Difficulty: Advanced
Reading Time: 35 min read
Last Updated: September 01, 2025
In the previous article, we explored B-trees and B+ trees — the clever self-balancing structures that keep data organized and fast to retrieve. But here’s the thing: data structures in textbooks are elegant… yet in the real world, they must fight against scale, disk I/O, and billions of records.
That’s where Indexing comes in.
Think of an index like the table of contents of a huge book. Without it, finding one topic means scanning page by page — painfully slow. With it, you jump directly to the right section in seconds. Databases face the exact same problem: without an index, every search is a full table scan. With the right index, queries drop from minutes to milliseconds.
And the magic behind most modern indexes?
👉 Exactly those B-trees and B+ trees we just studied.
In this article, we’ll move beyond abstract trees and see how databases harness them to power fast search, efficient range queries, and real-world performance. By the end, you’ll see why indexing isn’t just a feature — it’s the heartbeat of every database system.
Imagine asking a university database:
Without indexing, the database would scan every single row to check if it matches. That might be fine for a few hundred records, but for millions, it’s painfully slow.
That’s where indexing comes in.