Hybrid Relational-Vector Systems

• Hybrid relational-vector systems are unified platforms that integrate precise SQL queries with semantic, high-dimensional vector searches.
• They employ specialized indexing techniques like B⁺-trees and HNSW to manage and rapidly retrieve both structured attributes and unstructured embeddings.
• Optimized query operators fuse relational and semantic searches through adaptive cost-based planning, enhancing recall and performance in complex analytics.

Hybrid relational-vector/unstructured systems are data management platforms that provide unified storage, indexing, and query processing capabilities across both structured (relational) and unstructured (embedding/vector) data. They enable the integration of precise, attribute-based querying from classical RDBMS with fuzzy, semantic retrieval over high-dimensional embeddings derived from text, images, or other unstructured sources. The rise of large-scale unstructured data modalities in analytical, retrieval-augmented generation (RAG), and multimodal reasoning tasks has driven the development of a spectrum of architectures, ranging from extended SQL engines with embedded vector operations to native multimodal graph-index platforms and hybrid query processing frameworks. This entry surveys architectures, core operators, optimization theory, representative algorithms, evaluation results, and system integration found in current-generation hybrid relational-vector/unstructured systems.

1. Unified System Architectures and Data Models

Hybrid systems are engineered to support seamless interoperation between attribute-centric and embedding-centric operators in a singular query plan. Typical architectures include:

• Two-engine “glued” stacks: Systems such as SSQL employ a conventional SQL relational database for structured data and a vector index (e.g., FAISS) for dense embeddings, integrating them via a parser, optimizer, and result reconciler. The architecture enables queries containing both Boolean predicates and semantic/embedding-based clauses, with explicit language extensions in SQL for expressing semantic intent (Mittal et al., 5 Apr 2024).
• Native property-graph with vectors: The Hybrid Multimodal Graph Index (HMGI) and TigerVector embed high-dimensional vector attributes directly as node properties (or attributes) in a property-graph framework (e.g., Neo4j, TigerGraph), with modality labels and relationships. Embeddings are indexed per modality and partition for efficient retrieval, and graph traversals can be combined with vector search (Chandra et al., 11 Oct 2025, Liu et al., 20 Jan 2025).
• Composite Proximity Graphs: NHQ and HQANN eliminate the decomposition paradigm by constructing a single proximity (or navigable small-world) graph jointly over feature vectors and relational attributes, allowing hybrid queries to be processed as prioritized graph walks guided by fusion metrics (Wang et al., 2022, Wu et al., 2022).
• RAG-enabled multimodal architectures: Frameworks like MAHA construct a knowledge graph over multimodal "chunks," each chunk both embedded in a vector space and linked by type-specific or cross-modal edges, enabling queries that mix dense retrieval, graph traversal, and cross-modal reasoning (R et al., 16 Oct 2025).
• SQL Engine Extensions and Unified Dialects: Cognitive Database and Text2VectorSQL introduce learned vector-space models and cognitive UDFs alongside traditional relational plans, bridging natural language, structured SQL, and embedding-based retrieval natively in SQL. BlendSQL proposes a superset SQL dialect with embedded LLM invocations for unstructured retrieval, offering a fully interpretable query algebra (Bordawekar et al., 2017, Wang et al., 29 Jun 2025, Glenn et al., 27 Feb 2024).

2. Indexing, Storage Techniques, and Data Ingestion

Hybrid systems employ integrated indexing that enables both rapid attribute and vector-based access:

• Vector Indexes: HNSW (Hierarchical Navigable Small World), IVF-PQ (Inverted File Product Quantization), ANNS (Approximate Nearest Neighbor Structures), and partitioned HNSW per modality/cluster enable sublinear retrieval times for high-dimensional vectors (Pan et al., 2023, Chandra et al., 11 Oct 2025, Liu et al., 20 Jan 2025).
• Attribute Indexes: Traditional B⁺-trees, bitmaps, and hash indices are retained on structured columns for efficient range/query support (Ye et al., 31 Oct 2025).
• Composite/Fused Indexes: NHQ and HQANN construct graphs where both attribute and vector similarity are considered in edge formation and traversal, obviating separate filtering and merging phases (Wang et al., 2022, Wu et al., 2022).
• Decoupled Segmentation: TigerVector maintains one or more “embedding segments” per data segment; embeddings are stored off main vertex files and indexed separately, enabling asynchronous MVCC updates, fine-grained concurrency, and efficient distributed indexing and querying (Liu et al., 20 Jan 2025).
• Storage Quantization: Flash/PQ/Scalar Quantization compresses vector storage for memory and I/O savings, sometimes by up to 50%, with little loss in recall (Chandra et al., 11 Oct 2025, Pan et al., 2023).
• Incremental/MVCC Updates: Delta logs and asynchronous background index merging provide real-time freshness and minimize the cost of insertions and deletions in large datasets (Chandra et al., 11 Oct 2025, Liu et al., 20 Jan 2025).

