The author argues that in most companies you only need to write simple, single‑table or limited‑join queries and focus on index tuning and basic MySQL concepts such as InnoDB, locks, and execution flow, while complex SQL work often signals a poorly organized team.
During an Oracle‑to‑OceanBase migration, a client encountered extremely slow INSERT…SELECT batch jobs due to a global unique non‑partitioned index, and the article walks through extracting logical and physical plans, pinpointing the bottleneck operators, and demonstrates converting the index to a partitioned one to cut execution time from over 30 minutes to 14 minutes.
This article explains why massive tables become slow, identifies common bottlenecks such as disk I/O, missing indexes, deep pagination and lock contention, and provides a step‑by‑step guide covering table design, indexing, SQL tuning, sharding, caching and a real‑world case study to dramatically improve performance.
This article presents a comprehensive guide to database performance improvement, covering index tuning, query rewriting, proper table design, caching strategies, hardware choices, server parameter tweaks, monitoring, connection pooling, and data cleanup, all illustrated with practical examples and diagrams.
This article walks through a MySQL 5.6 scenario with course, student, and score tables, demonstrates why a naïve sub‑query is extremely slow, and shows step‑by‑step how adding single‑column, composite, and covering indexes, as well as rewriting the query to joins, can reduce execution time from hours to milliseconds.
The article details how Meituan built a MySQL autonomous platform by constructing kernel observability to split OnCPU/OffCPU wait time, capturing full SQL directly from the kernel with compression, designing a safe exception‑handling workflow, and generating cost‑based index‑tuning suggestions—including what‑if analysis and workload‑driven recommendations—to enable comprehensive SQL governance.
Facing severe IO-wait and read bottlenecks as product data grew from tens of millions to billions, this article analyzes root causes in Elasticsearch clusters and presents a comprehensive solution involving index parameter tuning, merge settings, translog async writes, query optimizations, and hardware upgrades to restore performance and stability.
This guide walks through systematic MySQL query optimization—from identifying slow statements via logs and EXPLAIN analysis, to profiling, tracing, and applying targeted fixes such as index tuning, query rewrites, and handling special cases like left‑most matching, implicit conversion, large pagination, and IN‑order‑by pitfalls.
Learn how to identify and fix slow MySQL queries by analyzing slow‑query logs, using EXPLAIN, profiling, and optimizer trace, understanding index types and extra flags, and applying practical case‑by‑case optimizations such as index tuning, query rewriting, pagination strategies, and handling large data sets.
This article walks through why SQL queries become bottlenecks as data grows and presents a step‑by‑step guide—including slow‑query analysis, EXPLAIN interpretation, profiling, optimizer tracing, index tuning, and scenario‑specific tricks—to dramatically improve MySQL performance.
This guide walks through essential MySQL performance tuning methods, from identifying slow queries via logs and EXPLAIN analysis to interpreting Extra information, using profiling and optimizer trace, and applying concrete index, query, and schema adjustments for common scenarios such as left‑most matching, implicit conversion, large pagination, IN‑order‑by, and complex queries.
This article outlines a systematic approach to identifying and fixing inefficient MySQL queries, covering slow‑query detection, EXPLAIN analysis, profiling, optimizer tracing, and practical case studies that demonstrate index tuning, query rewriting, and handling large‑scale pagination and complex conditions.
This article walks through a systematic MySQL SQL optimization workflow, explains EXPLAIN output types and Extra flags, demonstrates profiling and trace usage, and presents ten real‑world cases—from left‑most index matching to large pagination—offering practical measures and reference resources.
This article walks through a systematic SQL optimization workflow—identifying slow queries, interpreting EXPLAIN output, using SHOW PROFILE and TRACE, applying concrete tuning measures, and dissecting ten representative MySQL cases ranging from index ordering to large‑page handling and complex query pitfalls.
The second part of the Elasticsearch search‑tuning series explains how to pre‑index data, choose appropriate keyword or text mappings, minimize script use by preferring Painless or Lucene expressions, and efficiently force‑merge read‑only indices into single segments for better performance.
To meet a demanding monitoring project that required storing up to 400 million records per day in SQL Server 2012, the author details a step‑by‑step journey involving bulk‑copy insertion, removing indexes during load, hourly partitioned tables, strategic index creation, and query optimizations to achieve sub‑second query times.
