B-Trees reduce the number of page reads needed to find data by storing many keys per node. They are a cornerstone of databases, filesystems, and storage engines.
Why it shows up
Choose it when your data lives on disk or SSD pages and you need ordered lookup, insertion, deletion, and range scans.
Common domains
PostgreSQL, MySQL, SQLite, and similar systems
Ordered indexes commonly rely on B-Tree variants so the engine can locate rows quickly and also scan ranges efficiently.
Why this structure fits
Each node stores many keys, so finding a row requires very few page reads.
Directories, extents, and block maps
Filesystems use tree-based indexing to track file locations, names, and extents without linear scans across storage.
Why this structure fits
Shallow trees pair well with fixed-size blocks and ordered traversal.
On-disk ordered maps and range iterators
Storage engines that need sorted iteration and prefix ranges often rely on B-Tree style structures or close relatives.
Why this structure fits
The design supports exact lookup and sequential range walking from nearby leaves.
Pagination, range filtering, and sort-heavy queries
Administrative dashboards and analytics queries benefit when the backend index can satisfy ordered reads directly.
Why this structure fits
B-Trees naturally maintain sorted key order.
