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administrator
6d156f98c4 feat(docs): add management summary link in evaluation documents 
- include link to one-page executive summary in README
- update dashboard section in evaluation HTML to reference summary
- revise report metadata to reflect additional CSV tables generated
 2026-07-13 00:19:02 -04:00
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README.md
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**Results - interactive, single-file HTML (open in a browser or download and share):**
- **[Storage Decision](https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision.html)** -
one-page executive summary: the problem, the verdict (relational vs graph
database), and what it costs - written for management, every claim linked
to the full study
(source: [docs/research/Tribology_Storage_Decision.html](docs/research/Tribology_Storage_Decision.html)).
- **[Storage Format Evaluation](https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html)** -
the full study results: dashboard, measured matrix, projections to 6 TB,
hardware bill of materials, weighted scoring and verdict

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<header class="top">
<div class="logo">SE</div>
<div class="htitle">
<b>Storage Decision</b>
<span>Executive summary &middot; laboratory measurement data</span>
</div>
<div class="hright">
<a class="chip" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation" target="_blank" rel="noopener">Git repository</a>
<a class="chip-go" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html" target="_blank" rel="noopener">Open the full study &rarr;</a>
</div>
</header>
<div class="page">
<h1>Which database should hold the laboratory's data?</h1>
<div class="hero">
<div class="card">
<div class="cap">THE PROBLEM</div>
<p class="big"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#premises" target="_blank" rel="noopener"><b>600 GB</b></a> of laboratory data today.<br/><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#premises" target="_blank" rel="noopener"><b>6 TB</b></a> within a few years.<br/>It needs one working database.</p>
</div>
<div class="card">
<div class="cap">THE CANDIDATES</div>
<p class="big"><b>Relational</b> (PostgreSQL)<br/>vs<br/><b>Graph</b> (RDF triplestore).</p>
</div>
</div>
<div class="verdict">
<div class="mark">&#10003;</div>
<div style="flex:1">
<p><b>Decision: Relational vs Graph (RDF):</b></p>
<table class="vtab">
<tr>
<th></th>
<th class="win">Relational (PostgreSQL)</th>
<th class="lose">Graph (RDF)</th>
</tr>
<tr>
<td class="crit">Average report, today</td>
<td><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">~6 seconds</a></td>
<td class="lose"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">~1.6 hours</a></td>
</tr>
<tr>
<td class="crit">Average report at 6 TB</td>
<td><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">~30 seconds</a></td>
<td class="lose"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">~12 hours</a></td>
</tr>
<tr>
<td class="crit">Hardware at 6 TB</td>
<td><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#hardware" target="_blank" rel="noopener">one ordinary server</a></td>
<td class="lose"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#hardware" target="_blank" rel="noopener">a 14-server cluster</a></td>
</tr>
<tr>
<td class="crit">Ontology</td>
<td colspan="2">remains accessible with Relational (PostgreSQL)</td>
</tr>
</table>
</div>
</div>
<div class="kpis">
<div class="kpi"><div class="v"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#kpis" target="_blank" rel="noopener">1,000x</a></div><div class="l">faster on the average everyday report</div></div>
<div class="kpi"><div class="v"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#kpis" target="_blank" rel="noopener">$494k saved</a></div><div class="l">hardware at 6 TB: $33.6k for PGSQL server instead of a $527.7k RDF cluster</div></div>
<div class="kpi"><div class="v"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#kpis" target="_blank" rel="noopener">1 vs 14</a></div><div class="l">servers needed at 6 TB</div></div>
</div>
<div class="card">
<h2>Side by side</h2>
<table class="duel">
<tr>
<th></th>
<th class="win">Relational (PostgreSQL)<br><span class="pill">&#10003; recommended</span></th>
<th class="lose">Graph (RDF triplestore)<br><span class="pill">&#10007; rejected at scale</span></th>
<th></th>
</tr>
<tr>
<td class="crit">Average report, today<small>at 600 GB</small></td>
<td><span class="ok">&#10003;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">about 6 seconds</a></td>
<td><span class="no">&#10007;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">about 1.6 hours; 3 of 7 tasks need more than a day</a></td>
<td class="more"><a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html#proj" target="_blank" rel="noopener">details &rarr;</a></td>
</tr>
<tr>
<td class="crit">Average report at 6 TB<small>the 3-year horizon</small></td>
<td><span class="ok">&#10003;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">about 30 seconds</a></td>
<td><span class="no">&#10007;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">about 12 hours; 5 of 7 tasks need a day or more</a></td>
<td class="more"><a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html#proj" target="_blank" rel="noopener">details &rarr;</a></td>
</tr>
<tr>
<td class="crit">Servers needed today<small>at 600 GB</small></td>
<td><span class="ok">&#10003;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#hardware" target="_blank" rel="noopener">one standard server, $10.5k</a></td>
