DATA CHARACTERISTICS?
├── Fixed schema, shallow joins (≤3 hops)
│   └─ PostgreSQL (relational)
│
├── Already on PostgreSQL + simple graphs
│   └─ Apache AGE (PostgreSQL extension)
│
├── Deep traversals (4+ hops) + general purpose
│   └─ Neo4j (battle-tested, largest ecosystem)
│
├── Multi-model (documents + graph)
│   └─ ArangoDB
│
├── AWS-native, serverless
│   └─ Amazon Neptune
│
└── Real-time streaming, in-memory
    └─ Memgraph

(Person {name: "Alice", age: 28})-[:FRIEND {since: "2020-01-15"}]->(Person {name: "Bob"})

// Find all users at a company
MATCH (u:User)-[:WORKS_AT]->(c:Company {name: 'Acme Corp'})
RETURN u.name, u.title

// Find friends up to 3 degrees away
MATCH (u:User {name: 'Alice'})-[:FRIEND*1..3]->(friend)
WHERE u <> friend
RETURN DISTINCT friend.name
LIMIT 100

// Find shortest connection between two users
MATCH path = shortestPath(
  (a:User {name: 'Alice'})-[*]-(b:User {name: 'Bob'})
)
RETURN path, length(path) AS distance

// Collaborative filtering: Products liked by similar users
MATCH (u:User {id: $userId})-[:PURCHASED]->(p:Product)<-[:PURCHASED]-(similar)
MATCH (similar)-[:PURCHASED]->(rec:Product)
WHERE NOT exists((u)-[:PURCHASED]->(rec))
RETURN rec.name, count(*) AS score
ORDER BY score DESC
LIMIT 10

// Detect circular money flows
MATCH path = (a:Account)-[:SENT*3..6]->(a)
WHERE all(r IN relationships(path) WHERE r.amount > 1000)
RETURN path, [r IN relationships(path) | r.amount] AS amounts

# Python driver
pip install neo4j

# TypeScript driver
npm install neo4j-driver

# Rust driver
cargo add neo4rs

// BAD
(:Person {name: 'Alice', friend_ids: ['b123', 'c456']})

// GOOD
(:Person {name: 'Alice'})-[:FRIEND]->(:Person {id: 'b123'})
(:Person {name: 'Alice'})-[:FRIEND]->(:Person {id: 'c456'})

// Track interaction details on relationships
(:Person)-[:FRIEND {
  since: '2020-01-15',
  strength: 0.85,
  last_interaction: datetime()
}]->(:Person)

// SLOW: Unbounded traversal
MATCH (a)-[:FRIEND*]->(distant)
RETURN distant

// FAST: Bounded depth with index
MATCH (a)-[:FRIEND*1..4]->(distant)
WHERE distant.active = true
RETURN distant
LIMIT 100

// Instead of: (:User)-[:POSTED]->(:Post) [1M relationships]

// Use time partitioning:
(:User)-[:POSTED_IN]->(:Year {year: 2025})
       -[:HAS_MONTH]->(:Month {month: 12})
       -[:HAS_POST]->(:Post)

# Step 1: Vector search in Qdrant/pgvector
vector_results = qdrant.search(collection="concepts", query_vector=embedding)

# Step 2: Expand with graph relationships
concept_ids = [r.id for r in vector_results]
graph_context = neo4j.run("""
  MATCH (c:Concept) WHERE c.id IN $ids
  MATCH (c)-[:RELATED_TO|IS_A*1..2]-(related)
  RETURN c, related, relationships(path)
""", ids=concept_ids)

// Single-property index
CREATE INDEX user_email FOR (u:User) ON (u.email)

// Composite index (Neo4j 5.x+)
CREATE INDEX user_name_location FOR (u:User) ON (u.name, u.location)

// Full-text search
CREATE FULLTEXT INDEX product_search FOR (p:Product) ON EACH [p.name, p.description]

// Materialize friend count as property
MATCH (u:User)-[:FRIEND]->(f)
WITH u, count(f) AS friendCount
SET u.friend_count = friendCount

// Query becomes instant
MATCH (u:User) WHERE u.friend_count > 100
RETURN u.name, u.friend_count

from neo4j import GraphDatabase

class GraphDB:
    def __init__(self, uri: str, user: str, password: str):
        self.driver = GraphDatabase.driver(uri, auth=(user, password))

    def find_friends_of_friends(self, user_id: str, max_depth: int = 2):
        query = """
        MATCH (u:User {id: $userId})-[:FRIEND*1..$maxDepth]->(fof)
        WHERE u <> fof
        RETURN DISTINCT fof.id, fof.name
        LIMIT 100
        """
        with self.driver.session() as session:
            result = session.run(query, userId=user_id, maxDepth=max_depth)
            return [dict(record) for record in result]

# Usage
db = GraphDB("bolt://localhost:7687", "neo4j", "password")
friends = db.find_friends_of_friends("u123", max_depth=3)

import neo4j, { Driver } from 'neo4j-driver'

class Neo4jService {
  private driver: Driver

  constructor(uri: string, username: string, password: string) {
    this.driver = neo4j.driver(uri, neo4j.auth.basic(username, password))
  }

  async findFriendsOfFriends(userId: string, maxDepth: number = 2) {
    const session = this.driver.session()
    try {
      const result = await session.run(
        `MATCH (u:User {id: $userId})-[:FRIEND*1..$maxDepth]->(fof)
         WHERE u <> fof
         RETURN DISTINCT fof.id, fof.name
         LIMIT 100`,
        { userId, maxDepth }
      )
      return result.records.map(r => r.toObject())
    } finally {
      await session.close()
    }
  }
}

import (
    "github.com/arangodb/go-driver"
    "github.com/arangodb/go-driver/http"
)

func findFriendsOfFriends(db driver.Database, userId string, maxDepth int) ([]User, error) {
    query := `
        FOR vertex, edge, path IN 1..@maxDepth OUTBOUND @startVertex GRAPH 'socialGraph'
            FILTER vertex._id != @startVertex
            RETURN DISTINCT vertex
            LIMIT 100
    `

    cursor, err := db.Query(ctx, query, map[string]interface{}{
        "startVertex": userId,
        "maxDepth": maxDepth,
    })

    // Handle results...
}

python scripts/validate_graph_schema.py --database neo4j://localhost:7687

(:User)-[:PURCHASED]->(:Product)
(:User)-[:VIEWED]->(:Product)
(:User)-[:RATED]->(:Product)

(:CEO)-[:MANAGES]->(:VP)-[:MANAGES]->(:Director)

(:Event {timestamp})-[:NEXT]->(:Event {timestamp})