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Roman Baumer (Freenode: rba #raku or ##raku-infra) / 2022-08-11T02:19:56

Rakudo Weekly News: 2022.32 2nd Conf

Published by liztormato on 2022-08-08T14:11:14

Two days of just stuff about the Raku Programing Language at the second Raku Conference this weekend. Online only, alas. Hopefully in person next year! Ah, and if you would like to give a lightning talk, there’s still room for that! And don’t forget to register if you haven’t already!

Podlite server

Alexandr Zahatski, the author of Podlite (pod6 markup editor) announced Podlite-web, a lightweight starter for creating blogs/sites using nexjs and pod6 markup language (/r/rakulang comments). Intriguing usages and examples!

Anton’s Corner

Anton Antonov continues their work on Machine Learning, this week again with an extensive blog post, this time about Clustering analyses with the K-means and K-medoids algorithms, among many others! Good progress!

Did You Know?

That when you’re using a map, you can return more than one value from the Callable block?

$ say (^5).map({ $_, $_ + 1 })
# ((0 1) (1 2) (2 3) (3 4) (4 5))

Well, actually in this case, it’s still a single value, which happens to be a List. However, you can really return multiple values, as long as you put them in a Slip.

$ say (^5).map({ ($_, $_ + 1).Slip })
# (0 1 1 2 2 3 3 4 4 5)

Slip is a List that automatically flattens into an outer List (or other list-like container or iterable). You can also use the prefix pipe to create a Slip. In the above example, that would be: |($_, $_ + 1).


Weekly Challenge #177 is available for your perusal.

New Problem Solving Issues

New Pull Requests

Core Developments

Questions about Raku

Meanwhile on Twitter

Meanwhile on the mailing list

New Raku Modules

Updated Raku Modules

Winding down

A record week in Raku module land! Quite a number of new modules, and updates (with Jonathan Stowe updating and moving all of their modules to the zef ecosystem!).

This week’s image background again there to remind us to support Ukraine in their and our fight against the Russian aggression. Слава Україні!  Героям слава!

In the meantime, please stay safe, stay healthy, keep up the good work!

If you like what I’m doing, committing to a small sponsorship would mean a great deal!

Rakudo Weekly News: 2022.31 JustinTimeRelease

Published by liztormato on 2022-08-01T13:40:17

Justin DeVuyst was able to release the 2022.07 Rakudo Compiler Release just before the end of the month. Which added inode, dev and devtype methods to IO::Path, and a new .snip method (inspired by Haskell’s span). And many other improvements and fixes! Updated Linux packages are now also available on rakudo.pkg, thanks to Claudio Ramirez.

Comma Release

Jonathan Worthington and their team also released new versions of the Comma IDE, both the (paid) Comma Complete, as well as the (free) Comma Community edition. New features include better real-time reporting of IO and concurrency in your programs! Check it out!

Raku Conference

Quite a few presentations were added since the last listing in the weekly 🙂

These are all accepted presentations so far. Please register if you plan to attend this fine conference to allow for better resource planning! Ah, and if you would like to give a lightning talk, there’s still room for that!

Wenzel’s Corner

Wenzel P.P. Peppmeyer got too many Sundays to handle in Swarming Sundays.

Anton’s Corner

Anton Antonov continues their work on Machine Learning with an extensive blog post about the fast classification of DSL commands. Good to see Raku being used in the scientific world!

Did You Know?

That there are two ways of indicating a False value with Raku’s standard interpretation of command line arguments?

$ script-name --foo=False
$ script-name --/foo

However, many people are used to be able to specify --no-foo as a way to indicate that the foo option is False. And this is not supported by Raku. Fortunately, there’s a simple trick that you can add to your script to allow this transparently:

$_ .= subst(/^ '--no-' /, '--/') for @*ARGS;

This looks at the raw arguments (as available in @*ARGS) and changes any arguments starting with --no- to --/ (which is an accepted format in Raku’s standard way of interpreting command line arguments). After that, it’s just as if users of your script typed --/foo instead of --no-foo.


Weekly Challenge #176 is available for your perusal.

New Problem Solving Issues

New Pull Requests

Core Developments

Questions about Raku

Meanwhile on Twitter

Comments about Raku

New Raku Modules

Updated Raku Modules

Winding down

A bit of a quiet week, with a lot of people on vacation, and others preparing software releases and/or presentations for the 2022 Raku Conference!

This week’s image again there to remind us to support Ukraine in their and our fight against the Russian aggression. Слава Україні!  Героям слава!

In the meantime, please stay safe, stay healthy, keep up the good work! ‘Rona has gotten competition 😦

If you like what I’m doing, committing to a small sponsorship would mean a great deal! Rakudo compiler, Release #157 (2022.07)

Published on 2022-07-31T00:00:00

gfldex: Swarming Sundays

Published by gfldex on 2022-07-30T09:29:58

To not miss any Sundays, PWC 175 is asking us to find them. Raku helps us a great deal, because we can easily find the last day in a month, calculate it’s distance to the next Sunday and turn the clock back by that many days.

for 2022 -> $year {
    for 1..12 -> $month {
        my $last-day-in-month =$year, $month, *);
        my $Δsunday = $ % 7;
        say $last-day-in-month.earlier(:days($Δsunday));

If you follow this blog, you will have spotted my distaste for simple loops. They get in the way of using interesting language features and thus fun. will only ever return a single date. With an Array of dates I could use vector operations to calculate all last Sundays in a month in one go. So I need a way to create a swarm of Dates, depending on a pattern. Signatures are patterns (and just fancy lists), so let’s use those.

sub swarm(::Type, @sig is copy, Range $range) {
    my $var;
    my $marker-index = @sig.first([], :k);
    @sig[$marker-index] := $var;
    gather for @$range {
        $var = $_;
        take |@sig;

my @months = swarm(Date, (2022, @, *), 1..12);
my @Δsunday = @months».day-of-week »%» 7;
.say for{ .earlier(:days(@Δsunday.shift)) });

The sub swarm takes a type, a parameter-list meant to be used on that types’ new method and a range. It finds the empty list created by @ and replaced it with a container. That container is then filled with a value taken from the range. This is as neat as it is unnecessary. Raku will do the whole shebang for us if we use a Junction. Getting hold of the results requires a module by lizmat.

use eigenstates;

my @months =, (1..12).any, *).&eigenstates;
my @Δsunday = @months».day-of-week »%» 7;
.say for{ .earlier(:days(@Δsunday.shift)) });

# OUTPUT: No such method 'List' for invocant of type 'BOOTArray'.  Found 'List'
on type 'Any'
  in sub eigenstates at /usr/local/src/rakudo/install/share/perl6/site/sources/EDAA4AE0C8813633A2EA392374FB72F6B4D61047 (eigenstates) line 4

Thank you MoarVM, for creating a nice BOOTArray for us that we can’t easily turn into a List. Some bewilderment and a PR later, the result changed. You may wish to zef upgrade if you use eigenstates.


Just displaying those Sundays wont justify getting rid of the loop. If we need that in an API, returning a list of Dates without much hassle seems valuable. Please note, that the BOOTArray indicates eagerness and I don’t think it has to be. Junctions might get lazier and faster in the future.

Rakudo Weekly News: 2022.30 What

Published by liztormato on 2022-07-25T13:57:43

Someone with the nick MicrowaveOven86 asked a question on /r/rakulang: What do yall use raku for? With some nice, insightful, revealing and funny answers. Maybe more answers can be given by the readers of the Rakudo Weekly News?

Raku Conference

New presentations since the last listing in the weekly:

See also accepted presentations so far. If you have a nice module, or a nice use of the Raku Programming Language, please consider doing a presentation! You have until the 1st of August to submit your proposal!

And please register if you plan to attend to allow for better resource planning!

Wenzel’s Corner

Wenzel P.P. Peppmeyer got poetic in Are you a Raku poet?

Steve’s Corner

Steve Roe tries to work out how Raku (and the raku Dan module specifically) may have some resonance with the Data Scientists community in: Is raku Dan RubberSonic?

Did You Know?

Did you know that you can use the method .none (and friends) in a method call chain?

my \fib = 1, 1, * + * … ∞;
say so fib[^10];  # False
say so fib[^10];   # True
say so fib[5..9];  # True

A Junction is a parallel value that whats to collaps into a single Bool. Any method call to a definite Junction will be forwarded to its members, coerced to Bool and collapsed with the Junction-type. The above any-form is equivalent to:

say fib[^10].map(*.is-prime).reduce(&[||]);

Junctions come in many forms and can make you code quite neat!

This week’s “Did you know” contributed by Wenzel P.P. Peppmeyer! Other contributions are welcome!


Weekly Challenge #175 is available for your perusal.

New Problem Solving Issues

New Pull Requests

Core Developments

Questions about Raku

Meanwhile on Twitter

Meanwhile on the mailing list

Comments about Raku

New Raku Modules

Updated Raku Modules

Winding down

Good to see another crop of new modules and module updates!

This week’s image again there to remind us to support Ukraine in their and our fight against the Russian aggression. Слава Україні!  Героям слава!

In the meantime, please stay safe, stay healthy, keep up the good work! ‘Rona is most definitely not over yet.

If you like what I’m doing, committing to a small sponsorship would mean a great deal!

p6steve: Is raku Dan RubberSonic?

Published by p6steve on 2022-07-24T16:02:56


Raku is a great software language that draws on the scripting heritage of perl. The newly minted raku Dan modules provide Data ANalytics (geddit?) capabilities to raku for data science and engineering use cases. (Intro slides & demo video here). This is one in an occasional series of blog posts that seek to explore the whys & wherefores of raku Dan.

I you want to check out more, warts and all, visit /r/rakulang at Reddit and ask away!

Disclosure: I am the author of the raku Dan module family. ~p6steve

Why Dan?

It is tempting to say we need Dan for Raku just like Python needs Pandas – to meet the needs of the large community of Data Scientists. And there has been some healthy debate about this in the raku community around how this is valuable functionality that is otherwise lacking. Raku Dan is a necessary, but not a sufficient reason to switch.

The vast majority of Data Scientists are Python+Pandas users. To them, the question Why raku::Dan? is synonymous with the question Why raku? or even Why not Python?

Why not Python?

The majority of this vast majority is either (i) preoccupied with ascending the Python+Pandas learning curve, (ii) preoccupied with getting the data science bit to work (the problem) and not the toolset, (iii) value their team’s time investment in getting here and do not wish to risk a new and unproven path or (iv) simply cannot imagine a better way than Python+Pandas. There is no compelling reason to change that outweighs the perceived negatives and so they won’t.

A significant minority of this vast majority is looking for a better way. Some value speed and are trying Python+Polars. Changing to a similar, but faster library than Pandas is less disruptive than changing language.

Some are trying a purpose-built Data Science language like Julia to get both improved speed and a domain-oriented coding experience.

It seems that the reasons for making a change are a balance of:

SIDEBAR:- Notably, there is some limit to “raw speed at all costs” for most changers – otherwise folk would always choose R+data.table or Rust+Polars (the Polars library is written in Rust and uses Arrow2 fast columnar structures – bypassing Python would be an obvious speed up that would avoid the GIL and other overhead).

How Sonic is Raku?

Here, I use the term ‘Sonic’ to reflect raw speed.

Raku is not renowned for its build or execution speed – it’s a scripting language with on-the-fly build-and-run capability, after all. However, it does have very powerful & deep concurrency support such as hyper and map/reduce operations which are available in raku Dan from the outset:

say ~my \quants =[100, 15, 50, 15, 25]);
say ~my \prices =[1.1, 4.3, 2.2, 7.41, 2.89]); 
say ~my \costs  = quants >>*<< prices );

Yes – you can hyper a Dan::Series!

There are also some promising developments in sight for Raku (such as AST) and better VM optimisations.

This table illustrates how raku fits:

Script codePerlPython3RakuInterpreter / VMFlexible
System codeCC++RustCompilerFast

Polars is fast because Rust is fast. I love Rust and I believe that Rust and Raku is a great fit. That’s why raku Dan::Polars (a binding from Dan to Rust) is a vital member of the raku Dan module family and I am looking forward to its intro soon. With raku Dan::Polars we will get the same access to Rust and Arrow2 raw speed that Python/Polars has.

SIDEBAR:- a Rust compile (‘cargo build’ specifically) for dan/src/ (the custom Rust library that connects raku Dan::Polars to Rust/Polars) takes 2m 51s from scratch and 19.31s if I make a small change to the source file (incremental build). In contrast, the similar sized raku Dan/Polars.rakumod build never has a comparable initial compile overhead and an incremental change takes only 1.714s to recompile and run. Boy do I love scripting languages that avoid that 19s dev cycle time!!

So – raku is about as Sonic as Python.

How Rubber is Raku?

Here, I use the term ‘Rubber’ to reflect flexible coding experience.

I have spent the last 6 months learning to Rust. Rust is cool. But Rust is hard. Despite the alleviations of generic types and traits and so on, Rust is a strongly typed world (as you would expect from something that can make system code safely).

But, a Rust newby like me can spend ages to hammer down some type errors and untangle the <T>, into(), clone(), mut, Some(None), unwrap() knots just to get a few lines of code working. Strong typing means thinking as much about the language imposed patterns as about the problem-solution.

Obviously I am born scripter. I like to write a line or two that works in practice and then grow my code from there, refactoring with more structure as I start to repeat stuff and I need to harden against errors. So the raku model of “get some code working, then gradually layer in type safety as required” is a very natural fit.

This, I submit, is a crucial distinction for Data Science coders.

Strong typing puts substantial cognitive load on the coder and makes dealing with real-world data sources very onerous.

No wonder Python became the ‘darling child’ of the data scientist community. No wonder other strongly typed, though performant, languages of the day like Java or C++ didn’t pass muster. Simply put, Data science needs a flexible coding experience – it’s where the rubber hits the road.

So – raku is about as Rubbery as Python.

Why Raku?

Let’s say I have 5m sales records – maybe from a csv or some historic data files or data warehouse. Can I be sure that there are absolutely no errors such as (e.g.) ‘1’ (Int) instead of ‘1.0’ (Double), or an ‘l’ (Str) or maybe ‘𝟙’ (Unicode)?

Let’s see how that looks in raku:

> my @a = <1 1.0 l 𝟙>
[1 1.0 l 𝟙]                    #look I can suck up anything
(IntStr RatStr Str IntStr)     #I can just store them as allomorphs
> @a.are;
(Str)                          #and check for common parent type
> @a.grep(Real)
(1 1.0 𝟙)                      #I can extract the numbers
>{$_~~Real ?? .Num !! NaN})
(1 1 NaN 1)                    #or make them all Num (f64)

The .are method picks the narrowest type that is common to all the array items.

And it plays in the super well-structured raku gradual type system:

OK – so now I have a hierarchical type space for progressive munging and a set of type tests and an easy way to do type coercion / error rejection.

Raku also let’s me step up the enforcement by gradually adding types to my data structures.

Let’s gradually introduce typed variables. my Num @r = @a declares a new array @r whose items must be of type Num. It will reject other item types. This is a powerful way to specify and manage a “contract” between data capture / data munging phases and later data analysis code.

> my @a = <1 1.0 l 𝟙>
[1 1.0 l 𝟙]
> my Num @r = @a
Type check failed in assignment to @r; expected Num but got IntStr (, "1"))
  in block <unit> at <unknown file> line 1

Now, I can clean up my act like this (i) to replace unreal items with NaN and then (ii) to coerce all the remaining items to Num. I already know that there will be no coercion failures since they are all matched type Real.

>{$_=NaN if not $_~~Real})
> my Num() @r = @a
[1 1 NaN 1]

Here are some of the bits of raku I am using to do the work:

Many other horrors lurk in data munging and capture. Using raku and its gradual type system is a great way to contract and curate a data pipeline.

Here we have only scratched the surface with the full range of capabilities of raku and gradual types. What about DateTime formats? What about text extraction, regexes and unicode? So a lot more for next time…

Raku is RubberSonic

In summary, this blog has been trying to work out why raku::Dan in particular and therefore raku in general may have resonance with some the community of Data Scientists that typically use Python+Pandas today.

It has shown that when it comes to raw speed, Raku (when equipped with Dan::Polars) is about as Sonic as Python.

It has shown that when it comes to a flexible coding experience, Raku is about as Rubbery as Python.

This matters, I think, because the RubberSonic combination places raku Dan squarely in the tradition of the first Python/Pandas pioneers. It is a very good impedance match for the needs of data scientists – where the language does not get in the way of the solution.

Here I have set out a case for raku Gradual Typing and other unique capabilities which are not provided by the Python language to substantially improve consistency and control during the data munging and data capture phases of data analysis.

So, yes, raku Dan is definitely RubberSonic and is a very good fit for the needs of data scientists who feel constrained by the limits of the Python language around concurrency, gradual typing, formal OO with encapsulation, unicode and so on…

Please do leave a comment here, or come and join the raku debate over on reddit.


(c)2022 Henley Cloud Consulting Ltd.

Rakudo Weekly News: 2022.29 Hot

Published by liztormato on 2022-07-18T11:04:47

It is hot. It will keep getting hotter in the coming years. Enjoy the coolest summer you’ll ever have. Not much else to say.

Wenzel’s Corner

Wenzel P.P. Peppmeyer blogged about naming of bitmask values in Coercive Bits (/r/rakulang comments).

Steve’s Corner

Steve Roe blogged about how we tend to forget all the easy things we can do with the Raku Programming Language.

Did You Know?

That you can use the hyper operator >>op>> to run an operation on an array?

my @a = ^5;
say @a;          # [0 1 2 3 4]
say @a >>/>> 2;  # [0 0.5 1 1.5 2]

But what if you want to change the contents of the array? Then you can use the assigning version of the operator used. In this case: /= instead of / :

my @a = ^5;
@a >>/=>> 2;
say @a;  # [0 0.5 1 1.5 2]

Of course, you could also do @a = @a >>/>> 2, but that feels like you’re repeating yourself, and it is much less efficient. That’s because in the case of assignment by the hyperop, it does not need to create a result to be assigned to the array (because it can just return the left hand side of the hyperop).


Weekly Challenge #174 is available for your perusal.

New Problem Solving Issues

The Raku Problem Solving repository attempts to keep track of issues with the Raku Programming Language in all of its aspects. It is intended for discussions with the Raku community about the way to solve any issues that come up. The Rakudo Weekly News will mention any new problem solving issues from now on, to get more eyes to look at them. The most recent ones:

New Pull Requests

Core Developments

Questions about Raku

Meanwhile on Twitter

Comments about Raku

New Raku Modules

Updated Raku Modules

Winding down

Wow, looks like yours truly was on a roll last week with a big crop of new modules. 🙂

This week’s image was made enjoying another small bike ride in the neighborhood. It is there to remind us to support Ukraine in their and our fight against the Russian aggression. Слава Україні!  Героям слава!

In the meantime, please stay safe, stay healthy, keep up the good work! ‘Rona is most definitely not over yet.

If you like what I’m doing, committing to a small sponsorship would mean a great deal!

gfldex: Coercive bits

Published by gfldex on 2022-07-13T09:29:16

Altreus was looking for a way to convert a list of bitmask names, provided as a string by the user, to a bitmask. He wished for BUILDARGS, as provided by Perl’s Moose but was given good advise how to solve the problem without making object instantiation even more complex. Nobody can stop me from doing just that.

With Moose, BUILDARGS allows to modify values before they are bound to attributes of a class. We can do the same by using the COERCE-protocol, not just for Routine-arguments.

role BitMask[@names] {
    has int $.mask;
    sub names-to-bits(@a) {
        my int $mask;
        for @a -> $s {
            $mask = $mask +^ (1 +< @names.first($s, :k) // fail('bad‼'))

    sub bits-to-names($mask) {
        ($mask.base(2).comb.reverse Z @names).map: -> [Int() $is-it, $name] { $is-it ?? $name !! Empty }

    multi method COERCE(Str $s) { self.COERCE: $s.split(' ').list }
    multi method COERCE(List $l) { :mask($l.&names-to-bits) }

    method raku { "BitMask[<@names[]>](<{$!mask.&bits-to-names}>)" }
    method bits(--> Str) { $.mask.fmt('%b') }

class C {
    has BitMask[<ENODOC ENOSPEC LTA SEGV>]() $.attr is required;

my $c = :attr<ENODOC ENOSPEC SEGV>;
say $c;
say $c.attr.bits;
#         1011 

Here I build a role that carries the names of bits in a bit-field and will create the bit-field when give a Str or List containing those names. Since I use a parametrised role, my bitfield is type-safe and the type is tied to the actual names of the bits. As a consequence a user of the module that exports C can extend the accepted types by using that specific role-candidate.

enum IssueTypes ( ENODOC => 0b0001, ENOSPEC => 0b0010, LTA => 0b0100, SEGV => 0b1001 );

class IssueTypesBits does BitMask[<ENODOC ENOSPEC LTA SEGV>] {
    method new(*@a where .all ~~ IssueTypes) {
        self.bless: mask => [+|] @a

sub bitmask(*@a where .all ~~ IssueTypes) { |@a

my $c3 = attr => BEGIN bitmask(ENODOC, LTA, SEGV);
say $c3;
say $c3.attr.bits;

Here I define an enum and provide the same names in the same order as in does BitMask. With the shim bitmask I create the type-safety bridge between IssueTypes and BitMask[<ENODOC ENOSPEC LTA SEGV>].

It is very pleasing how well the new COERCE-protocol ties into the rest of the language, because we can use a coercing-type at any place in the source code which takes a normal type as well. What is not pleasing are the LTA-messages X::Coerce::Impossible is producing.

my $c4 = attr => 42;
# OUTPUT: Impossible coercion from 'Int' into 'BitMask[List]': method new returned a type object NQPMu
            in block <unit> at parameterised-attribute.raku line 60

This is surpring because we can get hold of the candidates for COERCE at runtime.

    when X::Coerce::Impossible && .target-type.HOW === Metamodel::CurriedRoleHOW {
        put "Sadly, COERCE has failed for {.from-type.^name}. Available candidates are: ",^lookup('COERCE').candidates».signature».params[*;1]»*.^name)

# OUTPUT: Sadly, COERCE has failed for Int. Available candidates are: Str List

What we can’t see are the candidates of IssueTypesBits, because that is hidden behind the constructor new. I tried to use the MOP to add another multi candidate but failed. When parametrising the underlying type-object gets cloned. Any change to the multi-chain wont propagate to any specialised types.

The COERCE-protocol is quite useful indeed. Maybe it’s time to document it.

p6steve: raku ‘KISS’

Published by p6steve on 2022-07-11T21:18:54

Occam’s razor, also known as Keep It Simple Stupid (KISS), is a sound principle. Larry says it this way ~ “make the easy things easy and the hard things possible”.

The raku community is a set of (deep) experts (yes, really) – who are intent on the hard things:

Me, I learnt raku after 4 years of perl and 10 years of not coding. So, my reboot was from (nearly) scratch and Think Raku was my guide.

I get the feeling that sometimes we forget all the easy stuff it can do….

SO… let’s just take a breath and remember that, if you want, raku is easy-peasy.

SO… to demonstrate this, I went to raku-Physics-Measure-Jupyter and clicked the Binder link. Binder is a really easy, on demand place where Python and Raku coders can host and share Jupyter notebooks. Jupyter is a really easy, live notebook for Python and Raku coders to dabble and test their ideas.

Well, I could spell out the details here. But I prefer to let the code speak for itself.

And here’s the result:



PS. Comments/feedback welcome.

Rakudo Weekly News: 2022.28 Announciations

Published by liztormato on 2022-07-11T12:59:22

The announcements of presentations of the second Raku Conference on 13-14 August 2022, keep coming in. So far, the following presentations have been accepted:

But that’s not nearly enough yet! So please consider submitting a presentation proposal, if you are:

Formats are lightning talk (5 minutes), short presentation (20 minutes), presentation (45 minutes), tutorial, class and workshop.

Documentation Meetup

The minutes of the online meeting of the documentation team on 9 July, are online. You can always check the #raku-doc IRC channel for more information about the documentation effort.

Anton’s Corner

Anton Antonov continues to be on a roll yet again this week, with two blog posts:

Steve’s Corner

Steve Dondley went spelunking in their code with Rockin’ with Raku – A Detailed Walk Through of Code for Finding Specific Prime Numbers (/r/rakulang comments).

Wenzel’s Corner

Wenzel P.P. Peppmeyer was in search of truth this week in The truth is a hard problem as a response on Steve Dondley‘s post (/r/rakulang comments)!

Steven’s Corner

Steven Lembark has updated their slide deck from a 2016 presentation to Raku: Climbing a Tree – Refactoring FindBin::libs for Raku.

Did You Know?

That you can use map as an alternative to grep?

my @a = ^10;
say @a.grep: * %% 2;  # (0 2 4 6 8)
say { $_ if $_ %% 2 }  # (0 2 4 6 8)

This is possible because a failed if (or with or elsif or etc…) will return Empty, which will cause the iterator to just skip that value. How can you check that? With do!

dd do if 0 { }  # Empty

As to why you would use map over grep? Well, map‘s handling of Empty is highly optimized, causing the same code with map to be up-to 2x as fast as using grep. But note, this only affects the overhead of iterating: if your code inside the grep or map is very expensive, you won’t see much difference!


Weekly Challenge #173 is available for your perusal.

New Pull Requests

Core Developments

Questions about Raku

Meanwhile on Twitter

Meanwhile on the mailing list

Comments about Raku

New Raku Modules

Updated Raku Modules

Winding down

Wow, yet another nice crop of new modules! Keep them coming 🙂

This week’s image was made enjoying a small bike ride in the neighborhood. It is there to remind us to support Ukraine in their fight against the Russian aggression. Слава Україні!  Героям слава!

In the mean time, please stay safe, stay healthy, keep up the good work! ‘Rona is most definitely not over yet.

If you like what I’m doing, committing to a small sponsorship would mean a great deal!

gfldex: The truth is a hard problem

Published by gfldex on 2022-07-11T08:28:53

In a recent article, stevied promised a detailed walk through of code. I asked him if he would be interested in a critique of his writings. He foolishly agreed.

He claims that Arrays are eager and fill all memory if given the chance. Let’s check this claim.

my @a = 1, 1, * + * … ∞;
say @a.WHAT;

# OUTPUT: (Array)

Array.STORE goes out of it’s way to maintain iterables (not all things that are iterable do Iterable) and only reify container slots that we actively ask for. It will cache lazy lists by default, assuming that when we use .AT-POS (usually indirectly with a positional subscript) we want to use the values again. Dealing with lazy lists is a hard problem and many get it wrong (as my next blog post will discuss in more detail).

