with just a simple text file that contains one filename/line number/routine name per line. However, I have been wanting something in MoarVM that allows much more involved configuration for many different parts of the VM, not just the profiler.
Obligatory cat photo.
That's why I quickly developed a small and simple "domain-specific language" for this and similar purposes.
The language had a few requirements:
There's also some things that aren't necessary:
While thinking about what exactly I should build – before I eventually settled on building a "programming language" for this task – I bounced back and forth between the simplest thing that could possibly work (for example, a text file with a list of file/line/name entries) and the most powerful thing that I can implement in a sensible timeframe (for example, allowing a full NQP script). A very important realization was that as long as I require the first line to identify what "version" of configuration program it is, I could completely throw away the current design and put something else instead, if the need ever arises. That allowed me to actually commit to a design that looked at least somewhat promising. And so I got started on what I call confprog.
Here's an example program. It doesn't do very much, but shows what it's about in general:
version = 1
entry profiler_static:
log = sf.name;
profile = sf.name eq "calculate-strawberries"
profile |= sf.cu.filename eq "CustomCode.pm6"
The entry decides which stage of profiling this program is applied to. In this case, the profiler_static means we're seeing a routine for the first time, before it is actually entered. That's why only the information every individual invocation of the frame in question shares is available via the variable sf, which stands for Static Frame. The Static Frame also allows access to the Compilation Unit (cu) that it was compiled into, which lets us find the filename.
The first line that actually does something assigns a value to the special variable log. This will output the name of the routine the program was invoked for.
The next line will turn on profiling only if the name of the routine is "calculate-strawberries". The line after that will also turn on profiling if the filename the routine is from is "CustomCode.pm6".
Apart from profiler_static, there are a couple more entry points available.
profiler_static, which runs the first time any routine is encountered, and stores the decision until the profile run is finished.profiler_dynamic will be evaluated every time a routine gets entered that would potentially be profiled otherwise (for example because a profiler_static program turned it on, or because there is no profiler_static program, which means that every routine is eligible for profiling).heapsnapshot will be run every time a GC run happens. It lets the user decide whether a GC run should result in a heap snapshot being taken or not, based on whether the run has been a minor or major collection, and arbitrary other factors, including time since start of the program, or time since the last heap snapshot was taken.spesh and jit I'm not entirely sure about yet. I want to allow turning the specializer or the jit off completely for certain routines, but I also want to offer control over individual optimizations in spesh (turning off inlining for one particular routine, preventing scalar replacement but only for Rat types, ...) and behaviour of the jit (use the template jit for everything except one specific piece of one particular routine, ...).The syntax is still subject to change, especially before the whole thing is actually in a released version of MoarVM.
There is a whole lot of other things I could imagine being of interest in the near or far future. One place I'm taking inspiration from is where "extended Berkeley Packet Filter" (eBPF for short) programs are being used in the linux kernel and related pieces of software:
Oversimplifying a bit, BPF was originally meant for tcpdump so that the kernel doesn't have to copy all data over to the userspace process so that the decision what is interesting or not can be made. Instead, the kernel receives a little piece of code in the special BPF language (or bytecode) and can calculate the decision before having to copy anything.
eBPF programs can now also be used as a complex ruleset for sandboxing processes (with "seccomp"), to decide how network packets are to be routed between sockets (that's probably for Software Defined Networks?), what operations a process may perform on a particular device, whether a trace point in the kernel or a user-space program should fire, and so on.
So what's the status of confprog? I've written a parser and compiler that feeds confprog "bytecode" (which is mostly the same as regular moarvm bytecode) to MoarVM. There's also a preliminary validator that ensures the program won't do anything weird, or crash, when run. It is much too lenient at the moment, though. Then there's an interpreter that actually runs the code. It can already take an initial value for the "decision output value" (great name, isn't it) and it will return whatever value the confprog has set when it runs. The heap snapshot profiler is currently the only part of MoarVM that will actually try to run a confprog, and it uses the value to decide whether to take a snapshot or not.
Next up on the list of things to work on:
Apart from improvements to the confprog programming language, the integration with MoarVM lacks almost everything, most importantly installing a confprog for the profiler to decide whether a frame should be profiled (which was the original purpose of this assignment).
After that, and after building a bit of GUI for Comma, the regular grant work can resume: Flame graphs are still not visible on the call graph explorer page, and heap snapshots can't be loaded into moarperf yet, either.
Thanks for sticking with me through this perhaps a little dry and technical post. I hope the next one will have a little more excitement! And if there's interest (which you can signal by sending me a message on irc, or posting on reddit, or reaching me via twitter @loltimo, on the Perl 6 discord server etc) I can also write a post on how exactly the compiler was made, and how you can build your own compiler with Perl 6 code. Until then, you can find Andrew Shitov's presentations about making tiny languages in Perl 6 on youtube.
