{"id":27944,"date":"2025-06-20T12:22:56","date_gmt":"2025-06-20T19:22:56","guid":{"rendered":"https:\/\/www.pingcap.com\/?post_type=article&p=27944"},"modified":"2025-08-18T23:26:15","modified_gmt":"2025-08-19T06:26:15","slug":"approximate-nearest-neighbor-ann-search-explained-ivf-vs-hnsw-vs-pq","status":"publish","type":"article","link":"https:\/\/www.pingcap.com\/ko\/article\/approximate-nearest-neighbor-ann-search-explained-ivf-vs-hnsw-vs-pq\/","title":{"rendered":"Approximate Nearest Neighbor (ANN) Search Explained: IVF vs HNSW vs PQ"},"content":{"rendered":"

<\/span>What Is Approximate Nearest Neighbor (ANN) Search?<\/span><\/h2>\n\n\n\n

Approximate Nearest Neighbor (ANN) search has become essential for applications dealing with high-dimensional data\u2014think natural language processing, image recognition, and recommendation engines. For a deeper understanding of TiDB’s approach, visit our Vector Search Overview<\/a>. Unlike exact methods, ANN strikes a practical balance between speed and accuracy, making it ideal for large-scale, real-time AI systems.<\/p>\n\n\n\n

At its core, ANN algorithms aim to identify data points that are closest to a given query point. They do so approximately, dramatically reducing computational load while maintaining acceptable accuracy levels. This efficiency is especially important when working with massive datasets.<\/p>\n\n\n\n

Applications of ANN span various industries. E-commerce platforms use it for personalized product recommendations. Social media and streaming services deploy ANN to surface relevant content. Healthcare leverages it for predictive analytics, diagnostics, and genome sequencing, enabling faster and more reliable results.<\/p>\n\n\n\n

To meet these diverse needs, several indexing techniques have been developed. For a detailed guide, check out Mastering Faiss Vector Database: A Beginner\u2019s Handbook<\/a>. Among the most popular are Inverted File (IVF), Hierarchical Navigable Small World (HNSW), and Product Quantization (PQ). These methods each take a different approach to indexing, offering trade-offs in speed, scalability, and accuracy.<\/p>\n\n\n\n

<\/span>Vector Indexing Techniques: IVF, HNSW, and PQ<\/span><\/h2>\n\n\n\n

Inverted File (IVF)<\/h3>\n\n\n\n

IVF divides the data space into clusters using algorithms like k-means. During a query, only the relevant clusters are searched, significantly speeding up the process. IVF is ideal for datasets that naturally segment into distinct clusters. However, its accuracy depends on how well the clusters (centroids) represent the overall data.<\/p>\n\n\n\n

Hierarchical Navigable Small World (HNSW)<\/h3>\n\n\n\n

HNSW builds a multi-layer graph where data points are connected across layers based on proximity. This graph structure enables fast navigation and high recall, making HNSW suitable for precision-focused applications like search engines. The downside? It requires more memory and longer build times.<\/p>\n\n\n\n

Product Quantization (PQ)<\/h3>\n\n\n\n

PQ compresses vectors by splitting them into subvectors and encoding each with a separate codebook. This reduces both memory usage and search time, which is especially useful for edge devices or environments with limited resources. While fast, PQ can suffer from reduced precision if not tuned properly.<\/p>\n\n\n\n

<\/span>IVF vs HNSW vs PQ: How to Choose the Right ANN Index<\/span><\/h2>\n\n\n\n

Choosing the right index type depends on your priorities:<\/p>\n\n\n\n