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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 why excessive MySQL joins can cause performance problems and provides a comprehensive set of techniques—including query splitting, temporary tables, denormalization, index tuning, query rewriting, result‑set reduction, configuration tweaks, big‑data tools, and summary tables—to effectively optimize multi‑table join queries.
This article explains the MySQL Connection Control Plugins introduced in 5.7.17, how improper configuration can lead to connection backlog and database hangs, and provides step‑by‑step installation, parameter tuning, testing procedures, and mitigation strategies to resolve the issue.
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.
A real‑world MySQL data‑migration case study shows how using INSERT INTO SELECT without proper indexing caused a full table scan, OOM errors, and data loss, and explains how to avoid the pitfall by adding appropriate indexes and understanding transaction locking.
This article explains MySQL's memory architecture, why memory usage often stays high after spikes, and provides practical steps—including connection checks, slow query analysis, workload scaling, and switching to jemalloc—to diagnose and reduce memory consumption on 360's internal cloud platform.
Although foreign keys enforce referential integrity, many enterprises avoid them because they can slow inserts, cause blocking cascade updates, trigger database update storms in high‑concurrency environments, and complicate sharding, leading developers to prefer redundant fields and handle relationships at the application layer.
This article explains OceanBase's full‑link tracing feature, covering its definition, internal working principle, trace‑log structure, span and tag details, configuration steps, and how to view trace data via the show‑trace command or the OCP visual interface, helping DBAs quickly diagnose performance issues.
This article explains the three MySQL EXPLAIN output formats—TRADITIONAL, JSON, and TREE—demonstrates how the JSON format provides richer cost and data‑read information, compares query costs of sample SQL statements, and offers practical optimization suggestions for poorly performing joins.
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.
This article examines MySQL's cost estimation process, explains why added indexes may still be ineffective, discusses the impact of memory fragmentation, and presents practical techniques such as prefix index handling, index merging, and resource monitoring to improve slow‑query performance.
The article explains that using IN and NOT IN in SQL can lead to poor performance, index misuse, and incorrect results especially with NULL values, and recommends replacing them with EXISTS/NOT EXISTS or JOIN constructs for more reliable and efficient queries.
This article explains three Redis-based methods—Hash, Bitmap, and HyperLogLog—for tracking website user visits, detailing how each structure works, their implementation steps, memory and accuracy trade‑offs, and guidance on choosing the best approach for different traffic scenarios.
This article explains why the SQL keywords IN and NOT IN often lead to poor performance and unexpected results, demonstrates the problems with large tables and NULL values, and recommends using EXISTS, NOT EXISTS, or JOIN alternatives for more reliable and efficient queries.
This article explains how to read MySQL's EXPLAIN EXTRA column, covering SEMI JOIN hints such as FirstMatch, Start/End temporary, LooseScan, and the Range‑checked‑for‑each‑record optimization, with concrete examples and command‑line output to illustrate each case.
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.
The article compares MySQL and PostgreSQL multi‑table join capabilities, explains why MySQL’s nested‑loop joins can be less efficient for complex queries, and discusses how decomposing joins into separate single‑table queries or moving logic to the service layer can improve performance, scalability, and caching.
Redis has unveiled a multi‑threaded query engine that dramatically increases query throughput and lowers latency for vector similarity searches, offering up to 16× performance gains and enabling real‑time Retrieval‑Augmented Generation (RAG) workloads in generative AI applications.
This article explains MySQL EXPLAIN type column, detailing the inefficiencies of type=ALL, how adding ORDER BY can change it to index, and demonstrates the use of INDEX_MERGE for UNION and INTERSECT queries, including practical examples and cost comparisons to improve query performance.
This article demonstrates how to use MySQL's EXPLAIN statement to examine execution plans, interpret key fields such as possible_keys, key, rows, and Extra, and apply index adjustments—including adding and forcing indexes—to improve query performance while weighing sorting costs.