turso-db

name

turso-db

description

Install, configure, and work with Turso DB — an in-process SQLite-compatible relational database engine written in Rust. Use when the user needs to (1) install Turso DB, (2) create or query databases with the tursodb CLI shell, (3) use Turso from JavaScript/Node.js via @tursodatabase/database, (4) work with vector search or embeddings in Turso, (5) set up full-text search with FTS indexes, (6) configure transactions including MVCC concurrent transactions, (7) enable encryption at rest, or (8) use Change Data Capture (CDC) for audit logging.

Turso DB

Turso is an in-process relational database engine aiming for full SQLite compatibility. Unlike client-server databases, it runs in your application's memory space with sub-microsecond read/write latencies.

Installation

# Via installer script
curl --proto '=https' --tlsv1.2 -LsSf \
  https://github.com/tursodatabase/turso/releases/latest/download/turso_cli-installer.sh | sh

# Via Homebrew
brew install turso

Quick Start

$ tursodb                        # transient in-memory database
$ tursodb mydata.db              # persistent database file

CREATE TABLE users (id INTEGER PRIMARY KEY, name TEXT, email TEXT);
INSERT INTO users VALUES (1, 'Alice', '[e-mail]');
SELECT * FROM users;

Core SQL

Standard SQLite-compatible SQL. See references/sql-reference.md for full statement syntax (CREATE/ALTER/DROP TABLE, INSERT, SELECT, UPDATE, DELETE, transactions).

Key differences from SQLite:

• No multi-process/multi-threading access
• No savepoints, triggers, or vacuum
• Views experimental (behind --experimental-views flag)
• UTF-8 only

JavaScript API

Install and use from Node.js:

npm i @tursodatabase/database        # native
npm i @tursodatabase/database --cpu wasm32  # WASM

import { connect } from '@tursodatabase/database';
const db = await connect('turso.db');
const row = db.prepare('SELECT 1').get();

See references/javascript-api.md for details.

Vector Search

Store embeddings and perform similarity search using built-in vector functions.

CREATE TABLE docs (id INTEGER PRIMARY KEY, content TEXT, embedding BLOB);
INSERT INTO docs VALUES (1, 'ML basics', vector32('[0.2, 0.5, 0.1]'));

SELECT content, vector_distance_cos(embedding, vector32('[0.3, 0.4, 0.2]')) AS dist
FROM docs ORDER BY dist LIMIT 5;

Supports: vector32, vector64, vector32_sparse, vector8, vector1bit. Distance functions: vector_distance_cos, vector_distance_l2, vector_distance_dot, vector_distance_jaccard.

See references/vector-search.md for all vector types, functions, and examples.

Full-Text Search (Experimental)

Tantivy-powered FTS with BM25 scoring. Requires fts feature at compile time.

CREATE INDEX fts_idx ON articles USING fts (title, body)
WITH (weights = 'title=2.0,body=1.0');

SELECT title, fts_score(title, body, 'database') as score
FROM articles
WHERE fts_match(title, body, 'database')
ORDER BY score DESC LIMIT 10;

See references/full-text-search.md for tokenizers, query syntax, and highlighting.

Advanced Features

See references/advanced-features.md for:

• Journal modes: WAL (default) and experimental MVCC (PRAGMA journal_mode = experimental_mvcc)
• Encryption: AES-GCM / AEGIS cipher support with --experimental-encryption flag
• CDC: Real-time change tracking via PRAGMA unstable_capture_data_changes_conn('full')
• Index methods: Custom data access methods (experimental, --experimental-index-method)
• C API: SQLite C API subset with Turso extensions