asterql

AsterQL

npm install asterql

A small language for shaping and analyzing JSON.

AsterQL is a JSON query and projection language for TypeScript. It is terse, JSON-shaped, synchronous, and built around the kind of data work application code does all the time: filter this API response, join that lookup table, group these nested records, and return exactly the shape a UI wants.

*.posts[status == "published"]
| sort(-date)[:10]{
  id,
  title,
  author: authorId -> *.users[id == @][0]{id, name},
  tags: tagIds -> *.tags[id in @]{name}
}

Why “AsterQL”? AsterQL starts at *, the full input value. The name points at the asterisk without making the language feel like a utility function. It is a small star for JSON data: a terse way to navigate, reshape, and summarize the structure in front of you.

AsterQL takes inspiration from Sanity GROQ's *[filter]{projection} shape, Sanity JSONMatch's tiny cursor-driven implementation style, and jq's compositional feel. It is not a clone of any of them. The goal is a JavaScript-native language for JSON data shaping, UI projections, and lightweight analytics.

Why AsterQL?

• 🧭 Starts from JSON: query plain data, API responses, config, fixtures, or generated objects.
• ✂️ Shapes for UI: return the exact nested structure your component wants.
• 📊 Does analytical work: filter, sort, group, join, and aggregate without moving into SQL-shaped tables first.
• 🧩 Composes deliberately: traversal and projection stay JSON-like; pipes mark collection-level transformations.
• 🔒 Runs locally and synchronously: no hidden I/O, mutation, modules, network calls, or resolver magic.

Install

pnpm add asterql

npm install asterql

Quick Start

import { evaluate, query, compile } from "asterql";

const data = {
  posts: [
    { id: "p1", status: "published", title: "Alpha", authorId: "u1" },
    { id: "p2", status: "draft", title: "Beta", authorId: "u2" },
  ],
  users: [
    { id: "u1", name: "Ada" },
    { id: "u2", name: "Grace" },
  ],
};

const result = evaluate(
  data,
  '*.posts[status == "published"]{title, author: authorId -> *.users[id == @][0].name}',
);

console.log(result);
// [{ title: "Alpha", author: "Ada" }]

const compiled = compile("*.posts[$status = status]{id, title}");
compiled.evaluate(data, { params: { status: "draft" } });
// [{ id: "p2", title: "Beta" }]

for (const entry of query(data, '*.posts[status == "published"]')) {
  console.log(entry.value, entry.path);
}

A Few Shapes

Project UI Data

*.products[active?]{
  id,
  label: name,
  price,
  badge: category -> *.categories[id == @][0].name
}

Join Across Collections

*.posts{
  title,
  author: authorId -> *.users[id == @][0]{id, name}
}

Group And Aggregate

*.orders
| group(status){
  status: @key,
  count: len(@items),
  revenue: @items | sum(total)
}

Flatten Nested JSON

*.orders[]{
  orderId: id,
  lines: items[]{sku, quantity, subtotal: quantity * price}
}

Language Shape

Scopes

• * is the root input value.
• @ is the current value.
• ^ is the parent current value.
• $name reads a parameter from the host.

Bare identifiers read attributes from the current value, so title is the same idea as @.title. Missing fields evaluate to null.

Traversal

*.posts[0]
*.posts[-1]
*.posts[:10]
*.posts[].tags[]
*.settings["theme-color"]
*.posts..slug

[] is the explicit fan-out operator. Ordinary field traversal does not implicitly flatten nested arrays.

Filtering

*.products[price > 10 && active?]
*.posts[id in $ids]

Filters keep truthy values. The postfix ? operator tests whether a value exists.

Projection

Projections are intentionally JSON-like:

*.cart.items{
  id,
  label: name,
  subtotal: price * quantity,
  ...metadata
}

Projection maps arrays and lazy sequences item-by-item. On a single object it returns one shaped object.

Joins

left -> right evaluates the right side with @ bound to the result of the left side and ^ bound to the previous current value.

*.posts{
  title,
  author: authorId -> *.users[id == @][0]{id, name}
}

Pipes

Pipes are deliberate collection boundaries. The left value becomes @ for the right expression.

*.posts[status == "published"] | sort(-date)[:5]{title, date}
*.posts | group(status){status: @key, count: len(@items)}
*.orders | fold{count: len(@), revenue: sum(total), statuses: unique(status)}

The initial collection tools are:

• sort(expr, ...) for stable sorting.
• group(expr) for {key, items} groups.
• fold{...} for aggregating a collection into one projected object.

Sorting, grouping, negative slices, and aggregation buffer because they require the whole collection. Traversal, filters, projections, joins, and positive slices remain lazy.

Built-ins

AsterQL keeps the default built-in set intentionally compact:

• len(value?)
• sum(expr?)
• avg(expr?)
• min(expr?)
• max(expr?)
• any(expr?)
• all(expr?)
• unique(expr?)
• keys(value?)
• type(value?)

sum(total) and avg(total) are the preferred “sum by” / “average by” shape: the first argument is evaluated once per item in the current collection. == and === are aliases for equality with the same deep JSON semantics as =; there is no JavaScript coercion.

Custom scalar functions can be passed through EvaluationOptions.functions. Custom collection reducers can be passed through EvaluationOptions.reducers.

API

parse(query: string): ProgramNode;
scan(query: string): Token[];
query(
  data: unknown,
  queryOrAst: string | ProgramNode,
  options?,
): Iterable<QueryEntry>;
evaluate(
  data: unknown,
  queryOrAst: string | ProgramNode,
  options?,
): JsonValue;
compile(query: string): CompiledQuery;

evaluate returns a materialized JSON result. query yields {value, path} entries; paths are preserved for real traversals and set to null for computed values.

Docs

• SPEC.md: language semantics and examples.
• GRAMMAR.md: implementation-driven grammar reference.
• CONTRIBUTING.md: local development and release checks.

Development

pnpm install
pnpm lint
pnpm typecheck
pnpm test
pnpm build

Status

AsterQL is early. The parser and evaluator are small enough to audit, and the current package is ready to consume as an ESM TypeScript library. The language surface should still be considered pre-1.0 while the analytics and codegen story settles.