This guide describes the current state of rust-analyzer as of 2019-01-20 (git tag guide-2019-01). Its purpose is to document various problems and architectural solutions related to the problem of building IDE-first compiler for Rust. There is a video version of this guide as well: https://youtu.be/ANKBNiSWyfc.
On the highest possible level, rust-analyzer is a stateful component. A client may
apply changes to the analyzer (new contents of foo.rs file is "fn main() {}")
and it may ask semantic questions about the current state (what is the
definition of the identifier with offset 92 in file bar.rs?). Two important
properties hold:
-
Analyzer does not do any I/O. It starts in an empty state and all input data is provided via
apply_changeAPI. -
Only queries about the current state are supported. One can, of course, simulate undo and redo by keeping a log of changes and inverse changes respectively.
To see the bigger picture of how the IDE features work, let's take a look at the AnalysisHost and
Analysis pair of types. AnalysisHost has three methods:
default()for creating an empty analysis instanceapply_change(&mut self)to make changes (this is how you get from an empty state to something interesting)analysis(&self)to get an instance ofAnalysis
Analysis has a ton of methods for IDEs, like goto_definition, or
completions. Both inputs and outputs of Analysis' methods are formulated in
terms of files and offsets, and not in terms of Rust concepts like structs,
traits, etc. The "typed" API with Rust specific types is slightly lower in the
stack, we'll talk about it later.
The reason for this separation of Analysis and AnalysisHost is that we want to apply
changes "uniquely", but we might also want to fork an Analysis and send it to
another thread for background processing. That is, there is only a single
AnalysisHost, but there may be several (equivalent) Analysis.
Note that all of the Analysis API return Cancellable<T>. This is required to
be responsive in an IDE setting. Sometimes a long-running query is being computed
and the user types something in the editor and asks for completion. In this
case, we cancel the long-running computation (so it returns Err(Cancelled)),
apply the change and execute request for completion. We never use stale data to
answer requests. Under the cover, AnalysisHost "remembers" all outstanding
Analysis instances. The AnalysisHost::apply_change method cancels all
Analysises, blocks until all of them are Dropped and then applies changes
in-place. This may be familiar to Rustaceans who use read-write locks for interior
mutability.
Next, let's talk about what the inputs to the Analysis are, precisely.
rust-analyzer never does any I/O itself, all inputs get passed explicitly via
the AnalysisHost::apply_change method, which accepts a single argument, a
Change. Change is a builder for a single change
"transaction", so it suffices to study its methods to understand all of the
input data.
The (add|change|remove)_file methods control the set of the input files, where
each file has an integer id (FileId, picked by the client), text (String)
and a filesystem path. Paths are tricky; they'll be explained below, in source roots
section, together with the add_root method. The add_library method allows us to add a
group of files which are assumed to rarely change. It's mostly an optimization
and does not change the fundamental picture.
The set_crate_graph method allows us to control how the input files are partitioned
into compilation units -- crates. It also controls (in theory, not implemented
yet) cfg flags. CrateGraph is a directed acyclic graph of crates. Each crate
has a root FileId, a set of active cfg flags and a set of dependencies. Each
dependency is a pair of a crate and a name. It is possible to have two crates
with the same root FileId but different cfg-flags/dependencies. This model
is lower than Cargo's model of packages: each Cargo package consists of several
targets, each of which is a separate crate (or several crates, if you try
different feature combinations).
Procedural macros are inputs as well, roughly modeled as a crate with a bunch of
additional black box dyn Fn(TokenStream) -> TokenStream functions.
Soon we'll talk how we build an LSP server on top of Analysis, but first,
let's deal with that paths issue.
This is a non-essential section, feel free to skip.
The previous section said that the filesystem path is an attribute of a file,
but this is not the whole truth. Making it an absolute PathBuf will be bad for
several reasons. First, filesystems are full of (platform-dependent) edge cases:
- It's hard (requires a syscall) to decide if two paths are equivalent.
- Some filesystems are case-sensitive (e.g. macOS).
- Paths are not necessarily UTF-8.
- Symlinks can form cycles.
Second, this might hurt the reproducibility and hermeticity of builds. In theory,
moving a project from /foo/bar/my-project to /spam/eggs/my-project should
not change a bit in the output. However, if the absolute path is a part of the
input, it is at least in theory observable, and could affect the output.
Yet another problem is that we really really want to avoid doing I/O, but with
Rust the set of "input" files is not necessarily known up-front. In theory, you
can have #[path="/dev/random"] mod foo;.
To solve (or explicitly refuse to solve) these problems rust-analyzer uses the
concept of a "source root". Roughly speaking, source roots are the contents of a
directory on a file systems, like /home/matklad/projects/rustraytracer/**.rs.
