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519 lines
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519 lines
27 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 in the right place!
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You might also enjoy ["Explaining Rust Analyzer"](https://www.youtube.com/playlist?list=PLhb66M_x9UmrqXhQuIpWC5VgTdrGxMx3y) series on YouTube.
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It goes deeper than what is covered in this document, but will take some time to watch.
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See also these implementation-related blog posts:
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* https://rust-analyzer.github.io/blog/2019/11/13/find-usages.html
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* https://rust-analyzer.github.io/blog/2020/07/20/three-architectures-for-responsive-ide.html
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* https://rust-analyzer.github.io/blog/2020/09/16/challeging-LR-parsing.html
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* https://rust-analyzer.github.io/blog/2020/09/28/how-to-make-a-light-bulb.html
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* https://rust-analyzer.github.io/blog/2020/10/24/introducing-ungrammar.html
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For older, by now mostly outdated stuff, see the [guide](./guide.md) and [another playlist](https://www.youtube.com/playlist?list=PL85XCvVPmGQho7MZkdW-wtPtuJcFpzycE).
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## Bird's Eye View
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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.
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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`.
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The crate graph specifies which files are crate roots, which cfg flags are specified for each crate and what dependencies exist between the crates.
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This is the input (ground) state.
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The analyzer keeps all this input data in memory and never does any IO.
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Because the input data is source code, which typically measures in tens of megabytes at most, keeping everything in memory is OK.
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A "structured semantic model" is basically an object-oriented representation of modules, functions and types which appear in the source code.
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This representation is fully "resolved": all expressions have types, all references are bound to declarations, etc.
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This is derived state.
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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.
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The underlying engine makes sure that model is computed lazily (on-demand) and can be quickly updated for small modifications.
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## Entry Points
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`crates/rust-analyzer/src/bin/main.rs` contains the main function which spawns LSP.
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This is *the* entry point, but it front-loads a lot of complexity, so it's fine to just skim through it.
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`crates/rust-analyzer/src/handlers.rs` implements all LSP requests and is a great place to start if you are already familiar with LSP.
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`Analysis` and `AnalysisHost` types define the main API for consumers of IDE services.
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## Code Map
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This section talks briefly about various important directories and data structures.
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Pay attention to the **Architecture Invariant** sections.
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They often talk about things which are deliberately absent in the source code.
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Note also which crates are **API Boundaries**.
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Remember, [rules at the boundary are different](https://www.tedinski.com/2018/02/06/system-boundaries.html).
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### `xtask`
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This is rust-analyzer's "build system".
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We use cargo to compile rust code, but there are also various other tasks, like release management or local installation.
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They are handled by Rust code in the xtask directory.
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### `editors/code`
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VS Code plugin.
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### `lib/`
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rust-analyzer independent libraries which we publish to crates.io.
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It's not heavily utilized at the moment.
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### `crates/parser`
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It is a hand-written recursive descent parser, which produces a sequence of events like "start node X", "finish node Y".
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It works similarly to
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[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.
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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.
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`TreeSink` and `TokenSource` traits bridge the tree-agnostic parser from `grammar` with `rowan` trees.
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**Architecture Invariant:** the parser is independent of the particular tree structure and particular representation of the tokens.
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It transforms one flat stream of events into another flat stream of events.
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Token independence allows us to parse out both text-based source code and `tt`-based macro input.
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Tree independence allows us to more easily vary the syntax tree implementation.
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It should also unlock efficient light-parsing approaches.
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For example, you can extract the set of names defined in a file (for typo correction) without building a syntax tree.
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**Architecture Invariant:** parsing never fails, the parser produces `(T, Vec<Error>)` rather than `Result<T, Error>`.
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### `crates/syntax`
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Rust syntax tree structure and parser.
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See [RFC](https://github.com/rust-lang/rfcs/pull/2256) and [./syntax.md](./syntax.md) 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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- `ast` provides a type safe API on top of the raw `rowan` tree.
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- `ungrammar` description of the grammar, which is used to generate `syntax_kinds` and `ast` modules, using `cargo test -p xtask` command.
