## Summary
Open cache files in parallel (again), brings the performance back to be roughly equal to the old implementation.
## Test Plan
Existing tests should keep working.
## Summary
I want to include URLs to the rule documentation in the LSP (the LSP has
a native `code_description` field for this, which, if specified, causes
the source to be rendered as a link to the docs). This PR exposes the
URL to the documentation in the Ruff JSON output.
## Summary
Maintain consistency while deserializing Jupyter notebook to JSON. The
following changes were made:
1. Use string array to store the source value as that's the default
(5781720423/nbformat/v4/nbjson.py (L56-L57))
2. Remove unused structs and enums
3. Reorder the keys in alphabetical order as that's the default.
(5781720423/nbformat/v4/nbjson.py (L51))
### Side effect
Removing the `preserve_order` feature means that the order of keys in
JSON output (`--format json`) will be in alphabetical order. This is
because the value is represented using `serde_json::Value` which
internally is a `BTreeMap`, thus sorting it as per the string key. For
posterity if this turns out to be not ideal, then we could define a
struct representing the JSON object and the order of struct fields will
determine the order in the JSON string.
## Test Plan
Add a test case to assert the raw JSON string.
## Summary
This changes the caching design from one cache file per source file, to
one cache file per package. This greatly reduces the amount of cache
files that are opened and written, while maintaining roughly the same
(combined) size as bincode is very compact.
Below are some very much not scientific performance tests. It uses
projects/sources to check:
* small.py: single, 31 bytes Python file with 2 errors.
* test.py: single, 43k Python file with 8 errors.
* fastapi: FastAPI repo, 1134 files checked, 0 errors.
Source | Before # files | After # files | Before size | After size
-------|-------|-------|-------|-------
small.py | 1 | 1 | 20 K | 20 K
test.py | 1 | 1 | 60 K | 60 K
fastapi | 1134 | 518 | 4.5 M | 2.3 M
One question that might come up is why fastapi still has 518 cache files
and not 1? That is because this is using the existing package
resolution, which sees examples, docs, etc. as separate from the "main"
source code (in the fastapi directory in the repo). In this future it
might be worth consider switching to a one cache file per repo strategy.
This new design is not perfect and does have a number of known issues.
First, like the old design it doesn't remove the cache for a source file
that has been (re)moved until `ruff clean` is called.
Second, this currently uses a large mutex around the mutation of the
package cache (e.g. inserting result). This could be (or become) a
bottleneck. It's future work to test and improve this (if needed).
Third, currently the packages and opened and stored in a sequential
loop, this could be done parallel. This is also future work.
## Test Plan
Run `ruff check` (with caching enabled) twice on any Python source code
and it should produce the same results.
## Summary
We want to ensure that once formatted content stays the same when
formatted again, which is known as formatter stability or formatter
idempotency, and that the formatter prints syntactically valid code. As
our test cases cover only a limited amount of code, this allows checking
entire repositories.
This adds a new subcommand to `ruff_dev` which can be invoked as `cargo
run --bin ruff_dev -- check-formatter-stability <repo>`. While initially
only intended to check stability, it has also found cases where the
formatter printed invalid syntax or panicked.
## Test Plan
Running this on cpython is already identifying bugs
(https://github.com/astral-sh/ruff/pull/5089)
## Summary
This adds a new subcommand that can be used as
```shell
cargo build --bin ruff_dev --profile=release-debug
perf record -g -F 999 target/release-debug/ruff_dev repeat --repeat 30 --exit-zero --no-cache path/to/cpython > /dev/null
flamegraph --perfdata perf.data
```
## Test Plan
This is a ruff internal script. I successfully used it to profile
cpython with the instructions above
## Summary
We now _always_ generate fixes, so `FixMode::None` and
`FixMode::Generate` are redundant. We can also remove the TODO around
`--fix-dry-run`, since that's our default behavior.
Closes#5081.
## Summary
This adds `json-lines` (https://jsonlines.org/ or http://ndjson.org/) as
an output format.
I'm sure you already know, but
* JSONL is more greppable (each record is a single line) than the pretty
JSON
* JSONL is faster to ingest piecewise (and/or in parallel) than JSON
## Test Plan
Snapshot test in the new module :)
## Summary
Add support for applying auto-fixes in Jupyter Notebook.
