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Graph Database Research

Source-audited research on graph database architecture, correctness, latency, resource efficiency, distributed execution, object-storage economics, and reproducible benchmark qualification.

This repository asks a deliberately difficult systems question: what would an engine need to prove to serve very-low-latency graph queries over PB-scale and trillion-edge datasets while using bounded compute and S3-class durable storage? It audits existing engines for reusable ideas and evidence gaps; it does not turn vendor claims into measured facts.

Research principles

  • Pin released source, documentation, images, and benchmark artifacts.
  • Separate shipped behavior from development branches.
  • Label source facts, official statements, vendor claims, issue reports, local observations, inferences, and unknowns.
  • Treat exact result semantics and durability as benchmark prerequisites.
  • Account for clients, caches, replicas, indexes, background work, recovery, and operator time when comparing resources or cost.
  • Publish raw, reproducible artifacts before making a 10x claim.

Repository layout

  • docs/ contains architecture specifications, the engine landscape, source audits, and benchmark protocols.
  • src/ contains corpus maintenance scripts and repository validation tools. Dedicated Aerospike research is maintained directly as reviewed Markdown rather than generated output.
  • CONTRIBUTING.md defines evidence and change standards.

Status

Research is current to the date recorded in each specification. Database releases, pricing, source heads, issues, and managed-service behavior change; revalidate decision-critical claims before adopting them.

Start with:

Deep source audits:

License

The repository's original text and scripts are available under the MIT License. Linked third-party sources, product names, and quoted material remain subject to their respective owners and licenses.

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Source-audited graph database research: architecture, correctness, scalability, S3 economics, and reproducible benchmarks.

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