Automatic synchronization method for dual server switching database based on FP algorithm

Insufficient optimization of log mapping in dual server switching databases leads to low efficiency in capturing changes during server switching, and association rule mining is susceptible to noise data interference. Moreover, dynamic environments further exacerbate synchronization bias. Therefore, a dual server switching database automatic synchronization method based on FP algorithm is proposed. Optimization of log mapping improves change capture efficiency and enables storage compression, ensuring, and compressing storage, the integrity and consistency of log records during dual server switching are ensured. Using the FP algorithm to mine log association rules, revealing the potential relationship between database operation behavior and log features, and providing a basis for anomaly detection. Design a database automatic synchronization mechanism based on deviation elimination to address potential biases in mining results. Dynamic filtering of redundant rules, correction of deviation patterns, and synchronization strategy optimization collectively enable efficient automatic synchronization. The experimental results show that the proposed method performs well in key indicators such as synchronization delay, synchronization accuracy, resource utilization, and anomaly detection capability. The experimental results show that the method has good performance in data version difference rate and has better synchronization performance.