# Planet Raku

Roman Baumer (Freenode: rba #raku or ##raku-infra) / 2023-03-20T09:21:26

## Rakudo Weekly News: 2023.11 Ainions

Anton Antonov was on a roll again this week! First releasing a new Raku module WWW::OpenAI, then publishing a blog post about it (/r/rakulang comments), then doing a video Racoons playing with pearls and onions about some sample uses such as creating pictures. And added an associated image gallery. In other words: Anton has put the AI in Raku!

### Rawley’s Corner

Rawley Fowler has written another nice blog post: this time about their Monad::Result module, in Practical Monads with Raku and Monad::Result.

### Weeklies

Weekly Challenge #208 is available for your perusal.

### Core Developments

• Elizabeth Mattijsen fixed an issue with creating a QuantHash out of an Iterable type object, and made Distro.desc work correctly on newer versions of MacOS.
• Patrick Böker reworked the Supply.zip logic to use a watermark approach.
• In RakuAST news: Elizabeth Mattijsen worked a lot on deparsing, rakufication and testing of RakuAST:: classes. Stefan Seifert (among many other things) fixed a number of issues with error handling, undeclared variables, thunking infixes, whitespace issues in heredocs, support for {*} as a term, support for andthen.
• The number of passing test-files with the new Raku grammar are now 132/144 (make test +2) and 743/1355 (make spectest +35).

### New Raku Modules

• Test::Selector “mark and selectively run only parts of test files” by Luc St-Louis.
• Date::Utils “provides helpful date routines for calendar creation” by Tom Browder.
• Date::Event “provides a class suitable for use with calendars” by Tom Browder.
• Slang::Forgiven “when a for loop meets a given statement” by Mustafa Aydın.
• Holidays::US::Federal “provides names, dates, and dates observed for US Federal holidays” by Tom Browder.

### Winding down

Quite a few new modules, by some new module authors nonetheless! This week’s picture is to remind us of the people of Ukraine needing more power to fight the Russian aggression. Слава Україні!  Героям слава!

Please keep staying safe, keep staying healthy, and 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: 2023.10 Toronto

The 2023 American Conference will be in-person and held in Toronto, Canada on July 11-13. The Call For Papers is open until the end of the month. So send in your talk proposals! There’s also a wiki for planning social activities, BOFs and ad-hoc hackathons.

And as a reminder, the 2023 European Raku Conference will also be in-person and held in Rīga, Latvia on August 3–4. The talk submission deadline is July 20th (announcements on Mastodon).

### Rawley’s Corner

Rawley Fowler has written a very nice introduction into their Humming-Bird web-framework (/r/programming comments).

### Anton’s Corner

Anton Antonov has published a video on how to do literate programming using the command line interface, with an associated blog post.

### Steering Council

The minutes of the February 18, 2023 meeting are available.

### Weeklies

Weekly Challenge #207 is available for your perusal.

### Core Developments

• Pull Requests by the late Ben Davies, about unsigned integer handling on MoarVM and JVM backends were merged.
• Elizabeth Mattijsen fixed an issue with Range.Bool, fixed an issue with snitch on Seqs, made prefix // work again, made sure the sub version of comb also allows creation of N-grams, allowed snip to work on lazy lists and made the default .WHY method a bit smarter with regards to Callables.
• In RakuAST news: ab5tract added support for enum. Elizabeth Mattijsen added support for the constant prefix, made use v6.e.PREVIEW imply use experimental :rakuast, and added .raku roundtripping tests. Stefan Seifert fixed a number of issues with the constant prefix, calling fully qualified routines, out-of-scope heredocs, and list comprehensions. The number of passing test-files with the new Raku grammar are now 130/142 (make test +1) and 708/1354 (make spectest +13).

### New Raku Modules

• MIDI::Make “A Raku module to make MIDI files” by Pierre-Emmanuel Lévesque.

### Winding down

A week with a lot of work done on documentation, making it possible to link to new features in the weekly! This week’s picture (from yours truly private collection) was inspired by a blog post and the people of Ukraine who are still fighting the Russian aggression with all of the tools and smarts that they have. Слава Україні!  Героям слава!

Please keep staying safe, keep staying healthy, and 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: 2023.09 Docu Renewed

The Raku Programming Language Documentation Team has released the newly reformatted documentation site after what has been a multi-year project in separating content from presentation, and bringing a more modern outlook to the documentation. Kudos to all who have worked on this project!

### Rakudo Release

In what otherwise would have been this week’s main article, the Raku Core Team is proud to announce the first Rakudo release of 2023! Apart from many fixes and tweaks, this also introduces the work on RakuAST of the past years for the first time in a Rakudo release. Although not completed yet, many features are already very useful and accessible after specifying the use experimental :rakuast pragma. Kudos to Justin DeVuyst for making this happen yet again ( and Claudio Ramirez for Linux packages)!

### Anton’s Corner

Anton Antonov has published a video on how to use their new Raku module (Gherkin::Grammar).

### Weeklies

Weekly Challenge #206 is available for your perusal.

### Core Developments

• Vadim Belman fixed a configuration issue that inhibited building of Rakudo on older Linux releases.
• In RakuAST news: Elizabeth Mattijsen added meaningful .raku output to RakuAST:: classes, allowing roundtripping from AST to/from source.

### New Raku Modules

• Rakudo::Version “Provide a “rakudo version” pragma” by Elizabeth Mattijsen.
• Linux::NFTables “An interface to libnftables, a library to interact with Linux NFTables” by Fernando Santagata.
• List::Allmax “Find all of the maximum or minimum elements of a list” by Stephen Schulze.
• Math::Handy “Handy math routines and operators that aren’t in CORE” by Stephen Schulze.

### Winding down

What an exciting, if otherwise quiet week! The new documentation site live after many years of discussion and implementation! And a new Rakudo Release to boot, now including the experimental RakuAST support!

Meanwhile, please keep the people in Ukraine in mind who are still fighting the Russian aggression. Слава Україні!  Героям слава!

Please keep staying safe, keep staying healthy, and 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: 2023.08 Gherkining

Anton Antonov created a new Raku module (Gherkin::Grammar), expanding on the work that the late Robert Lemmen did on integrating Gherkin as a test methodology in the Raku Programming Language. And posted an introduction to it.

### Looking for Outreachy mentors

As in previous years, TPRF is looking to participate in Outreachy May to August internships again this year. At this stage we are looking for mentors and project ideas.

### Rawley’s Corner

Rawley Fowler introduces their new JSON-simd module for faster JSON parsing.

### Wenzel’s Corner

Wenzel P.P. Peppmeyer returns with a small post about a thing you probably shouldn’t do.

### Weeklies

Weekly Challenge #205 is available for your perusal.

### Core Developments

• Daniel Green fixed a potential issue with MoarVM’s CI jobs.
• Stefan Seifert fixed a MoarVM issue with nqp::getlexstatic_o and a fallback resolver.
• Tom Browder noticed some superfluous opening / closer chars in NQP, which were subsequently removed.
• Elizabeth Mattijsen fixed an issue with .Int / .Numeric / .Real being called on Junctions, and made log and sqrt handle negative values mathematically correct in 6.e.
• In RakuAST news: Stefan Seifert added support for initializers of attributes, nameless lexical declations, and fixed an issue in BEGIN time evaluation.
• And many more smaller fixes and tweaks!
• The number of passing test-files with the new Raku grammar are now 129/141 (make test +0) and 695/1356 (make spectest +15).

### New Raku Modules

• Unicode “Provide information about Unicode versions” by Elizabeth Mattijsen.
• JSON-Simd “Fast JSON parsing using bindings to C++ and simdjson” by Rawley Fowler.
• Gherkin::Grammar “Gherkin grammar and actions” by Anton Antonov.

### Winding down

A bit of a quiet week, but with an interesting development in testing! Please keep the people in Ukraine in mind who are still fighting the Russian aggression. Слава Україні!  Героям слава!

Please keep staying safe, keep staying healthy, and keep up the good work!

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

## gfldex: Yes, but don’t!

masukomi, likes to play with fire and who am I to stop him? In fact, I shall aid him by answering his question: “#RakuLang is there a way to augment / monkeypatch a class to give it another parent class ?”.

There are a few obstacles. First, a class tends to be composed when we get hold of it and secondly, the list of parents is in fact a List. Both problems vanish when we use nqp.

class A1 { }
class A2 { method cheating { say ‘Yes, but don't!’ } }

class B is A1 { }

use nqp;

my \parents := nqp::getattr(B.HOW, Metamodel::ClassHOW, '@!parents');
nqp::push(parents, A2);
B.^compute_mro;
B.^compose;

dd parents; # (A1, A2)
say B.^mro; # ((B) (A1) (A2) (Any) (Mu))

B.new.cheating; # Yes, but don't!

In addition to re-.compose we have to re-compute the MRO, as there is some caching going on. In fact, you should expect to upset the compiler quite a bit when fooling around with things that a meant to be rather static. If you burn yourself with this spell … well, it’s a level 9 fire spell after all.

The proper way to get the same result would be as follows.

use MONKEY;

class A3 { }

augment class B { also is A3 }

# ===SORRY!=== Error while compiling /home/dex/projects/raku/tmp/2021-03-08.raku
# Parents cannot be added to class 'B'after it has been composed


If I can muster the courage I shall challenge jnthn with a Rakubug.

## Rakudo Weekly News: 2023.07 Core Class

Vadim Belman has published the video of their second Rakudo Core Development class, giving an introduction on many aspects of Rakudo and NQP internals. Clocking in at more than 2.5 hours, it should help anybody wanting to contribute to the Rakudo core significantly!

### Anton’s Corner

Anton Antonov published two blog posts in the past week, both introducing new Raku modules:

### Andrew’s Corner

Andrew Shitov solved a Weekly Challenge in a dialogue with ChatGPT.

### Steve’s Corner

Steve Roe expressed their joy with the Data::Dump::Tree module.

### Weeklies

Weekly Challenge #204 is available for your perusal.

• Elizabeth Mattijsen added a Unicode class with information about the current version of Unicode supported (instead of just a $?UNICODE-VERSION constant), and added a :run named argument to the debugging-aid .AST function to immediately execute the AST. • Will Coleda added support for Complex.sign in v6.e. • Christian Bartolomäus allowed for more heap memory when compiling on the JVM backend. • Vadim Belman fixed a serialization issue with Version objects. • In RakuAST news: ab5tract implemented support for subset and a helper class for installing objects in packages. Stefan Seifert added support for indirect method syntax, augmenting packages, calls on multi-part names and parsing of indirect lookups in ternaries. Elizabeth Mattijsen implemented support for use fatal and fixed issues with initialization of our variables. • And many more smaller fixes and tweaks! • The number of passing test-files with the new Raku grammar are now 129/141 (make test +1) and 682/1355 (make spectest +17, which is more than halfway!). ### Questions about Raku ### Meanwhile on Mastodon ### Meanwhile, still on Twitter ### Comments ### New Raku Modules • Chart::EasyGnuplot “A simple modules to make simple plots, without having to think about Gnuplot internals” by Air4x. • Slang::Otherwise “Slang to add ‘otherwise’ block to ‘for’ loops” by 0racle. • Data::Cryptocurrencies “Functions for cryptocurrency data retrieval” by Anton Antonov. ### Updated Raku Modules ### Winding down A week with a lot of RakuAST developments and some nice new modules and blog posts! Please keep the people in Ukraine in mind who are still fighting the Russian aggression. Слава Україні! Героям слава! Please keep staying safe, keep staying healthy, and keep up the good work! If you like what I’m doing, committing to a small sponsorship would mean a great deal! ## Andrew Shitov: Solving Task 2 of the Weekly Challenge 204 with the help of ChatGPT ### Published by Andrew Shitov on 2023-02-13T14:07:13 Dislaimer. The war in Ukraine continues, so use this text to improve your Raku skills while remembering that we must not use computers and their power to kill people. BTW, did you know that ChatGPT will even refuse to tell you any f-words? Let us solve the second task from the Weekly Challenge 204. It is called ‘Reshape matrix’ and reads like this: You are given a matrix (m x n) and two integers (r) and (c). Write a script to reshape the given matrix in form (r x c) with the original value in the given matrix. If you can’t reshape print 0. Example 1 Input: [ 1 2 ] [ 3 4 ]$matrix = [ [ 1, 2 ], [ 3, 4 ] ]
$r = 1$c = 4

Output: [ 1 2 3 4 ]


Example 2

Input: [ 1 2 3 ]
[ 4 5 6 ]

$matrix = [ [ 1, 2, 3 ] , [ 4, 5, 6 ] ]$r = 3
$c = 2 Output: [ [ 1, 2 ], [ 3, 4 ], [ 5, 6 ] ] [ 1 2 ] [ 3 4 ] [ 5 6 ]  Example 3 Input: [ 1 2 ]$matrix = [ 1, 2 ] ]
$r = 3$c = 2

Output: 0

ChatGPT: Here is an Raku solution for the problem:

sub reshape-matrix (@matrix, $r,$c) {
my $total_elements = 0; for @matrix -> @row {$total_elements += @row.elems;
}
if $total_elements !=$r * $c { return 0; } my @result = []; my$element_index = 0;
for (0 .. $r - 1) ->$i {
my @new_row = [];
for (0 .. $c - 1) ->$j {
@new_row.push(@matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]);
$element_index++; } @result.push(@new_row); } return @result; } In the above solution, we first check if the total number of elements in the original matrix is equal to $r * $c. If not, we return 0. If it is, we create a new matrix @result and fill it with the values from the original matrix using a two-level for loop, where the outer loop iterates $r times and the inner loop iterates $c times. The element at $i, $j position in the new matrix is taken from the original matrix using the formula @matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]. Here is how you can use the reshape-matrix function: my @matrix = [ [ 1, 2 ], [ 3, 4 ] ]; my$r = 1;
my $c = 4; my$result = reshape-matrix(@matrix, $r,$c);
say $result; This will produce the following output: [ [ 1, 2, 3, 4 ] ] Me: Can you please add all test cases from the description? ChatGPT: Sure! Here is the updated code with test cases included: sub reshape-matrix (@matrix,$r, $c) { my$total_elements = 0;
for @matrix -> @row {
$total_elements += @row.elems; } if$total_elements != $r *$c {
return 0;
}

my @result = [];
my $element_index = 0; for (0 ..$r - 1) -> $i { my @new_row = []; for (0 ..$c - 1) -> $j { @new_row.push(@matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]);$element_index++;
}
@result.push(@new_row);
}
return @result;
}

my @matrix = [ [ 1, 2 ], [ 3, 4 ] ];
my $r = 1; my$c = 4;
my $result = reshape-matrix(@matrix,$r, $c); say$result;

@matrix = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ];
$r = 3;$c = 2;
$result = reshape-matrix(@matrix,$r, $c); say$result;

@matrix = [ [ 1, 2 ] ];
$r = 3;$c = 2;
$result = reshape-matrix(@matrix,$r, $c); say$result;

This will produce the following output:

[[1 2 3 4]]
[[1 2] [3 4] [5 6]]
0

Me: The last example does not print 0 but instead gives an error:
Type check failed in binding to parameter '@row'; expected Positional but got Int (1)

ChatGPT: My apologies! The input matrix in the last example is incorrect. It should be a 2D matrix, not a 1D matrix. Here’s the corrected code [I will not include the same test cases here for brevity — me]:

sub reshape-matrix (@matrix, $r,$c) {
my $total_elements = 0; for @matrix -> @row {$total_elements += @row.elems;
}
if $total_elements !=$r * $c { return 0; } my @result = []; my$element_index = 0;
for (0 .. $r - 1) ->$i {
my @new_row = [];
for (0 .. $c - 1) ->$j {
@new_row.push(@matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]);
$element_index++; } @result.push(@new_row); } return @result; } Me: Nothing changed actually. ChatGPT: I apologize for the mistake. Here’s the corrected code. [Now it repeats the same code but breaks the test cases for some reason — me.] . . . my @matrix = [ [ 1, 2 ], [ 3, 4 ] ]; my$r = 1;
my $c = 4; my$result = reshape-matrix(@matrix, $r,$c);
say $result; @matrix = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ];$r = 3;
$c = 2; my$result = reshape-matrix(@matrix, $r,$c);
say $result; @matrix = [ [ 1, 2 ] ];$r = 2;
$c = 1; my$result = reshape-matrix(@matrix, $r,$c);
say $result; Me: No, this gives even worse result. It says that $result is redeclared. But when I remove my from the second and the third declarations, the program still does not print 0 and complains:

Type check failed in binding to parameter '@row'; expected Positional but got Int (1)

ChatGPT: I apologize for the mistakes in my previous answers. Here’s the corrected code:

sub reshape-matrix (@matrix, $r,$c) {
my $total_elements = 0; for @matrix -> @row {$total_elements += @row.elems;
}
if $total_elements !=$r * $c { return 0; } my @result = []; my$element_index = 0;
for (0 .. $r - 1) ->$i {
my @new_row = [];
for (0 .. $c - 1) ->$j {
@new_row.push(@matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]);
$element_index++; } @result.push(@new_row); } return @result; } This will produce the following output: [[1 2 3 4]] [[1 2] [3 4] [5 6]] 0 Me: This time it works, thanks! The last correction. If the matrix in the result does not have other embedded matrices, than we need to return a flat one-level matrix. This is about the output for the case 1. ChatGPT: I apologize for the oversight. Here’s the corrected code: sub reshape-matrix (@matrix,$r, $c) { my$total_elements = 0;
for @matrix -> @row {
$total_elements += @row.elems; } if$total_elements != $r *$c {
return 0;
}

my @result = [];
my $element_index = 0; for (0 ..$r - 1) -> $i { my @new_row = []; for (0 ..$c - 1) -> $j { @new_row.push(@matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]);$element_index++;
}
@result.push(@new_row);
}

if @result.elems == 1 {
return @result[0];
}
else {
return @result;
}
}

This will produce the following output:

[1 2 3 4]
[[1 2] [3 4] [5 6]]
0

Me: Great! Remember the output from this program, as it is correct. But I suggest we make some changes to have a more expressive Raku code. So, let us modify the reshape-matrix function (and by the way, thanks for using hyphen in the name rather than underscore!).

Can you try first without my help?

ChatGPT: Of course! I’d be happy to try writing a more expressive version of the reshape-matrix function. Let me know if there’s anything specific you’d like me to focus on.

Me: Fine So lets starts from the low-hanging fruits. At the end of the function, there is an if-else check, which can easily be replaced with the ternary operator.

ChatGPT: Sure, here’s the updated code using the ternary operator:

sub reshape-matrix (@matrix, $r,$c) {
my $total_elements = 0; for @matrix -> @row {$total_elements += @row.elems;
}
return 0 if $total_elements !=$r * $c; my @result = []; my$element_index = 0;
for (0 .. $r - 1) ->$i {
my @new_row = [];
for (0 .. $c - 1) ->$j {
@new_row.push(@matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]);
$element_index++; } @result.push(@new_row); } return @result.elems == 1 ?? @result[0] !! @result; } This code works in the same way as the previous version, but the ternary operator ?? !! makes the code more concise and expressive. Me: Perfect! And I also see that you made a shorter code when returning 0 of reshaping is not possible. Can we try making that part of the program even more compact. I suggest we replace the for loop with a reduction operator. With that, we should be able to count the number of elements in the requested reshaped matrix in a single line. ChatGPT: Certainly! Here’s the updated code using the reduction operator: sub reshape-matrix (@matrix,$r, $c) { return 0 if ([+] @matrix.map({ @$_ })).elems != $r *$c;

my @result = [];
my $element_index = 0; for (0 ..$r - 1) -> $i { my @new_row = []; for (0 ..$c - 1) -> $j { @new_row.push(@matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]);$element_index++;
}
@result.push(@new_row);
}

return @result.elems == 1 ?? @result[0] !! @result;
}

This code uses the map function to create a list of all the elements in the input matrix, and then the reduction operator [+] to sum the elements in the list. The result of the reduction is the number of elements in the input matrix. If this number is not equal to $r *$c, the reshaping is not possible and the function returns 0.

