How Salsa works

This chapter is based on the explanation given by Niko Matsakis in this video about Salsa. To find out more you may want to watch Salsa In More Depth, also by Niko Matsakis.

As of July 2021, although Salsa is inspired by (among other things) rustc's query system, it is not used directly in rustc. It is used in chalk and extensively in rust-analyzer, but there are no medium or long-term concrete plans to integrate it into the compiler.

What is Salsa?

Salsa is a library for incremental recomputation. This means it allows reusing computations that were already done in the past to increase the efficiency of future computations.

The objectives of Salsa are:

  • Provide that functionality in an automatic way, so reusing old computations is done automatically by the library
  • Doing so in a "sound", or "correct", way, therefore leading to the same results as if it had been done from scratch

Salsa's actual model is much richer, allowing many kinds of inputs and many different outputs. For example, integrating Salsa with an IDE could mean that the inputs could be the manifest (Cargo.toml), entire source files (, snippets and so on; the outputs of such an integration could range from a binary executable, to lints, types (for example, if a user selects a certain variable and wishes to see its type), completions, etc.

How does it work?

The first thing that Salsa has to do is identify the "base inputs" that are not something computed but given as input.

Then Salsa has to also identify intermediate, "derived" values, which are something that the library produces, but, for each derived value there's a "pure" function that computes the derived value.

For example, there might be a function ast(x: Path) -> AST. The produced AST isn't a final value, it's an intermediate value that the library would use for the computation.

This means that when you try to compute with the library, Salsa is going to compute various derived values, and eventually read the input and produce the result for the asked computation.

In the course of computing, Salsa tracks which inputs were accessed and which values are derived. This information is used to determine what's going to happen when the inputs change: are the derived values still valid?

This doesn't necessarily mean that each computation downstream from the input is going to be checked, which could be costly. Salsa only needs to check each downstream computation until it finds one that isn't changed. At that point, it won't check other derived computations since they wouldn't need to change.

It's is helpful to think about this as a graph with nodes. Each derived value has a dependency on other values, which could themselves be either base or derived. Base values don't have a dependency.

I <- A <- C ...
J <- B <--+

When an input I changes, the derived value A could change. The derived value B , which does not depend on I, A, or any value derived from A or I, is not subject to change. Therefore, Salsa can reuse the computation done for B in the past, without having to compute it again.

The computation could also terminate early. Keeping the same graph as before, say that input I has changed in some way (and input J hasn't) but, when computing A again, it's found that A hasn't changed from the previous computation. This leads to an "early termination", because there's no need to check if C needs to change, since both C direct inputs, A and B, haven't changed.

Key Salsa concepts


A query is some value that Salsa can access in the course of computation. Each query can have a number of keys (from 0 to many), and all queries have a result, akin to functions. 0-key queries are called "input" queries.


The database is basically the context for the entire computation, it's meant to store Salsa's internal state, all intermediate values for each query, and anything else that the computation might need. The database must know all the queries that the library is going to do before it can be built, but they don't need to be specified in the same place.

After the database is formed, it can be accessed with queries that are very similar to functions. Since each query's result is stored in the database, when a query is invoked N times, it will return N cloned results, without having to recompute the query (unless the input has changed in such a way that it warrants recomputation).

For each input query (0-key), a "set" method is generated, allowing the user to change the output of such query, and trigger previous memoized values to be potentially invalidated.

Query Groups

A query group is a set of queries which have been defined together as a unit. The database is formed by combining query groups. Query groups are akin to "Salsa modules".

A set of queries in a query group are just a set of methods in a trait.

To create a query group a trait annotated with a specific attribute (#[salsa::query_group(...)]) has to be created.

An argument must also be provided to said attribute as it will be used by Salsa to create a struct to be used later when the database is created.

Example input query group:

/// This attribute will process this tree, produce this tree as output, and produce
/// a bunch of intermediate stuff that Salsa also uses.  One of these things is a
/// "StorageStruct", whose name we have specified in the attribute.
/// This query group is a bunch of **input** queries, that do not rely on any
/// derived input.
pub trait Inputs {
    /// This attribute (`#[salsa::input]`) indicates that this query is a base
    /// input, therefore `set_manifest` is going to be auto-generated
    fn manifest(&self) -> Manifest;

    fn source_text(&self, name: String) -> String;

To create a derived query group, one must specify which other query groups this one depends on by specifying them as supertraits, as seen in the following example:

/// This query group is going to contain queries that depend on derived values a
/// query group can access another query group's queries by specifying the
/// dependency as a super trait query groups can be stacked as much as needed using
/// that pattern.
pub trait Parser: Inputs {
    /// This query `ast` is not an input query, it's a derived query this means
    /// that a definition is necessary.
    fn ast(&self, name: String) -> String;

When creating a derived query the implementation of said query must be defined outside the trait. The definition must take a database parameter as an impl Trait (or dyn Trait), where Trait is the query group that the definition belongs to, in addition to the other keys.

///This is going to be the definition of the `ast` query in the `Parser` trait.
///So, when the query `ast` is invoked, and it needs to be recomputed, Salsa is going to call this function
///and it's is going to give it the database as `impl Parser`.
///The function doesn't need to be aware of all the queries of all the query groups
fn ast(db: &impl Parser, name: String) -> String {
    //! Note, `impl Parser` is used here but `dyn Parser` works just as well
    /* code */
    ///By passing an `impl Parser`, this is allowed
    let source_text = db.input_file(name);
    /* do the actual parsing */
    return ast;

Eventually, after all the query groups have been defined, the database can be created by declaring a struct.

To specify which query groups are going to be part of the database an attribute (#[salsa::database(...)]) must be added. The argument of said attribute is a list of identifiers, specifying the query groups storages.

///This attribute specifies which query groups are going to be in the database
#[salsa::database(InputsStorage, ParserStorage)]
#[derive(Default)] //optional!
struct MyDatabase {
    ///You also need this one field
    runtime : salsa::Runtime<MyDatabase>,
///And this trait has to be implemented
impl salsa::Databse for MyDatabase {
    fn salsa_runtime(&self) -> &salsa::Runtime<MyDatabase> {

Example usage:

fn main() {
    let db = MyDatabase::default();
    loop {
        db.ast(...); //will reuse results