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ScaNN Index Integration

1 post · Jan 2026 – Apr 2026

What this trail covers

Milvus integrates the ScaNN index, a highly optimized Approximate Nearest Neighbor (ANN) search algorithm that builds upon IVFPQ. ScaNN enhances performance by employing a score-aware quantization objective to preserve ranking quality and a SIMD-optimized 4-bit PQ FastScan path to reduce memory bottlenecks. This integration allows Milvus to achieve significantly higher query throughput and a much smaller memory footprint, particularly for recall-insensitive workloads like recommendation systems.

All Milvus trails
  1. Interview with RaBitQ Authors: The TurboQuant Dispute and Why the Storage Selloff Was a False AlarmThis post discusses the technical debate surrounding vector quantization, specifically comparing RaBitQ and TurboQuant. It highlights that both methods use random rotation as a core technique, followed by quantization. RaBitQ's contribution is in its mathematical optimality and theoretical guarantees for error estimation, while TurboQuant introduces a minor optimization in grid allocation. The post also details the engineering implications for vector databases like Milvus, emphasizing the trade-offs between compression, memory usage, and retrieval quality. It critiques TurboQuant's benchmarking methodology and claims, noting that RaBitQ's vectorized and GPU implementations are more advanced than initially presented.
  2. A Brief Introduction to the ScaNN IndexThis post details the integration of the ScaNN index into Milvus. It explains ScaNN's core optimizations over IVFPQ: a score-aware quantization objective that improves ranking quality under compression, and the 4-bit PQ FastScan path which accelerates lookups using SIMD instructions to reduce memory load bottlenecks. Experimental results using VectorDBBench show that ScaNN, when integrated into Milvus, can achieve up to 5x the QPS of IVFFLAT and 6x that of IVF_PQ on the Cohere1M dataset at similar recall rates, with a significantly smaller memory footprint (1/16 of original data) when raw data is not loaded.