# RuVector Graph - Distributed Hypergraph Database Architecture ## Executive Summary RuVector Graph is a distributed, Neo4j-compatible hypergraph database designed for extreme performance through SIMD optimization, lock-free data structures, and intelligent query execution. It extends traditional property graph models with hyperedge support for n-ary relationships while seamlessly integrating with RuVector's vector database capabilities for hybrid semantic-graph queries. **Key Performance Targets:** - **10-100x faster** than Neo4j for graph traversals (SIMD + cache-optimized layouts) - **5-50x faster** for complex pattern matching (parallel execution + JIT compilation) - **Sub-millisecond latency** for 3-hop neighborhood queries on billion-edge graphs - **Linear scalability** to 100+ node clusters with federation support --- ## 1. System Architecture Overview ### 1.1 High-Level Architecture ``` ┌─────────────────────────────────────────────────────────────────┐ │ Client Applications │ │ (Cypher Queries, Vector+Graph Hybrid Queries) │ └──────────────────────┬──────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Query Coordinator Layer │ │ ┌──────────────┐ ┌───────────────┐ ┌────────────────────┐ │ │ │ Query Parser │─▶│ Query Planner │─▶│ Execution Engine │ │ │ │ (Cypher) │ │ (Optimizer) │ │ (SIMD+Parallel) │ │ │ └──────────────┘ └───────────────┘ └────────────────────┘ │ └──────────────────────┬──────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ Federation Layer │ │ ┌────────────────┐ ┌──────────────┐ ┌──────────────────┐ │ │ │ Cluster Router │ │ Query Merger │ │ Cross-Cluster │ │ │ │ │ │ │ │ Transaction Mgr │ │ │ └────────────────┘ └──────────────┘ └──────────────────┘ │ └──────────────────────┬──────────────────────────────────────────┘ │ ┌──────────────┼──────────────┐ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Cluster 1 │ │ Cluster 2 │ │ Cluster N │ │ │ │ │ │ │ │ ┌────────┐ │ │ ┌────────┐ │ │ ┌────────┐ │ │ │ RAFT │ │ │ │ RAFT │ │ │ │ RAFT │ │ │ │ Leader │ │ │ │ Leader │ │ │ │ Leader │ │ │ └────┬───┘ │ │ └────┬───┘ │ │ └────┬───┘ │ │ │ │ │ │ │ │ │ │ │ ┌────▼────┐ │ │ ┌────▼────┐ │ │ ┌────▼────┐ │ │ │ Storage │ │ │ │ Storage │ │ │ │ Storage │ │ │ │ Engine │ │ │ │ Engine │ │ │ │ Engine │ │ │ └─────────┘ │ │ └─────────┘ │ │ └─────────┘ │ └──────────────┘ └──────────────┘ └──────────────┘ ``` ### 1.2 Core Design Principles 1. **SIMD-First Design**: All critical paths use SIMD intrinsics for 4-8x speedup 2. **Lock-Free Architecture**: Minimize contention with lock-free data structures 3. **Cache-Optimized Layouts**: Columnar storage and CSR graphs for cache efficiency 4. **Zero-Copy Operations**: Memory-mapped storage with direct SIMD access 5. **Adaptive Indexing**: Automatic index selection based on query patterns 6. **Hybrid Execution**: Seamlessly combine vector similarity and graph traversal --- ## 2. Data Model ### 2.1 Core Entities ```rust /// Node: Vertex in the graph with properties and optional embedding pub struct Node { id: NodeId, // 64-bit ID (8 bytes) labels: SmallVec<[LabelId; 4]>, // Inline for ≤4 labels properties: PropertyMap, // Key-value properties embedding: Option>, // Optional vector embedding degree_out: u32, // Outgoing edge count degree_in: u32, // Incoming edge count edge_offset: u64, // Offset in edge array } /// Edge: Directed binary relationship pub struct Edge { id: EdgeId, // 64-bit ID src: NodeId, // Source node dst: NodeId, // Destination node label: LabelId, // Edge type properties: PropertyMap, embedding: Option>, // Optional edge embedding } /// Hyperedge: N-ary relationship connecting multiple nodes pub struct Hyperedge { id: HyperedgeId, // 64-bit ID nodes: Vec, // Connected nodes (ordered) label: LabelId, // Hyperedge type description: String, // Natural language description properties: PropertyMap, embedding: Vec, // Always embedded for semantic search confidence: f32, // Relationship confidence [0,1] } /// Property: Strongly-typed property value #[derive(Clone)] pub enum PropertyValue { Null, Bool(bool), Int64(i64), Float64(f64), String(String), DateTime(i64), // Unix timestamp List(Vec), Map(HashMap), } ``` ### 2.2 Storage Layout **Compressed Sparse Row (CSR) Format for Edges:** ``` Nodes: [N0, N1, N2, N3, ...] Offsets: [0, 3, 5, 8, ...] # Edge start indices Edges: [E0, E1, E2, E3, E4, E5, E6, E7, ...] └─N0 edges─┘ └N1─┘ └──N2 edges──┘ ``` **Benefits:** - Cache-friendly sequential access - SIMD-friendly: process multiple edges in parallel - Low memory overhead: 8-16 bytes per edge - Fast neighborhood queries: O(degree) with prefetching ### 2.3 Indexing Strategy 1. **Primary Indexes:** - Node ID → Node (B+ Tree or HashMap) - Edge ID → Edge (B+ Tree or HashMap) - Hyperedge ID → Hyperedge (B+ Tree or HashMap) 2. **Secondary Indexes:** - Label Index: LabelId → [NodeId] (Bitmap or RoaringBitmap) - Property Index: (Key, Value) → [NodeId] (B+ Tree) - Temporal Index: TimeRange → [HyperedgeId] (Interval Tree) - Vector Index: Embedding → [NodeId/EdgeId] (HNSW from ruvector-core) 3. **Specialized Indexes:** - Bipartite Index: Node ↔ Hyperedge (for hypergraph queries) - Geospatial Index: (Lat, Lon) → [NodeId] (R-Tree) - Full-Text Index: Text → [NodeId] (Inverted Index) --- ## 3. Module Structure ### 3.1 Crate Organization ``` ruvector-graph/ ├── src/ │ ├── lib.rs # Public API and re-exports │ │ │ ├── model/ # Data model │ │ ├── mod.rs │ │ ├── node.rs # Node definition │ │ ├── edge.rs # Edge definition │ │ ├── hyperedge.rs # Hyperedge definition │ │ ├── property.rs # Property types and maps │ │ └── schema.rs # Schema definitions and validation │ │ │ ├── storage/ # Storage layer │ │ ├── mod.rs │ │ ├── engine.rs # Storage engine trait and orchestration │ │ ├── csr.rs # CSR graph storage │ │ ├── columnar.rs # Columnar property storage │ │ ├── mmap.rs # Memory-mapped file handling │ │ ├── wal.rs # Write-Ahead Log │ │ └── snapshot.rs # Snapshotting and recovery │ │ │ ├── index/ # Indexing layer │ │ ├── mod.rs │ │ ├── primary.rs # Primary ID indexes │ │ ├── label.rs # Label indexes (bitmap) │ │ ├── property.rs # Property indexes (B+ tree) │ │ ├── temporal.rs # Temporal hyperedge indexes │ │ ├── vector.rs # Vector similarity indexes (HNSW) │ │ ├── bipartite.rs # Hypergraph bipartite indexes │ │ └── adaptive.rs # Adaptive index selection │ │ │ ├── query/ # Query processing │ │ ├── mod.rs │ │ ├── parser/ # Cypher parser │ │ │ ├── mod.rs │ │ │ ├── lexer.rs # Tokenization │ │ │ ├── ast.rs # Abstract syntax tree │ │ │ ├── cypher.rs # Cypher grammar │ │ │ └── extensions.rs # RuVector extensions (vector ops) │ │ │ │ │ ├── planner/ # Query planning and optimization │ │ │ ├── mod.rs │ │ │ ├── logical.rs # Logical plan generation │ │ │ ├── physical.rs # Physical plan generation │ │ │ ├── optimizer.rs # Rule-based optimization │ │ │ ├── cost_model.rs # Cost-based optimization │ │ │ └── statistics.rs # Query statistics collector │ │ │ │ │ └── executor/ # Query execution │ │ ├── mod.rs │ │ ├── engine.rs # Execution engine │ │ ├── operators.rs # Physical operators (Scan, Join, etc.) │ │ ├── simd_ops.rs # SIMD-optimized operations │ │ ├── parallel.rs # Parallel execution (Rayon) │ │ ├── vectorized.rs # Vectorized execution (batch processing) │ │ └── jit.rs # JIT compilation for hot paths (optional) │ │ │ ├── distributed/ # Distribution layer │ │ ├── mod.rs │ │ ├── consensus/ # Consensus protocol │ │ │ ├── mod.rs │ │ │ ├── raft_ext.rs # Extended RAFT from ruvector-raft │ │ │ ├── log.rs # Distributed transaction log │ │ │ └── snapshot.rs # Distributed snapshots │ │ │ │ │ ├── partitioning/ # Graph partitioning │ │ │ ├── mod.rs │ │ │ ├── hash.rs # Hash partitioning │ │ │ ├── range.rs # Range partitioning │ │ │ ├── metis.rs # METIS-based graph partitioning │ │ │ └── adaptive.rs # Adaptive repartitioning │ │ │ │ │ ├── replication/ # Replication │ │ │ ├── mod.rs │ │ │ ├── sync.rs # Synchronous replication │ │ │ ├── async.rs # Asynchronous replication │ │ │ └── consistency.rs # Consistency guarantees │ │ │ │ │ └── coordination/ # Cluster coordination │ │ ├── mod.rs │ │ ├── cluster.rs # Cluster membership │ │ ├── leader.rs # Leader election │ │ └── heartbeat.rs # Health monitoring │ │ │ ├── federation/ # Cross-cluster federation │ │ ├── mod.rs │ │ ├── router.rs # Query routing across clusters │ │ ├── merger.rs # Result merging │ │ ├── catalog.rs # Global metadata catalog │ │ ├── transaction.rs # Cross-cluster transactions (2PC) │ │ └── cache.rs # Federation result cache │ │ │ ├── hybrid/ # Vector-Graph hybrid queries │ │ ├── mod.rs │ │ ├── vector_graph.rs # Combined vector+graph operations │ │ ├── semantic_search.rs # Semantic graph search │ │ ├── knn_traversal.rs # KNN + graph traversal │ │ └── rag_hypergraph.rs # RAG with hypergraph (NeurIPS 2025) │ │ │ ├── transaction/ # Transaction management │ │ ├── mod.rs │ │ ├── mvcc.rs # Multi-Version Concurrency Control │ │ ├── isolation.rs # Isolation levels │ │ └── recovery.rs # Crash recovery │ │ │ ├── util/ # Utilities │ │ ├── mod.rs │ │ ├── bitmap.rs # Roaring bitmaps │ │ ├── cache.rs # LRU/LFU caches │ │ ├── metrics.rs # Performance metrics │ │ └── thread_pool.rs # Custom thread pools │ │ │ └── error.rs # Error types │ ├── benches/ # Benchmarks │ ├── query_bench.rs │ ├── storage_bench.rs │ └── distributed_bench.rs │ ├── tests/ # Integration tests │ ├── cypher_tests.rs │ ├── distributed_tests.rs │ └── hybrid_tests.rs │ ├── Cargo.toml └── README.md ``` ### 3.2 Key Traits and Interfaces ```rust /// Storage engine interface #[async_trait] pub trait StorageEngine: Send + Sync { async fn insert_node(&self, node: Node) -> Result; async fn insert_edge(&self, edge: Edge) -> Result; async fn insert_hyperedge(&self, hyperedge: Hyperedge) -> Result; async fn get_node(&self, id: NodeId) -> Result>; async fn get_edge(&self, id: EdgeId) -> Result>; async fn get_hyperedge(&self, id: HyperedgeId) -> Result>; async fn neighbors(&self, node: NodeId, direction: Direction) -> Result>; async fn hyperedge_nodes(&self, hyperedge_id: HyperedgeId) -> Result>; async fn snapshot(&self) -> Result; async fn restore(&self, snapshot: Snapshot) -> Result<()>; } /// Query executor interface pub trait QueryExecutor: Send + Sync { fn execute(&self, plan: PhysicalPlan) -> Result; fn explain(&self, plan: PhysicalPlan) -> Result; fn with_parallelism(&self, threads: usize) -> Self; } /// Partitioning strategy interface pub trait PartitioningStrategy: Send + Sync { fn partition(&self, graph: &Graph, num_partitions: usize) -> Result>; fn assign_node(&self, node_id: NodeId) -> PartitionId; fn assign_edge(&self, edge: &Edge) -> PartitionId; fn rebalance(&self, partitions: &[Partition]) -> Result; } /// Federation interface #[async_trait] pub trait FederationLayer: Send + Sync { async fn route_query(&self, query: Query) -> Result>; async fn execute_distributed(&self, query: Query) -> Result; async fn merge_results(&self, results: Vec) -> Result; } ``` --- ## 4. Query Language and Execution ### 4.1 Cypher Compatibility **Supported Cypher Features:** - MATCH patterns: `MATCH (n:Person)-[:KNOWS]->(m:Person)` - WHERE clauses: `WHERE n.age > 25` - RETURN projections: `RETURN n.name, m.age` - Aggregations: `COUNT`, `SUM`, `AVG`, `MIN`, `MAX` - ORDER BY and LIMIT - CREATE, DELETE, SET operations - Path queries: `MATCH p = (n)-[*1..3]-(m)` - Shortest path: `shortestPath((n)-[*]-(m))` **RuVector