### Solution
Cell offsets are the boundaries for each cell in the concatenated source
code. They are represented using `TextSize`. It includes the start and
end offset as well, thus creating a range for each cell. These offsets
are updated using the `SourceMap` markers.
### SourceMap
`SourceMap` contains markers constructed from each edits which tracks
the original source code position to the transformed positions. The
following drawing might make it clear:
The center column where the dotted lines are present are the markers
included in the `SourceMap`. The `Notebook` looks at these markers and
updates the cell offsets after each linter loop. If you notice closely,
the destination takes into account all of the markers before it.
The index is constructed only when required as it's only used to render
the diagnostics. So, a `OnceCell` is used for this purpose. The cell
offsets, cell content and the index will be updated after each iteration
of linting in the mentioned order. The order is important here as the
content is updated as per the new offsets and index is updated as per
the new content.
## Limitations
### 1
Styling rules such as the ones in `pycodestyle` will not be applicable
everywhere in Jupyter notebook, especially at the cell boundaries. Let's
take an example where a rule suggests to have 2 blank lines before a
function and the cells contains the following code:
```python
import something
# ---
def first():
pass
def second():
pass
```
(Again, the comment is only to visualize cell boundaries.)
In the concatenated source code, the 2 blank lines will be added but it
shouldn't actually be added when we look in terms of Jupyter notebook.
It's as if the function `first` is at the start of a file.
`nbqa` solves this by recording newlines before and after running
`autopep8`, then running the tool and restoring the newlines at the end
(refer https://github.com/nbQA-dev/nbQA/pull/807).
## Test Plan
Three commands were run in order with common flags (`--select=ALL
--no-cache --isolated`) to isolate which stage the problem is occurring:
1. Only diagnostics
2. Fix with diff (`--fix --diff`)
3. Fix (`--fix`)
### https://github.com/facebookresearch/segment-anything
```
-------------------------------------------------------------------------------
Jupyter Notebooks 3 0 0 0 0
|- Markdown 3 98 0 94 4
|- Python 3 513 468 4 41
(Total) 611 468 98 45
-------------------------------------------------------------------------------
```
```console
$ cargo run --all-features --bin ruff -- check --no-cache --isolated --select=ALL /path/to/segment-anything/**/*.ipynb --fix
...
Found 180 errors (89 fixed, 91 remaining).
```
### https://github.com/openai/openai-cookbook
```
-------------------------------------------------------------------------------
Jupyter Notebooks 65 0 0 0 0
|- Markdown 64 3475 12 2507 956
|- Python 65 9700 7362 1101 1237
(Total) 13175 7374 3608 2193
===============================================================================
```
```console
$ cargo run --all-features --bin ruff -- check --no-cache --isolated --select=ALL /path/to/openai-cookbook/**/*.ipynb --fix
error: Failed to parse /path/to/openai-cookbook/examples/vector_databases/Using_vector_databases_for_embeddings_search.ipynb:cell 4:29:18: unexpected token '-'
...
Found 4227 errors (2165 fixed, 2062 remaining).
```
### https://github.com/tensorflow/docs
```
-------------------------------------------------------------------------------
Jupyter Notebooks 150 0 0 0 0
|- Markdown 1 55 0 46 9
|- Python 1 402 289 60 53
(Total) 457 289 106 62
-------------------------------------------------------------------------------
```
```console
$ cargo run --all-features --bin ruff -- check --no-cache --isolated --select=ALL /path/to/tensorflow-docs/**/*.ipynb --fix
error: Failed to parse /path/to/tensorflow-docs/site/en/guide/extension_type.ipynb:cell 80:1:1: unexpected token Indent
error: Failed to parse /path/to/tensorflow-docs/site/en/r1/tutorials/eager/custom_layers.ipynb:cell 20:1:1: unexpected token Indent
error: Failed to parse /path/to/tensorflow-docs/site/en/guide/data.ipynb:cell 175:5:14: unindent does not match any outer indentation level
error: Failed to parse /path/to/tensorflow-docs/site/en/r1/tutorials/representation/unicode.ipynb:cell 30:1:1: unexpected token Indent
...
