Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector'
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ruv
367
vendor/ruvector/benchmarks/graph/src/graph-scenarios.ts
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367
vendor/ruvector/benchmarks/graph/src/graph-scenarios.ts
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/**
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* Graph benchmark scenarios for RuVector graph database
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* Tests various graph operations and compares with Neo4j
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*/
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export interface GraphScenario {
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name: string;
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description: string;
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type: 'traversal' | 'write' | 'aggregation' | 'mixed' | 'concurrent';
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setup: () => Promise<void>;
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execute: () => Promise<BenchmarkResult>;
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cleanup?: () => Promise<void>;
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}
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export interface BenchmarkResult {
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scenario: string;
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duration_ms: number;
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operations_per_second: number;
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memory_mb?: number;
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cpu_percent?: number;
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metadata?: Record<string, unknown>;
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}
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export interface GraphDataset {
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name: string;
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nodes: number;
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edges: number;
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labels: string[];
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relationshipTypes: string[];
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properties: Record<string, string>;
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}
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/**
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* Social Network Scenario
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* Simulates a social graph with users, posts, and relationships
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*/
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export const socialNetworkScenario: GraphScenario = {
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name: 'social_network_1m',
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description: 'Social network with 1M users and 10M friendships',
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type: 'mixed',
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setup: async () => {
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console.log('Setting up social network dataset...');
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// Will use agentic-synth to generate realistic social graph data
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},
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execute: async () => {
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const start = Date.now();
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// Benchmark operations:
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// 1. Create users (batch insert)
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// 2. Create friendships (batch edge creation)
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// 3. Friend recommendations (2-hop traversal)
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// 4. Mutual friends (intersection query)
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// 5. Influencer detection (degree centrality)
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const duration = Date.now() - start;
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return {
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scenario: 'social_network_1m',
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duration_ms: duration,
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operations_per_second: 1000000 / (duration / 1000),
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metadata: {
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nodes_created: 1000000,
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edges_created: 10000000,
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queries_executed: 5
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}
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};
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}
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};
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/**
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* Knowledge Graph Scenario
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* Tests entity relationships and multi-hop reasoning
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*/
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export const knowledgeGraphScenario: GraphScenario = {
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name: 'knowledge_graph_100k',
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description: 'Knowledge graph with 100K entities and 1M relationships',
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type: 'traversal',
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setup: async () => {
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console.log('Setting up knowledge graph dataset...');
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},
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execute: async () => {
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const start = Date.now();
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// Benchmark operations:
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// 1. Entity creation (Person, Organization, Location, Event)
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// 2. Relationship creation (works_at, located_in, participated_in)
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// 3. Multi-hop queries (person -> organization -> location)
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// 4. Path finding (shortest path between entities)
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// 5. Pattern matching (find all people in same organization and location)
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const duration = Date.now() - start;
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return {
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scenario: 'knowledge_graph_100k',
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duration_ms: duration,
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operations_per_second: 100000 / (duration / 1000)
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};
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}
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};
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/**
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* Temporal Graph Scenario
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* Tests time-based queries and event ordering
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*/
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export const temporalGraphScenario: GraphScenario = {
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name: 'temporal_graph_events',
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description: 'Temporal graph with time-series events and state transitions',
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type: 'mixed',
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setup: async () => {
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console.log('Setting up temporal graph dataset...');
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},
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execute: async () => {
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const start = Date.now();
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// Benchmark operations:
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// 1. Event insertion (timestamped nodes)
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// 2. State transitions (temporal edges)
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// 3. Time-range queries (events between timestamps)
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// 4. Temporal path finding (valid paths at time T)
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// 5. Event aggregation (count by time bucket)
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const duration = Date.now() - start;
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return {
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scenario: 'temporal_graph_events',
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duration_ms: duration,
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operations_per_second: 1000000 / (duration / 1000)
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};
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}
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};
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/**
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* Recommendation Engine Scenario
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* Tests collaborative filtering and similarity queries
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*/
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export const recommendationScenario: GraphScenario = {
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name: 'recommendation_engine',
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description: 'User-item bipartite graph for recommendations',
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type: 'traversal',
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setup: async () => {
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console.log('Setting up recommendation dataset...');
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},
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execute: async () => {
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const start = Date.now();
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// Benchmark operations:
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// 1. Create users and items
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// 2. Create rating/interaction edges
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// 3. Collaborative filtering (similar users)
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// 4. Item recommendations (2-hop: user -> items <- users -> items)
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// 5. Trending items (aggregation by interaction count)
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const duration = Date.now() - start;
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return {
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scenario: 'recommendation_engine',
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duration_ms: duration,
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operations_per_second: 500000 / (duration / 1000)
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};
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}
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};
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/**
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* Fraud Detection Scenario
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* Tests pattern matching and anomaly detection
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*/
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export const fraudDetectionScenario: GraphScenario = {
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name: 'fraud_detection',
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description: 'Transaction graph for fraud pattern detection',
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type: 'aggregation',
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setup: async () => {
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console.log('Setting up fraud detection dataset...');
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},
