karpal-core

Core algebraic structures for Rust: HKT encoding, a full functor hierarchy (Functor through Monad), Semigroup, Monoid, adjunctions, ends/coends, dinatural transformations, and do_!/ado_! notation macros.

What's inside

HKT encoding

GAT-based Higher-Kinded Type encoding with zero dependencies:

use karpal_core::hkt::{HKT, HKT2, OptionF, ResultF, VecF, ResultBF, TupleF};

// HKT  — OptionF::Of<T> = Option<T>, ResultF<E>::Of<T> = Result<T, E>, VecF::Of<T> = Vec<T>
// HKT2 — ResultBF::P<A, B> = Result<B, A>, TupleF::P<A, B> = (A, B)

Functor hierarchy

Chain and do_! — sequential, dependent computations

When each step depends on the result of the previous one, do_! replaces nested .and_then() chains with flat, readable notation:

use karpal_core::{do_, Applicative};
use karpal_core::hkt::OptionF;

// A multi-step lookup where each step can fail
fn resolve(items: &[(u32, &str)], aliases: &[(&str, &str)], id: u32) -> Option<String> {
    do_! { OptionF;
        name = items.iter().find(|(i, _)| *i == id).map(|(_, n)| *n);
        alias = aliases.iter().find(|(n, _)| *n == name).map(|(_, a)| *a);
        OptionF::pure(format!("{} ({})", name, alias))
    }
}

let items = vec![(1, "alice"), (2, "bob")];
let aliases = vec![("alice", "A"), ("bob", "B")];
assert_eq!(resolve(&items, &aliases, 1), Some("alice (A)".into()));
assert_eq!(resolve(&items, &aliases, 99), None); // first step fails → short-circuits

Applicative and ado_! — independent computations combined

Unlike do_!, ado_! makes it explicit that the computations don't depend on each other. All bindings are evaluated independently, then combined in the yield:

use karpal_core::{ado_, Applicative};
use karpal_core::hkt::OptionF;

// All three lookups are independent — if any fails, the whole thing fails
fn load_config(env: &[(&str, &str)]) -> Option<String> {
    let find = |key: &str| env.iter().find(|(k, _)| *k == key).map(|(_, v)| v.to_string());
    ado_! { OptionF;
        host = find("HOST");
        port = find("PORT");
        yield format!("{}:{}", host, port)
    }
}

let env = vec![("HOST", "localhost"), ("PORT", "5432")];
assert_eq!(load_config(&env), Some("localhost:5432".into()));

let env = vec![("HOST", "localhost")]; // PORT missing
assert_eq!(load_config(&env), None);

Traversable — batch operations that fail fast

"Run this fallible operation on every element; if any one fails, the whole batch fails." traverse does this without manual loops or early-return boilerplate:

use karpal_core::Traversable;
use karpal_core::hkt::{OptionF, VecF};

let ids = vec!["100", "200", "300"];
let parsed: Option<Vec<u64>> = VecF::traverse::<OptionF, _, _, _>(
    ids, |s| s.parse::<u64>().ok(),
);
assert_eq!(parsed, Some(vec![100, 200, 300]));

// One invalid entry poisons the whole batch
let ids = vec!["100", "not_a_number", "300"];
let parsed: Option<Vec<u64>> = VecF::traverse::<OptionF, _, _, _>(
    ids, |s| s.parse::<u64>().ok(),
);
assert_eq!(parsed, None);

Alt — fallback chains

Try multiple strategies in order, taking the first success:

use karpal_core::Alt;
use karpal_core::hkt::OptionF;

fn resolve_timeout(flag: Option<u64>, env: Option<u64>, default: u64) -> u64 {
    // flag overrides env, env overrides default
    OptionF::alt(OptionF::alt(flag, env), Some(default)).unwrap()
}

assert_eq!(resolve_timeout(Some(5), Some(30), 60), 5);
assert_eq!(resolve_timeout(None, Some(30), 60), 30);
assert_eq!(resolve_timeout(None, None, 60), 60);

