What You Can Build with Diesel, and Which Projects Use It

Diesel is a safe, extensible ORM and query builder for Rust, and you can use it to build database-backed applications that range from simple CRUD services to complex query-heavy systems. The official site lists crates.io, vaultwarden, and lemmy as popular projects using Diesel, which shows it is trusted for both high-traffic public services and self-hosted applications. You should consider Diesel if you want compile-time checked database interactions and are comfortable working within Rust's type system.

What kinds of database work Diesel supports

Diesel covers the full range of common database operations, from trivial lookups to nested queries, without dropping into raw SQL for everyday tasks.

Simple queries

Loading a whole table is a one-liner. The site's example loads all users with users::table.load(&mut connection), which executes SELECT * FROM users;. Loading related rows works the same way: Post::belonging_to(user).load(&mut connection) runs SELECT * FROM posts WHERE user_id = 1;. This is the pattern you would use for dashboards, list pages, or any endpoint that returns a collection.

Complex queries with filtering, ordering, and subqueries

Diesel's query builder composes. The documented example builds a query for the five most recent versions of a crate, then loads download records from the last 90 days for those versions, ordered by date:

let versions = Version::belonging_to(krate)
    .select(id)
    .order(num.desc())
    .limit(5);
let downloads = version_downloads
    .filter(date.gt(now - 90.days()))
    .filter(version_id.eq(any(versions)))
    .order(date)
    .load::<Download>(&mut conn)?;

The generated SQL uses a subquery with ANY(...), so you get the same result you would write by hand, but the query is checked when the code compiles. This is the kind of work you would build for analytics views, activity feeds, or reporting endpoints.

Inserts, updates, and raw SQL

The site's examples also cover inserting data, updating data, and ergonomic raw SQL. That means Diesel is not limited to reads: you can build full write paths for user registration, content editing, counters, and similar features. The raw SQL escape hatch matters when you have a query the builder does not express well; you keep the rest of the ORM benefits without being blocked.

Less boilerplate through codegen

Diesel derives row mapping for you. With #[derive(Queryable)] on a struct like Download, Diesel generates the mapping code; without it you would write the struct and the mapping by hand. For applications with many tables, this removes a large amount of repetitive code and keeps structs aligned with your domain model.

Which projects use Diesel

The official site names three popular projects:

These are useful reference points when you are deciding whether Diesel fits your project type. If your application resembles any of them — a registry, a self-hosted service with accounts and stored records, or a content platform with users, posts, and votes — Diesel has proven coverage for that shape of data.

How to decide whether Diesel fits your project

Diesel is built around three stated goals: preventing runtime errors by eliminating incorrect database interactions at compile time, performance through zero-cost abstractions (the site claims queries can run and load data faster than C), and extensibility so you can write reusable code in terms of your problem domain rather than SQL.

Choose Diesel when:

• You want database mistakes caught at compile time rather than in production.
• Your data model is relational and you expect to write nontrivial queries with filters, ordering, and subqueries.
• You want reusable, abstracted data-access code instead of SQL strings scattered through the app.
• You are building something in the same family as crates.io, vaultwarden, or lemmy.

Look elsewhere or plan extra work when:

• You need to support many database backends with minimal per-backend effort; the site mentions ergonomic multidatabase support, but you should verify your specific database is covered.
• Your team is not working in Rust, since Diesel's benefits depend on Rust's type system.
• Your queries are mostly dynamic and assembled at runtime in ways that fight compile-time checking; the raw SQL option helps, but it gives up some of the safety advantage.

To get started, the site points to a Getting Started guide and invites contributions. A practical first step is to model one table from your application, write a simple load query, then extend it with a filter and an order clause to confirm the compile-time workflow matches how your team expects to work.