Can this skill help with N+1 query issues?

Yes, it provides specific strategies for identifying N+1 query anti-patterns and refactoring them using JOINS or batch loading techniques.

How does this improve application performance?

By reducing query latency and database CPU usage, it directly improves application response times and enables the system to handle more concurrent users.

When should I use the sql-optimization-patterns skill?

Use it during the database schema design phase, when debugging application performance bottlenecks, or when preparing to scale for larger datasets.

What database systems does this skill support?

While it features specific PostgreSQL patterns, the core concepts of indexing, query plans, and optimization logic are applicable to MySQL, MariaDB, and most relational SQL databases.

Does it explain how to interpret EXPLAIN ANALYZE?

Yes, it provides a breakdown of key metrics like Seq Scans, Index Scans, and Costs to help you understand and improve query execution paths.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: carloscroc

bycarloscroc

Database Management

Optimizes SQL queries and database performance through advanced indexing strategies and execution plan analysis.

About

SQL Optimization Patterns empowers developers to transform slow database operations into high-performance queries by applying systematic optimization techniques. It provides expert guidance on interpreting EXPLAIN plans, implementing sophisticated indexing strategies like B-Tree, GIN, and partial indexes, and resolving common performance bottlenecks such as the N+1 query problem and inefficient offset-based pagination. Whether you are designing a new schema from scratch or debugging legacy application lag, this skill provides the implementation patterns needed to reduce database load, minimize costs, and ensure application scalability.

Key Features

High-performance pagination techniques using cursor-based logic
Deep analysis of SQL EXPLAIN and EXPLAIN ANALYZE execution plans
0 GitHub stars
Advanced indexing strategies including Composite, Partial, and GIN indexes
Systematic resolution of N+1 query anti-patterns using joins and batching
Implementation of Materialized Views and Table Partitioning for large datasets

Use Cases

Reducing cloud infrastructure costs by optimizing database resource utilization
Debugging and fixing slow-running production database queries
Designing performant schemas for high-growth application data

About

Key Features

High-performance pagination techniques using cursor-based logic
Deep analysis of SQL EXPLAIN and EXPLAIN ANALYZE execution plans
0 GitHub stars
Advanced indexing strategies including Composite, Partial, and GIN indexes
Systematic resolution of N+1 query anti-patterns using joins and batching
Implementation of Materialized Views and Table Partitioning for large datasets

Use Cases

Reducing cloud infrastructure costs by optimizing database resource utilization
Debugging and fixing slow-running production database queries
Designing performant schemas for high-growth application data