<td><span class="no">&#10007;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#hardware" target="_blank" rel="noopener">already two servers with 1.4 TB of memory, $58.7k</a></td>
<td class="more"><a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html#hw" target="_blank" rel="noopener">details &rarr;</a></td>
</tr>
<tr>
<td class="crit">Servers at 6 TB</td>
<td><span class="ok">&#10003;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#hardware" target="_blank" rel="noopener">still one server, $33.6k</a></td>
<td><span class="no">&#10007;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#hardware" target="_blank" rel="noopener">a 14-server cluster, $527.7k</a></td>
<td class="more"><a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html#hw" target="_blank" rel="noopener">details &rarr;</a></td>
</tr>
<tr>
<td class="crit">Disk for the same data<small>at 6 TB</small></td>
<td><span class="ok">&#10003;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#storage" target="_blank" rel="noopener">21 TB</a></td>
<td><span class="no">&#10007;</span><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#storage" target="_blank" rel="noopener">91 TB - the graph form stores the same data 4.4x bigger</a></td>
<td class="more"><a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html#storage" target="_blank" rel="noopener">details &rarr;</a></td>
</tr>
</table>
</div>
<div class="card">
<h2>Average everyday report</h2>
<div class="panels">
<div class="panel">
<h3>Today &middot; 600 GB</h3>
<div class="brow" title="PostgreSQL, average of the 7 everyday tasks at 600 GB: about 6 seconds (projected)">
<div class="lab">PostgreSQL</div>
<div class="trk"><div class="fill pg" style="width:0.4%"></div><span class="inval in-track"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">6 s</a></span></div>
</div>
<div class="brow" title="RDF triplestore, average of the 7 everyday tasks at 600 GB: about 1.6 hours (projected)">
<div class="lab">Graph (RDF)</div>
<div class="trk"><div class="fill rdf" style="width:100%"></div><span class="inval in-fill"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">1.6 h</a></span></div>
</div>
</div>
<div class="panel">
<h3>In a few years &middot; 6 TB</h3>
<div class="brow" title="PostgreSQL, average of the 7 everyday tasks at 6 TB: about 30 seconds (projected)">
<div class="lab">PostgreSQL</div>
<div class="trk"><div class="fill pg" style="width:0.4%"></div><span class="inval in-track"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">30 s</a></span></div>
</div>
<div class="brow" title="RDF triplestore, average of the 7 everyday tasks at 6 TB: about 12 hours (projected)">
<div class="lab">Graph (RDF)</div>
<div class="trk"><div class="fill rdf" style="width:100%"></div><span class="inval in-fill"><a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#performance" target="_blank" rel="noopener">12 h</a></span></div>
</div>
</div>
</div>
<p class="muted" style="margin-top:14px">Bars are to scale within each panel - the blue bar is real, it is just that small. An everyday report that takes hours is a report nobody runs.</p>
</div>
<div class="card">
<h2>What about the other formats?</h2>
<p><b>CSV</b> stays exactly where it belongs - the raw archive coming off the instruments (best compression, byte-reproducible). <b>JSON-LD</b> stays the exchange format for sending data between laboratories. Neither is a working database, so neither was a candidate. <b>SQLite</b> is excellent for a single user <a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#notes" target="_blank" rel="noopener">up to ~600 GB</a> but does not scale to the shared 6 TB horizon.</p>
<p><b>And the ontology?</b> It survives intact: semantic, ontology-style access is provided as a <b>virtual layer on top of PostgreSQL</b> (e.g. Ontop OBDA) - the graph view without the graph database's cost.</p>
</div>
<div class="card">
<h2>Recommended next steps</h2>
<ol class="steps">
<li>Adopt <b>PostgreSQL as the system of record</b> for laboratory measurement data; keep the raw CSV archive as the source of truth for reprocessing.</li>
<li>Provide ontology and semantic queries as a <b>virtual RDF layer</b> over PostgreSQL - no data is copied into a triplestore.</li>
<li>Revisit hardware once volume approaches 6 TB: the measured plan is <a class="fact" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html#hardware" target="_blank" rel="noopener">one 16-core server with 551 GB RAM (~$33.6k at July 2026 prices)</a>.</li>
</ol>
</div>
<div class="foot">
Figures at 600 GB and 6 TB are model projections calibrated on a measured benchmark (7 everyday retrieval tasks x 5 storage formats, every result checksum-validated); the "average report" is the geometric mean of the seven tasks, the same averaging the study's scoring uses. Method, raw data and hardware price sources: <a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision_Facts.html" target="_blank" rel="noopener">facts ledger (every number traced to its artifact)</a> &middot; <a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html" target="_blank" rel="noopener">full interactive study</a> &middot; <a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation" target="_blank" rel="noopener">git.boskadoff.com/Public/LabDataStorageEvaluation</a>.<br>
Study: Mary Goncharenko (Tribology Laboratory, University of Florida; internship at Sandia National Laboratories), with the technical assistance of Vasiliy Goncharenko (SoftCreator, LLC).
