Optimizing Queries

Optimizing SQL queries is the process of improving the efficiency of SQL statements to reduce execution time, minimize resource consumption, and enhance overall database performance.

This involves using techniques such as indexing, query restructuring, execution plan analysis, and proper use of SQL constructs to ensure that queries retrieve or manipulate data most effectively. Optimisation aims to handle large datasets, complex joins, or frequent transactions efficiently while maintaining accuracy and scalability.

Common Techniques for Query Optimization

Use Indexes Effectively

Indexes are essential for speeding up data retrieval. They act as pointers to the rows in a table, allowing the database to locate data without scanning the entire table.

Tips

• Create indexes on columns frequently used in WHERE, JOIN, GROUP BY, or ORDER BY clauses.
• Avoid over-indexing, as it can slow down INSERT, UPDATE, and DELETE operations.
• Use composite indexes for queries that filter on multiple columns.

To add an index on last_name in a SQL query:

Without an index:

SELECT *
FROM employees
WHERE last_name = 'Smith';

With an index:

CREATE INDEX idx_last_name ON employees(last_name);

SELECT *
FROM employees
WHERE last_name = 'Smith';

Avoid SELECT *

Fetching all columns from a table increases data transfer time and memory usage. Specify only the columns needed for your query.

Instead of:

SELECT *
FROM employees;

Use:

SELECT first_name, last_name
FROM employees;

Filter Early with WHERE

Reduce the number of rows processed by filtering data as early as possible in the query using the WHERE clause.

Instead of:

SELECT first_name, last_name
FROM employees
ORDER BY last_name;

Use:

SELECT first_name, last_name
FROM employees
WHERE department = 'Sales'
ORDER BY last_name;

Avoid Functions on Indexed Columns in WHERE Clauses

When using functions on columns in the WHERE clause, the database cannot use an index on that column effectively, leading to a full table scan. Rewrite queries to avoid applying functions directly to indexed columns.

Inefficient (function on indexed column):

SELECT *
FROM employees
WHERE YEAR(hire_date) = 2023;

This query applies the YEAR() function to every row’s hire_date, preventing the use of an index on the hire_date column.

Efficient:

SELECT *
FROM employees
WHERE hire_date BETWEEN '2023-01-01' AND '2023-12-31';

By transforming the query, the index on hire_date can be used effectively, improving performance.

Use Joins Instead of Subqueries

In many cases, joins perform better than subqueries because the database can optimize them more efficiently.

Subquery:

SELECT first_name, last_name
FROM employees
WHERE department_id = (SELECT id FROM departments WHERE name = 'Sales');

Join:

SELECT e.first_name, e.last_name
FROM employees e
JOIN departments d ON e.department_id = d.id
WHERE d.name = 'Sales';

Optimize JOIN

• Ensure the joined columns are indexed.
• Use the appropriate join type (e.g., INNER JOIN, LEFT JOIN) for the specific use case.

Limit the Use of DISTINCT

Using DISTINCT can be costly for performance, requiring sorting and deduplication. Use it only when absolutely necessary.

Instead of:

SELECT DISTINCT department
FROM employees;

Use:

SELECT department
FROM employees
GROUP BY department;

Partition Large Tables

Partitioning divides a table into smaller, more manageable pieces based on column values, improving query performance for specific subsets of data.

To partition a sales table by year:

CREATE TABLE sales_2023 PARTITION OF sales FOR VALUES IN (2023);

Use Caching

Cache frequently executed queries to reduce database load. This can be done using a caching layer like Redis or in-memory caching.

Optimize Data Types

Use appropriate data types for columns to minimize storage and processing overhead. For example, use INT instead of BIGINT when possible or CHAR instead of VARCHAR for fixed-length data.