Guide to SQL performance optimization with tips on query tuning, execution plans and improving database performance

6.How does denormalization enhance query performance?
Denormalization reduces the number of joins since it combines related tables in order to improve read performance at a cost of increased storage and maintenance.
Instead of separate employees and departments tables:
SELECT employees.name, departments.name
FROM employees JOIN departments ON employees.department_id = departments.department_id;
A denormalized table combines both data, reducing join complexity:
SELECT employee_name, department_name FROM employee_data;

7.SQL query hints are what and how do they help optimize performance?
Query hints are instructions to the SQL optimizer, telling it how queries must be executed. They bypass the default execution plan to give better performance.
Example: SQL Server
SELECT *
FROM orders WITH (INDEX(idx_order_date))
WHERE order_date > '2023-01-01';
This will force the use of the idx_order_date index, so that it optimizes the performance

8.Explain how query partitioning helps improve the performance.
Partitioning divides large tables into smaller, easier-to-handle pieces. The target queries are specific to certain partitions, reducing the scanned amount of data and increasing performance.
CREATE TABLE orders_partitioned (
order_id INT,
order_date DATE,
total_amount DECIMAL
)
PARTITION BY RANGE (order_date) (
PARTITION p1 VALUES LESS THAN ('2023-01-01'),
PARTITION p2 VALUES LESS THAN ('2024-01-01')
);
Queries based on certain date ranges only read relevant partitions.

9.Why is it so important to avoid correlated subqueries in optimization?
Correlated subqueries are executed for every row of the outer query, thus causing performance degradation. Instead, use joins or common table expressions.
Example Comparison: Bad:
SELECT name FROM employees WHERE salary > (SELECT AVG(salary) FROM employees);
Optimized :
WITH avg_salary AS (SELECT AVG(salary) AS avg_sal FROM employees)
SELECT name FROM employees WHERE salary > (SELECT avg_sal FROM avg_salary);

10.How do good type choices impact SQL performance?
Correct type choices have a reduced storage requirement and result in improved query performance.
For example, using INT instead of VARCHAR for ID reduces the storage and speeds up comparisons.
Example Comparison: Bad
CREATE TABLE users (user_id VARCHAR(10), name VARCHAR(100));
Optimized
CREATE TABLE users (user_id INT, name VARCHAR(100));
Optimized table using INT for user_id which improves indexing and query speed.