Binary trees and B+ trees are both tree data structures used for organizing data, but they differ significantly in their structure, purpose, and typical use cases. Binary trees have nodes with at most two children, while B+ trees have a high branching factor with data stored only in leaf nodes.
The key difference lies in branching factor. Binary trees have a maximum of 2 children per node, causing the tree height to grow quickly as data increases. In contrast, B+ trees have a high branching factor, often hundreds or thousands of children per node, resulting in much lower tree height for the same amount of data. This makes B+ trees ideal for disk storage where minimizing tree height reduces disk reads.
A crucial difference is data storage location. In binary search trees, data is stored in every node, including internal nodes. This means the search path encounters actual data values at each level. In contrast, B+ trees store data only in leaf nodes. Internal nodes contain only keys that guide the search down to the leaf level where the actual data resides. This separation allows B+ trees to pack more keys into internal nodes, further reducing tree height.
For traversal and range queries, the structures behave very differently. Binary tree traversal requires recursive visits following in-order pattern: left subtree, root, then right subtree. This makes range queries complex as you must navigate up and down the tree. B+ trees excel at range queries because all leaf nodes are linked sequentially. Once you find the starting point of a range, you can traverse directly along the leaf level without backtracking, making range operations highly efficient.
The choice between binary trees and B+ trees depends on the application context. Binary trees are ideal for in-memory data structures, symbol tables, expression parsing, and general-purpose searching with small to medium datasets. They provide fast O(log n) performance when data fits in memory. B+ trees excel in database systems, file systems, and scenarios involving large datasets stored on disk. Their design minimizes disk I/O operations and provides efficient range queries, making them the standard choice for database indexes and file system implementations where data persistence and large-scale operations are crucial.