Can this skill help with ORM-related performance issues?

Yes, it specifically addresses common ORM problems like the N+1 query anti-pattern and provides strategies to refactor them into efficient JOINs or batch loads.

Does this skill help with managing very large datasets?

Absolutely. It includes advanced techniques like table partitioning, materialized views, and BRIN indexes specifically designed for handling and querying massive data volumes.

How does it help with debugging slow queries?

It guides you through interpreting EXPLAIN and EXPLAIN ANALYZE output to identify sequential scans, high-cost nodes, and join bottlenecks within your query execution plans.

What database systems does this skill support?

While many patterns are universal to SQL, this skill provides specific implementation examples for PostgreSQL and MySQL, covering engine-specific query hints and specialized index types.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: HermeticOrmus

byHermeticOrmus

데이터베이스 관리

Optimizes database performance through advanced SQL query tuning, indexing strategies, and execution plan analysis.

소개

This skill provides a comprehensive toolkit for identifying and resolving database performance bottlenecks by mastering SQL execution plans, indexing strategies, and query optimization patterns. It enables developers to transform slow-running queries into efficient operations using techniques like cursor-based pagination, materialized views, and partition management. Whether you are debugging N+1 query problems in an ORM or designing a scalable schema for large datasets, this skill offers the specific implementation patterns and best practices needed to minimize database load and dramatically improve application response times.

주요 기능

Deep analysis of SQL execution plans using EXPLAIN and EXPLAIN ANALYZE
Optimization of complex JOINs, subqueries, and aggregation operations
0 GitHub stars
Resolution of common performance anti-patterns like N+1 queries and OFFSET pagination
Implementation of advanced indexing including B-Tree, GIN, Partial, and Covering indexes
Advanced data scaling techniques including table partitioning and materialized views

사용 사례

Refactoring slow legacy queries to reduce database CPU and memory consumption
Designing high-performance database schemas for applications with rapidly growing datasets
Optimizing batch data operations and bulk inserts to improve system throughput

소개

주요 기능

Deep analysis of SQL execution plans using EXPLAIN and EXPLAIN ANALYZE
Optimization of complex JOINs, subqueries, and aggregation operations
0 GitHub stars
Resolution of common performance anti-patterns like N+1 queries and OFFSET pagination
Implementation of advanced indexing including B-Tree, GIN, Partial, and Covering indexes
Advanced data scaling techniques including table partitioning and materialized views

사용 사례

Refactoring slow legacy queries to reduce database CPU and memory consumption
Designing high-performance database schemas for applications with rapidly growing datasets
Optimizing batch data operations and bulk inserts to improve system throughput