ANN Algorithm Complexity Analysis

An advanced quantization technique introduced by Google's ScaNN that prioritizes preserving parallel components between vectors rather than minimizing overall distance. Optimized for Maximum Inner Product Search (MIPS) and significantly improves retrieval accuracy.

Configuration options in distributed vector databases that trade off between data consistency, availability, and performance. Critical for understanding read/write behavior in production systems with replication.

A pagination technique for efficiently scrolling through large vector database result sets using cursors instead of offsets. Essential for retrieving all vectors in a collection or iterating through search results without performance degradation.

A search problem focused on finding vectors that maximize the inner product with a query vector. Common in recommendation systems and neural search where magnitude carries semantic meaning, requiring specialized algorithms like those in ScaNN.

A graph-based approximate nearest neighbor search algorithm that uses both long-range and short-range links to achieve poly-logarithmic search complexity. Foundation for the more advanced HNSW algorithm.

A major algorithmic advancement to Google's ScaNN that introduces controlled redundancy to the vector index, leading to improved search efficiency. Enables even faster vector search while maintaining or improving accuracy.