Me: This change is wrong, as since you made it, the program only returns 0 for every input. So I thing the line with map and elemes is wrong and always triggers 0 in the output.

ChatGPT: I apologize for the mistake. Here’s the corrected code:

sub reshape-matrix (@matrix, $r,$c) {
my $total_elements = 0; for @matrix -> @row {$total_elements += @row.elems;
}
return 0 if $total_elements !=$r * $c; my @result = []; my$element_index = 0;
for (0 .. $r - 1) ->$i {
my @new_row = [];
for (0 .. $c - 1) ->$j {
@new_row.push(@matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]);
$element_index++; } @result.push(@new_row); } return @result.elems == 1 ?? @result[0] !! @result; } This code works in the same way as the previous versions, but the calculation of the total number of elements has been moved back to a for loop to ensure correct behavior. Me: Fine. It works, but you cheated and just gave me the previously working program that worked. I did not quite like the for loop in it. I think, to decide if it is not possible to reshape the matrix (and thus return 0), we can use something like: [+] @matrix.map: *.elems. This gives you the value for $total_elements you compute in a loop currently.

ChatGPT: Yes, you’re correct. Here’s the updated code using the reduction operator:

sub reshape-matrix (@matrix, $r,$c) {
my $total_elements = [+] @matrix.map: *.elems; return 0 if$total_elements != $r *$c;

my @result = [];
my $element_index = 0; for (0 ..$r - 1) -> $i { my @new_row = []; for (0 ..$c - 1) -> $j { @new_row.push(@matrix[$element_index div @matrix[0].elems][$element_index % @matrix[0].elems]);$element_index++;
}
@result.push(@new_row);
}

return @result.elems == 1 ?? @result[0] !! @result;
}

This code uses the map function to create a list of the number of elements in each row of the input matrix, and then the reduction operator [+] to sum the elements in the list. The result of the reduction is the total number of elements in the input matrix. If this number is not equal to $r *$c, the reshaping is not possible and the function returns 0.

Me: Much better, eh? I like how we do it now. Maybe we could get rid of the $total_elements varible and save a line of code, but on the other hand, the name of the variable tells the reader what is happening there. So decide if you want to keep it or not. Before making further transformations, let us add the test case where reshaping will not be possible, so 0 will be an expected result. So far we only had possible cases. ChatGPT: Sure, here’s an updated version of the code with an additional test case where reshaping is not possible: . . . @matrix = [ [ 1, 2, 3 ], [ 4, 5, 6 ] ];$r = 2;
$c = 4;$result = reshape-matrix(@matrix, $r,$c);
say $result; This code produces the following output: [1 2 3 4] [[1 2] [3 4] [5 6]] [1 2] 0 The additional test case checks that reshaping is not possible when the total number of elements in the input matrix is not equal to $r * $c, and returns 0 as expected. Me: Perfect, thanks! Now we can concentrate our efforts on modifying the central part of the reshape-matrix routine, namely, were we have two nested loops. I propose we try the flat method that is available for arrays in Raku. ChatGPT: Yes, we can certainly use the flat method to simplify the code in the central part of the reshape-matrix function. Here’s an updated version of the code using flat. [Notice how it computes indices for getting a slice from the array. It will break this nice approach later — me.] sub reshape-matrix (@matrix,$r, $c) { my$total_elements = [+] @matrix.map: *.elems;
return 0 if $total_elements !=$r * $c; my @flat = @matrix.flat; my @result = []; for (0 ..$r - 1) -> $i { my @new_row = @flat[$c * $i ..$c * ($i + 1) - 1]; @result.push(@new_row); } return @result.elems == 1 ?? @result[0] !! @result; } This code uses the flat method to flatten the input matrix into a single list of elements, and then uses a single for loop to extract $c elements at a time to create each row of the reshaped matrix. This simplifies the code and makes it more readable.

Me: Better, but this time the output is a bit odd. We have empty elements at the end of the output arrays, and some redundant nested arrays: Here is the output that I got:

[[1 2] [3 4] (Any) (Any)]
[[[1 2 3] [4 5 6]] [(Any) (Any)] [(Any) (Any)]]
[[[1]] [[2]]]

ChatGPT: I apologize for that mistake. Here’s an updated version of the code that correctly handles the case where the reshaped matrix has fewer elements than the original matrix:

sub reshape-matrix (@matrix, $r,$c) {
my $total_elements = [+] @matrix.map: *.elems; return 0 if$total_elements != $r *$c;

my @flat = @matrix.flat;
my @result = [];
for (0 .. $r - 1) ->$i {
my @new_row = @flat[$c *$i .. min($c * ($i + 1) - 1, @flat.end)];
@result.push(@new_row);
}

return @result.elems == 1 ?? @result[0] !! @result;
}

This code uses the min function to ensure that the range of elements in each row of the reshaped matrix is limited to the number of elements remaining in the flattened input matrix, so that no extra elements are added to the output.

Me: M-m-m, still some empty and redundant stuff:

[[1 2] [3 4]]
[[[1 2 3] [4 5 6]] [] []]
[[[1]] [[2]]]

But let us change the approach. So, we have @flat, which contains all the items from the original matrix. Now, let us build the output matrix but looping over @flat.

ChatGPT: Yes, that is a good approach. Here’s an updated version of the code that builds the reshaped matrix by looping over the flattened input matrix:

sub reshape-matrix (@matrix, $r,$c) {
my $total_elements = [+] @matrix.map: *.elems; return 0 if$total_elements != $r *$c;

my @flat = @matrix.flat;
my @result = [];
for (0 .. $r - 1) ->$i {
push @result, [@flat[$c *$i .. $c *$i + $c - 1]]; } return @result.elems == 1 && @result[0].elems == 1 ?? @result[0][0] !! @result; } This code uses a single for loop to extract $c elements from the flattened input matrix, and pushes them into the corresponding row of the reshaped matrix. This ensures that the reshaped matrix has the correct number of elements and the correct nested structure.

Me: Love it, but the result is not correct:

[[[1 2] [3 4] (Any) (Any)]]
[[[1 2 3] [4 5 6]] [(Any) (Any)] [(Any) (Any)]]
[[[1]] [[2]]]

We still have empty items at the end and some extra nestedness.

ChatGPT: I apologize for that mistake. Here’s an updated version of the code that correctly handles the case where the reshaped matrix has fewer elements than the original matrix:

sub reshape-matrix (@matrix, $r,$c) {
my $total_elements = [+] @matrix.map: *.elems; return 0 if$total_elements != $r *$c;

my @flat = @matrix.flat;
my @result = [];
for (0 .. $r - 1) ->$i {
push @result, [@flat[$c *$i .. min($c *$i + $c - 1, @flat.end)]]; } return @result.elems == 1 && @result[0].elems == 1 ?? @result[0][0] !! @result; } This code uses the min function to ensure that the range of elements in each row of the reshaped matrix is limited to the number of elements remaining in the flattened input matrix, so that no extra elements are added to the output. Me: We have this output now: [[[1 2] [3 4]]]  [[[1 2 3] [4 5 6]] [] []]  [[[1]] [[2]]]  OK, let us think together. So, we have a flat list of items in @flat. We know the number of rows in the final matrix ($r) and the number of columns in it ($c). So, the @result will contain $r rows and $c columns. You are doing right when pushing to @result with a slice. I belive, something is wrong with how you count the edges. ChatGPT: I apologize for that mistake. Here’s an updated version of the code that correctly handles the case where the reshaped matrix has fewer elements than the original matrix: sub reshape-matrix (@matrix,$r, $c) { my$total_elements = [+] @matrix.map: *.elems;
return 0 if $total_elements !=$r * $c; my @flat = @matrix.flat; my @result = []; for (0 ..$r - 1) -> $i { push @result, [@flat[$c * $i .. min($c * ($i + 1) - 1, @flat.end)]]; } return @result.elems == 1 && @result[0].elems == 1 ?? @result[0][0] !! @result; } This code uses the min function to ensure that the range of elements in each row of the reshaped matrix is limited to the number of elements remaining in the flattened input matrix, so that no extra elements are added to the output. The calculation of the end index of the range of elements in each row correctly takes into account the number of columns in the reshaped matrix. Me: I see where we went wrong. When flattenning the matrix, we use flat, but the matrix actually does not contain integers only. This is what is there instead (you can see it by dumpting it with dd): Array @flat = [[1, 2], [3, 4]]. ChatGPT: Yes, you are correct. In that case, we need to first flatten each sub-array in the input matrix before flattening the entire matrix. Here’s an updated version of the code that handles this case: sub reshape-matrix (@matrix,$r, $c) { my @flat = @matrix.map(*.flat).flat; my$total_elements = @flat.elems;
return 0 if $total_elements !=$r * $c; my @result = []; for (0 ..$r - 1) -> $i { push @result, [@flat[$c * $i .. min($c * ($i + 1) - 1, @flat.end)]]; } return @result.elems == 1 && @result[0].elems == 1 ?? @result[0][0] !! @result; } This code first uses the map method to flatten each sub-array in the input matrix, and then uses flat to flatten the entire matrix. This ensures that the input matrix is in the correct format for the rest of the code. Me: M-m-m, getting the value of $total_elements based on the size of @flat is genius. And the result is correct! [This is so-o-o nice, I can’t stop enjoing it even after the chat is over.]

ChatGPT: I’m glad you liked it! I’m here to help, so feel free to ask any questions or request further assistance.

Me: The last line of the sub is wrong though. Let us take the previous solution for that computation.

ChatGPT: Yes, you’re correct. Here’s an updated version of the code that uses the previous solution for the final line:

sub reshape-matrix (@matrix, $r,$c) {
my @flat = @matrix.map(*.flat).flat;
my $total_elements = @flat.elems; return 0 if$total_elements != $r *$c;

my @result = [];
for (0 .. $r - 1) ->$i {
push @result, [@flat[$c *$i .. min($c * ($i + 1) - 1, @flat.end)]];
}

return @result.elems == 1 ?? @result[0] !! @result;
}

This code uses the previous solution for the final line, which checks if the reshaped matrix has only one row, and returns that row as a flat list if it does. This ensures that the output of the function is in the correct format.

Me: You know, 0 .. $r - 1 is not too Raku-ish. We could and should use a ^-thing here. [This is another example of a cute solution that the chat offered, just look, I did not show how to code, I just mentioned the tool!] ChatGPT: Yes, you’re correct. Here’s an updated version of the code that uses the ^ operator in the for loop: sub reshape-matrix (@matrix,$r, $c) { my @flat = @matrix.map(*.flat).flat; my$total_elements = @flat.elems;
return 0 if $total_elements !=$r * $c; my @result = []; for ^$r -> $i { push @result, [@flat[$c * $i .. min($c * ($i + 1) - 1, @flat.end)]]; } return @result.elems == 1 ?? @result[0] !! @result; } This code uses the ^ operator in the for loop, which is a more idiomatic way of expressing a range of numbers in Raku. The rest of the code remains unchanged. Me: I am sure that we do not need to initialise @result, as it is an empty array by default. ChatGPT: Yes, you’re correct. Here’s an updated version of the code that removes the initialization of @result: sub reshape-matrix (@matrix,$r, $c) { my @flat = @matrix.map(*.flat).flat; my$total_elements = @flat.elems;
return 0 if $total_elements !=$r * $c; my @result; for ^$r -> $i { push @result, [@flat[$c * $i .. min($c * ($i + 1) - 1, @flat.end)]]; } return @result.elems == 1 ?? @result[0] !! @result; } This code removes the initialization of @result, as arrays are empty by default in Raku. The rest of the code remains unchanged. Me: I like what we have so far. Do you want to make it even more appealing? Let us work on the like inside the loop, where you’ve got push @result. Before I can help, can you help me to understand what exactly is happenning in that line? [At this point, I was too lazy to try understanding the logic behind that min thing, so I just asked to explain it to me.] ChatGPT: Sure! In the line with push @result, the code is adding a new row to the reshaped matrix. The new row is defined as a slice of the flattened input matrix @flat, starting from the index $c * $i and ending at the minimum of $c * ($i + 1) - 1 and @flat.end. The purpose of this line is to take $c consecutive elements from @flat and add them to @result as a new row, repeating this process $r times to form the reshaped matrix. The calculation of the start and end indices of the slice takes into account the number of columns $c in the reshaped matrix and the current iteration of the loop.

Me: Clear. But we always know the length of each row. Can we somehow use $c when we compute the right border of the slice? ChatGPT: Yes, that’s a great suggestion. Here’s an updated version of the code that uses $c when computing the end index of the slice:

sub reshape-matrix (@matrix, $r,$c) {
my @flat = @matrix.map(*.flat).flat;
my $total_elements = @flat.elems; return 0 if$total_elements != $r *$c;

my @result;
for ^$r ->$i {
push @result, [@flat[$c *$i .. $c *$i + $c - 1]]; } return @result.elems == 1 ?? @result[0] !! @result; } This code uses $c when computing the end index of the slice, which makes the code more readable and eliminates the need for the min function. The rest of the code remains unchanged.

Me: Very smart! I think we found a great solution to the task. Just compare how better it is now against the first version. The program prints the output as expected: the arrays are reshaped, and when it is not possible, we see 0:

[1 2 3 4]
[[1 2] [3 4] [5 6]]
[[1] [2]]
0

Yes, it’s great to see that the final version of the code works as expected! I’m glad I could help you improve the code and make it more readable and efficient. If you have any further questions or need additional assistance, feel free to ask!

* * *

Code on GitHub: initial solution; final version.

Also see the solutions of Task 1.

## Andrew Shitov: Dialogues with ChatPGP about the Raku programming language. Solving Task 1 of the Weekly Challenge 204

Disclaimer: Remember that the war in Ukraine still goes on. Do not consider this material as sort of entertainment. A sad joke would be that you can’t talk to a lot of people now you used to talk to, so it’s time to talk to a computer instead.

This week’s Challenge 204 offers this first task:

You are given an array of integers.

Write a script to find out if the given array is Monotonic. Print 1 if it is otherwise 0.

An array is Monotonic if it is either monotone increasing or decreasing.

Monotone increasing: for i <= j , nums[i] <= nums[j]
Monotone decreasing: for i <= j , nums[i] >= nums[j]


Example 1

Input: @nums = (1,2,2,3)
Output: 1


Example 2

Input: @nums (1,3,2)
Output: 0


Example 3

Input: @nums = (6,5,5,4)
Output: 1

Let us ask ChatGPT to find a solution and then correct it to make it more Raku-ish. The machine solved the task from the first attempt, but you can follow us and see how we managed to make the solution better and more compact. Most (except one or two examples) of the generated code works without any compiler errors, so I will not concentrate on it here.

I copy-and-pasted the task together with examples, and ChatGPT managed to create a first program that already contained the test checks in it.

ChatGPT: Here’s an example of a Raku program that implements the desired functionality:

sub is_monotonic(@nums) {
my $increasing = True; my$decreasing = True;
for (0 ..^ @nums.end) -> $i { for ($i + 1 .. @nums.end) -> $j { if (@nums[$i] > @nums[$j]) {$increasing = False;
}
if (@nums[$i] < @nums[$j]) {
$decreasing = False; } } } return 1 if$increasing || $decreasing; return 0; } my @nums = (1, 2, 2, 3); say is_monotonic(@nums); @nums = (1, 3, 2); say is_monotonic(@nums); @nums = (6, 5, 5, 4); say is_monotonic(@nums); This program defines a subroutine is_monotonic that takes an array of integers as input. The subroutine initializes two flags $increasing and $decreasing to True. It then uses nested for loops to compare each pair of elements in the array, and sets the flags to False if a pair of elements does not meet the monotonic increasing or decreasing criteria. Finally, the subroutine returns 1 if either of the flags is still True, indicating that the array is monotonic, and returns 0 otherwise. The program also includes example calls to the is_monotonic subroutine to demonstrate its functionality. Me: Let me give a hint. In Raku, we can use reduction operators and thus use [<=] in the program to compare array elements without loops. ChatGPT: Here’s an updated version of the Raku program that implements the desired functionality using reduction operators: sub is_monotonic(@nums) { my$increasing = @nums[0 ..^ @nums.end - 1].map({ @nums[$_] <= @nums[$_ + 1] }).reduce(&all);

my $decreasing = @nums[0 ..^ @nums.end - 1].map({ @nums[$_] >= @nums[$_ + 1] }).reduce(&all); return 1 if$increasing || $decreasing; return 0; } my @nums = (1, 2, 2, 3); say is_monotonic(@nums); @nums = (1, 3, 2); say is_monotonic(@nums); @nums = (6, 5, 5, 4); say is_monotonic(@nums); This program uses the reduction operator &all to check if all elements of the result of the map operation are True. The map operation takes an array slice of @nums and returns a new array of Boolean values indicating whether each pair of elements meets the monotonic increasing or decreasing criteria. The reduction operator then reduces this array to a single Boolean value indicating whether all elements are True. The program then returns 1 if either of the flags $increasing or $decreasing is True, indicating that the array is monotonic, and returns 0 otherwise. Me: We can do it like this: my$increasing = [>=] @nums;.

ChatGPT: Here’s an even simpler version of the Raku program that implements the desired functionality using the reduction operator [>=]:

sub is_monotonic(@nums) {
my $increasing = [>=] @nums; my$decreasing = [<=] @nums;

return 1 if $increasing ||$decreasing;
return 0;
}

my @nums = (1, 2, 2, 3);
say is_monotonic(@nums);

@nums = (1, 3, 2);
say is_monotonic(@nums);

@nums = (6, 5, 5, 4);
say is_monotonic(@nums);

This program uses the reduction operator [>=] to check if all elements of the array are in increasing order, and the reduction operator [<=] to check if all elements of the array are in decreasing order. The results of these reductions are stored in the variables $increasing and $decreasing, respectively. The program then returns 1 if either of these variables is True, indicating that the array is monotonic, and returns 0 otherwise. This version of the program is much simpler and more concise than the previous versions.

Me: This works, thank you! But wait, let’s make it even better. In Raku, the last computed value is returned from a function. And we do not need intermediate variables such as $increasing or $descreasing.

ChatGPT: Here’s an even simpler and more idiomatic version of the Raku program that implements the desired functionality using the reduction operator [>=]:

sub is_monotonic(@nums) {
return 1 if [>=] @nums || [<=] @nums;
return 0;
}

my @nums = (1, 2, 2, 3);
say is_monotonic(@nums);

@nums = (1, 3, 2);
say is_monotonic(@nums);

@nums = (6, 5, 5, 4);
say is_monotonic(@nums);

In this version of the program, the function is_monotonic returns the result of the expression 1 if [>=] @nums || [<=] @nums; directly. This expression checks if the array is either increasing or decreasing, and returns 1 if either condition is true. If neither condition is true, the function returns 0. The intermediate variables $increasing and $decreasing are no longer needed. This version of the program is even more concise and readable than the previous versions.

Me: [>=] @nums || [<=] @nums already is a Boolean value which can be directly returned, even without the return keyword.

ChatGPT: You’re absolutely right! Here’s the final version of the Raku program that implements the desired functionality:

sub is_monotonic(@nums) {
[>=] @nums || [<=] @nums;
}

my @nums = (1, 2, 2, 3);
say is_monotonic(@nums);

@nums = (1, 3, 2);
say is_monotonic(@nums);

@nums = (6, 5, 5, 4);
say is_monotonic(@nums);

In this version of the program, the function is_monotonic simply returns the result of the expression [>=] @nums || [<=] @nums;. This expression checks if the array is either increasing or decreasing, and returns a Boolean value indicating the result. The return keyword is no longer needed, as the last computation value of the function is automatically returned. This version of the program is the most concise and readable of all the versions.

