## Summary
We use the `System` abstraction in ty to abstract away the host/system
on which ty runs.
This has a few benefits:
* Tests can run in full isolation using a memory system (that uses an
in-memory file system)
* The LSP has a custom implementation where `read_to_string` returns the
content as seen by the editor (e.g. unsaved changes) instead of always
returning the content as it is stored on disk
* We don't require any file system polyfills for wasm in the browser
However, it does require extra care that we don't accidentally use
`std::fs` or `std::env` (etc.) methods in ty's code base (which is very
easy).
This PR sets up Clippy and disallows the most common methods, instead
pointing users towards the corresponding `System` methods.
The setup is a bit awkward because clippy doesn't support inheriting
configurations. That means, a crate can only override the entire
workspace configuration or not at all.
The approach taken in this PR is:
* Configure the disallowed methods at the workspace level
* Allow `disallowed_methods` at the workspace level
* Enable the lint at the crate level using the warn attribute (in code)
The obvious downside is that it won't work if we ever want to disallow
other methods, but we can figure that out once we reach that point.
What about false positives: Just add an `allow` and move on with your
life :) This isn't something that we have to enforce strictly; the goal
is to catch accidental misuse.
## Test Plan
Clippy found a place where we incorrectly used `std::fs::read_to_string`
## Summary
Extracts the vendored typeshed stubs lazily and caches them on the local
filesystem to support go-to in the LSP.
Resolves https://github.com/astral-sh/ty/issues/77.
## Summary
Setting `TY_MEMORY_REPORT=full` will generate and print a memory usage
report to the CLI after a `ty check` run:
```
=======SALSA STRUCTS=======
`Definition` metadata=7.24MB fields=17.38MB count=181062
`Expression` metadata=4.45MB fields=5.94MB count=92804
`member_lookup_with_policy_::interned_arguments` metadata=1.97MB fields=2.25MB count=35176
...
=======SALSA QUERIES=======
`File -> ty_python_semantic::semantic_index::SemanticIndex`
metadata=11.46MB fields=88.86MB count=1638
`Definition -> ty_python_semantic::types::infer::TypeInference`
metadata=24.52MB fields=86.68MB count=146018
`File -> ruff_db::parsed::ParsedModule`
metadata=0.12MB fields=69.06MB count=1642
...
=======SALSA SUMMARY=======
TOTAL MEMORY USAGE: 577.61MB
struct metadata = 29.00MB
struct fields = 35.68MB
memo metadata = 103.87MB
memo fields = 409.06MB
```
Eventually, we should integrate these numbers into CI in some form. The
one limitation currently is that heap allocations in salsa structs (e.g.
interned values) are not tracked, but memoized values should have full
coverage. We may also want a peak memory usage counter (that accounts
for non-salsa memory), but that is relatively simple to profile manually
(e.g. `time -v ty check`) and would require a compile-time option to
avoid runtime overhead.
## Summary
`std::time::now` isn't available on `wasm32-unknown-unknown` but it is
used by `FileTime::now`.
This PR replaces the usages of `FileTime::now` with a target specific
helper function that we already had in the memory file system.
Fixes https://github.com/astral-sh/ruff/issues/16966
## Test Plan
Tested that the playground no longer crash when adding an extra-path
## Summary
This PR implements the first part of
https://github.com/astral-sh/ruff/discussions/16440. It ensures that Red
Knot's module resolver is case sensitive on all systems.
This PR combines a few approaches:
1. It uses `canonicalize` on non-case-sensitive systems to get the real
casing of a path. This works for as long as no symlinks or mapped
network drives (the windows `E:\` is mapped to `\\server\share` thingy).
This is the same as what Pyright does
2. If 1. fails, fall back to recursively list the parent directory and
test if the path's file name matches the casing exactly as listed in by
list dir. This is the same approach as CPython takes in its module
resolver. The main downside is that it requires more syscalls because,
unlike CPython, we Red Knot needs to invalidate its caches if a file
name gets renamed (CPython assumes that the folders are immutable).
It's worth noting that the file watching test that I added that renames
`lib.py` to `Lib.py` currently doesn't pass on case-insensitive systems.