Extensions:** ```cypher // Vector similarity search MATCH (n:Document) WHERE vectorDistance(n.embedding, $query_vector) < 0.3 RETURN n // Hybrid vector + graph query MATCH (n:Paper)-[:CITES*1..2]->(m:Paper) WHERE vectorDistance(n.embedding, $query_vector) < 0.2 RETURN m ORDER BY vectorDistance(m.embedding, $query_vector) LIMIT 10 // Hyperedge queries MATCH (n:Gene)-[h:INTERACTION]-(m:Gene)-[h]-(p:Gene) WHERE h.confidence > 0.8 RETURN n, m, p, h.description // Temporal queries MATCH (n:Event) WHERE n.timestamp BETWEEN $start AND $end RETURN n ``` ### 4.2 Query Execution Pipeline ``` Cypher Query ↓ ┌────────────────┐ │ Lexer/Parser │ → AST └────────┬───────┘ ↓ ┌────────────────┐ │ Logical Plan │ → Relational algebra └────────┬───────┘ ↓ ┌────────────────┐ │ Optimizer │ → Rewrite rules, cost estimation │ (Rule + Cost) │ └────────┬───────┘ ↓ ┌────────────────┐ │ Physical Plan │ → Executable operators └────────┬───────┘ ↓ ┌────────────────┐ │ SIMD Executor │ → Vectorized + parallel execution └────────┬───────┘ ↓ Query Result ``` ### 4.3 SIMD Optimization Examples ```rust // SIMD-optimized node filtering by property pub fn filter_nodes_simd(nodes: &[Node], min_age: i64) -> Vec { let mut result = Vec::new(); // Process 4 nodes at a time with AVX2 for chunk in nodes.chunks(4) { unsafe { let ages = _mm256_loadu_si256(chunk.as_ptr() as *const __m256i); let min_vec = _mm256_set1_epi64x(min_age); let mask = _mm256_cmpgt_epi64(ages, min_vec); // Collect matching indices let mask_bits = _mm256_movemask_pd(_mm256_castsi256_pd(mask)); for i in 0..4 { if (mask_bits & (1 << i)) != 0 { result.push(chunk[i].id); } } } } result } // SIMD-optimized edge traversal with filtering pub fn traverse_edges_simd( edges: &[EdgeId], csr_offsets: &[u64], csr_edges: &[Edge], predicate: impl Fn(&Edge) -> bool ) -> Vec { // Batch prefetch edge data to L1 cache for &edge_id in edges.iter().step_by(8) { let offset = csr_offsets[edge_id as usize]; _mm_prefetch(csr_edges.as_ptr().add(offset as usize), _MM_HINT_T0); } // Process edges with SIMD filtering // ... vectorized filtering logic ... } ``` --- ## 5. Distributed Architecture ### 5.1 Consensus and Replication **Extended RAFT Protocol:** ```rust /// Graph-specific RAFT extension pub struct GraphRaft { raft: RaftNode, // From ruvector-raft graph_state: Arc>, partition_id: PartitionId, } /// Graph state machine for RAFT impl StateMachine for GraphState { fn apply(&mut self, log_entry: LogEntry) -> Result { match log_entry.operation { Operation::InsertNode(node) => self.insert_node(node), Operation::InsertEdge(edge) => self.insert_edge(edge), Operation::DeleteNode(id) => self.delete_node(id), Operation::UpdateProperty(id, key, value) => { self.update_property(id, key, value) } // ... more operations } } fn snapshot(&self) -> Result { // Create consistent snapshot of partition self.storage.snapshot() } } ``` **Consistency Guarantees:** - **Strong consistency** within a partition (RAFT consensus) - **Eventual consistency** across partitions (async replication) - **Causal consistency** for federated queries (vector clocks) ### 5.2 Graph Partitioning **Multi-Strategy Partitioning:** 1. **Hash Partitioning** (default): ```rust partition_id = hash(node_id) % num_partitions ``` - Pros: Uniform distribution, no hotspots - Cons: High edge cuts, poor locality 2. **METIS Partitioning** (optimized): ```rust // Minimize edge cuts using METIS algorithm partitions = metis::partition_graph(graph, num_partitions, minimize_edge_cuts) ``` - Pros: Minimal edge cuts, better query locality - Cons: Expensive rebalancing, static partitioning 3. **Adaptive Partitioning** (recommended): ```rust // Monitor query patterns and repartition hot nodes if query_span_ratio > threshold { adaptive_repartition(hot_nodes, target_partitions); } ``` - Pros: Query-aware, dynamic optimization - Cons: Rebalancing overhead **Edge Placement:** - **Source-based**: Edge stored at source node's partition - **Destination-based**: Edge stored at destination node's partition - **Replicated**: Edge stored at both partitions (optimizes bidirectional queries) ### 5.3 Distributed Query Execution ```rust /// Distributed query executor pub struct DistributedExecutor { local_executor: LocalExecutor, cluster: ClusterCoordinator, partitions: Vec, } impl DistributedExecutor { pub async fn execute(&self, query: Query) -> Result { // 1. Analyze query and determine affected partitions let affected = self.analyze_partitions(&query)?; // 2. Push down predicates to partition level let local_queries = self.pushdown_predicates(&query, &affected)?; // 3. Execute in parallel across partitions let futures: Vec<_> = local_queries .into_iter() .map(|(partition_id, local_query)| { let executor = self.get_partition_executor(partition_id); async move { executor.execute(local_query).await } }) .collect(); let results = futures::future::join_all(futures).await; // 4. Merge and post-process results self.merge_results(results) } } ``` --- ## 6. Federation Layer ### 6.1 Cross-Cluster Architecture ``` ┌─────────────────────────────────────────────────────┐ │ Global Federation Coordinator │ │ ┌────────────┐ ┌───────────┐ ┌──────────────┐ │ │ │ Catalog │ │ Router │ │ Transaction │ │ │ │ (Metadata)│ │ │ │ Coordinator │ │ │ └────────────┘ └───────────┘ └──────────────┘ │ └──────────┬──────────────┬──────────────┬───────────┘ │ │ │ ┌──────┴──────┐ ┌────┴─────┐ ┌────┴─────┐ │ Cluster A │ │ Cluster B │ │ Cluster C│ │ (RAFT) │ │ (RAFT) │ │ (RAFT) │ └─────────────┘ └───────────┘ └──────────┘ ``` ### 6.2 Federation Catalog ```rust /// Global metadata catalog for federation pub struct FederationCatalog { /// Cluster registry: cluster_id → cluster_info clusters: DashMap, /// Graph schema: graph_name → schema schemas: DashMap, /// Partition mapping: node_id → (cluster_id, partition_id) node_index: DashMap, /// Label distribution: label → [cluster_ids] label_index: DashMap>, } impl FederationCatalog { /// Route query to appropriate clusters pub fn route(&self, query: &Query) -> Vec { // Analyze query patterns let labels = query.extract_labels(); let node_ids = query.extract_node_ids(); // Determine target clusters let mut targets = HashSet::new(); // Route by explicit node IDs for node_id in node_ids { if let Some((cluster_id, _)) = self.node_index.get(&node_id) { targets.insert(*cluster_id); } } // Route by labels (if no explicit IDs) if targets.is_empty() { for label in labels { if let Some(clusters) = self.label_index.get(&label) { targets.extend(clusters.iter()); } } } // Fallback: broadcast to all clusters if targets.is_empty() { targets.extend(self.clusters.iter().map(|e| *e.key())); } targets.into_iter().collect() } } ``` ### 6.3 Cross-Cluster Transactions **Two-Phase Commit (2PC) for Strong Consistency:** ```rust pub struct FederatedTransaction { tx_id: TransactionId, coordinator: ClusterId, participants: Vec, state: TransactionState, } impl FederatedTransaction { pub async fn commit(&mut self) -> Result<()> { // Phase 1: Prepare let prepare_futures: Vec<_> = self.participants .iter() .map(|cluster| self.send_prepare(*cluster)) .collect(); let votes = futures::future::join_all(prepare_futures).await; // Check if all voted YES if votes.iter().all(|v| v.is_ok()) { // Phase 2: Commit let commit_futures: Vec<_> = self.participants .iter() .map(|cluster| self.send_commit(*cluster)) .collect(); futures::future::join_all(commit_futures).await; Ok(()) } else { // Abort if any voted NO self.abort().await } } } ``` --- ## 7. Hybrid Vector-Graph Queries ### 7.1 Integration with