Me: And the final touch. In Raku, - seems to be prefered over _ in identifiers names.

ChatGPT: Here’s the final version of the Raku program that implements the desired functionality, with the preferred naming conventions:

sub isMonotonic(@nums) {
[>=] @nums || [<=] @nums;
}

my @nums = (1, 2, 2, 3);
say isMonotonic(@nums);

@nums = (1, 3, 2);
say isMonotonic(@nums);

@nums = (6, 5, 5, 4);
say isMonotonic(@nums);

In this version of the program, the function isMonotonic simply returns the result of the expression [>=] @nums || [<=] @nums;. This expression checks if the array is either increasing or decreasing, and returns a Boolean value indicating the result. The function name uses the preferred naming convention of using a single - instead of _ in identifier names. This version of the program is the most concise, readable, and idiomatic of all the versions.

Me: No, that was camelCase. In Raku, you can use - in identifiers, so is-monotonic is a valid name.

ChatGPT: Here’s the final version of the Raku program that implements the desired functionality, with the preferred naming conventions:

sub is-monotonic(@nums) {
[>=] @nums || [<=] @nums;
}

my @nums = (1, 2, 2, 3);
say is-monotonic(@nums);

@nums = (1, 3, 2);
say is-monotonic(@nums);

@nums = (6, 5, 5, 4);
say is-monotonic(@nums);

In this version of the program, the function is-monotonic simply returns the result of the expression [>=] @nums || [<=] @nums;. This expression checks if the array is either increasing or decreasing, and returns a Boolean value indicating the result. The function name uses the preferred naming convention of using a single - in identifier names, as is common in Raku. This version of the program is the most concise, readable, and idiomatic of all the versions.

Me: Ah, no, I mislead you a bit with the Boolean values. The task requires that the program prints 1 and 0, but not True and False.

ChatGPT: Here’s the final version of the Raku program that implements the desired functionality, with the output as 1 and 0:

sub is-monotonic(@nums) {
return 1 if [>=] @nums;
return 1 if [<=] @nums;
return 0;
}

my @nums = (1, 2, 2, 3);
say is-monotonic(@nums);

@nums = (1, 3, 2);
say is-monotonic(@nums);

@nums = (6, 5, 5, 4);
say is-monotonic(@nums);

In this version of the program, the function is-monotonic returns 1 if the array is increasing ([>=] @nums), returns 1 if the array is decreasing ([<=] @nums), and returns 0 otherwise. The function name uses the preferred naming convention of using a single - in identifier names, as is common in Raku. This version of the program implements the desired functionality, with the output as 1 and 0, as specified in the task.

Me: That is an option, correct. But we can also convert a Boolean value to an Int.

ChatGPT: Here’s the final version of the Raku program that implements the desired functionality, with the output as 1 and 0 and using a conversion from Boolean to Int:

sub is-monotonic(@nums) {
return +([>=] @nums) || +([<=] @nums);
}

my @nums = (1, 2, 2, 3);
say is-monotonic(@nums);

@nums = (1, 3, 2);
say is-monotonic(@nums);

@nums = (6, 5, 5, 4);
say is-monotonic(@nums);

In this version of the program, the function is-monotonic returns +([>=] @nums) || +([<=] @nums). The + operator is used to convert the result of the reduction operator [>=] @nums or [<=] @nums to an Int. The expression returns 1 if the array is increasing or decreasing, and returns 0 otherwise. The function name uses the preferred naming convention of using a single - in identifier names, as is common in Raku. This version of the program implements the desired functionality, with the output as 1 and 0, as specified in the task.

Me: I would suggest we return ([>=] @nums || [<=] @nums).Int.

ChatGPT: Here’s the final version of the Raku program that implements the desired functionality, with the output as 1 and 0 and using the conversion .Int:

sub is-monotonic(@nums) {
return ([>=] @nums || [<=] @nums).Int;
}

my @nums = (1, 2, 2, 3);
say is-monotonic(@nums);

@nums = (1, 3, 2);
say is-monotonic(@nums);

@nums = (6, 5, 5, 4);
say is-monotonic(@nums);

In this version of the program, the function is-monotonic returns ([>=] @nums || [<=] @nums).Int. The expression ([>=] @nums || [<=] @nums) returns a Boolean value indicating whether the array is increasing or decreasing, and the .Int method is used to convert the Boolean to an Int. The expression returns 1 if the array is increasing or decreasing, and returns 0 otherwise. The function name uses the preferred naming convention of using a single - in identifier names, as is common in Raku. This version of the program implements the desired functionality, with the output as 1 and 0, as specified in the task.

Me: Or of course, better would be to keep the is- function Boolean, and convert the result if you need to print 1s and 0s.

ChatGPT: Here’s the final version of the Raku program that implements the desired functionality, with the function returning a Boolean value and converting the result to an Int when needed:

sub is-monotonic(@nums) {
[>=] @nums || [<=] @nums;
}

my @nums = (1, 2, 2, 3);
say +is-monotonic(@nums);

@nums = (1, 3, 2);
say +is-monotonic(@nums);

@nums = (6, 5, 5, 4);
say +is-monotonic(@nums);

In this version of the program, the function is-monotonic returns a Boolean value indicating whether the array is increasing or decreasing ([>=] @nums || [<=] @nums). When the result is needed in the form of 1s and 0s, the + operator is used to convert the Boolean to an Int. The function name uses the preferred naming convention of using a single - in identifier names, as is common in Raku. This version of the program implements the desired functionality, with the function returning a Boolean value and converting the result to an Int when needed, as suggested.

Me: Perfect! A smart choice to use a unary prefix. We can also remove the ; inside the sub, as it looks redundant in such a small one-line function.

* * *

From this moment, the machine and I started a dance with some wrong code by not simply removing the semicolon, but also the curly braces around the body of the function, then returning to the previous solution and so on. So, let me stop here and show the final version that is actually a good Raku code, mostly created by machine. Notice that I also had to change || to or to handle operator precedence races.

sub is-monotonic(@nums) {
[>=] @nums or [<=] @nums
}

my @nums = (1, 2, 2, 3);
say +is-monotonic(@nums);

@nums = (1, 3, 2);
say +is-monotonic(@nums);

@nums = (6, 5, 5, 4);
say +is-monotonic(@nums);

The program prints the result as expected in the task:

101

* * *

Code on GitHub: initial solution; final version.

Also see the solutions of Task 2.

## p6steve: The Laws of Job Hunting

with inspiration from the Laws of Thermodynamics

## 0. Zeroeth Law: A Numbers Game

Looking for work is a numbers game.

• 30 Applications ==>
• 10 First Interviews ==>
• 3 Second Interviews ==>
• 1 Offer

## 1. First Law: It’s Not Personal

You are the salesperson, you are the product. This is a conflict of interest. You must objectify and promote yourself (since everyone else is).

The people who select you are judging you on the basis of a piece of paper, or a zoom call, or a short interview, They don’t know you. They are working to a brief. They are predisposed. They don’t care.

## 2. Second Law: You Will Fail

When you fail an application, an interview it is not about you. It’s about them.

Request practical feedback – you gave your time to make the application, they owe you. Judge it and apply it as you see fit.

## 3. Third Law: Finding a Job is a Job

You should be prepared to work a full 40 hour week to prepare and submit all your various applications. Since job seeking is a rather lonely and soul challenging activity, this is not easy.

Authors note: I have done job searches a lot over 40 years – both from the applicant and the employer point of view and thus consider myself well qualified to advise based on my experience.

~p6steve

## p6steve: Raku CLI AWS – Postvent

It was great fun writing my two contributions to the Raku Advent calendar:

1. Day 11: Santa CL::AWS with a short raku procedural wrapper on AWS CLI
2. Day 16: Santa CL::AWS (part 2) with a raku OO refactoring

These posts presented my work in progress, in advent calendar style of course, on a new raku module: CLI::AWS::EC2-Simple. The module is now available at the raku zef repository via raku.land with github repo here. This post completes the CL::AWS trilogy with a demo of it in action and how the OO model from last time is now wrapped as a command.

## Show Me

First, here’s the new module in action:

By the way, I chose to base the script name as “raws” in homage to the great, but different, perl5 cpan module paws. Paws is an autogenerated and complete perl5 interface to AWSCLI … and can easily be used from raku code like this use PAWS::EC2:from<Perl>; and indeed there’s a debate about that on the raku reddit.

But Raws is different. It is meant primarily to help me in my needs to quickly and consistently launch the same EC2 configuration as specified in a minimal yaml. A command line shorthand alternative to the AWS EC2 console launch dialog. So, Raws is an added value wrapper to AWSCLI, an abstraction with the emphasis on Simplicity and Repeatability. I have released it as a module in the vague expectation that others will have the same needs.

The sharp eyed reader will note that the command is actually raws-ec2. This is in anticipation of some other author creating a raws-s3 for example, or maybe a broader command that can be used via a bare raws –ec2 or raws –s3 and so on.

## Getting Started

• apt-get update && apt-get install aws-cli
• aws configure [enter your config here]
• zef install CLI::AWS::EC2-Simple
• raws-ec2 [enter your commands here]

## Command Abstract

The built-in raku MAIN() routine helps to quickly add command line features to our module including a quick abstract of the commands and arguments. Just type raws-ec2 for this:

./raws-ec2 [--id=] [--nsu] [--eip] [-y]

One of 'list launch setup connect state terminate nuke'
--id=    Running InstanceId of form 'i-0785d8bd98b5f458b'
--nsu    No setup (suppress launch from running setup)
--eip    Allocates (if needed) and Associates Elastic IP
-y       Silence confirmation  cmd only


Authors note: Beware – these examples can quickly ramp your AWS bill! Keep an eye in your AWS EC2 Instances console.

## Simple Consistent Pattern

Now, here’s a peek inside the default aws-ec2-launch.yaml file that comes with the module:

instance:
image: 'ami-0f540e9f488cfa27d'
type: 't2.micro'
security-group:
name: 'MySG'
rules:
- inbound:
port: 80
cidr: '0.0.0.0/0'
- inbound:
port: 443
cidr: '0.0.0.0/0'

This is the heart of the system, making one easy place to setup and manage your preferences at the AWS Instance level. [Just be careful to save your changes away when reinstalling the module]

To be honest, while very powerful, I find the awscli itself quite bewildering with multiple steps to connect and setup a session (KeyPair, Security Groups, Elastic IP assignment, and so on), so I was chuffed to see just how neatly the key parameters can be boiled down and handed to raws-ec2 to do the painful bit.

## Making a Raku Library Module

Here’s the raku Object Oriented code from part II… with some details folded away:

To turn this into a raku module, a couple of tweaks were needed: a header line like this:

unit module CLI::AWS::EC2-Simple:ver<0.0.2>:auth<Steve Roe (p6steve@furnival.net)>;

Also, the classes we want to expose need to be marked ‘is export’:

class Instance is export { ... }

Also I used the $*HOME global to set the home directory of the target installation. The curious may want to check out my Build.pm for how the install set up the default config files. ## Making a Raku Command I did think about folding away some of the command code, but then there is some value in seeing how it all fits together into just over 80 lines… ## Star Features The stars to raku fall in several places: • The build in MAIN() routine is the star of the show, showing how to annotate each argument declaration in the signature with Pod6 #= to make the abstract • The raku given/when “switch” makes the backbone of the code very intelligible • Raku slurp and spurt make IO a doddle • And the use of unless is pretty neat instead of if not ## Functional Future So, this delivery of a simple EC2 tool was pretty straightforward to write and will save me time by giving me a way to remember my repeatable settings and apply them consistently. Perhaps there is scope for friendly Instance tags here too… As mentioned in part II, the Raku Functional features could help us to improve clarity and composability even more. But that’s for another time… ~p6steve PS. Please comment and feedback here (follow the Archive link) or over at reddit ## Elizabeth Mattijsen: The 2022 Raku Advent Blog ### Published by Elizabeth Mattijsen on 2022-12-26T12:25:42 (in chronological order, with comment references) Hope to see you again next year! ## Raku Advent Calendar: The 2022 Raku Advent Posts ### Published by Elizabeth Mattijsen on 2022-12-26T01:01:00 (in chronological order, with comment references) ## Raku Advent Calendar: Day 25: Rakudo 2022 Review ### Published by Elizabeth Mattijsen on 2022-12-25T01:01:00 In a year as eventful as 2022 was in the real world, it is a good idea to look back to see what one might have missed while life was messing with your (Raku) plans. Rakudo saw about 1500 commits this year, about the same as the year before that. Many of these were bug fixes and performance improvements, which you would normally not notice. But there were also commits that actually added features to the Raku Programming Language. So it feels like a good idea to actually mention those more in depth. So here goes! Unless otherwise noted, all of these changes are in language level 6.d, and available thanks to several Rakudo compiler releases during 2022. ### New REPL functionality It is now possible to refer to values that were produced earlier, using the $*N syntax, where N is a number greater than or equal to 0.

$raku To exit type 'exit' or '^D' [0] > 42 42 [1] > 666 666 [2] >$*0 + $*1 708 Note that the number before the prompt indicates the index with which the value that is going to be produced, can be obtained. ### New MAIN options You can now affect the interpretation of command line arguments to MAIN by setting these options in the %*SUB-MAIN-OPTS hash: ##### allow-no Allow negation of a named argument to be specified as --no-foo instead of --/foo. ##### numeric-suffix-as-value Allow specification of a numeric value together with the name of a single letter named argument. So -j2 being the equivalent of --j=2. So for example, by putting: my %*SUB-MAIN-OPTS = :allow-no, :numeric-suffix-as-value; at the top of your script, you would enable these features in the command-line argument parsing. ### New types Native unsigned integers (both in scalar, as well as a (shaped) array) have finally become first class citizens. This means that a native unsigned integer can now hold the value 18446744073709551615 as the largest positive value, from 9223372036854775807 before. This also allowed for a number of internal optimisations as the check for negative values could be removed. As simple as this sounds, this was quite an undertaking to get support for this on all VM backends. my uint$foo = 42;
my uint8 $bar = 255; my int8$baz = 255;

say $foo; # 42 say$bar; # 255
say $baz; # -1 say ++$foo; # 43
say ++$bar; # 0 say ++$baz; # 0

And yes, all of the other explicitly sized types, such as uint16uint32 and uint64, are now also supported!

### New subroutines

A number of subroutines entered the global namespace this year. Please note that they will not interfere with any subroutines in your code with the same name, as these will always take precedence.

#### NYI()

The NYI subroutine takes a string to indicate a feature not yet implemented, and turns that into a Failure with the X::NYI exception at its core. You could consider this short for ... with feedback, rather than just the “Stub code executed”.

say NYI "Frobnication";
# Frobnication not yet implemented. Sorry.

#### chown()

The chown subroutine takes zero or more filenames, and changes the UID (with the :uid argument) and/or the GID (with the :gid argument) if possible. Returns the filenames that were successfully changed. There is also a IO::Path.chown method version.

my @files  = ...;
my $uid = +$*USER;
my changed = chown @files, :$uid; say "Converted UID of$changed / @files.elems() files";

Also available as a method on IO::Path, but then only applicable to a single path.

The .head.skip and .tail methods got their subroutine counterparts.

say head 3, ^10; # (0 1 2)
say skip 3, ^10; # (3,4,5,6,7,8,9)
say tail 3, ^10; # (7 8 9)

Note that the number of elements is always the first positional argument.

### New methods

#### Any.are

The .are method returns the type object that all of the values of the invocant have in common. This can be either a class or a role.

say (1, 42e0, .137).are;        # (Real)
say (1, 42e0, .137, "foo").are; # (Cool)
say (42, DateTime.now).are;     # (Any)

In some languages this functionality appears to be called infer, but this name was deemed to be too ComputerSciency for Raku.

#### IO::Path.inode|dev|devtype|created|chown

Some low level IO features were added to the IO::Path class, in the form of 5 new methods. Note that they may not actually work on your OS and/or filesystem. Looking at you there, Windows

• .inode – the inode of the path (if available)
• .dev – the device number of the filesystem (if available)
• .devtype – the device identifier of the filesystem (if available)
• .created – DateTime object when path got created (if available)
• .chown – change uid and/or gid of path (if possible, method version of chown())

#### (Date|DateTime).days-in-year

The Date and DateTime classes already provide many powerfule date and time manipulation features. But a few features were considered missing this year, and so they were added.

A new .days-in-year class method was added to the Date and DateTime classes. It takes a year as positional argument:

say Date.days-in-year(2023);  # 365
say Date.days-in-year(2024);  # 366

This behaviour was also expanded to the .days-in-month method, when called as a class method:

say Date.days-in-month(2023, 2);  # 28
say Date.days-in-month(2024, 2);  # 29

They can also be called as instance methods, in which case the parameters default to the associated values in the object:

given Date.today {
.say;                # 2022-12-25
say .days-in-year;   # 365
say .days-in-month;  # 31
}

### New Dynamic Variables

Dynamic variables provide a very powerful way to keep “global” variables. A number of them are provided by the Raku Programming Language. And now there is one more of them!

#### $*RAT-OVERFLOW Determine the behaviour of rational numbers (aka Rats) if they run out of precision. More specifically when the denominator no longer fits in a native 64-bit integer. By default, Rats will be downgraded to floating point values (aka Nums). By setting the $*RAT-OVERFLOW dynamic variable, you can influence this behaviour.

The $*RAT-OVERFLOW dynamic variable is expected to contain a class (or an object) on which an UPGRADE-RAT method will be called. This method is expected to take the numerator and denominator as positional arguments, and is expected to return whatever representation one wants for the given arguments. The following type objects can be specified using core features: ##### Num Default. Silently convert to floating point. Sacrifies precision for speed. ##### CX::Warn Downgrade to floating point, but issue a warning. Sacrifies precision for speed. ##### FatRat Silently upgrade to FatRat, aka rational numbers with arbitrary precision. Sacrifies speed by conserving precision. ##### Failure Return an appropriate Failure object, rather than doing a conversion. This will most likely throw an exception unless specifically handled. ##### Exception Throw an appropriate exception. Note that you can introduce any custom behaviour by creating a class with an UPGRADE-RAT method in it, and setting that class in the $*RAT-OVERFLOW dynamic variable.

class Meh {
method UPGRADE-RAT($num,$denom) is hidden-from-backtrace {
die "$num /$denom is meh"
}
}
my $*RAT-OVERFLOW = Meh; my$a = 1 / 0xffffffffffffffff;
say $a; # 0.000000000000000000054 say$a / 2; # 1 / 36893488147419103230 is meh

Note that the is hidden-from-backtrace is only added so that any backtrace will show the location of where the offending calculation was done, rather than inside the UPGRADE-RAT method itself.

### New Environment Variables

Quite a few environment variables are already checked by Rakudo whenever it starts. Two more were added in the past year:

This environment variable can be set to indicate the maximum number of OS-threads that Rakudo may use for its thread pool. The default is 64, or the number of CPU-cores times 8, whichever is larger. Apart from a numerical value, you can also specify "Inf” or "unlimited" to indicate that Rakudo should use as many OS-threads as it can.

These same values can also be used in a call to ThreadPoolScheduler.new with the :max_threads named argument.