3. Hybrid Query Operators, Optimization, and Execution

Key differentiator in hybrid systems is the co-existence and cost-based optimization of structured and semantic/embedding query operators:

• Extended Relational Algebra: Systems define hybrid join operators (e.g., E-join) for thresholded similarity between embeddings, supporting algebraic equivalences with selection, projection, and join, and enabling logical optimization such as selection and projection pushdown, and operator reordering (Sanca et al., 2023).
• Fusion Metrics: During query execution, a “fusion distance” is used: $$I'(e_1,e_2) = w_v \cdot \delta(\mathbf{v}_1,\mathbf{v}_2) + w_e \cdot \chi(\mathbf{l}_1,\mathbf{l}_2)$$, integrating vector and attribute similarity for single-phase search and pruning (Wang et al., 2022, Wu et al., 2022).
• Cost-based Optimization: Plans are chosen based on the estimated cost and selectivity of relational and semantic predicates. Classical decision points include whether to apply structured predicates (SQL filter) before vector search, or the converse. Optimizers incorporate cost models such as:

$$\textrm{Cost}(P) = \textrm{Cost}_\textrm{rel}(R) + \textrm{Sel}_\textrm{rel}(R) \cdot \textrm{Cost}_\textrm{vec}(V)$$

for relational-first, and similar for vector-first (Mittal et al., 5 Apr 2024, Sanca et al., 23 Mar 2024).

• Adaptive Query Planning: CHASE implements semantic query pattern recognition and logical plan rewriting to optimize hybrid queries, including pattern-specific vector top-K, range, and join operators, and code fusion strategies. Its optimizer dynamically selects best plans based on selectivity, operator costs, and join order (Ma et al., 9 Jan 2025).
• Human-in-the-loop and Threshold Tuning: SSQL employs interactive binary-search over similarity thresholds, allowing precise trade-offs in recall/precision by collecting user feedback during query execution (Mittal et al., 5 Apr 2024).
• Hybrid RAG Pipelines: In RAG architectures, hybrid retrievers (e.g., MAHA, HybGRAG) couple vector-based and graph-based retrieval with agentic refinement and LLM-based self-critique or interpretability modules, supporting multi-hop queries and dynamic adjustment of retrieval strategies (R et al., 16 Oct 2025, Lee et al., 20 Dec 2024).

4. Practical Algorithms, System Integration, and Query Languages

• SQL and Graph Query Extensions: Systems extend SQL with dedicated vector or semantic predicates (e.g., SEMANTIC, VECTOR_SEARCH, VECTOR_DIST), integrating these expressions as first-class citizens in query blocks and query execution (Mittal et al., 5 Apr 2024, Wang et al., 29 Jun 2025, Liu et al., 20 Jan 2025).
• LLM and Multimodal Extensions: BlendSQL and Text2VectorSQL show integration of LLM-based UDFs and embedding functions as algebraic query ingredients. MAHA and HybGRAG orchestrate modular hybrid retriever banks with LLM routers and critics for context-rich or multimodal data (Glenn et al., 27 Feb 2024, Wang et al., 29 Jun 2025, R et al., 16 Oct 2025, Lee et al., 20 Dec 2024).
• Operator Fusion and In-Engine Optimization: CHASE and TigerVector fuse ANN search, mapping, sorting, and early termination into a single execution kernel, reducing intermediate materialization and exploiting hardware-level optimization such as loop unrolling, SIMD vectorization, and GPU offload for dense kernels and batching (Liu et al., 20 Jan 2025, Ma et al., 9 Jan 2025, Sanca et al., 23 Mar 2024).
• Multi-modal Reasoning and Hybrid QA: MAHA uses modality-aware knowledge graphs with both vector-based scoring and weighted path-traversal for QA over documents containing text, images, tables, and more, achieving strong modality coverage and interpretability (R et al., 16 Oct 2025).

