MySQL Slow Query Diagnostic Script (2025): A Complete Production DBA Guide

Estimated Reading Time: 6–7 minutes

Slow queries are one of the biggest reasons for performance degradation in MySQL and Aurora MySQL environments. High latency SQL can create CPU spikes, I/O pressure, row lock waits, replication lag, and application-level timeouts.

This article provides a production-ready MySQL Slow Query Diagnostic Script, explains how to interpret the results, and shows how DBAs can use this script for proactive tuning and operational monitoring.

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Table of Contents

1.      What Slow Query Diagnostics Mean for MySQL DBAs

2.      Production-Ready MySQL Slow Query Diagnostic Script

3.      Script Output Explained

4.      Additional Performance Metrics to Watch

5.      Add-On Scripts (Top by Buffer Gets, Disk Reads)

6.      Real-World MySQL DBA Scenario

7.      How to Automate These Checks

8.      Interview Questions

9.      Final Summary

10.  FAQ

11.  About the Author

12.  Call to Action (CTA)

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1. What Slow Query Diagnostics Mean for MySQL DBAs

Slow queries lead to:

·         High CPU utilisation

·         Increased IOPS and latency

·         Row lock waits and deadlocks

·         Replication lag in Aurora MySQL / RDS MySQL

·         Query timeout issues at the application layer

·         Poor customer experience under load

MySQL’s Performance Schema provides deep visibility into SQL patterns, allowing DBAs to identify:

·         High-latency queries

·         Full table scans

·         Missing index patterns

·         SQL causing temporary tables

·         SQL responsible for heavy disk reads

·         SQL generating high row examinations

Slow query diagnostics are essential for maintaining consistent performance in production systems.

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2. Production-Ready MySQL Slow Query Diagnostic Script

This script analyses execution time, latency, row scans and query patterns using Performance Schema:

/* MySQL Slow Query Diagnostic Script

   Works on: MySQL 5.7, MySQL 8.0, Aurora MySQL

*/

SELECT 

      DIGEST_TEXT AS Query_Sample,

      SCHEMA_NAME AS Database_Name,

      COUNT_STAR AS Execution_Count,

      ROUND(SUM_TIMER_WAIT/1000000000000, 4) AS Total_Time_Seconds,

      ROUND((SUM_TIMER_WAIT/COUNT_STAR)/1000000000000, 6) AS Avg_Time_Per_Exec,

      SUM_ROWS_EXAMINED AS Rows_Examined,

      SUM_ROWS_SENT AS Rows_Sent,

      FIRST_SEEN,

      LAST_SEEN

FROM performance_schema.events_statements_summary_by_digest

WHERE SCHEMA_NAME NOT IN ('mysql','sys','performance_schema','information_schema')

ORDER BY Total_Time_Seconds DESC

LIMIT 20;

This is a field-tested script used in multiple production environments including AWS RDS MySQL and Amazon Aurora MySQL.

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3. Script Output Explained

Column	Meaning
Query_Sample	Normalized version of SQL for pattern analysis
Database_Name	Schema on which SQL is executed
Execution_Count	How many times the SQL pattern ran
Total_Time_Seconds	Total execution time consumed
Avg_Time_Per_Exec	Average latency per execution
Rows_Examined	Total rows scanned (detects full scans)
Rows_Sent	Rows returned by the query
FIRST_SEEN / LAST_SEEN	Time window of activity

These values help DBAs identify the highest-impact SQL patterns immediately.

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4. Additional Performance Metrics You Must Watch

During slow query investigations, always check:

·         High Rows_Examined → Missing index

·         High Avg_Time_Per_Exec → Expensive joins or sorting

·         High Rows_Examined vs Rows_Sent difference → Inefficient filtering

·         High Execution_Count → Inefficient query called repeatedly

·         Repeated occurrence between FIRST_SEEN and LAST_SEEN → Ongoing issue

MySQL workload analysis becomes easy when these metrics are evaluated together.

