rust/docs/dev/architecture.md

# Architecture

This document describes the high-level architecture of rust-analyzer.
If you want to familiarize yourself with the code base, you are just
in the right place!

See also the [guide](./guide.md), which walks through a particular snapshot of
rust-analyzer code base.

Yet another resource is this playlist with videos about various parts of the
analyzer:

https://www.youtube.com/playlist?list=PL85XCvVPmGQho7MZkdW-wtPtuJcFpzycE

## The Big Picture

![](https://user-images.githubusercontent.com/1711539/50114578-e8a34280-0255-11e9-902c-7cfc70747966.png)

On the highest level, rust-analyzer is a thing which accepts input source code
from the client and produces a structured semantic model of the code.

More specifically, input data consists of a set of test files (`(PathBuf,
String)` pairs) and information about project structure, captured in the so called
`CrateGraph`. The crate graph specifies which files are crate roots, which cfg
flags are specified for each crate (TODO: actually implement this) and what
dependencies exist between the crates. The analyzer keeps all this input data in
memory and never does any IO. Because the input data is source code, which
typically measures in tens of megabytes at most, keeping all input data in
memory is OK.

A "structured semantic model" is basically an object-oriented representation of
modules, functions and types which appear in the source code. This representation
is fully "resolved": all expressions have types, all references are bound to
declarations, etc.

The client can submit a small delta of input data (typically, a change to a
single file) and get a fresh code model which accounts for changes.

The underlying engine makes sure that model is computed lazily (on-demand) and
can be quickly updated for small modifications.


## Code generation

Some of the components of this repository are generated through automatic
processes. These are outlined below:

- `cargo xtask codegen`: The kinds of tokens that are reused in several places, so a generator
  is used. We use `quote!` macro to generate the files listed below, based on
  the grammar described in [grammar.ron]:
  - [ast/generated.rs][ast generated]
  - [syntax_kind/generated.rs][syntax_kind generated]

[grammar.ron]: ../../crates/ra_syntax/src/grammar.ron
[ast generated]: ../../crates/ra_syntax/src/ast/generated.rs
[syntax_kind generated]: ../../crates/ra_parser/src/syntax_kind/generated.rs

## Code Walk-Through

### `crates/ra_syntax`, `crates/ra_parser`

Rust syntax tree structure and parser. See
[RFC](https://github.com/rust-lang/rfcs/pull/2256) for some design notes.

- [rowan](https://github.com/rust-analyzer/rowan) library is used for constructing syntax trees.
- `grammar` module is the actual parser. It is a hand-written recursive descent parser, which
  produces a sequence of events like "start node X", "finish node Y". It works similarly to [kotlin's parser](https://github.com/JetBrains/kotlin/blob/4d951de616b20feca92f3e9cc9679b2de9e65195/compiler/frontend/src/org/jetbrains/kotlin/parsing/KotlinParsing.java),
  which is a good source of inspiration for dealing with syntax errors and incomplete input. Original [libsyntax parser](https://github.com/rust-lang/rust/blob/6b99adeb11313197f409b4f7c4083c2ceca8a4fe/src/libsyntax/parse/parser.rs)
  is what we use for the definition of the Rust language.
- `parser_api/parser_impl` bridges the tree-agnostic parser from `grammar` with `rowan` trees.
  This is the thing that turns a flat list of events into a tree (see `EventProcessor`)
- `ast` provides a type safe API on top of the raw `rowan` tree.
- `grammar.ron` RON description of the grammar, which is used to
  generate `syntax_kinds` and `ast` modules, using `cargo xtask codegen` command.
- `algo`: generic tree algorithms, including `walk` for O(1) stack
  space tree traversal (this is cool).

Tests for ra_syntax are mostly data-driven: `test_data/parser` contains subdirectories with a bunch of `.rs`
(test vectors) and `.txt` files with corresponding syntax trees. During testing, we check
`.rs` against `.txt`. If the `.txt` file is missing, it is created (this is how you update
tests). Additionally, running `cargo xtask codegen` will walk the grammar module and collect
all `// test test_name` comments into files inside `test_data/parser/inline` directory.

