Here are various techniques to tune database performance:
Database Normalization: Normalize tables to eliminate data redundancy and improve data integrity. However, over-normalization can lead to excessive table joins when querying, which could hamper performance.
Database Denormalization: In contrast to normalization, denormalization involves combining tables to reduce the complexity of queries. This technique can boost read performance but may increase storage requirements and risk data inconsistency.
Partitioning: Breaking a large table into smaller, more manageable pieces can improve performance. There's range partitioning (dividing by the range of column values), list partitioning (dividing by explicit lists of column values), hash partitioning (dividing by the result of a hashing function on specific columns), and composite partitioning (using multiple partitioning methods).
Caching: Databases often employ caching strategies to keep frequently accessed data in memory, reducing the need for disk access. System parameters can be often configured to optimize cache usage.
Concurrency Control: Techniques like multiversion concurrency control (MVCC) allow multiple users to access the database simultaneously without conflict, which is critical for performance in multi-user environments.
Remember that these techniques will have varying levels of impact depending on the specific workload and queries your database has to handle. Regularly monitor database performance and adjust as needed.