More precisely, all files (FileIds) are partitioned into disjoint
SourceRoots. Each file has a relative UTF-8 path within the SourceRoot.
SourceRoot has an identity (integer ID). Crucially, the root path of the
source root itself is unknown to the analyzer: A client is supposed to maintain a
mapping between SourceRoot IDs (which are assigned by the client) and actual
PathBufs. SourceRoots give a sane tree model of the file system to the
analyzer.
Note that mod, #[path] and include!() can only reference files from the
same source root. It is of course possible to explicitly add extra files to
the source root, even /dev/random.
Now let's see how the Analysis API is exposed via the JSON RPC based language server protocol. The
hard part here is managing changes (which can come either from the file system
or from the editor) and concurrency (we want to spawn background jobs for things
like syntax highlighting). We use the event loop pattern to manage the zoo, and
the loop is the main_loop_inner function. The main_loop does a one-time
initialization and tearing down of the resources.
Let's walk through a typical analyzer session!
First, we need to figure out what to analyze. To do this, we run cargo metadata to learn about Cargo packages for current workspace and dependencies,
and we run rustc --print sysroot and scan the "sysroot" (the directory containing the current Rust toolchain's files) to learn about crates like
std. Currently we load this configuration once at the start of the server, but
it should be possible to dynamically reconfigure it later without restart.
The ProjectModel we get after this step is very Cargo and sysroot specific,
it needs to be lowered to get the input in the form of Change. This
happens in ServerWorldState::new method. Specifically
- Create a
SourceRootfor each Cargo package and sysroot. - Schedule a filesystem scan of the roots.
- Create an analyzer's
Cratefor each Cargo target and sysroot crate. - Setup dependencies between the crates.
The results of the scan (which may take a while) will be processed in the body of the main loop, just like any other change. Here's where we handle:
After a single loop's turn, we group the changes into one Change and
apply it. This always happens on the main thread and blocks the loop.
To handle requests, like "goto definition", we create an instance of the
Analysis and schedule the task (which consumes Analysis) on the
threadpool. The task calls the corresponding Analysis method, while
massaging the types into the LSP representation. Keep in mind that if we are
executing "goto definition" on the threadpool and a new change comes in, the
task will be canceled as soon as the main loop calls apply_change on the
AnalysisHost.
This concludes the overview of the analyzer's programing interface. Next, let's dig into the implementation!
The most straightforward way to implement an "apply change, get analysis, repeat" API would be to maintain the input state and to compute all possible analysis information from scratch after every change. This works, but scales poorly with the size of the project. To make this fast, we need to take advantage of the fact that most of the changes are small, and that analysis results are unlikely to change significantly between invocations.
To do this we use salsa: a framework for incremental on-demand computation.
You can skip the rest of the section if you are familiar with rustc's red-green
algorithm (which is used for incremental compilation).
It's better to refer to salsa's docs to learn about it. Here's a small excerpt:
The key idea of salsa is that you define your program as a set of queries. Every
query is used like a function K -> V that maps from some key of type K to a value
of type V. Queries come in two basic varieties:
-
Inputs: the base inputs to your system. You can change these whenever you like.
-
Functions: pure functions (no side effects) that transform your inputs into other values. The results of queries are memoized to avoid recomputing them a lot. When you make changes to the inputs, we'll figure out (fairly intelligently) when we can re-use these memoized values and when we have to recompute them.
For further discussion, its important to understand one bit of "fairly
intelligently". Suppose we have two functions, f1 and f2, and one input,
z. We call f1(X) which in turn calls f2(Y) which inspects i(Z). i(Z)
returns some value V1, f2 uses that and returns R1, f1 uses that and
returns O. Now, let's change i at Z to V2 from V1 and try to compute
f1(X) again. Because f1(X) (transitively) depends on i(Z), we can't just
reuse its value as is. However, if f2(Y) is still equal to R1 (despite
i's change), we, in fact, can reuse O as result of f1(X). And that's how
salsa works: it recomputes results in reverse order, starting from inputs and
progressing towards outputs, stopping as soon as it sees an intermediate value
that hasn't changed. If this sounds confusing to you, don't worry: it is
confusing. This illustration by @killercup might help:
All analyzer information is stored in a salsa database. Analysis and
AnalysisHost types are newtype wrappers for RootDatabase -- a salsa
database.
Salsa input queries are defined in FilesDatabase (which is a part of
RootDatabase). They closely mirror the familiar Change structure:
indeed, what apply_change does is it sets the values of input queries.
The bulk of the rust-analyzer is transforming input text into a semantic model of Rust code: a web of entities like modules, structs, functions and traits.
An important fact to realize is that (unlike most other languages like C# or
Java) there is not a one-to-one mapping between the source code and the semantic model. A
single function definition in the source code might result in several semantic
functions: for example, the same source file might get included as a module in
several crates or a single crate might be present in the compilation DAG
several times, with different sets of cfgs enabled. The IDE-specific task of
mapping source code into a semantic model is inherently imprecise for
this reason and gets handled by the source_binder.
