Does this support specific databases like PostgreSQL?

While focusing on universal SQL patterns, it includes specific optimizations for PostgreSQL and MySQL, including advanced index types and execution plans.

Does it cover advanced features like partitioning?

Yes, it provides implementation patterns for table partitioning and materialized views to handle massive datasets and pre-compute expensive aggregations.

How does this skill help with slow queries?

It provides step-by-step guidance on using EXPLAIN tools to identify bottlenecks and suggests specific indexing or refactoring patterns to resolve them.

Is cursor-based pagination covered?

Yes, the skill explains why traditional OFFSET pagination degrades for large datasets and how to implement high-performance cursor-based pagination.

Can it help with the N+1 query problem?

Yes, it provides several patterns to identify and refactor N+1 issues into efficient JOINs or batch loading operations to minimize round-trips.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: HermeticOrmus

byHermeticOrmus

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데이터베이스 관리

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

소개

SQL Optimization Patterns is a specialized skill for Claude Code designed to transform slow-running database operations into high-performance queries. It provides expert guidance on analyzing EXPLAIN plans, implementing advanced indexing strategies like composite and partial indexes, and refactoring inefficient query structures such as the N+1 pattern or slow OFFSET pagination. Whether you are designing a new schema or debugging production performance bottlenecks, this skill ensures your database layer remains scalable, responsive, and cost-effective.

주요 기능

Performance-tuned pagination methods using cursor-based logic instead of OFFSET.
Implementation of advanced indexing strategies including B-Tree, GIN, and covering indexes.
Refactoring techniques to eliminate N+1 query problems and inefficient subqueries.
2 GitHub stars
Query plan analysis and metric interpretation using EXPLAIN and EXPLAIN ANALYZE.
Guidance on database scaling features like table partitioning and materialized views.

사용 사례

Designing performant database schemas for high-growth applications.
Debugging and fixing slow-running queries in production environments.
Reducing cloud infrastructure costs by optimizing database load and resource consumption.

소개

주요 기능

Performance-tuned pagination methods using cursor-based logic instead of OFFSET.
Implementation of advanced indexing strategies including B-Tree, GIN, and covering indexes.
Refactoring techniques to eliminate N+1 query problems and inefficient subqueries.
2 GitHub stars
Query plan analysis and metric interpretation using EXPLAIN and EXPLAIN ANALYZE.
Guidance on database scaling features like table partitioning and materialized views.

사용 사례

Designing performant database schemas for high-growth applications.
Debugging and fixing slow-running queries in production environments.
Reducing cloud infrastructure costs by optimizing database load and resource consumption.