When your SQL queries start slowing down, one of the first solutions that comes to mind is adding an index. But while indexes can dramatically speed up data scans and searches, they can also slow down data mutations like INSERT, UPDATE, and DELETE.

In this article, we’ll break down how indexes work, why they matter, and when you should (or shouldn’t) use them — all with practical SQL examples.

What Is an Index in SQL?

An index in a database is similar to an index in a book — it helps you find specific information faster.

Instead of scanning the entire table row by row, the database engine uses the index to jump directly to the relevant data.

Indexes are typically implemented using B-trees, which allow the database to quickly scan and retrieve the desired rows with logarithmic complexity instead of linear.

Why Indexes Improve Query Performance

When your table contains millions of rows and you run queries frequently, indexes can make a huge difference.

Let’s consider a simple table:

CREATE TABLE Users (
id SERIAL PRIMARY KEY,
name VARCHAR(255),
birthDate DATE
);

If you often search users by name:

SELECT * FROM Users WHERE name = 'John';

Adding an index on the name column can significantly improve the search performance:

CREATE INDEX idx_users_name ON Users(name);

Now, instead of scanning every row in the Users table, the database can use the index to locate matching entries instantly.

How Indexes Work Internally

Most relational databases (like PostgreSQL, MySQL, and SQL Server) use a B-tree or a balanced tree structure to store index data.

This structure allows for quick navigation from the root node down to the leaf nodes where the data resides.

The key point:

• Indexes reduce scan time for SELECT queries.
• Indexes increase write time for INSERT, UPDATE, and DELETE.

That’s because every time data changes, the database must rebuild or adjust the index structure — and that can slow down mutations.

When Indexes Hurt Performance

While it’s tempting to add indexes everywhere, that’s often a mistake.

Every additional index consumes:

• Disk space
• Memory
• CPU time for maintenance during data changes

For example, imagine a table with 30 indexes — every insert or update operation would need to update all those indexes, slowing down your system dramatically.

In some cases, it’s better to use caching technologies like Redis to handle frequently accessed data, especially when you’re only reading from the database.

Before adding dozens of indexes, run a proof of concept (POC) and verify if you really need that many. Most of the time, you don’t.

Final Considerations

Indexes are a powerful optimization tool, but like any optimization, they come with trade-offs. Start small, measure, and iterate.

Smarter caching or better query design will give you more performance than adding the 32,112th index to your table, so take your time to develop this solution.