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<header class="top">
<div class="logo">SE</div>
<div class="htitle">
<b>Decision Facts Ledger</b>
<span>Every fact on the Storage Decision page &middot; value, formula, source</span>
</div>
<div class="hright">
<a class="chip" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision.html" target="_blank" rel="noopener">&larr; Storage Decision</a>
<a class="chip" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html" target="_blank" rel="noopener">Full study</a>
<a class="chip" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation" target="_blank" rel="noopener">Git repository</a>
</div>
</header>
<div class="page">
<h1>Decision Facts Ledger</h1>
<p>Every number shown on the <a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision.html" target="_blank" rel="noopener">Storage Decision</a> executive summary, traced to the artifact that proves it. All source files are committed in the public repository and regenerate deterministically from seed 20260711. Projection tables carry one row per format x task x scale; "geometric mean" is the same averaging the study's scoring uses.</p>
<p class="muted">Sources: <span class="mono">t6</span> = <a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6_projected_all_scales.csv</a> &middot; <span class="mono">t7</span> = <a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t7_hardware_all_scales.csv" target="_blank" rel="noopener">t7_hardware_all_scales.csv</a> &middot; <span class="mono">t1</span> = <a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t1_storage_footprint.csv" target="_blank" rel="noopener">t1_storage_footprint.csv</a> &middot; <span class="mono">prices</span> = <a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/config/hw_prices.yaml" target="_blank" rel="noopener">hw_prices.yaml</a> &middot; <span class="mono">spec</span> = <a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/docs/specs/10_extrapolation_model.md" target="_blank" rel="noopener">10_extrapolation_model.md</a></p>
<h2 id="premises">Premises</h2>
<div class="card">
<table>
<tr><th>ID</th><th>Fact on the page</th><th>Exact value</th><th>How obtained</th><th>Source</th></tr>
<tr><td class="id">P1</td><td>"600 GB of laboratory data today"</td><td class="val">600 GB</td><td>The laboratory's current operational volume; also the first extrapolation scale of the study (scale_gb = 600).</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/docs/specs/10_extrapolation_model.md" target="_blank" rel="noopener">spec</a>, <a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
<tr><td class="id">P2</td><td>"6 TB within a few years"</td><td class="val">6,000 GB</td><td>The largest extrapolation scale of the study (scale_gb = 6000), the growth horizon.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/docs/specs/10_extrapolation_model.md" target="_blank" rel="noopener">spec</a>, <a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
</table>
</div>
<h2 id="performance">Performance (average everyday report)</h2>
<div class="card">
<table>
<tr><th>ID</th><th>Fact on the page</th><th>Exact value</th><th>How obtained</th><th>Source</th></tr>
<tr><td class="id">F1</td><td>PostgreSQL, average report today: "~6 seconds"</td><td class="val">5.66 s</td><td>Geometric mean of the 7 projected_wall_s values where format = PostgreSQL, scale_gb = 600.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
<tr><td class="id">F2</td><td>PostgreSQL, average report at 6 TB: "~30 seconds"</td><td class="val">29.6 s</td><td>Geometric mean, format = PostgreSQL, scale_gb = 6000.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
<tr><td class="id">F3</td><td>Graph (RDF), average report today: "~1.6 hours"</td><td class="val">5,902 s = 1.64 h</td><td>Geometric mean, format = RDF triplestore, scale_gb = 600.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
<tr><td class="id">F4</td><td>Graph (RDF), average report at 6 TB: "~12 hours"</td><td class="val">43,157 s = 11.99 h</td><td>Geometric mean, format = RDF triplestore, scale_gb = 6000.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
<tr><td class="id">F5</td><td>"3 of 7 tasks need more than a day" (RDF, today)</td><td class="val">q3 2.1 d &middot; q5 2.8 d &middot; q7 1.9 d</td><td>Count of RDF rows at scale_gb = 600 with projected_wall_s &gt; 86,400.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
<tr><td class="id">F6</td><td>"5 of 7 tasks need a day or more" (RDF, 6 TB)</td><td class="val">q2, q3, q4, q5, q7</td><td>Count of RDF rows at scale_gb = 6000 with projected_wall_s &gt;= 86,400.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
</table>
</div>
<h2 id="hardware">Hardware and cost</h2>
<div class="card">
<table>
