Understanding B+ Trees: From Binary Search Trees to MySQL Indexes

In MySQL, both InnoDB and MyISAM use B+‑trees for indexing; the article starts with the simplest binary search tree (BST) and shows how its unbalanced nature can degrade search performance to O(n) when the tree becomes skewed.

Balanced binary trees such as AVL trees enforce strict height balance, guaranteeing O(log n) operations, but the rotation overhead—especially during deletions—makes them less practical for many workloads.

Red‑black trees relax balance constraints, reducing rotation cost to a constant number of operations, which improves deletion performance and makes them popular in in‑memory structures like Java's TreeMap and HashMap, though their height remains too large for disk‑based storage.

B‑trees are multi‑way balanced trees designed for secondary storage; by allowing many children per node, they keep tree height low, reducing disk I/O and exploiting locality of reference.

B+‑trees further refine B‑trees: only leaf nodes store actual records, internal nodes store keys, and leaves are linked via a doubly‑linked list, enabling efficient range queries and even lower tree height, which translates to fewer I/O operations.

The article provides a practical estimation of B+‑tree height in InnoDB, showing that a three‑level B+‑tree can store around 100 million rows, whereas a comparable binary tree would need about 26 levels, illustrating the dramatic I/O savings.

Finally, the summary compares each tree type, concluding that B+‑trees combine low height, high cache hit rates, and efficient range scans, making them the preferred index structure for databases.