ZacharyZhang-NY
7e1fe649f6
The product's own search answered in p50 20.16s on 165,973 atoms. The SQL rewrite brought that to 0.68s; this brings it to 57ms and says why the rest was there. The cost was not SQL. An OR MATCH scores every row it touches, and a question carries words the whole library carries: `"parsing"* OR "error"* OR "in"* OR "the"* OR "pdf"* OR "converter"*` matched 127,145 atoms and took 305ms, while a CJK bigram phrase query with 32 matches took 1.0ms. Latency tracked match count. - text_index.rare_terms asks the index which of its own words are noise: count each term, drop the ones over 5% of the library, keep the rarest few when all of them are common. No stop-word list; a corpus of PDFs and a corpus of shell history get different answers. 0.05 is swept, not chosen — on n=173 recall@10 peaks there (0.899 / p50 58.6ms) against 0.893 / 112.9ms at 0.20 and 0.844 / 13.4ms at 0.01. - The bound is a cost, not a claim about rarity: `cap = max(int(total*fraction)+1, 2)`, so a word matching one atom in eleven is kept — scoring one candidate is free. A capped count cannot rank words that all saturate, so those few are counted exactly. - Schema v6 adds `term_df`: a word is counted once and the next question pays nothing. Every atom write clears it, so a stored count cannot outlive the library it described. Cold p50 73ms, warm 63ms, 1,324 terms cached. - The denominator is `count(*) FROM atoms` (0.01ms) rather than `count(*) FROM atom_fts` (37ms cold): FTS rows are written and deleted with their atoms, so the two counts move together and only one of them is free. SCALE.md, idle machine (load 1.76, 32 cores, 4 threads per query): 165,973 atoms — 1.66x the contract's 100k — 300 probes, p50 57ms, p95 138ms. Against the frozen baseline of 20.16s that is 354x. Holder-scoped (n=173): recall@10 0.870, MRR 0.740, p50 62ms. Verification: pytest backend/tests bench/tests -q -> 173 passed (5 new text_index tests: the common word is dropped and the rare one still answers, an all-common query keeps its rarest words in order, a one-word query is never filtered, an empty library keeps everything, and a write voids the counts); ruff clean; mypy fresh cache clean; pyright 0 errors on the touched files.
563 B
563 B
SCALE: the product's own search at corpus size
- database: bench/runs/baseline/data/ely_eye.sqlite
- atoms: 165973, probes: 300 (code, image, pdf, ui_screenshot, video)
- dense embeddings: False
- opening the store (migrations included): 0.000s
- p50 0.0570s · p95 0.1383s · max 0.5395s over 300 queries
- machine: Linux 6.18.40.1-microsoft-standard-WSL2, 32 cores, 4 threads per query, load [1.77, 1.62, 1.64]
Latency is wall time per search call on an idle machine with the thread budget the benchmark pins; a run on a loaded machine is not comparable.