Review: Vector Search + SQL — Combining Semantic Retrieval with Relational Queries

Semantic search powered by vector embeddings has unlocked new ways to query unstructured data. But teams often need both semantic retrieval and relational joins. This review examines prominent approaches for integrating vector search into SQL-based workflows and assesses trade-offs in latency, complexity, and accuracy.

Why Combine Vector Search with SQL?

Relational systems are great for structured joins, aggregations, and transactional logic. Vector stores excel at retrieving semantically similar documents or embeddings. Combining them lets you run a semantic match to get candidate keys and then execute relational joins and aggregates in the same or connected systems.

Common Integration Patterns

1. LRU (Local Retrieval + Join): Run a vector search in a specialized service to get top-N IDs, then query the relational store for details.
2. Embedded Vectors in Warehouse: Store embeddings in the data warehouse and use approximate nearest neighbor (ANN) functions (if supported) to directly run kNN queries.
3. Hybrid Query Engines: Use engines that natively support both vector indices and SQL (emerging solutions and vendors).

What We Tested

We evaluated three setups using a dataset of 2M customer support articles:

• Vector store (FAISS-based service) + Postgres for joins.
• Warehouse with ANN extension (embedding stored in BigQuery-like engine with UDF-based ANN).
• Hybrid cloud vendor solution offering native vector + SQL support.

Metrics

We measured:

• End-to-end latency for a typical semantic + join query.
• Throughput under concurrent loads.
• Developer effort for integration.
• Result quality (precision@K) for retrieval.

Findings

• Latency: The vector-store + relational join pattern often had the lowest median latency when the vector search returned a small candidate set. Network hops increased tail latency for higher concurrency.
• Throughput: Standalone vector services scale well; however, the relational join becomes the bottleneck unless you prefetch or denormalize.
• Developer effort: Embedding vectors into the warehouse simplifies the stack but requires ANN support or custom UDFs; hybrid solutions reduce integration work but are more vendor-bound.
• Quality: All approaches achieved similar retrieval quality when embeddings and index parameters were tuned.

Pros and Cons by Approach

Vector Store + Relational Join

Pros: Best modularity, choose best-of-breed components. Cons: More moving parts and potential network latency.

Embeddings In-Warehouse

Pros: Simpler operations, fewer data movements. Cons: Some warehouses lack native ANN support; custom implementations can be slow and costly.

Hybrid Vendor Solutions

Pros: Low integration friction and unified security. Cons: Vendor lock-in and unclear cost profiles for high-volume embedding storage.

Recommendations

• Start with a vector store + small candidate set if you need best performance and flexibility.
• Use hybrid or in-warehouse embeddings if you prioritize simpler operations and can accept vendor constraints.
• Precompute join keys or denormalize when possible to avoid hot relational joins under load.

Performance Scores (0-100)

• Latency: 78
• Throughput: 74
• Integration Effort: 85
• Result Quality: 90

Final Words

Combining vector search and SQL unlocks powerful capabilities for search, recommendations, and knowledge retrieval. The right pattern depends on your scale, latency requirements, and operational tolerance for additional components. For many teams, starting modular (vector store + SQL) provides the best balance of performance and flexibility.