← Home

sutraDB Theory

Interactive visualizations of database theory — from how graph and vector databases work individually, to the innovations that make sutraDB different.

Background: How Databases Work

How Graph Databases Work

SPARQL traverses RDF triples to answer multi-hop relationship queries. Watch a query find great-grandfathers step by step.

RDFSPARQL

How Vector Databases Work

HNSW graph traversal finds nearest neighbors by hopping through hierarchical layers. See greedy search in action.

HNSWEmbeddings

Traditional Hybrid Databases

The clunky handoff when a vector database and graph database are bolted together through JSON documents.

System BoundariesJSON Handoff

sutraDB: Unified Vector-Graph

Interleaved traversal — graph hops happen during the vector search, not after it. One graph, zero context switches.

sutraDBInterleaved Traversal

Innovations

Implementing HNSW in RDF

Vectors as typed RDF literals, HNSW neighbors as virtual triples. The vector index becomes the 4th index alongside SPO/POS/OSP, queryable by SPARQL.

Novelsutra:f32vecsutra:hnswNeighbor

Subgraph Indexing for SIMD

Auto-discover repeated subgraph patterns and flatten them into columnar pseudo-tables. Turns multi-hop joins into SIMD-accelerated column scans.

NovelPseudo-TablesAVX2

PageRank Entry Points & Traversal Counters

Use PageRank to pick structurally important starting nodes, and runtime traversal counters to materialize adjacency lists for hot areas.

NovelAdaptive Indexing

SPARQL Exit Conditions (UNTIL)

Extend SPARQL property paths with per-step exit predicates. Terminate traversal early instead of exhaustively scanning the graph.

NovelSPARQL+