fenecdb and PGlite

PGlite is PostgreSQL compiled to WebAssembly, run in the browser, Node.js and Bun, with extensions such as pgvector. fenecdb is a document database with vector and full-text search, built small for the page, which also runs in apps and as a server.

The facts about PGlite on this page were checked on 2026-10-02 against the pages each one links to, and its sizes measured from version 0.5.8. fenecdb is at version 0.1.8, and is alpha software.

Which to choose

Choose PGlite when

• You want PostgreSQL itself in the page: its SQL, joins, types and interactive transactions (API).
• You want its extensions: pgvector, pg_textsearch, pg_trgm, PostGIS and others (extensions).
• The page and the server should share schema and queries with a PostgreSQL server, or an ORM such as Drizzle (Drizzle).
• You want live queries that send only what changed (live.incrementalQuery, live.changes).

Choose fenecdb when

• Download size matters: its module is about a twentieth of PGlite's files (below).
• You search by meaning and by words in one query, fused by rank, with nothing to install (Hybrid search).
• The page writes while offline and syncs back to a server, which PGlite's sync does not do yet (Sync).
• You keep the database in a file in Safari too: fenecdb's file of the origin private file system works there (Keeping a database).
• The same database runs in an iOS, Android or Flutter app (Mobile and native apps).

Feature by feature

What the browser downloads

The WebAssembly and data files each needs before its first query, as each project publishes them, compressed the way a static file is served. PGlite's files are about 21 times fenecdb's module.

• PGlite's own figure is "under 3MB gzipped" (about); the files of 0.5.8 measure more.
• pgvector adds a 45 KB archive, gzipped as published.

Moving from PGlite

A table becomes a collection, a query becomes FenecQL or a builder chain, and parameters stay $1, $2. Opening a database kept in IndexedDB, with a vector and a text index:

// PGlite
import { PGlite } from '@electric-sql/pglite';
import { vector } from '@electric-sql/pglite-pgvector';

const pg = new PGlite('idb://notes', { extensions: { vector } });
await pg.exec(`
  CREATE EXTENSION IF NOT EXISTS vector;
  CREATE TABLE IF NOT EXISTS notes (id serial PRIMARY KEY, title text, body text, embed vector(384));
  CREATE INDEX IF NOT EXISTS notes_embed ON notes USING hnsw (embed vector_cosine_ops);
`);

// fenecdb
import { Fenec, restore } from './fenec.js';
import { fenecTable, text, vector, index } from './schema.js';

const notes = fenecTable('notes', {
  title: text(),
  body: text(),
  embed: vector({ dimensions: 384 }),
}, (t) => [
  index('notes_body').using('bm25', t.body),
  index('notes_embed').using('hnsw', t.embed.op('vector_cosine_ops')),
]);

const db = await Fenec.open('./fenec.wasm', { schema: { notes } });
await restore(db, 'notes');   // what persist(db, 'notes') kept, if anything

A write and a nearest-neighbour search:

// PGlite
await pg.query('INSERT INTO notes (title, body, embed) VALUES ($1, $2, $3)',
  [title, body, JSON.stringify(embedding)]);
const { rows } = await pg.query(
  'SELECT title FROM notes ORDER BY embed <=> $1 LIMIT 5', [JSON.stringify(queryVector)]);

// fenecdb, the builder
await db.from(notes).insert({ title, body, embed: embedding });
const rows = await db.from(notes).select('title').near('embed', queryVector).limit(5).rows();

// or FenecQL
const same = db.rows('get notes select title near embed $1 limit 5', [queryVector]);

Words and meaning in one ranking, which in PGlite is a query to write over pg_textsearch and pgvector:

const hits = await db.from(notes).select('title')
  .match('body', 'offline sync')
  .near('embed', queryVector)
  .fuse()
  .limit(10)
  .rows();

A live query:

// PGlite, with the live extension
const ret = pg.live.query('SELECT title FROM notes ORDER BY id DESC LIMIT 20', [], (res) => draw(res.rows));
await ret.unsubscribe();

// fenecdb
const stop = db.live(db.from(notes).select('title').order('id', 'desc').limit(20), draw);
stop();