What indexing strategies are covered?

It covers a wide range of indexes including B-Tree, GIN, GiST, BRIN, composite, partial, and covering indexes to suit different data types and query patterns.

Does this skill support specific databases?

While focusing heavily on PostgreSQL patterns, the core concepts of query optimization, indexing, and batching are applicable to most relational databases like MySQL and SQL Server.

Is this skill useful for schema design?

Absolutely. It provides best practices for data types, normalization, and partitioning strategies to ensure performance from the initial design phase.

How does this skill help with slow SQL queries?

It provides systematic patterns for analyzing query execution plans using EXPLAIN and suggests specific indexing or structural changes to reduce costs and execution time.

Can it solve N+1 query problems?

Yes, it includes specific patterns for identifying N+1 anti-patterns and refactoring them into efficient JOINs or batch loading operations.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Activer007

byActiver007

Database Management

Optimizes database performance through advanced indexing, query plan analysis, and efficient query architectural patterns.

About

This skill empowers Claude to act as a database performance engineer, specializing in identifying and resolving slow queries. It provides comprehensive patterns for PostgreSQL and general SQL, covering everything from interpreting EXPLAIN ANALYZE results to implementing sophisticated indexing strategies like B-Tree, GIN, and covering indexes. Whether you're debugging production bottlenecks, designing high-performance schemas, or eliminating N+1 query problems, this skill provides the systematic guidance needed to transform slow operations into lightning-fast database interactions and scalable architectures.

Key Features

Optimization techniques for complex JOINs, subqueries, and window functions
Refactoring patterns for common anti-patterns like N+1 queries and OFFSET pagination
Implementation of advanced strategies like partitioning and materialized views
Strategic indexing recommendations including Composite, Partial, and GIN indexes
Deep analysis of EXPLAIN and EXPLAIN ANALYZE query plans to identify bottlenecks
0 GitHub stars

Use Cases

Refactoring slow-running legacy queries to improve application response times
Designing high-performance database schemas for new scalable applications
Optimizing batch processing and bulk data operations to reduce database load

About

Key Features

Optimization techniques for complex JOINs, subqueries, and window functions
Refactoring patterns for common anti-patterns like N+1 queries and OFFSET pagination
Implementation of advanced strategies like partitioning and materialized views
Strategic indexing recommendations including Composite, Partial, and GIN indexes
Deep analysis of EXPLAIN and EXPLAIN ANALYZE query plans to identify bottlenecks
0 GitHub stars

Use Cases

Refactoring slow-running legacy queries to improve application response times
Designing high-performance database schemas for new scalable applications
Optimizing batch processing and bulk data operations to reduce database load