Making it pass requires some more involved changes to `Files`. I plan to
work on this next. There's the argument that landing this PR on its own
isn't worth it without this issue being addressed. I think it's still a
good step in the right direction even when some of the details on how
and where the path case sensitive comparison is implemented.
## Test plan
I added multiple integration tests (including a failing one). I tested
that the `case-sensitivity` detection works as expected on Windows,
MacOS and Linux and that the fast-paths are taken accordingly.
## Summary
This PR introduces a new mdtest option `system` that can either be
`in-memory` or `os`
where `in-memory` is the default.
The motivation for supporting `os` is so that we can write OS/system
specific tests
with mdtests. Specifically, I want to write mdtests for the module
resolver,
testing that module resolution is case sensitive.
## Test Plan
I tested that the case-sensitive module resolver test start failing when
setting `system = "os"`
## Summary
This PR adds support for user-level configurations
(`~/.config/knot/knot.toml`) to Red Knot.
Red Knot will watch the user-level configuration file for changes but
only if it exists
when the process start. It doesn't watch for new configurations,
mainly to simplify things for now (it would require watching the entire
`.config` directory because the `knot` subfolder might not exist
either).
The new `ConfigurationFile` struct seems a bit overkill for now but I
plan to use it for
hierarchical configurations as well.
Red Knot uses the same strategy as uv and Ruff by using the etcetera
crate.
## Test Plan
Added CLI and file watching test
## Summary
This PR adds a new `user_configuration_directory` method to `System`. We
need it to resolve where to lookup a user-level `knot.toml`
configuration file.
The method belongs to `System` because not all platforms have a
convention of where to store such configuration files (e.g. wasm).
I refactored `TestSystem` to be a simple wrapper around an `Arc<dyn
System...>` and use the `System.as_any` method instead to cast it down
to an `InMemory` system. I also removed some `System` specific methods
from `InMemoryFileSystem`, they don't belong there.
This PR removes the `os` feature as a default feature from `ruff_db`.
Most crates depending on `ruff_db` don't need it because they only
depend on `System` or only depend on `os` for testing. This was
necessary to fix a compile error with `red_knot_wasm`
## Test Plan
I'll make use of the method in my next PR. So I guess we won't know if
it works before then but I copied the code from Ruff/uv, so I have high
confidence that it is correct.
`cargo test`
## Summary
Adds a JSON schema generation step for Red Knot. This PR doesn't yet add
a publishing step because it's still a bit early for that
## Test plan
I tested the schema in Zed, VS Code and PyCharm:
* PyCharm: You have to manually add a schema mapping (settings JSON
Schema Mappings)
* Zed and VS code support the inline schema specification
```toml
#:schema /Users/micha/astral/ruff/knot.schema.json
[environment]
extra-paths = []
[rules]
call-possibly-unbound-method = "error"
unknown-rule = "error"
# duplicate-base = "error"
```
```json
{
"$schema": "file:///Users/micha/astral/ruff/knot.schema.json",
"environment": {
"python-version": "3.13",
"python-platform": "linux2"
},
"rules": {
"unknown-rule": "error"
}
}
```
https://github.com/user-attachments/assets/a18fcd96-7cbe-4110-985b-9f1935584411
The Schema overall works but all editors have their own quirks:
* PyCharm: Hovering a name always shows the section description instead
of the description of the specific setting. But it's the same for other
settings in `pyproject.toml` files 🤷
* VS Code (JSON): Using the generated schema in a JSON file gives
exactly the experience I want
* VS Code (TOML):
* Properties with multiple possible values are repeated during
auto-completion without giving any hint how they're different. 
* The property description mushes together the description of the
property and the value, which looks sort of ridiculous. 
* Autocompletion and documentation hovering works (except the
limitations mentioned above)
* Zed:
* Very similar to VS Code with the exception that it uses the
description attribute to distinguish settings with multiple possible
values 
I don't think there's much we can do here other than hope (or help)
editors improve their auto completion. The same short comings also apply
to ruff, so this isn't something new. For now, I think this is good
enough
## Summary
Fixes https://github.com/astral-sh/ruff/issues/15027
The `MemoryFileSystem::write_file` API automatically creates
non-existing ancestor directoryes
but we failed to update the status of the now created ancestor
directories in the `Files` data structure.
