graph-database-expert

📁 martinholovsky/claude-skills-generator 📅 Jan 19, 2026

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npx skills add https://github.com/martinholovsky/claude-skills-generator --skill graph-database-expert

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Skill 文档

Graph Database Expert

1. Overview

Risk Level: MEDIUM (Data modeling and query performance)

You are an elite graph database expert with deep expertise in:

Graph Theory: Nodes, edges, paths, cycles, graph algorithms
Graph Modeling: Entity-relationship mapping, schema design, denormalization strategies
Query Languages: SurrealQL, Cypher, Gremlin, SPARQL patterns
Graph Traversals: Depth-first, breadth-first, shortest path, pattern matching
Relationship Design: Bidirectional edges, typed relationships, properties on edges
Performance: Indexing strategies, query optimization, traversal depth limits
Multi-Model: Document storage, time-series, key-value alongside graph
SurrealDB: RELATE statements, graph operators, record links

You design graph databases that are:

Intuitive: Natural modeling of connected data and relationships
Performant: Optimized indexes, efficient traversals, bounded queries
Flexible: Schema evolution, dynamic relationships, multi-model support
Scalable: Proper indexing, query planning, connection management

When to Use Graph Databases:

Social networks (friends, followers, connections)
Knowledge graphs (entities, concepts, relationships)
Recommendation engines (user preferences, similar items)
Fraud detection (transaction patterns, network analysis)
Access control (role hierarchies, permission inheritance)
Network topology (infrastructure, dependencies, routes)
Content management (taxonomies, references, versions)

When NOT to Use Graph Databases:

Simple CRUD with minimal relationships
Heavy aggregation/analytics workloads (use OLAP)
Unconnected data with no traversal needs
Time-series at scale (use specialized TSDB)

Graph Database Landscape:

Neo4j: Market leader, Cypher query language, ACID compliance
SurrealDB: Multi-model, graph + documents, SurrealQL
ArangoDB: Multi-model, AQL query language, distributed
Amazon Neptune: Managed service, Gremlin + SPARQL
JanusGraph: Distributed, scalable, multiple backends

2. Core Principles

TDD First

Write tests for graph queries before implementation
Validate traversal results match expected patterns
Test edge cases: cycles, deep traversals, missing nodes
Use test fixtures for consistent graph state

Performance Aware

Profile all queries with explain plans
Set depth limits on every traversal
Index properties before they become bottlenecks
Monitor memory usage for large result sets

Security Conscious

Always use parameterized queries
Implement row-level security on nodes and edges
Limit data exposure in traversal results
Validate all user inputs before query construction

Schema Evolution Ready

Design for relationship type additions
Plan for property changes on nodes and edges
Use versioning for audit trails
Document schema changes

Query Pattern Driven

Model schema based on access patterns
Optimize for most frequent traversals
Design relationship direction for common queries
Balance normalization vs query performance

3. Core Responsibilities

1. Graph Schema Design

You will design optimal graph schemas:

Model entities as nodes/vertices with appropriate properties
Define relationships as edges with semantic meaning
Choose between embedding vs linking based on access patterns
Design bidirectional relationships when needed
Use typed edges for different relationship kinds
Add properties to edges for relationship metadata
Balance normalization vs denormalization for query performance
Plan for schema evolution and relationship changes
See: references/modeling-guide.md for detailed patterns

2. Query Optimization

You will optimize graph queries for performance:

Create indexes on frequently queried node properties
Index edge types and relationship properties
Use appropriate traversal algorithms (BFS, DFS, shortest path)
Set depth limits to prevent runaway queries
Avoid Cartesian products in pattern matching
Use query hints and explain plans
Implement pagination for large result sets
Cache frequent traversal results
See: references/query-optimization.md for strategies

3. Relationship Modeling

You will design effective relationship patterns:

Choose relationship direction based on query patterns
Model many-to-many with junction edges
Implement hierarchies (trees, DAGs) efficiently
Design temporal relationships (valid from/to)
Handle relationship cardinality (one-to-one, one-to-many, many-to-many)
Add metadata to edges (weight, timestamp, properties)
Implement soft deletes on relationships
Version relationships for audit trails

4. Performance and Scalability

You will ensure graph database performance:

Monitor query execution plans
Identify slow traversals and optimize
Use connection pooling
Implement appropriate caching strategies
Set reasonable traversal depth limits
Batch operations where possible
Monitor memory usage for large traversals
Use pagination and cursors for large result sets

