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Performance Optimization

SQL performance optimization is crucial for accelerating SQL queries and improving overall database performance. Most importantly, it ensures smooth and efficient execution of SQL statements, which can result in better application performance and user experience.

1. Indexes​

Creating indexes is one of the prominent ways to optimize SQL performance. They accelerate lookup and retrieval of data from a database.

CREATE INDEX index_name
ON table_name (column1, column2, ...);

Remember, though indexes speed up data retrieval, they can slow down data modification such as INSERT, UPDATE, and DELETE.

2. Avoid SELECT *​

Get only the required columns instead of fetching all columns using SELECT *. It reduces the amount of data that needs to be read from the disk.

SELECT required_column FROM table_name;

3. Use Join Instead of Multiple Queries​

Using join clauses can combine rows from two or more tables in a single query based on a related column between them. This reduces the number of queries hitting the database, improving performance.

SELECT Orders.OrderID, Customers.CustomerName
FROM Orders
INNER JOIN Customers
ON Orders.CustomerID=Customers.CustomerID;

4. Use LIMIT​

If only a certain number of rows are necessary, use the LIMIT keyword to restrict the number of rows returned by the query.

SELECT column FROM table LIMIT 10;

5. Avoid using LIKE Operator with Wildcards at the Start​

Using wildcard at the start of a query (LIKE '%search_term') can lead to full table scans.

SELECT column FROM table WHERE column LIKE 'search_term%';

6. Optimize Database Schema​

Database schema involves how data is organized and should be optimized for better performance.

7. Use EXPLAIN​

Many databases have 'explain plan' functionality that shows the plan of the database engine to execute the query.

EXPLAIN SELECT * FROM table_name WHERE column = 'value';

This can give insight into performance bottlenecks like full table scans, missing indices, etc.

8. Denormalization​

In some cases, it might be beneficial to denormalize the database to a certain extent to reduce complex joins and queries. Keep in mind that this is usually the last resort and may not always yield the desired results.

Remember, each query and database is unique, so what might work in one scenario might not work in another. It is always crucial to test the queries in a controlled and isolated environment before pushing them into production.