The phrase SQL + Vectors summarizes a powerful idea: one database engine that natively executes both OLAP-style analytics and semantic search over embeddings. This article outlines a practical blueprint for combining columnar storage, vector indices, and cost-based planning so teams can run complex aggregates and high-quality semantic retrieval from a single system without stitching multiple services together.
Why unify analytics and semantic search?
Separating OLAP and semantic retrieval creates friction: ETL pipelines to move vectors to a search engine, duplicated storage, different consistency models, and tricky joins between search results and analytic dimensions. A hybrid SQL + Vectors design reduces latency, simplifies data governance, and unlocks new queries—such as “top selling products similar to this description in the last 30 days” expressed as a single SQL statement.
Core components of a hybrid engine
Designing a successful hybrid database requires three core subsystems working together:
- Columnar storage optimized for OLAP: compressed, vectorized reads, and block-level metadata for fast scans and aggregation.
- Vector indices built into the engine: ANN structures (HNSW, IVF, PQ) that live alongside column files and support incremental updates.
- Cost-based planner extended to model ANN costs and ranking costs so it can choose plans that mix index probes, table scans, and aggregations efficiently.
Storage and index design
Columnar layout with embedding columns
Store embeddings as a dedicated column type (e.g., VECTOR(1536, float16)). Use columnar file formats (Arrow/Parquet-like) with per-block quantization metadata so an ANN index can operate on block-local centroids and avoid full-file deserialization for many queries.
Built-in vector indices
Embed ANN indexes at multiple granularities:
- Block-level summaries for quick pruning.
- Partitioned HNSW/IVF per segment for fast nearest-neighbor probes.
- Optional product/quantization layers (PQ, OPQ) to reduce memory footprint while preserving rerank accuracy.
Store index metadata with clear versioning to allow safe background reindexing and atomic index swaps.
Extending the cost-based planner
A hybrid planner must understand both OLAP primitives and ANN behaviors. Key planner extensions:
- ANN cost model: estimate probe cost as function of graph hops, degree, and estimated candidate set size; include IO and CPU contributions.
- Rank-and-retrieve modeling: consider cascaded plans: cheap ANN probe → small set → exact distance rerank with full-precision vectors.
- Hybrid join planning: allow nested-loop-style plans where the outer side is an ANN probe and the inner side is an aggregate or join stream, with cost estimates driving join order.
Execution strategies
Execution must be vectorized and resource-aware:
- Vectorized execution engine that operates on batches of rows and returns candidate sets that flow into ranking operators.
- Cascaded retrieval where ANN returns ~K candidates quickly and a precise distance operator (possibly GPU-accelerated) re-ranks them using full-precision vectors.
- Pushdown predicates and early aggregation to reduce candidate load: use column statistics to prune partitions before ANN probes whenever possible.
Ingestion, indexing, and maintenance
Embeddings arrive from ML pipelines and must be kept synchronized with analytic data:
- Streaming path: small updates produce delta segments and partial index inserts; useful for low-latency freshness.
- Batch reindexing: periodically rebuild dense indices with updated PQ/OPQ transforms for global quality improvements.
- Consistent visibility: MVCC or timestamped segments ensure queries see a coherent snapshot of table data and associated indices.
Schema and modeling patterns
Design schemas with both analytics and retrieval needs in mind:
- Keep an
embeddingcolumn alongside denormalized attributes used in OLAP filters (time, product category, region). - Use materialized views for expensive aggregations on top of semantic filters (e.g., daily popularity scores for items matching a semantic predicate).
- Design composite indexes combining low-cardinality columns with per-partition vector indices to accelerate queries filtered by those columns.
Example queries
Combining analytics and semantic retrieval in one SQL query:
SELECT p.category, COUNT(*) AS sales, AVG(r.score) AS avg_similarity
FROM products p
JOIN LATERAL (SELECT id, distance(embedding, VECTOR[...]) AS score FROM products ORDER BY score LIMIT 50) r
ON r.id = p.id
WHERE p.created_at BETWEEN '2026-01-01' AND '2026-02-01'
GROUP BY p.category
ORDER BY sales DESC;
This pattern runs an ANN probe (the LATERAL subquery) and aggregates results by category in the same engine, letting the planner choose whether to probe per-category partitions or probe globally then aggregate.
Operational considerations
Hybrid systems introduce operational complexity—plan for these practices:
- Resource isolation: separate pools for CPU-heavy ANN probes and IO/aggregation work to avoid noisy neighbor effects.
- Cost calibration: collect runtime metrics to update ANN cost models periodically so the planner adapts to real performance.
- Monitoring: track recall/precision for semantic queries, ANN probe latency, index sizes, and time-to-rebuild metrics.
- Fallback strategies: allow the planner to fall back to full-scan exact retrieval for small tables or when index recall degrades.
Trade-offs and pitfalls
Be explicit about trade-offs:
- Memory vs. latency: high-quality indices use more RAM but reduce probe time.
- Freshness vs. accuracy: streaming inserts simplify freshness but may require later full reindexing to regain top recall.
- Planner complexity: richer cost models produce better plans but must be maintained with telemetry and automated tuning.
Practical blueprint: step-by-step
- Introduce an embedding column type and store vectors in your columnar format with block-level metadata.
- Create per-segment ANN indices (HNSW/IVF + PQ) with versioned metadata enabling atomic swaps on rebuild.
- Extend the cost model to include ANN probe cost and re-ranking cost; expose tuning knobs (K, recall thresholds) in queries.
- Implement cascaded operators: ANN probe → candidate set → exact re-rank → downstream SQL operators (joins/aggregations).
- Instrument runtime metrics and run closed-loop calibration of cost estimates and index policies.
Following these steps produces a single engine that serves both analytics and semantic retrieval with less operational overhead and faster end-to-end queries.
Conclusion: A careful integration of columnar storage, built-in vector indices, and an ANN-aware cost-based planner allows a database to natively execute mixed OLAP and semantic workloads, simplifying pipelines and enabling powerful, composable queries.
Try prototyping by adding an embedding column and an ANN-backed LATERAL operator to a test dataset, then measure planner choices and tune index parameters accordingly.
