Optimizing Your Database Operations: Essential Techniques for Query Performance with Schema Design

1. ## Normalization

Normalization is a process of organizing the database schema to eliminate redundant data and minimize the data dependency. Normalization can help to reduce the amount of data that needs to be read and minimize the number of joins required to answer a query. For example, if you have a table that contains information about customers and their orders, you can normalize the schema by creating separate tables for customers and orders. This way, you can avoid duplicating customer information for every order and simplify the queries that join these two tables.

1. ## Denormalization

Denormalization is the opposite of normalization. It involves introducing redundant data into the schema to improve query performance. This can be particularly useful for read-heavy applications, where the cost of reading data from multiple tables outweighs the cost of storing redundant data. For example, you could denormalize a customer and order schema by adding a column for the customer name to the order table. This would eliminate the need to join the customer and order tables to retrieve the customer name for each order.

1. ## Clustering

Clustering is a technique for physically organizing data on disk to improve query performance. Clustering involves storing data that is frequently accessed together on the same disk pages. This way, the database engine can read all of the necessary data for a query in a single disk read operation. For example, if you have a table of sales data and frequently query by customer ID, you could cluster the table on the customer ID column so that all of the sales data for a given customer is stored together on disk.