<tr><th>ID</th><th>Fact on the page</th><th>Exact value</th><th>How obtained</th><th>Source</th></tr>
<tr><td class="id">F7</td><td>"one standard server, $10.5k" (PostgreSQL, today)</td><td class="val">$10,500 &middot; 1 node</td><td>Row format = PostgreSQL, scale_gb = 600: est_cost_usd, nodes; bill of materials in the configuration column.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t7_hardware_all_scales.csv" target="_blank" rel="noopener">t7</a></td></tr>
<tr><td class="id">F8</td><td>"already two servers with 1.4 TB of memory, $58.7k" (RDF, today)</td><td class="val">$58,740 &middot; 2 nodes &middot; 1,404.1 GB RAM</td><td>Row format = RDF triplestore, scale_gb = 600: est_cost_usd, nodes, ram_gb.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t7_hardware_all_scales.csv" target="_blank" rel="noopener">t7</a></td></tr>
<tr><td class="id">F9</td><td>"still one server, $33.6k" / "one 16-core server with 551 GB RAM" (PostgreSQL, 6 TB)</td><td class="val">$33,586 &middot; 1 node &middot; 16 cores &middot; 551.3 GB</td><td>Row format = PostgreSQL, scale_gb = 6000: est_cost_usd, nodes, cores, ram_gb.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t7_hardware_all_scales.csv" target="_blank" rel="noopener">t7</a></td></tr>
<tr><td class="id">F10</td><td>"a 14-server cluster, $527.7k" (RDF, 6 TB)</td><td class="val">$527,732 &middot; 14 nodes</td><td>Row format = RDF triplestore, scale_gb = 6000: est_cost_usd, nodes.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t7_hardware_all_scales.csv" target="_blank" rel="noopener">t7</a></td></tr>
<tr><td class="id">F11</td><td>"July 2026 prices"</td><td class="val">as_of 2026-07-11</td><td>Street prices with per-component source URLs recorded in the operator-editable price file.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/config/hw_prices.yaml" target="_blank" rel="noopener">prices</a></td></tr>
</table>
</div>
<h2 id="storage">Storage</h2>
<div class="card">
<table>
<tr><th>ID</th><th>Fact on the page</th><th>Exact value</th><th>How obtained</th><th>Source</th></tr>
<tr><td class="id">F12</td><td>"21 TB" disk (PostgreSQL, 6 TB)</td><td class="val">20.855 TB</td><td>Row format = PostgreSQL, scale_gb = 6000: disk_tb (includes 30% free-space headroom).</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t7_hardware_all_scales.csv" target="_blank" rel="noopener">t7</a></td></tr>
<tr><td class="id">F13</td><td>"91 TB" disk (RDF, 6 TB)</td><td class="val">91.268 TB</td><td>Row format = RDF triplestore, scale_gb = 6000: disk_tb.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t7_hardware_all_scales.csv" target="_blank" rel="noopener">t7</a></td></tr>
<tr><td class="id">F14</td><td>"stores the same data 4.4x bigger"</td><td class="val">11.70 / 2.67 = 4.38</td><td>Ratio of the measured working-footprint coefficients vs raw CSV (RDF 11.70x over PostgreSQL 2.67x).</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t1_storage_footprint.csv" target="_blank" rel="noopener">t1</a></td></tr>
</table>
</div>
<h2 id="kpis">Headline figures</h2>
<div class="card">
<table>
<tr><th>ID</th><th>Fact on the page</th><th>Exact value</th><th>How obtained</th><th>Source</th></tr>
<tr><td class="id">K1</td><td>"1,000x faster on the average everyday report"</td><td class="val">1,043x today &middot; 1,459x at 6 TB</td><td>F3 / F1 = 5,902 / 5.66; F4 / F2 = 43,157 / 29.6. Stated conservatively as 1,000x.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
<tr><td class="id">K2</td><td>"$494k saved" (hardware at 6 TB)</td><td class="val">$494,146</td><td>F10 - F9 = 527,732 - 33,586.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t7_hardware_all_scales.csv" target="_blank" rel="noopener">t7</a></td></tr>
<tr><td class="id">K3</td><td>"1 vs 14 servers needed at 6 TB"</td><td class="val">nodes: 1 vs 14</td><td>nodes column, rows PostgreSQL/6000 and RDF triplestore/6000.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t7_hardware_all_scales.csv" target="_blank" rel="noopener">t7</a></td></tr>
</table>
</div>
<h2 id="notes">Notes</h2>
<div class="card">
<table>
<tr><th>ID</th><th>Fact on the page</th><th>Exact value</th><th>How obtained</th><th>Source</th></tr>
<tr><td class="id">N1</td><td>"SQLite is excellent for a single user up to ~600 GB"</td><td class="val">all 7 tasks OK at 600 GB; q5 = 1.6 h, q7 = 1.5 h at 1.2 TB</td><td>SQLite rows in t6: every flag OK at scale_gb = 600; its single-threaded full scans exceed the 1-hour bound from scale_gb = 1200.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/src/branch/master/out/report/tables/t6_projected_all_scales.csv" target="_blank" rel="noopener">t6</a></td></tr>