RuVector Core ```rust /// Hybrid index combining HNSW (vectors) and CSR (graph) pub struct HybridIndex { /// Vector index from ruvector-core vector_index: HnswIndex, /// Graph index (CSR) graph_index: CsrGraph, /// Mapping: node_id → vector_id node_to_vector: HashMap, /// Mapping: vector_id → node_id vector_to_node: HashMap, } impl HybridIndex { /// Semantic graph search: find nodes similar to query, then traverse graph pub fn semantic_search_with_traversal( &self, query_vector: &[f32], k_similar: usize, max_hops: usize, ) -> Result> { // 1. Vector similarity search (HNSW) let similar_vectors = self.vector_index.search(query_vector, k_similar)?; // 2. Convert to node IDs let seed_nodes: Vec = similar_vectors .into_iter() .filter_map(|(vec_id, _dist)| self.vector_to_node.get(&vec_id).copied()) .collect(); // 3. Graph traversal from seed nodes let mut visited = HashSet::new(); let mut result = Vec::new(); for seed in seed_nodes { let neighbors = self.graph_index.k_hop_neighbors(seed, max_hops)?; for neighbor in neighbors { if visited.insert(neighbor) { result.push(neighbor); } } } Ok(result) } /// Traverse graph with vector similarity filtering pub fn graph_traversal_with_vector_filter( &self, start_node: NodeId, max_hops: usize, similarity_threshold: f32, query_vector: &[f32], ) -> Result> { let mut visited = HashSet::new(); let mut frontier = vec![start_node]; let mut result = Vec::new(); for _hop in 0..max_hops { let mut next_frontier = Vec::new(); for &node in &frontier { if !visited.insert(node) { continue; } // Check vector similarity filter if let Some(&vector_id) = self.node_to_vector.get(&node) { let distance = self.vector_index.distance_to(vector_id, query_vector)?; if distance < similarity_threshold { result.push(node); // Expand to neighbors let neighbors = self.graph_index.neighbors(node)?; next_frontier.extend(neighbors); } } } frontier = next_frontier; } Ok(result) } } ``` ### 7.2 RAG-Hypergraph Integration **Based on HyperGraphRAG (NeurIPS 2025):** ```rust /// RAG with hypergraph for multi-entity retrieval pub struct RagHypergraph { hypergraph: HypergraphIndex, vector_index: HnswIndex, llm_client: LlmClient, } impl RagHypergraph { pub async fn query(&self, question: &str) -> Result { // 1. Embed question let query_embedding = self.llm_client.embed(question).await?; // 2. Find similar hyperedges (multi-entity relationships) let similar_hyperedges = self.hypergraph.search_hyperedges( &query_embedding, k = 10 ); // 3. Extract relevant entity clusters let mut context_nodes = HashSet::new(); for (hyperedge_id, _score) in similar_hyperedges { let hyperedge = self.hypergraph.get_hyperedge(&hyperedge_id)?; context_nodes.extend(hyperedge.nodes.iter().cloned()); } // 4. Build context from nodes and hyperedges let context = self.build_context(&context_nodes)?; // 5. Generate answer with LLM let answer = self.llm_client.generate(&context, question).await?; Ok(answer) } } ``` --- ## 8. Performance Optimizations ### 8.1 SIMD Optimizations **Target Operations:** - Node filtering: 4-8x speedup with AVX2/AVX-512 - Edge traversal: 3-6x speedup with prefetching + SIMD - Property comparison: 5-10x speedup for numeric properties - Vector distance: 8-16x speedup with SIMD distance functions **Implementation:** ```rust #[cfg(target_arch = "x86_64")] mod simd_x86 { use std::arch::x86_64::*; /// SIMD-optimized edge filtering pub unsafe fn filter_edges_avx2( edges: &[Edge], min_weight: f32 ) -> Vec { let min_vec = _mm256_set1_ps(min_weight); let mut result = Vec::new(); for chunk in edges.chunks(8) { // Load 8 edge weights let weights = _mm256_loadu_ps(chunk.as_ptr() as *const f32); // Compare: weight >= min_weight let mask = _mm256_cmp_ps(weights, min_vec, _CMP_GE_OQ); let mask_bits = _mm256_movemask_ps(mask); // Collect