A public account focused on workplace, career growth, team management, and self-improvement. The author is the writer of books including 'SpringBoot Technology Insider' and 'Drools 8 Rule Engine: Core Technology and Practice'.
This article explains how MySQL's implicit type conversion during table joins can render indexes unusable or alter join order, and provides practical solutions such as aligning column types, using optimizer hints, and forcing indexes to restore query performance.
This article explores how MySQL decides whether queries with IS NULL or IS NOT NULL predicates can use an index, presenting three concrete examples with varying data sizes and NULL distributions, and explains the underlying storage of NULL values and optimizer cost decisions.
This article explains the five MySQL temporal column types—DATE, DATETIME, TIMESTAMP, YEAR, and TIME—detailing their storage size, valid ranges, typical use‑cases, timezone behavior, and the 2038 problem, helping developers pick the most suitable type for any scenario.
This article explains how MySQL's OR conditions can cause index loss, provides concrete query examples, analyzes why the optimizer falls back to full table scans, and offers practical solutions such as rewriting with UNION or adding missing indexes to restore efficient index usage.
This article explains MySQL’s native JSON data type introduced in 5.7, covering its binary storage format, built‑in functions, generated‑column indexing, performance advantages over string storage, and detailed CRUD examples with SQL syntax and practical code snippets.
This article explains what MySQL composite (multi‑column) indexes are, how they are built and ordered, the leftmost‑prefix rule that governs their usage, factors such as cardinality and selectivity for optimal design, and common scenarios where the index may become ineffective.
This article explains three frequent MySQL table‑design errors—choosing wrong data types and lengths, missing or redundant indexes, and improper storage structures—illustrating how they affect storage, query speed, and scalability, and offers practical guidelines to prevent them.
This article walks through the evolution from binary search trees to balanced trees, B‑trees and finally B+ trees, explaining how MySQL's InnoDB and MyISAM storage engines implement these structures to reduce disk I/O and boost query performance.
MySQL query optimization involves three distinct index processing stages—Index Key, Index Filter, and Table Filter—each handling different parts of condition matching and data retrieval, and mastering their roles and execution flow can dramatically improve query performance, especially with index condition pushdown.
This article explains the functional differences, performance considerations, and practical use cases of MySQL's SELECT DISTINCT and GROUP BY clauses, providing syntax details, example tables, query results, and a comparison table to help developers choose the right approach for deduplication or aggregation tasks.
