索引算法的进化史

mollasaddam

1. Spatial Partitioning and Clustering (IVF)

Early acceleration methods primarily relied on Inverted File (IVF) . It uses the K-Means algorithm to divide the vector space into multiple "cells." During the search, the algorithm first locates the nearest clusters and then compares them internally. While this significantly narrows the search range, maintaining a balance between recall and speed is difficult under extremely high-dimensional or massive datasets.

2. Peak Performance: Hierarchical Navigation Small World (HNSW)

The most mainstream algorithm in the industry at present is HNSW . It draws on the concepts of "skip lists" and "six degrees of separation" to construct a multi-layered neighbor graph architecture:

• Top layer: sparse points, responsible for "leapfrogging" to quickly locate the approximate area.
• The bottom layer consists of dense nodes, responsible for "fine-grained search" to find precise neighbors.
  This hierarchical structure reduces the search complexity from linear to logarithmic ($O(\log N)$), making retrieval in hundreds of millions of data points take only milliseconds.
  
  

3. Large-scale storage: The rise of DiskANN

As data volumes exceed one billion, memory costs become a bottleneck. The DiskANN algorithm, through a hybrid approach of "memory-compressed index + disk-based raw vectors," leverages the high random read throughput of SSDs in the latest databases . This ensures millisecond-level speeds while increasing the amount of data that a single machine can process by more than 10 times, and also solves the pain point of HNSW's difficulty in incremental updates.

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Technological Evolution in 2026: Hardware Acceleration and Quantization

To achieve peak performance, Product Quantization (PQ) and the ScaNN algorithm further reduce memory usage by compressing high-dimensional vectors into shorter code. Simultaneously, by leveraging GPU/FPGA to accelerate index building , the indexing time for massive vectors has been reduced from hours to minutes, ensuring real-time performance for AI applications.

In summary, the evolution of algorithms has transformed search from "finding identical results" to "finding similar results." This millisecond-level semantic retrieval capability is the underlying foundation supporting real-time AI reasoning and the Internet of Things by 2026.

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Do you want to understand how to configure these indexes in a specific database (such as Milvus or PostgreSQL), or do you want to compare the advantages and disadvantages of different algorithms in specific business scenarios (such as e-commerce image search)?

This video explains the working principle of the HNSW algorithm through vivid analogies, making it ideal for developers who want to intuitively understand complex graph index structures.