Create the right indexes based on query patterns to speed up data retrieval and optimize search performance.
Store pre-computed query results for quick access, reducing the need to process complex queries repeatedly.
Increase the capacity of the database server by adding more CPU, RAM, or storage resources.
Reduce complex joins by restructuring data, which can improve query performance at the cost of storage.
Store frequently accessed data in a faster storage layer to reduce load on the primary database.
Create copies of the primary database on different servers to distribute read load and enhance availability.
Divide the database into smaller, manageable pieces, or shards, to distribute load and improve performance.
Split large tables into smaller, more manageable pieces to improve query performance and maintenance.
Rewrite and fine-tune queries to execute more efficiently using proper syntax and execution plans.
Select the most efficient data types for each column to save space and speed up processing operations.
Avoid excessive indexing, which can slow down write operations; use indexes judiciously for optimal balance.
Move infrequently accessed data to an archive to keep the active database smaller and faster.