Can this skill help me choose the right index type?

Yes, it provides a detailed breakdown of different index types such as B-Tree, GIN, and BRIN, helping you choose based on your specific data types and query patterns.

Does it include guidance on handling very large tables?

Absolutely. It includes advanced techniques like range partitioning, materialized views, and BRIN indexes specifically designed for managing and querying massive datasets.

What database engines does this skill support?

While the skill focuses heavily on PostgreSQL patterns, the core concepts of indexing, execution plans, and batch operations are applicable to MySQL, SQLite, and most other relational databases.

How does this help with the N+1 query problem?

It provides specific SQL and code patterns to replace multiple iterative database calls with efficient JOINs or batch loading strategies, significantly reducing overhead.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Activer007

byActiver007

データベース管理

Optimizes SQL database performance through query plan analysis, advanced indexing strategies, and systematic query refactoring.

概要

This skill empowers developers to transform slow database operations into high-performance queries by providing deep insights into execution plans and modern database optimization techniques. It covers everything from interpreting EXPLAIN ANALYZE output to implementing complex indexing strategies like GIN and BRIN, as well as resolving common anti-patterns like N+1 queries and inefficient pagination. Whether you are scaling a growing application or debugging a production bottleneck, this skill provides the systematic patterns needed to reduce database load, minimize latency, and improve overall system scalability.

主な機能

N+1 query detection and batch-loading resolution patterns
Cursor-based pagination techniques for large-scale datasets
Implementation of materialized views and table partitioning
0 GitHub stars
Query execution plan (EXPLAIN) analysis and metric interpretation
Advanced indexing strategies including B-Tree, GIN, GiST, and BRIN

ユースケース

Debugging and fixing slow-running production database queries
Optimizing application response times by reducing database latency
Designing high-performance database schemas for scalable applications

概要

主な機能

N+1 query detection and batch-loading resolution patterns
Cursor-based pagination techniques for large-scale datasets
Implementation of materialized views and table partitioning
0 GitHub stars
Query execution plan (EXPLAIN) analysis and metric interpretation
Advanced indexing strategies including B-Tree, GIN, GiST, and BRIN

ユースケース

Debugging and fixing slow-running production database queries
Optimizing application response times by reducing database latency
Designing high-performance database schemas for scalable applications