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execute: async () => {
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const start = Date.now();
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// Benchmark operations:
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// 1. Create accounts and transactions
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// 2. Circular transfer detection (cycle detection)
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// 3. Velocity checks (count transactions in time window)
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// 4. Network analysis (connected components)
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// 5. Risk scoring (aggregation across relationships)
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const duration = Date.now() - start;
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return {
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scenario: 'fraud_detection',
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duration_ms: duration,
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operations_per_second: 200000 / (duration / 1000)
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};
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}
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};
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/**
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* Concurrent Write Scenario
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* Tests multi-threaded write performance
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*/
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export const concurrentWriteScenario: GraphScenario = {
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name: 'concurrent_writes',
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description: 'Concurrent node and edge creation from multiple threads',
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type: 'concurrent',
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setup: async () => {
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console.log('Setting up concurrent write test...');
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},
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execute: async () => {
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const start = Date.now();
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// Benchmark operations:
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// 1. Spawn multiple concurrent writers
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// 2. Each writes 10K nodes + 50K edges
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// 3. Test with 2, 4, 8, 16 threads
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// 4. Measure throughput and contention
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const duration = Date.now() - start;
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return {
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scenario: 'concurrent_writes',
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duration_ms: duration,
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operations_per_second: 100000 / (duration / 1000),
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metadata: {
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threads: 8,
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contention_rate: 0.05
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}
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};
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}
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};
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/**
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* Deep Traversal Scenario
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* Tests performance of deep graph traversals
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*/
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export const deepTraversalScenario: GraphScenario = {
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name: 'deep_traversal',
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description: 'Multi-hop traversals up to 6 degrees of separation',
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type: 'traversal',
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setup: async () => {
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console.log('Setting up deep traversal dataset...');
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},
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execute: async () => {
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const start = Date.now();
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// Benchmark operations:
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// 1. Create dense graph (avg degree = 50)
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// 2. 1-hop traversal (immediate neighbors)
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// 3. 2-hop traversal (friends of friends)
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// 4. 3-hop traversal
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// 5. 6-hop traversal (6 degrees of separation)
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const duration = Date.now() - start;
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return {
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scenario: 'deep_traversal',
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duration_ms: duration,
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operations_per_second: 1000 / (duration / 1000),
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metadata: {
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max_depth: 6,
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avg_results_per_hop: [50, 2500, 125000]
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}
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};
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}
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};
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/**
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* Aggregation Heavy Scenario
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* Tests aggregation and analytical queries
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*/
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export const aggregationScenario: GraphScenario = {
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name: 'aggregation_analytics',
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description: 'Complex aggregation and analytical queries',
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type: 'aggregation',
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setup: async () => {
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console.log('Setting up aggregation dataset...');
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},
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execute: async () => {
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const start = Date.now();
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// Benchmark operations:
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// 1. Count nodes by label
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// 2. Average property values
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// 3. Group by with aggregation
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// 4. Percentile calculations
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// 5. Graph statistics (degree distribution)
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const duration = Date.now() - start;
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return {
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scenario: 'aggregation_analytics',
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duration_ms: duration,
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operations_per_second: 1000000 / (duration / 1000)
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};
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}
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};
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/**
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* All benchmark scenarios
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*/
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export const allScenarios: GraphScenario[] = [
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socialNetworkScenario,
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knowledgeGraphScenario,
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temporalGraphScenario,
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recommendationScenario,
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fraudDetectionScenario,
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concurrentWriteScenario,
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deepTraversalScenario,
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aggregationScenario
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];
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/**
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* Dataset definitions for synthetic data generation
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*/
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export const datasets: GraphDataset[] = [
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{
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name: 'social_network',
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nodes: 1000000,
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edges: 10000000,
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labels: ['Person', 'Post', 'Comment', 'Group'],
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relationshipTypes: ['FRIENDS_WITH', 'POSTED', 'COMMENTED_ON', 'MEMBER_OF', 'LIKES'],
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properties: {
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Person: 'id, name, age, location, joinDate',
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Post: 'id, content, timestamp, likes',
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Comment: 'id, text, timestamp',
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Group: 'id, name, memberCount'
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}
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},
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{
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name: 'knowledge_graph',
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nodes: 100000,
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edges: 1000000,
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labels: ['Person', 'Organization', 'Location', 'Event', 'Concept'],
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relationshipTypes: ['WORKS_AT', 'LOCATED_IN', 'PARTICIPATED_IN', 'RELATED_TO', 'INFLUENCED_BY'],
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properties: {
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Person: 'id, name, birth_date, nationality',
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Organization: 'id, name, founded, industry',
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Location: 'id, name, country, coordinates',
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Event: 'id, name, date, description',
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Concept: 'id, name, domain, definition'
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}
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},
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{
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name: 'temporal_events',
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nodes: 500000,
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edges: 2000000,
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labels: ['Event', 'State', 'Entity'],
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relationshipTypes: ['TRANSITIONS_TO', 'TRIGGERED_BY', 'AFFECTS'],
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properties: {
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Event: 'id, timestamp, type, severity',
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State: 'id, value, validFrom, validTo',
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Entity: 'id, name, currentState'
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}
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}
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];
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