Complex data objects (such as pictures

text) are usually represented as high-dimensional feature vectors. The existing nearest neighbor search method KNN-Gist in PostgreSQL is based on the tree-structured index and cannot efficiently support the nearest neighbor search of high-dimensional data. The PostgreSQL system high-dimensional approximate nearest neighbor search extension: AKNN-Qalsh is introduced

which is based on the Locality-Sensitive Hashing scheme and supports approximate nearest neighbor search of large-scale

high-dimensional data objects. The effectiveness of the extension via extensive experiments on five real data sets is demonstrated.