b173ac82a9
- adapt meta-/code-/obs- rules to the local Python benchmark pipeline - replace db-sql-ddl, code-config-env-scope, test-e2e-pytest with pipeline equivalents - add code-python-style, data-determinism, data-naming-units, bench-methodology, build-pipeline-tasks - normalize specs and README typography to ASCII per code-data-formatting - rewrite root CLAUDE.md trigger table; add .cursorrules and .gitignore - specs: pipeline plan and task specs 00-11 - rules: 19 binding rule files adapted for this project - docs: CLAUDE.md rule-trigger table - config: .cursorrules commit convention, .gitignore excluding out/ and .venv/ - docs: rewrite README.md with pipeline diagrams, setup guide, result placeholders - config: requirements.txt for the closed dependency list - datagen: make_lab_config.py writes out/config/lab_config.yaml and .done marker
32 lines
2.1 KiB
Markdown
32 lines
2.1 KiB
Markdown
# 08 - Benchmark Query Definitions (Q1-Q7 × 5 formats)
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## Context / Goal
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Define the 7 laboratory-standard retrieval scenarios and their concrete implementation for each of the 5 formats. Every implementation must return identical result sets (validated by checksum of sorted result rows, tolerance 1e-9 on floats).
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## Dependencies
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04, 05, 06, 07 (all stores populated) + 03 (CSV baseline).
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## Scenarios
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| ID | Scenario | Result shape |
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|---|---|---|
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| Q1 | COF vs cycle curve for one track (`B722-W2-C13-T2`) | 1,000 rows (cycle, cof) |
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| Q2 | Steady-state COF for all coupons with mean Au = 10 ± 0.5 wt% | ~dozens of rows (coupon, env, cof_ss) |
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| Q3 | Hardness vs steady-state COF across all batches (join nano × track_summary) | 480 rows |
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| Q4 | Tracks in Dry N2, load 100 mN, cof_ss > 0.20 (anomaly filter) | filtered rows |
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| Q5 | Mean run-in cycles per batch (aggregation over ALL cycle rows, no summary tables allowed - force the full scan) | 4 rows |
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| Q6 | Wear volume vs load per coupon (join wear × tracks × coupons) | 480 rows |
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| Q7 | Stribeck-style aggregation: mean COF grouped by (environment, speed·load bucket) over all cycles past run-in | ~10 rows |
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## Implementations
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- **CSV**: pure Python + csv module streaming (pandas allowed only as a second measured variant, labeled separately). Q1 = direct path read (this is the honest CSV strength). Q2-Q7 = directory walks + programmatic joins.
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- **JSON**: variant FULL, `ijson` streaming parser; Q1 = load the single coupon file.
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- **SQLite**: SQL over `tribo.db`, cold and warm cache runs (`PRAGMA cache_size` default; drop OS cache not required, just report both run 1 and run 3).
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- **PostgreSQL**: SQL, `EXPLAIN (ANALYZE, BUFFERS)` captured alongside; parallel workers default.
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- **RDF**: SPARQL against the populated store; capture query text.
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- Q5 rule applies to all formats: query raw cycle data, not `track_summary`.
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## Output
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- `./out/bench/queries/` - one subfolder per format with 7 runnable query files (`q1.sql`, `q1.sparql`, `q1.py`, ...).
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- `./out/bench/expected/q<N>.sha256` - canonical result checksums (computed once from PostgreSQL, cross-validated against SQLite).
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- Marker `./out/.done/08.ok`.
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