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Tests for ra_syntax are mostly data-driven.
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`test_data/parser` contains subdirectories with a bunch of `.rs` (test vectors) and `.txt` files with corresponding syntax trees.
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During testing, we check `.rs` against `.txt`.
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If the `.txt` file is missing, it is created (this is how you update tests).
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Additionally, running the xtask test suite with `cargo test -p xtask` will walk the grammar module and collect all `// test test_name` comments into files inside `test_data/parser/inline` directory.
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To update test data, run with `UPDATE_EXPECT` variable:
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```bash
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env UPDATE_EXPECT=1 cargo qt
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```
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After adding a new inline test you need to run `cargo test -p xtask` and also update the test data as described above.
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Note [`api_walkthrough`](https://github.com/rust-lang/rust-analyzer/blob/2fb6af89eb794f775de60b82afe56b6f986c2a40/crates/ra_syntax/src/lib.rs#L190-L348)
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in particular: it shows off various methods of working with syntax tree.
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See [#93](https://github.com/rust-lang/rust-analyzer/pull/93) for an example PR which fixes a bug in the grammar.
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**Architecture Invariant:** `syntax` crate is completely independent from the rest of rust-analyzer. It knows nothing about salsa or LSP.
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This is important because it is possible to make useful tooling using only the syntax tree.
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Without semantic information, you don't need to be able to _build_ code, which makes the tooling more robust.
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See also https://mlfbrown.com/paper.pdf.
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You can view the `syntax` crate as an entry point to rust-analyzer.
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`syntax` crate is an **API Boundary**.
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**Architecture Invariant:** syntax tree is a value type.
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The tree is fully determined by the contents of its syntax nodes, it doesn't need global context (like an interner) and doesn't store semantic info.
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Using the tree as a store for semantic info is convenient in traditional compilers, but doesn't work nicely in the IDE.
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Specifically, assists and refactors require transforming syntax trees, and that becomes awkward if you need to do something with the semantic info.
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**Architecture Invariant:** syntax tree is built for a single file.
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This is to enable parallel parsing of all files.
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**Architecture Invariant:** Syntax trees are by design incomplete and do not enforce well-formedness.
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If an AST method returns an `Option`, it *can* be `None` at runtime, even if this is forbidden by the grammar.
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### `crates/base-db`
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We use the [salsa](https://github.com/salsa-rs/salsa) crate for incremental and on-demand computation.
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Roughly, you can think of salsa as a key-value store, but it can also compute derived values using specified functions.
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The `base-db` crate provides basic infrastructure for interacting with salsa.
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Crucially, it defines most of the "input" queries: facts supplied by the client of the analyzer.
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Reading the docs of the `base_db::input` module should be useful: everything else is strictly derived from those inputs.
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**Architecture Invariant:** particularities of the build system are *not* the part of the ground state.
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In particular, `base-db` knows nothing about cargo.
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For example, `cfg` flags are a part of `base_db`, but `feature`s are not.
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A `foo` feature is a Cargo-level concept, which is lowered by Cargo to `--cfg feature=foo` argument on the command line.
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The `CrateGraph` structure is used to represent the dependencies between the crates abstractly.
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**Architecture Invariant:** `base-db` doesn't know about file system and file paths.
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Files are represented with opaque `FileId`, there's no operation to get an `std::path::Path` out of the `FileId`.
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### `crates/hir-expand`, `crates/hir-def`, `crates/hir_ty`
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These crates are the *brain* of rust-analyzer.
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This is the compiler part of the IDE.
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`hir-xxx` crates have a strong [ECS](https://en.wikipedia.org/wiki/Entity_component_system) flavor, in that they work with raw ids and directly query the database.
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There's little abstraction here.
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These crates integrate deeply with salsa and chalk.
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Name resolution, macro expansion and type inference all happen here.
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These crates also define various intermediate representations of the core.
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`ItemTree` condenses a single `SyntaxTree` into a "summary" data structure, which is stable over modifications to function bodies.
