limbo/extensions/core

Limbo extension API

The limbo_ext crate simplifies the creation and registration of libraries meant to extend the functionality of Limbo, that can be loaded like traditional sqlite3 extensions, but are able to be written in much more ergonomic Rust.

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Currently supported features

• [ x ] Scalar Functions: Create scalar functions using the scalar macro.
• [ x ] Aggregate Functions: Define aggregate functions with AggregateDerive macro and AggFunc trait.
• [] Virtual tables: TODO

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Installation

Add the crate to your Cargo.toml:

[dependencies]
limbo_ext = { path = "path/to/limbo/extensions/core" } # temporary until crate is published

# mimalloc is required if you intend on linking dynamically. It is imported for you by the register_extension
# macro, so no configuration is needed. But it must be added to your Cargo.toml
[target.'cfg(not(target_family = "wasm"))'.dependencies]
mimalloc = { version = "*", default-features = false }

NOTE Crate must be of type cdylib if you wish to link dynamically

[lib]
crate-type = ["cdylib", "lib"]

cargo build will output a shared library that can be loaded with .load target/debug/libyour_crate_name

Extensions can be registered with the register_extension! macro:

register_extension!{
    scalars: { double }, // name of your function, if different from attribute name
    aggregates: { Percentile },
}

Scalar Example:

use limbo_ext::{register_extension, Value, scalar};

/// Annotate each with the scalar macro, specifying the name you would like to call it with
/// and optionally, an alias.. e.g. SELECT double(4); or SELECT twice(4);
#[scalar(name = "double", alias = "twice")]
fn double(&self, args: &[Value]) -> Value {
    if let Some(arg) = args.first() {
        match arg.value_type() {
            ValueType::Float => {
                let val = arg.to_float().unwrap();
                Value::from_float(val * 2.0)
            }
            ValueType::Integer => {
                let val = arg.to_integer().unwrap();
                Value::from_integer(val * 2)
            }
        }
    } else {
        Value::null()
    }
}

Aggregates Example:

use limbo_ext::{register_extension, AggregateDerive, AggFunc, Value};
/// annotate your struct with the AggregateDerive macro, and it must implement the below AggFunc trait
#[derive(AggregateDerive)]
struct Percentile;

impl AggFunc for Percentile {
    /// The state to track during the steps
    type State = (Vec<f64>, Option<f64>, Option<String>); // Tracks the values, Percentile, and errors

    /// Define the name you wish to call your function by. 
    /// e.g. SELECT percentile(value, 40);
     const NAME: &str = "percentile";

    /// Define the number of expected arguments for your function.
     const ARGS: i32 = 2;

    /// Define a function called on each row/value in a relevant group/column
    fn step(state: &mut Self::State, args: &[Value]) {
        let (values, p_value, error) = state;

        if let (Some(y), Some(p)) = (
            args.first().and_then(Value::to_float),
            args.get(1).and_then(Value::to_float),
        ) {
            if !(0.0..=100.0).contains(&p) {
                *error = Some("Percentile P must be between 0 and 100.".to_string());
                return;
            }

            if let Some(existing_p) = *p_value {
                if (existing_p - p).abs() >= 0.001 {
                    *error = Some("P values must remain consistent.".to_string());
                    return;
                }
            } else {
                *p_value = Some(p);
            }

            values.push(y);
        }
    }
    /// A function to finalize the state into a value to be returned as a result
    /// or an error (if you chose to track an error state as well)
    fn finalize(state: Self::State) -> Value {
        let (mut values, p_value, error) = state;

        if let Some(error) = error {
            return Value::custom_error(error);
        }

        if values.is_empty() {
            return Value::null();
        }

        values.sort_by(|a, b| a.partial_cmp(b).unwrap());
        let n = values.len() as f64;
        let p = p_value.unwrap();
        let index = (p * (n - 1.0) / 100.0).floor() as usize;

        Value::from_float(values[index])
    }
}