Which databases are supported by these patterns?

While the concepts are universal to relational databases, it provides specific syntax and optimization strategies for PostgreSQL and MySQL.

Does it include advice on database schema design?

Absolutely. It covers best practices for data types, normalization, and proactive indexing to ensure scalability from the initial design phase.

Can this skill help with N+1 query problems?

Yes, it includes specific implementation patterns for identifying N+1 issues and replacing them with efficient JOINs or batch loading strategies.

How does this skill improve query performance?

It provides standardized patterns for interpreting database execution plans and suggests optimal indexing or query refactoring techniques to reduce execution time.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: drgaciw

bydrgaciw

Database Management

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

About

The SQL Optimization Patterns skill empowers Claude to act as a database performance engineer, providing expert guidance on transforming slow queries into high-performance operations. It offers systematic approaches to interpreting EXPLAIN execution plans, implementing sophisticated indexing strategies—such as composite, partial, and covering indexes—and refactoring inefficient query logic to eliminate common anti-patterns like N+1 queries and expensive offsets. This skill is essential for developers looking to reduce database load, minimize infrastructure costs, and ensure application scalability across PostgreSQL, MySQL, and other relational databases.

Key Features

Deep analysis of EXPLAIN and EXPLAIN ANALYZE execution plans
Implementation of materialized views and table partitioning for big data
Advanced indexing patterns including B-Tree, GIN, and functional indexes
0 GitHub stars
Cursor-based pagination strategies for large-scale datasets
Systematic resolution of the N+1 query anti-pattern

Use Cases

Designing performant database schemas for data-intensive applications
Identifying and fixing performance bottlenecks in slow production queries
Refactoring ORM-generated queries to improve execution efficiency

About

Key Features

Deep analysis of EXPLAIN and EXPLAIN ANALYZE execution plans
Implementation of materialized views and table partitioning for big data
Advanced indexing patterns including B-Tree, GIN, and functional indexes
0 GitHub stars
Cursor-based pagination strategies for large-scale datasets
Systematic resolution of the N+1 query anti-pattern

Use Cases

Designing performant database schemas for data-intensive applications
Identifying and fixing performance bottlenecks in slow production queries
Refactoring ORM-generated queries to improve execution efficiency