The application discloses an
inverted index iterative self-index optimization method based on a Top-K query model, first, an initial
inverted index is constructed with the aid of an
open source tool, which is converted into an equal word compression format and reorganized into a block interleaved
storage structure; subsequently, based on the Top-K query model, a multi-layer iterative self-index is constructed through recursive segmentation and sampling; further, a query
performance model containing parameters such as disk I / O and decompression speed is established, the relationship between query benefits and storage overhead is quantified, and the optimal synchronous distance and the number of iterative
layers are solved; finally, fast
random access of a
mass disk compressed
inverted index is realized. Through the variable-length synchronous distance design, the Top-K
model parameter optimization and the multi-layer iterative index structure, the application significantly improves the query efficiency, reduces the data reading and decompression overhead, can maintain the
Pareto optimal balance of the query performance and the
storage efficiency in different hardware environments, and is suitable for a
mass data high-
concurrency query scene.