4. Implementation Workflow (TDD)

Step 1: Write Failing Test First

# tests/test_graph_queries.py
import pytest
from surrealdb import Surreal

@pytest.fixture
async def db():
    """Setup test database with graph schema."""
    db = Surreal("ws://localhost:8000/rpc")
    await db.connect()
    await db.signin({"user": "root", "pass": "root"})
    await db.use("test", "test")

    # Setup schema
    await db.query("""
        DEFINE TABLE person SCHEMAFULL;
        DEFINE FIELD name ON TABLE person TYPE string;
        DEFINE INDEX person_name ON TABLE person COLUMNS name;

        DEFINE TABLE follows SCHEMAFULL;
        DEFINE FIELD in ON TABLE follows TYPE record<person>;
        DEFINE FIELD out ON TABLE follows TYPE record<person>;
    """)

    yield db

    # Cleanup
    await db.query("REMOVE TABLE person; REMOVE TABLE follows;")
    await db.close()

@pytest.mark.asyncio
async def test_multi_hop_traversal(db):
    """Test that multi-hop traversal returns correct results."""
    # Arrange: Create test graph
    await db.query("""
        CREATE person:alice SET name = 'Alice';
        CREATE person:bob SET name = 'Bob';
        CREATE person:charlie SET name = 'Charlie';
        RELATE person:alice->follows->person:bob;
        RELATE person:bob->follows->person:charlie;
    """)

    # Act: Traverse 2 hops
    result = await db.query(
        "SELECT ->follows[..2]->person.name FROM person:alice"
    )

    # Assert: Should find Bob and Charlie
    names = result[0]['result'][0]['name']
    assert 'Bob' in names
    assert 'Charlie' in names

@pytest.mark.asyncio
async def test_depth_limit_respected(db):
    """Test that traversal depth limits are enforced."""
    # Arrange: Create chain of 5 nodes
    await db.query("""
        CREATE person:a SET name = 'A';
        CREATE person:b SET name = 'B';
        CREATE person:c SET name = 'C';
        CREATE person:d SET name = 'D';
        CREATE person:e SET name = 'E';
        RELATE person:a->follows->person:b;
        RELATE person:b->follows->person:c;
        RELATE person:c->follows->person:d;
        RELATE person:d->follows->person:e;
    """)

    # Act: Traverse only 2 hops
    result = await db.query(
        "SELECT ->follows[..2]->person.name FROM person:a"
    )

    # Assert: Should NOT include D or E
    names = result[0]['result'][0]['name']
    assert 'D' not in names
    assert 'E' not in names

@pytest.mark.asyncio
async def test_bidirectional_relationship(db):
    """Test querying in both directions."""
    # Arrange
    await db.query("""
        CREATE person:alice SET name = 'Alice';
        CREATE person:bob SET name = 'Bob';
        RELATE person:alice->follows->person:bob;
    """)

    # Act: Query both directions
    forward = await db.query(
        "SELECT ->follows->person.name FROM person:alice"
    )
    backward = await db.query(
        "SELECT <-follows<-person.name FROM person:bob"
    )

    # Assert
    assert 'Bob' in str(forward)
    assert 'Alice' in str(backward)

@pytest.mark.asyncio
async def test_weighted_edge_filter(db):
    """Test filtering edges by weight."""
    # Setup weighted edges
    await db.query("""
        DEFINE TABLE connected SCHEMAFULL;
        DEFINE FIELD in ON TABLE connected TYPE record<person>;
        DEFINE FIELD out ON TABLE connected TYPE record<person>;
        DEFINE FIELD weight ON TABLE connected TYPE float;

        CREATE person:alice SET name = 'Alice';
        CREATE person:bob SET name = 'Bob';
        CREATE person:charlie SET name = 'Charlie';
        RELATE person:alice->connected->person:bob SET weight = 0.9;
        RELATE person:alice->connected->person:charlie SET weight = 0.3;
    """)

    # Act: Filter by weight
    result = await db.query(
        "SELECT ->connected[WHERE weight > 0.5]->person.name FROM person:alice"
    )

    # Assert: Only Bob (high weight)
    assert 'Bob' in str(result)
    assert 'Charlie' not in str(result)

Step 2: Implement Minimum to Pass

# src/graph/queries.py
from surrealdb import Surreal

class GraphQueryService:
    def __init__(self, db: Surreal):
        self.db = db

    async def get_connections(
        self,
        node_id: str,
        relationship: str,
        depth: int = 2,
        min_weight: float | None = None
    ) -> list[dict]:
        """Get connected nodes with depth limit."""
        if depth > 5:
            raise ValueError("Maximum depth is 5 to prevent runaway queries")

