关键词: 实时计算, 高并发计算, 高性能缓存, 定址内存映射, 多维度自定义索引, 内存数据增量更新

Abstract: Relational databases face issues of low read efficiency in high-concurrency real-time scenarios.In efforts to enhance the performance of traditional database systems,NoSQL(Not Only SQL) in-memory databases and embedded databases are often employed as data caching layers for acceleration.However,these approaches have limited potential for improving read and write efficiency,and generally incur high migration costs due to differences in storage structures and read/write methods.This paper proposes a data caching solution MDBCache tailored for relational databases.By leveraging fixed-address memory-mapped caching,multi-dimensional custom index reading,and incremental update technology for in-memory data,it achieves efficient data sharing without the need for cross-process or cross-user-space operations,significantly reducing data request response times and migration costs.Experimental results demonstrate that,compared to the caching solutions of the in-memory database MMDB and the embedded database Berkeley DB,MDBCache achieves a maximum improvement of 1.49× and 6.88× in read efficiency(single-thread mode),and 8.59× and 5.44× in data update efficiency,respectively.This solution boasts mature underlying technology,high-performance service,low implementation complexity,and high practicality,making it a valuable reference in the design of data solutions for high-concurrency real-time scenarios.

Key words: Real-time computing, High-concurrency computing, High-performance caching, Fixed-address memory mapping, Multi-dimensional custom indexing, In-memory data incremental update