The MySQL optimizer can select an inappropriate index due to inaccurate statistics or sampling errors, but you can correct this by refreshing statistics with ANALYZE TABLE, increasing sample pages, or explicitly forcing the right index using the FORCE INDEX hint.
This article examines a puzzling MySQL case where the optimizer selects a full‑table scan despite suitable indexes, explains the role of cost constants, execution‑plan analysis, and demonstrates how to force the optimizer to use the optimal index for faster queries.
This article recounts a real‑world incident where MySQL 5.7 chose a sub‑optimal index, causing a three‑second slow query, and explains how the team identified the root cause, used EXPLAIN and optimizer trace, and applied fixes such as forced index hints and data cleanup.
This article explains MySQL's architecture, the server and storage‑engine layers, query execution phases, status states, query cache behavior, the optimizer's parsing and planning steps, EXPLAIN output fields, the cost model, and why the optimizer selects index_author_id instead of index_title for a given query.
A production MySQL query that should have returned instantly took 44 seconds because the optimizer selected the primary key index instead of a suitable composite index, and the article explains the root cause, the optimizer's decision process, and multiple practical solutions to prevent such slow‑query incidents.
This article summarizes a MySQL webinar covering cost‑based query optimization, monitoring and analysis tools, data‑access methods and index selection, join optimization techniques, and ways to influence the optimizer through hints and configuration, providing a concise technical reference for database practitioners.
