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The Hyperstore Motivation
Hyperstores emerge from a simple observation: If the domain model were fully declarative, complete, and authoritative, then an entire backend could be generated safely and deterministically from it. Hyperstores build on this foundation by ensuring: One sche...
Vektagraf as the First Hyperstore
Vektagraf embodies the Hyperstore paradigm by grounding the system in a declarative JSON schema. The schema is authoritative and descriptive, not imperative. There is no embedded code. The system resolves all behavior from the schema. The architecture of Vekta...
Schema Architecture and Design Rationale
Vektagraf schemas are composed of extension schemas and application schemas. This design was chosen for several reasons: Composability — Extensions encapsulate reusable domain components. Namespacing — Extensions avoid collision of types and models. Increment...
Schema-Driven Execution
A major design principle: Execution must be an interpretation of schema, not an implementation detail. Given a schema, Vektagraf generates: CRUD operations Query plans Validation logic Encryption behavior Provenance tracking Multi-tenancy filters Binary/fil...
Data and Storage Design
Vektagraf employs a model-aware object graph storage model. Every model and field in the schema influences storage layout. Key design considerations: Object Graph vs Relational vs Document Traditional relational systems enforce global schemas unsuitable for dy...
Multi-Tenancy
Vektagraf treats multi-tenancy as a first-class primitive. Design motivations: Multi-tenant SaaS requires strict isolation Application logic should not manually enforce tenant constraints Multi-tenancy must apply to data, files, vectors, logs, provenance, and...
Security and Cryptographic Architecture
This is one of the most scientifically rigorous parts of the system. Symmetric Encryption AES-256-GCM is applied at the field level for sensitive data. Homomorphic Encryption (Paillier) Chosen for its: Additive homomorphism Maturity Interoperability Efficient...
File and Binary Storage
Files are not treated as external attachments. Instead: Files are versioned Encrypted per tenant Included in provenance Stored in dedicated storage buckets Integrated with access policies Exposed through generated SDKs This unifies structured data and unstru...
Vector Search and AI Integration
Vektagraf supports vector fields: "embedding": { "type": "vector", "dimensions": 1536, "index": "hnsw" } Motivation: AI and LLM-based systems require semantic search Vector indexes must integrate with tenant boundaries Vector fields must participate in...
Code Generation
All client/server code is generated from schema: Types Validation Encryption behavior API routes Documentation Query helpers Design rationale: Code generation eliminates an entire class of integration failures between client and server. This yields strong ...
Plugin Architecture
Vektagraf’s plugin architecture is a critical component of the Hyperstore ecosystem. While the schema defines the structure, security model, provenance behavior, and data semantics of an application, real-world systems often require customizable logic that goe...
Hyperstore vs Traditional Systems
Vektagraf’s Hyperstore architecture represents a fundamental departure from the design principles of traditional data systems. To understand the scope and impact of this shift, it is important to compare Hyperstores not merely as a “more capable database,” but...
Performance Evaluation and Comparative Analysis
Based on Benchmark Execution: 2025-12-05T18:14:43.217086 This section evaluates Vektagraf’s performance characteristics as evidenced by the benchmark results and situates them within the landscape of established database systems. Although Vektagraf is not a tr...
Appendix A — Performance Evaluation
This appendix provides a structured, scientific presentation of the performance results collected in the benchmark executed on 2025-12-05 at 18:14:43.217086. The goal is not to assert superiority over any particular database system, but to characterize the ope...
Executive Summary
Data infrastructure has entered a period of fragmentation. Organizations increasingly rely on multiple systems—databases, caches, message queues, search engines, schema engines, object stores, workflow engines, metadata stores, identity systems, and AI vector ...
What Is a Hyperstore?
A Hyperstore is a next-generation data system that goes far beyond traditional databases. It integrates the essential capabilities of: A database A graph/object store A vector index for AI A schema enforcement system A provenance engine A validation engine A ...
Why Hyperstores Are Needed Now
Modern companies face new pressures: Explosion of AI models and vector workloads Rising regulatory scrutiny (GDPR, CCPA, NIS2, industry audits) Zero-trust environments requiring cryptographic guarantees Multi-tenant SaaS architectures becoming standard Hybrid...
Introducing Vektagraf
The world’s first Hyperstore. Vektagraf is a fully integrated Hyperstore platform engineered to unify: data storage schema intelligence cryptography automations vector intelligence multi-tenancy secure binaries and files workflow orchestration …in a single i...
Strategic Advantages for Executives and Boards
Reduced Technology Footprint Vektagraf consolidates up to 12 separate technologies, including: dattabase + search + graph + queue object store + file store vector store + AI embeddings index schema registry workflow automation provenance ledger identity-aware...
Architecture Overview (Board-Level)
A Hyperstore consists of three integrated layers: Storage Core Data blocks Index blocks Write-ahead log (WAL) Semantic Engine Object fabric Graph relationships Vector embeddings Binary and file blocks Intelligence and Governance Layer Provenance Automatio...