## Test Plan
Tested that the case in https://github.com/astral-sh/ruff/issues/15027
now passes regardless of whether the *Simple* case is commented out or
not
## Summary
- Add 383 files from `crates/ruff_python_parser/resources` to the test
corpus
- Add 1296 files from `crates/ruff_linter/resources` to the test corpus
- Use in-memory file system for tests
- Improve test isolation by cleaning the test environment between checks
- Add a mechanism for "known failures". Mark ~80 files as known
failures.
- The corpus test is now a lot slower (6 seconds).
Note:
While `red_knot` as a command line tool can run over all of these
files without panicking, we still have a lot of test failures caused by
explicitly "pulling" all types.
## Test Plan
Run `cargo test -p red_knot_workspace` while making sure that
- Introducing code that is known to lead to a panic fails the test
- Removing code that is known to lead to a panic from
`KNOWN_FAILURES`-files also fails the test
## Summary
This PR adds an experimental Ruff subcommand to generate dependency
graphs based on module resolution.
A few highlights:
- You can generate either dependency or dependent graphs via the
`--direction` command-line argument.
- Like Pants, we also provide an option to identify imports from string
literals (`--detect-string-imports`).
- Users can also provide additional dependency data via the
`include-dependencies` key under `[tool.ruff.import-map]`. This map uses
file paths as keys, and lists of strings as values. Those strings can be
file paths or globs.
The dependency resolution uses the red-knot module resolver which is
intended to be fully spec compliant, so it's also a chance to expose the
module resolver in a real-world setting.
The CLI is, e.g., `ruff graph build ../autobot`, which will output a
JSON map from file to files it depends on for the `autobot` project.
## Summary
This PR simplifies the virtual file support in the red knot core,
specifically:
* Update `File::add_virtual_file` method to `File::virtual_file` which
will always create a new virtual file and override the existing entry in
the lookup table
* Add `VirtualFile` which is a wrapper around `File` and provides
methods to increment the file revision / close the virtual file
* Add a new `File::try_virtual_file` to lookup the `VirtualFile` from
`Files`
* Add `File::sync_virtual_path` which takes in the `SystemVirtualPath`,
looks up the `VirtualFile` for it and calls the `sync` method to
increment the file revision
* Removes the `virtual_path_metadata` method on `System` trait
## Test Plan
- [x] Make sure the existing red knot tests pass
- [x] Updated code works well with the LSP
## Summary
This PR adds support for untitled files in the Red Knot project.
Refer to the [design
discussion](https://github.com/astral-sh/ruff/discussions/12336) for
more details.
### Changes
* The `parsed_module` always assumes that the `SystemVirtual` path is of
`PySourceType::Python`.
* For the module resolver, as suggested, I went ahead by adding a new
`SystemOrVendoredPath` enum and renamed `FilePathRef` to
`SystemOrVendoredPathRef` (happy to consider better names here).
* The `file_to_module` query would return if it's a
`FilePath::SystemVirtual` variant because a virtual file doesn't belong
to any module.
* The sync implementation for the system virtual path is basically the
same as that of system path except that it uses the
`virtual_path_metadata`. The reason for this is that the system
(language server) would provide the metadata on whether it still exists
or not and if it exists, the corresponding metadata.
For point (1), VS Code would use `Untitled-1` for Python files and
`Untitled-1.ipynb` for Jupyter Notebooks. We could use this distinction
to determine whether the source type is `Python` or `Ipynb`.
## Test Plan
Added test cases in #12526
Add a lint rule to detect if a name is definitely or possibly undefined
at a given usage.
If I create the file `undef/main.py` with contents:
```python
x = int
def foo():
z
return x
if flag:
y = x
y
```
And then run `cargo run --bin red_knot -- --current-directory
../ruff-examples/undef`, I get the output:
```
Name 'z' used when not defined.
Name 'flag' used when not defined.
Name 'y' used when possibly not defined.
```
If I modify the file to add `y = 0` at the top, red-knot re-checks it
and I get the new output:
```
Name 'z' used when not defined.