In the next paragraph I found another inaccurate statement. Int:D does not check for definedness.

multi sub bar(Any:D $) { say 'defined'; }
multi sub bar(Any:U $) { say 'undefined'; }

bar(Int); # undefined

class C { method defined { False } }
my $c =; # undefined
bar(C); # undefined
bar($c); # defined
say defined $c; # False
say $c.DEFINITE; # True
say $c // 'undefined'; # undefined

In Raku objects are either type objects or definite objects. The latter are the result of nqp::create. Raku and most user code expects objects to provide a basic set of methods that can be found in Mu or Any. The default type-check is against Mu. We can check if an object is definite with the macro .DEFINITE (you can overload a method of the same name, but Rakudo will ignore it) or, if we also want to know if we got an ordinary object, with $foo ~~ Mu:D. There is a clear distinction of definedness and being definite. Raku needs to make that differentiation to support “unusual” values and types like Nil and Failure. Please note, that type-objects are not definite but can be defined. Types are a hard problem.

In the same paragraph stevied writes: “defined integer object”, even though he makes a type-check against Int.

multi sub foo(Int:D $i) { }
multi sub foo($i where { $i - $i.truncate == 0 }) { say 'lolwut‽' }
multi sub foo($) { say 'not integer'; }


# OUTPUT: lolwut‽
          not integer

Int:D will fail for any value that doesn’t got (Int) in its .^mro or is a subset of Int or any of Int‘s sub-classes. Raku sports an excellent coercion protocol and there is no reason not use it.

subset PositiveInteger of Numeric:D() where { $^i > 0 && ($i - $i.truncate == 0) || fail("Expected a positive and an integer value but got $i.")}
sub get-prime(PositiveInteger $nth where * > 0) {
    say ($x.grep: *.is-prime)[$nth - 1];
# OUTPUT: 11
          Expected a positive and an integer value but got 0.5.
            in block  at 2021-03-08.raku line 1809
            in block <unit> at 2021-03-08.raku line 1288

A value check is needed in this instance because .AT-POS will coerce to Int (by truncating) what may give a reasonable answer for an unreasonable question. No matter how sharp the knife, we wont get the ½th prime number. Being positive and integer is a hard problem.

Please don’t use say unless you actually want to run .gist.

say Nil;
put Nil;
          Use of Nil in string context
            in block  at 2021-03-08.raku line 1818

When you say you may miss subtile bugs, especially when running CI-tests.

Further down we find Sequence but grep returns a Seq – in this example. I never had to make the distinction between Seq and HyperSeq but the latter is not a child of Cool so a lot of interfaces are missing. The same is true for the role Sequence. The build-in types are a hard problem.

If we must ask what exactly * is (I wouldn’t, because that’s a really hard question.), it is best to provide the correct answer.

my &b = * + *;
say .WHAT, .signature with &b;
my &b2 = { $^a + $^b };
say .WHAT, .signature with &b2;
# OUTPUT: (WhateverCode)(;; $whatevercode_arg_11 is raw, $whatevercode_arg_12 is raw)
          (Block)($a, $b)

A WhateverCode-object will have all its arguments as is raw, what means it will always have access to the containers used by the callee (There are many things we can do, but probably shouldn’t, with .VAR). It will never have control exceptions (no return or phasers, etc.), doesn’t have a proper scope (no use-statement) and a few more things. The idea is to have a subclass of Code that is so simple that the compiler can always inline it. Code-objects are a hard problem.

I would have written that code a little different.

sub get-primes(*@nth) {
    (^∞).hyper.grep(-> Int:D() $_ is raw { .is-prime })[@nth »-» 1]

.put for get-primes(5, 50, 500, 5000, 50000);

I spend a pretty penny on that Threadripper, so I better .hyper as often as I can. If sensible I try to use a slurpy to move the burden of dealing with plurals from the user to the implementer. We can ask .AT-POS for a list of values, so there is no reason not to.

There are quite a few more inaccuracies in Steve’s article. Bless him, Raku is a bitch and we are not good at explaining how the language actually works. For most programs that doesn’t matter, they will run just fine even if the programmer is sloppy. I learned most of the gritty details when tracking down bugs. Granted, I stick my arm deep into the machinery, so it’s my own bloody fault when I get pinched. Since bugs happen we need to get better at explaining how to hunt them down. Raku is a hard problem.

gfldex: Sinking Errors

Published by gfldex on 2022-06-26T19:15:56

I was looking for a way to output debug messages that can also carry additional values, when not output to the screen. That is easy. The tricky part is golfing the interface. After quite a bit of struggle. I ended up with the following.

use Log;

dd ERROR('foo') ~~ LogLevel::ERROR;
dd ERROR('foo') ~~ LogLevel::DEBUG;
say ERROR('foo').&{ .file, .line };
VERBOSE 'Detailed, very much so indeed.';
my $*LOGLEVEL = 2;
VERBOSE 'Detailed, very much so indeed.';

# Bool::True
# Bool::False
# (2021-03-08.raku 1661)
# 2021-03-08.raku:1664 Detailed, very much so indeed.

In sink-context, the “functions” will output to $*ERR and when not sunk return an object that provides a few useful methods. To get both, we need to implement the methods sink and CALL-ME.

# Log.rakumod

use v6.d;

my role Functor {
    has $.message;
    my $.level;
    has $.captured-loglevel;
    has $.file;
    has $.line;
    method CALL-ME(*@message) {
        my ($line, $file) = callframe(1).&{ .line, .file }; message => "$file:$line " ~ @message.join($?NL), :captured-loglevel($*LOGLEVEL // 0), :$line, :$file;
    method sink { $*ERR.put(self.message) if ($!captured-loglevel // 0) ≥ $.level }
    method Str(::?CLASS:D:) { self.message }
    method gist(::?CLASS:D:) { self.^shortname ~ ' ' ~ self.message }

my $ERROR;                                                                                                                                                                    my $VERBOSE;
my $DEBUG;

class LogLevel {
    constant ERROR := $ERROR;
    constant VERBOSE := $VERBOSE;
    constant DEBUG := $DEBUG;

$ERROR = class :: is LogLevel does Callable does Functor { my $.level = 1; }
$VERBOSE = class :: is LogLevel does Callable does Functor { my $.level = 2; }
$DEBUG = class :: is LogLevel does Callable does Functor { my $.level = 3; }

sub EXPORT { '&ERROR' => $ERROR,
             '&VERBOSE' => $VERBOSE,
             '&DEBUG' => $DEBUG,
             'LogLevel' => LogLevel,

To behave like a function a Callable must be bound to an &-sigiled symbol (or Rakudo complains when we use it as a term). As soon as Rakudo spots a class, it will stuff it into UNIT::EXPORT::ALL. When importing any &-sigiled key of sub EXPORT, with the same basename as a class with that basename, the symbol from EXPORT will be ignored. The only way to avoid that is to have anonymous classes and do the forward-declarations by hand. The latter is needed to allow ERROR('foo') ~~ LogLevel.

All that is a bit convoluted but works as expected. What I didn’t expect, is that dynvars that are user-defined are not visible in method sink. Not much of a problem in my case, as capturing the state of $*LOGLEVEL inside CALL-ME is the right thing to do anyway. What threw me off is the inconsistency with other functions and methods that are called by a time-machine. We type BEGIN, BUILD and friends in all-caps for that very reason. This may warrant a problem solving issue. It’s an ENODOC for sure.

I believe golfing APIs is a most desireable effort. Laziness isn’t just a virtue for programmers, often it’s a given. Rakudo compiler, Release #156 (2022.06)

Published on 2022-06-05T00:00:00

p6steve: raku & rust: a romance?

Published by p6steve on 2022-05-28T19:15:56

Rust is blazingly fast and memory-efficient: with no runtime or garbage collector, it can power performance-critical services, run on embedded devices, and easily integrate with other languages. Rust continues the spirit of C with emphasis on code safety and performance with a compiled approach.

Raku is an open source, gradually typed, Unicode-ready, concurrency friendly programming language made for at least the next hundred years. Raku continues the spirit of Perl with interpreter-like code generation (actually on MoarVM), one-liners, shell-centric, lightweight objects and expressiveness to get code up and running fast.

Both are modern languages and come from the Linux background:

In the same way that Perl and C were highly complementary technologies in their heyday, so Rust and Raku are naturally, romantically destined to be linked.

Introducing raku Inline::Rust

Following the nomenclature of raku modules such as Inline::Perl5, Inline::Python, Inline::Go and so on, Inline::Rust is a newly availably “interface” to connect Raku code to Rust dynamic libraries. Unlike some of its brethren, this is rather a zen module in that it provides worked examples and helpful Dockerfile builds to speed up the process, but no code is needed. The standard Rust FFI (Foreign Function Interface) on one side – the term comes from the specification for Common Lisp, which explicitly refers to the language features for inter-language calls as such; it is also used officially by the Haskell, Rust and Python programming languages. The core Raku NativeCall on the other – for calling into dynamic libraries that follow the C calling convention

Inline::Rust is inspired by the excellent Rust FFI Omnibus by Jake Goulding.

The Rust FFI Omnibus is a collection of 6 examples of using code written in Rust from other languages. Rust has drawn a large number of people who are interested in calling native code from higher-level languages. Many nearly duplicate questions have been asked on Stack Overflow, so the Omnibus was created as a central location for easy reference. This reference already covers C, Ruby, Python, Haskell, node.js, C# and Julia – with examples for each of these languages accessing the same Rust library code.

For the purposes of brevity, since the Rust code is common for all, this article will focus on the Raku consumption:


We need to start with some basics – follow the at Inline::Rust to try it yourself:

use NativeCall; 

constant $n-path = './ffi-omnibus/target/debug/foo';

Rust FFI Omnibus: Integers

Use the is native Trait to define the external function characteristics:

sub addition(int32, int32) returns int32 
    is native($n-path) { * }

say addition(1, 2);

Rust FFI Omnibus: String Arguments

Raku NativeCall provides traits to control Str encoding:

sub how_many_characters(Str is encoded('utf8')) returns int32 
    is native($n-path) { * }

say how_many_characters("göes to élevên");

Rust FFI Omnibus: String Return Values

Here we get a string back from Rust – the “free” sub means that Raku is responsible for releasing the memory. Raku Pointers can use the .deref method to get their contents:

sub theme_song_generate(uint8) returns Pointer[Str] is encoded('utf8') 
    is native($n-path) { * }
sub theme_song_free(Pointer[Str]) 
    is native($n-path) { * }

my \song = theme_song_generate(5);
say song.deref;

Rust FFI Omnibus: Slice Arguments

Here the for statement is used to cover over the lack of a direct assignment to CArray … a standard raku Array has a totally different memory layout so the CArray type is provided.

sub sum_of_even(CArray[uint32], size_t) returns uint32 
    is native($n-path) { * }

my @numbers := CArray[uint32].new;
@numbers[$++] = $_ for 1..6; 

say sum_of_even( @numbers, @numbers.elems );

Rust FFI Omnibus: Tuples

Full disclosure – there is an open issue with this pattern:

class Tuple is repr('CStruct') {
    has uint32 $.x;
    has uint32 $.y;
sub flip_things_around(Tuple) returns Tuple 
    is native($n-path) { * }

my \initial = x => 10, y => 20 );
my \result  = flip_things_around(initial);
say result.x, result.y;

Rust FFI Omnibus: Objects

Here we can use a Raku class to wrap a Rust structure, note the free method is now automatically called by the Raku Garbage Collector:

class ZipCodeDatabase is repr('CPointer') {
    sub zip_code_database_new() returns ZipCodeDatabase 
        is native($n-path) { * }
    sub zip_code_database_free(ZipCodeDatabase)         
        is native($n-path) { * }
    sub zip_code_database_populate(ZipCodeDatabase)     
        is native($n-path) { * }
    sub zip_code_database_population_of(ZipCodeDatabase, Str 
        is encoded('utf8'))returns uint32 is native($n-path) { * }

    method new {

    # Free data when the object is garbage collected.
    submethod DESTROY {        

    method populate {

    method population_of( Str \zip ) {
        zip_code_database_population_of(self, zip);

my \database =;

my \pop1 = database.population_of('90210');
my \pop2 = database.population_of('20500');
say pop1 - pop2;


The raku Nativecall syntax is very straightforward and the C-heritage on both sides shows in the seamless marriage (geddit?) of types.

This results in probably the most concise and natural code on the raku side – take a look at the other examples and make your own judgement!

More to come on some practical applications of this and support for concurrency and gradual typing…


PS. Please do leave comments/feedback on the blog page… here

PPS. Love that raku does not enforce indentation – I can make it fit the narrow width here!

gfldex: Reducing sets

Published by gfldex on 2022-05-25T20:41:22

This week´s PWC asks us for hexadecimal words and distinctly different directories.

sub term:<pwc-166-1> {
    sub less-substitutions-then ($_, $n) {
        .subst(/<-[olist]>/, :g, '').chars < $n

        .grep({.chars ≤ 8 && .&less-substitutions-then(4)})\
        .map(*.trans(<o l i s t> => <0 1 1 5 7>))\
        .grep(/^ <[0..9 a..f A..F]>+ $/)\

Once again, we write the algorithm down. Get the words, drop anything longer then 8 chars or what would need more then 4 substitutions. Then do the substitutions and grep anything that looks like a hexadecimal numeral. Sort for good measure and output the first 100 elements.

The second task provides us with a little challenge. We need to mock listing directories and working with them. Since dir returns a sequence of IO::Path I can create those by hand and mixin a role that mimics some filesystem operations. I can overload dir to provide a drop-in replacement.

sub term:<pwc-166-2> {
    sub basename(IO::Path $_) { .basename ~ (.d ?? '/' !! '') }
    sub pad(Str $_, $width, $padding = ' ') { .Str ~ $padding x ($width - .chars) }

    sub dir(Str $name) {
        sub mock-file(*@names) {{$_) but role :: { method f ( --> True ) {}; method e ( --> True ) {} } } ) }
        sub mock-dir(*@names) {{$_) but role :: { method d ( --> True ) {}; method e ( --> True) {} } }) }

        constant %dirs = dir_a => flat(mock-file(<Arial.ttf Comic_Sans.ttf Georgia.ttf Helvetica.ttf Impact.otf Verdana.ttf>), mock-dir(<Old_Fonts>)),
                         dir_b => mock-file(<Arial.ttf Comic_Sans.ttf Courier_New.ttf Helvetica.ttf Impact.otf Tahoma.ttf Verdana.ttf>),
                         dir_c => mock-file(<Arial.ttf Courier_New.ttf Helvetica.ttf Impact.otf Monaco.ttf Verdana.ttf>);


    sub dir-diff([email protected]) {
        my @content = @dirs».&dir».&basename;
        my @relevant = (([∪] @content) ∖ [∩] @content).keys.sort;

        my @columns => @col {{ $_ ∈ @col ?? $_ !! '' }) });
        my $col-width = [max] @columns[*;*]».chars;

        put @dirs».&pad($col-width).join(' | ');
        put (''.&pad($col-width, '-') xx 3).join('-+-');
        .put for ([Z] @columns)».&pad($col-width)».join(' | ');

    dir-diff(<dir_a dir_b dir_c>);

I’m asked to add a / to directories and do so with a custom basename. The rest is liberal application of set theory. Only names that don’t show up in all directories are relevant. Columns are created by matching the content of each directory against the relevant names. The width of columns is the longest string. The header is put on screen. To output the columns line by line, x and y are flipped with [Z].

After careful study of the solutions written in other languages, I believe it is fair to call Raku an eco-friendly language. Our keyboards are going to last at least twice a long. Rakudo compiler, Release #155 (2022.04)

Published on 2022-04-24T00:00:00

vrurg: He Tested Many Locks. See What Happened Then!

Published by Vadim Belman on 2022-04-23T00:00:00

These clickbaiting titles are so horrible, I couldn’t stand mocking them! But at least mine speaks truth.

My recent tasks are spinning around concurrency in one way or another. And where the concurrency is there are locks. Basically, introducing a lock is the most popular and the most straightforward solution for most race conditions one could encounter in their code. Like, whenever an investigation results in a resolution that data is being updated in one thread while used in another then just wrap both blocks into a lock and be done with it! Right? Are you sure?

They used to say about Perl that “if a problem is solved with regex then you got two problems”. By changing ‘regex’ to ‘lock’ we shift into another domain. I wouldn’t discuss interlocks here because it’s rather a subject for a big CS article. But I would mention an issue that is possible to stumble upon in a heavily multi-threaded Raku application. Did you know that Lock, Raku’s most used type for locking, actually blocks its thread? Did you also know that threads are a limited resource? That the default ThreadPoolScheduler has a maximum, which depends on the number of CPU cores available to your system? It even used to be a hard-coded value of 64 threads a while ago.

Put together, these two conditions could result in stuck code, like in this example:

BEGIN PROCESS::<$SCHEDULER> = max_threads => 32;

my Lock $l .= new;
my Promise $p .= new;
my @p; 

@p.push: start $l.protect: { await $p; };

for ^100 -> $idx {
    @p.push: start { $l.protect: { say $idx } }

@p.push: start { $p.keep; }

await @p;

Looks innocent, isn’t it? But it would never end because all available threads would be consumed and blocked by locks. Then the last one, which is supposed to initiate the unlock, would just never start in first place. This is not a bug in the language but a side effect of its architecture. I had to create Async::Workers module a while ago to solve a task which was hit by this issue. In other cases I can replace Lock with Lock::Async and it would just work. Why? The answer is in the following section. Why not always Lock::Async? Because it is rather slow. How much slower? Read on!

Lock vs. Lock::Async

What makes these different? To put it simple, Lock is based on system-level routines. This is why it is blocking: because this is the default system behavior.

Lock::Async is built around Promise and await. The point is that in Raku await tries to release a thread and return it back into the scheduler pool, making it immediately available to other jobs. So does Lock::Async too: instead of blocking, its protect method enters into await.

BTW, it might be surprising to many, but lock method of Lock::Async doesn’t actually lock by itself.


There is one more way to protect a block of code from re-entering. If you’re well familiar with atomic operations then you’re likely to know about it. For the rest I would briefly explain it in this section.

Let me skip the part about the atomic operations as such, Wikipedia has it. In particular we need CAS (Wikipedia again and Raku implementation). In a natural language terms the atomic approach can be “programmed” like this:

  1. Take a variable and set it to locked state if not set already; repeat otherwise
  2. Do your work.
  3. Set the variable to unlocked state.

Note that 1 and 3 are both atomic ops. In Raku code this is expressed in the following slightly simplified snippet:

my atomicint $lock = 0; # 0 is unlocked, 1 is locked
while cas($lock, 0, 1) == 1 {}  # lock
...                             # Do your work
$lock ⚛= 0;                     # unlock

Pretty simple, isn’t it? Let’s see what are the specs of this approach:

  1. It is blocking, akin to Lock
  2. It’s fast (will get back to this later)
  3. The lock operation might be a CPU hog

Item 2 is speculative at this moment, but guessable. Contrary to Lock, we don’t use a system call but rather base the lock on a purely computational trick.

Item 3 is apparent because even though Lock doesn’t release it’s thread for Raku scheduler, it does release a CPU core to the system.

Benchmarkers, let’s go benchmarking!

As I found myself in between of two big tasks today, I decided to make a pause and scratch the itch of comparing different approaches to locking. Apparently, we have three different kinds of locks at our hands, each based upon a specific approach. But aside of that, we also have two different modes of using them. One is explicit locking/unlocking withing the protected block. The other one is to use a wrapper method protect, available on Lock and Lock::Async. There is no data type for atomic locking, but this is something we can do ourselves and have the method implemented the same way, as Lock does.

Here is the code I used:

constant MAX_WORKERS = 50;  # how many workers per approach to start
constant TEST_SECS = 5;     # how long each worker must run

class Lock::Atomic {
    has atomicint $!lock = 0;

    method protect(&code) {
        while cas($!lock, 0, 1) == 1 { }
        LEAVE $!lock ⚛= 0;

my @tbl = <Wrkrs Atomic Lock Async Atomic.P Lock.P Async.P>;
my $max_w = max*.chars);
printf (('%' ~ $max_w ~ 's') xx [email protected]).join(" ") ~ "\n", |@tbl;
my $dfmt = (('%' ~ $max_w ~ 'd') xx [email protected]).join(" ") ~ "\n";

for 2..MAX_WORKERS -> $wnum {
    $*ERR.print: "$wnum\r";
    my Promise:D $starter .= new;
    my Promise:D @ready;
    my Promise:D @workers;
    my atomicint $stop = 0;

    sub worker(&code) {
        my Promise:D $ready .= new;
        @ready.push: $ready;
        @workers.push: start {
            await $starter;

    my atomicint $ia-lock = 0;
    my $ia-counter = 0;

    my $il-lock =;
    my $il-counter = 0;

    my $ila-lock =;
    my $ila-counter = 0;

    my $iap-lock =;
    my $iap-counter = 0;

    my $ilp-lock =;
    my $ilp-counter = 0;

    my $ilap-lock =;
    my $ilap-counter = 0;

    for ^$wnum {
        worker {
            until $stop {
                while cas($ia-lock, 0, 1) == 1 { } # lock
                LEAVE $ia-lock ⚛= 0; # unlock

        worker {
            until $stop {
                LEAVE $il-lock.unlock;

        worker {
            until $stop {
                await $ila-lock.lock;
                LEAVE $ila-lock.unlock;

        worker {
            until $stop {
                $iap-lock.protect: { ++$iap-counter }

        worker {
            until $stop {
                $ilp-lock.protect: { ++$ilp-counter }

        worker {
            until $stop {
                $ilap-lock.protect: { ++$ilap-counter }


    await @ready;
    sleep TEST_SECS;
    $*ERR.print: "stop\r";
    $stop ⚛= 1;
    await @workers;

    printf $dfmt, $wnum, $ia-counter, $il-counter, $ila-counter, $iap-counter, $ilp-counter, $ilap-counter;

The code is designed for a VM with 50 CPU cores available. By setting that many workers per approach, I also cover a complex case of an application over-utilizing the available CPU resources.

Let’s see what it comes up with:

   Wrkrs   Atomic     Lock    Async Atomic.P   Lock.P  Async.P
       2   918075   665498    71982   836455   489657    76854
       3   890188   652154    26960   864995   486114    27864
       4   838870   520518    27524   805314   454831    27535
       5   773773   428055    27481   795273   460203    28324
       6   726485   595197    22926   729501   422224    23352
       7   728120   377035    19213   659614   403106    19285
       8   629074   270232    16472   644671   366823    17020
       9   674701   473986    10063   590326   258306     9775
      10   536481   446204     8513   474136   292242     7984
      11   606643   242842     6362   450031   324993     7098
      12   501309   224378     9150   468906   251205     8377
      13   446031   145927     7370   491844   277977     8089
      14   444665   181033     9241   412468   218475    10332
      15   410456   169641    10967   393594   247976    10008
      16   406301   206980     9504   389292   250340    10301
      17   381023   186901     8748   381707   250569     8113
      18   403485   150345     6011   424671   234118     6879
      19   372433   127482     8251   311399   253627     7280
      20   343862   139383     5196   301621   192184     5412
      21   350132   132489     6751   315653   201810     6165
      22   287302   188378     7317   244079   226062     6159
      23   326460   183097     6924   290294   158450     6270
      24   256724   128700     2623   294105   143476     3101
      25   254587    83739     1808   309929   164739     1878
      26   235215   245942     2228   211904   210358     1618
      27   263130   112510     1701   232590   162413     2628
      28   244143   228978       51   292340   161485       54
      29   235120   104492     2761   245573   148261     3117
      30   222840   116766     4035   241322   140127     3515
      31   261837    91613     7340   221193   145555     6209
      32   206170    85345     5786   278407    99747     5445
      33   240815   109631     2307   242664   128062     2796
      34   196083   144639      868   182816   210769      664
      35   198096   142727     5128   225467   113573     4991
      36   186880   225368     1979   232178   179265     1643
      37   212517   110564       72   249483   157721       53
      38   158757    87834      463   158768   141681      523
      39   134292    61481       79   164560   104768       70
      40   210495   120967       42   193469   141113       55
      41   174969   118752       98   206225   160189     2094
      42   157983   140766      927   127003   126041     1037
      43   174095   129580       61   199023    91215       42
      44   251304   185317       79   187853    90355       86
      45   216065    96315       69   161697   134644      104
      46   135407    67411      422   128414   110701      577
      47   128418    73384       78    94186    95202       53
      48   113268    81380       78   112763   113826      104
      49   118124    73261      279   113389    90339       78
      50   121476    85438      308    82896    54521      510

Without deep analysis, I can make a few conclusions:

And to conclude with, the performance win of atomic approach doesn’t make it a clear winner due to it’s high CPU cost. I would say that it is a good candidate to consider when there is need to protect small, short-acting operations. Especially in performance-sensitive locations. And even then there are restricting conditions to be fulfilled:

In other words, the way we utilize CPU matters. If aggregated CPU time consumed by locking loops is larger than that needed for Lock to release+acquire the involved cores then atomic becomes a waste of resources.


By this moment I look at the above and wonder: are there any use for the atomic approach at all? Hm… 😉

By carefully considering this dilemma I would preliminary put it this way: I would be acceptable for an application as it knows the conditions it would be operated in and this makes it possible to estimate the outcomes.

But it is most certainly no go for a library/module which has no idea where and how would it be used.

It is much easier to formulate the rule of thumb for Lock::Async acceptance:

Sounds like some heavily parallelized I/O to me, for example. In such cases it is less important to be really fast but it does matter not to hit the max_threads limit.


This section would probably stay here for a while, until Ukraine wins the war. Until then, please, check out this page!

I have already received some donations on my PayPal. Not sure if I’m permitted to publish the names here. But I do appreciate your help a lot! In all my sincerity: Thank you!

vrurg: A New `will complain` Trait

Published by Vadim Belman on 2022-04-18T00:00:00

Long time no see, my dear reader! I was planning a lot for this blog, as well as for the Advanced Raku For Beginners series. But you know what they say: wanna make the God laugh – tell him your plans!

Anyway, there is one tradition I should try to maintain however hard the times are: whenever I introduce something new into the Raku language an update has to be published. No exception this time.

So, welcome a new will complain trait!

The idea of it came to be from discussion about a PR by @lizmat. The implementation as such could have taken less time would I be less busy lately. Anyway, at the moment when I’m typing these lines PR#4861 is undergoing CI testing and as soon as it is completed it will be merged into the master. But even after that the trait will not be immediately available as I consider it rather an experimental feature. Thus use experimental :will-complain; will be required to make use of it.

The actual syntax is very simple:

<declaration> will complain <code>;

The <declaration> is anything what could result in a type check exception thrown. I tried to cover all such cases, but not sure if something hasn’t been left behind. See the sections below.

<code> can be any Code object which will receive a single argument: the value which didn’t pass the type check. The code must return a string to be included into exception message. Something stringifiable would also do.

Less words, more examples!

