Difficulty: Advanced

Reading Time: 45 min read

Last Updated: September 01, 2025

Why Optimized Index Structures?

Efficient data access lies at the heart of every database system. Without the right indexing and storage strategies, even the most powerful hardware can feel sluggish under the weight of large datasets and complex queries.

Traditionally, B+-trees and simple hash tables have been the backbone of database indexing. But as applications scale, workloads diversify, and storage media like SSDs replace spinning disks, these classical techniques are no longer sufficient on their own. New challenges—ranging from write-heavy workloads to multi-dimensional queries—demand new solutions.

In this article, we’ll explore the full landscape of indexing and storage:

• Write-optimized structures like LSM Trees and Buffer Trees
• Special-purpose indices such as Bitmap Indices for analytics and R-Trees for spatial data.
• And finally, how these ideas connect to PostgreSQL’s real-world storage and index system, including its page layout, MVCC, and diverse index types (B-Tree, Hash, GiST, GIN, BRIN, and more).

By the end, you’ll see how theory meets practice: the evolution of indexing from fundamental concepts to the rich ecosystem powering modern databases.

1. Write-Optimized Index Structures

B+-Tree Performance Issue (Why an Alternative is Needed)

• When data is inserted out of order, each insert might touch a different leaf node.
• If those leaf nodes are not already in memory, the system must:
  • Read the disk page (random I/O)
  • Update it
  • Write it back to disk (another I/O)
• Disk-based systems (especially magnetic ones) perform very poorly under random writes.

We learned that B+-trees, while great for reads, can perform poorly in write-intensive scenarios. That’s because they involve random I/O when inserting, updating, or deleting data, which is expensive on disk.