Sled, the Fast and Lightweight Rust Database

#programming #rust #database #performance

Reliable storage with minimal footprint, tailored for Rust developers

Sled vs SQL vs Other Rust DB Crates (RocksDB, SeaORM, Rbatis)

Recently, I needed to integrate a database into a Rust application. Exploring the ecosystem, I encountered several options: from traditional SQL-based crates like _Diesel and SQLx, to embedded key-value stores such as RocksDB, and more lightweight solutions like Sled. Each option offers different trade-offs in terms of performance, ease of use, scalability, and flexibility.

After testing and researching, I realized why Sled stands out for embedded, high-performance, and safe data storage in Rust applications. Below is a comparison of Sled against other common Rust database crates to help clarify the differences and why Sled could be the ideal choice depending on your project needs.

Sled guarantees crash-safety by using a lock-free, append-only log structure and supports atomic batch operations, making it ideal for concurrent embedded use

Feature	Sled	SQL-based Crates (Diesel, SQLx)	Other Rust DB Crates (RocksDB, SeaORM, Rbatis)
Performance	High for embedded use; optimized for fast writes and reads	Depends on DB backend (Postgres, SQLite), generally it's good	Varies: RocksDB is very fast for key-value, ORM layers add overhead
Speed	Very fast in-memory and disk-based operations	Moderate to fast, depends on SQL backend and query complexity	RocksDB fast for key-value, ORMs slower due to abstraction
Safety	Memory safe, leverages Rust ownership; crash-safe design	Depends on backend; Rust crates provide compile-time query safety	Varies: RocksDB bindings safe but lower-level; ORMs type-safe
Scalability	Designed for embedded/single-node; not distributed	Scales well with DB backend (e.g., Postgres scales well)	RocksDB scales for local data; ORM scalability depends on backend
Size/Weight	Very lightweight (~1 MB crate size); minimal dependencies	Heavier; depends on features and SQL backend libraries	RocksDB larger due to C++ dependency; ORMs add abstraction layers
Flexibility	Key-value store, no SQL, supports trees and complex data structures	Full SQL support, complex queries, joins	Varies: RocksDB key-value; ORMs offer flexible schema and querying
Ease of Use	Simple API for embedded use, less boilerplate	Steeper learning curve; need to understand SQL and ORM paradigms	Varies: RocksDB lower-level, ORMs easier for relational models
Concurrency	Lock-free design, supports concurrent reads/writes	Depends on backend and crate support	RocksDB supports concurrent access; ORMs depend on DB
Use Case	Embedded apps, local storage, fast persistent KV store	Web apps, complex querying, relational DB needs	Embedded key-value, ORM abstraction for relational DBs
Maturity	Actively developed, modern Rust idioms	Mature ecosystems (Diesel, SQLx widely used)	Varies widely; RocksDB bindings stable, some ORMs newer

Code snippet

Here’s a simple example showing how easy it is to open a DB, and read/write data using Sled.

use sled::Db;

fn main() -> sled::Result<()> {
    let db: Db = sled::open("my_db")?;
    db.insert(b"key", b"value")?;
    let value = db.get(b"key")?;
    println!("Got: {:?}", value);
    Ok(())
}

Instead, a SQL example from the official documentation looks like this:

use sqlx::postgres::PgPoolOptions;
// use sqlx::mysql::MySqlPoolOptions;
// etc.

#[async_std::main] // Requires the `attributes` feature of `async-std`
// or #[tokio::main]
// or #[actix_web::main]
async fn main() -> Result<(), sqlx::Error> {
    // Create a connection pool
    //  for MySQL/MariaDB, use MySqlPoolOptions::new()
    //  for SQLite, use SqlitePoolOptions::new()
    //  etc.
    let pool = PgPoolOptions::new()
        .max_connections(5)
        .connect("postgres://postgres:password@localhost/test").await?;

    // Make a simple query to return the given parameter (use a question mark `?` instead of `$1` for MySQL/MariaDB)
    let row: (i64,) = sqlx::query_as("SELECT $1")
        .bind(150_i64)
        .fetch_one(&pool).await?;

    assert_eq!(row.0, 150);

    Ok(())
}

As you can see, it requires more configuration and async functions.

A direct comparison between sqlx and sled

Feature	SQLx	Sled
Async support	✅	❌
SQL queries	✅	❌
Embedded use	❌	✅
Performance	✅	✅
Type safety	✅	✅
Scalability	✅	❌
Ease of use	❌	✅
Weight	❌ (heavier, async & DB drivers)	✅ (lightweight, minimal dependencies)

When (and When Not) to Use Sled

When to use Sled:

You need a fast, embedded key-value store for local storage in Rust applications.
Your project requires crash-safe, concurrent data access without complex setup.
You want a lightweight, minimal-dependency database that’s easy to integrate.
Your application doesn’t require SQL queries or relational data modeling.
You’re building embedded systems, desktop apps, or small services with local persistence.

When not to use Sled:

You need full SQL support with complex queries, joins, and transactions.
Your application requires a distributed or scalable multi-node database.
You want to leverage existing relational database backends like Postgres or MySQL.
You’re working in a no_std or bare-metal embedded environment.
Your project demands extensive ORM features or schema migrations.

To summarize

Sled is a powerful option when you need reliable, lightweight, and high-performance local storage for Rust applications. It shines in scenarios where simplicity, speed, and safety matter more than SQL features or scalability. If your project doesn't need relational models or complex queries, sled might be the perfect tool for the job.