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
38 lines
2.3 KiB
Markdown
38 lines
2.3 KiB
Markdown
# 11 - Reporting: Tables, Charts, Weighted Scoring
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## Context / Goal
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Aggregate all measurements and projections into the final decision document that clearly ranks the storage formats for laboratory use cases: analysis, report generation, search/filtering, archiving, inter-lab exchange.
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## Dependencies
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09, 10 (and 02 for embedding process diagrams).
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## Processing instructions
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Write `report.py` using matplotlib (PNG, 150 dpi, log scales where ranges span decades) into `./out/report/charts/`, and assemble `./out/report/REPORT.md`.
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### Tables
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1. Storage footprint: measured @150 MB + coefficients vs CSV + projected @600 GB/1.2 TB/6 TB (incl. compressed archival sizes).
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2. Q1-Q7 median wall time / peak RAM / CPU% / read MB, per format (measured).
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3. Q1-Q7 projected wall time @600 GB with IMPRACTICAL/FAIL flags.
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4. Hardware sizing & cost per format @600 GB.
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5. Weighted scoring matrix (see below).
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### Charts (each measured + each projected @600 GB)
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- C1: disk footprint bar (log).
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- C2: RAM requirement bar (log).
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- C3: per-query grouped bars Q1-Q7 wall time (log), one chart per query, 5 formats each.
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- C4: degradation curves - wall time vs data size (150 MB → 6 TB), one line per format, per query class (point read / indexed / full aggregation), log-log.
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- C5: stacked-bar weighted score.
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- C6: cost vs performance scatter @600 GB (x = est cost, y = geometric-mean query time, log-log).
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### Weighted scoring (agreed methodology)
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Normalized 0-10 per metric, then weights: Search speed ×5 (0-50), RAM economy ×2 (0-20), Disk economy ×1 (0-10). Max 80. Search score = normalized inverse geometric mean of Q1-Q7 projected @600 GB (TIMEOUT/FAIL → 0). Present measured-scale and 600 GB-scale scoreboards side by side.
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### Narrative sections (concise, English)
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- Executive summary with the recommendation (expected, to be confirmed by data: PostgreSQL system of record; CSV retained as raw archive; hybrid JSON-LD for exchange; RDF as virtual layer only - reference Ontop OBDA).
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- Use-case mapping table: analysis / reports / search / archiving / exchange → recommended format(s).
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- Limitations: single-node, simulated data, extrapolation assumptions.
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## Output
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- `./out/report/REPORT.md` + `./out/report/charts/*.png` + all tables as CSV in `./out/report/tables/`.
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- Marker `./out/.done/11.ok`.
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