I hope you have a wonderful day; see you in the next one!
- Timo
PS: I would like to give a special shout-out to Nadim Khemir for the wonderful Data::Dump::Tree module which made it much easier to see what my parser was doing. Here's some example output from another simple confprog program:
[6] @0
├ 0 = .Label .Node @1
│ ├ $.name = heapsnapshot.Str
│ ├ $.type = entrypoint.Str
│ ├ $.position is rw = Nil
│ └ @.children = [0] @2
├ 1 = .Op .Node @3
│ ├ $.op = =.Str
│ ├ $.type is rw = Nil
│ └ @.children = [2] @4
│ ├ 0 = .Var .Node @5
│ │ ├ $.name = log.Str
│ │ └ $.type = CPType String :stringy @6
│ └ 1 = String Value ("we're in") @7
├ 2 = .Op .Node @8
│ ├ $.op = =.Str
│ ├ $.type is rw = Nil
│ └ @.children = [2] @9
│ ├ 0 = .Var .Node @10
│ │ ├ $.name = log.Str
│ │ └ $.type = CPType String :stringy §6
│ └ 1 = .Op .Node @12
│ ├ $.op = getattr.Str
│ ├ $.type is rw = CPType String :stringy §6
│ └ @.children = [2] @14
│ ├ 0 = .Var .Node @15
│ │ ├ $.name = sf.Str
│ │ └ $.type = CPType MVMStaticFrame @16
│ └ 1 = name.Str
├ 3 = .Op .Node @17
│ ├ $.op = =.Str
│ ├ $.type is rw = Nil
│ └ @.children = [2] @18
│ ├ 0 = .Var .Node @19
│ │ ├ $.name = log.Str
│ │ └ $.type = CPType String :stringy §6
│ └ 1 = String Value ("i am the confprog and i say:") @21
├ 4 = .Op .Node @22
│ ├ $.op = =.Str
│ ├ $.type is rw = Nil
│ └ @.children = [2] @23
│ ├ 0 = .Var .Node @24
│ │ ├ $.name = log.Str
│ │ └ $.type = CPType String :stringy §6
│ └ 1 = String Value (" no heap snapshots for you my friend!") @26
└ 5 = .Op .Node @27
├ $.op = =.Str
├ $.type is rw = Nil
└ @.children = [2] @28
├ 0 = .Var .Node @29
│ ├ $.name = snapshot.Str
│ └ $.type = CPType Int :numeric :stringy @30
└ 1 = Int Value (0) @31
Notice how it shows the type of most things, like name.Str, as well as cross-references for things that appear multiple times, like the CPType String. Particularly useful is giving your own classes methods that specify exactly how they should be displayed by DDT. Love It!
 |
| Both ADD and SUB refer to the same LOAD node |
 |
| The DO node is inserted for the LET operator. It ensures that the value of the LOAD node is computed before the reference in either branch |
This evening, I enjoyed the New Year’s fireworks display over the beautiful Prague skyline. Well, the bit of it that was between me and the fireworks, anyway. Rather than having its fireworks display at midnight, Prague puts it at 6pm on New Year’s Day. That makes it easy for families to go to, which is rather thoughtful. It’s also, for those of us with plans to dig back into work the following day, a nice end to the festive break.
So, tomorrow I’ll be digging back into work, which of late has involved a lot of Perl 6. Having spent so many years working on Perl 6 compiler and runtime design and implementation, it’s been fun to spend a good amount of 2018 using Perl 6 for commercial projects. I’m hopeful that will continue into 2019. Of course, I’ll be continuing to work on plenty of Perl 6 things that are for the good of all Perl 6 users too. In this post, I’d like to share some of the things I’m hoping to work on or achieve during 2019.
The MoarVM specializer learned plenty of new tricks this year, delivering some nice speedups for many Perl 6 programs. Many of my performance improvement hopes for 2019 center around escape analysis and optimizations stemming from it.
The idea is to analyze object allocations, and find pieces of the program where we can fully understand all of the references that exist to the object. The points at which we can cease to do that is where an object escapes. In the best cases, an object never escapes; in other cases, there are a number of reads and writes performed to its attributes up until its escape.
Armed with this, we can perform scalar replacement, which involves placing the attributes of the object into local registers up until the escape point, if any. As well as reducing memory operations, this means we can often prove significantly more program properties, allowing further optimization (such as getting rid of dynamic type checks). In some cases, we might never need to allocate the object at all; this should be a big win for Perl 6, which by its design creates lots of short-lived objects.