<tr><td class="id">N2</td><td>Method behind every projected number</td><td class="val">measured 35-cell benchmark</td><td>Scaling laws calibrated on the measured corpus; every measured run checksum-validated. Full methodology and measured medians in the interactive study.</td><td><a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Evaluation.html" target="_blank" rel="noopener">full study</a></td></tr>
</table>
</div>
<div class="foot">
Part of the LabDataStorageEvaluation study &middot; run of 2026-07-11, seed 20260711 &middot; canonical source: <a href="https://git.boskadoff.com/Public/LabDataStorageEvaluation" target="_blank" rel="noopener">git.boskadoff.com/Public/LabDataStorageEvaluation</a>.<br>
Study: Mary Goncharenko (Tribology Laboratory, University of Florida; internship at Sandia National Laboratories), with the technical assistance of Vasiliy Goncharenko (SoftCreator, LLC).
</div>
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@@ -151,7 +151,7 @@
<!-- ================= DASHBOARD ================= -->
<section id="dashboard" class="on">
<h1>Dashboard</h1>
<p class="sub">A 141.8 MiB physically plausible CSV corpus (the canonical raw format) was converted into four other storage formats and benchmarked on seven laboratory retrieval scenarios with subprocess-isolated resource metering; results were extrapolated to 600 GB, 1.2 TB and 6 TB. Every measured run validated its rows against canonical results (PostgreSQL cross-validated with SQLite, tolerance 1e-9).</p>
<p class="sub">A 141.8 MiB physically plausible CSV corpus (the canonical raw format) was converted into four other storage formats and benchmarked on seven laboratory retrieval scenarios with subprocess-isolated resource metering; results were extrapolated to 600 GB, 1.2 TB and 6 TB. Every measured run validated its rows against canonical results (PostgreSQL cross-validated with SQLite, tolerance 1e-9). Short on time? The one-page management summary is <a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision.html" target="_blank" rel="noopener">Storage Decision</a>.</p>
<div class="kpis">
<div class="kpi"><div class="l">Benchmark cells</div><div class="v v-good">35 / 35</div><div class="s">OK; 0 invalid, 0 timeouts</div></div>
<div class="kpi"><div class="l">Corpus</div><div class="v v-neutral">141.8 MiB</div><div class="s">8,718 CSV files, byte-reproducible</div></div>
@@ -450,7 +450,7 @@
<div class="card">
<h3>Data statement</h3>
<p>The measurement corpus is simulated: physically plausible models of a Pt-Au LDRD tribology study context at Sandia National Laboratories, generated from a fixed random seed. It contains no experimental measurements and no export-controlled data. Hardware prices cited in the cost model are public street prices retrieved 2026-07-11 (sources in the Hardware &amp; Cost section).</p>
<p>Related study in the same repository: <a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_FK_Architecture.html" target="_blank" rel="noopener">Foreign-Key Architecture Study</a>.</p>
<p>Companion pages in the same repository: <a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_Storage_Decision.html" target="_blank" rel="noopener">Storage Decision (one-page executive summary)</a> &middot; <a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation/raw/branch/master/docs/research/Tribology_FK_Architecture.html" target="_blank" rel="noopener">Foreign-Key Architecture Study</a>.</p>
</div>
<div class="foot">Standalone distributable file. If you received this document outside the repository, the canonical source is <a class="ext" href="https://git.boskadoff.com/Public/LabDataStorageEvaluation" target="_blank" rel="noopener">git.boskadoff.com/Public/LabDataStorageEvaluation</a>.</div>
</section>

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task: '11'
status: ok
charts: 14
tables: 5
tables: 7
report_bytes: 16091
winner_600gb: pg
prices_as_of: 2026-07-11

106
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@@ -0,0 +1,106 @@
format,query,scale_gb,projected_wall_s,projected_wall,flag
CSV,q1,600,0.06,0.06 s,OK
CSV,q1,1200,0.06,0.06 s,OK
CSV,q1,6000,0.06,0.06 s,OK
CSV,q2,600,428.301,7.1 min,OK
CSV,q2,1200,856.543,14.3 min,OK
CSV,q2,6000,4282.474,71.4 min,IMPRACTICAL
CSV,q3,600,4408.0,73.5 min,IMPRACTICAL
CSV,q3,1200,8815.94,2.4 h,IMPRACTICAL
CSV,q3,6000,44079.46,12.2 h,IMPRACTICAL
CSV,q4,600,2643.775,44.1 min,OK
CSV,q4,1200,5287.489,88.1 min,IMPRACTICAL
CSV,q4,6000,26437.206,7.3 h,IMPRACTICAL
CSV,q5,600,4176.725,69.6 min,IMPRACTICAL
CSV,q5,1200,8353.391,2.3 h,IMPRACTICAL
CSV,q5,6000,41766.715,11.6 h,IMPRACTICAL
CSV,q6,600,870.266,14.5 min,OK
CSV,q6,1200,1740.472,29.0 min,OK
CSV,q6,6000,8702.119,2.4 h,IMPRACTICAL
CSV,q7,600,4897.995,81.6 min,IMPRACTICAL
CSV,q7,1200,9795.931,2.7 h,IMPRACTICAL
CSV,q7,6000,48979.414,13.6 h,IMPRACTICAL