The semantic interface is declared in the code_model_api module. Each entity is
identified by an integer ID and has a bunch of methods which take a salsa database
as an argument and returns other entities (which are also IDs). Internally, these
methods invoke various queries on the database to build the model on demand.
Here's the list of queries.
The first step of building the model is parsing the source code.
An important property of the Rust language is that each file can be parsed in
isolation. Unlike, say, C++, an include can't change the meaning of the
syntax. For this reason, rust-analyzer can build a syntax tree for each "source
file", which could then be reused by several semantic models if this file
happens to be a part of several crates.
The representation of syntax trees that rust-analyzer uses is similar to that of Roslyn
and Swift's new libsyntax. Swift's docs give an excellent overview of the
approach, so I skip this part here and instead outline the main characteristics
of the syntax trees:
-
Syntax trees are fully lossless. Converting any text to a syntax tree and back is a total identity function. All whitespace and comments are explicitly represented in the tree.
-
Syntax nodes have generic
(next|previous)_sibling,parent,(first|last)_childfunctions. You can get from any one node to any other node in the file using only these functions. -
Syntax nodes know their range (start offset and length) in the file.
-
Syntax nodes share the ownership of their syntax tree: if you keep a reference to a single function, the whole enclosing file is alive.
-
Syntax trees are immutable and the cost of replacing the subtree is proportional to the depth of the subtree. Read Swift's docs to learn how immutable + parent pointers + cheap modification is possible.
-
Syntax trees are build on best-effort basis. All accessor methods return
Options. The tree forfn foowill contain a function declaration withNonefor parameter list and body. -
Syntax trees do not know the file they are built from, they only know about the text.
The implementation is based on the generic rowan crate on top of which a rust-specific AST is generated.
The next step in constructing the semantic model is ...
The algorithm for building a tree of modules is to start with a crate root
(remember, each Crate from a CrateGraph has a FileId), collect all mod
declarations and recursively process child modules. This is handled by the
module_tree_query, with two slight variations.
First, rust-analyzer builds a module tree for all crates in a source root
simultaneously. The main reason for this is historical (module_tree predates
CrateGraph), but this approach also enables accounting for files which are not
part of any crate. That is, if you create a file but do not include it as a
submodule anywhere, you still get semantic completion, and you get a warning
about a free-floating module (the actual warning is not implemented yet).
The second difference is that module_tree_query does not directly depend on
the "parse" query (which is confusingly called source_file). Why would calling
the parse directly be bad? Suppose the user changes the file slightly, by adding
an insignificant whitespace. Adding whitespace changes the parse tree (because
it includes whitespace), and that means recomputing the whole module tree.
We deal with this problem by introducing an intermediate submodules_query.
This query processes the syntax tree and extracts a set of declared submodule
names. Now, changing the whitespace results in submodules_query being
re-executed for a single module, but because the result of this query stays
the same, we don't have to re-execute module_tree_query. In fact, we only
need to re-execute it when we add/remove new files or when we change mod
declarations.
We store the resulting modules in a Vec-based indexed arena. The indices in
the arena becomes module IDs. And this brings us to the next topic:
assigning IDs in the general case.
One way to assign IDs is how we've dealt with modules: Collect all items into a single array in some specific order and use the index in the array as an ID. The main drawback of this approach is that these IDs are not stable: Adding a new item can shift the IDs of all other items. This works for modules, because adding a module is a comparatively rare operation, but would be less convenient for, for example, functions.
Another solution here is positional IDs: We can identify a function as "the
function with name foo in a ModuleId(92) module". Such locations are stable:
adding a new function to the module (unless it is also named foo) does not
change the location. However, such "ID" types ceases to be a Copyable integer and in
general can become pretty large if we account for nesting (for example: "third parameter of
the foo function of the bar impl in the baz module").
LocationInterner allows us to combine the benefits of positional and numeric
IDs. It is a bidirectional append-only map between locations and consecutive
integers which can "intern" a location and return an integer ID back. The salsa
database we use includes a couple of interners. How to "garbage collect"
unused locations is an open question.
For example, we use LocationInterner to assign IDs to definitions of functions,
structs, enums, etc. The location, DefLoc contains two bits of information:
- the ID of the module which contains the definition,
- the ID of the specific item in the modules source code.
We "could" use a text offset for the location of a particular item, but that would play badly with salsa: offsets change after edits. So, as a rule of thumb, we avoid using offsets, text ranges or syntax trees as keys and values for queries. What we do instead is we store "index" of the item among all of the items of a file (so, a positional based ID, but localized to a single file).