        # Build query with parameterization
        if min_weight is not None:
            query = f"""
                SELECT ->{relationship}[..{depth}][WHERE weight > $min_weight]->*.*
                FROM $node_id
            """
            params = {"node_id": node_id, "min_weight": min_weight}
        else:
            query = f"""
                SELECT ->{relationship}[..{depth}]->*.*
                FROM $node_id
            """
            params = {"node_id": node_id}

        result = await self.db.query(query, params)
        return result[0]['result']

    async def find_path(
        self,
        from_id: str,
        to_id: str,
        relationship: str,
        max_depth: int = 5
    ) -> list[str] | None:
        """Find shortest path between two nodes."""
        # BFS implementation with depth limit
        visited = set()
        queue = [(from_id, [from_id])]

        while queue and len(visited) < 1000:  # Safety limit
            current, path = queue.pop(0)
            if len(path) > max_depth:
                continue

            if current == to_id:
                return path

            if current in visited:
                continue
            visited.add(current)

            # Get neighbors
            result = await self.db.query(
                f"SELECT ->{relationship}->*.id FROM $node",
                {"node": current}
            )

            for neighbor in result[0]['result']:
                if neighbor not in visited:
                    queue.append((neighbor, path + [neighbor]))

        return None

Step 3: Refactor if Needed

# After tests pass, refactor for better performance
class GraphQueryService:
    def __init__(self, db: Surreal):
        self.db = db
        self._cache = {}  # Add caching

    async def get_connections_cached(
        self,
        node_id: str,
        relationship: str,
        depth: int = 2
    ) -> list[dict]:
        """Get connections with caching."""
        cache_key = f"{node_id}:{relationship}:{depth}"

        if cache_key in self._cache:
            return self._cache[cache_key]

        result = await self.get_connections(node_id, relationship, depth)
        self._cache[cache_key] = result

        return result

    def invalidate_cache(self, node_id: str = None):
        """Clear cache entries."""
        if node_id:
            self._cache = {
                k: v for k, v in self._cache.items()
                if not k.startswith(node_id)
            }
        else:
            self._cache.clear()

Step 4: Run Full Verification

# Run all graph database tests
pytest tests/test_graph_queries.py -v

# Run with coverage
pytest tests/test_graph_queries.py --cov=src/graph --cov-report=term-missing

# Run performance tests
pytest tests/test_graph_performance.py -v --benchmark-only

# Check for slow queries (custom marker)
pytest tests/test_graph_queries.py -m slow -v

5. Performance Patterns

Pattern 1: Indexing Strategy

Good: Create indexes before queries need them

-- Index frequently queried properties
DEFINE INDEX person_email ON TABLE person COLUMNS email UNIQUE;
DEFINE INDEX person_name ON TABLE person COLUMNS name;

-- Index edge properties used in filters
DEFINE INDEX follows_weight ON TABLE follows COLUMNS weight;
DEFINE INDEX employment_role ON TABLE employment COLUMNS role;
DEFINE INDEX employment_dates ON TABLE employment COLUMNS valid_from, valid_to;

-- Composite index for common filter combinations
DEFINE INDEX person_status_created ON TABLE person COLUMNS status, created_at;

Bad: Query without indexes

-- Full table scan on every query!
SELECT * FROM person WHERE email = 'alice@example.com';
SELECT ->follows[WHERE weight > 0.5]->person.* FROM person:alice;

Pattern 2: Query Optimization

Good: Bounded traversals with limits

-- Always set depth limits
SELECT ->follows[..3]->person.name FROM person:alice;

-- Use pagination for large results
SELECT ->follows->person.* FROM person:alice LIMIT 50 START 0;

-- Filter early to reduce traversal
SELECT ->follows[WHERE weight > 0.5][..2]->person.name
FROM person:alice
LIMIT 100;

Bad: Unbounded queries

-- Can traverse entire graph!
SELECT ->follows->person.* FROM person:alice;

-- No limits on results
SELECT * FROM person WHERE status = 'active';

Pattern 3: Caching Frequent Traversals

Good: Cache expensive traversals

from functools import lru_cache
from datetime import datetime, timedelta

class GraphCache:
    def __init__(self, ttl_seconds: int = 300):
        self.cache = {}
        self.ttl = timedelta(seconds=ttl_seconds)

    async def get_followers_cached(
        self,
        db: Surreal,
        person_id: str
    ) -> list[dict]:
        cache_key = f"followers:{person_id}"

        if cache_key in self.cache:
            entry = self.cache[cache_key]
            if datetime.now() - entry['time'] < self.ttl:
                return entry['data']

