Distributed Storage and Retrieval System for Large-scale Knowledge Graph Data
Knowledge graphs have been widely used in various fields.In order to solve the problems of low efficiency and high hardware pressure of traditional centralized query,the distributed retrieval and query of large-scale knowledge graph is studied.The lightweight repartitioning algorithm based on query load optimization is used to achieve query load balancing between servers by set-ting different weights,which significantly improves the query speed and system performance.At the same time,this paper designs a subgraph decomposition query algorithm based on query cost,which is based on the structural information of the query graph,so as to accelerate the query speed of the system.In the distributed microservice management system,Spring Cloud's distributed microser-vice architecture and Nginx's load balancing technology are used to ensure the reliability and high availability of the system in the case of high concurrency.Experimental results show that these algorithms are better than traditional algorithms in terms of query effi-ciency and system performance,and have practical application value.
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