b4270b0247
- update README to clarify the exclusion of bulk artifacts from git - specify committed curated results subset in README and rules - improve descriptions of output directories and their contents - refine details on the pipeline's data generation and reporting processes
326 lines
15 KiB
Markdown
326 lines
15 KiB
Markdown
# LabDataStorageEvaluation
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A reproducible pipeline that answers one practical question: **which storage
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format should a materials-science laboratory use for its measurement data?**
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The pipeline simulates a tribology laboratory (Pt-Au LDRD context, Sandia
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National Laboratories),
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generates ~150 MB of physically plausible raw measurement data in CSV,
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converts the corpus into four additional storage formats, benchmarks seven
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laboratory-standard retrieval scenarios against all five formats, extrapolates
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the measurements to 600 GB - 6 TB, and produces a ranked decision report for
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five laboratory use cases: analysis, reporting, search/filtering, archiving,
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and inter-lab exchange.
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- Task specifications: [docs/specs/](docs/specs/) (plan + tasks 01-11).
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- Binding conventions: [docs/rules/](docs/rules/) and [CLAUDE.md](CLAUDE.md).
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## Goals
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1. Define a simulated tribology laboratory with real instruments and a
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realistic test workflow.
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2. Generate ~150 MB of physically plausible measurement data in CSV (the
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canonical raw format), deterministically from seed `20260711`.
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3. Convert the CSV corpus into 4 target storage formats: JSON-LD, SQLite
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(optimized), PostgreSQL 16 (indexed + partitioned), RDF triplestore.
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4. Benchmark 7 retrieval scenarios (Q1-Q7) against all 5 formats, measuring
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wall time, peak RAM, CPU utilization, disk I/O, and disk footprint.
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5. Extrapolate measured results to 600 GB / 1.2 TB / 6 TB with
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complexity-aware scaling models.
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6. Produce summary tables, charts, and a weighted scoreboard that rank the
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formats per use case.
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## Pipeline
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```mermaid
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flowchart TD
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T01[01_lab_configuration] --> T02[02_process_flow_diagrams]
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T01 --> T03[03_csv_data_generation]
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T03 --> T04[04_convert_json]
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T03 --> T05[05_convert_sqlite]
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T03 --> T06[06_convert_postgresql]
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T03 --> T07[07_convert_rdf]
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T04 --> T08[08_benchmark_queries]
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T05 --> T08
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T06 --> T08
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T07 --> T08
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T03 --> T08
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T08 --> T09[09_benchmark_execution]
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T09 --> T10[10_extrapolation_model]
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T10 --> T11[11_reporting]
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T09 --> T11
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```
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Execution order: `01 -> 02 -> 03 -> (04 | 05 | 06 | 07) -> 08 -> 09 -> 10 -> 11`.
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Tasks 04-07 are independent and may run in parallel; task 09 requires a quiet
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machine (no parallel work). Every task writes a completion marker
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`./out/.done/<NN>.ok` and validates its dependencies' markers before starting.
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| Task | Script | Main output | Status |
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| 01 Lab configuration | `make_lab_config.py` | `out/config/lab_config.yaml` | implemented |
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| 02 Process flow diagrams | `make_process_flow.py` | `out/report/process_flow.md`, `out/report/diagrams/` | implemented |
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| 03 CSV data generation | `generate_data.py` | `out/csv/` tree + `MANIFEST.csv` | implemented |
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| 04 Convert to JSON-LD | `convert_json.py` | `out/json/full/`, `out/json/hybrid/dataset.jsonld` | implemented |
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| 05 Convert to SQLite | `convert_sqlite.py` | `out/sqlite/tribo.db` | implemented |
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| 06 Convert to PostgreSQL | `convert_postgresql.py` | live PostgreSQL DB (+ dump when pg_dump is available) | implemented |
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| 07 Convert to RDF | `convert_rdf.py` | `out/rdf/dataset.nt.gz`, `dataset.ttl`, oxigraph store | implemented |
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| 08 Benchmark queries | `make_queries.py` | `out/bench/queries/`, `out/bench/expected/` | implemented |
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| 09 Benchmark execution | `bench_runner.py` | `out/bench/results_raw.csv`, `results_median.csv` | implemented |
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| 10 Extrapolation | `extrapolate.py` | `out/bench/extrapolation.csv`, `hardware_sizing.csv` | implemented |
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| 11 Reporting | `report.py` | `out/report/REPORT.md`, charts, tables | implemented |
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## The simulated laboratory
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Ti-6Al-4V coupons (10 x 10 x 3 mm) with a sputtered Cr adhesion layer and a
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Pt-Au coating deposited as a composition gradient (film thickness
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0.3-1.1 um). Four deposition batches (B721-B724) vary gun tilt, power, and
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discharge voltage. Instruments: RAPID 6-probe tribometer (friction), Bruker
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TI980 (nanoindentation), Bruker M4 Tornado micro-XRF (composition), Kurt J.