        # Execute query
        result = await db.query(
            "SELECT <-follows<-person.* FROM $person LIMIT 100",
            {"person": person_id}
        )

        # Cache result
        self.cache[cache_key] = {
            'data': result[0]['result'],
            'time': datetime.now()
        }

        return result[0]['result']

    def invalidate(self, person_id: str):
        """Invalidate cache when graph changes."""
        keys_to_remove = [
            k for k in self.cache
            if person_id in k
        ]
        for key in keys_to_remove:
            del self.cache[key]

Bad: No caching for repeated queries

# Every call hits the database
async def get_followers(db, person_id):
    return await db.query(
        "SELECT <-follows<-person.* FROM $person",
        {"person": person_id}
    )

Pattern 4: Batch Operations

Good: Batch multiple operations

-- Batch create nodes
CREATE person CONTENT [
    { id: 'person:alice', name: 'Alice' },
    { id: 'person:bob', name: 'Bob' },
    { id: 'person:charlie', name: 'Charlie' }
];

-- Batch create relationships
LET $relations = [
    { from: 'person:alice', to: 'person:bob' },
    { from: 'person:bob', to: 'person:charlie' }
];
FOR $rel IN $relations {
    RELATE type::thing('person', $rel.from)->follows->type::thing('person', $rel.to);
};

# Python batch operations
async def batch_create_relationships(
    db: Surreal,
    relationships: list[dict]
) -> None:
    """Create multiple relationships in one transaction."""
    queries = []
    for rel in relationships:
        queries.append(
            f"RELATE {rel['from']}->follows->{rel['to']};"
        )

    # Execute as single transaction
    await db.query("BEGIN TRANSACTION; " + " ".join(queries) + " COMMIT;")

Bad: Individual operations

# N database round trips!
async def create_relationships_slow(db, relationships):
    for rel in relationships:
        await db.query(
            f"RELATE {rel['from']}->follows->{rel['to']};"
        )

Pattern 5: Connection Pooling

Good: Use connection pool

from contextlib import asynccontextmanager
import asyncio

class SurrealPool:
    def __init__(self, url: str, pool_size: int = 10):
        self.url = url
        self.pool_size = pool_size
        self._pool = asyncio.Queue(maxsize=pool_size)
        self._created = 0

    async def initialize(self):
        """Pre-create connections."""
        for _ in range(self.pool_size):
            conn = await self._create_connection()
            await self._pool.put(conn)

    async def _create_connection(self) -> Surreal:
        db = Surreal(self.url)
        await db.connect()
        await db.signin({"user": "root", "pass": "root"})
        await db.use("jarvis", "main")
        self._created += 1
        return db

    @asynccontextmanager
    async def acquire(self):
        """Get connection from pool."""
        conn = await self._pool.get()
        try:
            yield conn
        finally:
            await self._pool.put(conn)

    async def close(self):
        """Close all connections."""
        while not self._pool.empty():
            conn = await self._pool.get()
            await conn.close()

# Usage
pool = SurrealPool("ws://localhost:8000/rpc")
await pool.initialize()

async with pool.acquire() as db:
    result = await db.query("SELECT * FROM person LIMIT 10")

Bad: Create connection per query

# Connection overhead on every query!
async def query_slow(query: str):
    db = Surreal("ws://localhost:8000/rpc")
    await db.connect()
    await db.signin({"user": "root", "pass": "root"})
    result = await db.query(query)
    await db.close()
    return result

6. Top 7 Graph Modeling Patterns

Pattern 1: Entity Nodes with Typed Relationships (SurrealDB)

-- Define entity tables
DEFINE TABLE person SCHEMAFULL;
DEFINE FIELD name ON TABLE person TYPE string;
DEFINE FIELD email ON TABLE person TYPE string;
DEFINE FIELD created_at ON TABLE person TYPE datetime DEFAULT time::now();

DEFINE TABLE company SCHEMAFULL;
DEFINE FIELD name ON TABLE company TYPE string;
DEFINE FIELD industry ON TABLE company TYPE string;

-- Define relationship tables (typed edges)
DEFINE TABLE works_at SCHEMAFULL;
DEFINE FIELD in ON TABLE works_at TYPE record<person>;
DEFINE FIELD out ON TABLE works_at TYPE record<company>;
DEFINE FIELD role ON TABLE works_at TYPE string;
DEFINE FIELD start_date ON TABLE works_at TYPE datetime;
DEFINE FIELD end_date ON TABLE works_at TYPE option<datetime>;

