Turso is an in-process SQL database written in Rust, compatible with SQLite, offering async I/O, vector search, and multi-language support.
Turso Database is an in-process SQL database written in Rust, designed as a modern evolution of SQLite. It maintains compatibility with SQLite's SQL dialect, file formats, and C API while introducing performance enhancements like asynchronous I/O and new features such as vector search and change data capture for contemporary applications.
Developers building applications that require a lightweight, embeddable SQL database with modern features, especially those migrating from or compatible with SQLite but needing improved performance, real-time change tracking, or vector capabilities.
Developers choose Turso Database for its high compatibility with SQLite, enabling easy migration, combined with modern enhancements like native async I/O, multi-language bindings, and built-in vector search that are not available in standard SQLite.
Turso is an in-process SQL database, compatible with SQLite.
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Maintains full compatibility with SQLite's SQL dialect, file formats, and C API, enabling seamless migration from existing SQLite databases with minimal code changes.
Integrates native async I/O with io_uring on Linux, exact vector search, and change data capture, providing capabilities not available in standard SQLite for contemporary applications.
Offers bindings for Go, JavaScript, Java, .NET, Python, Rust, and WebAssembly, making it accessible across diverse tech stacks and deployment environments.
Runs on Linux, macOS, Windows, and browsers via WebAssembly, allowing versatile application deployment from servers to client-side applications.
Includes MVCC-based BEGIN CONCURRENT for improved write throughput and encryption at rest, pushing the boundaries of embedded database performance and security.
The README warns it's not ready for production, with potential bugs and data corruption, requiring caution and backups for any serious use.
Asynchronous I/O performance gains are restricted to Linux via io_uring, limiting benefits on macOS, Windows, or other platforms.
Vector indexing for approximate search is on the roadmap but not yet available, which may delay projects needing fast, scalable vector operations.
As a newer project, it lacks the extensive community, documentation, and third-party integrations that mature databases like SQLite offer, potentially increasing development friction.