Does this skill support advanced scaling features like partitioning?

Yes, it includes patterns for range partitioning, materialized views, and query hints to help manage and query very large datasets effectively.

How does this skill help with slow SQL queries?

It analyzes query execution plans to identify bottlenecks like full table scans and recommends specific indexes or query rewrites to significantly improve execution speed.

What is an N+1 query problem and can this skill fix it?

An N+1 problem occurs when an app makes one query for parents and N queries for children. This skill provides JOIN and batch-loading patterns to consolidate these into efficient single operations.

Can it handle different database engines like PostgreSQL and MySQL?

Yes, while it provides specific syntax for PostgreSQL and MySQL, the core optimization principles and patterns apply to most relational database management systems (RDBMS).

Does it help with database schema design from scratch?

Absolutely. It provides guidance on choosing optimal data types, normalization strategies, and indexing during the design phase to prevent performance issues before they occur.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Activer007

byActiver007

数据库管理

Optimizes SQL queries and database schemas through advanced indexing strategies, query plan analysis, and performance pattern implementation.

关于

This skill empowers Claude to transform sluggish database operations into high-performance queries by applying systematic optimization techniques. It covers everything from interpreting EXPLAIN execution plans and implementing sophisticated indexing (B-Tree, GIN, Partial) to resolving common anti-patterns like N+1 queries and inefficient pagination. Whether you are designing a new schema or debugging a production performance bottleneck, this skill provides the domain-specific logic needed to reduce database load, improve application response times, and ensure scalability for growing datasets.

主要功能

Deep analysis of SQL execution plans using EXPLAIN and EXPLAIN ANALYZE
Advanced database scaling techniques like table partitioning and materialized views
Optimization of complex JOINs, subqueries, and window functions for maximum efficiency
Implementation of advanced indexing strategies including Composite, GIN, and Partial indexes
Automated resolution of N+1 query anti-patterns and pagination bottlenecks
0 GitHub stars

使用场景

Reducing cloud database costs by optimizing query efficiency and reducing CPU/IO usage
Designing high-performance database schemas for data-intensive applications
Refactoring slow-running legacy queries to drastically improve application latency

关于

主要功能

Deep analysis of SQL execution plans using EXPLAIN and EXPLAIN ANALYZE
Advanced database scaling techniques like table partitioning and materialized views
Optimization of complex JOINs, subqueries, and window functions for maximum efficiency
Implementation of advanced indexing strategies including Composite, GIN, and Partial indexes
Automated resolution of N+1 query anti-patterns and pagination bottlenecks
0 GitHub stars

使用场景

Reducing cloud database costs by optimizing query efficiency and reducing CPU/IO usage
Designing high-performance database schemas for data-intensive applications
Refactoring slow-running legacy queries to drastically improve application latency