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`DefMap` contains the module tree of a crate and stores module scopes.
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`Body` stores information about expressions.
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**Architecture Invariant:** these crates are not, and will never be, an api boundary.
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**Architecture Invariant:** these crates explicitly care about being incremental.
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The core invariant we maintain is "typing inside a function's body never invalidates global derived data".
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i.e., if you change the body of `foo`, all facts about `bar` should remain intact.
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**Architecture Invariant:** hir exists only in context of particular crate instance with specific CFG flags.
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The same syntax may produce several instances of HIR if the crate participates in the crate graph more than once.
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### `crates/hir`
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The top-level `hir` crate is an **API Boundary**.
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If you think about "using rust-analyzer as a library", `hir` crate is most likely the façade you'll be talking to.
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It wraps ECS-style internal API into a more OO-flavored API (with an extra `db` argument for each call).
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**Architecture Invariant:** `hir` provides a static, fully resolved view of the code.
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While internal `hir-*` crates _compute_ things, `hir`, from the outside, looks like an inert data structure.
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`hir` also handles the delicate task of going from syntax to the corresponding `hir`.
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Remember that the mapping here is one-to-many.
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See `Semantics` type and `source_to_def` module.
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Note in particular a curious recursive structure in `source_to_def`.
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We first resolve the parent _syntax_ node to the parent _hir_ element.
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Then we ask the _hir_ parent what _syntax_ children does it have.
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Then we look for our node in the set of children.
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This is the heart of many IDE features, like goto definition, which start with figuring out the hir node at the cursor.
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This is some kind of (yet unnamed) uber-IDE pattern, as it is present in Roslyn and Kotlin as well.
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### `crates/ide`, `crates/ide-db`, `crates/ide-assists`, `crates/ide-completion`, `crates/ide-diagnostics`, `crates/ide-ssr`
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The `ide` crate builds on top of `hir` semantic model to provide high-level IDE features like completion or goto definition.
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It is an **API Boundary**.
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If you want to use IDE parts of rust-analyzer via LSP, custom flatbuffers-based protocol or just as a library in your text editor, this is the right API.
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**Architecture Invariant:** `ide` crate's API is build out of POD types with public fields.
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The API uses editor's terminology, it talks about offsets and string labels rather than in terms of definitions or types.
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It is effectively the view in MVC and viewmodel in [MVVM](https://en.wikipedia.org/wiki/Model%E2%80%93view%E2%80%93viewmodel).
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All arguments and return types are conceptually serializable.
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In particular, syntax trees and hir types are generally absent from the API (but are used heavily in the implementation).
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Shout outs to LSP developers for popularizing the idea that "UI" is a good place to draw a boundary at.
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`ide` is also the first crate which has the notion of change over time.
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`AnalysisHost` is a state to which you can transactionally `apply_change`.
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`Analysis` is an immutable snapshot of the state.
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Internally, `ide` is split across several crates. `ide-assists`, `ide-completion`, `ide-diagnostics` and `ide-ssr` implement large isolated features.
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`ide-db` implements common IDE functionality (notably, reference search is implemented here).
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The `ide` contains a public API/façade, as well as implementation for a plethora of smaller features.
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**Architecture Invariant:** `ide` crate strives to provide a _perfect_ API.
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Although at the moment it has only one consumer, the LSP server, LSP *does not* influence its API design.
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Instead, we keep in mind a hypothetical _ideal_ client -- an IDE tailored specifically for rust, every nook and cranny of which is packed with Rust-specific goodies.
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### `crates/rust-analyzer`
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This crate defines the `rust-analyzer` binary, so it is the **entry point**.
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It implements the language server.
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**Architecture Invariant:** `rust-analyzer` is the only crate that knows about LSP and JSON serialization.
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If you want to expose a data structure `X` from ide to LSP, don't make it serializable.
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Instead, create a serializable counterpart in `rust-analyzer` crate and manually convert between the two.
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`GlobalState` is the state of the server.
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The `main_loop` defines the server event loop which accepts requests and sends responses.
