## 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
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
- 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 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
Micha Reiser
Ibraheem Ahmed
Alex Waygood
David Peter
Charlie Marsh
Dhruv Manilawala