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Lesker PVD 200 (deposition), AFM (roughness), optical profilometry
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(thickness), SIMTRA (sputter transport simulation).
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```mermaid
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flowchart LR
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B[Batch x4] --> W[Wafer x3 per batch]
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W --> C[Coupon x49 per wafer, 7x7 grid]
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C --> X[xrf_map: 400 points]
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C --> N[nanoindentation: 25 indents]
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C --> A[afm: Ra / Rq]
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C --> P[profilometry: 100 points]
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C --> T[Track x3, friction coupons only]
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T --> CY[cof_vs_cycle: 1000 cycles]
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T --> L[friction_loops: 10 x 200 points]
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T --> WR[wear: 1 row]
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```
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| Entity | Count |
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| Batches | 4 |
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| Wafers | 12 |
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| Coupons | 588 (480 friction-tested + 108 reserve/QA) |
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| Tracks | 1,440 |
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| Friction cycle rows | 1,440,000 |
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| Friction loop points | 2,880,000 |
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| XRF map points | 235,200 |
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Friction runs R1/R2 execute in Lab Air, R3/R4 in Dry N2. Generated values
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follow explicit physical models (COF run-in exponential, steady-state COF and
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hardness vs Au content, Archard wear), so benchmark queries return physically
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meaningful results.
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## Storage formats under evaluation
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| # | Format | Role in the comparison |
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| 1 | CSV (raw tree) | Canonical instrument output; baseline |
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| 2 | JSON-LD (full + hybrid) | Ontology-annotated exchange format (FAIR) |
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| 3 | SQLite | Single-file relational, optimized for size and speed |
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| 4 | PostgreSQL 16 | Production relational: indexed, partitioned |
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| 5 | RDF triplestore (oxigraph) | Semantic-web store queried via SPARQL |
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## Benchmark scenarios (Q1-Q7)
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| ID | Scenario | Access pattern |
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| Q1 | COF vs cycle curve for one track | Point read |
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| Q2 | Steady-state COF for coupons with mean Au = 10 +/- 0.5 wt% | Indexed filter |
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| Q3 | Hardness vs steady-state COF across all batches | Indexed join |
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| Q4 | Tracks in Dry N2, 100 mN, cof_ss > 0.20 (anomaly filter) | Indexed filter |
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| Q5 | Mean run-in cycles per batch over ALL raw cycle rows | Forced full scan |
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| Q6 | Wear volume vs load per coupon | Indexed join |
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| Q7 | Stribeck-style mean COF by (environment, speed-load bucket) | Full aggregation |
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Protocol: every cell (format x query) runs in its own subprocess; 1 warm-up +
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3 measured runs; randomized cell order; 30-minute hard timeout recorded as
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`TIMEOUT`; every result validated against a canonical checksum before the
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measurement counts. See
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[docs/rules/bench-methodology.md](docs/rules/bench-methodology.md).
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## Repository layout
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```
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docs/
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Initial_Prompt.md the generative prompt behind specs 00-11 (provenance, non-binding)
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specs/ task specifications 00-11 (WHAT to build)
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rules/ binding conventions (HOW work is done)
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research/ measured design studies (e.g. FK key-schema study)
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examples/ imported reference materials (not binding)
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out/ ALL generated artifacts (reproducible; bulk git-ignored,
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report/ config/ .done/ committed - see .gitignore):
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config/ csv/ json/ sqlite/ pg/ rdf/ bench/ report/ .done/
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common/ shared helpers (storage_sizes.csv contract, ...)