Found 12726 errors (5140 fixed, 7586 remaining).
```
### https://github.com/tensorflow/models
```
-------------------------------------------------------------------------------
Jupyter Notebooks 46 0 0 0 0
|- Markdown 1 11 0 6 5
|- Python 1 328 249 19 60
(Total) 339 249 25 65
-------------------------------------------------------------------------------
```
```console
$ cargo run --all-features --bin ruff -- check --no-cache --isolated --select=ALL /path/to/tensorflow-models/**/*.ipynb --fix
...
Found 4856 errors (2690 fixed, 2166 remaining).
```
resolves: #1218fixes: #4556
## Summary
Ignore pyproject.toml file for adding noqa directives using `--add-noqa`
## Test Plan
`cargo run --bin ruff -- check --add-noqa .`
fixes: #5012
* Use dummy verbatim formatter for all nodes
* Use new formatter infrastructure in CLI and test
* Expose the new formatter in the CLI
* Merge import blocks
This adds a new rule `InvalidPyprojectToml` that lints pyproject.toml by checking if https://github.com/PyO3/pyproject-toml-rs can parse it. This means the linting is currently very basic, e.g. we don't check whether the name is actually a valid python project name or appropriately normalized. It does catch errors e.g. with invalid dependency requirements or problems withs the license specifications. It is open to be extended in the future (validate name, SPDX expressions, classifiers, ...), either in ruff or in pyproject-toml-rs.
Test plan:
```
scripts/ecosystem_all_check.sh check --select RUF200
```
This lead to a bunch of
```
RUF200 Failed to parse pyproject.toml: missing field `name`
```
(e.g. https://github.com/amitsk/fastapi-todos/blob/main/pyproject.toml) which is indeed invalid (https://packaging.python.org/en/latest/specifications/declaring-project-metadata/#specification).
Filtering those out, the following other problems were found by `cd target/ecosystem_all_results/ && rg RUF200`:
```
UCL-ARC:rred-reports.stdout.txt
1:pyproject.toml:27:16: RUF200 Failed to parse pyproject.toml: Version specifier `>='3.9'` doesn't match PEP 440 rules
EndlessTrax:python-start-project.stdout.txt
1:pyproject.toml:14:16: RUF200 Failed to parse pyproject.toml: Expected package name starting with an alphanumeric character, found '#'
redjax:gardening-api.stdout.txt
1:pyproject.toml:7:11: RUF200 Failed to parse pyproject.toml: Version `` doesn't match PEP 440 rules
ajslater:codex.stdout.txt
2: 3:17 RUF200 Failed to parse pyproject.toml: invalid type: sequence, expected a string
LDmitriy7:404_AvatarsBot.stdout.txt
1:pyproject.toml:3:11: RUF200 Failed to parse pyproject.toml: Version `` doesn't match PEP 440 rules
ajslater:comicbox.stdout.txt
1:pyproject.toml:3:17: RUF200 Failed to parse pyproject.toml: invalid type: sequence, expected a string
manueldevillena:forecast-earnings.stdout.txt
1:pyproject.toml:24:12: RUF200 Failed to parse pyproject.toml: Expected one of `@`, `(`, `<`, `=`, `>`, `~`, `!`, `;`, found `^`
redjax:ohio_utility_scraper.stdout.txt
1:pyproject.toml:11:11: RUF200 Failed to parse pyproject.toml: Version `` doesn't match PEP 440 rules
agronholm:typeguard.stdout.txt
1:pyproject.toml:40:8: RUF200 Failed to parse pyproject.toml: Expected a valid marker name, found 'python_implementation'
cyuss:decathlon-turnover.stdout.txt
1:pyproject.toml:7:12: RUF200 Failed to parse pyproject.toml: invalid type: string "Youcef", expected a table with 'name' and 'email' keys
ajslater:boilerplate.stdout.txt
1:pyproject.toml:3:17: RUF200 Failed to parse pyproject.toml: invalid type: sequence, expected a string
kaparoo:lightning-project-template.stdout.txt
1:pyproject.toml:56:16: RUF200 Failed to parse pyproject.toml: You can't mix a >= operator with a local version (`+cu117`)
dijital20:pytexas2023-decorators.stdout.txt
1:pyproject.toml:5:11: RUF200 Failed to parse pyproject.toml: Version `` doesn't match PEP 440 rules
pfouque:django-anymail-history.stdout.txt
