Creating an index is easy. Nearly every developer has created or used an index at some point, whether directly or indirectly. But knowing what to index is only one part of the equation. The more difficult question is understanding how the index works underneath.

Indexing isn’t a surface-level optimization. It’s a problem of data structures. The way an index organizes, stores, and retrieves data directly shapes the performance of read and write operations. Different data structures behave differently. 

• Some may excel at range scans. 

• Some are optimized for exact-match lookups. 

• Others are purpose-built for full-text search or geospatial queries. 

These decisions affect everything from query planning to I/O patterns to the amount of memory consumed under load.

When a query slows down or a system starts struggling with disk I/O, the index structure often sits at the heart of the issue. A poorly chosen index format can lead to inefficient access paths, unnecessary bloat, or slow inserts. Conversely, a well-aligned structure can turn a brute-force scan into a surgical lookup.

In this article, we will cover the core internal data structures that power database indexes. Each section will walk through how the structure works, what problems it solves, where it performs best, and what limitations it carries.