-- Create relationships
RELATE person:alice->works_at->company:acme SET
    role = 'Engineer',
    start_date = time::now();

-- Forward traversal: Who works at this company?
SELECT <-works_at<-person.* FROM company:acme;

-- Backward traversal: Where does this person work?
SELECT ->works_at->company.* FROM person:alice;

-- Filter on edge properties
SELECT ->works_at[WHERE role = 'Engineer']->company.*
FROM person:alice;

Generic concept: Model entities as nodes and relationships as edges with properties. Direction matters for query efficiency.

Pattern 2: Multi-Hop Graph Traversal

-- Schema: person -> follows -> person -> likes -> post
DEFINE TABLE follows SCHEMAFULL;
DEFINE FIELD in ON TABLE follows TYPE record<person>;
DEFINE FIELD out ON TABLE follows TYPE record<person>;

DEFINE TABLE likes SCHEMAFULL;
DEFINE FIELD in ON TABLE likes TYPE record<person>;
DEFINE FIELD out ON TABLE likes TYPE record<post>;

-- Multi-hop: Posts liked by people I follow
SELECT ->follows->person->likes->post.* FROM person:alice;

-- Depth limit to prevent runaway queries
SELECT ->follows[..3]->person.name FROM person:alice;

-- Variable depth traversal
SELECT ->follows[1..2]->person.* FROM person:alice;

-- DON'T: Unbounded traversal (dangerous!)
-- SELECT ->follows->person.* FROM person:alice; -- Could traverse entire graph!

Generic concept: Graph traversals follow edges to discover connected nodes. Always set depth limits to prevent performance issues.

Neo4j equivalent:

// Multi-hop in Cypher
MATCH (alice:Person {id: 'alice'})-[:FOLLOWS*1..2]->(person:Person)
RETURN person

Pattern 3: Bidirectional Relationships

-- Model friendship (symmetric relationship)
DEFINE TABLE friendship SCHEMAFULL;
DEFINE FIELD in ON TABLE friendship TYPE record<person>;
DEFINE FIELD out ON TABLE friendship TYPE record<person>;
DEFINE FIELD created_at ON TABLE friendship TYPE datetime DEFAULT time::now();

-- Create both directions for friendship
RELATE person:alice->friendship->person:bob;
RELATE person:bob->friendship->person:alice;

-- Query friends in either direction
SELECT ->friendship->person.* FROM person:alice;
SELECT <-friendship<-person.* FROM person:alice;

-- Alternative: Single edge with bidirectional query
-- Query both incoming and outgoing
SELECT ->friendship->person.*, <-friendship<-person.*
FROM person:alice;

Generic concept: Symmetric relationships need careful design. Either create bidirectional edges or query in both directions.

Design choices:

Duplicate edges: Faster queries, more storage
Single edge + bidirectional queries: Less storage, slightly slower
Undirected graph flag: Database-specific feature

Pattern 4: Hierarchical Data (Trees and DAGs)

-- Organization hierarchy
DEFINE TABLE org_unit SCHEMAFULL;
DEFINE FIELD name ON TABLE org_unit TYPE string;
DEFINE FIELD level ON TABLE org_unit TYPE string;

DEFINE TABLE reports_to SCHEMAFULL;
DEFINE FIELD in ON TABLE reports_to TYPE record<org_unit>;
DEFINE FIELD out ON TABLE reports_to TYPE record<org_unit>;

-- Create hierarchy
RELATE org_unit:eng->reports_to->org_unit:cto;
RELATE org_unit:product->reports_to->org_unit:cto;
RELATE org_unit:cto->reports_to->org_unit:ceo;

-- Get all ancestors (upward traversal)
SELECT ->reports_to[..10]->org_unit.* FROM org_unit:eng;

-- Get all descendants (downward traversal)
SELECT <-reports_to[..10]<-org_unit.* FROM org_unit:ceo;

-- Add materialized path for faster ancestor queries
DEFINE FIELD path ON TABLE org_unit TYPE string;
-- Store as: '/ceo/cto/eng' for fast LIKE queries

-- Add level for depth queries
UPDATE org_unit:eng SET level = 3;
SELECT * FROM org_unit WHERE level = 3;

Generic concept: Trees and hierarchies are special graph patterns. Consider materialized paths or nested sets for complex queries.

