mirror of
https://github.com/rust-lang/rust-analyzer
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178 lines
8.4 KiB
Markdown
178 lines
8.4 KiB
Markdown
# Architecture
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This document describes the high-level architecture of rust-analyzer.
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If you want to familiarize yourself with the code base, you are just
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in the right place!
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See also the [guide](./guide.md), which walks through a particular snapshot of
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rust-analyzer code base.
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Yet another resource is this playlist with videos about various parts of the
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analyzer:
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https://www.youtube.com/playlist?list=PL85XCvVPmGQho7MZkdW-wtPtuJcFpzycE
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## The Big Picture
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On the highest level, rust-analyzer is a thing which accepts input source code
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from the client and produces a structured semantic model of the code.
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More specifically, input data consists of a set of test files (`(PathBuf,
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String)` pairs) and information about project structure, captured in the so called
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`CrateGraph`. The crate graph specifies which files are crate roots, which cfg
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flags are specified for each crate (TODO: actually implement this) and what
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dependencies exist between the crates. The analyzer keeps all this input data in
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memory and never does any IO. Because the input data is source code, which
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typically measures in tens of megabytes at most, keeping all input data in
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memory is OK.
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A "structured semantic model" is basically an object-oriented representation of
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modules, functions and types which appear in the source code. This representation
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is fully "resolved": all expressions have types, all references are bound to
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declarations, etc.
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The client can submit a small delta of input data (typically, a change to a
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single file) and get a fresh code model which accounts for changes.
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The underlying engine makes sure that model is computed lazily (on-demand) and
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can be quickly updated for small modifications.
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## Code generation
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Some of the components of this repository are generated through automatic
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processes. These are outlined below:
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- `cargo xtask codegen`: The kinds of tokens that are reused in several places, so a generator
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is used. We use `quote!` macro to generate the files listed below, based on
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the grammar described in [grammar.ron]:
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- [ast/generated.rs][ast generated]
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- [syntax_kind/generated.rs][syntax_kind generated]
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[grammar.ron]: ../../crates/ra_syntax/src/grammar.ron
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[ast generated]: ../../crates/ra_syntax/src/ast/generated.rs
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[syntax_kind generated]: ../../crates/ra_parser/src/syntax_kind/generated.rs
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## Code Walk-Through
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### `crates/ra_syntax`, `crates/ra_parser`
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Rust syntax tree structure and parser. See
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[RFC](https://github.com/rust-lang/rfcs/pull/2256) for some design notes.
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- [rowan](https://github.com/rust-analyzer/rowan) library is used for constructing syntax trees.
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- `grammar` module is the actual parser. It is a hand-written recursive descent parser, which
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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),
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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)
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is what we use for the definition of the Rust language.
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- `parser_api/parser_impl` bridges the tree-agnostic parser from `grammar` with `rowan` trees.
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This is the thing that turns a flat list of events into a tree (see `EventProcessor`)
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- `ast` provides a type safe API on top of the raw `rowan` tree.
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- `grammar.ron` RON description of the grammar, which is used to
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generate `syntax_kinds` and `ast` modules, using `cargo xtask codegen` command.
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- `algo`: generic tree algorithms, including `walk` for O(1) stack
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space tree traversal (this is cool).
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Tests for ra_syntax are mostly data-driven: `test_data/parser` contains subdirectories with a bunch of `.rs`
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(test vectors) and `.txt` files with corresponding syntax trees. During testing, we check
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`.rs` against `.txt`. If the `.txt` file is missing, it is created (this is how you update
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tests). Additionally, running `cargo xtask codegen` will walk the grammar module and collect
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all `// test test_name` comments into files inside `test_data/parser/inline` directory.
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See [#93](https://github.com/rust-analyzer/rust-analyzer/pull/93) for an example PR which
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fixes a bug in the grammar.
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### `crates/ra_db`
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We use the [salsa](https://github.com/salsa-rs/salsa) crate for incremental and
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on-demand computation. Roughly, you can think of salsa as a key-value store, but
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it also can compute derived values using specified functions. The `ra_db` crate
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provides basic infrastructure for interacting with salsa. Crucially, it
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defines most of the "input" queries: facts supplied by the client of the
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analyzer. Reading the docs of the `ra_db::input` module should be useful:
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everything else is strictly derived from those inputs.
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### `crates/ra_hir`
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HIR provides high-level "object oriented" access to Rust code.
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The principal difference between HIR and syntax trees is that HIR is bound to a
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particular crate instance. That is, it has cfg flags and features applied (in
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theory, in practice this is to be implemented). So, the relation between
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syntax and HIR is many-to-one. The `source_binder` module is responsible for
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guessing a HIR for a particular source position.
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Underneath, HIR works on top of salsa, using a `HirDatabase` trait.
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### `crates/ra_ide_api`
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A stateful library for analyzing many Rust files as they change. `AnalysisHost`
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is a mutable entity (clojure's atom) which holds the current state, incorporates
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changes and hands out `Analysis` --- an immutable and consistent snapshot of
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the world state at a point in time, which actually powers analysis.
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One interesting aspect of analysis is its support for cancellation. When a
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change is applied to `AnalysisHost`, first all currently active snapshots are
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canceled. Only after all snapshots are dropped the change actually affects the
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database.
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APIs in this crate are IDE centric: they take text offsets as input and produce
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offsets and strings as output. This works on top of rich code model powered by
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`hir`.
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### `crates/ra_lsp_server`
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An LSP implementation which wraps `ra_ide_api` into a language server protocol.
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### `ra_vfs`
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Although `hir` and `ra_ide_api` don't do any IO, we need to be able to read
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files from disk at the end of the day. This is what `ra_vfs` does. It also
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manages overlays: "dirty" files in the editor, whose "true" contents is
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different from data on disk. This is more or less the single really
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platform-dependent component, so it lives in a separate repository and has an
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extensive cross-platform CI testing.
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### `crates/gen_lsp_server`
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A language server scaffold, exposing a synchronous crossbeam-channel based API.
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This crate handles protocol handshaking and parsing messages, while you
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control the message dispatch loop yourself.
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Run with `RUST_LOG=sync_lsp_server=debug` to see all the messages.
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### `crates/ra_cli`
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A CLI interface to rust-analyzer.
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## Testing Infrastructure
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Rust Analyzer has three interesting [systems
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boundaries](https://www.tedinski.com/2018/04/10/making-tests-a-positive-influence-on-design.html)
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to concentrate tests on.
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The outermost boundary is the `ra_lsp_server` crate, which defines an LSP
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interface in terms of stdio. We do integration testing of this component, by
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feeding it with a stream of LSP requests and checking responses. These tests are
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known as "heavy", because they interact with Cargo and read real files from
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disk. For this reason, we try to avoid writing too many tests on this boundary:
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in a statically typed language, it's hard to make an error in the protocol
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itself if messages are themselves typed.
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The middle, and most important, boundary is `ra_ide_api`. Unlike
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`ra_lsp_server`, which exposes API, `ide_api` uses Rust API and is intended to
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use by various tools. Typical test creates an `AnalysisHost`, calls some
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`Analysis` functions and compares the results against expectation.
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The innermost and most elaborate boundary is `hir`. It has a much richer
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vocabulary of types than `ide_api`, but the basic testing setup is the same: we
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create a database, run some queries, assert result.
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For comparisons, we use [insta](https://github.com/mitsuhiko/insta/) library for
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snapshot testing.
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To test various analysis corner cases and avoid forgetting about old tests, we
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use so-called marks. See the `marks` module in the `test_utils` crate for more.
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