Using indexes: Utilizing appropriate indexes in queries can significantly improve query performance. Creating suitable indexes based on query criteria can help avoid full table scans.
Optimize your queries: By optimizing the structure and logic of your queries, unnecessary calculations and data accesses can be reduced, improving query performance. Use appropriate keywords and operators, and avoid complex subqueries and joins.
Avoid unnecessary columns: only query the columns that are needed to prevent unnecessary data transmission and processing. Specify the columns to be queried by listing their names in the SELECT statement.
Using pagination queries: for large data tables, you can use a pagination query method to only retrieve a portion of the data each time, reducing the amount of data queried and resources consumed.
Avoid using wildcards: using specific condition values and operators instead of wildcards (such as “%”) can improve query performance.
Optimize table structure: By designing and optimizing table structure, it is possible to reduce data redundancy and duplication, improving query performance. Proper data types, constraints, and indexes can be utilized.
Using caching: You can utilize caching technology to store query results, reducing the number of database accesses and improving query performance.
Partitioned tables: For large data tables, consider using partitioned tables to improve query performance. By partitioning tables according to the values of a column, data can be stored and queried in a distributed manner, enhancing query efficiency.
By using stored procedures, you can bundle together a series of queries and actions to be executed on the database server, reducing network transfer costs and improving query performance.
Regular maintenance and optimization: Regularly maintaining and optimizing the database, including rebuilding indexes, compressing tables, updating statistics, can improve query performance.
