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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 how MySQL's optimizer calculates execution costs for different indexes, why it may select a full table scan instead of an apparently better index, and shows how to use EXPLAIN and optimizer trace to reveal and improve the chosen execution plan.
This article explains how MySQL calculates execution costs for different indexes using IO and CPU metrics, demonstrates with a sample table and queries, compares the optimizer's choices with actual performance, and shows how to use EXPLAIN and optimizer trace to fine‑tune query plans.
The article investigates why an UPDATE statement on a MySQL 5.7 InnoDB table with RR isolation experiences high lock time, demonstrates how missing indexes cause full‑table scans and blocking, and shows how adding an index and using optimizer trace can diagnose and resolve the issue.
This article explains how to enable MySQL's optimizer_trace feature, retrieve the detailed execution plan from the information_schema.OPTIMIZER_TRACE table, and interpret the cost‑based optimization phases (prepare, optimize, execute) to understand why the optimizer selects a particular index for a query.
This article explains why SQL queries become slow, how to capture slow queries using MySQL's slow query log, and provides detailed step‑by‑step methods for analyzing execution plans, profiling, optimizer tracing, and I/O parameters to identify and resolve performance bottlenecks.
This article explains the process by which MySQL transforms uncorrelated IN subqueries into correlated subqueries, details the differences in EXPLAIN output columns, explores optimizer trace information, compares materialization versus correlated execution costs, and provides best‑practice recommendations for writing efficient queries.
This article explains why MySQL may still use a composite index as a covering or range index despite the query not following the leftmost‑prefix rule, by analyzing optimizer trace output and demonstrating the behavior with concrete experiments.
This article examines how MySQL estimates execution costs for queries, explains why COUNT(*) can be optimal, demonstrates index selection with real‑world examples, and shows how optimizer_trace can reveal mismatches between estimated and actual performance.