How does this help with N+1 query problems?

It provides specific refactoring patterns using JOINs and batch loading to replace multiple database roundtrips with single, efficient operations.

What databases does this skill support?

While many examples focus on PostgreSQL, the core optimization concepts and patterns apply to most relational databases including MySQL, SQLite, and SQL Server.

Can it help with large-scale data migration?

Yes, it includes batch operation patterns and COPY command strategies to handle large volumes of data efficiently without locking tables.

Does this skill cover advanced features like partitioning?

Yes, it provides guidance on range partitioning and materialized views to handle very large datasets that outgrow standard table structures.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: amurata

byamurata

•

数据库管理

Optimizes database performance through advanced query patterns, indexing strategies, and detailed execution plan analysis.

关于

This skill empowers Claude to act as a database performance engineer, identifying and resolving bottlenecks in SQL-based applications. It provides comprehensive patterns for analyzing execution plans, implementing efficient indexing strategies like composite and partial indexes, and refactoring slow queries to eliminate anti-patterns such as N+1 issues and inefficient offsets. Whether you are designing a high-scale schema or debugging a slow production query, these patterns provide the systematic guidance needed to reduce database load, minimize costs, and significantly improve application response times.

主要功能

Execution plan analysis (EXPLAIN/ANALYZE) for deep bottleneck identification
Batch operation techniques to optimize bulk inserts and updates
3 GitHub stars
Comprehensive indexing strategies including B-Tree, GIN, and partial indexes
Query refactoring patterns to eliminate N+1 problems and slow offsets
Advanced database features like Materialized Views and Table Partitioning

使用场景

Accelerating slow-running reports and complex analytical queries
Refactoring application code to prevent database connection exhaustion
Designing scalable database schemas for high-traffic applications

关于

主要功能

Execution plan analysis (EXPLAIN/ANALYZE) for deep bottleneck identification
Batch operation techniques to optimize bulk inserts and updates
3 GitHub stars
Comprehensive indexing strategies including B-Tree, GIN, and partial indexes
Query refactoring patterns to eliminate N+1 problems and slow offsets
Advanced database features like Materialized Views and Table Partitioning

使用场景

Accelerating slow-running reports and complex analytical queries
Refactoring application code to prevent database connection exhaustion
Designing scalable database schemas for high-traffic applications