feat(bench): add SQLite and PostgreSQL wrappers for benchmark queries
- implement SQLite and PostgreSQL wrappers to execute benchmark queries - update README to reflect task 09 as implemented - enhance storage size updates to handle (format, variant) replacements - clarify benchmarking methodology for remote PostgreSQL setup
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docs/Initial_Prompt.md
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docs/Initial_Prompt.md
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# Prompt: Generate a Multi-File Specification for a Tribology Data Storage Evaluation Pipeline
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You are an AI assistant generating a specification for execution by Anthropic Claude Code
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in a Cursor development environment (local filesystem, on-premise only, no cloud).
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## Objective
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Produce 12 Markdown files — `00_PLAN.md` plus task files `01`–`11` — that specify a complete,
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reproducible pipeline: simulate a tribology laboratory, generate ~150 MB of realistic
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measurement data in CSV, convert it into 4 target storage formats, benchmark 7 retrieval
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scenarios across all 5 formats with resource metering, extrapolate results to 600 GB+,
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and produce a decision report with tables, charts, and weighted scoring.
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## File structure requirements
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Each task file must contain: Context/Goal, Dependencies, Input, Processing instructions,
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Output (artifacts + completion marker `./out/.done/<task>.ok`).
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`00_PLAN.md` must contain: goals, execution environment, a Mermaid dependency flowchart,
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parallelism notes, execution order, and global conventions (ISO-8601 UTC timestamps,
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SI-explicit column names, fixed random seed 20260711, Python with tabs for indentation).
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## Laboratory configuration (task 01)
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- Material: Ti-6Al-4V coupons 10×10×3 mm, Cr adhesion layer, Pt-Au coating sputtered in a
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composition gradient; film thickness 0.3–1.1 µm.
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- Instruments: RAPID custom high-throughput parallelized 6-probe tribometer (friction);
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Bruker TI980 TriboIndenter (nanoindentation); Bruker M4 Tornado micro-XRF (composition
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mapping); Kurt J. Lesker PVD 200 sputter-down (deposition); AFM (roughness); optical
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profilometry (thickness); SIMTRA (sputter-transport Monte-Carlo simulation).
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- Deposition matrix, 4 batches (B721–B724) with gun tilt (20°:0°, 20°:20°, 20°:20°, 0°:20°),
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Pt/Au power (150/50, 100/100, 150/50, 50/150 W) and discharge voltages.
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- Hierarchy and volumes: batch(×4) → wafer(×3) → coupon(×49, 7×7 grid) = 588 coupons;
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480 friction coupons (4 RAPID runs × 5 plates × 6 probes × 4 coupons/square),
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108 reserve; 2 environments (Lab Air / Dry N2, 2 runs each); 3 replicate tracks per
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coupon with counterface rotation → 1,440 tracks × 1,000 cycles = 1.44 M cycle rows;
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friction loops (200 points every 100th cycle) → 2.88 M loop points.
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- Physical models for simulation: exponential COF run-in with steady-state depending on
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Au% and environment; hardness/modulus linear in Au% with noise; log-normal roughness;
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Archard wear; Au gradient across wafer position (batch-dependent center: 8/25/10/60 wt%).
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## Process flow diagrams (task 02) — Mermaid
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Coupon assembly (clean → sonicate → Cr adhesive → PVD sputter); characterization &
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batch assembly (SIMTRA, profilometry, ×49/wafer, ×3/batch, batches 721–724); testing tree
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(friction µ_normal/µ_dry_nit, nanoindentation, AFM, XRF); tribometer session sequence
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(request → samples → ball holders, 3 counterfaces/holder → Excel test plan → setup →
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run → software stop → save avg files → teardown); execution loop (5 plates × 6 probes ×
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4 coupons × 3 tracks with counterface rotation); data-hierarchy ERD.
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## Data generation (task 03)
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Hierarchical CSV file tree mirroring the lab structure (batches, simtra, per-coupon
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xrf_map/profilometry/nanoindentation/afm, per-track track_info/cof_vs_cycle/
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friction_loops/wear), streaming generation, MANIFEST.csv with row/byte/sha256,
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size guard 120–200 MB.