5. Empirical Results, Benchmarks, and System Comparisons

Empirical studies report consistency across large-scale benchmarks, summarized as:

System	Throughput / QPS	Hybrid Query Speedup	Recall (approx.)	Unique Strengths
TigerVector	1079 QPS (100M)	5x vs Neo4j	90–99%	Segmental embedding, distributed HNSW
HMGI	4000 QPS (1B)	2–3x vs Milvus	20–30% higher recall	Modality partitioning, joint ANNS+traversal
NHQ/HQANN	10x vs Milvus	10x+ vs Vearch	≥99% @10	Single-index fusion, unified pruning
CHASE	13%–7500x vs baselines	Substantially faster	Up to 0.98	JIT codegen, full operator fusion
Compass	2–10x over NaviX	Matches SeRF in 1D/2D	Robust across filters	Generality, standard index integration
MAHA	ROUGE-L 0.486	+72% vs vector-only	MRR 0.74	Hybrid retrieval, modality graph, QA
HybGRAG	Hit@1 0.654	+51% over baselines		Agentic, interpretable refinement

Notably, performance, recall, and coverage are maintained or enhanced compared to independently optimized engines for structured and unstructured workloads (Liu et al., 20 Jan 2025, Chandra et al., 11 Oct 2025, Wang et al., 2022, Lee et al., 20 Dec 2024, Ye et al., 31 Oct 2025, R et al., 16 Oct 2025).

6. System Design Trade-offs and Limitations

Hybrid relational-vector/unstructured systems wrestle with:

• Fusion Overhead vs. Specialization: Unified execution can introduce minor overheads compared to pure vector search, particularly for ultra-low-latency use cases, unless further optimized via GPU/ASIC (Chandra et al., 11 Oct 2025).
• Selectivity-Driven Plan Choice: Scan-based access paths outperform ANNS at high selectivity due to linear overheads, while ANNS dominate at low selectivity or high dimension; adaptive path selection based on cost models is essential (Sanca et al., 23 Mar 2024, Pan et al., 2023).
• Index Maintenance and Updates: Supporting fine-grained, low-latency delta updates requires in-memory delta stores, batch merging, and robust MVCC mechanisms (Chandra et al., 11 Oct 2025, Liu et al., 20 Jan 2025).
• Model/Embedding Dependence: Embeddings are tightly coupled to specific encoders; evolution in input modalities or neural architectures may necessitate significant retraining or re-indexing (Chandra et al., 11 Oct 2025).
• Complex Query Semantics: Rich query expressiveness (arbitrary Boolean combinations with vector search, multi-modal retrieval, and context-enhanced joins) is supported but increases optimizer and engine complexity (Ye et al., 31 Oct 2025, Glenn et al., 27 Feb 2024, Sanca et al., 2023).
• Resource Utilization: Unified systems require higher baseline resource footprints than specialized vector or relational-only engines due to dual indexing and concurrent execution (Chandra et al., 11 Oct 2025).

7. Research Challenges and Directions

Key challenges for the next generation of hybrid systems include:

• End-to-end Learned Indexing: Jointly optimizing hybrid ANNS and relational/graph partitions; learned partitioners and adaptive index auto-tuning (Chandra et al., 11 Oct 2025).
• Deeper Hardware Integration: Direct support for graph traversal and vector operations on unified accelerators (GPU/ASIC co-execution) (Sanca et al., 23 Mar 2024, Chandra et al., 11 Oct 2025).
• AutoML-Driven Query Planning: Dynamic selection of plans, fusion weights, and multi-modal retrieval chains based on empirical model and data characteristics (Mittal et al., 5 Apr 2024, R et al., 16 Oct 2025).
• Native Lifecycle Operations: Real-time, high-throughput support for updates, deletions, and external model integration with minimal downtime (Chandra et al., 11 Oct 2025, Liu et al., 20 Jan 2025).
• Multimodal Semantics and Interoperability: Support for extensible modality graphs, LLM-powered interpretation of untyped or unknown modalities, and deeper integration with RAG and generative models (R et al., 16 Oct 2025, Lee et al., 20 Dec 2024).

Hybrid relational-vector/unstructured systems represent an overview of established relational principles and modern vector/multimodal data processing. Their design and optimization underlie emerging enterprise, scientific, and AI-driven analytics, providing both the expressiveness of semantic retrieval over unstructured data and the formal guarantees and efficiency of classical database systems.