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queries/ Q1-Q7 implementations for csv / json / rdf formats
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make_lab_config.py task 01 entry script (one script per task, repo root)
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make_process_flow.py task 02 entry script
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generate_data.py task 03 entry script
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convert_json.py task 04 entry script
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convert_sqlite.py task 05 entry script
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convert_postgresql.py task 06 entry script (TRIBO_PG_DSN, TRIBO_PROM_URL)
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convert_rdf.py task 07 entry script
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make_queries.py task 08 entry script (canonical results need TRIBO_PG_DSN)
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bench_runner.py task 09 entry script (TRIBO_PG_DSN, TRIBO_PROM_URL)
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extrapolate.py task 10 entry script (seeds editable out/config/hw_prices.yaml)
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report.py task 11 entry script (REPORT.md, charts, tables, scoring)
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requirements.txt closed dependency list (docs/rules/code-python-style.md)
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```
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The bulk of the `out/` tree - the ~150 MB CSV corpus, the JSON-LD variants,
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databases, stores, archives, and raw benchmark data - is **excluded from
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git**; every artifact regenerates deterministically from the specs, the
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rules, and the code. A curated results subset IS committed (decision of
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2026-07-11): `out/report/` (REPORT.md, charts, tables, diagrams),
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`out/config/` (lab_config.yaml, hw_prices.yaml), and the `out/.done/`
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markers - see `.gitignore`. [docs/Initial_Prompt.md](docs/Initial_Prompt.md)
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is the original prompt the 12 specification files were generated from - kept
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for provenance only, never authoritative: where it disagrees with the specs,
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rules, or code (e.g. the SQLite key schema, the JSON-LD vocabulary), those win.
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## Getting started
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Prerequisites: Python 3.11+ (3.12 tested). PostgreSQL 16 is required only for
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tasks 06/08/09; its DSN comes from the `TRIBO_PG_DSN` environment variable
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(default `postgresql://postgres@localhost:5432/tribo`).
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Windows (PowerShell):
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```powershell
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py -3 -m venv .venv
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.venv\Scripts\python -m pip install -r requirements.txt
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.venv\Scripts\python make_lab_config.py
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```
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Linux:
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```bash
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python3 -m venv .venv
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.venv/bin/python -m pip install -r requirements.txt
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.venv/bin/python make_lab_config.py
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```
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Each task script supports `--log-level {DEBUG,INFO,WARNING,ERROR}` and prints
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its protocol output (paths, counts, sizes) to stdout. A task refuses to run if
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a dependency's `out/.done/<NN>.ok` marker is missing.
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Note on cold-cache runs: the page-cache drop used by the cold-cache benchmark
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pass is Linux-only; on Windows hosts that pass is skipped and marked as such.
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Benchmark execution is planned for a dedicated Linux host.
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## Results
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Results of the full pipeline run of 2026-07-11 (seed 20260711, 141.8 MiB
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corpus, all 35 benchmark cells checksum-valid). The generated sources of
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truth are committed under [out/report/](out/report/) - the full decision
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document is [out/report/REPORT.md](out/report/REPORT.md); the tables below
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are copied from it. An interactive single-file companion (sidebar
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navigation, per-query and per-scale views, cost explorer) is
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[docs/research/Tribology_Storage_Evaluation.html](docs/research/Tribology_Storage_Evaluation.html).
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Bulk measurement files (`out/bench/`) are git-ignored and regenerable.
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### Process flow diagrams (task 02)
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`out/report/process_flow.md` with `out/report/diagrams/D1..D6.mermaid`:
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D1 coupon assembly, D2 characterization and batch assembly, D3 testing tree,
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D4 tribometer session sequence, D5 execution loop, D6 data hierarchy.
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### Measured results (task 09)
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Storage footprint per format (working + compressed archival):
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| Format | Measured | vs CSV | Archival (compressed) |
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| CSV | 149 MB | 1.00x | 52.8 MB (tar.gz) |
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| JSON-LD (full) | 311 MB | 2.09x | 57.2 MB (tar.gz) |
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| SQLite | 123 MB | 0.83x | 55.1 MB (gzip) |
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| PostgreSQL | 397 MB | 2.67x | 53.1 MB (pg_dump -Fc) |