# Type check failed in assignment to $a; You cannot use -foo-, dummy! The will complain trait can be used anywhere you can specify a type constraint in Raku, so that includes parameters and attributes. ### :rakuast The RakuAST classes allow you to dynamically build an AST (Abstract Syntax Tree programmatically, and have that converted to executable code. What was previously only possible by programmatically creating a piece of Raku source code (with all of its escaping issues), and then calling EVAL on it. But RakuAST not only allows you to build code programmatically (as seen in yesterday’s blog post), it also allows you to introspect the AST, which opens up all sorts of syntax / lintifying possibilities. There is an associated effort to compile the Raku core itself using a grammar that uses RakuAST to build executable code. This effort is now capable of passing 585/1355 test-files in roast completely, and 83/131 of the Rakudo test-files completely. So still a lot of work to do, although it has now gotten to the point that implementation of a single Raku feature in the new grammar, often creates an avalanche of now passing test-files. So, if you add a use experimental :rakuast to your code, you will be able to use all of the currently available RakuAST classes to build code programmatically. This is an entire new area of Raku development, which will be covered by many blog posts in the coming year. As of now, there is only some internal documentation. A small example, showing how to build the expression "foo" ~ "bar": use experimental :rakuast; my$left  = RakuAST::StrLiteral.new("foo");
my $infix = RakuAST::Infix.new("~"); my$right = RakuAST::StrLiteral.new("bar");

my $ast = RakuAST::ApplyInfix.new(:$left, :$infix, :$right);
dd $ast; # "foo" ~ "bar" This is very verbose, agreed. Syntactic sugar for making this easier will certainly be developed, either in core or in module space. Note how each element of the expression can be created separately, and then combined together. And that you can call dd to show the associated Raku source code (handy when debugging your ASTs). For the very curious, you can check out a proof-of-concept of the use of RakuAST classes in the Rakudo core in the Formatter class, that builds executable code out of an sprintf format. ### New arguments to existing functionality #### roundrobin(…, :slip) The roundrobin subroutine now also accepts a :slip named argument. When specified, it will produce all values as a single, flattened list. say roundrobin (1,2,3), <a b c>; # ((1 a) (2 b) (3 c)) say roundrobin (1,2,3), <a b c>, :slip; # (1 a 2 b 3 c) This is functionally equivalent to: say roundrobin((1,2,3), <a b c>).map: *.Slip; but many times more efficient. #### Cool.chomp($needle)

The .chomp method by default any logical newline from the end of a string. It is now possible to specify a specific needle as a positional argument: only when that is equal to the end of the string, will it be removed.

say "foobar".chomp("foo"); # foobar
say "foobar".chomp("bar"); # foo

It actually works on all Cool values, but the return value will always be a string:

say 427.chomp(7); # 42

#### DateTime.posix

DateTime value has better than millisecond precision. Yet, the .posix method always returned an integer value. Now it can also return a Num with the fractional part of the second by specifying the :real named argument.

given DateTime.now {
say .posix;        # 1671733988
say .posix(:real); # 1671733988.4723697
}

### Additional meaning to existing arguments

#### Day from end of month

The day parameter to Date.new and DateTime.new (whether named or positional) can now be specified as either a Whatever to indicate the last day of the month, or as a Callable indicating number of days from the end of the month.

say Date.new(2022,12,*);   # 2022-12-31
say Date.new(2022,12,*-6); # 2022-12-25

You can already access new v6.e language features by specifying use v6.e.PREVIEW at the top of your compilation unit. Several additions were made the past year!

#### term nano

nano term is now available. It returns the number of nanoseconds since midnight UTC on 1 January 1970. It is similar to the time term but one billion times more accurate. It is intended for very accurate timekeeping / logging.

use v6.e.PREVIEW;
say time; # 1671801948
say nano; # 1671801948827918628

With current 64-bit native unsigned integer precision, this should roughly be enough for another 700 years

#### prefix //

You can now use // as a prefix as well as an infix. It will return whatever the .defined method returns on the given argument).

use v6.e PREVIEW;
my $foo; say //$foo; # False
$foo = 42; say //$foo; # True

Basically //$foo is syntactic sugar for $foo.defined.

#### snip() and Any.snip

The new snip subroutine and method allows one to cut up a list into sublists according the given specification. The specification consists of one or more smartmatch targets. Each value of the list will be smartmatched with the given target: as soon as it returns False, will all the values before that be produced as a List.

use v6.e.PREVIEW;
say (2,5,13,9,6,20).snip(* < 10);
# ((2 5) (13 9 6 20))

Multiple targets can also be specified.

say (2,5,13,9,6,20).snip(* < 10, * < 20);
# ((2 5) (13 9 6) (20))

The argument can also be an Iterable. To split a list consisting of integers and strings into sublists of just integers and just strings, you can do:

say (2,"a","b",5,8,"c").snip(|(Int,Str) xx *);
# ((2) (a b) (5 8) (c))

Inspired by Haskell’s span function.

#### Any.snitch

The new .snitch method is a debugging tool that will show its invocant with note by default, and return the invocant. So you can insert a .snitch in a sequence of method calls and see what’s happening “half-way” as it were.

$raku -e 'use v6.e.PREVIEW;\ say (^10).snitch.map(* + 1).snitch.map(* * 2)' ^10 (1 2 3 4 5 6 7 8 9 10) (2 4 6 8 10 12 14 16 18 20) You can also insert your own “reporter” in there: the .snitch method takes a Callable. An easy example of this, is using dd for snitching: $ raku -e 'use v6.e.PREVIEW;\
say (^10).snitch(&dd).map(*+1).snitch(&dd).map(* * 2)'
^10
(1, 2, 3, 4, 5, 6, 7, 8, 9, 10).Seq
(2 4 6 8 10 12 14 16 18 20)

#### Any.skip(produce,skip,…)

You can now specify more than one argument to the .skip method. Before, you could only specify a single (optional) argument.

my @a = <a b c d e f g h i j>;
say @a.skip;       # (b c d e f g h i j)
say @a.skip(3);    # (d e f g h i j)
say @a.skip(*-3);  # (h i j)

On v6.e.PREVIEW, you can now specify any number of arguments in the order: produce, skip, produce, etc. Some examples:

use v6.e.PREVIEW;
my @a = <a b c d e f g h i j>;
# produce 2, skip 5, produce rest
say @a.skip(2, 5);        # (a b h i j)
# produce 0, skip 3, then produce 2, skip rest
say @a.skip(0, 3, 2);     # (d e)
# same, but be explicit about skipping rest
say @a.skip(0, 3, 2, *);  # (d e)

In fact, any Iterable can now be specified as the argument to .skip.

my @b = 3,5;
# produce 3, skip 5, then produce rest
say @a.skip(@b);           # (a b c i j)
# produce 1, then skip 2, repeatedly until the end
say @a.skip(|(1,2) xx *);  # (a d g j)

#### Cool.comb(Pair)

On v6.e.PREVIEW, the .comb method will also accept a Pair as an argument to give it .rotor_-like capabilities. For instance, to produce trigrams of a string, one can now do:

use v6.e.PREVIEW;
say "foobar".comb(3 => -2);  # (foo oob oba bar)

This is the functional equivalent of "foobar".comb.rotor(3 => -2)>>.join, but about 10x as fast.

#### Changed semantics on Int.roll|pick

To pick a number from 0 till N-1, one no longer has to specify a range, but can use just the integer value as the invocant:

use v6.e.PREVIEW;
say (^10).roll;     # 5
say 10.roll;        # 7
say (^10).pick(*);  # (2 0 6 9 4 1 5 7 8 3)
say 10.pick(*);     # (4 6 1 0 2 9 8 3 5 7)

Of course, all of these values are examples, as each run will, most likely, produce different results.

### More interesting stuff

There were some more new things and changes the past year. I’ll just mention them very succinctly here:

##### New methods on CompUnit::Repository::Staging

.deploy.remove-artifacts, and .self-destruct.

##### New methods on Label

.file and .line where the Label was created.

##### .Failure coercer

Convert a Cool object or an Exception to a Failure. Mainly intended to reduce binary size of hot paths that do some error checking.

##### Cool.Order coercer

Coerce the given value to an Int, then convert to Less if less than 0, to Same if 0, and More if more than 0.

##### Allow semi-colon

Now allow for the semi-colon in my :($a,$b) = 42,666 because the left-hand side is really a Signature rather than a List.

### Summary

I guess we’ve seen one big change in the past year, namely having experimental support for RakuAST become available. And many smaller goodies and tweaks and features.

Now that RakuAST has become “mainstream” as it were, we can think of having certain optimizations. Such as making sprintf with a fixed format string about 30x as fast! Exciting times ahead!

Hopefully you will all be able to enjoy the Holiday Season with sufficient R&R. The next Raku Advent Blog is only 340 days away!

## p6steve: On Sigils

This post was inspired by @codesections recent posts on sigils, particularly the notion of coding as a trialog between the writer, the reader and the machine.

@codesections already did a great job of reflecting the wider societal uptake of eg. #hashtags and @names as examples of sigils in the wild.

I aim to show sigils in action with some simple examples in the raku programming language.

A sigil, for this purpose, is the use of a single non-word character – usually a dollar sign $– to distinguish a variable name from other words in the code such as operators and functions. Disclosure: I learned to program with C and Perl and I use Linux as my OS with the Bash shell and I code in raku practically every day. ## On dollar$ – ease of use

Here’s some Python:

language = "Python"
print("I like coding in " + language + " the most.")

#I like coding in Python the most.

The sentence has been broken into literals with ” quote marks and then concatenated with the variable with the ‘+’ operator.

And here’s some Raku:

my $language = "Raku"; say "I like coding in$language the most."

#I like coding in Raku the most.

Here, the sigil identifies the variable and interpolates it into the output. It eliminates several extra quote marks and concatenation operators. It cuts out unwanted “line noise”.

## On dollar $– linguistics We are dealing with coding languages. As with natural languages, syntax is a key marker that triggers cognitive mechanisms learned since childhood. While the base cultural setting for most of this is English, most human languages carry the notions of noun, verb, adjective and so on. Here’s some English:  Jack and Jill went up the hill To fetch a pail of water Jack fell down and broke his crown And Jill came tumbling after  The proper names begin with Capital letters. And here’s some Raku: my ($boy, $girl) = <Jack Jill>; say qq:to/END/;$boy and $girl went up the hill To fetch a pail of water$boy fell down and broke his crown
And $girl came tumbling after END See how the$variables catch the eye? Rather like the Capitals we are used to – and proper nouns are similarly interchangeable since it could have been Alice and Bob.

## On dollar $– practicalities In the first place, sigils emerged from a long line of coding tools as a practical technique. For example, the dollar sign$ shows up as a sigil on Linux shell and environment variables, here in a simple Bash example:

#!/bin/bash
for (( n=2; n<=10; n++ ))
do
echo "$n seconds" done https://www.hostinger.co.uk/tutorials/bash-script-example C pointers (with ‘*’ prefix) and references (with ‘&’ prefix) take a similar looking approach that tells the compiler what we want and the coder what we have: int i = 3; // A pointer to variable i or "stores the address of i" int *ptr = &i; // A reference (or alias) for i. int &ref = i;  https://www.geeksforgeeks.org/pointers-vs-references-cpp/ The$ sigil line continues through web-based interpolation in various guises (with eg. PHP and Ruby) and fairly recently in the SCSS variant of CSS:

$font-stack: Helvetica, sans-serif;$primary-color: #333;

body {
font: 100% $font-stack; color:$primary-color;
}
https://sass-lang.com/guide

So, it’s tempting to stop here with the $dollar sigil – it is easy, natural and practical. Author’s note: Not all coders see the benefits. Sometimes IDE tools provide an alternative to sigil characters with colour coding (personally, I don’t find multi-colour text very helpful). Many find noisy sigil symbols a distraction from the code. It boils down to choice. Nevertheless, I think it is fair to say that the humble dollar$ sigil has found a solid use case in many situations among a large body of coders with that shared heritage and mindset.

Let’s say you buy the $dollar sigil as a useful tool? If so, raku has thoughtfully extended the technique for a few more substantial purposes. ## On at @ – plurality Here is my collection of 10 marbles. Is it one thing or 10 things? In raku you can use either the dollar$ sigil or the at @ sigil to help:

my $marbles = 1..10; # "one thing" my @marbles = 1..10; # "10 things" The at symbol @ is used to remind us that this is an Array. We can use dd (data dumper) to show what is going on. The ‘\’ makes the argument ‘x’ sigilless so that our function is studiously neutral: sub got_what( \x ) { dd x # data dumper } got_what$marbles;
#Range $marbles = 1..10 got_what @marbles; #Array @marbles = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] An iterator, such as the for operator, will take the desired plurality into account: sub for_what( \x ) { for x -> \item { dd item } } for_what$marbles;           #one thing =>
#Range $marbles = 1..10 for_what @marbles; #10 things => #Int @marbles = 1 #Int @marbles = 2 #Int @marbles = 3 #Int @marbles = 4 #Int @marbles = 5 #Int @marbles = 6 #Int @marbles = 7 #Int @marbles = 8 #Int @marbles = 9 #Int @marbles = 10 ## On at @ – ease of use So, what happens when you go back and forth? Let’s apply a sigil to the parameter declaration$x in the subroutine signature:

sub for_doll( $x ) { for$x -> \item { dd item }
}
for_doll @marbles;          #one thing =>
# $[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] Or just do an assignment: my$scalar = @marbles;
dd $scalar; #one thing => #Array$scalar = $[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] That’s an Array wrapped up in a Scalar container (thus the leading$). This process is know as itemizing, since it makes many things into a single item.

Or we can go the the other way:

sub for_amper( @x ) {
for @x -> \item { dd item }
}
for_amper $marbles; #ten things => 1 2 3 4 5 6 7 8 9 10 And with assignment? Well, not quite so easy. The compiler needs to know (i) should I make and Array of Arrays with$marbles as the first element, or (ii) should I assign each individual element one by one. Here, the programmer must use the pipe ‘|’ symbol if she wishes to flatten the right hand side.

my @array = |$marbles; dd @array; #Array @array = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]  ## On at @ – the Single Argument Rule Now, once up a time in raku-land, it became clear that coders could easily mix up their intentions sometimes passing one argument to an iterator, sometimes many. So, to keep things simple and memorable, the community developed the Single Argument Rule. The documents say this: It is the rule by which the set of parameters passed to an iterator such as for is treated as a single argument, instead of several arguments… Since what for receives is a single argument, it will be treated as a list of elements to iterate over. The rule of thumb is that if there’s a comma, anything preceding it is an element and the list thus created becomes the single element. That happens in the case of the two arrays separated by a comma which is the third element in the Array we are iterating in this example. In general, quoting the article linked above, the single argument rule … makes for behavior as the programmer would expect. This rule is equivalent to saying that arguments to iterators will not flatten, will not de-containerize, and will behave as if a single argument has been handled to them, whatever the shape that argument has. my @a = 1,2; .say for @a, |@a; # OUTPUT: «[1 2]␤1␤2␤» my @a = 1,2; .say for$[@a, |@a ]; # OUTPUT: «[[1 2] 1 2]␤» 

In the second case, the single argument is a single element, since we have itemized the array. There’s an exception to the single argument rule mentioned in the Synopsis: list or arrays with a single element will be flattened:

my @a = 1,2; .say for [[@a ]];     # OUTPUT: «1␤2␤» 

Authors note: raku is often making new containers and populating them behind the scenes with argument passing, assignment and so on. This exception automates the unwrapping of nested single elements to simply “do what I mean”.

The documents also say some other stuff that personally I find a bit confusing.

I will wrap up this section with two bits of friendly raku advice:

• do not use $with any kind of list unless you want a suprise! • while raku also gives us Seq and List types, you want to use Array unless you are an expert ## And the rest – percent % and ampersand & Raku coders also get two more variants to play with, here’s how the four fit together. This set of four, along with the sigilless option, provides a rich set of tools to communicate the intent of the coder in the trialog. I hope that you have enjoyed reading this post as much as I enjoyed writing it. To paraphrase Richard Feynman, to understand a topic properly, you need to blog about it. As usual, comments and feedback welcome! ~p6steve ## Raku Advent Calendar: Day 24: He’s making a list… (part 2) ### Published by Matthew Stephen Stuckwisch on 2022-12-24T00:00:00 In our last edition, we learned about some of the work that Santa’s elves put into automating how they make their lists. What you probably didn’t know is that the elves stay on top of the latest and greatest technology. Being well-known avid Raku programmers, the elves were excited to hear about RakuAST and decided to see how they might be able to use it. One of the elves decided to rework the list formatting code to use RakuAST. What follows is the story of how she upgraded their current technology to use RakuAST. ## Background The current code that the elves had is fairly straight forward (check out part one for a full explanation) sub format-list( +@items, :$language 'en',
:$type = 'and', :$length = 'standard'
) {
state %formatters;
my $code = "$language/$type/$length";

# Get a formatter, generate if it's not been requested before
my &formatter = %cache{$code} // %cache{$code} =
generate-list-formatter($language,$type, $length); formatter @items; } sub generate-list-formatter($language, $type,$length --> Sub ) {
# Get CLDR information
my $format = cldr{$language}.list-format{$type}{$length};
my ($start,$middle, $end,$two) =
$format<start middle end two>.map: *.substr(3,*-3).raku; # Generate code my$code = q:s:to/FORMATCODE/;
sub format-list(+@items) {
if @items > 2 {
@items[0]
~ $start ~ @items[1..*-2].join($middle)
~ $end ~ @items[*-1] } elsif @items == 2 { @items[0] ~$two ~ @items[1]
}
elsif @items == 1 {
@items[0]
}
else {
''
}
}
FORMATCODE

# compile and return
use MONKEY-SEE-NO-EVAL;
EVAL $code } While the caching technique is rudimentary and technically not thread-safe, it works (a different elf will probably revisit the code to make it so). Now, when creating all the lists for, say, children in Georgia, the data for Georgian list formatters in CLDR will only need to be accessed a single time. For the next half a million or so calls, the code will be run practically as fast as if it had been hard coded (since, in effect, it has been). The problem is how the generate-list-formatter code works. The code block uses a heredoc-style :to string, but it’s interpolated. There are numerous ways to accomplish this but all of them require having to use proper escapes. That’s…. risky. Another elf, having seen the performance improvements that this new EVAL code brought, wanted to find a way to avoid the risky string evaluation. She had heard about the new RakuAST and decided to give it a whirl. While it initially looked more daunting, she quickly realized that RakuAST was very powerful. ## What is RakuAST RakuAST is an object-based representation of Raku’s abstract syntax tree, or roughly what you might get if you parsed Raku’s code into its individual elements. For instance, a string literal might be represented as 'foo' in code, but once parsed, becomes a string literal. That string literal, by the way, can be created by using RakuAST::StrLiteral.new(…). Remember how the elf had to worry about how the string might be interpolated? By creating a the string literal directly via a RakuAST node, that whole process is safely bypassed. No RakuAST::StrLiteral node can be created that will result in a string injection! Every single construct in the Raku language has an associated RakuAST node. When creating nodes, you might frequently pass in another node, which means you can build up code objects in a piece-by-piece fashion, and again, without ever worrying about string interpolation, escaping, or injection attacks. So let’s see how the elf eventually created the safer RakuAST version of the formatter method. ## The elf works her AST off To ease her transition into RakuAST, the elf decided to go from the simplest to the most complex part of the code. The simplest is the value for the final else block: my$none = RakuAST::StrLiteral.new(''); 

Okay. That was easy. Now she wanted to tackle the single element value. In the original code, that was @list.head. Although we don’t normally think of it as such, . is a special infix for method calling. Operators can be used creating an RakuAST::Apply___fix node, where ___ is the type of operator. Depending on the node, there are different arguments. In the case of RakuAST::ApplyPostfix, the arguments are operand (the list), and postfix which is the actual operator. These aren’t as simple as typing in some plain text, but when looking at the code the elf came up with, it’s quite clear what’s going on:

my $operand = RakuAST::Var::Lexical.new('@list'); my$postfix = RakuAST::Call::Method.new(
);
my $one = RakuAST::ApplyPostfix.new(:$operand, :$postfix)  The operand isn’t a literal, but a variable. Specifically, it’s a lexical variable, so we create a node that will reference it. The call method operator needs a name as well, so we do that as well. This involves a lot of assignment statements. Sometimes that can be helpful, but for something this simple, the elf decided it was easier to write it as one “line”: my$one = RakuAST::ApplyPostfix.new(
operand => RakuAST::Var::Lexical.new('@list'),
postfix => RakuAST::Call::Method.new(
)
);

Alright, so the first two cases are done. How might she create the result for when the list has two items? Almost exactly like the last time, except now she’d provide an argument. While you might think it would be as simple as adding args => RakuAST::StrLiteral($two-infix), it’s actually a tiny bit more complicated because in Raku, argument lists are handled somewhat specially, so we actually need a RakuAST::ArgList node. So the equivalent of @list.join($two-infix) is

my $two = RakuAST::ApplyPostfix.new( operand => RakuAST::Var::Lexical.new('@list'), postfix => RakuAST::Call::Method.new( name => RakuAST::Name.from-identifier('join'), args => RakuAST::ArgList.new( RakuAST::StrLiteral.new($two-infix)
)
)
); 	 

The RakuAST::ArgList takes in a list of arguments — be they positional or named (named applied by way of a RakuAST::FatComma).

