What types of indexes are covered?

It covers a wide range of indexing strategies including B-Tree, GIN, Hash, composite, partial, expression, and covering indexes to match various data types and query requirements.

Can it help with ORM performance issues?

Yes, it includes specific patterns for identifying and fixing common ORM-related anti-patterns such as N+1 queries by using JOINs or batch loading techniques.

Does it include advanced scaling techniques?

Yes, it covers advanced database performance topics such as materialized views, table partitioning, and batch update operations for large datasets.

Which databases are supported by these patterns?

While the core optimization concepts apply to most relational databases, the skill specifically provides syntax and strategies optimized for PostgreSQL and MySQL.

How does this skill help with slow SQL queries?

It provides systematic patterns for analyzing EXPLAIN plans to identify bottlenecks like sequential scans and suggests specific indexing or refactoring strategies to resolve them.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Microck

byMicrock

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114

Gestión de Bases de Datos

Optimizes database performance through advanced indexing strategies, query plan analysis, and efficient data retrieval patterns.

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This Claude Code skill empowers developers to eliminate database bottlenecks by providing expert guidance on SQL query optimization and schema design. It offers structured patterns for analyzing EXPLAIN execution plans, implementing high-performance indexing strategies—including composite, partial, and covering indexes—and resolving common performance anti-patterns like N+1 queries and inefficient pagination. Whether you are scaling a growing dataset or debugging slow-running production queries, this skill provides the systematic implementation patterns needed to ensure your database layer remains responsive, scalable, and cost-effective.

Características Principales

High-performance pagination patterns using cursor-based methods
Optimization for complex aggregations, subqueries, and window functions
Expert analysis of EXPLAIN and execution plans for PostgreSQL and MySQL
Advanced indexing strategies including GIN, BRIN, and covering indexes
Techniques for resolving N+1 query anti-patterns and Cartesian products
114 GitHub stars

Casos de Uso

Debugging and fixing slow-running production database queries to improve UX
Designing high-performance database schemas for data-intensive applications
Reducing database server load and infrastructure costs through query tuning

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Características Principales

High-performance pagination patterns using cursor-based methods
Optimization for complex aggregations, subqueries, and window functions
Expert analysis of EXPLAIN and execution plans for PostgreSQL and MySQL
Advanced indexing strategies including GIN, BRIN, and covering indexes
Techniques for resolving N+1 query anti-patterns and Cartesian products
114 GitHub stars

Casos de Uso

Debugging and fixing slow-running production database queries to improve UX
Designing high-performance database schemas for data-intensive applications
Reducing database server load and infrastructure costs through query tuning