Type Objects

my enum FOO 
    will complain { "need something FOO-ish, got {.raku}" } 
    <foo1 foo2 foo3>;
my subset IntD of Int:D 
    will complain { "only non-zero positive integers, not {.raku}" } 
    where * > 0;
my class Bar 
    will complain -> $val { "need something Bar-like, got {$val.^name}" } {}

Basically, any type object can get the trait except for composables, i.e. – roles. This is because there is no unambiguous way to chose the particular complain block to be used when a type check fails:

role R will complain { "only R" } {}
role R[::T] will complain { "only R[::T]" } {}
my R $v;
$v = 13; # Which role candidate to choose from??

There are some cases when the ambiguity is pre-resolved, like my R[Int] $v;, but I’m not ready to get into these details yet.


A variable could have specific meaning. Some like to use our to configure modules (my heavily multi-threaded soul is grumbling, but we’re tolerant to people’s mistakes, aren’t we?). Therefore providing them with a way to produce less cryptic error messages is certainly for better than for worse:

our Bool:D $disable-something
    will complain { "set disable-something something boolean!" } = False;

And why not to help yourself with a little luxury of easing debugging when an assignment fails:

my Str $a-lexical 
   will complain { "string must contain 'foo'" } 
   where { !.defined || .contains("foo") }; 

The trait works with hashes and arrays too, except that it is applied not to the actual hash or array object but to its values. Therefore it really only makes sense for their typed variants:

my Str %h will complain { "hash values are to be strings, not {.^name}" };
my Int @a will complain { "this array is all about integers, not {.^name}" };

Also note that this wouldn’t work for hashes with typed keys when a key of wrong type is used. But it doesn’t mean there is no solution:

subset IntKey of Int will complain { "hash key must be an Int" };
my %h{IntKey};
%h<a> = 13;


class Foo {
    has Int $.a 
        is rw 
        will complain { "you offer me {.raku}, but with all the respect: an integer, please!" };


sub foo( Str:D $p will complain { "the first argument must be a string with 'foo'" } 
                  where *.contains('foo') ) {}


By this time all CI has passed with no errors and I have merged the PR.


You all are likely to know about the Russia’s war in Ukraine. Some of you know that Ukraine is my homeland. What I never told is that since the first days of the invasion we (my family) are trying to help our friends back there who fight against the aggressor. By ‘fight’ I mean it, they’re literally at the front lines. Unfortunately, our resources are not limitless. Therefore I would like to ask for any donations you could make by using the QR code below.

I’m not asking this for myself. I didn’t even think of this when I started this post. I never took a single penny for whatever I was doing for the Raku language. Even more, I was avoiding applying for any grants because it was always like “somebody would have better use for them”.

But this time I’m asking because any help to Ukrainian militaries means saving lives, both theirs and the people they protect.

PayPal Donate Rakudo compiler, Release #154 (2022.03)

Published on 2022-03-20T00:00:00

p6steve: raku Physics::Unit vs. Python Pint

Published by p6steve on 2022-02-16T12:26:50

[Health warning – the following is written by the author of raku Physics::Unit & Physics::Measure – so it is an opinion piece with a strong pro-raku Physics::Measure bias…]

The raku Physics::Unit and Physics::Measure module family (“rPM”) was built to make the most of raku’s unique blend of innovative programming language features. During the build, I had the opportunity to use a lot of raku capabilities and principles, both within the module code and in the way it is used.

These raku modules took inspiration from the perl5 Physics::Unit module on cpan written by Joel Berger, in particular the smart handling of type derivations in math operations and parsing, but otherwise were written ‘blind’ without reference to other languages’ support for units of measurement.

Now that the first wave of build is done for raku Physics::Measure, it is time to take stock:

I have chosen the Python Pint package to be the “best of breed” Python equivalent of raku Physics::Measure for the comparison examples – by way of a compliment to its authors. My detailed Pint to rPM comparison table is here.

rPM Design Principles

raku is already very good at embedding perl5 and Python packages via the Inline:: modules and, since it is a direct descendant of perl5 (raku was formerly known as perl6), many current raku modules are straight translations from perl5. This is not the case with Physics::Measure which was designed and built from scratch with raku.

Here is what I set out to achieve…

Since my scoring “system” is focused on “what did raku bring to the party”, it is highly subjective, totally one sided and biased. But hopefully -Ofun!

Overall it awards Python Pint 30/70 whereas raku Physics::Measure wins with 62/70.

See down below for the blow-by-blow comparison…

My Conclusion

After performing this comparison, I feel that raku Physics::Measure offers a substantial step forward in code clarity, readability and maintainability over Python Pint for anyone who wants to use units of measurement for educational, engineering or scientific problems.

This example typifies what I mean:

raku helps in three distinct ways

 – language features such as Grammars and Class Types were used to develop the rPM modules. In comparison Pint has had to implement these functions without similar Python core support

– particularly in the case of the lack of core Type constructs in Python, this limits the usability and type safety of Pint in comparison to rPM

– language features such as unicode, Rats and postfix operators help to make the rPM modules more usable in the problem domain and a more natural language extension (or slang)


– yes raku brings a lot to the party

– yes raku is better than Python

Do I think that these local improvements in the “units of measurement domain” are sufficient to convince scientific programmers to switch to raku to avoid the limitations of Python? No. 

Do I think that raku is a next generation language with a wide range of small, but significant improvements that, put together, amount to a real step change in the toolkit available to scientific programmers? Yes. 

Do I think that raku is worth (another) look by anyone who feels constrained by Python’s emphasis on conformity? Yes.

Please do provide your feedback and comments…


Units, Errors & Measures [Python Pint 5/10, rPM 8/10]

The family of four modules – Physics::Measure, Physics::Unit, Physics::Error and Physics::Constants extend the core raku object model like this.

At the highest level, in raku code terms this looks like…

At face value this is a vast improvement in consistency and type safety versus Python – however, Pint has recognised the need for Quantity Types and implemented them at the module level.

So, many practical benefits of Raku types are also there in Python Pint

But … on scratching the surface, it becomes clear that there are many disadvantages of handling types in the Pint module:

These raku examples (there are many more) have no direct equivalent in Python / Pint:

given $measurement {
    when Length { say 'long' }
    when Time    { say 'long' }
    when Speed   { say 'fast'  }

– or –

subset Limited of Speed where 0 <= *.in('mph') <= 70;

Rat math (round trip fidelity) [Python Pint 1/10, rPM 10/10]

This core raku feature is a slam dunk vs. Python. You will also note that rPM automagically knows that kilometer2 meter is m^3 (Volume).

thickness = 68 * ureg.m
area = 60 ***2
n2g = 0.5 * ureg.dimensionless 
phi = 0.2
sat = 0.7  

volume = area * thickness * n2g * phi * sat

285.59999999999997 kilometer2 meter
my \thickness = 68m;
my \area = ♎ '60 sq km';
my \n2g = ♎ '0.5 ①';
my \φ = 0.2;
my \sat = 0.7;

my \volume = area * thickness * n2g * φ * sat;


Natural Unicode [Python Pint 0/10, rPM 10/10]

The rPM example below is from the Physics::Constants synopsis here … there are many other rPM areas where unicode is cool like my \θ1 = ♎ <45°30′30″>;  #45°30′30″ .

Postfix notation [Python Pint 0/10, rPM 10/10]

Mathematicians and Physicists have spent centuries applying the art of concision to concepts in order to help humans think at a higher level. This example clearly shows the potential for raku to declutter scientific code and improve perspicacity.

Grammar notation [Python Pint 7/10, rPM 9/10]

I thought this would be a big win for rPM … but it turns out that using a raku Grammar to parse Unit strings is matched by the functionality of the Pint string parser.

Both handle a similar input range:

rPM output defaults to the abbreviated initial(s)

rPM goes beyond Pint by knowing that “kg m^2/s^2” is the same as “J” and automatically applies the SI Derived Unit relationship (so my Pint score is off a couple of pips)

Pint does this…

> m | meter | metre
> ft | foot | feet
> km | kilom | kilometer
> J | joule | joules
> kg m^2 | s^2 | kg m^2/s^2 | kg m**2/s**2 |  kg·m²/s²

rPM does this…

> m | meter | metre
> ft | foot | feet
> km | kilom | kilometer
> J | joule | joules
> kg m^2 | s^2 | kg m^2/s^2 | kg m**2/s**2 |  kg·m²/s²

All the same I would still lean on the raku saves 70% post I wrote some time back that shows just how powerful and concise raku Grammars are compared to rolling your own recursive descent parser (in that case compared to perl)

SI and US/Imperial conversions [Python Pint 9/10, rPM 8/10]

Both Python Pint and rPM do a thorough job here. In fact during my study of Python Pint I found that they have better support for the slight difference between beer barrel and oil barrel volume … so an overall win for Pint.

Errors control rounding [Python Pint 8/10, rPM 7/10]

The raku Physics::Error module introspects the precision and relative size of the Error value and uses this to control the rounding of the Physics::Measure value that is output.

my $val1 = 2.8275793cm ±5%;
#2.8276cm ±0.141

my $val2 = 2.8275793cm   #without Error

This is a cool trick that Python Pint does not have. However, Pint uses the Python uncertainties module that has more stats oriented standard deviation uncertainty propagation whereas raku Physics::Error is a more basic single value model. So, for now, Pint has the edge on this until the raku eco-system catches up…

And finally…

And, for the record here are the raku Type Classes implemented: Rakudo compiler, Release #153 (2022.02)

Published on 2022-02-11T00:00:00

p6steve: raku for yachting

Published by p6steve on 2022-02-07T21:55:45

On Saturday, I was honoured to be among the leaders of the raku community in the FOSDEM 2022 raku devroom. Thanks to Andrew Shitov for organising and to all those who were able to join.

Here’s the official video of my presentation for those who missed the talk (and so that I can find it again in future):

If you prefer to roll up your sleeves and try this yourself, rather than watch the video, then just open the Yacht::Navigation Jupyter Binder … eg/Chapter 9 covers Navigation Lights.

To recap on the main points:

The bit about Raku Grammar for Navigation Lights seemed like a great raku feature to present in the context of a usable (if rather controlled) real world problem and to illustrate how the combination of Grammars and OO are a big game changer for coders with intermediate skills (like me) who would not naturally reach for the Rec::Descent big guns.


Raku Advent Calendar: All the blogs posts of 2021

Published by liztormato on 2022-01-10T19:51:57

Raku Advent Calendar: Day 25 – Future-proofing the Raku Programming Language

Published by liztormato on 2021-12-25T01:00:03

Around this time last year, Jonathan Worthington was writing their Advent Post called Reminiscence, refinement, revolution. Today, yours truly finds themselves writing a similar blog post after what can only be called a peculiar year in the world.

The Language

Visible Changes

The most visible highlights in the Raku Programming Language are basically:

last / next with a value

use v6.e.PREVIEW;
say (^5).grep: { $_ == 3 ?? last False !! True } # (0 1 2)
say (^5).grep: { $_ == 3 ?? last True  !! True } # (0 1 2 3)

Normally, last just stops an iteration, but now you can give it a value as well, which can be handy e.g. in a grep where you know the current value is the last, but you still want to include it.

use v6.e.PREVIEW;
say (^5).map: { next    if $_ == 2; $_ } # (0 1 3 4)
say (^5).map: { next 42 if $_ == 2; $_ } # (0 1 42 3 4)

Similarly with map, if you want to skip a value (which was already possible), you can now replace that value by another value.

Note that you need to activate the upcoming 6.e Raku language level to enable this feature, as there were some potential issues when activated in 6.d. But that’s just one example of future proofing the Raku Programming Language.


The .pick(*) call will produce all possible values of the Iterable on which it is called in random order, and then stop. The .pick(**) will do the same, but then start again producing values in (another) random order until exhausted, ad infinitum.

.say for (^5).pick(* ); # 3␤4␤0␤2␤1␤
.say for (^5).pick(**); # 3␤4␤0␤2␤1␤0␤2␤1␤4␤3␤3␤4␤2␤1␤0␤....

Nothing essential, but it is sure nice to have 😀.

is implementation-detail trait

The is implementation-detail trait indicates that something that is publicly visible, still should be considered off-limits as it is a detail of the implementation of something (whether that is your own code, or the core). This will also mark something as invisible for standard introspection:

class A {
    method foo() is implementation-detail { }
    method bar() { }
.name.say for A.^methods; # bar␤BUILDALL␤

Subroutines and methods in the core that are considered to be an implementation-detail, have been marked as such. This should make it more clear which parts of the Rakudo implementation are game, and which parts are off-limits for developers (knowing that they can be changed without notice). Yet another way to make sure that any Raku programs will continue to work with future versions of the Raku Programming Language.

Invisible Changes

There were many smaller and bigger fixes and improvements “under the hood” of the Raku Programming Language. Some code refactoring that e.g. made Allomorph a proper class, without changing any functionality of allomorphs in general. Or speeding up by using smarter algorithms, or by refactoring so that common hot code paths became smaller than the inlinining limit, and thus become a lot faster.

But the BIG thing in the past year, was that the so-called “new-disp” work was merged. In short, you could compare this to ripping out a gasoline engine from a car (with all its optimizations for fuel efficiency of 100+ years of experience) and replacing this by an electrical engine, while its being driven running errands. And although the electrical engine is already much more efficient to begin with, it still can gain a lot from more optimizations.

For yours truly, the notion that it is better to remove certain optimizations written in C in the virtual machine engine, and replace them by code written in NQP, was the most eye-opening one. The reason for this is that all of the optimization work that MoarVM does at runtime, can only work on the parts it understands. And C code, is not what MoarVM understands, so it can not optimize that at runtime. Simple things such as assignment had been optimized in C code and basically had become an “island” of unoptimization. But no more!

The current state of this work, is that it for now is a step forward, but also a step back in some aspects (at least for now). Some workflows most definitely have benefitted from the work so far (especially if you dispatch on anything that has a where clause in it, or use NativeCall directly, or indirectly with e.g. Inline::Perl5). But the bare startup time of Rakudo has increased. Which has its effect on the speed with which modules are installed, or testing is being done.

The really good thing about this work, is that it will allow more people to work on optimizing Rakudo, as that optimizing work can now be done in NQP, rather than in C. The next year will most definitely see one or more blog posts and/or presentations about this, to lower the already lowered threshold even further.

In any case, kudos to Jonathan WorthingtonStefan SeifertDaniel GreenNicholas Clark and many, many others for pulling this off! The Virtual Machine of choice for the Raku Programming Language is now ready to be taken for many more spins!

The Ecosystem

Thanks to Cro, a set of libraries for building reactive distributed systems (lovingly crafted to take advantage of all Raku has to offer), a number of ecosystem related services have come into development and production.

zef ecosystem

The new zef ecosystem has become of age and is now supported by various developer apps, such as App::Mi6, which basically reduces the distribution upload / commit process to a single mi6 release↵. Recommended by yours truly, especially if you are about to develop a Raku module from scratch. There are a number of advantages to using the zef ecosystem.

direct availability

Whenever you upload a new distribution to the zef ecosystem, it becomes (almost) immediately available for download by users. This is particularly handy for CI situations, if you are first updating one or more dependencies of a distribution: the zef CLI wouldn’t know about your upload upto an hour later on the older ecosystem backends.

better ecosystem security

Distributions from the older ecosystem backends could be removed by the author without the ecosystem noticing it (p6c), or not immediately noticing it (CPAN). Distributions, once uploaded to the zef ecosystem, can not be removed.

more dogfooding

The zef ecosystem is completely written in the Raku Programming Language itself. And you could argue that’s one more place where Raku is in production. Kudos to Nick Logan and Tony O’Dell for making this all happen! is a place where one can browse the Raku ecosystem. A website entirely developed with the Raku Programming Language, it should be seen as the successor of the website, which is not based on Raku itself. Although some of the features are still missing, it is an excellent piece of work by James Raspass and very much under continuous development.

Not forgetting the past

“Those who cannot remember the past are condemned to repeat it.” George Santanaya has said. And that is certainly true in the context of the Raku Programming Language with its now 20+ year history.

Permanent Distributions

Even though distributions can not be removed from the zef ecosystem, there’s of course still a chance that it may become unavailable temporarily, or more permanently. And there are still many distributions in the old ecosystems that can still disappear for whatever reason. Which is why the Raku Ecosystem Archive has been created: this provides a place where (ideally) all distributions ever to be available in the Raku ecosystem, are archived. In Perl terms: a BackPAN if you will. Before long, this repository will be able to serve as another backend for zef, in case a distribution one needs, is no longer available.

Permanent Blog Posts

A lot of blog post have been written in the 20+ year history of what is now the Raku Programming Language. They provide sometimes invaluable insights into the development of all aspects of the Raku Programming Language. Sadly, some of these blog posts have been lost in the mists of time. To prevent more memory loss, the CCR – The Raku Collect, Conserve and Remaster Project was started. I’m pretty sure a Cro-driven website will soon emerge that will make these saved blog posts more generally available. In the meantime, if you know of any old blog posts not yet collected, please make an issue for it.

Permanent IRC Logs

Ever since 2005, IRC has been the focal point of discussions between developers and users of the Raku Programming Language. In order to preserve all these discussions, a repository was started to store all of these logs, up to the present. Updating of the repository is not yet completey automated, but if you want to search something in the logs, or just want to keep up-to-date without using an IRC client, you can check out the experimental IRC Logs server (completely written in the Raku Programming Language).

Looking forward

So what will the coming year(s) bring? That is a good question.

The Raku Programming Language is an all volunteer Open Source project without any big financial backing. As such, it is dependent on the time that people put into it voluntarily. That doesn’t mean that plans cannot be made. But sometimes, sometimes even regularly, $work and Real Life take precedence and change the planning. And things take longer than expected.

If you want to make things happen in the Raku Programming Language, you can start by reporting problems in a manner that other people can easily reproduce. Or if it is a documentation problem, create a Pull Request with the way you think it should be. In other words: put some effort into it yourself. It will be recognized and appreciated by other people in the Raku Community.

Now that we’ve established that, let’s have a look at some of the developments now that we ensured the Raku Programming Language is a good base to build more things upon.

new-disp based improvements

The tools that “new-disp” has made available, haven’t really been used all that much yet: the emphasis was on making things work again (after the engine had been ripped out)! So one can expect quite a few performance improvements to come to fruition now that it all works. Which in turn will make some language changes possible that were previously deemed too hard, or affecting the general performance of Raku too much.


Jonathan Worthington‘s focus has been mostly on the “new-disp” work, but the work on RakuAST will be picked up again as well. This should give the Raku Programming Language a very visible boost, by adding full blown macro and after that proper slang support. While making all applications that depend to an extent on generating Raku code and then executing it, much easier to make and maintain (e.g. Cro routing and templates, printf functionality that doesn’t depend on running a grammar every time it is called).

More Cro driven websites

It looks like most, if not all Raku related websites, will be running on Cro in the coming year. With a few new ones as well (no, yours truly will not reveal more at this moment).

A new language level

After the work on RakuAST has become stable enough, a new language level (tentatively still called “6.e”) will become the default. The intent is to come with language levels more frequently than before (the last language level increase was in 2018), targeting a yearly language level increase.

More community

The new #raku-beginner channel has become pretty active. It’s good to see a lot of new names on that channel, also thanks to a Discord bridge (kudos to Wenzel P.P. Peppmeyer for that).

The coming year will see some Raku-only events. First, there is the Raku DevRoom at FOSDEM (5-6 February), which will be an online-only event (you can still sign up for a presentation or a lightning talk!). And if all goes ok, there will be an in-person/online hybrid Raku Conference in Riga (August 11-13 2022).

And of course there are other events where Raku users are welcome: the German Perl/Raku Workshop (30 March/1 April in Leipzig), and the Perl and Raku Conference in Houston (21-25 June).

And who knows, once Covid restrictions have been lifted, how many other Raku related events will be organized!


This year saw the loss of a lot of life. Within the Raku Community, we sadly had to say goodbye to Robert Lemmen and David H. Adler. Belated kudos to them for their contributions to what is now the Raku Programming Language, and Open Source in general. You are missed!

Which brings yours truly to instill in all of you again: please be careful, stay healthy and keep up the good work!

Raku Advent Calendar: Day 24 – Packaging and unpackaging real good

Published by jjmerelo on 2021-12-24T01:01:00

After all Rakuing along all Christmas, Santa realizes it’s a pretty good idea to keep things packed and ready to ship whenever it’s needed. So it looks at containers. Not the containers that might or might not actually be doing all the grunt work for bringing gifts to all good boys and girls in the world, but containers that are used to pack Raku and ship it or use it for testing. Something you need to do sooner or later, and need to do real fast.

The base container

The base container needs to be clean, and tiny, and contain only what’s strictly necessary to build your application on. So it needs a bunch of binaries and that’s that. No ancillary utilities, nothing like that. Enter jjmerelo/raku, a very bare bones container, that takes 15 MBytes and contains only the Rakudo compiler, and everything it needs to work. It’s also available from GHCR, if that’s more to your taste.

You only need that to run your Raku programs. For instance, just print all environment variables that are available inside the container:

time podman run --rm -it -e 'say %*ENV'

Which takes around 6 seconds in my machine, most of it devoted to downloading the container. Not a bad deal, really, all things considered.

The thing it, it comes in two flavors. Alternative is called jj/raku-gha, for obvious reasons: It’s the one that will actually work in side GitHub actions, which is where many of you will eventually use it. The difference? Well, a tiny difference, but one that took some time to discover: its default user, called raku, uses 1001 as UID, instead of the default 1000.

Right, I could have directly used 1001 as the single UID for all of them, but then I might have to do some more changes for GitHub Actions, so why bother?

Essentially, the user that runs GitHub actions uses that UID. We want our package user to be in harmony with the GHA user. We achieve harmony with that.

But we want a tiny bit more.

We will probably need zef to install new modules. And while we’re at it, we might also need to use a REPL in an easier way. Enter alpine-raku, once again in two flavors: regular and gha. Same difference as above: different UID for the user.

Also, this is the same jjmerelo/alpine-raku container I have been maintaining for some time. Its plumbing is now completely different, but its functionality is quite the same. Only it’s slimmer, so faster to download. Again

time podman run --rm -it -e 'say %*ENV'

Will take a bit north of 7 seconds, with exactly the same result. But we will see an interesting bit in that result:

{ENV => /home/raku/.profile, HOME => /home/raku, HOSTNAME => 2b6b1ac50f73, PATH => /home/raku/.raku/bin:/home/raku/.raku/share/perl6/site/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin, PKGS => git, PKGS_TMP => make gcc linux-headers musl-dev, RAKULIB => inst#/home/raku/.raku, TERM => xterm, container => podman}

And that’s the RAKULIB bit. What I’m saying it is that, no matter what the environment says, we’re gonna have an installation of Raku in that precise directory. Which is the home directory, and it should work, right? Only it does not, because GitHub Actions change arbitrarily the HOME variable, which is where Raku picks it from.

This was again something that required a bit of work and understanding where Rakudo picks its configuration. If we run

raku -e 'dd $*REPO.repo-chain'

We will obtain something like this:

( => "/home/jmerelo/.raku"), => "/home/jmerelo/.rakubrew/versions/moar-2021.10/install/share/perl6/site"), => "/home/jmerelo/.rakubrew/versions/moar-2021.10/install/share/perl6/vendor"), => "/home/jmerelo/.rakubrew/versions/moar-2021.10/install/share/perl6/core"), => => => CompUnit::Repository))), => => CompUnit::Repository)), => CompUnit::Repository))

We’re talking about the repository chain, where Raku (through Rakudo) keeps the information or where to find the, effectively, CompUnit repositories or the libraries, precompiled (those are the CompUnit::Repository::Installation) or not (CompUnit::Repository::AbsolutePath). But let’s look at the first one, which is where it will start looking. It’s effectively our home directory, or more precisely, a subdirectory where things are installed in the normal course of things. Where does Rakudo picks that from? Let’s change the HOME environment variable and we’ll see, or rather not, because depending on the installation, it will simply hang. With the RAKULIB defined as above, however, say $*REPO.repo-chain will print

(inst#/home/raku/.raku inst#/tmp/.raku inst#/usr/share/perl6/site inst#/usr/share/perl6/vendor inst#/usr/share/perl6/core ap# nqp# perl5#)

Our CompUnit::Repository::Installation become here inst#/home/raku/.raku, but, what’s more important, the HOME environment variable gets tacked a .raku in the end and an inst# in front, implying that’s the place where Rakudo expects to find it.

This brings us back again to GitHub actions, which change that variable for no good reason, leaving our Rakudo installation effectively unusable. But no fear, a simple environment variable baked in the alpine-raku container (and its GHCR variants) will keep the actual Rakudo installation in check for GitHub actions to come.

Now we’re all set

And we can write our own GitHub actions using this image. Directly run all our stuff inside a container that has Raku. For instance, this way:

name: "Test in a Raku container"
on: [ push, pull_request ]
runs-on: ubuntu-latest
packages: read
name: Checkout
uses: actions/[email protected]
name: Install modules
run: zef install .
name: Test
run: zef –debug test .
view raw raku-test.yaml hosted with ❤ by GitHub
GHA used in Pod::Load

This is decevingly simply, doing exactly what you would do in your console. Install, and then test, right? That’s it. Underneath, however the fact that the container is using the right UID and Raku knows where to find its own installation despite all the moving and shaking that’s going on is what makes it run.

You can even do a bit more. Use Raku as a shell for running anything. Add this step:

  - name: Use Raku to run
    shell: raku {0}
    run: say $*REPO.repo-chain

And with the shell magic, it will actually run that directly on the Raku interpreter. You can do anything else you want: install stuff, run Cro if you want. All within the GitHub action! For instance, do you want to chart how many files were changed in the latest commits using Raku? Here you go:

name: Install Text::Chart
run: zef install Text::Chart
name: Chart files changed latest commits
shell: raku {0}
run: |
use Text::Chart;
my @changed-files = qx<git log –oneline –shortstat -$COMMITS>
.lines.grep( /file/ )
.map( * ~~ /$<files>=(\d+) \s+ file/ )
.map: +*<files>;
say vertical(
:max( @changed-files[0..*-2].max),
This can be added to the action above

Couple of easy steps: install whatever you need, and then use Text::Chart to chart those files. This needs a bit of explaining, or maybe directly checking the source to have the complete picture: it’s using an environment variable called COMMITS, which is one more than the commits we want to chart, has been used to check out all those commits, and then, of course, we need to pop the last one since it’s a squashed commit that contains all older changes in the repo, uglifying our chart (which we don’t want). Essentially, however, is a pipe that takes the text content of the log that includes the number of file changed, extracts that number via a regex match, and feeds it into the vertical function to create the text chart. Which will show something like this (click on the > sign to show the chart):

Files changed in the last 10 commits in Pod::Load

With thousands of files at your disposal, the sky’s the limit. Do you want to install fez and upload automatically when tagging? Why not? Just do it. Upload your secret, and Bob’s your uncle. Do you want to do some complicated analytics on the source using Raku or generate thumbnails? Go ahead!