JSON-LD,q1,600,0.111,0.11 s,OK
JSON-LD,q1,1200,0.111,0.11 s,OK
JSON-LD,q1,6000,0.111,0.11 s,OK
JSON-LD,q2,600,5519.622,92.0 min,IMPRACTICAL
JSON-LD,q2,1200,11039.133,3.1 h,IMPRACTICAL
JSON-LD,q2,6000,55195.222,15.3 h,IMPRACTICAL
JSON-LD,q3,600,39673.993,11.0 h,IMPRACTICAL
JSON-LD,q3,1200,79347.876,22.0 h,IMPRACTICAL
JSON-LD,q3,6000,396738.933,4.6 d,FAIL
JSON-LD,q4,600,39597.306,11.0 h,IMPRACTICAL
JSON-LD,q4,1200,79194.5,22.0 h,IMPRACTICAL
JSON-LD,q4,6000,395972.054,4.6 d,FAIL
JSON-LD,q5,600,39760.772,11.0 h,IMPRACTICAL
JSON-LD,q5,1200,79521.432,22.1 h,IMPRACTICAL
JSON-LD,q5,6000,397606.717,4.6 d,FAIL
JSON-LD,q6,600,39076.231,10.9 h,IMPRACTICAL
JSON-LD,q6,1200,78152.351,21.7 h,IMPRACTICAL
JSON-LD,q6,6000,390761.312,4.5 d,FAIL
JSON-LD,q7,600,40228.164,11.2 h,IMPRACTICAL
JSON-LD,q7,1200,80456.218,22.3 h,IMPRACTICAL
JSON-LD,q7,6000,402280.643,4.7 d,FAIL
SQLite,q1,600,0.062,0.06 s,OK
SQLite,q1,1200,0.063,0.06 s,OK
SQLite,q1,6000,0.063,0.06 s,OK
SQLite,q2,600,1.271,1.27 s,OK
SQLite,q2,1200,2.482,2.48 s,OK
SQLite,q2,6000,12.169,12 s,OK
SQLite,q3,600,339.238,5.7 min,OK
SQLite,q3,1200,699.328,11.7 min,OK
SQLite,q3,6000,3739.195,62.3 min,IMPRACTICAL
SQLite,q4,600,0.06,0.06 s,OK
SQLite,q4,1200,0.06,0.06 s,OK
SQLite,q4,6000,0.06,0.06 s,OK
SQLite,q5,600,2847.603,47.5 min,OK
SQLite,q5,1200,5695.146,94.9 min,IMPRACTICAL
SQLite,q5,6000,28475.49,7.9 h,IMPRACTICAL
SQLite,q6,600,5.18,5.18 s,OK
SQLite,q6,1200,10.615,11 s,OK
SQLite,q6,6000,56.5,56 s,OK
SQLite,q7,600,2619.558,43.7 min,OK
SQLite,q7,1200,5239.055,87.3 min,IMPRACTICAL
SQLite,q7,6000,26195.034,7.3 h,IMPRACTICAL
PostgreSQL,q1,600,0.222,0.22 s,OK
PostgreSQL,q1,1200,0.222,0.22 s,OK
PostgreSQL,q1,6000,0.222,0.22 s,OK
PostgreSQL,q2,600,0.221,0.22 s,OK
PostgreSQL,q2,1200,0.221,0.22 s,OK
PostgreSQL,q2,6000,0.221,0.22 s,OK
PostgreSQL,q3,600,3.42,3.42 s,OK
PostgreSQL,q3,1200,6.817,6.82 s,OK
PostgreSQL,q3,6000,35.495,35 s,OK
PostgreSQL,q4,600,10.715,11 s,OK
PostgreSQL,q4,1200,21.209,21 s,OK
PostgreSQL,q4,6000,105.162,105 s,OK
PostgreSQL,q5,600,222.111,3.7 min,OK
PostgreSQL,q5,1200,444.001,7.4 min,OK
PostgreSQL,q5,6000,2219.125,37.0 min,OK
PostgreSQL,q6,600,4.06,4.06 s,OK
PostgreSQL,q6,1200,8.137,8.14 s,OK
PostgreSQL,q6,6000,42.55,43 s,OK
PostgreSQL,q7,600,114.849,115 s,OK
PostgreSQL,q7,1200,229.477,3.8 min,OK
PostgreSQL,q7,6000,1146.503,19.1 min,OK
RDF triplestore,q1,600,0.113,0.11 s,OK
RDF triplestore,q1,1200,0.113,0.11 s,OK
RDF triplestore,q1,6000,0.113,0.11 s,OK
RDF triplestore,q2,600,10866.789,3.0 h,IMPRACTICAL
RDF triplestore,q2,1200,21733.465,6.0 h,IMPRACTICAL
RDF triplestore,q2,6000,108666.874,30.2 h,FAIL
RDF triplestore,q3,600,185722.881,2.1 d,FAIL
RDF triplestore,q3,1200,371445.649,4.3 d,FAIL
RDF triplestore,q3,6000,1857227.792,21.5 d,FAIL
RDF triplestore,q4,600,79559.375,22.1 h,IMPRACTICAL
RDF triplestore,q4,1200,159118.636,44.2 h,FAIL
RDF triplestore,q4,6000,795592.727,9.2 d,FAIL
RDF triplestore,q5,600,240054.647,2.8 d,FAIL
RDF triplestore,q5,1200,480109.181,5.6 d,FAIL
RDF triplestore,q5,6000,2400545.453,27.8 d,FAIL
RDF triplestore,q6,600,352.729,5.9 min,OK
RDF triplestore,q6,1200,727.088,12.1 min,OK
RDF triplestore,q6,6000,3887.402,64.8 min,IMPRACTICAL
RDF triplestore,q7,600,163225.475,45.3 h,FAIL
RDF triplestore,q7,1200,326450.837,3.8 d,FAIL
RDF triplestore,q7,6000,1632253.733,18.9 d,FAIL
1 format query scale_gb projected_wall_s projected_wall flag
2 CSV q1 600 0.06 0.06 s OK
3 CSV q1 1200 0.06 0.06 s OK
4 CSV q1 6000 0.06 0.06 s OK
5 CSV q2 600 428.301 7.1 min OK
6 CSV q2 1200 856.543 14.3 min OK
7 CSV q2 6000 4282.474 71.4 min IMPRACTICAL
8 CSV q3 600 4408.0 73.5 min IMPRACTICAL
9 CSV q3 1200 8815.94 2.4 h IMPRACTICAL
10 CSV q3 6000 44079.46 12.2 h IMPRACTICAL
11 CSV q4 600 2643.775 44.1 min OK
12 CSV q4 1200 5287.489 88.1 min IMPRACTICAL
13 CSV q4 6000 26437.206 7.3 h IMPRACTICAL
14 CSV q5 600 4176.725 69.6 min IMPRACTICAL
15 CSV q5 1200 8353.391 2.3 h IMPRACTICAL
16 CSV q5 6000 41766.715 11.6 h IMPRACTICAL
17 CSV q6 600 870.266 14.5 min OK
18 CSV q6 1200 1740.472 29.0 min OK
19 CSV q6 6000 8702.119 2.4 h IMPRACTICAL
20 CSV q7 600 4897.995 81.6 min IMPRACTICAL
21 CSV q7 1200 9795.931 2.7 h IMPRACTICAL
22 CSV q7 6000 48979.414 13.6 h IMPRACTICAL
23 JSON-LD q1 600 0.111 0.11 s OK
24 JSON-LD q1 1200 0.111 0.11 s OK
25 JSON-LD q1 6000 0.111 0.11 s OK
26 JSON-LD q2 600 5519.622 92.0 min IMPRACTICAL
27 JSON-LD q2 1200 11039.133 3.1 h IMPRACTICAL
28 JSON-LD q2 6000 55195.222 15.3 h IMPRACTICAL
29 JSON-LD q3 600 39673.993 11.0 h IMPRACTICAL
30 JSON-LD q3 1200 79347.876 22.0 h IMPRACTICAL
31 JSON-LD q3 6000 396738.933 4.6 d FAIL
32 JSON-LD q4 600 39597.306 11.0 h IMPRACTICAL
33 JSON-LD q4 1200 79194.5 22.0 h IMPRACTICAL
34 JSON-LD q4 6000 395972.054 4.6 d FAIL
35 JSON-LD q5 600 39760.772 11.0 h IMPRACTICAL
