Does it provide solutions for the N+1 query problem?

Absolutely. It includes specific patterns for refactoring N+1 query loops into efficient JOINs or batch loading operations to significantly reduce round-trips to the database.

Can I use this for databases other than PostgreSQL?

Yes. While many examples focus on PostgreSQL, the core optimization concepts—such as B-Tree indexing, batching, and JOIN optimization—are applicable to MySQL, MariaDB, and other SQL-based systems.

Is this skill useful for high-level schema design?

Yes, it covers proactive design patterns like table partitioning, materialized views, and choosing appropriate data types to ensure performance from day one.

How does this skill help identify slow SQL queries?

The skill provides a structured approach to analyzing EXPLAIN plans, helping you identify Seq Scans, high-cost operations, and areas where indexes are missing or underutilized.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Microck

byMicrock

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数据库管理

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

关于

This skill empowers developers to transform sluggish database operations into high-performance queries by applying systematic optimization techniques. It provides expert guidance on interpreting EXPLAIN plans, implementing sophisticated indexing strategies such as composite and partial indexes, and resolving common performance anti-patterns like N+1 queries. Whether you are debugging a slow production endpoint or designing a scalable schema for millions of rows, this skill offers the implementation patterns and best practices needed to minimize database load and maximize application responsiveness.

主要功能

Advanced database scaling via partitioning and materialized views
Proven patterns to eliminate N+1 query performance bottlenecks
81 GitHub stars
Comprehensive indexing strategies including B-Tree, GIN, and covering indexes
Efficient pagination techniques using cursor-based logic
In-depth EXPLAIN and EXPLAIN ANALYZE output interpretation

使用场景

Designing high-performance database schemas for rapidly growing datasets
Refactoring slow-running legacy queries to improve application speed
Reducing cloud database costs by optimizing query efficiency and resource usage

关于

主要功能

Advanced database scaling via partitioning and materialized views
Proven patterns to eliminate N+1 query performance bottlenecks
81 GitHub stars
Comprehensive indexing strategies including B-Tree, GIN, and covering indexes
Efficient pagination techniques using cursor-based logic
In-depth EXPLAIN and EXPLAIN ANALYZE output interpretation

使用场景

Designing high-performance database schemas for rapidly growing datasets
Refactoring slow-running legacy queries to improve application speed
Reducing cloud database costs by optimizing query efficiency and resource usage