There will be various code-generation and static optimizer improvements to be done in Rakudo in support of this work also, which should result in its own set of speedups.
Expect to hear plenty about this in my posts here in the year ahead.
The current Rakudo startup time is quite high. I’d really like to see it fall to around half of what it currently is during 2019. I’ve got some concrete ideas on how that can be achieved, including changing the way we store and deserialize NFAs used by the parser, and perhaps also dealing with the way we currently handle method caches to have less startup impact.
Both of these should also decrease the base memory use, which is also a good bit higher than I wish.
Some folks – myself included – are developing increasingly large applications in Perl 6. For the current major project I’m working on, runtime performance is not an issue by now, but I certainly feel myself waiting a bit on compiles. Part of it is parse performance, and I’d like to look at that; in doing so, I’d also be able to speed up handling of all Perl 6 grammars.
I suspect there are some good wins to be had elsewhere in the compilation pipeline too, and the startup time improvements described above should also help, especially when we pre-compile deep dependency trees. I’d also like to look into if we can do some speculative parallel compilation.
In Perl 6.d, we got non-blocking await and react support, which greatly improved the scalability of Perl 6 concurrent and parallel programs. Now many thousands of outstanding tasks can be juggled across just a handful of threads (the exact number chosen according to demand and CPU count).
For Perl 6.e, which is still a good way off, I’d like to having something to offer in terms of making Perl 6 concurrent and parallel programming safer. While we have a number of higher-level constructs that eliminate various ways to make mistakes, it’s still possible to get into trouble and have races when using them.
So, I plan to spend some time this year quietly exploring and prototyping in this space. Obviously, I want something that fits in with the Perl 6 language design, and that catches real and interesting bugs – probably by making things that are liable to occasionally explode in weird ways instead reliably do so in helpful ways, such that they show up reliably in tests.
In the 16 months since I revealed it, Cro has become a popular choice for implementing HTTP APIs and web applications in Perl 6. It has also attracted code contributions from a couple of dozen contributors. This year, I aim to see Cro through to its 1.0 release. That will include (to save you following the roadmap link):
I founded Comma IDE in order to bring Perl 6 a powerful Integrated Development Environment. We’ve come a long way since the Minimum Viable Product we shipped back in June to the first subscribers to the Comma Supporter Program. In recent months, I’ve used Comma almost daily on my various Perl 6 projects, and by this point honestly wouldn’t want to be without it. Like Cro, I built Comma because it’s a product I wanted to use, which I think is a good place to be in when building any product.
In a few months time, we expect to start offering Comma Community and Comma Complete. The former will be free of charge, and the latter a commercial offering under a subscribe-for-updates model (just like how the supporter program has worked so far). My own Comma wishlist is lengthy enough to keep us busy for a lot more than the next year, and that’s before considering things Comma users are asking for. Expect plenty of exciting new features, as well as ongoing tweaks to make each release feel that little bit nicer to use.
This year will see me giving my first keynote at a European Perl Conference. I’m looking forward to being in Riga again; it’s a lovely city to wander around, and I remember having some pretty nice food there too. The keynote will focus on the concurrent and parallel aspects of Perl 6; thankfully, I’ve still a good six months to figure out exactly what angle I wish to take on that, having spoken on the topic many times before!
I also plan to submit a talk or two for the German Perl Workshop, and will probably find the Swiss Perl Workshop hard to resist attending once more. And, more locally, I’d like to try and track down other Perl folks here in Prague, and see if I can help some kind of Praha.pm to happen again.
I need to keep my travel down to sensible levels, but might be able to fit in the odd other bit of speaking during the year, if it’s not too far away.
While I want to spend most of my time building stuff rather than talking about it, I’m up for the occasional bit of teaching. I’m considering pitching a 1-day Perl 6 concurrency workshop to the Riga organizers. Then we’ll see if there’s enough folks interested in taking it. It’ll cost something, but probably much less than any other way of getting a day of teaching from me. :-)
Well, a good night’s sleep first. :-) But tomorrow, another year of fun begins. I’m looking forward to it, and to working alongside many wonderful folks in the Perl community.
I want to revive Carl Mäsak's Coding Contest as a crowd-sourced contest.
The contest will be in four phases:
For the first phase, development of tasks, I am looking for volunteers who come up with coding tasks collaboratively. Sadly, these volunteers, including myself, will be excluded from participating in the second phase.
I am looking for tasks that ...
This is non-trivial, so I'd like to have others to discuss things with, and to come up with some more tasks.
If you want to help with task creation, please send an email to [email protected], stating your intentions to help, and your freenode IRC handle (optional).
There are other ways to help too:
In these cases you can use the same email address to contact me,
or use IRC (moritz on freenode) or twitter.