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This guide explains what the MySQL slow query log is, why it matters for performance, how to check its status, enable it temporarily or permanently in Docker‑based deployments, troubleshoot command‑not‑found errors, read the log file, and filter entries to focus on the truly slow queries.
This article explains MySQL's storage architecture, compares the DELETE, TRUNCATE, and DROP commands, shows their performance and side‑effects, and provides practical guidance on choosing the safest and most efficient way to remove data from a MySQL table.
This article investigates a slow OceanBase SQL query by examining CPU and TopSQL metrics, dissecting the execution plan, identifying batch join inefficiencies, rewriting the SQL for better performance, and demonstrating a reduction in execution time from over 2600 seconds to under one second.
This article introduces MySQL's tree‑style execution plan output using EXPLAIN FORMAT=TREE, explains its advantages over traditional and JSON formats, and demonstrates its practical use through eight detailed SQL examples with full plan analysis and performance insights.
This article explains how to use MySQL's EXPLAIN command to analyze slow queries, interprets each field in the execution plan, provides practical examples and optimization techniques, and offers advanced diagnostics for improving index usage and overall SQL performance.
This article presents a real‑world case study of a high‑traffic backend API that originally took 30 seconds to process over two million records, detailing problem diagnosis, SQL and Java code analysis, and a series of optimizations—including SQL rewrites, database‑side aggregation, and Caffeine caching—that ultimately cut the response time to under one second.
This article presents a comprehensive set of MySQL optimization techniques, covering the avoidance of SELECT *, proper use of specific fields, index-friendly query patterns, join strategies, handling of NULL and default values, efficient use of UNION, batch inserts, and numerous other performance‑enhancing tips for database developers.
This article presents a series of MySQL performance‑tuning techniques—including smarter LIMIT usage, avoiding implicit type conversion, rewriting joins, handling mixed ordering, replacing EXISTS with joins, pushing predicates into subqueries, early row limiting, and using WITH clauses—to dramatically reduce query execution time across common scenarios.
The case study details how an activity‑middleware platform tackled slow SQL problems by cleaning historic data, introducing sharding and Elasticsearch off‑loading, optimizing queries with proper indexes and simplified joins, and automating nightly deletions, ultimately cutting daily slow queries from thousands to double‑digit levels and boosting system stability.
The article shows how common MySQL performance pitfalls—such as large LIMIT offsets, implicit type conversions, sub‑query‑based updates or deletes, mixed ORDER BY, EXISTS clauses, and misplaced predicates—can be rewritten into index‑friendly forms using filters, joins, early limits, UNION ALL, and CTEs to achieve execution times that drop from seconds to milliseconds.
After upgrading from MySQL 5.7 to 8.0, a client observed that ORDER BY queries on wide SELECTs became dramatically slower because MySQL 8.0 deprecated the max_length_for_sort_data setting, causing the optimizer to perform full‑field sorting instead of index‑based sorting; adding an index on the ordered column restores performance.
After upgrading a client from MySQL 5.7 to 8.0, query performance slowed dramatically due to changes in ORDER BY handling, and the article analyzes the root cause, presents verification tests, and recommends adding indexes to restore millisecond‑level response times.
This article shows how AI tools like DeepSeek can dramatically speed up data‑engineering tasks—such as fixing long‑running SQL queries, building real‑time data pipelines with Flink, and deciphering legacy stored procedures—while offering concrete prompts, real‑world case studies, and five time‑saving techniques.
This article explains the meaning and optimization strategies for various MySQL EXPLAIN EXTRA hints—including Impossible WHERE/HAVING, No matching rows, Recursive queries, Rematerialize, and canned N databases—provides example execution plans, code snippets, and performance comparisons to help developers write more efficient SQL statements.
This article explains the meaning of the Extra column in MySQL EXPLAIN results, covering common values such as Using index, Backward index scan, Select tables optimized away, and Zero limit, and shows how to interpret and optimize each case with concrete examples.
This tutorial walks through MySQL's EXPLAIN TYPE column, illustrating how different join types such as const, eq_ref, ref, range, and index affect query performance using concrete SQL examples and execution‑plan analysis on a sample table with a composite primary key.
The article details the background, architectural analysis, multi‑tenant redesign, engine selection enhancements, compatibility adaptations, stability fixes, containerized deployment, performance optimizations, and measurable business outcomes of the Intelligent Acceleration Engine upgrade using Apache Kyuubi and StarRocks within the 58 big data platform.
This article introduces four open‑source MySQL performance tools—mysqltuner.pl, tuning‑primer.sh, pt‑variable‑advisor, and pt‑query‑digest—explaining their purpose, how to download and run them, and how to interpret the diagnostic reports they generate for database optimization.
This article explains how multi‑table joins in MySQL can become performance bottlenecks and walks through the four join algorithms—Simple Nested‑Loop, Block Nested‑Loop, Hash, and Index Nested‑Loop—showing their execution plans, code examples, and step‑by‑step optimizations using indexes and join buffers to dramatically improve query speed.
This article explains common MySQL performance pitfalls such as large‑offset LIMIT queries, implicit type conversion, sub‑query updates, mixed sorting, inefficient EXISTS clauses, predicate push‑down limitations, and demonstrates how rewriting with proper indexes, JOINs, early range reduction, and WITH (CTE) statements can reduce execution time from seconds to milliseconds.