Happy packaging!

After this, Santa was incredibly happy, since all his Raku stuff was properly checked, and even containerized if needed! So he sit down to enjoy his corncob pipe, which Meta-Santa brought for him last Christmas.

And, with that, Merry Christmas to all and everyone of you!

Raku Advent Calendar: Day 23 – The Life of Raku Module Authoring

Published by liztormato on 2021-12-23T01:01:00

by Tony O’Dell

Hello, world! This article is a lot about fez and how you can get started writing your first module and making it available to other users. Presuming you have rakudo and zef installed, install fez!

$ zef install fez
===> Searching for: fez
===> Updating fez mirror:
===> Updated fez mirror:
===> Testing: fez:ver<32>:auth<zef:tony-o>:api<0>
[fez]   Fez - Raku / Perl6 package utility
[fez]   USAGE
[fez]     fez command [args]
[fez]   COMMANDS
[fez]     register              registers you up for a new account
[fez]     login                 logs you in and saves your key info
[fez]     upload                creates a distribution tarball and uploads
[fez]     meta                  update your public meta info (website, email, name)
[fez]     reset-password        initiates a password reset using the email
[fez]                           that you registered with
[fez]     list                  lists the dists for the currently logged in user
[fez]     remove                removes a dist from the ecosystem (requires fully
[fez]                           qualified dist name, copy from `list` if in doubt)
[fez]     org                   org actions, use `fez org help` for more info
[fez]     FEZ_CONFIG            if you need to modify your config, set this env var
[fez]   CONFIGURATION (using: /home/tonyo/.fez-config.json)
[fez]     Copy this to a cool location and write your own requestors/bundlers or
[fez]     ignore it and use the default curl/wget/git tools for great success.
===> Testing [OK] for fez:ver<32>:auth<zef:tony-o>:api<0>
===> Installing: fez:ver<32>:auth<zef:tony-o>:api<0>

1 bin/ script [fez] installed to:

Make sure that the last line is in your $PATH so the next set of commands all run smoothly. Now we can start writing the actual module, let’s write ROT13 since it’s a fairly easy problem to solve and this article really is less about module content than how to get working with fez.

Writing the Module

Our module directory structure:

├── lib
│   └── ROT13.rakumod
├── t
│   ├── 00-use.rakutest
│   └── 01-tests.rakutest
└── META6.json

lib is the main content of your module, it’s where all of your module’s utilities, helpers, and organization happens. Each file corresponds to one or more modules or classes, more on in the META6.json paragraph below.

META6.json is how zef knows what the module is, it’s how fez knows what it’s uploading, and it’s how rakudo knows how to load what and from where. Let’s take a look at the structure of META6.json:

t contains all of your module’s tests. If you have “author only” tests then you’d also have a directory xt and that directory works roughly the same. For your users’ sanity WRITE TESTS!

  "name": "ROT13",
  "auth": "zef:tony-o",
  "version": "0.0.1",
  "api": 0,

  "provides": {
    "ROT13": "lib/ROT13.rakumod"

  "depends":       [],
  "build-depends": [],
  "test-depends":  [],

  "tags":        [ "ROT13", "crypto" ],
  "description": "ROT13 everything!"

A quick discussion about dists. A dist is the fully qualified name of your module and it contains the name, auth, and version. It’s how zef can differentiate your ROT13 module from mine. It works in conjunction with use, such as use ROT13:auth<zef:tony-o>, and in zef: zef install ROT13:auth<tony-o>:ver<0.0.1>. The dist string is always qualified with both the :auth and the :ver internally to raku and the ecosystem, but the end user isn’t required to type the fully qualified dist if they’re less picky about what version/author of the module they’d like. In use statements you can combine auth and ver to get the author or version you’re expecting or you can omit one or both.

It’s better practice to fully qualify your use statements; as more modules hit the ecosystem with the same name, this practice will help keep your modules running smoothly.

Let’s whip up a quick ROT13 module, in lib/ROT13.rakumod dump the following

unit module ROT13;

sub rot13(Str() $text) is export {

Great, you can test it now (from the root of your module directory) with raku -I. -e 'use ROT13; say rot13("hello, WoRlD!");. You should get output of uryyb, JbEyQ!.

Now fill in your test files and run the tests with zef test .

Publishing Your Module


If you’re not registered with fez, now’s the time!

$ fez register
>>= Email: [email protected]
>>= Username: tony-o
>>= Password:
>>= Registration successful, requesting auth key
>>= Username: tony-o
>>= Password:
>>= Login successful, you can now upload dists

Check Yourself

$ fez checkbuild
>>= Inspecting ./META6.json
>>= meta<provides> looks OK
>>= meta<resources> looks OK
>>= ROT13:ver<0.0.1>:auth<zef:tony-o> looks OK

Oh snap, we’re lookin’ good!


$ fez upload
>>= Hey! You did it! Your dist will be indexed shortly.

Only thing to note here is that if there’s a problem indexing your module then you’ll receive an email with the gripe.

Further Reading

You can read more about fez here:

Perhaps you’d prefer listening:

That’s it! If there’s other things you’d like to know about fez, zef, or ecosystems then send tony-o some chat in IRC or an email!

Raku Advent Calendar: Day 22 – Santa Claus is Rakuing Along

Published by Tom Browder (@tbrowder) on 2021-12-22T01:01:00

Part 4 – The Santa Claus Reports


A Christmas ditty sung to the tune of Santa Claus is Coming to Town:

 He’s making a list,
 He’s checking it closely,
 He’s gonna find out who’s Rakuing mostly,
 Santa Claus is Rakuing along.

Santa Claus Operations Update 2021

Part 1 of this article reported on the new journaling process for Santa’s employees and how they keep their individual work logs. Part 2 was a side step to show how to better manage Santa’s code by using the new Zef module repository. Part 3 was another side step because Santa was running out of time.

This article, written by yours truly, junior Elf Rodney, will attempt to showcase the use of Raku’s built-in date, time, and sorting functions along with the ease of class construction to handle the new journals in the aggregate to automate many of the reports that used to take goodly amounts of time. They can now be prepared quickly and thus free resources to research more deeply-observed problem areas.

The Reporting System

The journals are frequently read and manipulated by system-wide programs (most found in the Raku module SantaClaus::Utils) run by root. Individual journals are periodically shortened by extracting older entries which are then concatenated onto hidden .journal.YYYY.MM files (owned by root but readable by all) in the user’s home directory.

The data in the journal files are converted to class instances which are deployed for the moment in two global Raku hashes keyed by task-id and user-id, respectively. (When the new persistent types in Raku are ready, they will be a natural fit to form a large on-disk database).

Before designing classes to use with the journals let’s take a quick look at how we want the data to be accessed and used.

First, the raw data give us, for each user and his assigned task (which may be a sub-task):

Second, the raw data give us, for each task and sub-task

It seems that the data we have so far collected don’t yield the task/sub-task relations, but that is solved with a task-id system designed with that in mind. As a start, the task-id is a two-field number with the first field being the task number and the second field being the sub-task number. Supervisors will normally use the task number and their subordinates the sub-task number.

For example, a task number might be 103458 with sub-tasks of 200 and 202. The three numbers entered by the different employees working them would enter:

The final system could be as detailed as desired, but the two-level task-id is sufficient for now.

[Sorry, this article will  be finished later–I am needed for last minute jobs in the factory!]


Santa now has a personnel and task reporting system that automatically produces continuously updated reports on current tasks and the personnel resources used for them. Raku’s built-in date, time, and sorting functions help ease the work of the IT department in their job of programming and maintaining the system.

Santa’s Epilogue

Don’t forget the “reason for the season:” ✝

As I always end these jottings, in the words of Charles Dickens’ Tiny Tim, “may God bless Us , Every one! [1]”


  1. A Christmas Carol, a short story by Charles Dickens (1812-1870), a well-known and popular Victorian author whose many works include The Pickwick Papers, Oliver Twist, David Copperfield, Bleak House, Great Expectations, and A Tale of Two Cities.

vrurg: Merging Symbols Issue

Published by Vadim Belman on 2021-10-05T00:00:00

First of all, I’d like to apologize for all the errors in this post. I just haven’t got time to properly proof-read it.

A while ago I was trying to fix a problem in Rakudo which, under certain conditions, causes some external symbols to become invisible for importing code, even if explicit use statement is used. And, indeed, it is really confusing when:

use L1::L2::L3::Class;;

fails with “Class symbol doesn’t exists in L1::L2::L3” error! It’s ok if use throws when there is no corresponding module. But .new??

Skip This Unless You Know What A Package Is

This section is needed to understand the rest of the post. A package in Raku is a typeobject which has a symbol table attached. The table is called stash (stands for “symbol table hash”) and is represented by an instance of Stash class, which is, basically, is a hash with minor tweaks. Normally each package instance has its own stash. For example, it is possible to manually create two different packages with the same name:

my $p1a := Metamodel::PackageHOW.new_type(:name<P1>); 
my $p1b := Metamodel::PackageHOW.new_type(:name<P1>); 
say $p1a.WHICH, " ", $p1a.WHO.WHICH; # P1|U140722834897656 Stash|140723638807008
say $p1b.WHICH, " ", $p1b.WHO.WHICH; # P1|U140722834897800 Stash|140723638818544

Note that they have different stashes as well.

A package is barely used in Raku as is. Usually we deal with packagy things like modules and classes.

Back On The Track

Back then I managed to trace the problem down to deserialization process within MoarVM backend. At that point I realized that somehow it pulls in packagy objects which are supposed to be the same thing, but they happen to be different and have different stashes. Because MoarVM doesn’t (and must not) have any idea about the structure of high-level Raku objects, there is no way it could properly handle this situation. Instead it considers one of the conflicting stashes as “the winner” and drops the other one. Apparently, symbols unique to the “loser” are lost then.

It took me time to find out what exactly happens. But not until a couple of days ago I realized what is the root cause and how to get around the bug.

Package Tree

What happens when we do something like:

module Foo {
    module Bar {

How do we access Bar, speaking of the technical side of things? Foo::Bar syntax basically maps into Foo.WHO<Bar>. In other words, Bar gets installed as a symbol into Foo stash. We can also rewrite it with special syntax sugar: Foo::<Bar> because Foo:: is a representation for Foo stash.

So far, so good; but where do we find Foo itself? In Raku there is a special symbol called GLOBAL which is the root namespace (or a package if you wish) of any code. GLOBAL::, or GLOBAL.WHO is where one finds all the top-level symbols.

Say, we have a few packages like L11::L21, L11::L22, L12::L21, L12::L22. Then the namespace structure would be represented by this tree:

`- L11
   `- L21
   `- L22
`- L12
   `- L21
   `- L22

Normally there is one per-process GLOBAL symbol and it belongs to the compunit which used to start the program. Normally it’s a .raku file, or a string supplied on command line with -e option, etc. But each compunit also gets its own GLOBALish package which acts as compunit’s GLOBAL until it is fully incorporated into the main code. Say, we declare a module in file Foo.rakumod:

unit module Foo;
sub print-GLOBAL($when) is export {
    say "$when: ", GLOBAL.WHICH, " ", GLOBALish.WHICH;
print-GLOBAL 'LOAD';

And use it in a script:

use Foo;
print-GLOBAL 'RUN ';

Then we can get an ouput like this:

LOAD: GLOBAL|U140694020262024 GLOBAL|U140694020262024
RUN : GLOBAL|U140694284972696 GLOBAL|U140694020262024

Notice that GLOBALish symbol remains the same object, whereas GLOBAL gets different. If we add a line to the script which also prints GLOBAL.WHICH then we’re going to get something like:

MAIN: GLOBAL|U140694284972696

Let’s get done with this part of the story for a while a move onto another subject.

Compunit Compilation

This is going to be a shorter story. It is not a secret that however powerful Raku’s grammars are, they need some core developer’s attention to make them really fast. In the meanwhile, compilation speed is somewhat suboptimal. It means that if a project consist of many compunits (think of modules, for example), it would make sense to try to compile them in parallel if possible. Unfortunately, the compiler is not thread-safe either. To resolve this complication Rakudo implementation parallelizes compilation by spawning individual processes per each compunit.

For example, let’s refer back to the module tree example above and imagine that all modules are used by a script. In this case there is a chance that we would end up with six rakudo processes, each compiling its own L* module. Apparently, things get slightly more complicated if there are cross-module uses, like L11::L21 could refer to L21, which, in turn, refers to L11::L22, or whatever. In this case we need to use topological sort to determine in what order the modules are to be compiled; but that’s not the point.

The point is that since each process does independent compilation, each compunit needs independent GLOBAL to manage its symbols. For the time being, what we later know as GLOBALish serves this duty for the compiler.

Later, when all pre-compiled modules are getting incorporated into the code which uses them, symbols installed into each individual GLOBAL are getting merged together to form the final namespace, available for our program. There are even methods in the source, using merge_global in their names.


(Note the clickable section header; I love the guy!)

Now, you can feel the catch. Somebody might have even guessed what it is. It crossed my mind after I was trying to implement legal symbol auto-registration which doesn’t involve using QAST to install a phaser. At some point I got an idea of using GLOBAL to hold a register object which would keep track of specially flagged roles. Apparently it failed due to the parallelized compilation mentioned above. It doesn’t matter, why; but at that point I started building a mental model of what happens when merge is taking place. And one detail drew my special attention: what happens if a package in a long name is not explicitly declared?

Say, there is a class named Foo::Bar::Baz one creates as:

unit class Foo::Bar;
class Baz { }

In this case the compiler creates a stub package for Foo. The stub is used to install class Bar. Then it all gets serialized into bytecode.

At the same time there is another module with another class:

unit class Foo::Bar::Fubar;

It is not aware of Foo::Bar::Baz, and the compiler has to create two stubs: Foo and Foo::Bar. And not only two versions of Foo are different and have different stashes; but so are the two versions of Bar where one is a real class, the other is a stub package.

Most of the time the compiler does damn good job of merging symbols in such cases. It took me stripping down a real-life code to golf it down to some minimal set of modules which reproduces the situation where a require call comes back with a Failure and a symbol becomes missing. The remaining part of this post will be dedicated to this example. In particular, this whole text is dedicated to one line.

Before we proceed further, I’d like to state that I might be speculating about some aspects of the problem cause because some details are gone from my memory and I don’t have time to re-investigate them. Still, so far my theory is backed by working workaround presented at the end.

To make it a bit easier to analyze the case, let’s start with namespace tree:

`- L1
   `- App
   `- L2
      `- Collection
         `- Driver
         `- FS

Rough purpose is for application to deal with some kind of collection which stores its items with help of a driver which is loaded dynamically, depending, say, on a user configuration. We have the only driver implemented: File System (FS).

If you checkout the repository and try raku -Ilib symbol-merge.raku in the examples/2021-10-05-merge-symbols directory, you will see some output ending up with a line like Failure|140208738884744 (certainly true for up until Rakudo v2021.09 and likely to be so for at least a couple of versions later).

The key conflict in this example are modules Collection and Driver. The full name of Collection is L1::L2::Collection. L1 and L2 are both stubs. Driver is L1::L2::Collection::Driver and because it imports L1::L2, L2 is a class; but L1 remains to be a stub. By commenting out the import we’d get the bug resolved and the script would end up with something like:


This means that the driver module was successfully loaded and the driver class symbol is available.

Ok, uncomment the import and start the script again. And then once again to get rid of the output produced by compilation-time processes. We should see something like this:

[7329] L1 in L1::L2         : L1|U140360937889112
[7329] L1 in Driver         : L1|U140361742786216
[7329] L1 in Collection     : L1|U140361742786480
[7329] L1 in App            : L1|U140361742786720
[7329] L1 in MAIN           : L1|U140361742786720
[7329] L1 in FS             : L1|U140361742788136

We already know that L1 is a stub. Dumping object IDs also reveals that each compunit has its own copy of L1, except for App and the script (marked as MAIN). This is pretty much expected because each L1 symbol is installed at compile-time into per-compunit GLOBALish. This is where each module finds it. App is different because it is directly imported by the script and was compiled by the same compiler process, and shared its GLOBAL with the script.

Now comes the black magic. Open lib/L1/L2/Collection/FS.rakumod and uncomment the last line in the file. Then give it a try. The output would seem impossible at first; hell with it, even at second glance it is still impossible:

[17579] Runtime Collection syms      : (Driver)

Remember, this line belongs to L1::L2::Collection::FS! How come we don’t see FS in Collection stash?? No wonder that when the package cannot see itself others cannot see it too!

Here comes a bit of my speculation based on what I vaguely remember from the times ~2 years ago when I was trying to resolve this bug for the first time.

When Driver imports L1::L2, Collection gets installed into L2 stash, and Driver is recorded in Collection stash. Then it all gets serialized with Driver compunit.

Now, when FS imports Driver to consume the role, it gets the stash of L2 serialized at the previous stage. But its own L2 is a stub under L1 stub. So, it gets replaced with the serialized “real thing” which doesn’t have FS under Collection! Bingo and oops…

A Workaround

Walk through all the example files and uncomment use L1 statement. That’s it. All compunits will now have a common anchor to which their namespaces will be attached.

The common rule would state that if a problem of the kind occurs then make sure there’re no stub packages in the chain from GLOBAL down to the “missing” symbol. In particular, commenting out use L1::L2 in Driver will get our error back because it would create a “hole” between L1 and Collection and get us back into the situation where conflicting Collection namespaces are created because they’re bound to different L2 packages.

It doesn’t really matter how exactly the stubs are avoided. For example, we can easily move use L1::L2 into Collection and make sure that use L1 is still part of L2. So, for simplicity a child package may import its parent; and parent may then import its parent; and so on.

Sure, this adds to the boilerplate. But I hope the situation is temporary and there will be a fix.


The one I was playing with required a compunit to serialize its own GLOBALish stash at the end of the compilation in a location where it would not be at risk of overwriting. Basically, it means cloning and storing it locally on the compunit (the package stash is part of the low-level VM structures). Then compunit mainline code would invoke a method on the Stash class which would forcibly merge the recorded symbols back right after deserialization of compunit’s bytecode. It was seemingly working, but looked more of a kind of a hack, than a real fix. This and a few smaller issues (like a segfault which I failed to track down) caused it to be frozen.

As I was thinking of it lately, more proper fix must be based upon a common GLOBAL shared by all compunits of a process. In this case there will be no worry about multiple stub generated for the same package because each stub will be shared by all compunits until, perhaps, the real package is found in one of them.

Unfortunately, the complexity of implementing the ‘single GLOBAL’ approach is such that I’m unsure if anybody with appropriate skill could fit it into their schedule.

6guts: The new MoarVM dispatch mechanism is here!

Published by jnthnwrthngtn on 2021-09-29T16:16:31

Around 18 months ago, I set about working on the largest set of architectural changes that Raku runtime MoarVM has seen since its inception. The work was most directly triggered by the realization that we had no good way to fix a certain semantic bug in dispatch without either causing huge performance impacts across the board or increasingly complexity even further in optimizations that were already riding their luck. However, the need for something like this had been apparent for a while: a persistent struggle to optimize certain Raku language features, the pain of a bunch of performance mechanisms that were all solving the same kind of problem but each for a specific situation, and a sense that, with everything learned since I founded MoarVM, it was possible to do better.

The result is the development of a new generalized dispatch mechanism. An overview can be found in my Raku Conference talk about it (slidesvideo); in short, it gives us a far more uniform architecture for all kinds of dispatch, allowing us to deliver better performance on a range of language features that have thus far been glacial, as well as opening up opportunities for new optimizations.

Today, this work has been merged, along with the matching changes in NQP (the Raku subset we use for bootstrapping and to implement the compiler) and Rakudo (the full Raku compiler and standard library implementation). This means that it will ship in the October 2021 releases.

In this post, I’ll give an overview of what you can expect to observe right away, and what you might expect in the future as we continue to build upon the possibilities that the new dispatch architecture has to offer.

The big wins

The biggest improvements involve language features that we’d really not had the architecture to do better on before. They involved dispatch – that is, getting a call linked to a destination efficiently – but the runtime didn’t provide us with a way to “explain” to it that it was looking at a dispatch, let alone with the information needed to have a shot at optimizing it.

The following graph captures a number of these cases, and shows the level of improvement, ranging from a factor of 3.3 to 13.3 times faster.

Graph showing benchmark results, described textually below

Let’s take a quick look at each of these. The first, new-buf, asks how quickly we can allocate Bufs.

for ^10_000_000 {

Why is this a dispatch benchmark? Because Buf is not a class, but rather a role. When we try to make an instance of a role, it is “punned” into a class. Up until now, it works as follows:

  1. We look up the new method
  2. The find_method method would, if needed, create a pun of the role and cache it
  3. It would return a forwarding closure that takes the arguments and gives them to the same method called on the punned class, or spelt in Raku code, -> $role-discarded, |args { $pun."$name"(|args) }
  4. This closure would be invoked with the arguments

This had a number of undesirable consequences:

  1. While the pun was cached, we still had a bit of overhead to check if we’d made it already
  2. The arguments got slurped and flattened, which costs something, and…
  3. …the loss of callsite shape meant we couldn’t look up a type specialization of the method, and thus lost a chance to inline it too

With the new dispatch mechanism, we have a means to cache constants at a given program location and to replace arguments. So the first time we encounter the call, we:

  1. Get the role pun produced if needed
  2. Resolve the new method on the class punned from the role
  3. Produce a dispatch program that caches this resolved method and also replaces the role argument with the pun

For the next thousands of calls, we interpret this dispatch program. It’s still some cost, but the method we’re calling is already resolved, and the argument list rewriting is fairly cheap. Meanwhile, after we get into some hundreds of iterations, on a background thread, the optimizer gets to work. The argument re-ordering cost goes away completely at this point, and new is so small it gets inlined – at which point the buffer allocation is determined dead and so goes away too. Some remaining missed opportunities mean we still are left with a loop that’s not quite empty: it busies itself making sure it’s really OK to do nothing, rather than just doing nothing.

Next up, multiple dispatch with where clauses.

multi fac($n where $n <= 1) { 1 }
multi fac($n) { $n * fac($n - 1) }
for ^1_000_000 {

These were really slow before, since:

  1. We couldn’t apply the multi-dispatch caching mechanism at all as soon as we had a where clause involved
  2. We would run where clauses twice in the event the candidate was chosen: once to see if we should choose that multi candidate, and once again when we entered it

With the new mechanism, we:

  1. On the first call, calculate a multiple dispatch plan: a linked list of candidates to work through
  2. Invoke the one with the where clause, in a mode whereby if the signature fails to bind, it triggers a dispatch resumption. (If it does bind, it runs to completion)
  3. In the event of a bind failure, the dispatch resumption triggers, and we attempt the next candidate

Once again, after the setup phase, we interpret the dispatch programs. In fact, that’s as far as we get with running this faster for now, because the specializer doesn’t yet know how to translate and further optimize this kind of dispatch program. (That’s how I know it currently stands no chance of turning this whole thing into another empty loop!) So there’s more to be had here also; in the meantime, I’m afraid you’ll just have to settle for a factor of ten speedup.

Here’s the next one:

proto with-proto(Int $n) { 2 * {*} }
multi with-proto(Int $n) { $n + 1 }
sub invoking-nontrivial-proto() {
    for ^10_000_000 {

Again, on top form, we’d turn this into an empty loop too, but we don’t quite get there yet. This case wasn’t so terrible before: we did get to use the multiple dispatch cache, however to do that we also ended up having to allocate an argument capture. The need for this also blocked any chance of inlining the proto into the caller. Now that is possible. Since we cannot yet translate dispatch programs that resume an in-progress dispatch, we don’t yet get to further inline the called multi candidate into the proto. However, we now have a design that will let us implement that.

This whole notion of a dispatch resumption – where we start doing a dispatch, and later need to access arguments or other pre-calculated data in order to do a next step of it – has turned out to be a great unification. The initial idea for it came from considering things like callsame:

class Parent {
    method m() { 1 }
class Child is Parent {
    method m() { 1 + callsame }
for ^10_000_000 {

Once I started looking at this, and then considering that a complex proto also wants to continue with a dispatch at the {*}, and in the case a where clauses fails in a multi it also wants to continue with a dispatch, I realized this was going to be useful for quite a lot of things. It will be a bit of a headache to teach the optimizer and JIT to do nice things with resumes – but a great relief that doing that once will benefit multiple language features!

Anyway, back to the benchmark. This is another “if we were smart, it’d be an empty loop” one. Previously, callsame was very costly, because each time we invoked it, it would have to calculate what kind of dispatch we were resuming and the set of methods to call. We also had to be able to locate the arguments. Dynamic variables were involved, which cost a bit to look up too, and – despite being an implementation details – these also leaked out in introspection, which wasn’t ideal. The new dispatch mechanism makes this all rather more efficient: we can cache the calculated set of methods (or wrappers and multi candidates, depending on the context) and then walk through it, and there’s no dynamic variables involved (and thus no leakage of them). This sees the biggest speedup of the lot – and since we cannot yet inline away the callsame, it’s (for now) measuring the speedup one might expect on using this language feature. In the future, it’s destined to optimize away to an empty loop.

A module that makes use of callsame on a relatively hot path is OO::Monitors,, so I figured it would be interesting to see if there is a speedup there also.

use OO::Monitors;
monitor TestMonitor {
    method m() { 1 }
my $mon =;
for ^1_000_000 {

monitor is a class that acquires a lock around each method call. The module provides a custom meta-class that adds a lock attribute to the class and then wraps each method such that it acquires the lock. There are certainly costly things in there besides the involvement of callsame, but the improvement to callsame is already enough to see a 3.3x speedup in this benchmark. Since OO::Monitors is used in quite a few applications and modules (for example, Cro uses it), this is welcome (and yes, a larger improvement will be possible here too).

Caller side decontainerization

I’ve seen some less impressive, but still welcome, improvements across a good number of other microbenchmarks. Even a basic multi dispatch on the + op:

my $i = 0;
for ^10_000_000 {
    $i = $i + $_;

Comes out with a factor of 1.6x speedup, thanks primarily to us producing far tighter code with fewer guards. Previously, we ended up with duplicate guards in this seemingly straightforward case. The infix:<+> multi candidate would be specialized for the case of its first argument being an Int in a Scalar container and its second argument being an immutable Int. Since a Scalar is mutable, the specialization would need to read it and then guard the value read before proceeding, otherwise it may change, and we’d risk memory safety. When we wanted to inline this candidate, we’d also want to do a check that the candidate really applies, and so also would deference the Scalar and guard its content to do that. We can and do eliminate duplicate guards – but these guards are on two distinct reads of the value, so that wouldn’t help.