1:pyproject.toml:137:12: RUF200 Failed to parse pyproject.toml: Version specifier `> = 1.2.0` doesn't match PEP 440 rules
pfouque:django-fakemessages.stdout.txt
1:pyproject.toml:130:12: RUF200 Failed to parse pyproject.toml: Version specifier `> = 1.2.0` doesn't match PEP 440 rules
pypa:build.stdout.txt
1:tests/packages/test-invalid-requirements/pyproject.toml:2:12: RUF200 Failed to parse pyproject.toml: Expected one of `@`, `(`, `<`, `=`, `>`, `~`, `!`, `;`, found `i`
4:tests/packages/test-no-requires/pyproject.toml:1:1: RUF200 Failed to parse pyproject.toml: missing field `requires`
UnoYakshi:FRAAND.stdout.txt
2: 3:11 RUF200 Failed to parse pyproject.toml: Version `` doesn't match PEP 440 rules
DHolmanCoding:python-template.stdout.txt
1:pyproject.toml:22:1: RUF200 Failed to parse pyproject.toml: missing field `requires`
```
Overall, this emitted errors in 43 out of 3408 projects (`rg -c RUF200 target/ecosystem_all_results/ | wc -l`)
Co-authored-by: Micha Reiser <micha@reiser.io>
* Create dummy format CLI
* Hide format from clap, too
Missed that this is a separate option from `#[doc(hidden)]`
* Remove cargo feature and replace with warning
* No-alloc files parameter matching
* beta warning: warn -> warn_user_once
* Rephrase warning
I noticed in the byte-offsets refactor that the `JsonEmitter` uses one indexed column numbers for the diagnostic start and end locations but not for `edits`.
This PR changes the `JsonEmitter` to emit one-indexed column numbers for edits, as we already do for `Message::location` and `Message::end_location`.
## Open questions
~We'll need to change the LSP to subtract 1 from the columns in `_parse_fix`~
6e44fadf8a/ruff_lsp/server.py (L129-L150)
~@charliermarsh is there a way to get the ruff version in that method? If not, then I recommend adding a `version` that we increment whenever we make incompatible changes to the serialized message. We can then use it in the LSP to correctly compute the column offset.~
I'll use the presence of the `Fix::applicability` field to detect if the Ruff version uses one or zero-based column indices.
See https://github.com/charliermarsh/ruff-lsp/pull/103
* Add basic jupyter notebook support behind a feature flag
* Address review comments
* Rename in separate commit to make both git and clippy happy
* cfg(feature = "jupyter_notebook") another test
* Address more review comments
* Address more review comments
* and clippy and windows
* More review comment
## Summary
This PR moves `Diagnostic`, `DiagnosticKind`, and `Fix` into their own crate, which will enable us to further split up Ruff, since sub-linter crates (which need to implement functions that return `Diagnostic`) can now depend on `ruff_diagnostics` rather than Ruff.
This PR introduces a new `CacheKey` trait for types that can be used as a cache key.
I'm not entirely sure if this is worth the "overhead", but I was surprised to find `HashableHashSet` and got scared when I looked at the time complexity of the `hash` function. These implementations must be extremely slow in hashed collections.
I then searched for usages and quickly realized that only the cache uses these `Hash` implementations, where performance is less sensitive.
This PR introduces a new `CacheKey` trait to communicate the difference between a hash and computing a key for the cache. The new trait can be implemented for types that don't implement `Hash` for performance reasons, and we can define additional constraints on the implementation: For example, we'll want to enforce portability when we add remote caching support. Using a different trait further allows us not to implement it for types without stable identities (e.g. pointers) or use other implementations than the standard hash function.
Thomas de Zeeuw
Charlie Marsh
Dhruv Manilawala
konstin
Aarni Koskela
Micha Reiser
Jonathan Plasse
Aaron Cunningham
Mikko Leppänen
Zanie Adkins
Evan Rittenhouse
Chris Chan
Agriya Khetarpal
StefanBRas