36 JSON-LD q5 1200 79521.432 22.1 h IMPRACTICAL
37 JSON-LD q5 6000 397606.717 4.6 d FAIL
38 JSON-LD q6 600 39076.231 10.9 h IMPRACTICAL
39 JSON-LD q6 1200 78152.351 21.7 h IMPRACTICAL
40 JSON-LD q6 6000 390761.312 4.5 d FAIL
41 JSON-LD q7 600 40228.164 11.2 h IMPRACTICAL
42 JSON-LD q7 1200 80456.218 22.3 h IMPRACTICAL
43 JSON-LD q7 6000 402280.643 4.7 d FAIL
44 SQLite q1 600 0.062 0.06 s OK
45 SQLite q1 1200 0.063 0.06 s OK
46 SQLite q1 6000 0.063 0.06 s OK
47 SQLite q2 600 1.271 1.27 s OK
48 SQLite q2 1200 2.482 2.48 s OK
49 SQLite q2 6000 12.169 12 s OK
50 SQLite q3 600 339.238 5.7 min OK
51 SQLite q3 1200 699.328 11.7 min OK
52 SQLite q3 6000 3739.195 62.3 min IMPRACTICAL
53 SQLite q4 600 0.06 0.06 s OK
54 SQLite q4 1200 0.06 0.06 s OK
55 SQLite q4 6000 0.06 0.06 s OK
56 SQLite q5 600 2847.603 47.5 min OK
57 SQLite q5 1200 5695.146 94.9 min IMPRACTICAL
58 SQLite q5 6000 28475.49 7.9 h IMPRACTICAL
59 SQLite q6 600 5.18 5.18 s OK
60 SQLite q6 1200 10.615 11 s OK
61 SQLite q6 6000 56.5 56 s OK
62 SQLite q7 600 2619.558 43.7 min OK
63 SQLite q7 1200 5239.055 87.3 min IMPRACTICAL
64 SQLite q7 6000 26195.034 7.3 h IMPRACTICAL
65 PostgreSQL q1 600 0.222 0.22 s OK
66 PostgreSQL q1 1200 0.222 0.22 s OK
67 PostgreSQL q1 6000 0.222 0.22 s OK
68 PostgreSQL q2 600 0.221 0.22 s OK
69 PostgreSQL q2 1200 0.221 0.22 s OK
70 PostgreSQL q2 6000 0.221 0.22 s OK
71 PostgreSQL q3 600 3.42 3.42 s OK
72 PostgreSQL q3 1200 6.817 6.82 s OK
73 PostgreSQL q3 6000 35.495 35 s OK
74 PostgreSQL q4 600 10.715 11 s OK
75 PostgreSQL q4 1200 21.209 21 s OK
76 PostgreSQL q4 6000 105.162 105 s OK
77 PostgreSQL q5 600 222.111 3.7 min OK
78 PostgreSQL q5 1200 444.001 7.4 min OK
79 PostgreSQL q5 6000 2219.125 37.0 min OK
80 PostgreSQL q6 600 4.06 4.06 s OK
81 PostgreSQL q6 1200 8.137 8.14 s OK
82 PostgreSQL q6 6000 42.55 43 s OK
83 PostgreSQL q7 600 114.849 115 s OK
84 PostgreSQL q7 1200 229.477 3.8 min OK
85 PostgreSQL q7 6000 1146.503 19.1 min OK
86 RDF triplestore q1 600 0.113 0.11 s OK
87 RDF triplestore q1 1200 0.113 0.11 s OK
88 RDF triplestore q1 6000 0.113 0.11 s OK
89 RDF triplestore q2 600 10866.789 3.0 h IMPRACTICAL
90 RDF triplestore q2 1200 21733.465 6.0 h IMPRACTICAL
91 RDF triplestore q2 6000 108666.874 30.2 h FAIL
92 RDF triplestore q3 600 185722.881 2.1 d FAIL
93 RDF triplestore q3 1200 371445.649 4.3 d FAIL
94 RDF triplestore q3 6000 1857227.792 21.5 d FAIL
95 RDF triplestore q4 600 79559.375 22.1 h IMPRACTICAL
96 RDF triplestore q4 1200 159118.636 44.2 h FAIL
97 RDF triplestore q4 6000 795592.727 9.2 d FAIL
98 RDF triplestore q5 600 240054.647 2.8 d FAIL
99 RDF triplestore q5 1200 480109.181 5.6 d FAIL
100 RDF triplestore q5 6000 2400545.453 27.8 d FAIL
101 RDF triplestore q6 600 352.729 5.9 min OK
102 RDF triplestore q6 1200 727.088 12.1 min OK
103 RDF triplestore q6 6000 3887.402 64.8 min IMPRACTICAL
104 RDF triplestore q7 600 163225.475 45.3 h FAIL
105 RDF triplestore q7 1200 326450.837 3.8 d FAIL
106 RDF triplestore q7 6000 1632253.733 18.9 d FAIL

16
out/report/tables/t7_hardware_all_scales.csv Normal file
View File

@@ -0,0 +1,16 @@
format,scale_gb,configuration,disk_tb,ram_gb,cores,nodes,ram_modules,nvme_drives,base_usd,ram_usd,disk_usd,extra_nodes_usd,est_cost_usd
CSV,600,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2,0.78,16.0,16,1,1,1,4618,1887,3995,0,10500
CSV,1200,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2,1.56,16.0,16,1,1,1,4618,1887,3995,0,10500
CSV,6000,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 2 x 7.68 TB NVMe U.2,7.8,16.0,16,1,1,2,4618,1887,7990,0,14495
JSON-LD,600,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2,1.633,16.0,16,1,1,1,4618,1887,3995,0,10500
JSON-LD,1200,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2,3.267,16.0,16,1,1,1,4618,1887,3995,0,10500
JSON-LD,6000,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 3 x 7.68 TB NVMe U.2,16.334,16.0,16,1,1,3,4618,1887,11985,0,18490
SQLite,600,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2,0.647,21.0,16,1,1,1,4618,1887,3995,0,10500
SQLite,1200,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2,1.295,38.0,16,1,1,1,4618,1887,3995,0,10500
SQLite,6000,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 3 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2,6.475,173.9,16,1,3,1,4618,5661,3995,0,14274
PostgreSQL,600,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2,2.086,58.7,16,1,1,1,4618,1887,3995,0,10500
PostgreSQL,1200,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 2 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2,4.171,113.5,16,1,2,1,4618,3774,3995,0,12387
PostgreSQL,6000,1 x 1U Supermicro AS-1015CS-TNR (16 cores); 9 x 64 GB DDR5 ECC RDIMM; 3 x 7.68 TB NVMe U.2,20.855,551.3,16,1,9,3,4618,16983,11985,0,33586