Since in the new dispatch mechanism we can rewrite arguments, we can now quite easily do caller-side removal of Scalar containers around values. So easily, in fact, that the change to do it took me just a couple of hours. This gives a lot of benefits. Since dispatch programs automatically eliminate duplicate reads and guards, the read and guard by the multi-dispatcher and the read in order to pass the decontainerized value are coalesced. This means less repeated work prior to specialization and JIT compilation, and also only a single read and guard in the specialized code after it. With the value to be passed already guarded, we can trivially select a candidate taking two bare Int values, which means there’s no further reads and guards needed in the callee either.

A less obvious benefit, but one that will become important with planned future work, is that this means Scalar containers escape to callees far less often. This creates further opportunities for escape analysis. While the MoarVM escape analyzer and scalar replacer is currently quite limited, I hope to return to working on it in the near future, and expect it will be able to give us even more value now than it would have been able to before.

Further results

The benchmarks shown earlier are mostly of the “how close are we to realizing that we’ve got an empty loop” nature, which is interesting for assessing how well the optimizer can “see through” dispatches. Here are a few further results on more “traditional” microbenchmarks:

Graph showing benchmark results, described textually below

The complex number benchmark is as follows:

my $total-re = 0e0;
for ^2_000_000 {
    my $x = 5 + 2i;
    my $y = 10 + 3i;
    my $z = $x * $x + $y;
    $total-re = $total-re + $
say $total-re;

That is, just a bunch of operators (multi dispatch) and method calls, where we really do use the result. For now, we’re tied with Python and a little behind Ruby on this benchmark (and a surprising 48 times faster than the same thing done with Perl’s Math::Complex), but this is also a case that stands to see a huge benefit from escape analysis and scalar replacement in the future.

The hash read benchmark is:

my %h = a => 10, b => 12;
my $total = 0;
for ^10_000_000 {
    $total = $total + %h<a> + %h<b>;

And the hash store one is:

my @keys = 'a'..'z';
for ^500_000 {
    my %h;
    for @keys {
        %h{$_} = 42;

The improvements are nothing whatsoever to do with hashing itself, but instead look to be mostly thanks to much tighter code all around due to caller-side decontainerization. That can have a secondary effect of bringing things under the size limit for inlining, which is also a big help. Speedup factors of 2x and 1.85x are welcome, although we could really do with the same level of improvement again for me to be reasonably happy with our results.

The line-reading benchmark is:

my $fh = open "longfile";
my $chars = 0;
for $fh.lines { $chars = $chars + .chars };
say $chars

Again, nothing specific to I/O got faster, but when dispatch – the glue that puts together all the pieces – gets a boost, it helps all over the place. (We are also decently competitive on this benchmark, although tend to be slower the moment the UTF-8 decoder can’t take it’s “NFG can’t possibly apply” fast path.)

And in less micro things…

I’ve also started looking at larger programs, and hearing results from others about theirs. It’s mostly encouraging:

Smaller profiler output

One unpredicted (by me), but also welcome, improvement is that profiler output has become significantly smaller. Likely reasons for this include:

  1. The dispatch mechanism supports producing value results (either from constants, input arguments, or attributes read from input arguments). It entirely replaces an earlier mechanism, “specializer plugins”, which could map guards to a target to invoke, but always required a call to something – even if that something was the identity function. The logic was that this didn’t matter for any really hot code, since the identity function will trivially be inlined away. However, since profile size of the instrumenting profiler is a function of the number of paths through the call tree, trimming loads of calls to the identity function out of the tree makes it much smaller.
  2. We used to make lots of calls to the sink method when a value was in sink context. Now, if we see that the type simply inherits that method from Mu, we elide the call entirely (again, it would inline away, but a smaller call graph is a smaller profile).
  3. Multiple dispatch caching would previously always call the proto when the cache was missed, but would then not call an onlystar proto again when it got cache hits in the future. This meant the call tree under many multiple dispatches was duplicated in the profile. This wasn’t just a size issue; it was a bit annoying to have this effect show up in the profile reports too.

To give an example of the difference, I took profiles from Agrammon to study why it might have become slower. The one from before the dispatcher work weighed in at 87MB; the one with the new dispatch mechanism is under 30MB. That means less memory used while profiling, less time to write the profile out to disk afterwards, and less time for tools to load the profiler output. So now it’s faster to work out how to make things faster.

Is there any bad news?

I’m afraid so. Startup time has suffered. While the new dispatch mechanism is more powerful, pushes more complexity out of the VM into high level code, and is more conducive to reaching higher peak performance, it also has a higher warmup time. At the time of writing, the impact on startup time seems to be around 25%. I expect we can claw some of that back ahead of the October release.

What will be broken?

Changes of this scale always come with an amount of risk. We’re merging this some weeks ahead of the next scheduled monthly release in order to have time for more testing, and to address any regressions that get reported. However, even before reaching the point of merging it, we have:

What happens next?

As I’ve alluded to in a number of places in this post, while there are improvements to be enjoyed right away, there are also new opportunities for further improvement. Some things that are on my mind include:

Thank you

I would like to thank TPF and their donors for providing the funding that has made it possible for me to spend a good amount of my working time on this effort.

While I’m to blame for the overall design and much of the implementation of the new dispatch mechanism, plenty of work has also been put in by other MoarVM and Rakudo contributors – especially over the last few months as the final pieces fell into place, and we turned our attention to getting it production ready. I’m thankful to them not only for the code and debugging contributions, but also much support and encouragement along the way. It feels good to have this merged, and I look forward to building upon it in the months and years to come.

vrurg: Secure JSONification?

Published by Vadim Belman on 2021-09-14T00:00:00

There was an interesting discussion on IRC today. In brief, it was about exposing one’s database structures over API and security implications of this approach. I’d recommend reading the whole thing because Altreus delivers a good (and somewhat emotional 🙂) point on why such practice is most definitely bad design decision. Despite having minor objections, I generally agree to him.

But I’m not wearing out my keyboard on this post just to share that discussion. There was something in it what made me feel as if I miss something. And it came to me a bit later, when I was done with my payjob and got a bit more spare resources for the brain to utilize.

First of all, a bell rang when a hash was mentioned as the mediator between a database and API return value. I’m somewhat wary about using hashes as return values primarily for a reason of performance price and concurrency unsafety.

Anyway, the discussion went on and came to the point where it touched the ground of blacklisting of a DB table fields vs. whitelisting. The latter is really worthy approach of marking those fields we want in a JSON (or a hash) rather than marking those we don’t want because blacklisting requires us to remember to mark any new sensitive field as prohibited explicitly. Apparently, it is easy to forget to stick the mark onto it.

Doesn’t it remind you something? Aren’t we talking about hashes now? Isn’t it what we sometimes blame JavaScript for, that its objects are free-form with barely any reliable control over their structure? Thanks TypeScript for trying to get this fixed in some funky way, which I personally like more than dislike.

That’s when things clicked together. I was giving this answer already on a different occasion: using a class instance is often preferable over a hash. In the light of the JSON/API safety this simple rule gets us to another rather interesting aspect. Here is an example SmokeMachine provided on IRC:

to-json %( name => "{ .first-name } { .last-name }", 
           password => "***" )
    given $model

This was about returning basic user account information to a frontend. This is supposed to replace JSONification of a Red model like the following:

model Account {
    has UInt $.id is serial is json-skip;
    has Str $.username is column{ ... };
    has Str $.password is column{ ... } is json-skip;
    has Str $.first-name is column{ ... };
    has Str $.last-name is column{ ... };

The model example is mine.

By the way, in my opinion, neither first name nor last name do not belong to this model and must be part of a separate table where user’s personal data is kept. In more general case, a name must either be a long single field or an array where one can fit something like “Pablo Diego José Francisco de Paula Juan Nepomuceno María de los Remedios Cipriano de la Santísima Trinidad Ruiz y Picasso”.

The model clearly demonstrates the blacklist approach with two fields marked as non-JSONifiable. Now, let’s make it the right way, as I see it:

class API::Data::User {
    has Str:D $.username is required;
    has Str $.first-name;
    has Str $.last-name;

    method !FROM-MODEL($model) { username   => .username,
                  first-name => .first-name,
                  last-name  => .last-name
            given $model

    multi method new(Account:D $model) {

    method COERCE(Account:D $model) {

And now, somewhere in our code we can do:

method get-user-info(UInt:D $id) {
    to-json API::Data::User(Account.^load: :$id)

With Cro::RPC::JSON module this could be part of a general API class which would provide common interface to both front- and backend:

use Cro::RPC::JSON;
class API::User {
    method get-user-info(UInt:D $id) is json-rpc {
        API::Data::User(Account.^load: :$id)

With such an implementation our Raku backend would get an instance of API::Data::User. In a TypeScript frontend code of a private project of mine I have something like the following snippet, where connection is an object derived from jayson module:"get-user-info", id).then(
    (user: User | undefined | null) => { ... }

What does it all eventually give us? First, API::Data::User provides the mechanism of whilelisting the fields we do want to expose in API. Note that with properly defined attributes we’re as explicit about that as only possible. And we do it declaratively one single place.

Second, the class prevents us from mistyping field names. It wouldn’t be possible to have something like %( usrname => $model.username, ... ) somewhere else in our codebase. Or, perhaps even more likely, to try %user<frst-name> and wonder where did the first name go? We also get the protection against wrong data types or undefined values.

It is also likely that working with a class instance would be faster than with a hash. I have this subject covered in another post of mine.

Heh, at some point I thought this post could fit into IRC format… 🤷

vrurg: My Work Environment

Published by Vadim Belman on 2021-06-24T00:00:00

Just have noticed that normally I have 4 editors/IDEs running at the same time:

Only Vim I could quit on occasion.

What is your state of affairs?

6guts: Raku multiple dispatch with the new MoarVM dispatcher

Published by jnthnwrthngtn on 2021-04-15T09:54:30

I recently wrote about the new MoarVM dispatch mechanism, and in that post noted that I still had a good bit of Raku’s multiple dispatch semantics left to implement in terms of it. Since then, I’ve made a decent amount of progress in that direction. This post contains an overview of the approach taken, and some very rough performance measurements.

My goodness, that’s a lot of semantics

Of all the kinds of dispatch we find in Raku, multiple dispatch is the most complex. Multiple dispatch allows us to write a set of candidates, which are then selected by the number of arguments:

multi ok($condition, $desc) {
    say ($condition ?? 'ok' !! 'not ok') ~ " - $desc";
multi ok($condition) {
    ok($condition, '');

Or the types of arguments:

multi to-json(Int $i) { ~$i }
multi to-json(Bool $b) { $b ?? 'true' !! 'false' }

And not just one argument, but potentially many:

multi truncate(Str $str, Int $chars) {
    $str.chars < $chars ?? $str !! $str.substr(0, $chars) ~ '...'
multi truncate(Str $str, Str $after) {
    with $str.index($after) -> $pos {
        $str.substr(0, $pos) ~ '...'
    else {

We may write where clauses to differentiate candidates on properties that are not captured by nominal types:

multi fac($n where $n <= 1) { 1 }
multi fac($n) { $n * fac($n - 1) }

Every time we write a set of multi candidates like this, the compiler will automatically produce a proto routine. This is what is installed in the symbol table, and holds the candidate list. However, we can also write our own proto, and use the special term {*} to decide at which point we do the dispatch, if at all.

proto mean($collection) {
    $collection.elems == 0 ?? Nil !! {*}
multi mean(@arr) {
    @arr.sum / @arr.elems
multi mean(%hash) {
    %hash.values.sum / %hash.elems

Candidates are ranked by narrowness (using topological sorting). If multiple candidates match, but they are equally narrow, then that’s an ambiguity error. Otherwise, we call narrowest one. The candidate we choose may then use callsame and friends to defer to the next narrowest candidate, which may do the same, until we reach the most general matching one.

Multiple dispatch is everywhere

Raku leans heavily on multiple dispatch. Most operators in Raku are compiled into calls to multiple dispatch subroutines. Even $a + $b will be a multiple dispatch. This means doing multiple dispatch efficiently is really important for performance. Given the riches of its semantics, this is potentially a bit concerning. However, there’s good news too.

Most multiple dispatches are boring

The overwhelmingly common case is that we have:

This isn’t to say the other cases are unimportant; they are really quite useful, and it’s desirable for them to perform well. However, it’s also desirable to make what savings we can in the common case. For example, we don’t want to eagerly calculate the full set of possible candidates for every single multiple dispatch, because the majority of the time only the first one matters. This is not just a time concern: recall that the new dispatch mechanism stores dispatch programs at each callsite, and if we store the list of all matching candidates at each of those, we’ll waste a lot of memory too.

How do we do today?

The situation in Rakudo today is as follows:

Effectively, the situation today is that you simply don’t use where clauses in a multiple dispatch if its anywhere near a hot path (well, and if you know where the hot paths are, and know that this kind of dispatch is slow). Ditto for callsame, although that’s less commonly reached for. The question is, can we do better with the new dispatcher?

Guard the types

Let’s start out with seeing how the simplest cases are dealt with, and build from there. (This is actually what I did in terms of the implementation, but at the same time I had a rough idea where I was hoping to end up.)

Recall this pair of candidates:

multi truncate(Str $str, Int $chars) {
    $str.chars < $chars ?? $str !! $str.substr(0, $chars) ~ '...'
multi truncate(Str $str, Str $after) {
    with $str.index($after) -> $pos {
        $str.substr(0, $pos) ~ '...'
    else {

We then have a call truncate($message, "\n"), where $message is a Str. Under the new dispatch mechanism, the call is made using the raku-call dispatcher, which identifies that this is a multiple dispatch, and thus delegates to raku-multi. (Multi-method dispatch ends up there too.)

The record phase of the dispatch – on the first time we reach this callsite – will proceed as follows:

  1. Iterate over the candidates
  2. If a candidate doesn’t match on argument count, just discard it. Since the shape of a callsite is a constant, and we calculate dispatch programs at each callsite, we don’t need to establish any guards for this.
  3. If it matches on types and concreteness, note which parameters are involved and what kinds of guards they need.
  4. If there was no match or an ambiguity, report the error without producing a dispatch program.
  5. Otherwise, having established the type guards, delegate to the raku-invoke dispatcher with the chosen candidate.

When we reach the same callsite again, we can run the dispatch program, which quickly checks if the argument types match those we saw last time, and if they do, we know which candidate to invoke. These checks are very cheap – far cheaper than walking through all of the candidates and examining each of them for a match. The optimizer may later be able to prove that the checks will always come out true and eliminate them.

Thus the whole of the dispatch processes – at least for this simple case where we only have types and arity – can be “explained” to the virtual machine as “if the arguments have these exact types, invoke this routine”. It’s pretty much the same as we were doing for method dispatch, except there we only cared about the type of the first argument – the invocant – and the value of the method name. (Also recall from the previous post that if it’s a multi-method dispatch, then both method dispatch and multiple dispatch will guard the type of the first argument, but the duplication is eliminated, so only one check is done.)

That goes in the resumption hole

Coming up with good abstractions is difficult, and therein lies much of the challenge of the new dispatch mechanism. Raku has quite a number of different dispatch-like things. However, encoding all of them directly in the virtual machine leads to high complexity, which makes building reliable optimizations (or even reliable unoptimized implementations!) challenging. Thus the aim is to work out a comparatively small set of primitives that allow for dispatches to be “explained” to the virtual machine in such a way that it can deliver decent performance.

It’s fairly clear that callsame is a kind of dispatch resumption, but what about the custom proto case and the where clause case? It turns out that these can both be neatly expressed in terms of dispatch resumption too (the where clause case needing one small addition at the virtual machine level, which in time is likely to be useful for other things too). Not only that, but encoding these features in terms of dispatch resumption is also quite direct, and thus should be efficient. Every trick we teach the specializer about doing better with dispatch resumptions can benefit all of the language features that are implemented using them, too.

Custom protos

Recall this example:

proto mean($collection) {
    $collection.elems == 0 ?? Nil !! {*}

Here, we want to run the body of the proto, and then proceed to the chosen candidate at the point of the {*}. By contrast, when we don’t have a custom proto, we’d like to simply get on with calling the correct multi.

To achieve this, I first moved the multi candidate selection logic from the raku-multi dispatcher to the raku-multi-core dispatcher. The raku-multi dispatcher then checks if we have an “onlystar” proto (one that does not need us to run it). If so, it delegates immediately to raku-multi-core. If not, it saves the arguments to the dispatch as the resumption initialization state, and then calls the proto. The proto‘s {*} is compiled into a dispatch resumption. The resumption then delegates to raku-multi-core. Or, in code:

nqp::dispatch('boot-syscall', 'dispatcher-register', 'raku-multi',
    # Initial dispatch, only setting up resumption if we need to invoke the
    # proto.
    -> $capture {
        my $callee := nqp::captureposarg($capture, 0);
        my int $onlystar := nqp::getattr_i($callee, Routine, '$!onlystar');
        if $onlystar {
            # Don't need to invoke the proto itself, so just get on with the
            # candidate dispatch.
            nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'raku-multi-core', $capture);
        else {
            # Set resume init args and run the proto.
            nqp::dispatch('boot-syscall', 'dispatcher-set-resume-init-args', $capture);
            nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'raku-invoke', $capture);
    # Resumption means that we have reached the {*} in the proto and so now
    # should go ahead and do the dispatch. Make sure we only do this if we
    # are signalled to that it's a resume for an onlystar (resumption kind 5).
    -> $capture {
        my $track_kind := nqp::dispatch('boot-syscall', 'dispatcher-track-arg', $capture, 0);
        nqp::dispatch('boot-syscall', 'dispatcher-guard-literal', $track_kind);
        my int $kind := nqp::captureposarg_i($capture, 0);
        if $kind == 5 {
            nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'raku-multi-core',
                nqp::dispatch('boot-syscall', 'dispatcher-get-resume-init-args'));
        elsif !nqp::dispatch('boot-syscall', 'dispatcher-next-resumption') {
            nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'boot-constant',
                nqp::dispatch('boot-syscall', 'dispatcher-insert-arg-literal-obj',
                    $capture, 0, Nil));

Two become one

Deferring to the next candidate (for example with callsame) and trying the next candidate because a where clause failed look very similar: both involve walking through a list of possible candidates. There’s some details, but they have a great deal in common, and it’d be nice if that could be reflected in how multiple dispatch is implemented using the new dispatcher.

Before that, a slightly terrible detail about how things work in Rakudo today when we have where clauses. First, the dispatcher does a “trial bind”, where it asks the question: would this signature bind? To do this, it has to evaluate all of the where clauses. Worse, it has to use the slow-path signature binder too, which interprets the signature, even though we can in many cases compile it. If the candidate matches, great, we select it, and then invoke it…which runs the where clauses a second time, as part of the compiled signature binding code. There is nothing efficient about this at all, except for it being by far more efficient on developer time, which is why it happened that way.

Anyway, it goes without saying that I’m rather keen to avoid this duplicate work and the slow-path binder where possible as I re-implement this using the new dispatcher. And, happily, a small addition provides a solution. There is an op assertparamcheck, which any kind of parameter checking compiles into (be it type checking, where clause checking, etc.) This triggers a call to a function that gets the arguments, the thing we were trying to call, and can then pick through them to produce an error message. The trick is to provide a way to invoke a routine such that a bind failure, instead of calling the error reporting function, will leave the routine and then do a dispatch resumption! This means we can turn failure to pass where clause checks into a dispatch resumption, which will then walk to the next candidate and try it instead.

Trivial vs. non-trivial

This gets us most of the way to a solution, but there’s still the question of being memory and time efficient in the common case, where there is no resumption and no where clauses. I coined the term “trivial multiple dispatch” for this situation, which makes the other situation “non-trivial”. In fact, I even made a dispatcher called raku-multi-non-trivial! There are two ways we can end up there.

  1. The initial attempt to find a matching candidate determines that we’ll have to consider where clauses. As soon as we see this is the case, we go ahead and produce a full list of possible candidates that could match. This is a linked list (see my previous post for why).
  2. The initial attempt to find a matching candidate finds one that can be picked based purely on argument count and nominal types. We stop there, instead of trying to build a full candidate list, and run the matching candidate. In the event that a callsame happens, we end up in the trivial dispatch resumption handler, which – since this situation is now non-trivial – builds the full candidate list, snips the first item off it (because we already ran that), and delegates to raku-multi-non-trivial.

Lost in this description is another significant improvement: today, when there are where clauses, we entirely lose the ability to use the MoarVM multiple dispatch cache, but under the new dispatcher, we store a type-filtered list of candidates at the callsite, and then cheap type guards are used to check it is valid to use.

Preliminary results

I did a few benchmarks to see how the new dispatch mechanism did with a couple of situations known to be sub-optimal in Rakudo today. These numbers do not reflect what is possible, because at the moment the specializer does not have much of an understanding of the new dispatcher. Rather, they reflect the minimal improvement we can expect.

Consider this benchmark using a multi with a where clause to recursively implement factorial.

multi fac($n where $n <= 1) { 1 }
multi fac($n) { $n * fac($n - 1) }
for ^100_000 {
say now - INIT now;

This needs some tweaks (and to be run under an environment variable) to use the new dispatcher; these are temporary, until such a time I switch Rakudo over to using the new dispatcher by default:

use nqp;
multi fac($n where $n <= 1) { 1 }
multi fac($n) { $n * nqp::dispatch('raku-call', &fac, $n - 1) }
for ^100_000 {
    nqp::dispatch('raku-call', &fac, 10);
say now - INIT now;

On my machine, the first runs in 4.86s, the second in 1.34s. Thus under the new dispatcher this runs in little over a quarter of the time it used to – a quite significant improvement already.

A case involving callsame is also interesting to consider. Here it is without using the new dispatcher:

multi fallback(Any $x) { "a$x" }
multi fallback(Numeric $x) { "n" ~ callsame }
multi fallback(Real $x) { "r" ~ callsame }
multi fallback(Int $x) { "i" ~ callsame }
for ^1_000_000 {
say now - INIT now;

And with the temporary tweaks to use the new dispatcher:

use nqp;
multi fallback(Any $x) { "a$x" }
multi fallback(Numeric $x) { "n" ~ new-disp-callsame }
multi fallback(Real $x) { "r" ~ new-disp-callsame }
multi fallback(Int $x) { "i" ~ new-disp-callsame }
for ^1_000_000 {
    nqp::dispatch('raku-call', &fallback, 4+2i);
    nqp::dispatch('raku-call', &fallback, 4.2);
    nqp::dispatch('raku-call', &fallback, 42);
say now - INIT now;

On my machine, the first runs in 31.3s, the second in 11.5s, meaning that with the new dispatcher we manage it in a little over a third of the time that current Rakudo does.

These are both quite encouraging, but as previously mentioned, a majority of multiple dispatches are of the trivial kind, not using these features. If I make the most common case worse on the way to making other things better, that would be bad. It’s not yet possible to make a fair comparison of this: trivial multiple dispatches already receive a lot of attention in the specializer, and it doesn’t yet optimize code using the new dispatcher well. Of note, in an example like this:

multi m(Int) { }
multi m(Str) { }
for ^1_000_000 {
say now - INIT now;

Inlining and other optimizations will turn this into an empty loop, which is hard to beat. There is one thing we can already do, though: run it with the specializer disabled. The new dispatcher version looks like this:

use nqp;
multi m(Int) { }
multi m(Str) { }
for ^1_000_000 {
    nqp::dispatch('raku-call', &m, 1);
    nqp::dispatch('raku-call', &m, "x");
say now - INIT now;

The results are 0.463s and 0.332s respectively. Thus, the baseline execution time – before the specializer does its magic – is less using the new general dispatch mechanism than it is using the special-case multiple dispatch cache that we currently use. I wasn’t sure what to expect here before I did the measurement. Given we’re going from a specialized mechanism that has been profiled and tweaked to a new general mechanism that hasn’t received such attention, I was quite ready to be doing a little bit worse initially, and would have been happy with parity. Running in 70% of the time was a bigger improvement than I expected at this point.

I expect that once the specializer understands the new dispatch mechanism better, it will be able to also turn the above into an empty loop – however, since more iterations can be done per-optimization, this should still show up as a win for the new dispatcher.

Final thoughts

With one relatively small addition, the new dispatch mechanism is already handling most of the Raku multiple dispatch semantics. Furthermore, even without the specializer and JIT really being able to make a good job of it, some microbenchmarks already show a factor of 3x-4x improvement. That’s a pretty good starting point.

There’s still a good bit to do before we ship a Rakudo release using the new dispatcher. However, multiple dispatch was the biggest remaining threat to the design: it’s rather more involved than other kinds of dispatch, and it was quite possible that an unexpected shortcoming could trigger another round of design work, or reveal that the general mechanism was going to struggle to perform compared to the more specialized one in the baseline unoptimized, case. So far, there’s no indication of either of these, and I’m cautiously optimistic that the overall design is about right.

Pawel bbkr Pabian: Asynchronous, parallel and... dead. My Perl 6 daily bread.

Published by Pawel bbkr Pabian on 2015-09-06T14:00:56

I love Perl 6 asynchronous features. They are so easy to use and can give instant boost by changing few lines of code that I got addicted to them. I became asynchronous junkie. And finally overdosed. Here is my story...

I was processing a document that was divided into chapters, sub-chapters, sub-sub-chapters and so on. Parsed to data structure it looked like this:

    my %document = (
        '1' => {
            '1.1' => 'Lorem ipsum',
            '1.2' => {
                '1.2.1' => 'Lorem ipsum',
                '1.2.2' => 'Lorem ipsum'
        '2' => {
            '2.1' => {
                '2.1.1' => 'Lorem ipsum'

Every chapter required processing of its children before it could be processed. Also processing of each chapter was quite time consuming - no matter which level it was and how many children did it have. So I started by writing recursive function to do it:

    sub process (%chapters) {
        for %chapters.kv -> $number, $content {
            note "Chapter $number started";
            &?ROUTINE.($content) if $content ~~ Hash;
            sleep 1; # here the chapter itself is processed
            note "Chapter $number finished";

So nothing fancy here. Maybe except current &?ROUTINE variable which makes recursive code less error prone - there is no need to repeat subroutine name explicitly. After running it I got expected DFS (Depth First Search) flow:

    $ time perl6
    Chapter 1 started
    Chapter 1.1 started
    Chapter 1.1 finished
    Chapter 1.2 started
    Chapter 1.2.1 started
    Chapter 1.2.1 finished
    Chapter 1.2.2 started
    Chapter 1.2.2 finished
    Chapter 1.2 finished
    Chapter 1 finished
    Chapter 2 started
    Chapter 2.1 started
    Chapter 2.1.1 started
    Chapter 2.1.1 finished
    Chapter 2.1 finished
    Chapter 2 finished
    real    0m8.184s

It worked perfectly, but that was too slow. Because 1 second was required to process each chapter in serial manner it ran for 8 seconds total. So without hesitation I reached for Perl 6 asynchronous goodies to process chapters in parallel.

    sub process (%chapters) {
        await do for %chapters.kv -> $number, $content {
            start {
                note "Chapter $number started";
                &?ROUTINE.outer.($content) if $content ~~ Hash;
                sleep 1; # here the chapter itself is processed
                note "Chapter $number finished";

Now every chapter is processed asynchronously in parallel and first waits for its children to be also processed asynchronously in parallel. Note that after wrapping processing in await/start construct &?ROUTINE must now point to outer scope.

