Here's how you can optimize database performance using logical reasoning as a Database Developer.

Before going to actual point, giving simple and understanding manner may be newer can read this article. Query execution plans are like roadmaps for your database. They show the best way to find the data you need. By understanding these maps, you can find shortcuts and avoid traffic jams, making your database run faster. Think of it this way: If your database is a city, and the data you want is a specific house, the query execution plan is the best route to get there. A good plan uses highways (indexes) to get there quickly. A bad plan might make you drive through every street (a table scan), which takes forever. So, indexing, normalisation of data, regularly monitor the execution data are vital role places here to achieve it.

Indexes allow for smarter retrieval of data by creating a structured reference to them. They speed up search operations , sorting and filtering. Columns used in "IN" clauses are usual suspects that need an index, and also the ones used in WHERE clause , JOINS and ORDER BY clauses must be examined. Take the advice of bult in database index advisors frequently , to identify the need for new ones , and also the ones that are currently obsolete. However , indexes can slow write operations, so index only the columns that provide performance improvements to each use case. Also be aware of the storage overhead an index can cause and examine the potential drawbacks against your disk space and memory. The world of indexing , is one based on balance.

To add to this point, normalization is a very essential process of simplifying our database schema for querying purpose by separating the data on to different tables based on their dependency to each other(column). But sometimes over normalization could be problematic especially if there is multiple point of update in each normalized tables. This will cause complicated query execution(really not pleasant sql code) and also can hurt the performance.

Everyone knows how important normalization is. It prevents duplication of data which would result in waste of disk space and inevitable data error. It is impossible for anyone to remember replicating the data correctly. Choose the normalization level that reflects the current business operation model well so that a new IT person would understand the business operation model in a short period of time by looking at the schema diagram. Data and business go hand in hand.

I agree that joins are fundamental , they are also the main source of bottlenecks and delay in modern database so one must be careful how to shape them. First of all avoid unnecessary tables, then , try to use inner joins where possible because they are faster than outer joins most of the time. Filter your data using the WHERE clause and make sure that the columns used in the joins are indexed. Use the most selective column first in composite indexes , to achieve efficiency. Place the most restrictive of joins together , to be clear about the queries intent, and help developers optimize them. Lastly , take the advice of the database execution plan, which will inform you of the performance , and help in achieving optimal join strategy