This article demonstrates a step‑by‑step method to confirm that InnoDB secondary index leaf pages store rows sorted first by the secondary key and then by the primary key, using bcview, mysqlblock, and a small C utility to read raw .ibd pages.
This article explains the theoretical foundations of relational databases, outlines the essential storage, engine, and UI layers, and walks through a concrete minimal implementation using fixed‑length tables, B+‑tree indexes, simple SQL parsing with regular expressions, and a TCP‑based client interface.
This article explains MySQL’s indexing fundamentals, the left‑most prefix rule, B+‑tree structure, disk I/O considerations, and provides step‑by‑step methods and real‑world cases for diagnosing and optimizing slow queries in production systems.
This article explains MySQL index fundamentals, B+‑tree structures, disk I/O considerations, and practical steps for diagnosing and optimizing slow queries, illustrated with multiple real‑world examples and concrete indexing guidelines.
This article provides a comprehensive overview of MySQL indexing, covering the theoretical foundations of B‑Tree and B+Tree data structures, the differences between MyISAM and InnoDB index implementations, and practical optimization techniques such as left‑most prefix usage, selectivity analysis, and prefix indexing.
This article examines MySQL indexing theory, covering B‑Tree and B+Tree structures, storage‑engine differences between MyISAM and InnoDB, left‑most prefix rules, index selectivity, prefix indexes, and practical optimization techniques for high‑performance query execution.
This article explains the characteristics and usage scenarios of B-Tree and Hash indexes in MySQL, including supported operators, example queries, when indexes are applied or ignored, and performance considerations for different storage engines such as MEMORY.
This article illustrates the structure of a B+ tree, explains how leaf and non‑leaf nodes store data and pointers, and walks through a concrete search for the key 29, highlighting the three disk I/O operations required to locate the value.
This article explains the fundamentals of B+Tree indexes, why they matter for SQL performance, and presents three concise indexing techniques—single, composite, and covering indexes—to reduce resource consumption, I/O, and sorting overhead in relational databases.
This article explains how to build a minimal database by storing records in a fixed‑length text file, using B‑tree structures for efficient lookup, creating index files for non‑primary fields, and outlines additional features such as SQL parsing, joins, transactions, backup and remote access.