    $ time perl6
    Chapter 1 started
    Chapter 2 started
    Chapter 1.1 started
    Chapter 1.2 started
    Chapter 2.1 started
    Chapter 1.2.1 started
    Chapter 2.1.1 started
    Chapter 1.2.2 started
    Chapter 1.1 finished
    Chapter 1.2.1 finished
    Chapter 1.2.2 finished
    Chapter 2.1.1 finished
    Chapter 2.1 finished
    Chapter 1.2 finished
    Chapter 1 finished
    Chapter 2 finished
    real    0m3.171s

Perfect. Time dropped to expected 3 seconds - it was not possible to go any faster because document had 3 nesting levels and each required 1 second to process. Still smiling I threw bigger document at my beautiful script - 10 chapters, each with 10 sub-chapters, each with 10 sub-sub-chapters. It started processing, run for a while... and DEADLOCKED.

Friedrich Nietzsche said that "when you gaze long into an abyss the abyss also gazes into you". Same rule applies to code. After few minutes me and my code were staring at each other. And I couldn't find why it worked perfectly for small documents but was deadlocking in random moments for big ones. Half an hour later I blinked and got defeated by my own code in staring contest. So it was time for debugging.

I noticed that when it was deadlocking there was always constant amount of 16 chapters that were still in progress. And that number looked familiar to me - thread pool!

    $ perl6 -e 'say start { }'
        scheduler =>
            initial_threads => 0,
            max_threads => 16,
            uncaught_handler => Callable
        status => PromiseStatus::Kept

Every asynchronous task that is planned needs free thread so it can be executed. And on my system only 16 concurrent threads are allowed as shown above. To analyze what happened let's use document from first example but also assume thread pool is limited to 4:

    $ perl6          # 4 threads available by default
    Chapter 1 started       # 3 threads available
    Chapter 1.1 started     # 2 threads available
    Chapter 2 started       # 1 thread available
    Chapter 1.1 finished    # 2 threads available again
    Chapter 1.2 started     # 1 thread available
    Chapter 1.2.1 started   # 0 threads available
                            # deadlock!

At this moment chapter 1 subtree holds three threads and waits for one more for chapter 1.2.2 to complete everything and start ascending from recursion. And subtree of chapter 2 holds one thread and waits for one more for chapter 2.1 to descend into recursion. In result processing gets to a point where at least one more thread is required to proceed but all threads are taken and none can be returned to thread pool. Script deadlocks and stops here forever.

How to solve this problem and maintain parallel processing? There are many ways to do it :)
The key to the solution is to process asynchronously only those chapters that do not have unprocessed chapters on lower level.

Luckily Perl 6 offers perfect tool - promise junctions. It is possible to create a promise that waits for other promises to be kept and until it happens it is not sent to thread pool for execution. Following code illustrates that:

    my $p = Promise.allof(, );
    sleep 1;
    say "Promise after 1 second: " ~ $p.perl;
    sleep 3;
    say "Promise after 4 seconds: " ~ $p.perl;


    Promise after 1 second:
        ..., status => PromiseStatus::Planned
    Promise after 4 seconds:
        ..., status => PromiseStatus::Kept

Let's rewrite processing using this cool property:

    sub process (%chapters) {
        return Promise.allof(
            do for %chapters.kv -> $number, $content {
                my $current = {
                    note "Chapter $number started";
                    sleep 1; # here the chapter itself is processed
                    note "Chapter $number finished";
                if $content ~~ Hash {
                    Promise.allof( &?ROUTINE.($content) )
                        .then( $current );
                else {
                    Promise.start( $current );
    await process(%document);

It solves the problem when chapter was competing with its sub-chapters for free threads but at the same time it needed those sub-chapters before it can process itself. Now awaiting for sub-chapters to complete does not require free thread. Let's run it:

    $ perl6
    Chapter 1.1 started
    Chapter 1.2.1 started
    Chapter 1.2.2 started
    Chapter 2.1.1 started
    Chapter 1.1 finished
    Chapter 1.2.1 finished
    Chapter 1.2.2 finished
    Chapter 1.2 started
    Chapter 2.1.1 finished
    Chapter 2.1 started
    Chapter 1.2 finished
    Chapter 1 started
    Chapter 2.1 finished
    Chapter 2 started
    Chapter 1 finished
    Chapter 2 finished
    real    0m3.454s

I've added separator for each second passed so it is easier to understand. When script starts chapters 1.1, 1.2.1, 1.2.2 and 2.1.1 do not have sub-chapters at all. So they can take threads from thread pool immediately. When they are completed after one second then Promises that were awaiting for all of them are kept and chapters 1.2 and 2.1 can be processed safely on thread pool. It keeps going until getting out of recursion.

After trying big document again it was processed flawlessly in 72 seconds instead of linear 1000.

I'm high on asynchronous processing again!

You can download script here and try different data sizes and algorithms for yourself (params are taken from command line).

6guts: Towards a new general dispatch mechanism in MoarVM

Published by jnthnwrthngtn on 2021-03-15T02:08:42

My goodness, it appears I’m writing my first Raku internals blog post in over two years. Of course, two years ago it wasn’t even called Raku. Anyway, without further ado, let’s get on with this shared brainache.

What is dispatch?

I use “dispatch” to mean a process by which we take a set of arguments and end up with some action being taken based upon them. Some familiar examples include:

At first glance, perhaps the first two seem fairly easy and the third a bit more of a handful – which is sort of true. However, Raku has a number of other features that make dispatch rather more, well, interesting. For example:

Thanks to this, dispatch – at least in Raku – is not always something we do and produce an outcome, but rather a process that we may be asked to continue with multiple times!

Finally, while the examples I’ve written above can all quite clearly be seen as examples of dispatch, a number of other common constructs in Raku can be expressed as a kind of dispatch too. Assignment is one example: the semantics of it depend on the target of the assignment and the value being assigned, and thus we need to pick the correct semantics. Coercion is another example, and return value type-checking yet another.

Why does dispatch matter?

Dispatch is everywhere in our programs, quietly tieing together the code that wants stuff done with the code that does stuff. Its ubiquity means it plays a significant role in program performance. In the best case, we can reduce the cost to zero. In the worst case, the cost of the dispatch is high enough to exceed that of the work done as a result of the dispatch.

To a first approximation, when the runtime “understands” the dispatch the performance tends to be at least somewhat decent, but when it doesn’t there’s a high chance of it being awful. Dispatches tend to involve an amount of work that can be cached, often with some cheap guards to verify the validity of the cached outcome. For example, in a method dispatch, naively we need to walk a linearization of the inheritance graph and ask each class we encounter along the way if it has a method of the specified name. Clearly, this is not going to be terribly fast if we do it on every method call. However, a particular method name on a particular type (identified precisely, without regard to subclassing) will resolve to the same method each time. Thus, we can cache the outcome of the lookup, and use it whenever the type of the invocant matches that used to produce the cached result.

Specialized vs. generalized mechanisms in language runtimes

When one starts building a runtime aimed at a particular language, and has to do it on a pretty tight budget, the most obvious way to get somewhat tolerable performance is to bake various hot-path language semantics into the runtime. This is exactly how MoarVM started out. Thus, if we look at MoarVM as it stood several years ago, we find things like:

These are all still there today, however are also all on the way out. What’s most telling about this list is what isn’t included. Things like:

A few years back I started to partially address this, with the introduction of a mechanism I called “specializer plugins”. But first, what is the specializer?

When MoarVM started out, it was a relatively straightforward interpreter of bytecode. It only had to be fast enough to beat the Parrot VM in order to get a decent amount of usage, which I saw as important to have before going on to implement some more interesting optimizations (back then we didn’t have the kind of pre-release automated testing infrastructure we have today, and so depended much more on feedback from early adopters). Anyway, soon after being able to run pretty much as much of the Raku language as any other backend, I started on the dynamic optimizer. It gathered type statistics as the program was interpreted, identified hot code, put it into SSA form, used the type statistics to insert guards, used those together with static properties of the bytecode to analyze and optimize, and produced specialized bytecode for the function in question. This bytecode could elide type checks and various lookups, as well as using a range of internal ops that make all kinds of assumptions, which were safe because of the program properties that were proved by the optimizer. This is called specialized bytecode because it has had a lot of its genericity – which would allow it to work correctly on all types of value that we might encounter – removed, in favor of working in a particular special case that actually occurs at runtime. (Code, especially in more dynamic languages, is generally far more generic in theory than it ever turns out to be in practice.)

This component – the specializer, known internally as “spesh” – delivered a significant further improvement in the performance of Raku programs, and with time its sophistication has grown, taking in optimizations such as inlining and escape analysis with scalar replacement. These aren’t easy things to build – but once a runtime has them, they create design possibilities that didn’t previously exist, and make decisions made in their absence look sub-optimal.

Of note, those special-cased language-specific mechanisms, baked into the runtime to get some speed in the early days, instead become something of a liability and a bottleneck. They have complex semantics, which means they are either opaque to the optimizer (so it can’t reason about them, meaning optimization is inhibited) or they need special casing in the optimizer (a liability).

So, back to specializer plugins. I reached a point where I wanted to take on the performance of things like $obj.?meth (the “call me maybe” dispatch), $obj.SomeType::meth() (dispatch qualified with a class to start looking in), and private method calls in roles (which can’t be resolved statically). At the same time, I was getting ready to implement some amount of escape analysis, but realized that it was going to be of very limited utility because assignment had also been special-cased in the VM, with a chunk of opaque C code doing the hot path stuff.

But why did we have the C code doing that hot-path stuff? Well, because it’d be too espensive to have every assignment call a VM-level function that does a bunch of checks and logic. Why is that costly? Because of function call overhead and the costs of interpretation. This was all true once upon a time. But, some years of development later:

I solved the assignment problem and the dispatch problems mentioned above with the introduction of a single new mechanism: specializer plugins. They work as follows:

The vast majority of cases are monomorphic, meaning that only one set of guards are produced and they always succeed thereafter. The specializer can thus compile those guards into the specialized bytecode and then assume the given target invocant is what will be invoked. (Further, duplicate guards can be eliminated, so the guards a particular plugin introduces may reduce to zero.)

Specializer plugins felt pretty great. One new mechanism solved multiple optimization headaches.

The new MoarVM dispatch mechanism is the answer to a fairly simple question: what if we get rid of all the dispatch-related special-case mechanisms in favor of something a bit like specializer plugins? The resulting mechanism would need to be a more powerful than specializer plugins. Further, I could learn from some of the shortcomings of specializer plugins. Thus, while they will go away after a relatively short lifetime, I think it’s fair to say that I would not have been in a place to design the new MoarVM dispatch mechanism without that experience.

The dispatch op and the bootstrap dispatchers

All the method caching. All the multi dispatch caching. All the specializer plugins. All the invocation protocol stuff for unwrapping the bytecode handle in a code object. It’s all going away, in favor of a single new dispatch instruction. Its name is, boringly enough, dispatch. It looks like this:

dispatch_o result, 'dispatcher-name', callsite, arg0, arg1, ..., argN

Which means:

(Aside: this implies a new calling convention, whereby we no longer copy the arguments into an argument buffer, but instead pass the base of the register set and a pointer into the bytecode where the register argument map is found, and then do a lookup registers[map[argument_index]] to get the value for an argument. That alone is a saving when we interpret, because we no longer need a loop around the interpreter per argument.)

Some of the arguments might be things we’d traditionally call arguments. Some are aimed at the dispatch process itself. It doesn’t really matter – but it is more optimal if we arrange to put arguments that are only for the dispatch first (for example, the method name), and those for the target of the dispatch afterwards (for example, the method parameters).

The new bootstrap mechanism provides a small number of built-in dispatchers, whose names start with “boot-“. They are:

That’s pretty much it. Every dispatcher we build, to teach the runtime about some other kind of dispatch behavior, eventually terminates in one of these.

Building on the bootstrap

Teaching MoarVM about different kinds of dispatch is done using nothing less than the dispatch mechanism itself! For the most part, boot-syscall is used in order to register a dispatcher, set up the guards, and provide the result that goes with them.

Here is a minimal example, taken from the dispatcher test suite, showing how a dispatcher that provides the identity function would look:

nqp::dispatch('boot-syscall', 'dispatcher-register', 'identity', -> $capture {
    nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'boot-value', $capture);
sub identity($x) {
    nqp::dispatch('identity', $x)
ok(identity(42) == 42, 'Can define identity dispatch (1)');
ok(identity('foo') eq 'foo', 'Can define identity dispatch (2)');

In the first statement, we call the dispatcher-register MoarVM system call, passing a name for the dispatcher along with a closure, which will be called each time we need to handle the dispatch (which I tend to refer to as the “dispatch callback”). It receives a single argument, which is a capture of arguments (not actually a Raku-level Capture, but the idea – an object containing a set of call arguments – is the same).

Every user-defined dispatcher should eventually use dispatcher-delegate in order to identify another dispatcher to pass control along to. In this case, it delegates immediately to boot-value – meaning it really is nothing except a wrapper around the boot-value built-in dispatcher.

The sub identity contains a single static occurrence of the dispatch op. Given we call the sub twice, we will encounter this op twice at runtime, but the two times are very different.

The first time is the “record” phase. The arguments are formed into a capture and the callback runs, which in turn passes it along to the boot-value dispatcher, which produces the result. This results in an extremely simple dispatch program, which says that the result should be the first argument in the capture. Since there’s no guards, this will always be a valid result.

The second time we encounter the dispatch op, it already has a dispatch program recorded there, so we are in run mode. Turning on a debugging mode in the MoarVM source, we can see the dispatch program that results looks like this:

Dispatch program (1 temporaries)
    Load argument 0 into temporary 0
    Set result object value from temporary 0

That is, it reads argument 0 into a temporary location and then sets that as the result of the dispatch. Notice how there is no mention of the fact that we went through an extra layer of dispatch; those have zero cost in the resulting dispatch program.

Capture manipulation

Argument captures are immutable. Various VM syscalls exist to transform them into new argument captures with some tweak, for example dropping or inserting arguments. Here’s a further example from the test suite:

nqp::dispatch('boot-syscall', 'dispatcher-register', 'drop-first', -> $capture {
    my $capture-derived := nqp::dispatch('boot-syscall', 'dispatcher-drop-arg', $capture, 0);
    nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'boot-value', $capture-derived);
ok(nqp::dispatch('drop-first', 'first', 'second') eq 'second',
    'dispatcher-drop-arg works');

This drops the first argument before passing the capture on to the boot-value dispatcher – meaning that it will return the second argument. Glance back at the previous dispatch program for the identity function. Can you guess how this one will look?

Well, here it is:

Dispatch program (1 temporaries)
    Load argument 1 into temporary 0
    Set result string value from temporary 0

Again, while in the record phase of such a dispatcher we really do create capture objects and make a dispatcher delegation, the resulting dispatch program is far simpler.

Here’s a slightly more involved example:

my $target := -> $x { $x + 1 }
nqp::dispatch('boot-syscall', 'dispatcher-register', 'call-on-target', -> $capture {
    my $capture-derived := nqp::dispatch('boot-syscall',
            'dispatcher-insert-arg-literal-obj', $capture, 0, $target);
    nqp::dispatch('boot-syscall', 'dispatcher-delegate',
            'boot-code-constant', $capture-derived);
sub cot() { nqp::dispatch('call-on-target', 49) }
ok(cot() == 50,
    'dispatcher-insert-arg-literal-obj works at start of capture');
ok(cot() == 50,
    'dispatcher-insert-arg-literal-obj works at start of capture after link too');

Here, we have a closure stored in a variable $target. We insert it as the first argument of the capture, and then delegate to boot-code-constant, which will invoke that code object and pass the other dispatch arguments to it. Once again, at the record phase, we really do something like:

And the resulting dispatch program? It’s this:

Dispatch program (1 temporaries)
    Load collectable constant at index 0 into temporary 0
    Skip first 0 args of incoming capture; callsite from 0
    Invoke MVMCode in temporary 0

That is, load the constant bytecode handle that we’re going to invoke, set up the args (which are in this case equal to those of the incoming capture), and then invoke the bytecode with those arguments. The argument shuffling is, once again, gone. In general, whenever the arguments we do an eventual bytecode invocation with are a tail of the initial dispatch arguments, the arguments transform becomes no more than a pointer addition.


All of the dispatch programs seen so far have been unconditional: once recorded at a given callsite, they shall always be used. The big missing piece to make such a mechanism have practical utility is guards. Guards assert properties such as the type of an argument or if the argument is definite (Int:D) or not (Int:U).

Here’s a somewhat longer test case, with some explanations placed throughout it.

# A couple of classes for test purposes
my class C1 { }
my class C2 { }

# A counter used to make sure we're only invokving the dispatch callback as
# many times as we expect.
my $count := 0;

# A type-name dispatcher that maps a type into a constant string value that
# is its name. This isn't terribly useful, but it is a decent small example.
nqp::dispatch('boot-syscall', 'dispatcher-register', 'type-name', -> $capture {
    # Bump the counter, just for testing purposes.

    # Obtain the value of the argument from the capture (using an existing
    # MoarVM op, though in the future this may go away in place of a syscall)
    # and then obtain the string typename also.
    my $arg-val := nqp::captureposarg($capture, 0);
    my str $name := $$arg-val);

    # This outcome is only going to be valid for a particular type. We track
    # the argument (which gives us an object back that we can use to guard
    # it) and then add the type guard.
    my $arg := nqp::dispatch('boot-syscall', 'dispatcher-track-arg', $capture, 0);
    nqp::dispatch('boot-syscall', 'dispatcher-guard-type', $arg);

    # Finally, insert the type name at the start of the capture and then
    # delegate to the boot-constant dispatcher.
    nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'boot-constant',
        nqp::dispatch('boot-syscall', 'dispatcher-insert-arg-literal-str',
            $capture, 0, $name));

# A use of the dispatch for the tests. Put into a sub so there's a single
# static dispatch op, which all dispatch programs will hang off.
sub type-name($obj) {
    nqp::dispatch('type-name', $obj)

# Check with the first type, making sure the guard matches when it should
# (although this test would pass if the guard were ignored too).
ok(type-name(C1) eq 'C1', 'Dispatcher setting guard works');
ok($count == 1, 'Dispatch callback ran once');
ok(type-name(C1) eq 'C1', 'Can use it another time with the same type');
ok($count == 1, 'Dispatch callback was not run again');

# Test it with a second type, both record and run modes. This ensures the
# guard really is being checked.
ok(type-name(C2) eq 'C2', 'Can handle polymorphic sites when guard fails');
ok($count == 2, 'Dispatch callback ran a second time for new type');
ok(type-name(C2) eq 'C2', 'Second call with new type works');

# Check that we can use it with the original type too, and it has stacked
# the dispatch programs up at the same callsite.
ok(type-name(C1) eq 'C1', 'Call with original type still works');
ok($count == 2, 'Dispatch callback only ran a total of 2 times');

This time two dispatch programs get produced, one for C1:

Dispatch program (1 temporaries)
    Guard arg 0 (type=C1)
    Load collectable constant at index 1 into temporary 0
    Set result string value from temporary 0

And another for C2:

Dispatch program (1 temporaries)
    Guard arg 0 (type=C2)
    Load collectable constant at index 1 into temporary 0
    Set result string value from temporary 0

Once again, no leftovers from capture manipulation, tracking, or dispatcher delegation; the dispatch program does a type guard against an argument, then produces the result string. The whole call to $$arg-val) is elided, the dispatcher we wrote encoding the knowledge – in a way that the VM can understand – that a type’s name can be considered immutable.

This example is a bit contrived, but now consider that we instead look up a method and guard on the invocant type: that’s a method cache! Guard the types of more of the arguments, and we have a multi cache! Do both, and we have a multi-method cache.

The latter is interesting in so far as both the method dispatch and the multi dispatch want to guard on the invocant. In fact, in MoarVM today there will be two such type tests until we get to the point where the specializer does its work and eliminates these duplicated guards. However, the new dispatcher does not treat the dispatcher-guard-type as a kind of imperative operation that writes a guard into the resultant dispatch program. Instead, it declares that the argument in question must be guarded. If some other dispatcher already did that, it’s idempotent. The guards are emitted once all dispatch programs we delegate through, on the path to a final outcome, have had their say.

Fun aside: those being especially attentive will have noticed that the dispatch mechanism is used as part of implementing new dispatchers too, and indeed, this ultimately will mean that the specializer can specialize the dispatchers and have them JIT-compiled into something more efficient too. After all, from the perspective of MoarVM, it’s all just bytecode to run; it’s just that some of it is bytecode that tells the VM how to execute Raku programs more efficiently!

Dispatch resumption

A resumable dispatcher needs to do two things:

  1. Provide a resume callback as well as a dispatch one when registering the dispatcher
  2. In the dispatch callback, specify a capture, which will form the resume initialization state

When a resumption happens, the resume callback will be called, with any arguments for the resumption. It can also obtain the resume initialization state that was set in the dispatch callback. The resume initialization state contains the things needed in order to continue with the dispatch the first time it is resumed. We’ll take a look at how this works for method dispatch to see a concrete example. I’ll also, at this point, switch to looking at the real Rakudo dispatchers, rather than simplified test cases.

The Rakudo dispatchers take advantage of delegation, duplicate guards, and capture manipulations all having no runtime cost in the resulting dispatch program to, in my mind at least, quite nicely factor what is a somewhat involved dispatch process. There are multiple entry points to method dispatch: the normal boring $obj.meth(), the qualified $obj.Type::meth(), and the call me maybe $obj.?meth(). These have common resumption semantics – or at least, they can be made to provided we always carry a starting type in the resume initialization state, which is the type of the object that we do the method dispatch on.

Here is the entry point to dispatch for a normal method dispatch, with the boring details of reporting missing method errors stripped out.

# A standard method call of the form $obj.meth($arg); also used for the
# indirect form $obj."$name"($arg). It receives the decontainerized invocant,
# the method name, and the the args (starting with the invocant including any
# container).
nqp::dispatch('boot-syscall', 'dispatcher-register', 'raku-meth-call', -> $capture {
    # Try to resolve the method call using the MOP.
    my $obj := nqp::captureposarg($capture, 0);
    my str $name := nqp::captureposarg_s($capture, 1);
    my $meth := $obj.HOW.find_method($obj, $name);

    # Report an error if there is no such method.
    unless nqp::isconcrete($meth) {
        !!! 'Error reporting logic elided for brevity';

    # Establish a guard on the invocant type and method name (however the name
    # may well be a literal, in which case this is free).
    nqp::dispatch('boot-syscall', 'dispatcher-guard-type',
        nqp::dispatch('boot-syscall', 'dispatcher-track-arg', $capture, 0));
    nqp::dispatch('boot-syscall', 'dispatcher-guard-literal',
        nqp::dispatch('boot-syscall', 'dispatcher-track-arg', $capture, 1));

    # Add the resolved method and delegate to the resolved method dispatcher.
    my $capture-delegate := nqp::dispatch('boot-syscall',
        'dispatcher-insert-arg-literal-obj', $capture, 0, $meth);
    nqp::dispatch('boot-syscall', 'dispatcher-delegate',
        'raku-meth-call-resolved', $capture-delegate);

Now for the resolved method dispatcher, which is where the resumption is handled. First, let’s look at the normal dispatch callback (the resumption callback is included but empty; I’ll show it a little later).

# Resolved method call dispatcher. This is used to call a method, once we have
# already resolved it to a callee. Its first arg is the callee, the second and
# third are the type and name (used in deferral), and the rest are the args to
# the method.
nqp::dispatch('boot-syscall', 'dispatcher-register', 'raku-meth-call-resolved',
    # Initial dispatch
    -> $capture {
        # Save dispatch state for resumption. We don't need the method that will
        # be called now, so drop it.
        my $resume-capture := nqp::dispatch('boot-syscall', 'dispatcher-drop-arg',
            $capture, 0);
        nqp::dispatch('boot-syscall', 'dispatcher-set-resume-init-args', $resume-capture);

        # Drop the dispatch start type and name, and delegate to multi-dispatch or
        # just invoke if it's single dispatch.
        my $delegate_capture := nqp::dispatch('boot-syscall', 'dispatcher-drop-arg',
            nqp::dispatch('boot-syscall', 'dispatcher-drop-arg', $capture, 1), 1);
        my $method := nqp::captureposarg($delegate_capture, 0);
        if nqp::istype($method, Routine) && $method.is_dispatcher {
            nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'raku-multi', $delegate_capture);
        else {
            nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'raku-invoke', $delegate_capture);
    # Resumption
    -> $capture {
        ... 'Will be shown later';

There’s an arguable cheat in raku-meth-call: it doesn’t actually insert the type object of the invocant in place of the invocant. It turns out that it doesn’t really matter. Otherwise, I think the comments (which are to be found in the real implementation also) tell the story pretty well.

One important point that may not be clear – but follows a repeating theme – is that the setting of the resume initialization state is also more of a declarative rather than an imperative thing: there isn’t a runtime cost at the time of the dispatch, but rather we keep enough information around in order to be able to reconstruct the resume initialization state at the point we need it. (In fact, when we are in the run phase of a resume, we don’t even have to reconstruct it in the sense of creating a capture object.)

Now for the resumption. I’m going to present a heavily stripped down version that only deals with the callsame semantics (the full thing has to deal with such delights as lastcall and nextcallee too). The resume initialization state exists to seed the resumption process. Once we know we actually do have to deal with resumption, we can do things like calculating the full list of methods in the inheritance graph that we want to walk through. Each resumable dispatcher gets a single storage slot on the call stack that it can use for its state. It can initialize this in the first step of resumption, and then update it as we go. Or more precisely, it can set up a dispatch program that will do this when run.

A linked list turns out to be a very convenient data structure for the chain of candidates we will walk through. We can work our way through a linked list by keeping track of the current node, meaning that there need only be a single thing that mutates, which is the current state of the dispatch. The dispatch program mechanism also provides a way to read an attribute from an object, and that is enough to express traversing a linked list into the dispatch program. This also means zero allocations.

