What advanced database features are covered?

The skill covers advanced techniques like materialized views, table partitioning, and functional indexes to handle high-scale data requirements.

Does it support databases other than PostgreSQL?

While many examples use PostgreSQL syntax, the core optimization patterns and indexing principles are applicable to MySQL, MariaDB, and other relational databases.

Is it useful for initial schema design?

Absolutely; it offers guidance on choosing the right data types and index types during the design phase to prevent performance debt before it occurs.

How does this skill help with slow SQL queries?

It provides systematic patterns for analyzing EXPLAIN plans and implementing the most effective indexing strategies to reduce query execution time.

Can this skill help with N+1 query problems?

Yes, it includes specific code patterns for identifying N+1 issues and replacing them with efficient JOINs or batch loading operations.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: pur3v4d3r

bypur3v4d3r

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Database Management

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

About

This Claude Code skill provides a comprehensive framework for transforming slow-running database queries into high-performance operations. It guides developers through the intricacies of SQL optimization, covering critical areas such as interpreting EXPLAIN ANALYZE output, selecting appropriate index types (B-Tree, GIN, GiST), and eliminating N+1 query anti-patterns. Whether you are debugging production bottlenecks, designing scalable schemas, or managing massive datasets via partitioning and materialized views, this skill offers the implementation patterns needed to ensure your database layer remains responsive and cost-effective.

Key Features

Proven patterns to eliminate N+1 query performance bottlenecks
1 GitHub stars
In-depth interpretation of EXPLAIN and EXPLAIN ANALYZE query plans
High-performance pagination techniques using cursor-based logic
Comprehensive indexing strategies including composite, partial, and covering indexes
Optimization workflows for complex joins, subqueries, and batch operations

Use Cases

Reducing cloud database costs by optimizing query efficiency and resource usage
Designing performant schemas for applications with rapidly growing datasets
Troubleshooting and fixing slow-running production database queries

About

Key Features

Proven patterns to eliminate N+1 query performance bottlenecks
1 GitHub stars
In-depth interpretation of EXPLAIN and EXPLAIN ANALYZE query plans
High-performance pagination techniques using cursor-based logic
Comprehensive indexing strategies including composite, partial, and covering indexes
Optimization workflows for complex joins, subqueries, and batch operations

Use Cases

Reducing cloud database costs by optimizing query efficiency and resource usage
Designing performant schemas for applications with rapidly growing datasets
Troubleshooting and fixing slow-running production database queries