MySQL's query optimizer is generally reliable, but it can occasionally choose the wrong index, causing significant performance degradation. This article delves into the common root causes behind this issue, such as stale table statistics, inaccurate cardinality estimates, and the optimizer's cost model limitations. It provides concrete examples of problematic execution plans and explains how to diagnose them using EXPLAIN and other tools. Practical mitigation strategies include manually updating statistics with ANALYZE TABLE, using index hints like FORCE INDEX, or rewriting queries to guide the optimizer. For persistent cases, adjusting optimizer parameters or considering alternative indexing strategies (e.g., covering indexes) may be necessary. Understanding these pitfalls is essential for any developer or DBA responsible for database performance. The article emphasizes that while the optimizer is powerful, it is not infallible, and proactive monitoring is key to avoiding surprises in production.
This article explores why MySQL's optimizer sometimes selects suboptimal indexes, leading to slow queries. It explains root causes like outdated statistics and cardinality misestimates, and offers practical fixes such as using FORCE INDEX or updating statistics. This is crucial for developers maintaining high-performance databases.