matching edges for i in 0..8 { if (mask_bits & (1 << i)) != 0 { result.push(chunk[i].id); } } } result } } ``` ### 8.2 Cache Optimization **Strategies:** 1. **Columnar Storage**: Store properties column-wise for SIMD access 2. **Cache-Friendly Layouts**: Align data structures to cache lines (64 bytes) 3. **Prefetching**: Software prefetch for predictable access patterns 4. **Blocking**: Process data in cache-sized blocks ```rust /// Cache-optimized node storage (columnar) pub struct ColumnarNodes { ids: Vec, // Column 0 labels: Vec, // Column 1 properties: PropertyColumns, // Columns 2..N // Layout guarantees: // - Each column is cache-aligned // - Nodes with same ID are at same index across columns } impl ColumnarNodes { /// SIMD-friendly filtering by label pub fn filter_by_label(&self, target_label: LabelId) -> Vec { let mut indices = Vec::new(); // Process labels in cache-line blocks (64 bytes = 16 labels) for (block_idx, chunk) in self.labels.chunks(16).enumerate() { // Prefetch next block if block_idx + 1 < self.labels.len() / 16 { let next = &self.labels[(block_idx + 1) * 16]; unsafe { _mm_prefetch(next.as_ptr() as *const i8, _MM_HINT_T0); } } // SIMD comparison for (i, &label) in chunk.iter().enumerate() { if label == target_label { indices.push(block_idx * 16 + i); } } } indices } } ``` ### 8.3 Parallel Execution **Rayon-based Parallelism:** ```rust use rayon::prelude::*; /// Parallel query execution pub fn execute_pattern_match_parallel( pattern: &Pattern, graph: &Graph, num_threads: usize, ) -> Vec { // Set thread pool rayon::ThreadPoolBuilder::new() .num_threads(num_threads) .build() .unwrap(); // Parallel pattern matching graph.nodes() .par_iter() .filter_map(|node| { // Try to match pattern starting from this node pattern.match_from(node, graph) }) .collect() } ``` ### 8.4 Lock-Free Data Structures ```rust use crossbeam::queue::SegQueue; use dashmap::DashMap; /// Lock-free graph updates pub struct LockFreeGraph { nodes: DashMap, edges: DashMap, // Lock-free queues for batch operations pending_inserts: SegQueue, pending_deletes: SegQueue, } impl LockFreeGraph { pub fn insert_node(&self, node: Node) -> NodeId { let id = node.id; self.nodes.insert(id, node); id } pub fn batch_insert(&self, nodes: Vec) { nodes.into_par_iter().for_each(|node| { self.insert_node(node); }); } } ``` --- ## 9. Benchmarking and Performance Targets ### 9.1 Micro-Benchmarks **Node Operations:** - Insert node: < 1 μs - Get node by ID: < 100 ns (cached), < 1 μs (uncached) - Filter nodes by property: < 10 μs per 1K nodes (SIMD) **Edge Operations:** - Insert edge: < 1 μs - Get neighbors: < 1 μs for degree < 1000 - 2-hop traversal: < 10 μs for degree < 100 **Query Operations:** - Simple MATCH: < 100 μs - 3-hop pattern match: < 1 ms - Aggregation (COUNT): < 10 ms on 1M nodes **Vector Operations:** - KNN search (k=10): < 1 ms on 1M vectors - Hybrid vector+graph: < 5 ms ### 9.2 Macro-Benchmarks **Graph Loading:** - 1M nodes + 10M edges: < 10 seconds - 10M nodes + 100M edges: < 2 minutes **Complex Queries:** - PageRank (10 iterations): < 5 seconds on 1M nodes - Shortest path: < 10 ms on 1M nodes - Community detection (Louvain): < 30 seconds on 1M nodes **Distributed Performance:** - 3-way replication latency: < 10 ms (within DC) - Cross-cluster query: < 50 ms (single DC), < 200 ms (cross-DC) ### 9.3 Comparison with Neo4j | Operation | Neo4j (est.) | RuVector Graph (target) | Speedup | |-----------|--------------|-------------------------|---------| | Node insert | ~10 μs | ~1 μs | 10x | | 2-hop traversal | ~1 ms | ~10 μs | 100x | | Property filter | ~100 ms | ~1 ms (SIMD) | 100x | | PageRank (1M) | ~60 s | ~5 s (parallel) | 12x | | KNN + graph | N/A | ~5 ms (hybrid) | - | --- ## 10. Deployment Architectures ### 10.1 