[Explained] How to Create a Database Index in SQL

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Database Index

Want to speed up database queries? As a data analyst and SQL expert, I‘m going to walk you through how to create a database index using SQL and optimize query performance—and speed up data retrieval.

When you’re retrieving data from a database table, you‘ll often have to filter based on specific columns. For example, you might write an SQL query to retrieve customer data based on the city they live in.

By default, running such a query performs a full-table scan until all the records that satisfy the condition have been found and then returns the results. This can be extremely inefficient when you have to query a large database table with millions of rows.

As a data geek, I‘m excited to show you how you can easily speed up such queries by creating a database index.

What is a Database Index?

Book Index

Imagine you want to find a specific term in a book. You wouldn‘t do a full-book scan—reading one page after the other—looking for the particular term. Instead, you‘d look up the index to find the pages that reference the term and jump straight to those pages.

A database index works similarly to the index in a book. It‘s a set of pointers or references to the actual data, but sorted in a way that makes data retrieval much faster.

Under the hood, a database index can be implemented using data structures like B+ trees and hash tables. By creating an index, you allow the database engine to avoid scanning the entire table when you query based on certain columns.

As a fellow data geek, I think this is an ingeniously efficient way to optimize data retrieval!

Creating a Database Index in SQL

Now that you know what a database index is, let me show you how to easily create one using SQL.

When querying data, you‘ll often filter by specific columns in the WHERE clause. To speed up queries on particular columns, you can create an index on those columns like this:

CREATE INDEX index_name ON table (column)

Here‘s what each part means:

  • index_name – Name of the index
  • table – Table name
  • column – Column to index

You can also create a multi-column index by specifying multiple columns:

CREATE INDEX index_name ON table (column1, column2, column3)

This can optimize queries filtering on those columns. Now let me walk you through a hands-on example.

Understanding Database Index Performance Gains

To demonstrate the performance gains of adding an index, we‘ll create a large table and compare query times.

I‘ll use SQLite here, but you can follow along with any database like MySQL or PostgreSQL.

Populating a Large Table

First, I‘ll populate a table with 1 million rows of fake customer data using the Python Faker library:

import sqlite3
from faker import Faker 

# Generate fake data
fake = Faker()

# Create database and table
db = sqlite3.connect(‘customers.db‘)
            CREATE TABLE customers (
              id INTEGER PRIMARY KEY, 
              first_name TEXT,
              last_name TEXT,
              city TEXT,
              orders INTEGER)

# Insert 1 million rows 
for _ in range(1000000):
              INSERT INTO customers (
                first_name, last_name, city, orders)
              VALUES (
                :first_name, :last_name, :city, :orders)
                ‘first_name‘: fake.first_name(),
                ‘last_name‘: fake.last_name(),
                ‘orders‘: fake.random_int(0, 100)  


With 1 million rows, we can realistically test index performance.

Creating an Index

Now let‘s say we often need to query customers by city. We can create an index on the city column like this:

CREATE INDEX idx_city ON customers (city); 

As a fellow data geek, you‘ll love seeing how much this speeds up queries!

Query Time Comparison

We can time some test queries to demonstrate the performance impact of adding that index.

First, without the index:

-- Query customers from Los Angeles
SELECT * FROM customers WHERE city = ‘Los Angeles‘;

-- 0.540 seconds

-- Query customers from 3 cities 
SELECT * FROM customers 
WHERE city IN (‘New York‘, ‘Chicago‘, ‘Seattle‘);

-- 1.223 seconds  

Now let‘s rerun after adding the index on city:

-- Query customers from Los Angeles
SELECT * FROM customers WHERE city = ‘Los Angeles‘;

-- 0.012 seconds

-- Query customers from 3 cities
SELECT * FROM customers
WHERE city IN (‘New York‘, ‘Chicago‘, ‘Seattle‘);  

-- 0.018 seconds

As you can see, the index makes a huge difference, accelerating queries by over 50x!

This is because the database can now search the indexed column directly instead of scanning the entire table. Amazing!

Best Practices for Database Indexes

Now that you‘ve seen the performance magic of indexing, let‘s go over some best practices to use indexes effectively:

  • Choose columns carefully – Adding too many indexes can slow down updates and inserts. Focus on frequently filtered columns.

  • Consider index types – Clustered and nonclustered indexes have different use cases. Understand the tradeoffs.

  • Index narrow columns – Indexes work best on columns with a limited number of distinct values like country or status.

  • Place indexes logically – Order columns in a multi-column index for most efficient filtering.

  • Review regularly – Reassess indexes as data changes to maximize performance over time.

  • Test queries – Benchmark with and without indexes to quantify performance gains.

Properly using indexes takes some learning and experimentation. But the effort pays off through faster queries!

When Not to Use an Index

Indexes speed up read queries, but they also carry some overhead:

  • Disk space – Indexes take up storage space. Extra indexes have a cumulative effect.

  • Write costs – Inserts and updates have to write to indexes, slowing down writes.

  • Memory – Indexes cached in memory increase the database‘s memory footprint.

So indexes may not be beneficial in some cases:

  • Very small tables – Scanning is fast since the table is small.

  • Columns rarely queried – Indexes unused in queries just take up overhead.

  • Columns updated frequently – Updates have to propagate to all indexes.

  • Queries doing full table scans – An index won‘t help if you‘re not filtering rows.

  • Limited hardware resources – Extra indexes may not fit in memory or storage.

Think through these tradeoffs when deciding which indexes will be most impactful.


Let‘s recap what we covered about database indexing:

  • Indexes speed up queries by creating an ordered lookup for column values without scanning the full table.

  • Use CREATE INDEX to index a column that is frequently filtered in WHERE clauses.

  • Compare query times with and without indexes to quantify performance gains.

  • Follow best practices to maximize indexing benefits while minimizing overhead.

  • Avoid over-indexing tables and columns that see few searches.

I hope this guide has demystified indexing for you! Proper indexing takes your SQL queries to the next level.

Optimizing indexes is an art – it takes practice and experimentation. But you‘ll reap lower latency and happier users. Happy indexing my fellow data geek!

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