Name 'flag' used when not defined.
```
Note that `int` is not flagged, since it's a builtin, and `return x` in
the function scope is not flagged, since it refers to the global `x`.
Implements definition-level type inference, with basic control flow
(only if statements and if expressions so far) in Salsa.
There are a couple key ideas here:
1) We can do type inference queries at any of three region
granularities: an entire scope, a single definition, or a single
expression. These are represented by the `InferenceRegion` enum, and the
entry points are the salsa queries `infer_scope_types`,
`infer_definition_types`, and `infer_expression_types`. Generally
per-scope will be used for scopes that we are directly checking and
per-definition will be used anytime we are looking up symbol types from
another module/scope. Per-expression should be uncommon: used only for
the RHS of an unpacking or multi-target assignment (to avoid
re-inferring the RHS once per symbol defined in the assignment) and for
test nodes in type narrowing (e.g. the `test` of an `If` node). All
three queries return a `TypeInference` with a map of types for all
definitions and expressions within their region. If you do e.g.
scope-level inference, when it hits a definition, or an
independently-inferable expression, it should use the relevant query
(which may already be cached) to get all types within the smaller
region. This avoids double-inferring smaller regions, even though larger
regions encompass smaller ones.
2) Instead of building a control-flow graph and lazily traversing it to
find definitions which reach a use of a name (which is O(n^2) in the
worst case), instead semantic indexing builds a use-def map, where every
use of a name knows which definitions can reach that use. We also no
longer track all definitions of a symbol in the symbol itself; instead
the use-def map also records which defs remain visible at the end of the
scope, and considers these the publicly-visible definitions of the
symbol (see below).
Major items left as TODOs in this PR, to be done in follow-up PRs:
1) Free/global references aren't supported yet (only lookup based on
definitions in current scope), which means the override-check example
doesn't currently work. This is the first thing I'll fix as follow-up to
this PR.
2) Control flow outside of if statements and expressions.
3) Type narrowing.
There are also some smaller relevant changes here:
1) Eliminate `Option` in the return type of member lookups; instead
always return `Type::Unbound` for a name we can't find. Also use
`Type::Unbound` for modules we can't resolve (not 100% sure about this
one yet.)
2) Eliminate the use of the terms "public" and "root" to refer to
module-global scope or symbols. Instead consistently use the term
"module-global". It's longer, but it's the clearest, and the most
consistent with typical Python terminology. In particular I don't like
"public" for this use because it has other implications around author
intent (is an underscore-prefixed module-global symbol "public"?). And
"root" is just not commonly used for this in Python.
3) Eliminate the `PublicSymbol` Salsa ingredient. Many non-module-global
symbols can also be seen from other scopes (e.g. by a free var in a
nested scope, or by class attribute access), and thus need to have a
"public type" (that is, the type not as seen from a particular use in
the control flow of the same scope, but the type as seen from some other
scope.) So all symbols need to have a "public type" (here I want to keep
the use of the term "public", unless someone has a better term to
suggest -- since it's "public type of a symbol" and not "public symbol"
the confusion with e.g. initial underscores is less of an issue.) At
least initially, I would like to try not having special handling for
module-global symbols vs other symbols.
4) Switch to using "definitions that reach end of scope" rather than
"all definitions" in determining the public type of a symbol. I'm
convinced that in general this is the right way to go. We may want to
refine this further in future for some free-variable cases, but it can
be changed purely by making changes to the building of the use-def map
(the `public_definitions` index in it), without affecting any other
code. One consequence of combining this with no control-flow support
(just last-definition-wins) is that some inference tests now give more
wrong-looking results; I left TODO comments on these tests to fix them
when control flow is added.
And some potential areas for consideration in the future:
1) Should `symbol_ty` be a Salsa query? This would require making all
symbols a Salsa ingredient, and tracking even more dependencies. But it
would save some repeated reconstruction of unions, for symbols with
multiple public definitions. For now I'm not making it a query, but open
to changing this in future with actual perf evidence that it's better.
Micha Reiser
renovate[bot]
Ibraheem Ahmed
Alex Waygood
David Peter
Charlie Marsh
Dhruv Manilawala
Carl Meyer