What database engines are supported?

The skill focuses on universal SQL optimization principles with specific implementation patterns for PostgreSQL and MySQL, including engine-specific indexing and analysis tools.

Can it help fix the N+1 query problem?

Yes, it includes specific patterns for identifying N+1 anti-patterns and transforming them into efficient JOINs or batch loading operations.

How does this skill help with slow SQL queries?

It provides systematic patterns for analyzing EXPLAIN plans to find bottlenecks, identifying missing indexes, and refactoring inefficient query structures for maximum speed.

Does this cover advanced database features?

Yes, it provides guidance on advanced topics such as materialized views, table partitioning, and the use of specialized index types like GIN and BRIN.

How do I use this skill to optimize my schema?

You can use it during the design phase to select appropriate data types, plan composite indexes based on query patterns, and implement partitioning for large datasets.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: HermeticOrmus

byHermeticOrmus

Database Management

Optimizes SQL database performance through systematic query plan analysis, advanced indexing strategies, and efficient data access patterns.

About

This skill empowers developers to transform slow database operations into high-performance queries by applying industry-standard optimization techniques. It provides expert guidance on interpreting EXPLAIN execution plans, implementing specialized indexing (like GIN, BRIN, and covering indexes), and refactoring common performance killers such as N+1 query patterns and inefficient OFFSET pagination. Whether you are designing a scalable schema from scratch or debugging production bottlenecks, this skill provides the patterns needed to reduce database load and improve application responsiveness.

Key Features

Comprehensive analysis of EXPLAIN and EXPLAIN ANALYZE execution plans
Advanced indexing patterns including composite, partial, and expression indexes
Refactoring techniques for N+1 queries, subqueries, and complex JOINs
Implementation of advanced features like materialized views and table partitioning
0 GitHub stars
High-performance pagination strategies using cursor-based methods

Use Cases

Debugging and accelerating slow-running queries in production environments
Designing performant database schemas for data-intensive applications
Optimizing batch data operations and reducing database infrastructure costs

About

Key Features

Comprehensive analysis of EXPLAIN and EXPLAIN ANALYZE execution plans
Advanced indexing patterns including composite, partial, and expression indexes
Refactoring techniques for N+1 queries, subqueries, and complex JOINs
Implementation of advanced features like materialized views and table partitioning
0 GitHub stars
High-performance pagination strategies using cursor-based methods

Use Cases

Debugging and accelerating slow-running queries in production environments
Designing performant database schemas for data-intensive applications
Optimizing batch data operations and reducing database infrastructure costs