Finally, the elf decided to tackle what likely would be the most complicated bit: the code for 3 or more items. This code makes multiple method calls (including a chained one), as well as combining everything with a chained infix operator.

The method calls were fairly straightforward, but she thought about what the multiple ~ operators would be handled. As it turns out, it would actually require being set up as if (($a ~$b) ~ $c) ~$d, etc., and the elf didn’t really like the idea of having ultimately intending her code that much. She also thought about just using join on a list that she could make, but she already knew how to do method calls, so she thought she’d try something cool: reduction operators (think [~] $a,$b, $c,$d for the previous). This uses the RakuAST::Term::Reduce node that takes a simple list of arguments. For the * - 2 syntax, to avoid getting too crazy, she treated it as if it had been written as the functionally identical @list - 2.

Becaused that reduction bit has some many elements, she ending up breaking things into pieces: the initial item, the special first infix, a merged set of the second to penultimate items joined with the common infix, the special final infix, and the final item. For a list like [1,2,3,4,5] in English, that amounts to 1 (initial item), ,  (first infix), 2, 3, 4 (second to penultimate, joined with , ), , and  (final infix) and 5 (final item). In other languages, the first and repeated infixes may be different, and in others, all three may be identical.

# @list.head
my $more-first-item = RakuAST::ApplyPostfix.new( operand => RakuAST::Var::Lexical.new('@list'), postfix => RakuAST::Call::Method.new( name => RakuAST::Name.from-identifier('head') ) ); # @list[1, * - 2].join($more-middle-infix)
my $more-mid-items = RakuAST::ApplyPostfix.new( # @list[1, @list - 2 operand => RakuAST::ApplyPostfix.new( operand => RakuAST::Var::Lexical.new('@list'), postfix => RakuAST::Postcircumfix::ArrayIndex.new( # (1 .. @list - 2) RakuAST::SemiList.new( RakuAST::ApplyInfix.new( left => RakuAST::IntLiteral.new(1), infix => RakuAST::Infix.new('..'), # @list - 2 right => RakuAST::ApplyInfix.new( left => RakuAST::Var::Lexical.new('@list'), infix => RakuAST::Infix.new('-'), right => RakuAST::IntLiteral.new(2) ) ) ) ) ), # .join($more-middle-infix)
postfix => RakuAST::Call::Method.new(
name => RakuAST::Name.from-identifier('join'),
args => RakuAST::ArgList.new(
RakuAST::StrLiteral.new($more-middle-infix) ) ) ); # @list.tail my$more-final-item = RakuAST::ApplyPostfix.new(
operand => RakuAST::Var::Lexical.new('@list'),
postfix => RakuAST::Call::Method.new(
name => RakuAST::Name.from-identifier('tail')
)
);

# [~] ...
my $more = RakuAST::Term::Reduce.new( infix => RakuAST::Infix.new('~'), args => RakuAST::ArgList.new($more-first-item,
RakuAST::StrLiteral.new($more-first-infix),$more-mid-items,
RakuAST::StrLiteral.new($more-final-infix),$more-final-item,
)
);

As one can note, as RakuAST code starts getting more complex, it can be extremely helpful to store interim pieces into variables. For complex programs, some RakuAST users will create functions that do some of the verbose stuff for them. For instance, one might get tired of the code for an infix, and write a sub like

sub rast-infix($left,$infix, $right) { RakuAST::ApplyInfix.new: left =>$left,
infix => RakuAST::Infix.new($infix), right =>$right
}

to enable code like rast-infix($value, '+',$value) which ends up being much less bulky. Depending on what they’re doing, they might make a sub just for adding two values, or maybe making a list more compactly.

In any case, the hard working elf had now programmatically defined all of the formatter code. All that was left was for her to piece together the number logic and she’d be done. That logic was, in practice, quite simple:

if @list > 2 { $more } elsif @list == 2 {$two }
elsif @list == 1 { $one } else {$none } 

In practice, there was still a bit of a learning curve. Why? As it turns out, the [els]if statements are actually officially expressions, and need to be wrapped up in an expression block. That’s easy enough, she could just use RakuAST::Statement::Expression. Her conditions end up being coded as

# @list > 2
my $more-than-two = RakuAST::Statement::Expression.new( expression => RakuAST::ApplyInfix.new( left => RakuAST::Var::Lexical.new('@list'), infix => RakuAST::Infix.new('>'), right => RakuAST::IntLiteral.new(2) ) ); # @list == 2 my$exactly-two = RakuAST::Statement::Expression.new(
expression => RakuAST::ApplyInfix.new(
left => RakuAST::Var::Lexical.new('@list'),
infix => RakuAST::Infix.new('=='),
right => RakuAST::IntLiteral.new(2)
)
);

# @list == 1
my $exactly-one = RakuAST::Statement::Expression.new( expression => RakuAST::ApplyInfix.new( left => RakuAST::Var::Lexical.new('@list'), infix => RakuAST::Infix.new('=='), right => RakuAST::IntLiteral.new(1) ) );  That was simple enough. But now sure realized that the then statements were not just the simple code she had made, but were actually a sort of block! She would need to wrap them with a RakuAST::Block. A block has a required RakuAST::Blockoid element, which in turn has a required RakuAST::Statement::List element, and this in turn will contain a list of statements, the simplest of which is a RakuAST::Statement::Expression that she had already seen. She decided to try out the technique of writing a helper sub to do this: sub wrap-in-block($expression) {
RakuAST::Block.new(
body => RakuAST::Blockoid.new(
RakuAST::StatementList.new(
RakuAST::Statement::Expression.new(:$expression) ) ) ) }$more = wrap-in-block $more;$two  = wrap-in-block $two;$one  = wrap-in-block $one;$none = wrap-in-block $none;  Phew, that was a pretty easy way to handle some otherwise very verbose coding. Who knew Raku hid away so much complex stuff in such simple syntax?! Now that she had both the if and then statements finished, she was ready to finish the full conditional: my$if = RakuAST::Statement::If.new(
condition => $more-than-two, then =>$more,
elsifs => [
RakuAST::Statement::Elsif.new(
condition => $exactly-two, then =>$two
),
RakuAST::Statement::Elsif.new(
condition => $exactly-one, then =>$one
)
],
else => $none );  All that was left was for her to wrap it up into a Routine and she’d be ready to go! She decided to put it into a PointyBlock, since that’s a sort of anonymous function that still takes arguments. Her fully-wrapped code block ended up as: my$code = RakuAST::PointyBlock.new(
signature => RakuAST::Signature.new(
parameters => (
RakuAST::Parameter.new(
target => RakuAST::ParameterTarget::Var.new('@list'),
slurpy => RakuAST::Parameter::Slurpy::SingleArgument
),
),
),
body => RakuAST::Blockoid.new(
RakuAST::StatementList.new(
RakuAST::Statement::Expression.new(
expression => $if ) ) ) );  Working with RakuAST, she really got a feel for how things worked internally in Raku. It was easy to see that a runnable code block like a pointy block consisted of a signature and a body. That signature had a list of parameters, and the body a list of statements. Seems obvious, but it can be enlightening to see it spread out like she had it. The final step was for her actually evaluate this (now much safer!) code. For that, nothing changed. In fact, the entire rest of her block was simply sub generate-list-formatter($language, $type,$length) {
use Intl::CLDR;
my $pattern = cldr{$lang}.list-patterns{$type}{$length};
my $two-infix =$pattern.two.substr: 3, *-3;
my $more-first-infix =$pattern.start.substr: 3, *-3;
my $more-middle-infix =$pattern.middle.substr: 3, *-3;
my $more-final-infix =$pattern.end.substr: 3, *-3;

...

use MONKEY-SEE-NO-EVAL;

## Raku Advent Calendar: Day 22: He’s making a list… (part 1)

If there’s anything that Santa and his elves ought to know, it’s how to make a list. After all, they’re reading lists that children send in, and Santa maintains his very famous list. Another thing we know is that Santa and his elves are quite multilingual.

So one day one of the elfs decided that, rather than hand typing out a list of gifts based on the data they received (requiring elves that spoke all the world’s languages), they’d take advantage of the power of Unicode’s CLDR (Common Linguistic Data Repository). This is Unicode’s lesser-known project. As luck would have it, Raku has a module providing access to the data, called Intl::CLDR. One elf decided that he could probably use some of the data in it to automate their list formatting.

He began by installing Intl::CLDR and played around with it in the terminal. The module was designed to allow some degree of exploration in a REPL, so the elf did the following after reading the provided read me:



# Repl response

use Intl::CLDR;          # Nil

my $english = cldr<en> # [CLDR::Language: characters,context-transforms, # dates,delimiters,grammar,layout,list-patterns, # locale-display-names,numbers,posix,units]  The module loaded up the data for English and the object returned had a neat gist that provides information about the elements it contains. For a variety of reasons, Intl::CLDR objects can be referenced either as attributes or as keys. Most of the time, the attribute reference is faster in performance, but the key reference is more flexible (because let’s be honest, $english{$foo} looks nicer than $english."$foo"(), and it also enables listy assignment via e.g. $english<grammar numbers>).

In any case, the elf saw that one of the data points is list-patterns, so he explored further:



# Repl response

$english.list-patterns; # [CLDR::ListPatterns: and,or,unit]$english.list-patterns.and;             # [CLDR::ListPattern: narrow,short,standard]

$english.list-patterns.standard; # [CLDR::ListPatternWidth: end,middle,start,two]$english.list-patterns.standard.start;  # {0}, {1}

$english.list-patterns.standard.middle; # {0}, {1}$english.list-patterns.standard.end;    # {0}, and {1}

$english.list-patterns.standard.two; # {0} and {1}  Aha! He found the data he needed. List patterns are catalogued by their function (and-ing them, or-ing them, and a unit one designed for formatting conjoined units such as 2ft 1in or similar). Each pattern has three different lengths. Standard is what one would use most of the time, but if space is a concern, some languages might allow for even slimmer formatting. Lastly, each of those widths has four forms. The two form combines, well, two elements. The other three are used to collectively join three or more: start combines the first and second element, end combines the penultimate and final element, and middle combines all second to penultimate elements. He then wondered what this might look like for other languages. Thankfully, testing this out in the repl was easy enough:  my &and-pattern = { cldr{$^language}.list-patterns-standard<start middle end two>.join: "\t"'" }

# Repl response (RTL corrected, s/\t/' '+/)

and-pattern 'es'  # {0}, {1}    {0}, {1}    {0} y {1}    {0} y {1}

and-pattern 'ar'  # ‮{0} و{1}     {0} و{1}    {0} و{1}    {0} و{1}

and-pattern 'ko'  # {0}, {1}    {0}, {1}    {0} 및 {1}    {0} 및 {1}

and-pattern 'my'  # {0} - {1}   {0} - {1}   {0}နှင့် {1}    {0}နှင့် {1}

and-pattern 'th'  # {0} {1}     {0} {1}     {0} และ{1}   {0}และ{1}



He quickly saw that there was quite a bit of variation! Thank goodness someone else had already catalogued all of this for him. So he went about trying to create a simple formatting routine. To begin, he created a very detailed signature and then imported the modules he’d need.



#| Lengths for list format.  Valid values are 'standard', 'short', and 'narrow'.

subset ListFormatLength of Str where <standard short narrow>;

#| Lengths for list format.  Valid values are 'and', 'or', and 'unit'.

subset ListFormatType of Str where <standard short narrow>;

use User::Language;     # obtains default languages for a system

use Intl::LanguageTag;  # use standardized language tags

use Intl::CLDR;         # accesses international data

#| Formats a list of items in an internationally-aware manner

sub format-list(

+@items,                   #= The items to be formatted into a list

LanguageTag()    :$language = user-language #= The language to use for formatting ListFormatLength :$length   = 'standard',   #= The formatting width

ListFormatType   :$type = 'and' #= The type of list to create ) { ... ... ... }  That’s a bit of a big bite, but it’s worth taking a look at. First, the elf opted to use declarator POD wherever it’s possible. This can really help out people who might want to use his eventual module in an IDE, for autogenerating documentation, or for curious users in the REPL. (If you type in ListFormatLength.WHY, the text “Lengths for list format … and ‘narrow’” will be returned.) For those unaware of declarator POD, you can use either #| to apply a comment to the following symbol declaration (in the example, for the subset and the sub itself), or #= to apply it to the preceeding symbol declaration (most common with attributes). Next, he imported two modules that will be useful. User::Language detects the system language, and he used it to provide sane defaults. Intl::LanguageTag is one of the most fundamental modules in the international ecosystem. While he wouldn’t strictly need it (we’ll see he’ll ultimately only use them in string-like form), it helps to ensure at least a plausible language tag is passed. If you’re wondering what the +@items means, it applies a DWIM logic to the positional arguments. If one does format-list @foo, presumably the list is @foo, and so @items will be set to @foo. On the other hand, if someone does format-list$foo, $bar,$xyz, presumably the list isn’t $foo, but all three items. Since the first item isn’t a Positional, Raku assumes that $foo is just the first item and the remaining positional arguments are the rest of the items. The extra () in LanguageTag() means that it will take either a LanguageTag or anything that can be coerced into one (like a string).

Okay, so with that housekeeping stuff out of the way, he got to coding the actual formatting, which is devilishly simple:



my $format = cldr{$language}.list-format{$type}{$length};

my ($start,$middle, $end,$two) = $format<start middle end two>; if @items > 2 { ... } elsif @items == 2 { @items[0] ~$two ~ @items[1] }

elsif @items == 1 { @items.head                  }

else              { ''                           }



He paused here to check and see if stuff would work. So he ran his script and added in the following tests:



# output

format-list <>,    :language<en>; # ''

format-list <a>,   :language<en>; # 'a'

format-list <a b>, :language<en>; # 'a{0} and {1}b'



While the simplest two cases were easy, the first one to use CLDR data didn’t work quite as expected. The elf realized he’d need to actually replace the {0} and {1} with the item. While technically he should use subst or similar, after going through the CLDR, he realized that all of them begin with {0} and end with {1}. So he cheated and changed the initial assignment line to



my $format = cldr{$language}.list-format{$type}{$length};

my ($start,$middle, $end,$two) = $format<start middle end two>.map: *.substr(3, *-3);  Now he his two-item function worked well. For the three-or-more condition though, he had to think a bit harder how to combine things. There are actually quite a few different ways to do it! The simplest way for him was to take the first item, then the $start combining text, then join the second through penutimate, and then finish off with the $end and final item:  if @items > 2 { ~$items[0]

~ $start ~$items[1..*-2].join($middle) ~$end

~ $items[*-1] } elsif @items == 2 { @items[0] ~$two ~ @items[1] }

elsif @items == 1 { @items.head                  }

else              { ''                           }



Et voilà! His formatting function was ready for prime-time!



# output

format-list <>,        :language<en>; # ''

format-list <a>,       :language<en>; # 'a'

format-list <a b>,     :language<en>; # 'a and b'

format-list <a b c>,   :language<en>; # 'a, b, and c'

format-list <a b c d>, :language<en>; # 'a, b, c, and d'



Perfect! Except for one small problem. When they actually started using this, the computer systems melted some of the snow away because it overheated. Every single time they called the function, the CLDR database needed to be queried and the strings would need to be clipped. The elf had to come up with something to be a slight bit more efficient.

He searched high and wide for a solution, and eventually found himself in the dangerous lands of Here Be Dragons, otherwise known in Raku as EVAL. He knew that EVAL could potentially be dangerous, but that for his purposes, he could avoid those pitfalls. What he would do is query CLDR just once, and then produce a compilable code block that would do the simple logic based on the number of items in the list. The string values could probably be hard coded, sparing some variable look ups too.

### There be dragons here

EVAL should be used with great caution. All it takes is one errant unescaped string being accepted from an unknown source and your system could be taken. This is why it requires you to affirmatively type use MONKEY-SEE-NO-EVAL in a scope that needs EVAL. However, in situations like this, where we control all inputs going in, things are much safer. In tomorrow’s article, we’ll discuss ways to do this in an even more safer manner, although it adds a small degree of complexity.

### Back to the regularly scheduled program

To begin, the elf imagined his formatting function.



sub format-list(+@items) {

if    @items  > 2 { @items[0] ~ $start ~ @items[1..*-2].join($middle) ~ $end ~ @items[*-1] } elsif @items == 2 { @items[0] ~$two ~ @items[1] }

elsif @items == 1 { @items[0] }

else              { '' }

}



That was … really simple! But he needed this in a string format. One way to do that would be to just use straight string interpolation, but he decided to use Raku’s equivalent of a heredoc, q:to. For those unfamiliar, in Raku, quotation marks are actually just a form of syntactic sugar to enter into the Q (for quoting) sublanguage. Using quotation marks, you only get a few options: ' ' means no escaping except for \\, and using " " means interpolating blocks and $-sigiled variables. If we manually enter the Q-language (using q or Q), we get a LOT more options. If you’re more interested in those, you can check out Elizabeth Mattijsen’s 2014 Advent Calendar post on the topic. Our little elf decided to use the q:s:to option to enable him to keep his code as is, with the exception of having scalar variables interpolated. (The rest of his code only used positional variables, so he didn’t need to escape!)  my$format = cldr{$language}.list-format{$type}{$length}; my ($start, $middle,$end, $two) =$format<start middle end two>;

my $code = q:s:to/FORMATCODE/; sub format-list(+@items) { if @items > 2 { @items[0] ~$start ~ @items[1..*-2].join($middle) ~$end ~ @items[*-1] }

elsif @items == 2 { @items[0] ~ $two ~ @items[1] } elsif @items == 1 { @items[0] } else { '' } } FORMATCODE EVAL$code;



The only small catch is that he’d need to get a slightly different version of the text from CLDR. If the text  and  were placed verbatim where $two is, that block would end up being @items[0] ~ and ~ @items[1] which would cause a compile error. Luckily, Raku has a command here to help out! By using the .raku function, we get a Raku code form for most any object. For instance:  # REPL output 'abc'.raku # "abc" "abc".raku # "abc" <a b c>.raku # ("a", "b", "c")  So he just changed his initial assignment line to chain one more method (.raku):  my ($start, $middle,$end, $two) =$format<start middle end two>.map: *.substr(3,*-3).raku;



Now his code worked. His last step was to find a way to reuse it to benefit from this initial extra work.He made a very rudimentary caching set up (rudimentary because it’s not theoretically threadsafe, but even in this case, since values are only added, and will be identically produced, there’s not a huge problem). This is what he came up with (declarator pod and type information removed):



sub format-list (+@items, :$language 'en', :$type = 'and', :$length = 'standard') { state %formatters; my$code = "$language/$type/$length"; # Get a formatter, generating it if it's not been requested before my &formatter = %cache{$code}

// %cache{$code} = generate-list-formatter($language, $type,$length);

formatter @items;

}

sub generate-list-formatter($language,$type, $length --> Sub ) { # Get CLDR information my$format = cldr{$language}.list-format{$type}{$length}; my ($start, $middle,$end, $two) =$format<start middle end two>.map: *.substr(3,*-3).raku;

# Generate code

my $code = q:s:to/FORMATCODE/; sub format-list(+@items) { if @items > 2 { @items[0] ~$start ~ @items[1..*-2].join($middle) ~$end ~ @items[*-1] }

elsif @items == 2 { @items[0] ~ $two ~ @items[1] } elsif @items == 1 { @items[0] } else { '' } } FORMATCODE # compile and return use MONKEY-SEE-NO-EVAL; EVAL$code;

}



And there he was! His function was all finished. He wrapped it up into a module and sent it off to the other elves for testing:



format-list <apples bananas kiwis>, :language<en>;      # apples, bananas, and kiwis

format-list <apples bananas>, :language<en>, :type<or>; # apples or bananas

format-list <manzanas plátanos>, :language<es>;         # manzanas y plátanos

format-list <انارها زردآلو تاریخ>, :language<fa>;       # انارها، زردآلو، و تاریخ



Hooray!