Pattern 5: Temporal Relationships (Time-Based Edges)

-- Track relationship validity periods
DEFINE TABLE employment SCHEMAFULL;
DEFINE FIELD in ON TABLE employment TYPE record<person>;
DEFINE FIELD out ON TABLE employment TYPE record<company>;
DEFINE FIELD role ON TABLE employment TYPE string;
DEFINE FIELD valid_from ON TABLE employment TYPE datetime;
DEFINE FIELD valid_to ON TABLE employment TYPE option<datetime>;

-- Create temporal relationship
RELATE person:alice->employment->company:acme SET
    role = 'Engineer',
    valid_from = d'2020-01-01T00:00:00Z',
    valid_to = d'2023-12-31T23:59:59Z';

-- Query current relationships
LET $now = time::now();
SELECT ->employment[WHERE valid_from <= $now AND (valid_to = NONE OR valid_to >= $now)]->company.*
FROM person:alice;

-- Query historical relationships
SELECT ->employment[WHERE valid_from <= d'2021-06-01']->company.*
FROM person:alice;

-- Index temporal fields
DEFINE INDEX employment_valid_from ON TABLE employment COLUMNS valid_from;
DEFINE INDEX employment_valid_to ON TABLE employment COLUMNS valid_to;

Generic concept: Add timestamps to edges for temporal queries. Essential for audit trails, historical analysis, and versioning.

Pattern 6: Weighted Relationships (Graph Algorithms)

-- Social network with relationship strength
DEFINE TABLE connected_to SCHEMAFULL;
DEFINE FIELD in ON TABLE connected_to TYPE record<person>;
DEFINE FIELD out ON TABLE connected_to TYPE record<person>;
DEFINE FIELD weight ON TABLE connected_to TYPE float;
DEFINE FIELD interaction_count ON TABLE connected_to TYPE int DEFAULT 0;

-- Create weighted edges
RELATE person:alice->connected_to->person:bob SET
    weight = 0.8,
    interaction_count = 45;

-- Filter by weight threshold
SELECT ->connected_to[WHERE weight > 0.5]->person.* FROM person:alice;

-- Sort by relationship strength
SELECT ->connected_to->person.*, ->connected_to.weight AS strength
FROM person:alice
ORDER BY strength DESC;

-- Use cases:
-- - Shortest weighted path algorithms
-- - Recommendation scoring
-- - Fraud detection patterns
-- - Network flow analysis

Generic concept: Edge properties enable graph algorithms. Weight is fundamental for pathfinding, recommendations, and network analysis.

Pattern 7: Avoiding N+1 Queries with Graph Traversal

-- N+1 ANTI-PATTERN: Multiple queries
-- First query
SELECT * FROM person;
-- Then for each person (N queries)
SELECT * FROM company WHERE id = (SELECT ->works_at->company FROM person:alice);
SELECT * FROM company WHERE id = (SELECT ->works_at->company FROM person:bob);

-- CORRECT: Single graph traversal
SELECT
    *,
    ->works_at->company.* AS companies
FROM person;

-- With FETCH to include related data
SELECT * FROM person FETCH ->works_at->company;

-- Complex traversal in one query
SELECT
    name,
    ->works_at->company.name AS company_name,
    ->follows->person.name AS following,
    <-follows<-person.name AS followers
FROM person:alice;

Generic concept: Graph databases excel at joins. Use traversal operators instead of multiple round-trip queries.

7. Testing

Unit Tests for Graph Queries

# tests/test_graph_service.py
import pytest
from unittest.mock import AsyncMock, MagicMock

@pytest.fixture
def mock_db():
    """Create mock database for unit tests."""
    db = AsyncMock()
    return db

@pytest.mark.asyncio
async def test_get_connections_enforces_depth_limit(mock_db):
    """Test that depth limit is enforced."""
    from src.graph.queries import GraphQueryService

    service = GraphQueryService(mock_db)

    with pytest.raises(ValueError) as exc_info:
        await service.get_connections("person:alice", "follows", depth=10)

    assert "Maximum depth is 5" in str(exc_info.value)

@pytest.mark.asyncio
async def test_cache_invalidation(mock_db):
    """Test cache invalidation works correctly."""
    from src.graph.queries import GraphQueryService

    mock_db.query.return_value = [{'result': [{'name': 'Bob'}]}]

    service = GraphQueryService(mock_db)

    # First call
    result1 = await service.get_connections_cached("person:alice", "follows")
    # Second call (should use cache)
    result2 = await service.get_connections_cached("person:alice", "follows")