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| RDF triplestore | 1.74 GB | 11.70x | 126 MB (nt.gz) |
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Median wall time on the 150 MB corpus, warm cache (peak RAM / CPU / read
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bytes: [out/report/tables/t2_measured_medians.csv](out/report/tables/t2_measured_medians.csv)):
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| Format | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 |
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| CSV | 0.06 s | 0.17 s | 1.15 s | 0.72 s | 1.09 s | 0.28 s | 1.27 s |
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| JSON-LD | 0.11 s | 1.48 s | 9.9 s | 9.9 s | 10 s | 9.8 s | 10 s |
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| SQLite | 0.06 s | 0.06 s | 0.11 s | 0.06 s | 0.77 s | 0.06 s | 0.71 s |
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| PostgreSQL | 0.22 s | 0.22 s | 0.22 s | 0.22 s | 0.44 s | 0.22 s | 0.33 s |
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| RDF triplestore | 0.11 s | 2.81 s | 46 s | 20 s | 60 s | 0.17 s | 41 s |
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### Projections (task 10)
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Projected wall times at 600 GB - `(!)` = IMPRACTICAL (over 1 hour),
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`FAIL` = over 24 hours; 1.2 TB and 6 TB scales:
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[out/report/tables/t3_projected_600gb.csv](out/report/tables/t3_projected_600gb.csv)
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and `out/bench/extrapolation.csv`:
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| Format | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 |
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| CSV | 0.06 s | 7.1 min | 1.2 h (!) | 44 min | 1.2 h (!) | 15 min | 1.4 h (!) |
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| JSON-LD | 0.11 s | 1.5 h (!) | 11 h (!) | 11 h (!) | 11 h (!) | 10.9 h (!) | 11.2 h (!) |
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| SQLite | 0.06 s | 1.3 s | 5.7 min | 0.06 s | 47 min | 5.2 s | 44 min |
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| PostgreSQL | 0.22 s | 0.22 s | 3.4 s | 11 s | 3.7 min | 4.1 s | 1.9 min |
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| RDF triplestore | 0.11 s | 3.0 h (!) | 2.1 d FAIL | 22 h (!) | 2.8 d FAIL | 5.9 min | 1.9 d FAIL |
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Hardware sizing at 600 GB - bill of materials with street prices as of
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2026-07-11 (component sources: REPORT.md section 7 and
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[out/config/hw_prices.yaml](out/config/hw_prices.yaml)):
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| Format | Configuration | Total |
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| CSV, JSON-LD, SQLite, PostgreSQL (each) | 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM ($1,887); 1 x 7.68 TB NVMe U.2 ($3,995) | $10,500 |
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| RDF triplestore | 2 nodes; 22 x 64 GB DDR5 ECC RDIMM; 2 x 7.68 TB NVMe U.2 | $58,740 |
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At 6 TB PostgreSQL still fits one 16-core node - 9 RDIMM modules, 3 NVMe
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drives, $33,586; the RDF triplestore needs 14 nodes and $527,732
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(projected; full table: `out/bench/hardware_sizing.csv`).
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### Charts (task 11)
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(Task 11 writes the charts to `out/report/charts/` under exactly these
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names; c3 covers all seven queries - `c3_q1..q7_wall_time.png` - and c4 has
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three query classes: `point_read`, `indexed`, `full_scan`.)
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### Final scoreboard (task 11)
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Weighted scoring, max 80 points: search speed x5 (0-50), RAM economy x2
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(0-20), disk economy x1 (0-10); a FAIL projection at 600 GB zeroes the
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search subscore. Values below are the projected-600 GB scoreboard from the
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2026-07-11 pipeline run (seed 20260711); the generated source of truth is
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`out/report/tables/t5_weighted_scores.csv`, and REPORT.md also carries the
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measured-scale scoreboard.
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| Format | Search (0-50) | RAM (0-20) | Disk (0-10) | Total (0-80) |
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| CSV | 0.6 | 20.0 | 8.3 | 28.9 |
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| JSON-LD | 0.1 | 20.0 | 4.0 | 24.0 |
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| SQLite | 21.8 | 15.2 | 10.0 | 47.0 |
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| PostgreSQL | 50.0 | 5.5 | 3.1 | 58.6 |
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| RDF | 0.0 | 0.2 | 0.7 | 0.9 |
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| Use case | Recommended format |
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| Analysis | PostgreSQL (SQLite acceptable single-user up to ~600 GB) |
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| Report generation | PostgreSQL (track_summary materialized view) |
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| Search / filtering | PostgreSQL or SQLite (indexed) |
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| Archiving | CSV tree + tar.gz, plus pg_dump of the system of record |
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| Inter-lab exchange | Hybrid JSON-LD (metadata + sourceFile links to CSV) |
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## Conventions
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- IDs: `batch:B721`, `wafer:B721-W1`, `coupon:B721-W1-C07`,
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`track:B721-W1-C07-T2`, runs `R1..R4`.
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- Timestamps: ISO-8601 UTC; all corpus timestamps are simulated.
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- Units are explicit ASCII suffixes in column names (`_mN`, `_um`, `_GPa`,
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`_wtpct`, `_nm`).
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- Random seed `20260711`, read from `lab_config.yaml`; regeneration is
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byte-identical (proof: `MANIFEST.csv` sha256).
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- Python: tabs for indentation; closed dependency list; streaming discipline
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(the corpus never fits in memory by design).
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