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Requests that modify the state or might block user's typing are handled on the main thread.
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All other requests are processed in background.
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**Architecture Invariant:** the server is stateless, a-la HTTP.
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Sometimes state needs to be preserved between requests.
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For example, "what is the `edit` for the fifth completion item of the last completion edit?".
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For this, the second request should include enough info to re-create the context from scratch.
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This generally means including all the parameters of the original request.
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`reload` module contains the code that handles configuration and Cargo.toml changes.
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This is a tricky business.
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**Architecture Invariant:** `rust-analyzer` should be partially available even when the build is broken.
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Reloading process should not prevent IDE features from working.
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### `crates/toolchain`, `crates/project-model`, `crates/flycheck`
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These crates deal with invoking `cargo` to learn about project structure and get compiler errors for the "check on save" feature.
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They use `crates/paths` heavily instead of `std::path`.
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A single `rust-analyzer` process can serve many projects, so it is important that server's current directory does not leak.
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### `crates/mbe`, `crates/tt`, `crates/proc-macro-api`, `crates/proc-macro-srv`, `crates/proc-macro-srv-cli`
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These crates implement macros as token tree -> token tree transforms.
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They are independent from the rest of the code.
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`tt` crate defined `TokenTree`, a single token or a delimited sequence of token trees.
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`mbe` crate contains tools for transforming between syntax trees and token tree.
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And it also handles the actual parsing and expansion of declarative macro (a-la "Macros By Example" or mbe).
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For proc macros, the client-server model are used.
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We start a separate process (`proc-macro-srv-cli`) which loads and runs the proc-macros for us.
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And the client (`proc-macro-api`) provides an interface to talk to that server separately.
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And then token trees are passed from client, and the server will load the corresponding dynamic library (which built by `cargo`).
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And due to the fact the api for getting result from proc macro are always unstable in `rustc`,
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we maintain our own copy (and paste) of that part of code to allow us to build the whole thing in stable rust.
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**Architecture Invariant:**
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Bad proc macros may panic or segfault accidentally. So we run it in another process and recover it from fatal error.
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And they may be non-deterministic which conflict how `salsa` works, so special attention is required.
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### `crates/cfg`
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This crate is responsible for parsing, evaluation and general definition of `cfg` attributes.
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### `crates/vfs`, `crates/vfs-notify`, `crates/paths`
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These crates implement a virtual file system.
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They provide consistent snapshots of the underlying file system and insulate messy OS paths.
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**Architecture Invariant:** vfs doesn't assume a single unified file system.
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i.e., a single rust-analyzer process can act as a remote server for two different machines, where the same `/tmp/foo.rs` path points to different files.
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For this reason, all path APIs generally take some existing path as a "file system witness".
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### `crates/stdx`
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This crate contains various non-rust-analyzer specific utils, which could have been in std, as well
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as copies of unstable std items we would like to make use of already, like `std::str::split_once`.
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### `crates/profile`
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This crate contains utilities for CPU and memory profiling.
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### `crates/intern`
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This crate contains infrastructure for globally interning things via `Arc`.
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### `crates/load-cargo`
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This crate exposes several utilities for loading projects, used by the main `rust-analyzer` crate
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and other downstream consumers.
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### `crates/rustc-dependencies`
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This crate wraps the `rustc_*` crates rust-analyzer relies on and conditionally points them to
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mirrored crates-io releases such that rust-analyzer keeps building on stable.
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### `crates/span`
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This crate exposes types and functions related to rust-analyzer's span for macros.
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A span is effectively a text range relative to some item in a file with a given `SyntaxContext` (hygiene).
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## Cross-Cutting Concerns
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This sections talks about the things which are everywhere and nowhere in particular.
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### Stability Guarantees
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One of the reasons rust-analyzer moves relatively fast is that we don't introduce new stability guarantees.
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Instead, as much as possible we leverage existing ones.
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Examples:
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* The `ide` API of rust-analyzer are explicitly unstable, but the LSP interface is stable, and here we just implement a stable API managed by someone else.
