This article explains why multi‑way search trees such as B‑trees, B+‑trees, B*‑trees and R‑trees are crucial for reducing disk I/O in large‑scale storage systems, covering their hardware background, structural definitions, height analysis, insertion and deletion algorithms, and practical examples with code and diagrams.
This article explains the fundamentals of database indexes, covering their purpose, underlying structures such as hash tables, B‑Tree and B+‑Tree, the advantages and drawbacks, and the various index types in MySQL including clustered, non‑clustered, primary, secondary, unique, prefix, and full‑text indexes.
This article provides a comprehensive overview of B‑tree, B+‑tree, B*‑tree and R‑tree data structures, explaining their definitions, node layouts, height analysis, insertion and deletion algorithms with examples, and their roles in external‑memory indexing for databases and file systems.
This article explains how database indexes improve query performance by exploring binary trees, binary search, balanced trees, B‑trees, and B+‑trees, illustrating their structures, advantages, disadvantages, and the impact of disk I/O on overall efficiency.
This article explains the concepts, properties, and operations of binary trees, binary search trees, AVL trees, 2‑3 and 2‑3‑4 trees, B‑trees, and red‑black trees, illustrating how they maintain balance, avoid degeneration, and are applied in modern software systems.