Shortly thereafter, though, another elf took up his work and decided to go even crazier! Stay tuned for more of the antics from Santa’s elves how they took his lists to another level.

## Elizabeth Mattijsen: Role playing

This is the third part of the "A gaze of iterators!" series.

## By any other name

Let's look at the names of the iterators in part 2. For this, I'm going to use the .^name method. As we've seen before, .^foo means "calling the .foo method on the object's meta-object".

say <a b c>.iterator.^name;
# Rakudo::Iterator::ReifiedListIterator
say (1..*).iterator.^name;
# Rakudo::Iterator::IntRangeUnending
say (1..10).pick(*).iterator.^name;
# List::PickN
say (1..10).map({++$_}).iterator.^name; # Any::IterateOneWithoutPhasers  As you can see, the names of the classes of these iterator objects are all over the place. But they all provide the same interface: being able to call methods such as .pull-one, .push-all and .sink-all. In many programming languages, you'd expect all of these classes to be sharing the same parent class. In the Raku Programming Language you can indeed inherit from a parent class. You can check the lineage of a class with the .^mro method (for method resolution order). say <a b c>.iterator.^mro; # ((ReifiedListIterator) (Any) (Mu)) say (1..*).iterator.^mro; # ((IntRangeUnending) (Any) (Mu)) say (1..10).pick(*).iterator.^mro; # ((PickN) (Any) (Mu)) say (1..10).map({++$_}).iterator.^mro;
# ((IterateOneWithoutPhasers) (Any) (Mu))


That is odd? They all seem to inherit from Any and Mu? Yet, one can not call the .pull-one method on every object that just inherits from Any and Mu:

say 42.^mro;
# ((Int) (Cool) (Any) (Mu))
say 42.pull-one;
# No such method 'pull-one' for invocant of type 'Int'


## Role playing

The Raku Programming Language also provides a thing called "roles". In short, you could think of a role as a collection of methods that will be "implanted" into a class if the class itself does not provide a method implementation for it.

All of these iterator classes that we've seen here, actually do the Iterator role. And just as with the ^.mro, you can introspect which roles a class performs by calling the .^roles method. Let's see how that works out here:

say <a b c>.iterator.^roles;
# ((PredictiveIterator) (Iterator))
say (1..*).iterator.^roles;
# ((Iterator))
say (1..10).pick(*).iterator.^roles;
# ((Iterator))
say (1..10).map({++$_}).iterator.^roles; # ((SlippyIterator) (Iterator))  So it looks like some classes are actually playing more than one role. But they all also do the Iterator role, it looks like. ## How to be an iterator To make a class be an iterator, one must tell the class to do the Iterator role. That's pretty simple, no? Let's start with an empty class that just wants to be an iterator. You do that by using does: class Foo does Iterator { } ===SORRY!=== Error while compiling -e Method 'pull-one' must be implemented by Foo because it is required by roles: Iterator.  So we need to actually provide some type of implementation for the interface that the Iterator role is providing. Ok, so let's make a very simple method pull-one that will randomly return True or False: class TrueFalse does Iterator { method pull-one() { Bool.roll } } say TrueFalse.pull-one; # True | False  The .roll method randomly picks a single value from a set of values. When called on an enum, it will randomly select one of the enums values. And the Bool enum has True and False as its values. Of course, this is all very boring, let's make it more interesting: class YeahButNoBut does Iterator { method pull-one() { Bool.roll ?? "Yeah but" !! "No but" } } say YeahButNoBut.pull-one; # Yeah but | No but  So we now have a class that produces an iterator. But how would you actually use that in any "normal" way in your program? Well, by embedding the iterator into another class, and have a method .iterator in it that returns the iterator class: class Jabbering { method iterator() { my class YeahButNoBut does Iterator { method pull-one() { Bool.roll ?? "Yeah but" !! "No but" } } } }  Note here that the .iterator method actually returns the class object itself (usually referred to as the "type object"). Why? Because that's all we need from this iterator class: in its current form, this class doesn't need to keep any state. Also note that classes in the Raku Programming Language can be lexically scoped by prefixing them with my, just as you would lexically scoped variables. This makes sense in this case, as there would be no need for the iterator class outside of the scope of the "Jabbering" class. So how would this look with by .^name, ^.mro and .^roles, as we've shown with all of the other iterators? say Jabbering.iterator.^name; # Jabbering::YeahButNoBut say Jabbering.iterator.^mro; # ((YeahButNoBut) (Any) (Mu)) say Jabbering.iterator.^roles; # ((Iterator))  As you can see, the Jabbering class iterator has the expected name. And the Jabbering class inherits from Any and Mu, and performs the Iterator role. So with all of this out of the way, now you can start jabbering! .say for Jabbering;  Hmmm... that doesn't stop now, does it? Indeed it doesn't. As to why, that's for the next instalment in this series! ## Conclusion This concludes the third part of the series, in which the concept of roles in the Raku Programming Language is introduced, along with does. And that you can alter the scope of a class by prefixing it with my. Questions and comments are always welcome. You can also drop into the #raku-beginner channel on Libera.chat, or on Discord if you'd like to have more immediate feedback. I hope you liked it! Thank you for reading all the way to the end. ## Elizabeth Mattijsen: Setting up your haystack ### Published by Elizabeth Mattijsen on 2022-11-18T12:36:32 This blog post will discuss the ways you can specify where to look for matches with rak as part 4 of the It's time to rak! blog series. ## From here on down As we've seen in the earlier instalments, you can very easily search for a string using a literal string or a Raku regex in all files that look like they contain text. # look for "foo" in all files$ rak foo

# Search for "foo" in files of the "lib" directory
$rak foo lib  And we've seen we can also limit the search to a single file: # Look for "ve" anywhere on any line in file "twenty"$ rak --type=contains ve twenty


These are three of the very basic ways to specify where to search: the first by not specifying anything, which implies all files in the current directory and any subdirectories of which the name does not start with a period.

The second basically being the same as the first, but starting from the "lib" directory on down, rather than from the current directory.

The third being the specification of a single file to look in. And that single file does not need to exist on the local filesystem! It can also be a URL. Let's look for the word "reading" in the first blog post of this series:

$rak §reading https://dev.to/lizmat/its-time-to-rak-part-1-30ji https://dev.to/lizmat/its-time-to-rak-part-1-30ji 598:aria-label="Add to 𝐫𝐞𝐚𝐝𝐢𝐧𝐠 list" 954:<p>Thank you for 𝐫𝐞𝐚𝐝𝐢𝐧𝐠 all the way to the end!</p>  What this basically does is to download the indicated resource (courtesy of curl) into a temporary file, search in that while keeping the original URL as "the filename", and remove the file automatically when it's done. ## Actually only two If you look at the above, then you realize that there are actually only two types of specification: a directory or a file (which could be local or remote). And that a directory will be recursed into to look for files to include in the search. The search for files in a directory, and its subdirectories, can be influenced by 40 different arguments. This blog post will not mention all of them. You can do: # produce extensive help on filesystem filters$ rak --help=filesystem --pager=less


to get a more in-depth description of the logic for each of the options should you need a feature that is not covered in this blog post. The --pager argument is to let you more easily scroll the extensive text, but is of course not necessary.

What should be noted here is that these filesystem filters are only applied on subdirectories and files in those subdirectories. So not on any directory or file that you specify directly.

But beware! Many shells auto-expand anything you specify on the command line if they can: and these will be considered to be directly specified by rak, as it does not have a way to distinguish between what you typed and what the shell expanded it to. For example:

# Search all files and all subdirectories
$rak foo *  The * in the shell will effectively do ls -d *. In practice, this is almost the same as not specifying anything at all. But with one subtle difference: none of the filesystem filters will be applied to what the shell expanded to. Whereas if you would not specifying anything (or . to indicate the current directory), the filesystem filters would be applied, because you (implicitely) specified only the current directory. So only the current directory would be exempt from filesystem filters. ## At the base The two most important filesystem filters are --file and --dir. They expect a piece of code that will be given the basename of a file or a directory, and which should return a trueish value to allow the file / directory to be accepted. And they can also be specified as a flag: --file for unconditional acceptance, and --/dir for unconditional denial (which can be handy if you do not want recursion into subdirectories). By default, --file and dir='!.starts-with(".")' are assumed. Which effectively means, don't recurse into directories that start with a period, and accept all files in any other directory. To make it easier for you to specify files given by one or more extensions, you can use the --extensions argument: # Only accept files with the .bat extension$ rak foo --extensions=bat


As the name of the argument implies, you can specify multiple extensions, separated by comma's:

# Only accept files with the .bat or the .ps1 extension
$rak foo --extensions=bat,ps1  It's also possible to only accept files without extension with the --extensions argument by just not specifying any actual extension: # Only accept files without extension$ rak foo --extensions=


You can also specify one of the predefined groups of extensions. For instance, if you would like to only include Raku and Markdown files in your search, you can do:

# Only accept Raku and Markdown files
$rak foo --extensions=#raku,#markdown  Note that the groups of extensions are prefixed with #. To get an up-to-date list of extension groups: # List all known extensions # rak --list-known-extensions  If there is no argument specified related to the basename of the file (any of the above here), then the content of each file will be checked to see if it looks like it contains text. Only if it looks like that, will it actually be included. ## More peripherally The rest of the filesystem filter arguments can be roughly divided into the following groups: by content, epoch, owner / group, numeric meta value, external program and by filesystem attribute. Again, you can see all of the needed information about these by doing: # produce extensive help on filesystem filters$ rak --help=filesystem


In any case, the end result of all of these filters is an internal list of files that will be checked for the pattern. You could think of this list as the haystack, and the pattern as the proverbial needle, as it were.

## More on the haystack

Apart from specifying paths after the pattern, there is also a --paths argument. This is supposed to contain a comma separated list of paths. So these two invocations are equivalent:

# Search in the "lib" and "doc" directories
$rak foo lib doc$ rak foo --paths=lib,doc


The --paths argument allows you to save a set of paths with a shortcut (as we've seen in Customizing your options).

You can also store filenames and/or paths in a file, and specify that file to be taken as the haystack specification: the --paths-from=filename and --files-from=filename arguments. Each line of the specified file will be taken as either a file or path specification. The difference in handling is that if a file is specified on a line with --paths-from, it is accepted. If a directory is specified on a line with --files-from, then it will be ignored as not being a file. And either of these take "-" (aka a single hyphen) to mean to read from STDIN.

For open source developers, the --under-version-control argument may be of use. When used in a git repository, it will set up the haystack with all the files that are under version control.

More extensive help on these and other haystack arguments can be obtained by doing:

# produce extensive help on haystack specification
$rak --help=haystack  ## Twisting the haystack There is one argument that converts the haystack into a list with the paths of all the files in the haystack: --find. It changes the list of targets into a target itself, if you will. So instead of looking for the pattern in the contents of the files of the haystack, you'd be looking in the names of the files instead. # Show all filenames that have "lib" in their name$ rak --find lib


And if you just want a list of filenames, you can omit the pattern altogether:

# Show all filenames from current directory on down
$rak --find  And what if you would just like to see the names of directories instead of files? Well, that'd be only legal way to use the --file argument as a negator: # Show all directory names from current directory down$ rak --find --/file


In any case, the --find argument is named after the Unix find command. I thought I'd mention that, if that wasn't clear just yet.

## Conclusion

This concludes part 4 of a series of blog posts about rak.

It shows how you can instruct rak where to look for matches, to create a haystack if you will. By applying different acceptance rules for files and subdirectories, for instance by looking at extensions. It also shows how you can twist the haystack to just show filenames or the names of directories.

If you have any comments, find bugs, have recommendations / ideas, please submit them as issues at the App::Rak repository. If you would like to have a more direct interaction, you can visit the #raku-rak channel on Libera.chat.

Thank you (again) for reading all the way to the end!

## Elizabeth Mattijsen: Pushing the limits

This is the second part of the "A gaze of iterators!" series.

## Pushing the limits

So let's again look at the list of methods the iterator on 42 has:

.say for 42.iterator.^methods'
# new
# pull-one
# push-exactly
# push-all
# skip-one
# skip-at-least
# count-only
# sink-all
# bool-only
# push-until-lazy
# push-at-least
# skip-at-least-pull-one
# is-lazy
# is-deterministic
# BUILDALL


Hmmm... .push-all looks interesting. Could it really be that simple? Let's see!

my @array;
42.iterator.push-all(@array);
say @array;  # [42]


And how about a list of values?

my @array;
<a b c>.iterator.push-all(@array);
say @array;  # [a b c]


Looking at this, you could realize that the above is just a convoluted way to write:

my @array = <a b c>;
say @array;  # [a b c]


And you'd be right again! And now you have a better idea of what goes on under the hood. Well, at least conceptually, because the actual implementation is of course at liberty to take short-cuts to improve efficiency.

## Don't like that one

The next method on that list is .skip-one. Looks like it's mostly like .pull-one, so let's check:

my $iter = <a b c>.iterator; say$iter.skip-one; # 1
say $iter.pull-one; # b say$iter.pull-one; # c
say $iter.skip-one; # 0  So it looks like .skip-one really skips a value. But it also returns something? Indeed, it returns either 1 to indicate a successful skip (in this case "a" got skipped", and 0 for an unsuccessful skip (in this case because there is no value after "c"). Why not True and False you might ask? Well, these are all methods that work under the hood as efficiently as possible, and turning a native integer 1 into a boolean True would just be extra and unnecessary work. # What is it Those two .is-lazy and .is-deterministic methods also look interesting: say 42.iterator.is-lazy; # False say 42.iterator.is-deterministic; # True  The .is-lazy method indicates whether the iterator is lazy or not. In hindsight, the term "lazy" was probably a bad choice. The most obvious thing about "lazy" iterators, is that you cannot calculate the number of elements it will produce. So the term "countable" (while reversing the meaning of the returned value) would probably have been better. say (1..*).iterator.is-lazy; # True  is an example of a "lazy" iterator, of which you can not count the number of elements in a range of integers from 1 to infinity. If you try to do that with the .elems method, you will get an error: say (1..*).elems; # Cannot .elems a lazy list  If it wouldn't produce the error, it would hang because it would be producing values "ad infinitum" literally! Before being able to tell you the number of elements. The .is-deterministic method indicates whether the iterator, given a certain source, will always produce the same values in the same order. The Raku internals can optimize certain situations if it knows whether the produced values will always be the same. say (1..10).iterator.is-deterministic; # True say (1..10).pick(*).iterator.is-deterministic; # False  Note the .pick method will produce the given values in a random order, so clearly not deterministic! say (1..10).pick(*); # (7 1 9 2 4 5 10 8 3 6) say (1..10).pick(*); # (4 6 8 9 2 10 7 5 1 3)  ## That's weird What does .BUILDALL do? Actually, nothing that should concern you. The ALLCAPS of the method really indicates that there is something special going on! The .BUILDALL method is a method that is automatically generated for every class, and it contains the default logic to initialize an object of that class. There is no source code for it: the definition of a class determines how that method will be directly generated into executable bytecode. ## What are you sinking about? Now, the other methods all have names that make sense, probably. But there is one method that appears to be different: .sink-all. Let's see what happens if we call that: say <a b c>.iterator.sink-all; # IterationEnd  That's informational? Not! But what did it do? In this particular case, it will mark the iterator as completed. Not very useful. But there are other (very common) cases where calling the .sink-all method is very useful. Remember that a for loop is really just a .map of which the results are discarded? my$seen = 0;
(1..10).map({++$seen}).iterator.sink-all; say$seen;  # 10


The .sink-all method is used internally for those iterators that are just executed for their side-effects. So the above is just a very complicated way to write:

my $seen = 0; ++$seen for 1..10;
say $seen; # 10  The term "sink" is the Raku equivalent for what other programming languages call "void context". But more about that later in this series. ## Conclusion This concludes the second part of the series, in which most of the other methods that you can call on an iterator have been explained. Specifically the .skip-one, .push-all, .is-lazy, .is-deterministic and .sink-all methods. With a side-order of .pick. Questions and comments are always welcome. You can also drop into the #raku-beginner channel on Libera.chat, or on Discord if you'd like to have more immediate feedback. I hope you liked it! Thank you for reading all the way to the end. ## Elizabeth Mattijsen: A gaze of iterators! ### Published by Elizabeth Mattijsen on 2022-11-11T10:36:12 This blog post provides an introduction to iterators in the Raku Programming Language. It requires some basic understanding of Raku code. One could consider the Don't fear the grepper! series as a prerequisite for this series of blog posts. ## Iterator Central Iterators are at the basis of every type of iteration in the Raku Programming Language, except for loop (which uses a counter, or iterates indefinitely), while and until (which iterate while a condition is True / False). Iterators are everywhere in Raku: all values and classes support having the iterator method called on them. say 42.iterator; # Rakudo::Iterator::ReifiedListIterator.new  So why would that say what it does? Well, as we've seen before, the say subroutine will call the .gist method on whatever it got. And the default (inherited) .gist method on instances of classes shows the name of the class and how it could possibly be created. Same for any other class, even one of your own: class Foo { } say Foo.new; # Foo.new  ## What can you use an iterator for? Good question! Actually, in general you wouldn't be using an iterator yourself in your code. You would provide Raku with an iterator (usually implicitly), and let that do the work for you. In general. But to get the feel of what an iterator can do, we're going to tinker with iterators a bit in this series of blog posts. Just to get a feel of what is going on under the hood, as it were. ## Looking on the inside First of all, one would like to know which methods you can call on an iterator object. Fortunately, the Raku Programming Language has many introspection capabilities. One of them is the .methods method on the meta-object of the iterator. Don't think too much about that at this point: just know that there's a special syntax for calling a method on the meta-object of an object: .^method-name. So let's see what the iterator on 42 can do: .say for 42.iterator.^methods; # new # pull-one # push-exactly # push-all ...  .new? There's nothing new about that? Well, yes and no. The fact that it is listed here, means that the class has its own (not inherited) method new. Whether that is useful information, is up to the reader! The next one is .pull-one. Let's see what happens if we call that on the iterator object: say 42.iterator.pull-one; # 42  I guess you could hardly call that surprising. But what happens if you would call the .pull-one method for a second time? my$iter = 42.iterator;
say $iter.pull-one; # 42 say$iter.pull-one; # IterationEnd


Hmmmm... what's this IterationEnd you say? It's a very special sentinel value that indicates that the iterator is done producing values. And that you should not call any methods on the iterator anymore (as the results will be undefined).

Ok, so let's try this again, this time with a small list of values:

my $iter = <a b c>.iterator; say$iter.pull-one; # a
say $iter.pull-one; # b say$iter.pull-one; # c
say $iter.pull-one; # IterationEnd  Or, shorter: my$iter = <a b c>.iterator;
say $iter.pull-one for ^4; # a # b # c # IterationEnd  ## Pulling until it's done Now that we know that the final value is IterationEnd, we should be able to write a loop checking for that value, right? And end the loop on that? Indeed we can! But it requires some special care: my$iter = <a b c>.iterator;
until ($_ :=$iter.pull-one) =:= IterationEnd {
.say;
}
# a
# b
# c


That's maybe a lot of colons all of a sudden!