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* Rust language and Cargo are stable, and they are the primary inputs to rust-analyzer.
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* The `rowan` library is published to crates.io, but it is deliberately kept under `1.0` and always makes semver-incompatible upgrades
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Another important example is that rust-analyzer isn't run on CI, so, unlike `rustc` and `clippy`, it is actually ok for us to change runtime behavior.
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At some point we might consider opening up APIs or allowing crates.io libraries to include rust-analyzer specific annotations, but that's going to be a big commitment on our side.
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Exceptions:
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* `rust-project.json` is a de-facto stable format for non-cargo build systems.
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It is probably ok enough, but was definitely stabilized implicitly.
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Lesson for the future: when designing API which could become a stability boundary, don't wait for the first users until you stabilize it.
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By the time you have first users, it is already de-facto stable.
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And the users will first use the thing, and *then* inform you that now you have users.
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The sad thing is that stuff should be stable before someone uses it for the first time, or it should contain explicit opt-in.
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* We ship some LSP extensions, and we try to keep those somewhat stable.
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Here, we need to work with a finite set of editor maintainers, so not providing rock-solid guarantees works.
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### Code generation
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Some components in this repository are generated through automatic processes.
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Generated code is updated automatically on `cargo test`.
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Generated code is generally committed to the git repository.
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In particular, we generate:
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* API for working with syntax trees (`syntax::ast`, the [`ungrammar`](https://github.com/rust-analyzer/ungrammar) crate).
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* Various sections of the manual:
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* features
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* assists
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* config
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* Documentation tests for assists
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See the `xtask\src\codegen\assists_doc_tests.rs` module for details.
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**Architecture Invariant:** we avoid bootstrapping.
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For codegen we need to parse Rust code.
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Using rust-analyzer for that would work and would be fun, but it would also complicate the build process a lot.
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For that reason, we use syn and manual string parsing.
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### Cancellation
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Let's say that the IDE is in the process of computing syntax highlighting, when the user types `foo`.
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What should happen?
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`rust-analyzer`s answer is that the highlighting process should be cancelled -- its results are now stale, and it also blocks modification of the inputs.
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The salsa database maintains a global revision counter.
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When applying a change, salsa bumps this counter and waits until all other threads using salsa finish.
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If a thread does salsa-based computation and notices that the counter is incremented, it panics with a special value (see `Canceled::throw`).
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That is, rust-analyzer requires unwinding.
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`ide` is the boundary where the panic is caught and transformed into a `Result<T, Cancelled>`.
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### Testing
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rust-analyzer has three interesting [system boundaries](https://www.tedinski.com/2018/04/10/making-tests-a-positive-influence-on-design.html) to concentrate tests on.
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The outermost boundary is the `rust-analyzer` crate, which defines an LSP interface in terms of stdio.
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We do integration testing of this component, by feeding it with a stream of LSP requests and checking responses.
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These tests are known as "heavy", because they interact with Cargo and read real files from disk.
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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.
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Heavy tests are only run when `RUN_SLOW_TESTS` env var is set.
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The middle, and most important, boundary is `ide`.
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Unlike `rust-analyzer`, which exposes API, `ide` uses Rust API and is intended for use by various tools.
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A typical test creates an `AnalysisHost`, calls some `Analysis` functions and compares the results against expectation.
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The innermost and most elaborate boundary is `hir`.
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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.
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For comparisons, we use the `expect` crate for snapshot testing.
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To test various analysis corner cases and avoid forgetting about old tests, we use so-called marks.
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See the [cov_mark](https://docs.rs/cov-mark/latest/cov_mark/) crate documentation for more.
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**Architecture Invariant:** rust-analyzer tests do not use libcore or libstd.
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All required library code must be a part of the tests.
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This ensures fast test execution.
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**Architecture Invariant:** tests are data driven and do not test the API.
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Tests which directly call various API functions are a liability, because they make refactoring the API significantly more complicated.
