# 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_api` 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_api` into a language server protocol. ### `ra_vfs` Although `hir` and `ra_ide_api` 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_api`. Unlike `ra_lsp_server`, which exposes API, `ide_api` 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_api`, 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.