## Summary
This PR stores the mapping from `ExprName` node to resolved `BindingId`,
which lets us skip scope lookups in `resolve_call_path`. It's enabled by
#6045, since that PR ensures that when we analyze a node (and thus call
`resolve_call_path`), we'll have already visited its `ExprName`
elements.
In more detail: imagine that we're traversing over `foo.bar()`. When we
read `foo`, it will be an `ExprName`, which we'll then resolve to a
binding via `handle_node_load`. With this change, we then store that
binding in a map. Later, if we call `collect_call_path` on `foo.bar`,
we'll identify `foo` (the "head" of the attribute) and grab the resolved
binding in that map. _Almost_ all names are now resolved in advance,
though it's not a strict requirement, and some rules break that pattern
(e.g., if we're analyzing arguments, and they need to inspect their
annotations, which are visited in a deferred manner).
This improves performance by 4-6% on the all-rules benchmark. It looks
like it hurts performance (1-2% drop) in the default-rules benchmark,
presumedly because those rules don't call `resolve_call_path` nearly as
much, and so we're paying for these extra writes.
Here's the benchmark data:
```
linter/default-rules/numpy/globals.py
time: [67.270 µs 67.380 µs 67.489 µs]
thrpt: [43.720 MiB/s 43.792 MiB/s 43.863 MiB/s]
change:
time: [+0.4747% +0.7752% +1.0626%] (p = 0.00 < 0.05)
thrpt: [-1.0514% -0.7693% -0.4724%]
Change within noise threshold.
Found 1 outliers among 100 measurements (1.00%)
1 (1.00%) high severe
linter/default-rules/pydantic/types.py
time: [1.4067 ms 1.4105 ms 1.4146 ms]
thrpt: [18.028 MiB/s 18.081 MiB/s 18.129 MiB/s]
change:
time: [+1.3152% +1.6953% +2.0414%] (p = 0.00 < 0.05)
thrpt: [-2.0006% -1.6671% -1.2981%]
Performance has regressed.
linter/default-rules/numpy/ctypeslib.py
time: [637.67 µs 638.96 µs 640.28 µs]
thrpt: [26.006 MiB/s 26.060 MiB/s 26.113 MiB/s]
change:
time: [+1.5859% +1.8109% +2.0353%] (p = 0.00 < 0.05)
thrpt: [-1.9947% -1.7787% -1.5611%]
Performance has regressed.
linter/default-rules/large/dataset.py
time: [3.2289 ms 3.2336 ms 3.2383 ms]
thrpt: [12.563 MiB/s 12.581 MiB/s 12.599 MiB/s]
change:
time: [+0.8029% +0.9898% +1.1740%] (p = 0.00 < 0.05)
thrpt: [-1.1604% -0.9801% -0.7965%]
Change within noise threshold.
linter/all-rules/numpy/globals.py
time: [134.05 µs 134.15 µs 134.26 µs]
thrpt: [21.977 MiB/s 21.995 MiB/s 22.012 MiB/s]
change:
time: [-4.4571% -4.1175% -3.8268%] (p = 0.00 < 0.05)
thrpt: [+3.9791% +4.2943% +4.6651%]
Performance has improved.
Found 8 outliers among 100 measurements (8.00%)
2 (2.00%) low mild
3 (3.00%) high mild
3 (3.00%) high severe
linter/all-rules/pydantic/types.py
time: [2.5627 ms 2.5669 ms 2.5720 ms]
thrpt: [9.9158 MiB/s 9.9354 MiB/s 9.9516 MiB/s]
change:
time: [-5.8304% -5.6374% -5.4452%] (p = 0.00 < 0.05)
thrpt: [+5.7587% +5.9742% +6.1914%]
Performance has improved.
Found 7 outliers among 100 measurements (7.00%)
6 (6.00%) high mild
1 (1.00%) high severe
linter/all-rules/numpy/ctypeslib.py
time: [1.3949 ms 1.3956 ms 1.3964 ms]
thrpt: [11.925 MiB/s 11.931 MiB/s 11.937 MiB/s]
change:
time: [-6.2496% -6.0856% -5.9293%] (p = 0.00 < 0.05)
thrpt: [+6.3030% +6.4799% +6.6662%]
Performance has improved.
Found 7 outliers among 100 measurements (7.00%)
3 (3.00%) high mild
4 (4.00%) high severe
linter/all-rules/large/dataset.py
time: [5.5951 ms 5.6019 ms 5.6093 ms]
thrpt: [7.2527 MiB/s 7.2623 MiB/s 7.2711 MiB/s]
change:
time: [-5.1781% -4.9783% -4.8070%] (p = 0.00 < 0.05)
thrpt: [+5.0497% +5.2391% +5.4608%]
Performance has improved.
```
Still playing with this (the concepts need better names, documentation,
etc.), but opening up for feedback.
e15b9c5572