Does it provide specific SQL code for the optimizations?

Yes, it provides ready-to-use SQL for creating indexes and rewritten versions of inefficient queries.

Which database systems are supported by this skill?

It supports major relational databases including MySQL, PostgreSQL, and cloud-managed services like AWS RDS.

Do I need to provide my database credentials to use this?

No, the skill works by analyzing downloaded log files (.log or .json) that you provide within your project directory.

Can it suggest complex indexes like compound or covering indexes?

Yes, it analyzes WHERE clauses, JOIN conditions, and selected columns to suggest optimal multi-column and covering indexes.

How does the skill prioritize which queries to fix first?

It calculates a total impact score by multiplying average execution time by execution frequency, highlighting the queries that cause the most total delay.

Slow Query Analysis & DB Tuning

Name: Slow Query Analysis & DB Tuning
Author: jo-minjun

byjo-minjun

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Database Management

Analyzes database slow query logs to identify performance bottlenecks and provide actionable indexing and optimization strategies.

About

This skill automates the diagnosis of database performance issues by parsing slow query logs from AWS RDS, MySQL, and PostgreSQL. It groups query patterns, calculates impact scores based on execution frequency and duration, and provides specific recommendations for SQL rewrites and index creation. By interpreting execution plans like EXPLAIN and ANALYZE, it helps developers transition from resource-heavy full table scans to efficient index-based lookups, significantly reducing database load and improving response times.

Key Features

1 GitHub stars
Intelligent index recommendations including composite and covering indexes
Automated parsing of MySQL, PostgreSQL, and AWS RDS slow query logs
Identification of high-impact query patterns through statistical aggregation
SQL rewriting suggestions to avoid index invalidation and unnecessary data retrieval
Detailed execution plan interpretation with performance improvement projections

Use Cases

Diagnosing sudden database latency spikes in production environments
Optimizing resource-heavy queries identified by AWS RDS Performance Insights
Reviewing and improving index efficiency for large-scale data retrieval

About

Key Features

1 GitHub stars
Intelligent index recommendations including composite and covering indexes
Automated parsing of MySQL, PostgreSQL, and AWS RDS slow query logs
Identification of high-impact query patterns through statistical aggregation
SQL rewriting suggestions to avoid index invalidation and unnecessary data retrieval
Detailed execution plan interpretation with performance improvement projections

Use Cases

Diagnosing sudden database latency spikes in production environments
Optimizing resource-heavy queries identified by AWS RDS Performance Insights
Reviewing and improving index efficiency for large-scale data retrieval