    # Only one DB call
    assert mock_db.query.call_count == 1

    # Invalidate and call again
    service.invalidate_cache("person:alice")
    result3 = await service.get_connections_cached("person:alice", "follows")

    # Should hit DB again
    assert mock_db.query.call_count == 2

Integration Tests with Real Database

# tests/integration/test_graph_integration.py
import pytest
from surrealdb import Surreal

@pytest.fixture(scope="module")
async def test_db():
    """Setup test database."""
    db = Surreal("ws://localhost:8000/rpc")
    await db.connect()
    await db.signin({"user": "root", "pass": "root"})
    await db.use("test", "graph_test")

    yield db

    # Cleanup
    await db.query("REMOVE DATABASE graph_test;")
    await db.close()

@pytest.mark.integration
@pytest.mark.asyncio
async def test_full_graph_workflow(test_db):
    """Test complete graph workflow."""
    # Setup schema
    await test_db.query("""
        DEFINE TABLE person SCHEMAFULL;
        DEFINE FIELD name ON TABLE person TYPE string;
        DEFINE INDEX person_name ON TABLE person COLUMNS name;

        DEFINE TABLE follows SCHEMAFULL;
        DEFINE FIELD in ON TABLE follows TYPE record<person>;
        DEFINE FIELD out ON TABLE follows TYPE record<person>;
    """)

    # Create nodes
    await test_db.query("""
        CREATE person:alice SET name = 'Alice';
        CREATE person:bob SET name = 'Bob';
    """)

    # Create relationship
    await test_db.query(
        "RELATE person:alice->follows->person:bob"
    )

    # Query relationship
    result = await test_db.query(
        "SELECT ->follows->person.name FROM person:alice"
    )

    assert 'Bob' in str(result)

Performance Tests

# tests/performance/test_graph_performance.py
import pytest
import time

@pytest.mark.slow
@pytest.mark.asyncio
async def test_traversal_performance(test_db):
    """Test that traversal completes within time limit."""
    # Setup large graph
    await test_db.query("""
        FOR $i IN 1..100 {
            CREATE person SET name = $i;
        };
        FOR $i IN 1..99 {
            RELATE type::thing('person', $i)->follows->type::thing('person', $i + 1);
        };
    """)

    start = time.time()

    # Run bounded traversal
    result = await test_db.query(
        "SELECT ->follows[..5]->person.* FROM person:1"
    )

    elapsed = time.time() - start

    # Should complete in under 100ms
    assert elapsed < 0.1, f"Traversal took {elapsed}s"

    # Should return limited results
    assert len(result[0]['result']) <= 5

8. Security

8.1 Access Control

-- Row-level security on nodes
DEFINE TABLE document SCHEMAFULL
    PERMISSIONS
        FOR select WHERE public = true OR owner = $auth.id
        FOR create WHERE $auth.id != NONE
        FOR update, delete WHERE owner = $auth.id;

-- Relationship permissions
DEFINE TABLE friendship SCHEMAFULL
    PERMISSIONS
        FOR select WHERE in = $auth.id OR out = $auth.id
        FOR create WHERE in = $auth.id
        FOR delete WHERE in = $auth.id OR out = $auth.id;

-- Prevent unauthorized traversal
DEFINE TABLE follows SCHEMAFULL
    PERMISSIONS
        FOR select WHERE in.public = true OR in.id = $auth.id;

8.2 Injection Prevention

-- SECURE: Parameterized queries
LET $person_id = "person:alice";
SELECT ->follows->person.* FROM $person_id;

-- With SDK
const result = await db.query(
    'SELECT ->follows->person.* FROM $person',
    { person: `person:${userId}` }
);

-- VULNERABLE: String concatenation
-- const query = `SELECT * FROM person:${userInput}`;

8.3 Query Depth Limits

-- SAFE: Bounded traversal
SELECT ->follows[..3]->person.* FROM person:alice;

-- SAFE: Limit results
SELECT ->follows->person.* FROM person:alice LIMIT 100;

-- DANGEROUS: Unbounded traversal
-- SELECT ->follows->person.* FROM person:alice;
-- Could traverse millions of nodes!

8.4 Data Exposure

-- Filter sensitive data in traversals
SELECT
    name,
    ->follows->person.{name, public_bio} AS following
FROM person:alice;

-- DON'T: Expose all fields in traversal
-- SELECT ->follows->person.* FROM person:alice;
-- May include email, phone, private data

9. Common Mistakes

Mistake 1: Unbounded Graph Traversals

-- DON'T: No depth limit
SELECT ->follows->person.* FROM person:alice;
-- Could traverse entire social network!

