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.

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.
• [ x ] Virtual tables: Create a module for a virtual table with the VTabModuleDerive macro and VTabCursor trait.
• [] VFS Modules

Installation

On the root of the workspace run:

cargo new --lib extensions/your_crate_name

Add the crate to your extensions/your_crate_name/Cargo.toml:

[features]
static = ["limbo_ext/static"]

[dependencies]
limbo_ext = { path = "path/to/limbo/extensions/core", features = ["static"] } # 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 = "0.1", 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 by the following options:

CLI:

`.load target/debug/libyour_crate_name`

SQL:

SELECT load_extension('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 },
    vtabs: { CsvVTable },
}

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 your error type, must impl Display
    type Error = String;

    /// 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) -> Result<Value, Self::Error> {
        let (mut values, p_value, error) = state;

        if let Some(error) = error {
            return Err(error);
        }

        if values.is_empty() {
            return Ok(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;

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

Virtual Table Example:

/// Example: A virtual table that operates on a CSV file as a database table.
/// This example assumes that the CSV file is located at "data.csv" in the current directory.
#[derive(Debug, VTabModuleDerive)]
struct CsvVTable;

impl VTabModule for CsvVTable {
    type VCursor = CsvCursor;
    /// Define your error type. Must impl Display and match VCursor::Error
    type Error = &'static str;
    /// Declare the name for your virtual table
    const NAME: &'static str = "csv_data";

    /// Declare the type of vtable (TableValuedFunction or VirtualTable)
    const VTAB_KIND: VTabKind = VTabKind::VirtualTable;

    /// Function to initialize the schema of your vtable 
    fn create_schema(_args: &[Value]) -> &'static str {
        "CREATE TABLE csv_data(
            name TEXT,
            age TEXT,
            city TEXT
        )"
    }

    /// Open to return a new cursor: In this simple example, the CSV file is read completely into memory on connect.
    fn open(&self) -> Result<Self::VCursor, Self::Error> {
        // Read CSV file contents from "data.csv"
        let csv_content = fs::read_to_string("data.csv").unwrap_or_default();
        // For simplicity, we'll ignore the header row.
        let rows: Vec<Vec<String>> = csv_content
            .lines()
            .skip(1)
            .map(|line| {
                line.split(',')
                    .map(|s| s.trim().to_string())
                    .collect()
            })
            .collect();
        Ok(CsvCursor { rows, index: 0 })
    }

    /// Filter through result columns. (not used in this simple example)
    fn filter(_cursor: &mut Self::VCursor, _args: &[Value]) -> ResultCode {
        ResultCode::OK
    }

    /// Return the value for the column at the given index in the current row.
    fn column(cursor: &Self::VCursor, idx: u32) -> Result<Value, Self::Error> {
        cursor.column(idx)
    }

    /// Next advances the cursor to the next row.
    fn next(cursor: &mut Self::VCursor) -> ResultCode {
        if cursor.index < cursor.rows.len() - 1 {
            cursor.index += 1;
            ResultCode::OK
        } else {
            ResultCode::EOF
        }
    }

    /// Return true if the cursor is at the end.
    fn eof(cursor: &Self::VCursor) -> bool {
        cursor.index >= cursor.rows.len()
    }

    /// *Optional* methods for non-readonly tables

    /// Update the value at rowid
    fn update(&mut self, _rowid: i64, _args: &[Value]) -> Result<(), Self::Error> {
        Ok(())
    }

    /// Insert the value(s)
    fn insert(&mut self, _args: &[Value]) -> Result<i64, Self::Error> {
        Ok(0)
    }
    /// Delete the value at rowid
    fn delete(&mut self, _rowid: i64) -> Result<(), Self::Error> {
        Ok(())
    }
}

/// The cursor for iterating over CSV rows.
#[derive(Debug)]
struct CsvCursor {
    rows: Vec<Vec<String>>,
    index: usize,
}

/// Implement the VTabCursor trait for your cursor type
impl VTabCursor for CsvCursor {
    type Error = &'static str;

    fn next(&mut self) -> ResultCode {
        CsvCursor::next(self)
    }

    fn eof(&self) -> bool {
        self.index >= self.rows.len()
    }

    fn column(&self, idx: u32) -> Result<Value, Self::Error> {
        let row = &self.rows[self.index];
        if (idx as usize) < row.len() {
            Ok(Value::from_text(&row[idx as usize]))
        } else {
            Ok(Value::null())
        }
    }

    fn rowid(&self) -> i64 {
        self.index as i64
    }
}

Cargo.toml Config

Edit the workspace Cargo.toml to include your extension as a workspace dependency, e.g:

[workspace.dependencies]
limbo_core = { path = "core", version = "0.0.16" }
limbo_crypto = { path = "extensions/crypto", version = "0.0.16" }
limbo_ext = { path = "extensions/core", version = "0.0.16" }
limbo_macros = { path = "macros", version = "0.0.16" }
limbo_uuid = { path = "extensions/uuid", version = "0.0.16" }
...
+limbo_csv = { path = "extensions/csv", version = "0.0.16" }

And add your extension as a feature in core/Cargo.toml:

[features]
default = ["fs", "json", "uuid", "time"]
fs = []
json = ["dep:jsonb", "dep:pest", "dep:pest_derive", "dep:serde", "dep:indexmap"]
uuid = ["limbo_uuid/static"]
...
+csv = ["limbo_csv/static"]

Register Extension in Core

Lastly, you have to register your extension statically in the core crate:

pub fn register_builtins(&self) -> Result<(), String> {
        #[allow(unused_variables)]
        let ext_api = self.build_limbo_ext();
        #[cfg(feature = "uuid")]
        if unsafe { !limbo_uuid::register_extension_static(&ext_api).is_ok() } {
            return Err("Failed to register uuid extension".to_string());
        }
+        #[cfg(feature = "csv")]
+        if unsafe { !limbo_csv::register_extension_static(&ext_api).is_ok() } {
+            return Err("Failed to register csv extension".to_string());
+        }
        Ok(())
    }