Foldable — generic aggregation with Monoid

Summarize any foldable structure using any Monoid. The fold knows nothing about the container or the summary type — it just needs combine and empty:

use karpal_core::{Foldable, Monoid, Semigroup};
use karpal_core::hkt::VecF;

struct Histogram { buckets: Vec<(String, u32)> }

impl Semigroup for Histogram {
    fn combine(mut self, other: Self) -> Self {
        for (key, count) in other.buckets {
            if let Some(entry) = self.buckets.iter_mut().find(|(k, _)| *k == key) {
                entry.1 += count;
            } else {
                self.buckets.push((key, count));
            }
        }
        self
    }
}
impl Monoid for Histogram {
    fn empty() -> Self { Histogram { buckets: vec![] } }
}

let events = vec!["click", "view", "click", "view", "view"];
let hist: Histogram = VecF::fold_map(events, |e| {
    Histogram { buckets: vec![(e.to_string(), 1)] }
});
let clicks = hist.buckets.iter().find(|(k, _)| k == "click").unwrap().1;
let views = hist.buckets.iter().find(|(k, _)| k == "view").unwrap().1;
assert_eq!(clicks, 2);
assert_eq!(views, 3);

Bifunctor — map both sides of a Result or tuple

use karpal_core::Bifunctor;
use karpal_core::hkt::{ResultBF, TupleF};

// Map the error to a structured type and the success to a display string
let result: Result<&str, &str> = Err("connection refused");
let mapped = ResultBF::bimap(result, |e| format!("NetworkError: {}", e), |v| v.len());
assert_eq!(mapped, Err("NetworkError: connection refused".to_string()));

// Transform both halves of a key-value pair
let entry = ("temperature", 98.6_f64);
let display = TupleF::bimap(entry, |k| k.to_uppercase(), |v| format!("{:.1}F", v));
assert_eq!(display, ("TEMPERATURE".to_string(), "98.6F".to_string()));

FunctorFilter — map and filter in one pass

use karpal_core::FunctorFilter;
use karpal_core::hkt::VecF;

// Parse valid entries, silently skip malformed ones
let raw = vec!["42", "bad", "7", "", "13"];
let valid: Vec<i32> = VecF::filter_map(raw, |s| s.parse().ok());
assert_eq!(valid, vec![42, 7, 13]);

Semigroup / Monoid

use karpal_core::{Semigroup, Monoid};

// Combine anything associative
assert_eq!(vec![1, 2].combine(vec![3, 4]), vec![1, 2, 3, 4]);
assert_eq!(Some(3i32).combine(Some(4)), Some(7));

// Monoid adds an identity element
assert_eq!(Vec::<i32>::empty().combine(vec![1, 2]), vec![1, 2]);

// Tuple instances combine component-wise
assert_eq!((1i32, 10i32).combine((2, 20)), (3, 30));

Instances: all numeric types (additive), String, Vec<T>, Option<T: Semigroup>, (A, B).

Newtype wrappers

Select alternative Semigroup/Monoid instances for numeric types:

use karpal_core::{Sum, Product, Min, Max, First, Last, Semigroup, Monoid, Foldable};
use karpal_core::hkt::VecF;

// Product uses multiplication instead of addition
let product = VecF::fold_map(vec![1, 2, 3, 4], |x| Product(x));
assert_eq!(product, Product(24));

// Min/Max for ordered types
assert_eq!(Min(3i32).combine(Min(7)), Min(3));
assert_eq!(Max(3i32).combine(Max(7)), Max(7));

// First/Last pick the first/last Some value
assert_eq!(First(None::<i32>).combine(First(Some(2))), First(Some(2)));
assert_eq!(Last(Some(1i32)).combine(Last(None)), Last(Some(1)));

Features

License

MIT OR Apache-2.0