-- DO: Set depth limits
SELECT ->follows[..2]->person.* FROM person:alice;
SELECT ->follows[1..3]->person.* FROM person:alice LIMIT 100;

Mistake 2: Missing Indexes on Traversal Paths

-- DON'T: Query without indexes
SELECT * FROM person WHERE email = 'alice@example.com';
-- Full table scan!

-- DO: Create indexes
DEFINE INDEX email_idx ON TABLE person COLUMNS email UNIQUE;
DEFINE INDEX name_idx ON TABLE person COLUMNS name;

-- Index edge properties used in filters
DEFINE INDEX works_at_role ON TABLE works_at COLUMNS role;

Mistake 3: Wrong Relationship Direction

-- Inefficient: Traversing against primary direction
SELECT <-authored<-post WHERE author = person:alice;

-- Better: Traverse with primary direction
SELECT ->authored->post.* FROM person:alice;

-- Design rule: Model edges in the direction of common queries

Mistake 4: N+1 Query Pattern in Graphs

-- DON'T: Multiple round trips
SELECT * FROM person;
-- Then for each person:
SELECT * FROM post WHERE author = person:1;

-- DO: Single graph traversal
SELECT *, ->authored->post.* FROM person;

Mistake 5: Over-Normalizing Relationship Data

-- DON'T: Over-normalize simple properties
-- Separate table for single property
DEFINE TABLE person_email;

-- DO: Embed simple properties
DEFINE TABLE person;
DEFINE FIELD email ON TABLE person TYPE string;

-- Use relationships for:
-- - Many-to-many associations
-- - Entities with independent lifecycle
-- - Rich metadata on relationships

Mistake 6: Not Handling Cycles

-- Circular references can cause issues
-- Example: A follows B, B follows C, C follows A

-- Set depth limit to prevent infinite loops
SELECT ->follows[..5]->person.* FROM person:alice;

-- Track visited nodes in application logic
-- Use cycle detection in graph algorithms

Mistake 7: Ignoring Query Explain Plans

-- Always check query plans for slow queries
-- (Database-specific syntax)

-- SurrealDB: Monitor query performance
-- Neo4j: EXPLAIN / PROFILE
-- EXPLAIN SELECT ->follows->person.* FROM person:alice;

-- Look for:
-- - Full table scans
-- - Missing indexes
-- - Cartesian products
-- - Excessive traversal depth

10. Pre-Implementation Checklist

Phase 1: Before Writing Code

Read the PRD section for graph requirements
Identify entities (nodes) and relationships (edges)
Design schema based on query patterns
Plan indexes for frequently queried properties
Determine traversal depth limits
Review security requirements (permissions, data exposure)
Write failing tests for expected query behavior

Phase 2: During Implementation

Use parameterized queries (prevent injection)
Set depth limits on all traversals
Implement pagination for large result sets
Add caching for frequent queries
Use batch operations for bulk inserts
Monitor query performance with explain plans
Filter sensitive fields in traversal results

Phase 3: Before Committing

All graph query tests pass
Integration tests with real database pass
Performance tests meet latency requirements
No unbounded traversals in codebase
All queried properties have indexes
Security review for data exposure
Documentation updated for schema changes

12. Summary

You are a graph database expert focused on:

Graph Modeling – Entities as nodes, relationships as edges, typed connections
Query Optimization – Indexes, depth limits, explain plans, efficient traversals
Relationship Design – Bidirectional edges, temporal data, weighted connections
Performance – Avoid N+1, bounded traversals, proper indexing
Security – Row-level permissions, injection prevention, data exposure

Key Principles:

Model queries first, then design your graph schema
Always set depth limits on recursive traversals
Use graph traversal instead of joins or multiple queries
Index both node properties and edge properties
Add metadata to edges (timestamps, weights, properties)
Design relationship direction based on common queries
Monitor query performance with explain plans

Graph Database Resources:

SurrealDB Docs: https://surrealdb.com/docs
Neo4j Graph Academy: https://neo4j.com/graphacademy/
Graph Database Theory: https://neo4j.com/docs/getting-started/appendix/graphdb-concepts/

Reference Documentation:

Query Optimization: See references/query-optimization.md
Modeling Guide: See references/modeling-guide.md

Graph databases excel at connected data. Model relationships as first-class citizens and leverage traversal operators for powerful, efficient queries.

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