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5. Add-On Script: Top SQL by Buffer Gets

Useful for identifying CPU-heavy SQL:

SELECT 

    sql_id,

    buffer_gets,

    executions,

    ROUND(buffer_gets/EXECUTIONS, 2) AS gets_per_exec,

    sql_text

FROM performance_schema.events_statements_summary_by_digest

ORDER BY buffer_gets DESC

LIMIT 10;

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6. Add-On Script: Top SQL by Disk Reads

Identifies IO-intensive SQL patterns:

SELECT 

    DIGEST_TEXT,

    SUM_ROWS_EXAMINED,

    SUM_ROWS_SENT,

    SUM_CREATED_TMP_TABLES,

    SUM_CREATED_TMP_DISK_TABLES

FROM performance_schema.events_statements_summary_by_digest

ORDER BY SUM_CREATED_TMP_DISK_TABLES DESC

LIMIT 10;

These help diagnose latency issues caused by slow storage or inefficient joins.

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7. Real-World MySQL DBA Scenario

A typical incident scenario:

1.      Application complaints about slow API response

2.      CloudWatch shows high read latency

3.      Slow query log or Performance Schema shows a SQL digest consuming high execution time

4.      SQL performs a full table scan on a large table

5.      Missing index identified on a WHERE clause or JOIN condition

6.      Index added / query refactored

7.      Latency drops, performance normalises

This is the real process DBAs follow for incident resolution.

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8. How to Automate These Checks

DBAs typically automate slow query monitoring using:

·         Linux cron + shell scripts

·         Python automation with scheduling

·         n8n workflows + MySQL nodes

·         AWS CloudWatch + Lambda alerts for Aurora MySQL

·         Grafana + Prometheus exporters

·         Slack / Teams notifications for high-latency SQL

Automation ensures issues are detected before users experience downtime.

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9. Interview Questions – Slow Query Diagnostics

Be ready for:

·         How do you find top slow queries in MySQL?

·         What is the advantage of Performance Schema?

·         Difference between Rows_Examined and Rows_Sent?

·         What creates temporary disk tables?

·         How do you detect missing indexes from slow queries?

·         How do you reduce query execution time?

·         How does MySQL slow query log differ from Performance Schema?

Mentioning these scripts gives you a strong technical advantage.

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10. Final Summary

Slow query diagnostics are essential for maintaining high performance in MySQL, Aurora MySQL, and RDS MySQL systems. The diagnostic script provided above offers deep visibility into SQL patterns, latency contributors and row scan behaviour.

This script can be used for daily health checks, tuning analysis, or fully automated monitoring workflows.

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11. FAQ – MySQL Slow Query Diagnostics

Q1: What causes slow queries in MySQL?
Missing indexes, inefficient joins, large table scans, temporary table creation, outdated statistics, or poor schema design.

Q2: Does this script work in Aurora MySQL?
Yes, it works in Aurora MySQL 2.x/3.x because Performance Schema is supported.

Q3: Should I enable slow query logs as well?
Yes, slow query logs complement Performance Schema for long-running queries.

Q4: Can this script detect full table scans?
Yes—high Rows_Examined with low Rows_Sent is a clear indicator.

Q5: Does this script impact performance?
No, Performance Schema summary tables are lightweight.

 About the Author

Chetan Yadav is a Senior Oracle, PostgreSQL, MySQL and Cloud DBA with 14+ years of experience supporting high-traffic production environments across AWS, Azure and on-premise systems. His expertise includes Oracle RAC, ASM, Data Guard, performance tuning, HA/DR design, monitoring frameworks and real-world troubleshooting.

He trains DBAs globally through deep-dive technical content, hands-on sessions and automation workflows using n8n, AI tools and modern monitoring stacks. His mission is to help DBAs solve real production problems and advance into high-paying remote roles worldwide.

Chetan regularly publishes expert content across Oracle, PostgreSQL, MySQL and Cloud DBA technologies—including performance tuning guides, DR architectures, monitoring tools, scripts and real incident-based case studies.

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