So, without further ado, here is the linked list (rather less pretty in NQP, the restricted Raku subset, than it would be in full Raku):

# A linked list is used to model the state of a dispatch that is deferring
# through a set of methods, multi candidates, or wrappers. The Exhausted class
# is used as a sentinel for the end of the chain. The current state of the
# dispatch points into the linked list at the appropriate point; the chain
# itself is immutable, and shared over (runtime) dispatches.
my class DeferralChain {
    has $!code;
    has $!next;
    method new($code, $next) {
        my $obj := nqp::create(self);
        nqp::bindattr($obj, DeferralChain, '$!code', $code);
        nqp::bindattr($obj, DeferralChain, '$!next', $next);
    method code() { $!code }
    method next() { $!next }
my class Exhausted {};

And finally, the resumption handling.

nqp::dispatch('boot-syscall', 'dispatcher-register', 'raku-meth-call-resolved',
    # Initial dispatch
    -> $capture {
        ... 'Presented earlier;
    # Resumption. The resume init capture's first two arguments are the type
    # that we initially did a method dispatch against and the method name
    # respectively.
    -> $capture {
        # Work out the next method to call, if any. This depends on if we have
        # an existing dispatch state (that is, a method deferral is already in
        # progress).
        my $init := nqp::dispatch('boot-syscall', 'dispatcher-get-resume-init-args');
        my $state := nqp::dispatch('boot-syscall', 'dispatcher-get-resume-state');
        my $next_method;
        if nqp::isnull($state) {
            # No state, so just starting the resumption. Guard on the
            # invocant type and name.
            my $track_start_type := nqp::dispatch('boot-syscall', 'dispatcher-track-arg', $init, 0);
            nqp::dispatch('boot-syscall', 'dispatcher-guard-type', $track_start_type);
            my $track_name := nqp::dispatch('boot-syscall', 'dispatcher-track-arg', $init, 1);
            nqp::dispatch('boot-syscall', 'dispatcher-guard-literal', $track_name);

            # Also guard on there being no dispatch state.
            my $track_state := nqp::dispatch('boot-syscall', 'dispatcher-track-resume-state');
            nqp::dispatch('boot-syscall', 'dispatcher-guard-literal', $track_state);

            # Build up the list of methods to defer through.
            my $start_type := nqp::captureposarg($init, 0);
            my str $name := nqp::captureposarg_s($init, 1);
            my @mro := nqp::can($start_type.HOW, 'mro_unhidden')
                ?? $start_type.HOW.mro_unhidden($start_type)
                !! $start_type.HOW.mro($start_type);
            my @methods;
            for @mro {
                my %mt := nqp::hllize($_.HOW.method_table($_));
                if nqp::existskey(%mt, $name) {

            # If there's nothing to defer to, we'll evaluate to Nil (just don't set
            # the next method, and it happens below).
            if nqp::elems(@methods) >= 2 {
                # We can defer. Populate next method.
                @methods.shift; # Discard the first one, which we initially called
                $next_method := @methods.shift; # The immediate next one

                # Build chain of further methods and set it as the state.
                my $chain := Exhausted;
                while @methods {
                    $chain :=, $chain);
                nqp::dispatch('boot-syscall', 'dispatcher-set-resume-state-literal', $chain);
        elsif !nqp::istype($state, Exhausted) {
            # Already working through a chain of method deferrals. Obtain
            # the tracking object for the dispatch state, and guard against
            # the next code object to run.
            my $track_state := nqp::dispatch('boot-syscall', 'dispatcher-track-resume-state');
            my $track_method := nqp::dispatch('boot-syscall', 'dispatcher-track-attr',
                $track_state, DeferralChain, '$!code');
            nqp::dispatch('boot-syscall', 'dispatcher-guard-literal', $track_method);

            # Update dispatch state to point to next method.
            my $track_next := nqp::dispatch('boot-syscall', 'dispatcher-track-attr',
                $track_state, DeferralChain, '$!next');
            nqp::dispatch('boot-syscall', 'dispatcher-set-resume-state', $track_next);

            # Set next method, which we shall defer to.
            $next_method := $state.code;
        else {
            # Dispatch already exhausted; guard on that and fall through to returning
            # Nil.
            my $track_state := nqp::dispatch('boot-syscall', 'dispatcher-track-resume-state');
            nqp::dispatch('boot-syscall', 'dispatcher-guard-literal', $track_state);

        # If we found a next method...
        if nqp::isconcrete($next_method) {
            # Call with same (that is, original) arguments. Invoke with those.
            # We drop the first two arguments (which are only there for the
            # resumption), add the code object to invoke, and then leave it
            # to the invoke or multi dispatcher.
            my $just_args := nqp::dispatch('boot-syscall', 'dispatcher-drop-arg',
                nqp::dispatch('boot-syscall', 'dispatcher-drop-arg', $init, 0),
            my $delegate_capture := nqp::dispatch('boot-syscall',
                'dispatcher-insert-arg-literal-obj', $just_args, 0, $next_method);
            if nqp::istype($next_method, Routine) && $next_method.is_dispatcher {
                nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'raku-multi',
            else {
                nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'raku-invoke',
        else {
            # No method, so evaluate to Nil (boot-constant disregards all but
            # the first argument).
            nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'boot-constant',
                nqp::dispatch('boot-syscall', 'dispatcher-insert-arg-literal-obj',
                    $capture, 0, Nil));

That’s quite a bit to take in, and quite a bit of code. Remember, however, that this is only run for the record phase of a dispatch resumption. It also produces a dispatch program at the callsite of the callsame, with the usual guards and outcome. Implicit guards are created for the dispatcher that we are resuming at that point. In the most common case this will end up monomorphic or bimorphic, although situations involving nestings of multiple dispatch or method dispatch could produce a more morphic callsite.

The design I’ve picked forces resume callbacks to deal with two situations: the first resumption and the latter resumptions. This is not ideal in a couple of ways:

  1. It’s a bit inconvenient for those writing dispatch resume callbacks. However, it’s not like this is a particularly common activity!
  2. The difference results in two dispatch programs being stacked up at a callsite that might otherwise get just one

Only the second of these really matters. The reason for the non-uniformity is to make sure that the overwhelming majority of calls, which never lead to a dispatch resumption, incur no per-dispatch cost for a feature that they never end up using. If the result is a little more cost for those using the feature, so be it. In fact, early benchmarking shows callsame with wrap and method calls seems to be up to 10 times faster using the new dispatcher than in current Rakudo, and that’s before the specializer understands enough about it to improve things further!

What’s done so far

Everything I’ve discussed above is implemented, except that I may have given the impression somewhere that multiple dispatch is fully implemented using the new dispatcher, and that is not the case yet (no handling of where clauses and no dispatch resumption support).

Next steps

Getting the missing bits of multiple dispatch fully implemented is the obvious next step. The other missing semantic piece is support for callwith and nextwith, where we wish to change the arguments that are being used when moving to the next candidate. A few other minor bits aside, that in theory will get all of the Raku dispatch semantics at least supported.

Currently, all standard method calls ($obj.meth()) and other calls (foo() and $foo()) go via the existing dispatch mechanism, not the new dispatcher. Those will need to be migrated to use the new dispatcher also, and any bugs that are uncovered will need fixing. That will get things to the point where the new dispatcher is semantically ready.

After that comes performance work: making sure that the specializer is able to deal with dispatch program guards and outcomes. The goal, initially, is to get steady state performance of common calling forms to perform at least as well as in the current master branch of Rakudo. It’s already clear enough there will be some big wins for some things that to date have been glacial, but it should not come at the cost of regression on the most common kinds of dispatch, which have received plenty of optimization effort before now.

Furthermore, NQP – the restricted form of Raku that the Rakudo compiler and other bits of the runtime guts are written in – also needs to be migrated to use the new dispatcher. Only when that is done will it be possible to rip out the current method cache, multiple dispatch cache, and so forth from MoarVM.

An open question is how to deal with backends other than MoarVM. Ideally, the new dispatch mechanism will be ported to those. A decent amount of it should be possible to express in terms of the JVM’s invokedynamic (and this would all probably play quite well with a Truffle-based Raku implementation, although I’m not sure there is a current active effort in that area).

Future opportunities

While my current focus is to ship a Rakudo and MoarVM release that uses the new dispatcher mechanism, that won’t be the end of the journey. Some immediate ideas:

Some new language features may also be possible to provide in an efficient way with the help of the new dispatch mechanism. For example, there’s currently not a reliable way to try to invoke a piece of code, just run it if the signature binds, or to do something else if it doesn’t. Instead, things like the Cro router have to first do a trial bind of the signature, and then do the invoke, which makes routing rather more costly. There’s also the long suggested idea of providing pattern matching via signatures with the when construct (for example, when * -> ($x) {}; when * -> ($x, *@tail) { }), which is pretty much the same need, just in a less dynamic setting.

In closing…

Working on the new dispatch mechanism has been a longer journey than I first expected. The resumption part of the design was especially challenging, and there’s still a few important details to attend to there. Something like four potential approaches were discarded along the way (although elements of all of them influenced what I’ve described in this post). Abstractions that hold up are really, really, hard.

I also ended up having to take a couple of months away from doing Raku work at all, felt a bit crushed during some others, and have been juggling this with the equally important RakuAST project (which will be simplified by being able to assume the presence of the new dispatcher, and also offers me a range of softer Raku hacking tasks, whereas the dispatcher work offers few easy pickings).

Given all that, I’m glad to finally be seeing the light at the end of the tunnel. The work that remains is enumerable, and the day we ship a Rakudo and MoarVM release using the new dispatcher feels a small number of months away (and I hope writing that is not tempting fate!)

The new dispatcher is probably the most significant change to MoarVM since I founded it, in so far as it sees us removing a bunch of things that have been there pretty much since the start. RakuAST will also deliver the greatest architectural change to the Rakudo compiler in a decade. Both are an opportunity to fold years of learning things the hard way into the runtime and compiler. I hope when I look back at it all in another decade’s time, I’ll at least feel I made more interesting mistakes this time around.

brrt to the future: Why bother with Scripting?

Published by Bart Wiegmans on 2021-03-14T14:33:00

Many years back, Larry Wall shared his thesis on the nature of scripting. Since recently even Java gained 'script' support I thought it would be fitting to revisit the topic, and hopefully relevant to the perl and raku language community.

The weakness of Larry's treatment (which, to be fair to the author, I think is more intended to be enlightening than to be complete) is the contrast of scripting with programming. This contrast does not permit a clear separation because scripts are programs. That is to say, no matter how long or short, scripts are written commands for a machine to execute, and I think that's a pretty decent definition of a program in general.

A more useful contrast - and, I think, the intended one - is between scripts and other sorts of programs, because that allows us to compare scripting (writing scripts) with 'programming' (writing non-script programs). And to do that we need to know what other sorts of programs there are.

The short version of that answer is - systems and applications, and a bunch of other things that aren't really relevant to the working programmer, like (embedded) control algorithms, spreadsheets and database queries. (The definition I provided above is very broad, by design, because I don't want to get stuck on boundary questions). Most programmers write applications, some write systems, virtually all write scripts once in a while, though plenty of people who aren't professional programmers also write scripts.

I think the defining features of applications and systems are, respectively:

Consider for instance a mail client (like thunderbird) in comparison to a mailer daemon (like sendmail) - one provides an interface to read and write e-mails (the model) and the other provides functionality to send that e-mail to other servers.

Note that under this (again, broad) definition, libraries are also system software, which makes sense, considering that their users are developers (just as for, say, PostgreSQL) who care about things like performance, reliability, and correctness. Incidentally, libraries as well as 'typical' system software (such as database engines and operating system kernels) tend to be written in languages like C and C++ for much the same reasons.

What then, are the differences between scripts, applications, and systems? I think the following is a good list:

Obviously these distinctions aren't really binary - 'short' versus 'long', 'ad-hoc' versus 'general purpose'  - and can't be used to conclusively settle the question whether something is a script or an application. (If, indeed, that question ever comes up). More important is that for the 10 or so scripts I've written over the past year - some professionally, some not - all or most of these properties held, and I'd be surprised if the same isn't true for most readers. 

And - finally coming at the point that I'm trying to make today - these features point to a specific niche of programs more than to a specific technology (or set of technologies). To be exact, scripts are (mostly) short, custom programs to automate ad-hoc tasks, tasks that are either to specific or too small to develop and distribute another program for.

This has further implications on the preferred features of a scripting language (taken to mean, a language designed to enable the development of scripts). In particular:

As an example of the last point - Python 3 requires users to be exact about the encoding of their input, causing all sorts of trouble for unsuspecting scripters when they accidentally try to read ISO-8551 data as UTF-8, or vice versa. Python 2 did not, and for most scripts - not applications - I actually think that is the right choice.

This niche doesn't always exist. In computing environments where everything of interest is adequately captured by an application, or which lacks the ability to effectively automate ad-hoc tasks (I'm thinking in particular of Windows before PowerShell), the practice of scripting tends to not develop. Similarily, in a modern 'cloud' environment, where system setup is controlled by a state machine hosted by another organization, scripting doesn't really have much of a future.

To put it another way, scripting only thrives in an environment that has a lot of 'scriptable' tasks; meaning tasks for which there isn't already a pre-made solution available, environments that have powerful facilities available for a script to access, and whose users are empowered to automate those tasks. Such qualities are common on Unix/Linux 'workstations' but rather less so on smartphones and (as noted before) cloud computing environments.

Truth be told I'm a little worried about that development. I could point to, and expound on, the development and popularity of languages like go and rust, which aren't exactly scripting languages, or the replacement of Javascript with TypeScript, to make the point further, but I don't think that's necessary. At the same time I could point to the development of data science as a discipline to demonstrate that scripting is alive and well (and indeed perhaps more economically relevant than before).

What should be the conclusion for perl 5/7 and raku? I'm not quite sure, mostly because I'm not quite sure whether the broader perl/raku community would prefer their sister languages to be scripting or application languages. (As implied above, I think the Python community chose that they wanted Python 3 to be an application language, and this was not without consequences to their users). 

Raku adds a number of features common to application languages (I'm thinking of it's powerful type system in particular), continuing a trend that perl 5 arguably pioneered. This is indeed a very powerful strategy - a language can be introduced for scripts and some of those scripts are then extended into applications (or even systems), thereby ensuring its continued usage. But for it to work, a new perl family language must be introduced on its scripting merits, and there must be a plentiful supply of scriptable tasks to automate, some of which - or a combination of which - grow into an application.

For myself, I would like to see scripting have a bright future. Not just because scripting is the most accessible form of programming, but also because an environment that permits, even requires scripting, is one were not all interesting problems have been solved, one where it's users ask it to do tasks so diverse that there isn't an app for that, yet. One where the true potential of the wonderful devices that surround is can be explored.

In such a world there might well be a bright future for scripting.

Andrew Shitov: Computing factorials using Raku

Published by Andrew Shitov on 2021-01-31T18:19:33

In this post, I’d like to demonstrate a few ways of computing factorials using the Raku programming language.

1 — reduction

Let me start with the basic and the most effective (non necessarily the most efficient) form of computing the factorial of a given integer number:

say [*] 1..10; # 3628800

In the below examples, we mostly will be dealing with the factorial of 10, so remember the result. But to make the programs more versatile, let us read the number from the command line:

unit sub MAIN($n);

say [*] 1..$n;

To run the program, pass the number:

$ raku 00-cmd.raku 10

The program uses the reduction meta-operator [ ] with the main operator * in it.

You can also start with 2 (you can even compute 0! and 1! this way).

unit sub MAIN($n);

say [*] 2..$n;

2 — for

The second solution is using a postfix for loop to multiply the numbers in the range:

unit sub MAIN($n);

my $f = 1;
$f *= $_ for 2..$n;

say $f;

This solution is not that expressive but still demonstrates quite a clear code.

3 — map

You can also use map that is applied to a range:

unit sub MAIN($n);

my $f = 1;
(2..$n).map: $f *= *;

say $f;

Refer to my article All the stars of Perl 6, or * ** * to learn more about how to read *= *.

4 — recursion

Let’s implement a recursive solution.

unit sub MAIN($n);

sub factorial($n) {
    if $n < 2 {
        return 1;
    else {
        return $n * factorial($n - 1);

say factorial(n);

There are two branches, one of which terminates recursion.

5 — better recursion

The previous program can be rewritten to make a code with less punctuation:

unit sub MAIN($n);

sub factorial($n) {
    return 1 if $n < 2;
    return $n * factorial($n - 1);

say factorial($n);

Here, the first return is managed by a postfix if, and the second return can only be reached if the condition in if is false. So, neither an additional Boolean test nor else is needed.

6 — big numbers

What if you need to compute a factorial of a relatively big number? No worries, Raku will just do it:

say [*] 1..500;

The speed is more than acceptable for any practical application:

raku 06-long-factorial.raku  0.14s user 0.02s system 124% cpu 0.127 total

7 — small numbers

Let’s try something opposite and compute a factorial, which can fit a native integer:

unit sub MAIN($n);

my int $f = 1;
$f *= $_ for 2..$n;

say $f;

I am using a for loop here, but notice that the type of $f is a native integer (thus, 4 bytes). This program works with the numbers up to 20:

$ raku 07-int-factorial.raku 20

8 — sequence

The fun fact is that you can add a dot to the first program 🙂

unit sub MAIN($n);

say [*] 1 ... $n;

Now, 1 ... $n is a sequence. You can start it with 2 if you are not planning to compute a factorials of 0 and 1.

9 — reversed sequence

Unlike the solution with a range, it is possible to swap the ends of the sequence:

unit sub MAIN($n);

say [*] $n ... 1;

10 — sequence with definition

Nothing stops us from defining the elements of the sequence with a code block. The next program shows how you do it:

unit sub MAIN($n);

my @f = 1, * * ++$ ... *;
say @f[$n];

This time, the program generates a sequence of factorials from 1! to $n!, and to print the only one we need, we take the value from the array as @f[$n]. Notice that the sequence itself is lazy and its right end is undefined, so you can’t use @f[*-1], for example.

The rule here is * * ++$ (multiply the last computed value by the incremented index); it is using the built-in state variable $.

11 — multi functions

The idea of the solutions 4 and 5 with two branches can be further transformed to using multi-functions:

unit sub MAIN($n);

multi sub factorial(1)  { 1 }
multi sub factorial($n) { $n * factorial($n - 1) }

say factorial($n);

For the numbers above 1, Raku calls the second variant of the function. When the number comes down to 1, recursion stops, because the first variant is called. Notice how easily you can create a variant of a function that only reacts to the given value.

12 — where

The previous program loops infinitely if you try to set $n to 0. One of the simplest solution is to add a where clause to catch that case too.

unit sub MAIN($n);

multi sub factorial($n where $n < 2)  { 1 }
multi sub factorial($n) { $n * factorial($n - 1) }

say factorial($n);

13 — operator

Here’s another classical Raku solution: modifying its grammar to allow mathematical notation $n!.

unit sub MAIN($n);

sub postfix:<!>($n) {
    [*] 1..$n

say $n!;

14 — methodop

A rarely seen Raku’s feature called methodop (method operator) that allows you to call a function as it if was a method:

unit sub MAIN($n);

sub factorial($n) { 
    [*] 1..$n

say $n.&factorial;

15 — cached

Recursive solutions are perfect subjects for result caching. The following program demonstrates this approach.

unit sub MAIN($n);

use experimental :cached;

sub f($n) is cached {
    say "Called f($n)";
    return 1 if $n < 2;
    return $n * f($n - 1);

say f($n div 2);
say f(10);

This program first computes a factorial of the half of the input number, and then of the number itself. The program logs all the calls of the function. You can clearly see that, say, the factorial of 10 is using the results that were already computed for the factorial of 5:

$ raku 15-cached-factorial.raku 10
Called f(5)
Called f(4)
Called f(3)
Called f(2)
Called f(1)
Called f(10)
Called f(9)
Called f(8)
Called f(7)
Called f(6)

Note that the feature is experimental.

16 — triangular reduction

The reduction operator that we already used has a special variant [\ ] that allows to keep all the intermediate results. This is somewhat similar to using a sequence in the example 10.

unit sub MAIN($n);

my @f = [\*] 1..$n;

say @f[$n - 1];

17 — division of factorials

Now a few programs that go beyond the factorials themselves. The first program computes the value of the expression a! / b!, where both a and b are integer numbers, and a is not less than b.

The idea is to optimise the solution to skip the overlapping parts of the multiplication sequences. For example, 10! / 5! is 6 * 7 * 8 * 9 * 10.

To have more fun, let us modify Raku’s grammar so that it really parses the above expression.

unit sub MAIN($a, $b where $a >= $b);

class F {
    has $.n;    

sub postfix:<!>(Int $n) { => $n)

sub infix:</>(F $a, F $b) { 
    [*] $b.n ^.. $a.n

say $a! / $b!;

We already have seen the postfix:<!> operator. To catch division, another operator is defined, but to prevent catching the division of data of other types, a proxy class F is introduced.

To keep proper processing of expression such as 4 / 5, define another / operator that catches things which are not F. Don’t forget to add multi to both options. The callsame built-in routine dispatches control to built-in operator definitions.

. . .

multi sub infix:</>(F $a, F $b) { 
    [*] $b.n ^.. $a.n

multi sub infix:</>($a, $b) {

say $a! / $b!;
say 4 / 5;

18 — optimisation

Let’s try to reduce the number of multiplications. Take a factorial of 10:

10 * 9 * 8 * 7 * 6 * 5 * 4 * 3 * 2 * 1

Now, take one number from each end, multiply them, and repeat the procedure:

10 * 1 = 10
 9 * 2 = 18
 8 * 3 = 24
 7 * 4 = 28
 6 * 5 = 30

You can see that every such result is bigger than the previous one by 8, 6, 4, and 2. In other words, the difference reduces by 2 on each iteration, starting from 10, which is the input number.

The whole program that implements this algorithm is shown below:

unit sub MAIN(
    $n is copy where $n %% 2 #= Even numbers only

my $f = $n;

my $d = $n - 2;
my $m = $n + $d;

while $d > 0 {
    $f *= $m;
    $d -= 2;
    $m += $d;

say $f;

It only works for even input numbers, so it contains a restriction reflected in the where clause of the MAIN function. As homework, modify the program to accept odd numbers too.

19 — integral

Before wrapping up, let’s look at a couple of exotic methods, which, however, can be used to compute factorials of non-integer numbers (or, to be stricter, to compute what can be called extended definition of it).

The proper way would be to use the Gamma function, but let me illustrate the method with a simpler formula:

An integral is a sum by definition, so let’s make a straightforward loop:

unit sub MAIN($n);

my num $f = 0E0;
my num $dx = 1E-6;
loop (my $x = $dx; $x <= 1; $x += $dx) {
    $f += (-log($x)) ** $n;

say $f * $dx;

With the given step of 1E-6, the result is not that exact:

$ raku 19-integral-factorial.raku 10

But you can compute a ‘factorial’ of a floating-point number. For example, 5! is 120 and 6! is 720, but what is 5.5!?

$ raku 19-integral-factorial.raku 5.5

20 — another formula

And finally, the Stirling’s formula for the rescue. The bigger the n, the more correct is the result.

The implementation can be as simple as this:

unit sub MAIN($n);

# τ = 2 * π
say (τ * $n).sqrt * ($n / e) ** $n;

But you can make it a bit more outstanding if you have a fixed $n:

say sqrt(τ * 10) * (10 / e)¹⁰;

* * *

And that’s it for now. You can find the source code of all the programs shown here in the GitHub repository

Andrew Shitov: The course of Raku

Published by Andrew Shitov on 2021-01-13T08:44:00

I am happy to report that the first part of the Raku course is completed and published. The course is available at

The grant was approved a year and a half ago right before the PerlCon conference in Rīga. I was the organiser of the event, so I had to postpone the course due to high load. During the conference, it was proposed to rename Perl 6, which, together with other stuff, made me think if the course is needed.

After months, the name was settled, the distinction between Perl and Raku became clearer, and, more importantly, external resourses and services, e.g., Rosettacode and started using the new name. So, now I think it is still a good idea to create the course that I dreamed about a couple of years ago. I started the main work in the middle of November 2020, and by the beginning of January 2021, I had the first part ready.

The current plan includes five parts:

  1. Raku essentials
  2. Advanced Raku subjects
  3. Object-oriented programming in Raku
  4. Regexes and grammars
  5. Functional, concurrent, and reactive programming

It differs a bit from the original plan published in the grant proposal. While the material stays the same, I decided to split it differently. Initially, I was going to go through all the topics one after another. Now, the first sections reveal the basics of some topics, and we will return to the same topics on the next level in the second part.

For example, in the first part, I only talk about the basic data types: IntRatNumStrRangeArrayList, and Hash and basic usage of them. The rest, including other types (e.g., Date or DateTime) and the methods such as @array.rotate or %hash.kv is delayed until the second part.

Contrary, functions were a subject of the second part initially, but they are now discussed in the first part. So, we now have Part 1 “Raku essentials” and Part 2 “Advanced Raku topics”. This shuffling allowed me to create a liner flow in such a way that the reader can start writing real programs already after they finish the first part of the course.

I must say that it is quite a tricky task to organise the material without backward links. In the ideal course, any topic may only be based on the previously explained information. A couple of the most challenging cases were ranges and typed variables. They both causes a few chicken-and-egg loops.

During the work on the first part, I also prepared a ‘framework’ that generates the navigation through the site and helps with quiz automation. It is hosted as GitHub Pages and uses Jekyll and Liquid for generating static pages, and a couple of Raku programs to automate the process of adding new exercises and highlighting code snippets. Syntax highlighting is done with Pygments.

Returning the to course itself, it includes pages of a few different types:

The quizzes were not part of the grant proposal, but I think they help making a better user experience. All the quizzes have answers and comments. All the exercises are solved and published with the comments to explain the solution, or even to highlight some theoretical aspects.

The first part covers 91 topics and includes 73 quizzes and 65 exercises (with 70 solutions :-). There are about 330 pages in total. The sources are kept in a GitHub repository, so people can send pull requiest, etc.

At this point, the first part is fully ready. I may slightly update it if the following parts require additional information about the topics covered in Part 1.

This text is a grant report, and it is also (a bit modified) published at on 13 January 2021.

Andrew Shitov: Raku Challenge, Week 92, Issue 1

Published by Andrew Shitov on 2020-12-22T08:24:00

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

Andrew Shitov: Advent of Code 2020 Day 18/25 in the Raku programming language

Published by Andrew Shitov on 2020-12-18T22:07:44

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.

sub infix:<m>($a, $b) { $a * $b }

say [+] ('input.txt' *.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

Andrew Shitov: The second wave of

Published by Andrew Shitov on 2020-12-15T22:15:33

When I started 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/ 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/ (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!

6guts: Taking a break from Raku core development

Published by jnthnwrthngtn on 2020-10-05T19:44:26

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.

brrt to the future: Reverse Linear Scan Allocation is probably a good idea

Published by Bart Wiegmans on 2019-03-21T15:52:00

Hi hackers! Today First of all, I want to thank everybody who gave such useful feedback on my last post.  For instance, I found out that the similarity between the expression JIT IR and the Testarossa Trees IR is quite remarkable, and that they have a fix for the problem that is quite different from what I had in mind.

