Squirreling SQL Engine

!squirreling engine

!coverage

Squirreling is a streaming async SQL engine in pure JavaScript. Built for the browser from the ground up: streaming input and output, pluggable data sources, and lazy async cell evaluation. This makes Squirreling ideal for querying data from network sources, APIs, or LLMs where latency and cost matter.

- Standard SQL: Full SQL support for querying data (read-only)
- Async UDFs: User-defined functions can call APIs or models
- Tiny: 13 kb bundle, zero dependencies, instant startup

The key idea is cell-level lazy evaluation: rows are native AsyncGenerators and cells are async thunks () => Promise. This means expensive operations only execute for cells that actually appear in your query results. Unlike WebAssembly databases, Squirreling is fully async with true streaming during network fetches.

Usage

Squirreling returns an AsyncGenerator of AsyncRows, allowing you to process rows one at a time without loading everything into memory. AsyncRows are made up of AsyncCells, allowing for late materialization of values.

``typescript
import { executeSql } from 'squirreling'

// Input table (in-memory for this example)
const users = [
{ id: 1, name: 'Alice', active: true },
{ id: 2, name: 'Bob', active: false },
{ id: 3, name: 'Charlie', active: true },
// ...more rows
]

// Squirreling return types
interface AsyncRow {
columns: string[]
cells: Record
}
type AsyncCell = () => Promise

// Returns an AsyncIterable of rows with async cell loading
const asyncRows: AsyncIterable = executeSql({
tables: { users },
query: 'SELECT * FROM users',
})

// Process rows as they arrive (streaming)
for await (const { id, name } of asyncRows) {
console.log(User id=${await id()}, name=${await name()})
}
`

Squirreling exports a helper function collect to gather all rows into an array:

`javascript
import { collect, executeSql } from 'squirreling'

// Collect all rows and cells into a materialized array
const rows: Record[] = await collect(executeSql({
tables: { users },
query: 'SELECT active, count(*) as cnt FROM users GROUP BY active',
}))
console.log(Collected rows:, rows)
// Collected rows: [ { active: true, cnt: 2 }, { active: false, cnt: 1 } ]
`

$3

Pass custom functions via the functions option. UDFs can be sync or async, making them ideal for calling APIs, models, or other external services:

`javascript
const rows = await collect(executeSql({
tables: { products },
query: 'SELECT name,AI_SCORE(description) AS score FROM products',
functions: {
AI_SCORE: {
apply: async (text) => completions(Rate the following product description from 1 to 10: ${text}),
arguments: { min: 1, max: 1 },
},
},
}))
`

Because Squirreling uses lazy cell evaluation, the AI_SCORE function only executes for cells that are actually materialized. Combined with LIMIT or WHERE, you can efficiently query expensive operations.

Supported SQL Syntax

Squirreling mostly follows the SQL standard. The following features are supported:

- SELECT statements with WHERE, ORDER BY, LIMIT, OFFSET
- WITH clause for Common Table Expressions (CTEs)
- Subqueries in SELECT, FROM, and WHERE clauses
- JOIN operations: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, POSITIONAL JOIN
- GROUP BY and HAVING clauses

$3

- Aggregate: COUNT, SUM, AVG, MIN, MAX, JSON_ARRAYAGG
- String: CONCAT, SUBSTRING, REPLACE, LENGTH, UPPER, LOWER, TRIM, LEFT, RIGHT, INSTR
- Math: ABS, SIGN, CEIL, FLOOR, ROUND, MOD, RAND, RANDOM, LN, LOG10, EXP, POWER, SQRT
- Trig: SIN, COS, TAN, COT, ASIN, ACOS, ATAN, ATAN2, DEGREES, RADIANS, PI
- Date: CURRENT_DATE, CURRENT_TIME, CURRENT_TIMESTAMP, INTERVAL
- Json: JSON_VALUE, JSON_QUERY, JSON_OBJECT
- Regex: REGEXP_SUBSTR, REGEXP_REPLACE
- Conditional: COALESCE, NULLIF`
- User-defined functions (UDFs)