Does this skill support both PostgreSQL and MySQL?

Yes, it covers universal SQL optimization patterns as well as specific techniques for both PostgreSQL and MySQL, including dialect-specific EXPLAIN syntax and index types.

Can it help resolve high latency in API responses?

Absolutely. This skill specializes in identifying database-level bottlenecks like N+1 queries and inefficient JOINs that are the primary causes of slow API performance.

Is it useful for managing very large datasets?

Yes, it includes advanced strategies like table partitioning, BRIN indexes, and materialized views specifically designed for maintaining performance with large volumes of data.

How does it help with indexing strategy?

It provides guidance on selecting the right index type (B-Tree, GIN, Hash) and designing composite or covering indexes to ensure the database can satisfy queries without full table scans.

Can it suggest improvements for batch operations?

Yes, it provides patterns for optimized batch INSERTs and UPDATEs, including the use of temporary tables and the COPY command for bulk data handling.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: EricGrill

byEricGrill

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データベース管理

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

概要

SQL Optimization Patterns is a comprehensive skill designed to transform slow database queries into high-performance operations by providing Claude with specialized knowledge in query tuning and schema design. It enables the systematic analysis of EXPLAIN plans, the implementation of sophisticated indexing strategies like partial and covering indexes, and the resolution of common performance bottlenecks such as N+1 query patterns. Whether you are debugging production latency, designing scalable schemas, or implementing batch operations, this skill provides the patterns necessary to reduce database load and improve application responsiveness across various SQL dialects.

主な機能

2 GitHub stars
Advanced optimization via materialized views and table partitioning
Detection and elimination of N+1 query anti-patterns
Advanced indexing strategies including Composite, GIN, and Partial indexes
Query execution plan (EXPLAIN) analysis and interpretation
Efficient cursor-based pagination and batch processing techniques

ユースケース

Debugging and fixing slow-running legacy queries in production environments
Reducing database infrastructure costs by optimizing resource-heavy queries
Designing high-performance database schemas for data-intensive applications

概要

主な機能

2 GitHub stars
Advanced optimization via materialized views and table partitioning
Detection and elimination of N+1 query anti-patterns
Advanced indexing strategies including Composite, GIN, and Partial indexes
Query execution plan (EXPLAIN) analysis and interpretation
Efficient cursor-based pagination and batch processing techniques

ユースケース

Debugging and fixing slow-running legacy queries in production environments
Reducing database infrastructure costs by optimizing resource-heavy queries
Designing high-performance database schemas for data-intensive applications