One thing we've glossed over for the time being is support for macros. We have only proof of concept handling of macros at the moment, but they are extremely interesting from an "assigning IDs" perspective.
The tricky bit about macros is that they effectively create new source files.
While we can use FileIds to refer to original files, we can't just assign them
willy-nilly to the pseudo files of macro expansion. Instead, we use a special
ID, HirFileId to refer to either a usual file or a macro-generated file:
enum HirFileId {
FileId(FileId),
Macro(MacroCallId),
}MacroCallId is an interned ID that specifies a particular macro invocation.
Its MacroCallLoc contains:
ModuleIdof the containing moduleHirFileIdof the containing file or pseudo file- an index of this particular macro invocation in this file (positional id again).
Note how HirFileId is defined in terms of MacroCallLoc which is defined in
terms of HirFileId! This does not recur infinitely though: any chain of
HirFileIds bottoms out in HirFileId::FileId, that is, some source file
actually written by the user.
Now that we understand how to identify a definition, in a source or in a macro-generated file, we can discuss name resolution a bit.
Name resolution faces the same problem as the module tree: if we look at the syntax tree directly, we'll have to recompute name resolution after every modification. The solution to the problem is the same: We lower the source code of each module into a position-independent representation which does not change if we modify bodies of the items. After that we loop resolving all imports until we've reached a fixed point.
And, given all our preparation with IDs and a position-independent representation, it is satisfying to test that typing inside function body does not invalidate name resolution results.
An interesting fact about name resolution is that it "erases" all of the
intermediate paths from the imports: in the end, we know which items are defined
and which items are imported in each module, but, if the import was use foo::bar::baz, we deliberately forget what modules foo and bar resolve to.
To serve "goto definition" requests on intermediate segments we need this info
in the IDE, however. Luckily, we need it only for a tiny fraction of imports, so we just ask
the module explicitly, "What does the path foo::bar resolve to?". This is a
general pattern: we try to compute the minimal possible amount of information
during analysis while allowing IDE to ask for additional specific bits.
Name resolution is also a good place to introduce another salsa pattern used throughout the analyzer:
Due to an obscure edge case in completion, IDE needs to know the syntax node of a use statement which imported the given completion candidate. We can't just store the syntax node as a part of name resolution: this will break incrementality, due to the fact that syntax changes after every file modification.
We solve this problem during the lowering step of name resolution. The lowering
query actually produces a pair of outputs: LoweredModule and SourceMap.
The LoweredModule module contains imports, but in a position-independent form.
The SourceMap contains a mapping from position-independent imports to
(position-dependent) syntax nodes.
The result of this basic lowering query changes after every modification. But there's an intermediate projection query which returns only the first position-independent part of the lowering. The result of this query is stable. Naturally, name resolution uses this stable projection query.
First of all, implementation of type inference in rust-analyzer was spearheaded by @flodiebold. #327 was an awesome Christmas present, thank you, Florian!
Type inference runs on per-function granularity and uses the patterns we've discussed previously.
First, we lower the AST of a function body into a position-independent
representation. In this representation, each expression is assigned a
positional ID. Alongside the lowered expression, a source map is produced,
which maps between expression ids and original syntax. This lowering step also
deals with "incomplete" source trees by replacing missing expressions by an
explicit Missing expression.
Given the lowered body of the function, we can now run type inference and
construct a mapping from ExprIds to types.
To conclude the overview of the rust-analyzer, let's trace the request for (type-inference powered!) code completion!
We start by receiving a message from the language client. We decode the message as a request for completion and schedule it on the threadpool. This is the place where we catch canceled errors if, immediately after completion, the client sends some modification.
In the handler, we deserialize LSP requests into rust-analyzer specific data
types (by converting a file url into a numeric FileId), ask analysis for
completion and serialize results into the LSP.
The completion implementation is finally the place where we start doing the actual
work. The first step is to collect the CompletionContext -- a struct which
describes the cursor position in terms of Rust syntax and semantics. For
example, function_syntax: Option<&'a ast::FnDef> stores a reference to
the enclosing function syntax, while function: Option<hir::Function> is the
Def for this function.
To construct the context, we first do an "IntelliJ Trick": we insert a dummy
identifier at the cursor's position and parse this modified file, to get a
reasonably looking syntax tree. Then we do a bunch of "classification" routines
to figure out the context. For example, we find an ancestor fn node and we get a
semantic model for it (using the lossy source_binder infrastructure).
The second step is to run a series of independent completion routines. Let's
take a closer look at complete_dot, which completes fields and methods in
foo.bar|. First we extract a semantic function and a syntactic receiver
expression out of the Context. Then we run type-inference for this single
function and map our syntactic expression to ExprId. Using the ID, we figure
out the type of the receiver expression. Then we add all fields & methods from
the type to completion.