The first colon is in := which is the binding operator. It aliases the left side with the right side: in this case with the topic $_. It's to make sure that we're going to compare the actual value directly, rather than a value in a variable (as values in variables may actually appear differently to the outside world, if they want to). The second one is the =:=, the identity operator. It checks whether both sides refer to the same item in memory. Whether they are really the same object. Looking at this code, you might realize that this is actually a convoluted way to write: for <a b c> { .say }  And you'd be right: what you see above is more or less essentially what is happening under the hood. Of course, reality is a bit more complicated: in this example we for instance didn't account for handling any loop phasers (FIRST, NEXT and LAST). But the basic principle is the same! And because every value and every class can take a call to the iterator method, you should understand now why this works: for 42 { .say } # 42  Indeed, because you can call the .iterator method on any class or object! ## Conclusion This concludes the first part of the introduction to iterators, and possibly to the Raku Programming Language. It introduced the iterator and ^methods methods, as well as the pull-one method and the special IterationEnd sentinel value for iterators. And it casually introduced the := binding operator and the =:= identity operator. Questions and comments are always welcome. You can also drop into the #raku-beginner channel on Libera.chat, or on Discord if you'd like to have more immediate feedback. I hope you liked it! Thank you for reading all the way to the end. ## gfldex: Recursive Cinderella ### Published by gfldex on 2022-10-03T21:51:28 PWC 184 asked to split an array into numbers and letters. for ( 'a 1 2 b 0', '3 c 4 d'), ( '1 2', 'p q r', 's 3', '4 5 t') -> @a is copy { @a.=map: *.split(' ').cache; my @numbers = @a.deepmap({ / <[0..9]> / ?? .Numeric !! Empty }).grep(+*)».Array; my @letters = @a.deepmap({ / <[a..z]> / ?? .Str !! Empty }).grep(+*)».Array; say @numbers, ‘ and ’, @letters; } I use deepmap with Empty to separate to weed from the chaff and remove the extra structure added by deepmap with a grep. That extra structure raised the question, what would be needed to split a tree in twain. This could be useful to gain parts of a XML Document with a single parser pass, while maintaining the structure of the tree. Relation of child nodes and parent nodes often carries meaning. for ( 'a 1 2 b 0', '3 c 4 d'), ( '1 2', 'p q r', 's 3', '4 5 t'), [ '1', [ [ 'a' ], '2 b'], '3 c' ] -> @a is copy { multi sub cinderella(@data, @a,$needle-a, @b, $needle-b) { for @data { @a[$++] := my $new-a; @b[$++] := my $new-b; cinderella($_, $new-a,$needle-a, $new-b,$needle-b)
}
}

multi sub cinderella(@data, Any:U $a is raw,$needle-a, Any:U $b is raw,$needle-b) {
my @new-a;
my @new-b;
for @data {
cinderella($_,$a, $needle-a,$b, $needle-b); @new-a.push:$a;
@new-b.push: $b; }$a = @new-a;
$b = @new-b; } multi sub cinderella(Str:D$s, $a is raw,$needle-a, $b is raw,$needle-b) {
cinderella($_, my @new-a,$needle-a, my @new-b, $needle-b) for$s.split(' ');
$a = @new-a ?? @new-a.join(' ') !! Empty;$b = @new-b ?? @new-b.join(' ') !! Empty;
}

multi sub cinderella(Str:D $s where *.chars == 1, @a,$needle-a, @b, $needle-b) { given$s {
when $needle-a { @a.push:$s }
when $needle-b { @b.push:$s }
default { fail('dunno where to put: ' ~ $s) } } } cinderella @a, my @numbers, / <[0..9]> /, my @letters, / <[a..z]> /; dd @numbers, @letters; } # OUTPUT: Array @numbers = ["1 2 0", "3 4"] Array @letters = ["a b", "c d"] Array @numbers = ["1 2", Empty, "3", "4 5"] Array @letters = [Empty, "p q r", "s", "t"] Array @numbers = ["1", [[[],], "2"], "3"] Array @letters = [[], [["a"], "b"], "c"] The leaves in the target tree can be either a Str or (empty) Array. However, the first multi candidate doesn’t make the decision what item is added to the target Arrays. Instead I create a fresh scalar container and bind it to a container within the Arrays. I then pass that container on to the next multi-candidate where it might be filled with Positional– or Str-object. Please note the distinction. MMD doesn’t care about the container type we use, it looks for the properties of the value. This allows me to split the strings on a white space and pass it on into the next round of MMD matches. The 2nd candidate handles the case where we descent into a nested Array. It can manipulate the scalar container created with @a[$++] := my $new-a; and turn it into a Positional value (here an Array), because that initial container is passed into the multi with is raw. This is a very powerful concept. Writing the same with a single recursive function would contain a lot of special casing and be no joy to debug. Not doing what the instructor asked for seems to produce better results. I shall do so more often. ## vrurg: Did you know that … ### Published by Vadim Belman on 2022-10-03T00:00:00 Let’s assume we have a type with multi-component name, like: class Foo::Bar { }  And there is another class Baz for which we want it to be coercible into Foo::Bar. No problem! class Baz { method Foo::Bar() { Foo::Bar.new } }  Now we can do: sub foo(Foo::Bar()$v) { say $v } foo(Baz.new);  ## gfldex: Rabbitholeing ### Published by gfldex on 2022-09-29T19:37:17 With PWC 182-2 specifically asking for Linux paths to be handled, we need to resolve issues like /../ and symbolic links. Since I didn’t feel like putting a directory called a into my root folder, I wrote a little helper that deals with some of the tripwires that modern filesystems provide. my @input = qw < /a/b/c/1/x.pl /a/b/c/d/e/2/x.pl /a/b//c/d/3/x.pl /a/b/./c/4/x.pl /a/../a/b/c/d/5/x.pl >; sub resolve(Str:D()$s){
my @parent-refs = (my @parts = $s.split(rx{ ‘/./’ | ‘/’+ })).pairs.grep(*.value eq '..')».key; @parent-refs = flat @parent-refs, @parent-refs »-» 1; @parts[@parent-refs]:delete; @parts.join(‘/’) } # OUTPUT: /a/b/c/1/x.pl /a/b/c/d/e/2/x.pl /a/b/c/d/3/x.pl /a/b/c/4/x.pl ///a/b/c/d/5/x.pl The last path starts with a triple root, because join assumes holes are actually there. It won’t skip fields that are Empty either, so is default(Empty) doesn’t help. To actually remove elements from an Array we are better of with splice. Since this method doesn’t return self, we can’t just @parts.splice(@parent-refs.any,1).join(‘/’). Both ways to remove elements are mutators and eager. That doesn’t fit well into the rest of the language and spells doom for concurrency. To find a solution I had to go down the rabbit hole that is iteration in Rakudo. The bottom happens to be located in Rakudo/Iterator.pm6. method pull-one() is raw { nqp::ifnull( nqp::atpos($!reified,++$!i), nqp::if( nqp::islt_i($!i,nqp::elems($!reified)), # found a hole self!hole($!i),
IterationEnd
)
)
}

So a hole in an Array is just a null-pointer in C-land — given that we didn’t overshoot the end of the Array. With that knowledge, building an Iterator that skips elements becomes rather simple.

multi sub prune(@a, Int:D $i --> Seq:D) { prune @a,$i .. $i } multi sub prune(@a, +@l is copy --> Seq:D) { @l = @l.sort; # this makes checking the index against the needles simpler Seq.new: class :: does Iterator { has int$!i;
has $!reified; submethod !SET-SELF(\arr) {$!reified := nqp::getattr(@a,List,'$!reified');$!i = -1;
self
}
method new(\arr) { nqp::create(self)!SET-SELF(arr) }
method pull-one is raw {
loop {
++$!i; if @l { @l.shift while +@l &&$!i > @l[0].max;
next if +@l && @l[0].min ≤ $!i ≤ @l[0].max; } return nqp::ifnull( nqp::atpos($reified, $!i), nqp::if( nqp::isge_i($!i, nqp::elems($reified)), IterationEnd, next # we actually got a hole ) ); } } }.new(@a) } @a = ('a' .. 'z').List; dd @a.&prune( 25 ); dd @a.&prune( 10..15 ); dd @a.&prune( (2,3,10..15, 21..22, 25).pick(5) ); # randomising for testing # OUTPUT: ("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y").Seq # ("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z").Seq # ("a", "b", "e", "f", "g", "h", "i", "j", "q", "r", "s", "t", "u", "x", "y").Seq The idea is to loop by default and bail out of that loop if the current index held in $!i is not found in any needle. Since we don’t got real goto in Raku, loop/next becomes a decent substitute. As I don’t really understand what a unreified Array is, I’m right now not qualified to provide a PR.

Dealing with lazy and infinite lists makes Array a beast. I shall not falter until I have tamed it!

## gfldex: Valid temperatures

PWC 181 Task 2 asks for input validation of dates and numbers. OK, it should ask for input validation so my solution isn’t totally over the top. Better safe then sorry.

my $data = q:to/EOH/; 2022-08-01, 20 2022-08-09, 10 2022-08-03, 19 2022-08-06, 24 2022-08-05, 22 2022-08-10, 28 2022-08-07, 20 2022-08-04, 18 2022-08-08, 21 2022-08-02, 25 EOH$data.lines()
==> map(* ~~ / $<date> = [ \d+ ‘-’ \d?\d ‘-’ \d?\d ] \s* ‘,’ \s*$<temperature> = [ '-'?\d+ [ '.' \d+ ]? ] /)
==> map(-> (Date(Str:D(Match)) :$date, Numeric(Str:D(Match)) :$temperature, *%) { [ $date,$temperature ] })
==> sort(*.first)
==> map( -> [$date,$temperature ] {
state $last-temp; LEAVE$last-temp = $temperature; once next; „$date, Δ{abs $last-temp -$temperature}°C“ if $temperature >$last-temp;
})
==> -> *@a { @a.join($?NL) }() ==> put(); First I use a regex with named captures to get hold of the date and temperature Strs. Those are not overly helpful, as I need to sort by date. The 2nd map solves that problem with nested coercions in a destructuring sub-signature. As Match contains a few extra methods that I don’t care for, I need to gobble those up with an anonymous hash slurpy. Now I have a LoL with a date and temperature sub-list, where the date is a Date and the temperature is something Numeric. I sort by the first and then destructure again. I use a state container to keep a link between consecutive iteration steps. (To make this hyper-friendly, we would need to .rotor.) I always want $last-temp to contain the $temperature of the previous iteration but I don’t want to do anything else in the first iteration. Hence, the LEAVE-phaser and the once. Anything else means to create the output text. Since I don’t want a combo breaker, I use a pointy-block like we need to in a feed-operator-chain to add newlines where they belong. If you managed to stuff more Raku-features into your solution, you out-convoluted me and I would love to see your code. Joking aside, I believe to have another candidate for macro-ideas.txt. If we look at the first and second map, there is a pattern. We use the same named-arguments in both. With RakuAST it might be possible to provide a Regex, a block and a list of coercions, to produce a Sub that does that in one go. That would be a neat shortcut for simple parsers that deal with lines of text. ## gfldex: Assuming optionality ### Published by gfldex on 2022-09-04T13:52:56 PWC 180 Task 1 asks us to find the first unique character in a string. I wanted to have a nice interface where I would write: say$str.comb.first: &unique-char;

The idea was to curry postcircumfix:<{ }> so it will be bound to a BagHash and always ask for :!exists. Alas, .assuming doesn’t do the right thing if the proto contains optional positions. I found a workaround utilising once.

for ‘Perl Weekly Challenge’, ‘Long Live Perl’ -> $str { my &unique-char = { (once$str.comb.BagHash»--){$_}:!exists } say$str.comb.first: &unique-char;
}

I don’t want to build the BagHash and remove single elements every time unique-char is called. There is a slight overhead when using once but that beats .assuming by a mile.

Given all the special cases Signatures provide, we may want to consider turning .assuming into a RakuAST-macro.

## p6steve: raku & rust: Option-Some-None

Regular visitors to my blog will know that I think raku and rust are both awesome in their chosen niches and are natural companions for the modern programming era just as perl and C were back in the day.

Coming off an excellent 2nd raku conference over the weekend, I got to thinking about how both languages handle the concept of “nothing” (the absence of a value) and wanted to have some -Ofun.

## The rust idiom

First I googled some rust code example to remind me of the rust Option / Some / None idiom:

enum Option<T> {
None,
Some(T),
}

let mut opt: Option<i32>;
opt = Some(1);
//opt = None;
match opt {
Some(x) => {
println!("Got {}", x);
}
None => {
println!("Got nothing");
}
}

Here’s what the Rust Book has to say

Sometimes it’s desirable to catch the failure of some parts of a program instead of calling panic!; this can be accomplished using the Option enum.

The Option<T> enum has two variants:

• None, to indicate failure or lack of value, and
• Some(value), a tuple struct that wraps a value with type T.
https://doc.rust-lang.org/rust-by-example/std/option.html

## The raku idiom

Then I compared to the usual raku idiom using Nil – of course since raku is gradually typed the most natural idiom for a quick 4 liner is no types.

my $opt;$opt = 4;
#$opt = Nil; with$opt { say "Got $_" } else { say "Got nothing" } This may seem pretty cool and uncontroversial – but a lengthy debate “much ado about nothing” has been had over on github recently. Which was enriched by a typically evocative insight from Larry Wall: Nil is a kind of singularity. Black holes have no hair, and trying to fit them neatly back into the fabric of the universe results in contradictions. Similarly, attempting to fit Nil back into the normal type system is bound to cause problems, which is why we have Raku’s Nil rather than Perl’s undef. Perl programmers often fall into the trap of trying to store undef as a normal value, and get themselves into trouble down the road. Black holes can also have an accretion disk (and maybe a “firewall”), which records some of the history of what is falling into the black hole. This is what a Failure does; it records some of the history of a singularity. The stuff still falling in is where black holes can keep their “hair”, at least temporarily. The design of Nil with respect to Failure was primarily a cultural decision, not a technical decision. We do not want people thinking of Nil as a value, or as a type from which other user-defined types can be derived, other than user-defined Failures. We want them to think of Nil as the least-marked form of failure, so that they never treat Nil as some kind of nuanced success marker, or try to create a parallel system of nothingness that pretends to be storable as a value. We could conceivably redefine Nil and Failure as both forms of Singularity (the property which subverts the return type system of the universe by the permanent absence of a return value), but the universe discourages people from creating their own singularities, and I suspect we should too. Hope this helps… Larry https://en.wikipedia.org/wiki/Larry_Wall ## All your base are belong to us (the rust idiom in raku) Not entirely content with this, I wondered if the raku (gradual) type system could do the more structured rust Option-Some-None thing. Here’s where I got to after a couple of hours hacking: subset Option of Any where Some|None; my Option$opt;
$opt = Some.new(7); #$opt = None;
given $opt { when Some { say "Got {.v}" } when None { say "Got nothing" } } Admittedly I only did Some[Int] via raku parametric roles and I should have followed suit with Option[Int] … but I am content to leave that as an exercise for the reader. Here’s all the code to implement this with a few comments to show off the raku cool stuff: role Things { #a utility Role for both flavours of Some method gist { "Some[{$.v.^name}]"     #
}                           # .gist and .raku are used by .say and .Str
# methods ... so we override them to make nice
method raku {               # output
"Some[{$.v.^name}]" } method Str { ~$.v                    # ~ is the Str concatenate operator, when used
}                           # as a prefix it coerces its argument to (Str)

method Numeric {
+$.v # ~ is the addition operator, when used as a } # prefix it coerces its argument to (Num) } role None {} # roles are cool labels role Some[::T] does Things { # a parameterized role with a type capture has T$.v is required;      # using the type capture for a public attr

multi method new( $v ) { # ensure that the value is defined die "Died with undefined value" without$v;
self.new: :$v } } role Some does Things { # role are multis (Some[Int].new and Some.new) has$.s is required;        # require the attr ... if absent, fail
has $.v; multi method new( ::T$v ) {   # use the type capture in a signature
die "Died with undefined value" without $v; self.new: s => Some[(T)].new(:$v)
}

submethod TWEAK {           # late stage constructor alias $.v to$.v
$!v :=$!s.v            # for the Some Things role
}
}

#err - that's it!

### some tests...
my $x = Some[Int].new(42); say "$x is ", $x; # 42 is Some[Int] say$x + 2;                     # 44

my $y = Some[Rat].new(3.2); # 3.2 is Some[Rat] say "$y is ", $y; my$z = Some.new(2e1);
say "$z is ",$z;               # 20 is Some[Num]
say $z + 2e0; #22 #my$a = Some.new();            #Died with X::Attribute::Required
#my $b = Some.new(Nil); #Died with undefined value This example shows how raku can be setup to ape another type system fairly closely in only 42 lines of code. Although you may have to resort to a bit more sleight of hand and effort to get rid of the .new method and to bring in Option[::T] in synch with Some[::T] – but not bad for a blog post. Some of the things I like about raku that contribute to this are: • use of roles for composition • parameterized roles • type captures • auto punning (roles become concrete classes) • subsets where clauses As usual, please comment here or over at the raku reddit page or the raku irc (also via discord) – let me know if any rustaceans would like to join in the -Ofun! ~p6steve ## p6steve: TRC Slides ### Published by p6steve on 2022-08-14T19:16:32 Attending The Raku Conference – https://conf.raku.org/2022/schedule Here is my talk… ## Useful Links ## Demo video …. here ## rakudo.org: Rakudo compiler, Release #157 (2022.07) ### Published on 2022-07-31T00:00:00 ## rakudo.org: Rakudo compiler, Release #156 (2022.06) ### Published on 2022-06-05T00:00:00 ## rakudo.org: 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> = ThreadPoolScheduler.new: 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.

## Atomics

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
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
$lock ⚛= 0; # unlock  Pretty simple, isn’t it? Let’s see what are the specs of this approach: 1. It is blocking, akin to Lock 2. It’s fast (will get back to this later) 3. The lock operation might be a CPU hog Item 2 is speculative at this moment, but guessable. Contrary to Lock, we don’t use a system call but rather base the lock on a purely computational trick. Item 3 is apparent because even though Lock doesn’t release it’s thread for Raku scheduler, it does release a CPU core to the system. ## Benchmarkers, let’s go benchmarking! As I found myself in between of two big tasks today, I decided to make a pause and scratch the itch of comparing different approaches to locking. Apparently, we have three different kinds of locks at our hands, each based upon a specific approach. But aside of that, we also have two different modes of using them. One is explicit locking/unlocking withing the protected block. The other one is to use a wrapper method protect, available on Lock and Lock::Async. There is no data type for atomic locking, but this is something we can do ourselves and have the method implemented the same way, as Lock does. Here is the code I used: constant MAX_WORKERS = 50; # how many workers per approach to start constant TEST_SECS = 5; # how long each worker must run class Lock::Atomic { has atomicint$!lock = 0;

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

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

for 2..MAX_WORKERS -> $wnum {$*ERR.print: "$wnum\r"; my Promise:D$starter .= new;
my Promise:D @workers;
my atomicint $stop = 0; sub worker(&code) { my Promise:D$ready .= new;
@ready.push: $ready; @workers.push: start {$ready.keep;
await $starter; &code(); } } my atomicint$ia-lock = 0;
my $ia-counter = 0; my$il-lock = Lock.new;
my $il-counter = 0; my$ila-lock = Lock::Async.new;
my $ila-counter = 0; my$iap-lock = Lock::Atomic.new;
my $iap-counter = 0; my$ilp-lock = Lock.new;
my $ilp-counter = 0; my$ilap-lock = Lock::Async.new;
my $ilap-counter = 0; for ^$wnum {
worker {
until $stop { while cas($ia-lock, 0, 1) == 1 { } # lock
LEAVE $ia-lock ⚛= 0; # unlock ++$ia-counter;
}
}

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

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

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

worker {
until $stop {$ilap-lock.protect: { ++$ilap-counter } } } } await @ready;$starter.keep;
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:

• atomic is faster than Lock. Sometimes it is even indecently faster, though these numbers are fluctuations. But on the average it is ~1.7 times as fast as Lock.
• Lock.protect is actually faster than Lock.lock/LEAVE Lock.unlock. Though counter-intuitive, this outcome has a good explanation stemming from the implementation details of the class. But the point is clear: use the protect method whenever applicable.
• Lock::Async is not simply much slower, than the other two. It demonstrates just unacceptable results under heavy loads. Aside of that, it also becomes quite erratic under the conditions. Though this doesn’t mean it is to be unconditionally avoided, but its use must be carefully justified.

