Data Model

CoordiNode uses a property graph extended with vector, spatial, time-series, and document capabilities — all within a single unified model.

Core Primitives

Nodes (Vertices)

A node represents an entity. It has:

• One or more labels (type tags): :Person, :Document, :Sensor
• A map of properties (typed key-value pairs)

CREATE (p:Person {name: "Alice", age: 30})

Edges (Relationships)

An edge connects two nodes directionally. It has:

• Exactly one relationship type: KNOWS, RELATED_TO, ABOUT
• A map of properties (weight, timestamp, confidence, …)

CREATE (a)-[:KNOWS {since: 2024}]->(b)

Edges are first-class citizens — they can be traversed, filtered, and returned like nodes.

Properties

CoordiNode supports all standard property types:

Type	Example	Notes
String	"Alice"	UTF-8
Integer	42	i64
Float	3.14	f64
Boolean	true
List	[1, 2, 3]	Homogeneous or mixed
Map	{x: 1, y: 2}	Nested document
Bytes	$blob	Arbitrary binary
Vector	[0.1, 0.2, ...]	Dense float array
DateTime	datetime("2024-01-01")	ISO 8601, timezone-aware

Labels and Modalities

Labels are arbitrary user-defined strings — :Person, :Article, :Sensor. CoordiNode does not have reserved "special" labels for storage modes.

A node becomes multi-modal through its property types. A node with a vector-typed property participates in vector search; a node with a text property participates in full-text search — regardless of its label name.

-- One node: graph + vector + full-text, defined by its properties
CREATE (d:Document {
  title: "Attention Is All You Need",
  body: "We propose a new simple network architecture...",
  embedding: [0.1, 0.2, ...]   -- stored as a Vector property → HNSW index
})

Property type	Storage	Use case
String, Int, Float, Bool	Graph record	Standard entities
Vector ([f32, ...])	HNSW index	Embedding similarity search
Geo point	Spatial index	Points, distance queries
Map	Nested document	Config, structured data
Bytes/Blob	BlobStore	Large binary objects

Indexes

CoordiNode maintains indexes when you create them:

-- Exact property index (B-tree in LSM) — implemented
CREATE INDEX :Person(email)

-- Full-text index (Tantivy) — implemented
CREATE TEXT INDEX doc_body ON :Document(body)
  OPTIONS {analyzer: "english"}

Note: Vector index DDL (CREATE VECTOR INDEX) is currently available via the programmatic API (Database::create_vector_index()). Cypher DDL syntax is planned.

EXPLAIN SUGGEST analyzes a query and recommends missing indexes. Available via gRPC or, when running Docker, via the REST proxy on port 7081:

curl -X POST http://localhost:7081/v1/query/cypher/explain \
  -H "Content-Type: application/json" \
  -d '{"query": "MATCH (u:User) WHERE u.email = $e RETURN u", "parameters": {}}'

Transactions and MVCC

All reads and writes are wrapped in MVCC (Multi-Version Concurrency Control) transactions with Snapshot Isolation. See MVCC Transactions for details.

Next Step

• MVCC Transactions — how reads, writes, and conflicts work
• Hybrid Retrieval — combining graph + vector + text in one query
• Quick Start — hands-on example