How to resolve lagging caused by excessive SQLite data?

When the performance is degraded due to excessive data in an SQLite database, several solutions can be considered: 1. Database index optimization to ensure key fields have proper indexes, speeding up queries. Use EXPLAIN statement to check query execution plan and optimize indexes accordingly. 2. Database caching by storing commonly used query results in memory to reduce database accesses and improve query speed. Use SQLite’s built-in caching feature or external caching tools. 3. Regular data cleanup to delete unnecessary data and reduce database size. Delete expired data, archive old data, or use partitioned tables. 4. Database sharding to split data into multiple independent database files, reducing size and query complexity. Use SQLite’s ATTACH and DETACH statements to manage multiple database files. 5. Database optimization configuration by adjusting parameters like buffer size and thread pool size based on specific application scenarios and hardware conditions to enhance performance. 6. Database upgrade to the latest SQLite version for better performance and stability, especially if using an older version. 7. Database restructuring may be necessary if other methods are ineffective, redesigning the database structure or refactoring related code to optimize usage. In conclusion, addressing performance issues from excessive data in SQLite can involve optimizing indexes, caching, regular data cleanup, sharding, configuration optimization, upgrades, and restructuring. The choice of method depends on specific application scenarios and requirements.