Today I want to write something about register allocation, however. Register allocation is probably not my favorite problem, on account of being both messy and thankless. It is a messy problem because - aside from being NP-hard to solve optimally - hardware instruction sets and software ABI's introduce all sorts of annoying constraints. And it is a thankless problem because the case in which a good register allocator is useful - for instance, when there's lots of intermediate values used over a long stretch of code - are fairly rare. Much more common are the cases in which either there are trivially sufficient registers, or ABI constraints force a spill to memory anyway (e.g. when calling a function, almost all registers can be overwritten).

So, on account of this being not my favorite problem, and also because I promised to implement optimizations in the register allocator, I've been researching if there is a way to do better. And what better place to look than one of the fastest dynamic language implementations arround, LuaJIT? So that's what I did, and this post is about what I learned from that.

Truth be told, LuaJIT is not at all a learners' codebase (and I don't think it's author would claim this). It uses a rather terse style of C and lots and lots of preprocessor macros. I had somewhat gotten used to the style from hacking dynasm though, so that wasn't so bad. What was more surprising is that some of the steps in code generation that are distinct and separate in the MoarVM JIT - instruction selection, register allocation and emitting bytecode - were all blended together in LuaJIT. Over multiple backend architectures, too. And what's more - all these steps were done in reverse order - from the end of the program (trace) to the beginning. Now that's interesting...

I have no intention of combining all phases of code generation like LuaJIT has. But processing the IR in reverse seems to have some interesting properties. To understand why that is, I'll first have to explain how linear scan allocation currently works in MoarVM, and is most commonly described:

  1. First, the live ranges of program values are computed. Like the name indicates, these represent the range of the program code in which a value is both defined and may be used. Note that for the purpose of register allocation, the notion of a value shifts somewhat. In the expression DAG IR, a value is the result of a single computation. But for the purposes of register allocation, a value includes all its copies, as well as values computed from different conditional branches. This is necessary because when we actually start allocating registers, we need to know when a value is no longer in use (so we can reuse the register) and how long a value will remain in use -
  2. Because a value may be computed from distinct conditional branches, it is necessary to compute the holes in the live ranges. Holes exists because if a value is defined in both sides of a conditional branch, the range will cover both the earlier (in code order) branch and the later branch - but from the start of the later branch to its definition that value doesn't actually exist. We need this information to prevent the register allocator from trying to spill-and-load a nonexistent value, for instance.
  3. Only then can we allocate and assign the actual registers to instructions. Because we might have to spill values to memory, and because values now can have multiple definitions, this is a somewhat subtle problem. Also, we'll have to resolve all architecture specific register requirements in this step.
In the MoarVM register allocator, there's a fourth step and a fifth step. The fourth step exists to ensure that instructions conform to x86 two-operand form (Rather than return the result of an instruction in a third register, x86 reuses one of the input registers as the output register. E.g. all operators are of the form a = op(a, b)  rather than a = op(b, c). This saves on instruction encoding space). The fifth step inserts instructions that are introduced by the third step; this is done so that each instruction has a fixed address in the stream while the stream is being processed.

Altogether this is quite a bit of complexity and work, even for what is arguably the simplest correct global register allocation algorithm. So when I started thinking of the reverse linear scan algorithm employed by LuaJIT, the advantages became clear:
There are downsides as well, of course. Not knowing exactly how long a value will be live while processing it may cause the algorithm to make worse choices in which values to spill. But I don't think that's really a great concern, since figuring out the best possible value is practically impossible anyway, and the most commonly cited heuristic - evict the value that is live furthest in the future, because this will release a register over a longer range of code, reducing the chance that we'll need to evict again - is still available. (After all, we do always know the last use, even if we don't necessarily know the first definition).

Altogether, I'm quite excited about this algorithm; I think it will be a real simplification over the current implementation. Whether that will work out remains to be seen of course. I'll let you know!

brrt to the future: Something about IR optimization

Published by Bart Wiegmans on 2019-03-17T06:23:00

Hi hackers! Today I want to write about optimizing IR in the MoarVM JIT, and also a little bit about IR design itself.

One of the (major) design goals for the expression JIT was to have the ability to optimize code over the boundaries of individual MoarVM instructions. To enable this, the expression JIT first expands each VM instruction into a graph of lower-level operators. Optimization then means pattern-matching those graphs and replacing them with more efficient expressions.

As a running example, consider the idx operator. This operator takes two inputs (base and element) and a constant parameter scale and computes base+element*scale. This represents one of the operands of an  'indexed load' instruction on x86, typically used to process arrays. Such instructions allow one instruction to be used for what would otherwise be two operations (computing an address and loading a value). However, if the element of the idx operator is a constant, we can replace it instead with the addr instruction, which just adds a constant to a pointer. This is an improvement over idx because we no longer need to load the value of element into a register. This saves both an instruction and valuable register space.

Unfortunately this optimization introduces a bug. (Or, depending on your point of view, brings an existing bug out into the open). The expression JIT code generation process selects instructions for subtrees (tile) of the graph in a bottom-up fashion. These instructions represent the value computed or work performed by that subgraph. (For instance, a tree like (load (addr ? 8) 8) becomes mov ?, qword [?+8]; the question marks are filled in during register allocation). Because an instruction is always represents a tree, and because the graph is an arbitrary directed acyclic graph, the code generator projects that graph as a tree by visiting each operator node only once. So each value is computed once, and that computed value is reused by all later references.

It is worth going into some detail into why the expression graph is not a tree. Aside from transformations that might be introduced by optimizations (e.g. common subexpression elimination), a template may introduce a value that has multiple references via the let: pseudo-operator. See for instance the following (simplified) template:

(let: (($foo (load (local))))
    (add $foo (sub $foo (const 1))))

Both ADD and SUB refer to the same LOAD node

In this case, both references to $foo point directly to the same load operator. Thus, the graph is not a tree. Another case in which this occurs is during linking of templates into the graph. The output of an instruction is used, if possible, directly as the input for another instruction. (This is the primary way that the expression JIT can get rid of unnecessary memory operations). But there can be multiple instructions that use a value, in which case an operator can have multiple references. Finally, instruction operands are inserted by the compiler and these can have multiple references as well.

If each operator is visited only once during code generation, then this may introduce a problem when combined with another feature - conditional expressions. For instance, if two branches of a conditional expression both refer to the same value (represented by name $foo) then the code generator will only emit code to compute its value when it encounters the first reference. When the code generator encounters $foo for the second time in the other branch, no code will be emitted. This means that in the second branch, $foo will effectively have no defined value (because the code in the first branch is never executed), and wrong values or memory corruption is then the predictable result.

This bug has always existed for as long as the expression JIT has been under development, and in the past the solution has been not to write templates which have this problem. This is made a little easier by a feature the let: operator, in that it inserts a do operator which orders the values that are declared to be computed before the code that references them. So that this is in fact non-buggy:

(let: (($foo (load (local))) # code to compute $foo is emitted here
  (if (...)  
    (add $foo (const 1)) # $foo is just a reference
    (sub $foo (const 2)) # and here as well

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

Alternatively, if a value $foo is used in the condition of the if operator, you can also be sure that it is available in both sides of the condition.

All these methods rely on the programmer being able to predict when a value will be first referenced and hence evaluated. An optimizer breaks this by design. This means that if I want the JIT optimizer to be successful, my options are:

  1. Fix the optimizer so as to not remove references that are critical for the correctness of the program
  2. Modify the input tree so that such references are either copied or moved forward
  3. Fix the code generator to emit code for a value, if it determines that an earlier reference is not available from the current block.
In other words, I first need to decide where this bug really belongs - in the optimizer, the code generator, or even the IR structure itself. The weakness of the expression IR is that expressions don't really impose a particular order. (This is unlike the spesh IR, which is instruction-based, and in which every instruction has a 'previous' and 'next' pointer). Thus, there really isn't a 'first' reference to a value, before the code generator introduces the concept. This is property is in fact quite handy for optimization (for instance, we can evaluate operands in whatever order is best, rather than being fixed by the input order) - so I'd really like to preserve it. But it also means that the property we're interested in - a value is computed before it is used in, in all possible code flow paths - isn't really expressible by the IR. And there is no obvious local invariant that can be maintained to ensure that this bug does not happen, so any correctness check may have to check the entire graph, which is quite impractical.

I hope this post explains why this is such a tricky problem! I have some ideas for how to get out of this, but I'll reserve those for a later post, since this one has gotten quite long enough. Until next time!

brrt to the future: A short post about types and polymorphism

Published by Bart Wiegmans on 2019-01-14T13:34:00

Hi all. I usually write somewhat long-winded posts, but today I'm going to try and make an exception. Today I want to talk about the expression template language used to map the high-level MoarVM instructions to low-level constructs that the JIT compiler can easily work with:

This 'language' was designed back in 2015 subject to three constraints:
Recently I've been working on adding support for floating point operations, and  this means working on the type system of the expression language. Because floating point instructions operate on a distinct set of registers from integer instructions, a floating point operator is not interchangeable with an integer (or pointer) operator.

This type system is enforced in two ways. First, by the template compiler, which attempts to check if you've used all operands correctly. This operates during development, which is convenient. Second, by instruction selection, as there will simply not be any instructions available that have the wrong combinations of types. Unfortunately, that happens at runtime, and such errors so annoying to debug that it motivated the development of the first type checker.

However, this presents two problems. One of the advantages of the expression IR is that, by virtue of having a small number of operators, it is fairly easy to analyze. Having a distinct set of operators for each type would undo that. But more importantly, there are several MoarVM instructions that are generic, i.e. that operate on integer, floating point, and pointer values. (For example, the set, getlex and bindlex instructions are generic in this way). This makes it impossible to know whether its values will be integers, pointers, or floats.

This is no problem for the interpreter since it can treat values as bags-of-bits (i.e., it can simply copy the union MVMRegister type that holds all values of all supported types). But the expression JIT works differently - it assumes that it can place any value in a register, and that it can reorder and potentially skip storing them to memory. (This saves work when the value would soon be overwritten anyway). So we need to know what register class that is, and we need to have the correct operators to manipulate a value in the right register class.

To summarize, the problem is:
There are two ways around this, and I chose to use both. First, we know as a fact for each local or lexical value in a MoarVM frame (subroutine) what type it should have. So even a generic operator like set can be resolved to a specific type at runtime, at which point we can select the correct operators. Second, we can introduce generic operators of our own. This is possible so long as we can select the correct instruction for an operator based on the types of the operands.

For instance, the store operator takes two operands, an address and a value. Depending on the type of the value (reg or num), we can always select the correct instruction (mov or movsd). It is however not possible to select different instructions for the load operator based on the type required, because instruction selection works from the bottom up. So we need a special load_num operator, but a store_num operator is not necessary. And this is true for a lot more operators than I had initially thought. For instance, aside from the (naturally generic) do and if operators, all arithmetic operators and comparison operators are generic in this way.

I realize that, despite my best efforts, this has become a rather long-winded post anyway.....

Anyway. For the next week, I'll be taking a slight detour, and I aim to generalize the two-operand form conversion that is necessary on x86. I'll try to write a blog about it as well, and maybe it'll be short and to the point. See you later!

brrt to the future: New years post

Published by Bart Wiegmans on 2019-01-06T13:15:00

Hi everybody! I recently read jnthns Perl 6 new years resolutions post, and I realized that this was an excellent example to emulate. So here I will attempt to share what I've been doing in 2018 and what I'll be doing in 2019.

In 2018, aside from the usual refactoring, bugfixing and the like:
So 2019 starts with me trying to complete the goals specified in that grant request. I've already partially completed one goal (as explained in the interim report) - ensuring that register encoding works correctly for SSE registers in DynASM. Next up is actually ensuring support for SSE (and floating point) registers in the JIT, which is surprisingly tricky, because it means introducing a type system where there wasn't really one previously. I will have more to report on that in the near future.

After that, the first thing on my list is the support for irregular register requirements in the register allocator, which should open up the possibility of supporting various instructions.

I guess that's all for now. Speak you later!

6guts: My Perl 6 wishes for 2019

Published by jnthnwrthngtn on 2019-01-02T01:35:51

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.

Prague fireworks over Narodni Divadlo

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.

Partial Escape Analysis and related optimizations in MoarVM

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.

Decreasing startup time and base memory use

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.

Improving compilation times

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.

Research into concurrency safety

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.

Get Cro to its 1.0 release

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):

Comma Community, and lots of improvements and features

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.

Speaking, conferences, workshops, etc.

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 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. :-)

So, down to work!

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. Perl 6 Coding Contest 2019: Seeking Task Makers

Published by Moritz Lenz on 2018-11-10T23:00:01

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.

stmuk: Swiss Perl Workshop 2017

Published by stmuk on 2017-08-30T17:48:17


After a perilous drive up a steep, narrow, winding road from Lake Geneva we arrived at an attractive Alpine village (Villars-sur-Ollon) to meet with fellow Perl Mongers in a small restaurant.  There followed much talk and a little clandestine drinking of exotic spirits including Swiss whisky. The following morning walking to the conference venue there was an amazing view of mountain ranges. On arrival I failed to operate the Nespresso machine which I later found was due to it simply being off.  Clearly software engineers should never try to use hardware. At least after an evening of drinking.

Wendy’s stall was piled high with swag including new Bailador (Perl 6 dancer like framework) stickers, a Shadowcat booklet about Perl 6 and the new O’Reilly “Thinking in Perl 6″. Unfortunately she had sold out of Moritz’s book “Perl 6 Fundamentals” (although there was a sample display copy present). Thankfully later that morning I discovered I had a £3 credit on Google Play Books so I bought the ebook on my phone.

The conference started early with Damian Conway’s Three Little Words.  These were “has”, “class” and “method” from Perl 6 which he liked so much that he had added them to Perl 5 with his “Dios” – “Declarative Inside-Out Syntax” module.  PPI wasn’t fast enough so he had to replace it with a 50,000 character regex PPR. Practical everyday modules mentioned included Regexp::Optimizer and Test::Expr. If the video  doesn’t appear shortly on youtube a version of his talk dating from a few weeks earlier is available at

Jonathan Worthington returned with his Perl 6 talk on “How does deoptimization help us go faster?” giving us insight into why Perl 6 was slow at the Virtual Machine level (specifically MoarVM). Even apparently simple and fast operations like indexing an array were slow due to powerful abstractions, late binding and many levels of Multiple Dispatch. In short the flexibility and power of such an extensible language also led to slowness due to the complexity of code paths. The AST optimizer helped with this at compile time but itself took time and it could be better to do this at a later compile time (like Just In Time).  Even with a simple program reading lines from a file it was very hard to determine statically what types were used (even with type annotations) and whether it was worth optimizing (since the file could be very short).

The solution to these dynamic problems was also dynamic but to see what was happening needed cheap logging of execution which was passed to another thread.  This logging is made visible by setting the environment variable MVM_SPESH_LOG to a filename. Better tooling for this log would be a good project for someone.

For execution planning we look for hot (frequently called) code, long blocks of bytecode (slow to run) and consider how many types are used (avoiding “megamorphic” cases with many types which needs many versions of code).  Also analysis of the code flow between different code blocks and SSA.  Mixins made the optimization particularly problematic.

MoarVM’s Spesh did statistical analysis of the code in order to rewrite it in faster, simpler ways. Guards (cheap check for things like types) were placed to catch cases where it got it wrong and if these were triggered (infrequently) it would deoptimize as well, hence the counterintuitive title since “Deoptimization enables speculation” The slides are at with the video at The older and more dull witted of us (including myself) might find the latter part of the video more comprehensible at 0.75 Youtube speed.

After a superb multi-course lunch (the food was probably the best I’d had at any Perl event) we returned promptly to hear Damian talk of “Everyday Perl 6”. He pointed out that it wasn’t necessary to code golf obfuscated extremes of Perl 6 and that the average Perl 5 programmer would see many things simpler in Perl 6.  Also a rewrite from 5 to 6 might see something like 25% fewer lines of code since 6 was more expressive in syntax (as well as more consistent) although performance problems remained (and solutions in progress as the previous talk had reminded us).

Next Liz talked of a “gross” (in the numerical sense of 12 x 12 rather than the American teen sense) of Perl 6 Weeklies as she took us down memory lane to 2014 (just about when MoarVM was launched and when unicode support was poor!)  with some selected highlights and memories of Perl 6 developers of the past (and hopefully future again!). Her talk was recorded at


Cal then spoke of Perl 6 maths which he thought was good with its Rats and FatRats but not quite good enough and his ideas of fixing it.  On the following day he showed us he had started some TDD work on TrimRats. He also told us that Newton’s Method wasn’t very good but generated a pretty fractal. See

Lee spoke about how to detect Perl 5 memory leaks with various CPAN modules and his examples are at

The day finished with Lightning Talks and a barbecue at givengain — a main sponsor.

On the second day I noticed the robotic St Bernards dog in a tourist shop window had come to life.


Damian kicked off the talks with my favourite of his talks,  “Standing on the Shoulders of Giants”, starting with the Countess of Lovelace and her Bernoulli number program. This generated a strange sequence with many zeros. The Perl 6 version since it used rational numbers not floating point got the zeros right whereas the Perl 5 version initially suffered from floating point rounding errors (which are fixable).

Among other things he showed us how to define a new infix operator in Perl 6. He also showed us a Perl 6 sort program that looked exactly like LISP even down to the Lots of Irritating Superfluous Parentheses. I think this was quicksort (he certainly showed us a picture of Sir Tony Hoare at some point). Also a very functional (Haskell-like) equivalent  with heavy use of P6 Multiple Dispatch.  Also included was demonstration of P6 “before” as a sort of typeless/multi-type comparison infix. Damian then returned to his old favourite of Quantum Computing.

My mind and notes got a bit jumbled at this point but I particularly liked the slide that explained how factorisation could work by observing the product of possible inputs since this led to a collapse that revealed the factors.  To do this on RSA etc., of course, needs real hardware support which probably only the NSA and friends have (?). Damian’s code examples are at with  an earlier version of his talk at Around this point there was a road race of classic cars going on outside up the main road into the village and there were car noises in the background that strangely were more relaxing than annoying.


After Quantum Chaos Paul Johnson brought us all back down to ground with an excellent practical talk on modernising legacy Perl 5 applications based on his war stories. Hell, of course, is “Other People’s Code”, often dating from Perl’s early days and lacking documentation and sound engineering.

Often the original developers had long since departed or, in the worse cases, were still there.  Adding tests and logging (with stack traces) were particularly useful. As was moving to git (although its steep learning curve meant mentoring was needed) and handling CPAN module versioning with pinto.  Many talks had spoken of the Perl 6 future whereas this spoke of the Perl 5 past and present and the work many of us suffer to pay the bills. It’s at

File_000 (1)

Jonathan then spoke of reactive distributed software.  A distributed system is an async one where “Is it working?” means “some of it is working but we don’t know which bits”.  Good OO design is “tell don’t ask” — you tell remote service to do something for you and not parse the response and do it yourself thus breaking encapsulation.  This is particularly important in building well designed distributed systems since otherwise the systems are less responsive and reliable.  Reactive (async) works better for distributed software than interactive (blocking or sync).

We saw a table that used a Perl 6 promise for one value and a supply for many values for reactive (async) code and the equivalent (one value) and a Perl 6 Seq for interactive code. A Supply could be used for pub/sub and the Observer Pattern. A Supply could either be live (like broadcast TV) or, for most Perl 6 supplies, on-demand (like Netflix). Then samples of networking (socket) based code were discussed including a web client, web server and SSH::LibSSH (async client bindings often very useful in practical applications like port forwarding)

Much of the socket code had a pattern of “react { whenever {” blocks with “whenever” as a sort of async loop.He then moved on from sockets to services (using a Supply pipeline) and amazed us by announcing the release of “cro”, a microservices library that even supports HTTP/2 and Websockets, at  This is installable using Perl 6 by “zef install –/test cro”.

Slides at and video at

Next Lee showed Burp Scanner which is payware but probably the best web vulnerabilities scanner. I wondered if anyone had dare run it on ACT or the hotel’s captive portal.

Wendy did some cheerleading in her “Changing Image of Perl”.  An earlier version is at

Sue’s talk was “Spiders, Gophers, Butterflies” although the latter were mostly noticeably absent. She promises me that a successor version of the talk will use them more extensively. Certainly any Perl 6 web spidering code is likely to fit better on one slide than the Go equivalent.

During the lightning talks Timo showed us a very pretty Perl 6 program using his SDL2::Raw to draw an animated square spiral with hypnotic colour cycling type patterns. Also there was a talk by the author about— a distributed bug tracking system (which worked offline like git).

Later in the final evening many of us ate and chatted in another restaurant where we witnessed a dog fight being narrowly averted and learnt that Wendy didn’t like Perl 5’s bless for both technical and philosophical reasons. My Ten Years of Perl 6

Published by Moritz Lenz on 2017-08-08T22:00:01

Time for some old man's reminiscence. Or so it feels when I realize that I've spent more than 10 years involved with the Perl 6 community.

How I Joined the Perl 6 Community

It was February 2007.

I was bored. I had lots of free time (crazy to imagine that now...), and I spent some of that answering (Perl 5) questions on perlmonks. There was a category of questions where I routinely had no good answers, and those were related to threads. So I decided to play with threads, and got frustrated pretty quickly.

And then I remember that a friend in school had told me (about four years earlier) that there was this Perl 6 project that wanted to do concurrency really well, and even automatically parallelize some stuff. And this was some time ago, maybe they had gotten anywhere?

So I searched the Internet, and found out about Pugs, a Perl 6 compiler written in Haskell. And I wanted to learn more, but some of the links to the presentations were dead. I joined the #perl6 IRC channel to report the broken link.

And within three minutes I got a "thank you" for the report, the broken links were gone, and I had an invitation for a commit bit to the underlying SVN repo.

I stayed.

The Early Days

Those were they wild young days of Perl 6 and Pugs. Audrey Tang was pushing Pugs (and Haskell) very hard, and often implemented a feature within 20 minutes after somebody mentioned it. Things were unstable, broken often, and usually fixed quickly. No idea was too crazy to be considered or even implemented.

We had bots that evaluated Perl 6 and Haskell code, and gave the result directly on IRC. There were lots of cool (and sometimes somewhat frightening) automations, for example for inviting others to the SVN repo, to the shared hosting system (called feather), for searching SVN logs and so on. Since git was still an obscure and very unusable, people tried to use SVK, an attempt to implement a decentralized version control system on top of of the SVN protocol.

Despite some half-hearted attempts, I didn't really make inroads into compiler developments. Having worked with neither Haskell nor compilers before proved to be a pretty steep step. Instead I focused on some early modules, documentation, tests, and asking and answering questions. When the IRC logger went offline for a while, I wrote my own, which is still in use today.

I felt at home in that IRC channel and the community. When the community asked for mentors for the Google Summer of Code project, I stepped up. The project was a revamp of the Perl 6 test suite, and to prepare for mentoring task, I decided to dive deeper. That made me the maintainer of the test suite.

Pet Projects

I can't recount a full history of Perl 6 projects during that time range, but I want to reflect on some projects that I considered my pet projects, at least for some time.

It is not quite clear from this (very selected) timeline, but my Perl 6 related activity dropped around 2009 or 2010. This is when I started to work full time, moved in with my girlfriend (now wife), and started to plan a family.


The technologies and ideas in Perl 6 are fascinating, but that's not what kept me. I came for the technology, but stayed for the community.

There were and are many great people in the Perl 6 community, some of whom I am happy to call my friends. Whenever I get the chance to attend a Perl conference, workshop or hackathon, I find a group of Perl 6 hackers to hang out and discuss with, and generally have a good time.

Four events stand out in my memory. In 2010 I was invited to the Open Source Days in Copenhagen. I missed most of the conference, but spent a day or two with (if memory serve right) Carl Mäsak, Patrick Michaud, Jonathan Worthington and Arne Skjærholt. We spent some fun time trying to wrap our minds around macros, the intricacies of human and computer language, and Japanese food. (Ok, the last one was easy). Later the same year, I attended my first YAPC::EU in Pisa, and met most of the same crowd again -- this time joined by Larry Wall, and over three or four days. I still fondly remember the Perl 6 hallway track from that conference. And 2012 I flew to Oslo for a Perl 6 hackathon, with a close-knit, fabulous group of Perl 6 hackers. Finally, the Perl Reunification Summit in the beautiful town of Perl in Germany, which brought together Perl 5 and Perl 6 hackers in a very relaxed atmosphere.

For three of these four events, different private sponsors from the Perl and Perl 6 community covered travel and/or hotel costs, with their only motivation being meeting folks they liked, and seeing the community and technology flourish.

The Now

The Perl 6 community has evolved a lot over the last ten years, but it is still a very friendly and welcoming place. There are lots of "new" folks (where "new" is everybody who joined after me, of course :D), and a surprising number of the old guard still hang around, some more involved, some less, all of them still very friendly and supportive

The Future

I anticipate that my family and other projects will continue to occupy much of my time, and it is unlikely that I'll be writing another Perl 6 book (after the one about regexes) any time soon. But the Perl 6 community has become a second home for me, and I don't want to miss it.

In the future, I see myself supporting the Perl 6 community through infrastructure (community servers, IRC logs, running IRC bots etc.), answering questions, writing a blog article here and there, but mostly empowering the "new" guard to do whatever they deem best. Perl 6 Fundamentals Now Available for Purchase

Published by Moritz Lenz on 2017-07-21T22:00:01

After about nine months of work, my book Perl 6 Fundamentals is now available for purchase on and

The ebook can be purchased right now, and comes in the epub and PDF formats (with watermarks, but DRM free). The print form can be pre-ordered from Amazon, and will become ready for shipping in about a week or two.

I will make a copy of the ebook available for free for everybody who purchased an earlier version, "Perl 6 by Example", from LeanPub.

The book is aimed at people familiar with the basics of programming; prior Perl 5 or Perl 6 knowledge is not required. It features a practical example in most chapters (no mammal hierarchies or class Rectangle inheriting from class Shape), ranging from simple input/output and text formatting to plotting with python's matplotlib libraries. Other examples include date and time conversion, a Unicode search tool and a directory size visualization.

I use these examples to explain subset of Perl 6, with many pointers to more documentation where relevant. Perl 6 topics include the basic lexicographic structure, testing, input and output, multi dispatch, object orientation, regexes and grammars, usage of modules, functional programming and interaction with python libraries through Inline::Python.

Let me finish with Larry Wall's description of this book, quoted from his foreword:

It's not just a reference, since you can always find such materials online. Nor is it just a cookbook. I like to think of it as an extended invitation, from a well-liked and well-informed member of our circle, to people like you who might want to join in on the fun. Because joy is what's fundamental to Perl. The essence of Perl is an invitation to love, and to be loved by, the Perl community. It's an invitation to be a participant of the gift economy, on both the receiving and the giving end.