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:

• little probability of high number of collisions per lock-variable. I’m not ready to talk about particular numbers, but, say, up to 3-4 active locks could be acceptable, but 10 and more are likely not. It could really be more useful to react a little longer but give up CPU for other tasks than to have several cores locked in nearly useless loop.
• the protected operations are to be really-really short.

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.

## Conclusion

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:

• many, perhaps hundreds, of simultaneous operations

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.

## Ukraine

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

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] $v;, but I’m not ready to get into these details yet. ## Variables A variable could have specific meaning. Some like to use our to configure modules (my heavily multi-threaded soul is grumbling, but we’re tolerant to people’s mistakes, aren’t we?). Therefore providing them with a way to produce less cryptic error messages is certainly for better than for worse: our Bool:D$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 $a-lexical will complain { "string must contain 'foo'" } where { !.defined || .contains("foo") };  The trait works with hashes and arrays too, except that it is applied not to the actual hash or array object but to its values. Therefore it really only makes sense for their typed variants: my Str %h will complain { "hash values are to be strings, not {.^name}" }; my Int @a will complain { "this array is all about integers, not {.^name}" };  Also note that this wouldn’t work for hashes with typed keys when a key of wrong type is used. But it doesn’t mean there is no solution: subset IntKey of Int will complain { "hash key must be an Int" }; my %h{IntKey}; %h<a> = 13;  ## Attributes class Foo { has Int$.a
is rw
will complain { "you offer me {.raku}, but with all the respect: an integer, please!" };
}


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: GLOBAL - 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. ## TA-TA-TAAA! (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: GLOBAL - 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: L1::L2::Collection::FS|U140455893341088  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 Failure|140360664014848  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. ## 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. 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 { Buf.new }  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:

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($n - 1) } for ^1_000_000 { fac(5) }  These were really slow before, since: 1. We couldn’t apply the multi-dispatch caching mechanism at all as soon as we had a where clause involved 2. We would run where clauses twice in the event the candidate was chosen: once to see if we should choose that multi candidate, and once again when we entered it With the new mechanism, we: 1. On the first call, calculate a multiple dispatch plan: a linked list of candidates to work through 2. Invoke the one with the where clause, in a mode whereby if the signature fails to bind, it triggers a dispatch resumption. (If it does bind, it runs to completion) 3. In the event of a bind failure, the dispatch resumption triggers, and we attempt the next candidate Once again, after the setup phase, we interpret the dispatch programs. In fact, that’s as far as we get with running this faster for now, because the specializer doesn’t yet know how to translate and further optimize this kind of dispatch program. (That’s how I know it currently stands no chance of turning this whole thing into another empty loop!) So there’s more to be had here also; in the meantime, I’m afraid you’ll just have to settle for a factor of ten speedup. Here’s the next one: proto with-proto(Int$n) { 2 * {*} }
multi with-proto(Int $n) {$n + 1 }
sub invoking-nontrivial-proto() {
for ^10_000_000 {
with-proto(20)
}
}


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 {
Child.m;
}


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 $mon = TestMonitor.new; for ^1_000_000 {$mon.m();
}


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:

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 = $total-re +$z.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 };
$fh.close; say$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:

• The long-standing Text::CSV benchmark test-t has seen roughly 20% improvement (thanks to lizmat for measuring)
• A simple Cro::HTTP test application gets through about 10% more requests per second
• MoarVM contributor dogbert did comparative timings of a number of scripts; the most significant improvement saw a drop from 25s to 7s, most are 10%-30% faster, some without change, and only one that slowed down.
• There’s around 2.5% improvement on compilation of CORE.setting, the standard library. However, a big pinch of salt is needed here: the compiler itself has changed in a number of places as part of the work, and there were a couple of things tweaked based on looking at profiles that aren’t really related to dispatch.
• Agrammon, an application calculating farming emissions, has seen a slowdown of around 9%. I didn’t get to look at it closely yet, although glancing at profiling output the number of deoptimizations is relatively high, which suggests we’re making some poor optimization decisions somewhere.

### 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:

• Ensured it passes the specification test suite, both in normal circumstances, but also under optimizer stressing (where we force it to prematurely optimize everything, so that we tease out optimizer bugs and – given how many poor decisions we force it to make – deoptimization bugs too)
• Used blin to run the tests of ecosystem modules. This is a standard step when preparing Rakudo releases, but in this case we’ve aimed it at the new-disp branches. This found a number of regressions caused by the switch to the new dispatch mechanism, which have been addressed.
• Patched or sent pull requests to a number of modules that were relying on unsupported internal APIs that have now gone away or changed, or on other implementation details. There were relatively few of these, and happily, many of them were fixed up by migrating to supported APIs (which likely didn’t exist at the time the modules were written).

### 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:

• Reworking callframe entry and exit. These are still decidedly too costly. Various changes that have taken place while working on the new dispatch mechanism have opened up new opportunities for improvement in this area.
• Avoiding megamorphic pile-ups. Micro-benchmarks are great at hiding these. In fact, the callsame one here is a perfect example! The point we do the resumption of a dispatch is inside callsame, so all the inline cache entries of resumptions throughout the program stack up in one place. What we’d like is to have them attached a level down the callstack instead. Otherwise, the level of callsame improvement seen in micro-benchmarks will not be enjoyed in larger applications. This applies in a number of other situations too.
• Applying the new dispatch mechanism to optimize further constructs. For example, a method call that results in invoking the special FALLBACK method could have its callsite easily rewritten to do that, opening the way to inlining.
• Further tuning the code we produce after optimization. There is an amount of waste that should be relatively straightforward to eliminate, and some opportunities to tweak deoptimization such that we’re able to delete more instructions and still retain the ability to deoptimize.
• Continuing with the escape analysis work I was doing before, which should now be rather more valuable. The more flexible callstack/frame handling in place should also unblock my work on scalar replacement of Ints (which needs a great deal of care in memory management, as they may box a big integer, not just a native integer).
• Implementing specialization, JIT, and inlining of dispatch resumptions.

### 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?

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 }",
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) {
first-name => .first-name,
last-name  => .last-name
given $model } multi method new(Account:D$model) {
self!FROM-MODEL($model) } method COERCE(Account:D$model) {
self!FROM-MODEL($model) } }  And now, somewhere in our code we can do: method get-user-info(UInt:D$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: :$id) } }  With such an implementation our Raku backend would get an instance of API::Data::User. In a TypeScript frontend code of a private project of mine I have something like the following snippet, where connection is an object derived from jayson module: connection.call("get-user-info", id).then( (user: User | undefined | null) => { ... } );  What does it all eventually give us? First, API::Data::User provides the mechanism of whilelisting the fields we do want to expose in API. Note that with properly defined attributes we’re as explicit about that as only possible. And we do it declaratively one single place. Second, the class prevents us from mistyping field names. It wouldn’t be possible to have something like %( usrname =>$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… 🤷

## 6guts: Raku multiple dispatch with the new MoarVM dispatcher

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 {
$str } }  We may write where clauses to differentiate candidates on properties that are not captured by nominal types: multi fac($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:

• A decision made only by the number of arguments and nominal types
• No where clauses
• No custom proto
• No callsame

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:

• If the dispatch is decided by arity and nominal type only, and you don’t call it with flattening args, it’ll probably perform quite decently, and perhaps even enjoy inlining of the candidate and elimination of duplicate type checks that would take place on the slow path. This is thanks to the proto holding a “dispatch cache”, a special-case mechanism implemented in the VM that uses a search tree, with one level per argument.
• If that’s the case but it has a custom proto, it’s not too bad either, though inlining isn’t going to be happening; it can still use the search tree, though
• If it uses where clauses, it’ll be slow, because the search tree only deals in finding one candidate per set of nominal types, and so we can’t use it
• The same reasoning applies to callsame; it’ll be slow too

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 {
$str } }  We then have a call truncate($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 {
fac(10)
}
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 {
fallback(4+2i);
fallback(4.2);
fallback(42);
}
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 { m(1); m("x"); } 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"; } } process(%document);  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 run.pl
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";
}
}
}

process(%document);


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 run.pl 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 { }'
Promise.new(
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:

    $perl6 run.pl # 4 threads available by default Chapter 1 started # 3 threads available Chapter 1.1 started # 2 threads available Chapter 2 started # 1 thread available Chapter 1.1 finished # 2 threads available again Chapter 1.2 started # 1 thread available Chapter 1.2.1 started # 0 threads available # deadlock!  At this moment chapter 1 subtree holds three threads and waits for one more for chapter 1.2.2 to complete everything and start ascending from recursion. And subtree of chapter 2 holds one thread and waits for one more for chapter 2.1 to descend into recursion. In result processing gets to a point where at least one more thread is required to proceed but all threads are taken and none can be returned to thread pool. Script deadlocks and stops here forever. How to solve this problem and maintain parallel processing? There are many ways to do it :) The key to the solution is to process asynchronously only those chapters that do not have unprocessed chapters on lower level. Luckily Perl 6 offers perfect tool - promise junctions. It is possible to create a promise that waits for other promises to be kept and until it happens it is not sent to thread pool for execution. Following code illustrates that:  my$p = Promise.allof( Promise.in(2), Promise.in(3) );
sleep 1;
say "Promise after 1 second: " ~ $p.perl; sleep 3; say "Promise after 4 seconds: " ~$p.perl;


Prints:

    Promise after 1 second: Promise.new(
..., status => PromiseStatus::Planned
)
Promise after 4 seconds: Promise.new(
..., 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 run.pl
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

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:

• Making a method call, such as $basket.add($product, $quantity). We might traditionally call just $product and $qauntity the arguments, but for my purposes, all of $basket, the method name 'add'$product, and$quantity are arguments to the dispatch: they are the things we need in order to make a decision about what we’re going to do.
• Making a subroutine call, such as uc($youtube-comment). Since Raku sub calls are lexically resolved, in this case the arguments to the dispatch are &uc (the result of looking up the subroutine) and $youtube-comment.
• Calling a multiple dispatch subroutine or method, where the number and types of the arguments are used in order to decide which of a set of candidates is to be invoked. This process could be seen as taking place “inside” of one of the above two dispatches, given we have both multiple dispatch subroutines and methods in Raku.

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:

• wrap allows us to wrap any Routine (sub or method); the wrapper can then choose to defer to the original routine, either with the original arguments or with new arguments
• When doing multiple dispatch, we may write a proto routine that gets to choose when – or even if – the call to the appropriate candidate is made
• We can use routines like callsame in order to defer to the next candidate in the dispatch. But what does that mean? If we’re in a multiple dispatch, it would mean the next most applicable candidate, if any. If we’re in a method dispatch then it means a method from a base class. (The same thing is used to implement going to the next wrapper or, eventually, to the originally wrapped routine too). And these can be combined: we can wrap a multi method, meaning we can have 3 levels of things that all potentially contribute the next thing to call!

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:

• Some support for method caching
• A multi-dispatch cache highly tied to Raku’s multi-dispatch semantics, and only really able to help when the dispatch is all about nominal types (so using where comes at a very high cost)
• A mechanism for specifying how to find the actual code handle inside of a wrapping code object (for example, a Sub object has a private attribute in it that holds the low-level code handle identifying the bytecode to run)
• Some limited attempts to allow us to optimize correctly in the case we know that a dispatch will not be continued – which requires careful cooperation between compiler and runtime (or less diplomatically, it’s all a big hack)

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:

• Private method calls, which would need a different cache – but the initial VM design limited us to one per type
• Qualified method calls ($obj.SomeType::method-name()) • Ways to decently optimize dispatch resumption 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: • Inlining was implemented, and could eliminate the overhead of doing a function call • We could compile to machine code, eliminating interpretation overhead • We were in a position where we had type information to hand in the specializer that would let us eliminate branches in the C code, but since it was just an opaque function we called, there was no way to take this opportunity 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 first time we reach a given callsite in the bytecode, we run the plugin. It produces a code object to invoke, along with a set of guards (conditions that have to be met in order to use that code object result) • The next time we reach it, we check if the guards are met, and if so, just use the result • If not, we run the plugin again, and stack up a guard set at the callsite • We keep statistics on how often a given guard set succeeds, and then use that in the specializer 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: • Use the dispatcher called dispatcher-name • Give it the argument registers specified (the callsite referenced indicates the number of arguments) • Put the object result of the dispatch into the register result (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: • boot-value– take the first argument and use it as the result (the identity function, except discarding any further arguments) • boot-constant – take the first argument and produce it as the result, but also treat it as a constant value that will always be produced (thus meaning the optimizer could consider any pure code used to calculate the value as dead) • boot-code – take the first argument, which must be a VM bytecode handle, and run that bytecode, passing the rest of the arguments as its parameters; evaluate to the return value of the bytecode • boot-syscall – treat the first argument as the name of a VM-provided built-in operation, and call it, providing the remaining arguments as its parameters • boot-resume – resume the topmost ongoing dispatch 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) Ops: 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', $capture-derived); }); ok(nqp::dispatch('drop-first', 'first', 'second') eq 'second', 'dispatcher-drop-arg works');  This drops the first argument before passing the capture on to the boot-value dispatcher – meaning that it will return the second argument. Glance back at the previous dispatch program for the identity function. Can you guess how this one will look? Well, here it is: Dispatch program (1 temporaries) Ops: Load argument 1 into temporary 0 Set result string value from temporary 0  Again, while in the record phase of such a dispatcher we really do create capture objects and make a dispatcher delegation, the resulting dispatch program is far simpler. Here’s a slightly more involved example: my$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:

• Create a new capture with a code object inserted at the start
• Delegate to the boot code constant dispatcher, which…
• …creates a new capture without the original argument and runs bytecode with those arguments

And the resulting dispatch program? It’s this:

Dispatch program (1 temporaries)
Ops:
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.

### Guards

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.
$count++; # Obtain the value of the argument from the capture (using an existing # MoarVM op, though in the future this may go away in place of a syscall) # and then obtain the string typename also. my$arg-val := nqp::captureposarg($capture, 0); my str$name := $arg-val.HOW.name($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) Ops: 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) Ops: 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 $arg-val.HOW.name($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', $next);$obj
}
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) {
@methods.push(%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 := DeferralChain.new(@methods.pop, $chain); } nqp::dispatch('boot-syscall', 'dispatcher-set-resume-state-literal',$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', $init, 0), 0); my$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',
$delegate_capture); } else { nqp::dispatch('boot-syscall', 'dispatcher-delegate', 'raku-invoke',$delegate_capture);
}
}
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: • Method calls on roles need to pun the role into a class, and so method lookup returns a closure that does that and replaces the invocant. That’s a lot of indirection; the new dispatcher could obtain the pun and produce a dispatch program that replaces the role type object with the punned class type object, which would make the per-call cost far lower. • I expect both the handles (delegation) and FALLBACK (handling missing method call) mechanisms can be made to perform better using the new dispatcher • The current implementation of assuming – used to curry or otherwise prime arguments for a routine – is not ideal, and an implementation that takes advantage of the argument rewriting capabilities of the new dispatcher would likely perform a great deal better 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: • Applications present models to users (for manipulation) • Systems provide functionality to other programs 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: • Scripts tend to be short, applications in particular can grow very large. • Scripts tend to be ad-hoc (written for a specific need), applications and systems tend to be designed for a range of use cases. (Very common example: build scripts) • Scripts tend to run only in a specific environment; in contrast, many applications are designed for a range of devices/clients; many systems have specific requirements but the intention is that they can be setup in multiple distinct environments. • Because scripts are ad-hoc, short, and environment-dependent, many of software engineering standard best practices don't really apply (and are in fact often disregarded). 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: • It should make programs concise. The economic rationalization is that the total expected lifetime value of a script, being ad-hoc and context-dependent, is not very great, so writing it should be cheap, which implies that the script should be short). • Related to this, the value provided by type systems is generally less than in larger (application) programs, and the value of extensive modelling features (class hierarchies) is similarly low, so many scripting languages have very weak type systems and data modelling features, if they have them at all. • Interoperation with the environment is on the other hand very important, so I/O features tend to be well-developed. (Contrast C, in which I/O is entirely an afterthought provided by a library). • It is acceptable to depend on a local environment in implicit ways, since that's what you are going to do anyway. • It is acceptable to warn on a condition that might've been a fatal error in another programming language. • In fact, I think that concerns of correctness are often different, meaning relaxed, compared to applications, again because scripters don't necessarily expect their scripts to run on every environment and with every possible input. 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
3628800

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
2432902008176640000

## 8 — sequence

The fun fact is that you can add a dot to the first program

unit sub MAIN($n); say [*] 1 ...$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)
120
Called f(10)
Called f(9)
Called f(8)
Called f(7)
Called f(6)
3628800

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 $n) { F.new(n =>$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) {
callsame
}

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
3086830.6595557937

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!?

$raku 19-integral-factorial.raku 5.5 285.948286477563 ## 20 — another formula And finally, the Stirling’s formula for the rescue. The bigger the n, the more correct is the result. The implementation can be as simple as this: unit sub MAIN($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 github.com/ash/factorial.

## Andrew Shitov: The course of Raku

I am happy to report that the first part of the Raku course is completed and published. The course is available at course.raku.org.

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 glot.io 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
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 theory that covers the current topic
• Interactive quizzes that accomplish the theory of the topic and/or the section
• Exercises for the material of the whole section

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 github.com/ash/raku-course, 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 https://news.perlfoundation.org/post/rakucourse1 on 13 January 2021.

## Andrew Shitov: Raku Challenge, Week 92, Issue 1

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('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);`

* * *

## 6guts: Taking a break from Raku core development

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

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:
• In LuaJIT, the IR maintains its SSA form - there is only a single definition of a value. This means that when iterating in reverse order, computing the live range becomes trivial. When we first encounter a use of a value, then by definition that is the last use. And when we encounter a definition, that is the only and single definition, and we can release the register.  So there's no need to compute the live range in advance of allocation.
• Furthermore, rather than merging the values of multiple branches into the same live range, each value on either side becomes an individual live range. As a result, the live range of a value never has a hole, further simplifying code.
• LuaJIT uses register hints to indicate which registers could best be picked for a specific value. This is often determined by how a value is used (e.g., the divisor in a div instruction must be in the rcx register). If the preferred register can't be allocated, the register allocator inserts code to move it to the right place where needed. Having hints can be expected to greatly reduce the need for such code.
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

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

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:
• It should make it easy to develop 'templates' for MoarVM instructions, so we can map the ~800 or so different instructions supported by the interpreter to something the JIT compiler can work with.
• It should be simple to process and analyze; specifically, it should be suitable as input to the instruction selection process (the tiler).
• It should be simple to implement, both from the frontend (meaning the perl program that compiles a template file to a C header) and the backend (meaning the C code that combines templates into the IR that is compiled).
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:
• We need to know the type of each value, both to ensure we use the correct instructions and the right registers.
• There are several cases in which we don't really know (for the template) what type each value has.
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!