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So most of the tests look like this:
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```rust
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#[track_caller]
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fn check(input: &str, expect: expect_test::Expect) {
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// The single place that actually exercises a particular API
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}
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#[test]
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fn foo() {
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check("foo", expect![["bar"]]);
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}
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#[test]
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fn spam() {
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check("spam", expect![["eggs"]]);
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}
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// ...and a hundred more tests that don't care about the specific API at all.
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```
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To specify input data, we use a single string literal in a special format, which can describe a set of rust files.
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See the `Fixture` its module for fixture examples and documentation.
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**Architecture Invariant:** all code invariants are tested by `#[test]` tests.
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There's no additional checks in CI, formatting and tidy tests are run with `cargo test`.
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**Architecture Invariant:** tests do not depend on any kind of external resources, they are perfectly reproducible.
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### Performance Testing
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TBA, take a look at the `metrics` xtask and `#[test] fn benchmark_xxx()` functions.
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### Error Handling
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**Architecture Invariant:** core parts of rust-analyzer (`ide`/`hir`) don't interact with the outside world and thus can't fail.
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Only parts touching LSP are allowed to do IO.
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Internals of rust-analyzer need to deal with broken code, but this is not an error condition.
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rust-analyzer is robust: various analysis compute `(T, Vec<Error>)` rather than `Result<T, Error>`.
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rust-analyzer is a complex long-running process.
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It will always have bugs and panics.
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But a panic in an isolated feature should not bring down the whole process.
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Each LSP-request is protected by a `catch_unwind`.
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We use `always` and `never` macros instead of `assert` to gracefully recover from impossible conditions.
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### Observability
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rust-analyzer is a long-running process, so it is important to understand what's going on inside.
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We have several instruments for that.
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The event loop that runs rust-analyzer is very explicit.
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Rather than spawning futures or scheduling callbacks (open), the event loop accepts an `enum` of possible events (closed).
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It's easy to see all the things that trigger rust-analyzer processing, together with their performance
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rust-analyzer includes a simple hierarchical profiler (`hprof`).
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It is enabled with `RA_PROFILE='*>50'` env var (log all (`*`) actions which take more than `50` ms) and produces output like:
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```
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85ms - handle_completion
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68ms - import_on_the_fly
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67ms - import_assets::search_for_relative_paths
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0ms - crate_def_map:wait (804 calls)
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0ms - find_path (16 calls)
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2ms - find_similar_imports (1 calls)
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0ms - generic_params_query (334 calls)
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59ms - trait_solve_query (186 calls)
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0ms - Semantics::analyze_impl (1 calls)
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1ms - render_resolution (8 calls)
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0ms - Semantics::analyze_impl (5 calls)
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```
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This is cheap enough to enable in production.
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Similarly, we save live object counting (`RA_COUNT=1`).
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It is not cheap enough to enable in prod, and this is a bug which should be fixed.
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### Configurability
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rust-analyzer strives to be as configurable as possible while offering reasonable defaults where no configuration exists yet.
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The rule of thumb is to enable most features by default unless they are buggy or degrade performance too much.
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There will always be features that some people find more annoying than helpful, so giving the users the ability to tweak or disable these is a big part of offering a good user experience.
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Enabling them by default is a matter of discoverability, as many users don't know about some features even though they are presented in the manual.
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Mind the code--architecture gap: at the moment, we are using fewer feature flags than we really should.
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### Serialization
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In Rust, it is easy (often too easy) to add serialization to any type by adding `#[derive(Serialize)]`.
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This easiness is misleading -- serializable types impose significant backwards compatibility constraints.
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If a type is serializable, then it is a part of some IPC boundary.
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You often don't control the other side of this boundary, so changing serializable types is hard.
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For this reason, the types in `ide`, `base_db` and below are not serializable by design.
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If such types need to cross an IPC boundary, then the client of rust-analyzer needs to provide a custom, client-specific serialization format.
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This isolates backwards compatibility and migration concerns to a specific client.
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For example, `rust-project.json` is its own format -- it doesn't include `CrateGraph` as is.
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Instead, it creates a `CrateGraph` by calling appropriate constructing functions.
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