This article examines how MySQL stores rows in InnoDB, compares the four row formats, shows how NULL and default values affect storage, hidden columns, and record headers, and discusses the trade‑offs of defining columns as NOT NULL or allowing NULL values.
This article consolidates theory and practical calculations to reveal how MySQL's B+‑tree storage, page structure, and row size determine the realistic maximum number of records a single table can store, ranging from millions to billions depending on schema choices.
This article explains the differences between B‑trees and B+‑trees, covering row vs. column storage, index construction, binary search trees, and how B+‑trees provide faster insert, delete, and range queries in modern database systems.
This article explains the differences between row-based and column-based storage, comparing their write and read performance, outlining advantages and disadvantages, and describing suitable scenarios such as OLAP queries, column families, compression, and indexing, to help choose the appropriate storage model.
This article explains the fundamental differences between row‑based and column‑based storage in databases, compares their write and read performance, lists pros and cons, presents an openGauss experiment with sample tables, and offers practical guidance on when to choose each storage model.
Tongcheng Yilong’s BaikalDB deployment combines high‑availability multi‑Raft HA, HTAP support, and share‑nothing scalability to deliver over 72K TPS OLTP and ten‑fold faster OLAP queries while cutting operational costs up to a hundredfold through dual‑center, columnar storage and cloud‑native elasticity.
This article explains fundamental big‑data concepts by comparing offline (batch) and real‑time (stream) computing, distinguishing real‑time queries from ad‑hoc queries, clarifying OLTP versus OLAP workloads, and outlining the differences between row‑based and column‑based storage architectures.