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## Conversions (tasks 04–07), each recording sizes into storage_sizes.csv
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- JSON-LD: vocab + QUDT + PROV-O; two variants — FULL (bulk embedded, per-coupon files,
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compact) and HYBRID (metadata only, sourceFile links to CSV).
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- SQLite: single file, integer surrogate keys, WITHOUT ROWID bulk tables, precomputed
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track_summary, indexes for the benchmark queries, load PRAGMAs, VACUUM/ANALYZE.
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- PostgreSQL 16: same logical schema, RANGE partitioning of bulk tables, COPY loading,
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b-tree + BRIN indexes, materialized track_summary, pg_dump archive.
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- RDF: N-Triples.gz + Turtle serializations; queryable embedded store (oxigraph preferred,
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Fuseki/TDB2 fallback); document triple-modeling decisions; expect 25–45 M triples.
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## Benchmarks (tasks 08–09)
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Seven scenarios: Q1 single-track COF curve; Q2 steady-state COF at Au=10±0.5 wt%;
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Q3 hardness vs COF join; Q4 condition filter (Dry N2, 100 mN, cof>0.20); Q5 mean run-in
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per batch aggregating ALL raw cycles (summary tables forbidden); Q6 wear volume vs load
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join; Q7 Stribeck-style grouped aggregation. Implement per format (direct path read /
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ijson streaming / SQL / SQL+EXPLAIN / SPARQL); identical result sets validated by checksum.
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Harness: each cell in a separate subprocess; psutil metering (wall time, peak RSS, CPU
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time/utilization, read bytes); 1 warm-up + 3 measured runs, medians; 30-min timeout;
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outputs results_raw.csv / results_median.csv.
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## Extrapolation (task 10)
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Project storage linearly and query time by access-pattern complexity laws
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(O(1)/O(log n)/O(n)/single-threaded vs parallel partitioned scan) from the 150 MB
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calibration point to 600 GB, 1.2 TB, 6 TB; flag >1 h IMPRACTICAL, >24 h FAIL; RAM models
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per format; hardware sizing and cost table driven by an editable hw_prices.yaml
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(June 2026 defaults: DDR5 RDIMM ~$14/GB, enterprise NVMe ~$250/TB, 16-core 1U ~$6k).
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## Reporting (task 11)
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Tables (footprint + coefficients vs CSV, measured metrics, projected times with flags,
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hardware costs, scoring matrix); matplotlib charts (log scales): footprint, RAM,
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per-query bars, degradation curves 150 MB→6 TB, stacked weighted score, cost-vs-performance
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scatter. Weighted scoring: search speed ×5 (0–50), RAM economy ×2 (0–20), disk economy
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×1 (0–10), max 80; search score from inverse geometric mean of Q1–Q7 projected at 600 GB.
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Concise English narrative: executive summary and use-case mapping (analysis, reports,
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search/filter, archiving, inter-lab exchange), limitations.
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## Style
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Specification prose in English, concise and directive. No implementation code in the
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spec — only precise instructions an autonomous coding agent can execute.
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@@ -82,6 +82,15 @@ invalidates everything downstream.
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pass is therefore SKIPPED and marked as such in results and report -
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never approximated by a fake (e.g. file re-copy tricks) without a
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rules update describing the method.
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- **PostgreSQL is remote on this setup** (dedicated DB host on the LAN),
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so spec 09's backend-PID sampling via `pg_stat_activity` + psutil is
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impossible - psutil cannot attribute a remote process. Instead the
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runner records (a) client-side child metrics exactly like every other
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format (wall times therefore include LAN round-trips - stated in the
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report), (b) best-effort Prometheus / node_exporter host CPU/RAM
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windows per PG cell and for the whole session (`TRIBO_PROM_URL`), and
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(c) best-effort `pg_stat_statements` per-query session deltas
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(server-side execution time and buffer traffic).
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- Quiesce the machine during measured runs (no parallel pipeline tasks,
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no background loads); the runner runs cells sequentially by design.
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