## 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
Another salsa upgrade.
The main motivation is to stay on a recent salsa version because there
are still a lot of breaking changes happening.
The most significant changes in this update:
* Salsa no longer derives `Debug` by default. It now requires
`interned(debug)` (or similar)
* This version ships the foundation for garbage collecting interned
values. However, this comes at the cost that queries now track which
interned values they created (or read). The micro benchmarks in the
salsa repo showed a significant perf regression. Will see if this also
visible in our benchmarks.
## Test Plan
`cargo test`
## 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 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
