Martin Vogel ecfbbf48d6 perf(py_lsp): cap dedup window at 256 entries — 91.8x -> 31.4x scaling ...

Audit for O(n^2) hot paths after Round 10. The dedup loop in
py_emit_resolved_call scanned ALL prior entries linearly, making
emission O(N) and total file work O(N^2). On a synthetic 2000-class
fixture with 8000 calls, that dominated time at 5083 ms.

Cap the scan window at the last 256 entries. Most duplicate
emissions come from the same expression being walked twice (resolver
+ emitter passes), so they're temporally adjacent — a 256-entry
window catches every common case while keeping the dedup O(1) per
emission.

Scale measurements (test_py_lsp_scale.c added):
| Classes | Calls | Resolved | Before | After |
|--------:|------:|---------:|-------:|------:|
|     100 |   400 |      300 |   55ms |  57ms |
|     500 |  2000 |     1500 |  333ms | 323ms |
|    2000 |  8000 |     6000 | 5083ms | 1804ms|

Ratio for 20x input: 91.8x -> 31.4x (linear would be 20x; quadratic
would be 400x). No remaining quadratic hot path in code added by
this branch.

Other hot paths surveyed:
- py_register_def -> cbm_registry_lookup_type: O(types) per def at
  registration, O(D x types) for D defs. Pre-existing linear scan in
  type_registry.c; hash-indexing it would be a cross-language
  optimization (Go and C++ LSPs benefit too).
- py_lookup_attribute / py_lookup_field: O(types) per call site.
  Same pre-existing linear scan. With ~900 stdlib types + per-file
  defs, accounts for ~75µs/call at 2000-class scale.
- MODULE submodule prefix scan: O(funcs) per module-attribute access,
  early-exits on first match. Bounded by registry size.
- lambda / dict-literal registries: linear, but bounded by per-file
  N which is small.

Bench unchanged at 100% / 12ms (52/52 calls, 178 LOC).

2026-05-09 15:44:09 +02:00

..

cbm

perf(py_lsp): cap dedup window at 256 entries — 91.8x -> 31.4x scaling

2026-05-09 15:44:09 +02:00