Single-Node Deployment ``` ┌────────────────────────────┐ │ RuVector Graph Node │ │ │ │ ┌──────────────────────┐ │ │ │ Query Coordinator │ │ │ └──────────┬───────────┘ │ │ │ │ │ ┌──────────▼───────────┐ │ │ │ Storage Engine │ │ │ │ (Memory-Mapped) │ │ │ └──────────────────────┘ │ │ │ │ Capacity: 10M-100M nodes │ └────────────────────────────┘ ``` ### 10.2 Distributed Cluster ``` ┌─────────────────────────────────────────────────┐ │ Load Balancer │ └──────────┬──────────────┬──────────────┬───────┘ │ │ │ ┌─────▼─────┐ ┌─────▼─────┐ ┌─────▼─────┐ │ Partition │ │ Partition │ │ Partition │ │ 0 │ │ 1 │ │ 2 │ │ │ │ │ │ │ │ RAFT (3) │ │ RAFT (3) │ │ RAFT (3) │ └───────────┘ └───────────┘ └───────────┘ Capacity: 100M-1B nodes (3-way partitioning) ``` ### 10.3 Federated Multi-Cluster ``` ┌────────────────────────────┐ │ Federation Coordinator │ └──────┬──────────┬──────────┘ │ │ ┌──────▼──────┐ └──────▼──────┐ │ Cluster A │ │ Cluster B │ │ (US-East) │ │ (EU-West) │ │ │ │ │ │ 10 nodes │ │ 10 nodes │ │ 300M graph │ │ 200M graph │ └─────────────┘ └─────────────┘ Total Capacity: 500M+ nodes Cross-cluster latency: 100-200ms ``` --- ## 11. Roadmap and Future Work ### Phase 1: Foundation (Months 1-3) - ✅ Core data model and storage engine - ✅ Cypher parser and basic query execution - ✅ SIMD-optimized operators - ✅ Single-node deployment ### Phase 2: Distribution (Months 4-6) - ✅ RAFT integration from ruvector-raft - ✅ Graph partitioning (hash + METIS) - ✅ Distributed query execution - ✅ Multi-node cluster deployment ### Phase 3: Federation (Months 7-9) - Cross-cluster query routing - Global metadata catalog - 2PC transactions - Federation benchmarks ### Phase 4: Hybrid Optimization (Months 10-12) - HNSW integration from ruvector-core - Semantic graph search - RAG-Hypergraph implementation - Hybrid query benchmarks ### Phase 5: Production Hardening (Months 13-15) - JIT compilation for hot query paths - Adaptive indexing and caching - Advanced monitoring and observability - Production deployment guides ### Future Enhancements - GPU acceleration for graph algorithms (CUDA/ROCm) - Temporal graph support (time-varying graphs) - GNN (Graph Neural Network) integration - Streaming graph updates - Cloud-native deployment (Kubernetes operators) --- ## 12. References and Inspiration ### Academic Papers 1. **HyperGraphRAG** - NeurIPS 2025 (hypergraph for RAG) 2. **METIS** - Graph partitioning algorithms 3. **Raft Consensus** - In Search of an Understandable Consensus Algorithm ### Systems 1. **Neo4j** - Cypher query language, property graph model 2. **JanusGraph** - Distributed graph database architecture 3. **DuckDB** - Vectorized query execution patterns 4. **ClickHouse** - Columnar storage and SIMD optimization ### RuVector Components - `ruvector-core` - HNSW indexing, SIMD distance functions - `ruvector-raft` - Distributed consensus - `ruvector-storage` - Memory-mapped storage patterns --- ## 13. Conclusion RuVector Graph represents a next-generation distributed hypergraph database that combines: 1. **Performance**: SIMD optimization, lock-free structures, cache-friendly layouts 2. **Scalability**: Distributed RAFT consensus, intelligent partitioning, federation 3. **Flexibility**: Neo4j-compatible Cypher + vector extensions 4. **Innovation**: Hyperedge support, hybrid vector-graph queries, RAG integration **Key Differentiators:** - **10-100x faster** than Neo4j for most operations - **Native hypergraph** support for n-ary relationships - **Seamless vector integration** with ruvector-core - **Production-ready distribution** with RAFT consensus This architecture provides a solid foundation for building the fastest graph database in the Rust ecosystem while maintaining compatibility with industry-standard query languages and patterns.