Source
How AlloyDB ScaNN scales vector search to 10 billion vectors
AlloyDB has introduced a four-level tree architecture for its ScaNN index, enabling vector search to scale up to 10 billion vectors. This update is intended to satisfy the requirements of enterprise-grade agentic AI applications.
Why it matters
Organizations building large-scale AI applications can now process massive datasets more efficiently, ensuring faster and more accurate search results for end users.
The details
- The four-level tree uses hierarchical partitioning to reduce the volume of scanned vectors.
- Tests show <= 51 ms p95 latency and 95% recall at 10 billion vectors.
- Balanced tree shapes and sampling optimizations are implemented to maximize memory efficiency.
Show entities and relationshipsHide entities and relationships
In this article
Key connections
Google developed the ScaNN vector search index.
AlloyDB uses the ScaNN index to scale vector search to 10 billion vectors.
AlloyDB is built with PostgreSQL
AlloyDB is engineered as a fully managed PostgreSQL-compatible database service.
AlloyDB is related to Agentic AI
AlloyDB is optimized to handle demanding workloads for enterprise-grade agentic AI applications.
ScaNN is related to Vector Search
ScaNN provides high-performance vector search indexing.
Related events
AlloyDB ScaNN Scales Vector Search to 10 Billion Vectors
Get the weekly recap
The stories like this one, picked and explained — once a week, straight to your inbox.