Distributed data storage and efficient query optimization method for tobacco manufacturing information system

In this paper, a distributed data storage and efficient query optimization method for tobacco manufacturing information system is proposed to meet the massive and complex data processing requirements. Through in-depth analysis of data storage requirements, a multi-dimensional data partitioning strategy including spatio-temporal joint partitioning and intelligent copy placement algorithm is designed, which combines hierarchical redundancy mechanism and consistency protocol optimization to realize efficient data storage and high reliability guarantee. In the aspect of query optimization, an intelligent index optimization scheme based on multi-level index system is adopted, and a distributed query optimization engine including parallel execution framework and materialized view acceleration technology is constructed. At the same time, dynamic cache and replication strategy and query scheduling and load balancing mechanism are used to further improve data access efficiency and system stability. The experimental results show that this scheme is significantly superior to the traditional architecture in many performance indicators, such as the write throughput is increased by 597%, the query delay is reduced by more than 90%, and it shows good scalability. After it is actually deployed in a tobacco group, the production report generation time is shortened to 1/15 of the original, the success rate of historical data query is increased to 99.3%, and the storage cost is reduced by 62%, which proves the remarkable effect and application value of the scheme in improving efficiency and reducing costs.