Vector indexes

Advanced search and indexing capabilities

Vector indexes benefit AI and machine learning workloads that rely on vector comparisons. Vector indexes provide the following enhancements to Db2:

• Semantic search.
• Recommendations and personalization.
• Image and document matching.
• Embedding-driven classification.

Specialized indexing

The Db2 VECTOR search feature uses a brute-force approach. It works well for small datasets or when queries limit the search space. However, interactive and high-volume scenarios—such as semantic search, retrieval-augmented generation, or fraud detection—typically issue multiple similarity queries and operate over very large datasets. These workloads require faster ways to locate relevant vectors without scanning everything.

Modern embeddings are large, often between 768 and 1500 dimensions. Regular searching in such high-dimensional spaces can negatively affect your vector operations:

• Exponentially growing search areas.
• Sparse data distribution even at massive scale.
• High per-query computational cost.

Traditional indexing techniques degrade rapidly under these conditions and become slower than brute force. Specialized vector indexes are essential to overcome these challenges.

Approximate similarity search

Conventional indexing structures were originally optimized for low-dimensional spatial data. When extended to hundreds or thousands of dimensions, they become inefficient. To address this, a new class of indexing methods trades perfect accuracy for speed—these are known as Approximate Nearest Neighbor (ANN) structures.

ANN structures focus on retrieving close-enough matches rather than exhaustive exact neighbors. The resulting performance gains are substantial and typically acceptable for most AI-driven use cases. Accuracy is measured using RECALL, which indicates how many true nearest neighbors are returned.

For more information, see ANN index implementation.

Similarity searches with vector indexes

Performance considerations

Vector indexes deliver substantial performance improvements for large-scale similarity search. They can efficiently handle datasets ranging from millions to billions of vectors, with typical queries completing in milliseconds rather than seconds. The disk-backed design supports datasets larger than available RAM, making it practical for production workloads without requiring massive memory resources. Additionally, Product Quantization compression reduces storage requirements while enabling faster initial filtering during search operations.

However, these performance gains come with important trade-offs. Vector indexes return approximate nearest neighbors rather than guaranteed exact matches, typically achieving 90% or higher recall. As the insert journal accumulates new vectors, query performance gradually degrades, requiring periodic index recreation to maintain optimal speed. Initial index creation can take considerable time for large datasets, and the auxiliary tables consume additional disk space beyond the base table storage.

When evaluating whether to use vector indexes, consider both the performance benefits for your query workload and the operational overhead of maintaining the index structure over time.

Restrictions