Which databases are supported by these optimization patterns?

While the skill features detailed PostgreSQL examples, the core concepts of query plans, indexing strategies, and batch operations are applicable to most relational databases like MySQL and SQL Server.

Is this skill useful for database schema design?

Absolutely. It offers best practices for data types, normalization, and proactive index placement to ensure your database is built for performance from day one.

Does it include guidance on advanced scaling techniques?

Yes, it covers advanced topics like Table Partitioning, Materialized Views, and functional indexing to handle very large datasets that standard queries can't manage efficiently.

How does this skill help with slow database queries?

It provides a systematic approach to performance tuning by using EXPLAIN analysis to identify bottlenecks and recommending specific indexing or query refactoring patterns to resolve them.

Can this skill solve the N+1 query problem?

Yes, it includes clear implementation patterns for replacing inefficient iterative queries with optimized JOINs or batch loading techniques in both SQL and application code.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Tahir-yamin

byTahir-yamin

•

データベース管理

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

概要

This skill provides Claude with a comprehensive framework for identifying and resolving database performance bottlenecks across full-stack applications. It covers essential techniques such as interpreting EXPLAIN query plans, implementing diverse index types including B-Tree and GIN, and applying advanced optimization patterns like cursor-based pagination and batch operations. Whether you are debugging slow-running legacy queries, designing scalable database schemas from scratch, or refactoring application code to eliminate N+1 query issues, this skill offers the specific guidance and implementation patterns needed to minimize database load and maximize application responsiveness.

主な機能

Advanced EXPLAIN query plan analysis and metric interpretation
Comprehensive indexing strategies including composite, partial, and covering indexes
Advanced database features such as Materialized Views and Partitioning
High-performance pagination techniques to replace slow OFFSET methods
3 GitHub stars
N+1 query detection and resolution using JOINs or batch loading

ユースケース

Designing performant database schemas for large-scale or rapidly growing datasets
Optimizing backend application logic to reduce database server costs and load
Refactoring slow legacy queries for improved application response times

概要

主な機能

Advanced EXPLAIN query plan analysis and metric interpretation
Comprehensive indexing strategies including composite, partial, and covering indexes
Advanced database features such as Materialized Views and Partitioning
High-performance pagination techniques to replace slow OFFSET methods
3 GitHub stars
N+1 query detection and resolution using JOINs or batch loading

ユースケース

Designing performant database schemas for large-scale or rapidly growing datasets
Optimizing backend application logic to reduce database server costs and load
Refactoring slow legacy queries for improved application response times