RDF triplestore,600,2 x 1U Supermicro AS-1015CS-TNR (16 cores); 22 x 64 GB DDR5 ECC RDIMM; 2 x 7.68 TB NVMe U.2,9.127,1404.1,16,2,22,2,4618,41514,7990,4618,58740
RDF triplestore,1200,3 x 1U Supermicro AS-1015CS-TNR (16 cores); 44 x 64 GB DDR5 ECC RDIMM; 3 x 7.68 TB NVMe U.2,18.254,2808.3,16,3,44,3,4618,83028,11985,9236,108867
RDF triplestore,6000,14 x 1U Supermicro AS-1015CS-TNR (16 cores); 220 x 64 GB DDR5 ECC RDIMM; 12 x 7.68 TB NVMe U.2,91.268,14041.3,16,14,220,12,4618,415140,47940,60034,527732
1 format scale_gb configuration disk_tb ram_gb cores nodes ram_modules nvme_drives base_usd ram_usd disk_usd extra_nodes_usd est_cost_usd
2 CSV 600 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2 0.78 16.0 16 1 1 1 4618 1887 3995 0 10500
3 CSV 1200 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2 1.56 16.0 16 1 1 1 4618 1887 3995 0 10500
4 CSV 6000 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 2 x 7.68 TB NVMe U.2 7.8 16.0 16 1 1 2 4618 1887 7990 0 14495
5 JSON-LD 600 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2 1.633 16.0 16 1 1 1 4618 1887 3995 0 10500
6 JSON-LD 1200 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2 3.267 16.0 16 1 1 1 4618 1887 3995 0 10500
7 JSON-LD 6000 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 3 x 7.68 TB NVMe U.2 16.334 16.0 16 1 1 3 4618 1887 11985 0 18490
8 SQLite 600 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2 0.647 21.0 16 1 1 1 4618 1887 3995 0 10500
9 SQLite 1200 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2 1.295 38.0 16 1 1 1 4618 1887 3995 0 10500
10 SQLite 6000 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 3 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2 6.475 173.9 16 1 3 1 4618 5661 3995 0 14274
11 PostgreSQL 600 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 1 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2 2.086 58.7 16 1 1 1 4618 1887 3995 0 10500
12 PostgreSQL 1200 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 2 x 64 GB DDR5 ECC RDIMM; 1 x 7.68 TB NVMe U.2 4.171 113.5 16 1 2 1 4618 3774 3995 0 12387
13 PostgreSQL 6000 1 x 1U Supermicro AS-1015CS-TNR (16 cores); 9 x 64 GB DDR5 ECC RDIMM; 3 x 7.68 TB NVMe U.2 20.855 551.3 16 1 9 3 4618 16983 11985 0 33586
14 RDF triplestore 600 2 x 1U Supermicro AS-1015CS-TNR (16 cores); 22 x 64 GB DDR5 ECC RDIMM; 2 x 7.68 TB NVMe U.2 9.127 1404.1 16 2 22 2 4618 41514 7990 4618 58740
15 RDF triplestore 1200 3 x 1U Supermicro AS-1015CS-TNR (16 cores); 44 x 64 GB DDR5 ECC RDIMM; 3 x 7.68 TB NVMe U.2 18.254 2808.3 16 3 44 3 4618 83028 11985 9236 108867
16 RDF triplestore 6000 14 x 1U Supermicro AS-1015CS-TNR (16 cores); 220 x 64 GB DDR5 ECC RDIMM; 12 x 7.68 TB NVMe U.2 91.268 14041.3 16 14 220 12 4618 415140 47940 60034 527732

21
report.py
View File

@@ -3,8 +3,9 @@
Aggregates the measured matrix (task 09), the projections (task 10) and the
process diagrams (task 02) into the final decision document. Writes 14 PNG
charts (150 dpi) under ./out/report/charts/ with the exact names fixed in
README.md, five CSV tables under ./out/report/tables/, and
./out/report/REPORT.md.
README.md, seven CSV tables under ./out/report/tables/ (t6/t7 carry every
projection and hardware row for all three scales - they are the committed
proof artifacts the executive summary links to), and ./out/report/REPORT.md.
Chart readability contract (owner feedback 2026-07-11): linear scales with
actual units everywhere (panels instead of log axes when magnitudes clash),
@@ -636,6 +637,22 @@ def main() -> int:
money(sizing[(fmt, REPORT_SCALE_GB)]["est_cost_usd"])] for fmt in FORMATS],
)
tables["t6_projected_all_scales"] = (
["format", "query", "scale_gb", "projected_wall_s", "projected_wall", "flag"],
[[FMT_NAMES[fmt], f"q{qn}", s, proj[(fmt, qn, s)][0], human_time(proj[(fmt, qn, s)][0]), proj[(fmt, qn, s)][1]]
for fmt in FORMATS for qn in QUERY_NUMBERS for s in SCALES_GB],
)
tables["t7_hardware_all_scales"] = (
["format", "scale_gb", "configuration", "disk_tb", "ram_gb", "cores", "nodes",
"ram_modules", "nvme_drives", "base_usd", "ram_usd", "disk_usd", "extra_nodes_usd", "est_cost_usd"],
[[FMT_NAMES[fmt], s, config_string(sizing[(fmt, s)], prices),
sizing[(fmt, s)]["disk_tb"], sizing[(fmt, s)]["ram_gb"],
sizing[(fmt, s)]["cores"], sizing[(fmt, s)]["nodes"],
sizing[(fmt, s)]["ram_modules"], sizing[(fmt, s)]["nvme_drives"],
round(sizing[(fmt, s)]["base_usd"]), round(sizing[(fmt, s)]["ram_usd"]),
round(sizing[(fmt, s)]["disk_usd"]), round(sizing[(fmt, s)]["extra_nodes_usd"]),
round(sizing[(fmt, s)]["est_cost_usd"])] for fmt in FORMATS for s in SCALES_GB],
)
scoreboards = build_scoreboards(medians, proj, sizes, sizing)
tables["t5_weighted_scores"] = (
["scale", "format", "search_pts", "ram_pts", "disk_pts", "total"],