See [#93](https://github.com/rust-analyzer/rust-analyzer/pull/93) for an example PR which
fixes a bug in the grammar.

### `crates/ra_db`

We use the [salsa](https://github.com/salsa-rs/salsa) crate for incremental and
on-demand computation. Roughly, you can think of salsa as a key-value store, but
it also can compute derived values using specified functions. The `ra_db` crate
provides basic infrastructure for interacting with salsa. Crucially, it
defines most of the "input" queries: facts supplied by the client of the
analyzer. Reading the docs of the `ra_db::input` module should be useful:
everything else is strictly derived from those inputs.

### `crates/ra_hir`

HIR provides high-level "object oriented" access to Rust code.

The principal difference between HIR and syntax trees is that HIR is bound to a
particular crate instance. That is, it has cfg flags and features applied (in
theory, in practice this is to be implemented). So, the relation between
syntax and HIR is many-to-one. The `source_binder` module is responsible for
guessing a HIR for a particular source position.

Underneath, HIR works on top of salsa, using a `HirDatabase` trait.

### `crates/ra_ide`

A stateful library for analyzing many Rust files as they change. `AnalysisHost`
is a mutable entity (clojure's atom) which holds the current state, incorporates
changes and hands out `Analysis` --- an immutable and consistent snapshot of
the world state at a point in time, which actually powers analysis.

One interesting aspect of analysis is its support for cancellation. When a
change is applied to `AnalysisHost`, first all currently active snapshots are
canceled. Only after all snapshots are dropped the change actually affects the
database.

APIs in this crate are IDE centric: they take text offsets as input and produce
offsets and strings as output. This works on top of rich code model powered by
`hir`.

### `crates/ra_lsp_server`

An LSP implementation which wraps `ra_ide` into a language server protocol.

### `ra_vfs`

Although `hir` and `ra_ide` don't do any IO, we need to be able to read
files from disk at the end of the day. This is what `ra_vfs` does. It also
manages overlays: "dirty" files in the editor, whose "true" contents is
different from data on disk. This is more or less the single really
platform-dependent component, so it lives in a separate repository and has an
extensive cross-platform CI testing.

### `crates/gen_lsp_server`

A language server scaffold, exposing a synchronous crossbeam-channel based API.
This crate handles protocol handshaking and parsing messages, while you
control the message dispatch loop yourself.

Run with `RUST_LOG=sync_lsp_server=debug` to see all the messages.

### `crates/ra_cli`

A CLI interface to rust-analyzer.


## Testing Infrastructure

Rust Analyzer has three interesting [systems
boundaries](https://www.tedinski.com/2018/04/10/making-tests-a-positive-influence-on-design.html)
to concentrate tests on.

The outermost boundary is the `ra_lsp_server` crate, which defines an LSP
interface in terms of stdio. We do integration testing of this component, by
feeding it with a stream of LSP requests and checking responses. These tests are
known as "heavy", because they interact with Cargo and read real files from
disk. For this reason, we try to avoid writing too many tests on this boundary:
in a statically typed language, it's hard to make an error in the protocol
itself if messages are themselves typed.

The middle, and most important, boundary is `ra_ide`. Unlike
`ra_lsp_server`, which exposes API, `ide` uses Rust API and is intended to
use by various tools. Typical test creates an `AnalysisHost`, calls some
`Analysis` functions and compares the results against expectation.

The innermost and most elaborate boundary is `hir`. It has a much richer
vocabulary of types than `ide`, but the basic testing setup is the same: we
create a database, run some queries, assert result.

For comparisons, we use [insta](https://github.com/mitsuhiko/insta/) library for
snapshot testing.

To test various analysis corner cases and avoid forgetting about old tests, we
use so-called marks. See the `marks` module in the `test_utils` crate for more.