Day 6: Immutable data structures and reduction in Raku

For a little compiler I’ve been writing, I felt increasingly the need for immutable data structures to ensure that nothing was passed by references between passes. I love Perl and Raku but I am a functional programmer at heart, so I prefer map and reduce over loops. It bothered me to run reductions on a mutable data structure. So I made a small library to make it easier to work with immutable maps and lists.

A reduction combines all elements of a list into a result. A typical example is the sum of all elements in a list. According to the Raku docs, reduce() has the following signature

multi sub reduce (&with, +list)

In general, if we have a list of elements of type T1 and a result of type T2, Raku’s reduce() function takes as first argument a function of the form

    -> T2 \acc, T1 \elt --> T2 { ... }

I use the form of reduce that takes three arguments: the reducing function, the accumulator (what the Raku docs call the initial value) and the list. As explained in the docs, Raku’s reduce operates from left to right. (In Haskell speak, it is a foldl :: (b -> a -> b) -> b -> [a].)

The use case is the traversal of a role-based datastructure ParsedProgram which contains a map and an ordered list of keys. The map itself contains elements of type ParsedCodeBlock which is essentially a list of tokens.

    role ParsedProgram {
        has Map $.blocks = Map.new; # {String => ParsedCodeBlock}
        has List $.blocks-sequence = List.new; # [String]
        ...
    }
    
    role ParsedCodeBlock {
        has List $.code = List.new; # [Token]
        ...
    }

List and Map are immutable, so we have immutable datastructures. What I want to do is update these datastructures using a nested reduction where I iterate over all the keys in the blocks-sequence List and then modify the corresponding ParsedCodeBlock. For that purpose, I wrote a small API, and in the code below, append and insert are part of that API. What they do is create a fresh List resp. Map rather than updating in place.

I prefer to use sigil-less variables for immutable data, so that sigils in my code show where I have use mutable variables.

The code below is an example of a typical traversal. We iterate over a list of code blocks in a program, parsed_program.blocks-sequence; on every iteration, we update the program parsed_program (the accumulator). The reduce() call takes a lambda function with the accumulator (ppr_) and a list element (code_block_label).

We get the code blocks from the program’s map of blocks, and use reduce() again to update the tokens in the code block. So we iterate over the original list of tokens (parsed_block.code) and build a new list. The lambda function therefore has as accumulator the updated list (mod_block_code_) and as element a token (token_).

The inner reduce creates a modified token and puts it in the updated list using append. Then the outer reduce updates the block code using clone and updates the map of code blocks in the program using insert, which updates the entry if it was present. Finally, we update the program using clone.

    reduce(
        -> ParsedProgram \ppr_, String \code_block_label {
            my ParsedCodeBlock \parsed_block =
                ppr_.blocks{code_block_label};
    
            my List \mod_block_code = reduce(
                -> \mod_block_code_,\token_ {
                    my Token \mod_token_ = ...;
                    append(mode_block_code_,mod_token_);
                },
                List.new,
                |parsed_block.code
            );
            my ParsedCodeBlock \mod_block_ =
                parsed_block.clone(code=>mode_block_code);
            my Map \blocks_ = insert(
                ppr_glob.blocks,code_block_label,mod_block_);
            ppr_.clone(blocks=>blocks_);
        },
        parsed_program,
        |parsed_program.blocks-sequence
    );
    

The entire library is only a handful of functions. The naming of the functions is based on Haskell’s, except where Raku already claimed a name as a keyword.

Map manipulation

Insert, update and remove entries in a Map. Given an existing key, insert will update the entry.

    sub insert(Map \m_, Str \k_, \v_ --> Map )
    sub update(Map \m_, Str \k_, \v_ --> Map )
    sub remove(Map \m_, Str \k_ --> Map )
    

List manipulation

There are more list manipulation functions because reductions operate on lists.

Add/remove an element at the front:

    # push
    sub append(List \l_, \e_ --> List)
    # unshift
    sub prepend(List \l_, \e_ --> List)
    

Split a list into its first element and the rest:

# return the first element, like shift
sub head(List \l_ --> Any)
# drops the first element
sub tail(List \l_ --> List)

# This is like head:tail in Haskell
sub headTail(List \l_ --> List) # List is a tuple (head, tail)

The typical use of headTail is something like:

    my (Str \leaf, List \leaves_) = headTail(leaves);
    

Similar operations but for the last element:

    # drop the last element
    sub init(List \l_ --> List)
    # return the last element, like pop.
    sub top(List \l_ --> Any) ,
    # Split the list on the last element
    sub initLast(List \l_ --> List) # List is a tuple (init, top)
    

The typical use of initLast is something like:

    my (List \leaves_, Str \leaf) = initLast(leaves);
    

4 thoughts on “Day 6: Immutable data structures and reduction in Raku

  1. Very nice post, as always!

    What I’m curious about is the balance between descriptive names versus more symbolic (but explicit) operations. For example, if I have a list names, with your library, I can write append names, "Larry". But without your library, I could achieve the same with (|names, "Larry") which is shorter and (imo) clearer/more explicit about what it’s doing. And the similar alternatives are available for many of the functions in the library.

    I suppose you prefer the API provided by the name, but I’m curious about how you’d compare the two.

    Like

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