Choose the appropriate data structure based on the query requirement, such as hash tables or tree structures, in order to improve query efficiency.
Index optimization: Creating appropriate indexes in a database can improve query performance by helping the system quickly locate the data being queried and reducing scan time.
Partitioning a table allows for the distribution of data across different partitions, reducing the amount of data that needs to be scanned during queries and improving query performance for large datasets.
Sharding of data involves storing data in separate shards according to certain rules, allowing for improved query efficiency by only accessing relevant data shards during queries.
Caching data: Frequently accessed data can be cached in memory to reduce database queries and improve query performance.
Vertical and horizontal partitioning: By splitting data vertically and horizontally based on access frequency and relevance, it can reduce the amount of data that needs to be scanned during queries and improve query performance.
Avoid excessive indexing: having too many indexes can increase the cost of database write operations, so indexes should be set up according to actual needs.
Data compression: For large data tables, compressing data for storage can reduce disk space usage and improve query performance.
Regular data cleansing: Periodically cleaning out unnecessary data can reduce the data volume in the database and improve query performance.
Regularly optimizing the database, such as rebuilding indexes and optimizing query statements, can improve query performance.
