Does it offer guidance on advanced indexing?

Absolutely. It covers everything from standard B-Tree indexes to complex composite, partial, functional, and GIN indexes for JSONB or full-text search.

Is this skill useful for managing very large tables?

Yes, it includes advanced architectural patterns such as range partitioning, BRIN indexes for correlated data, and materialized views to handle massive datasets efficiently.

How does it help with debugging slow queries?

It provides a systematic approach to using EXPLAIN ANALYZE to identify sequential scans, high-cost nodes, and join bottlenecks, then suggests specific refactoring techniques to address them.

Which databases does this skill support?

While the optimization patterns are universal to relational databases, it provides specific implementation examples for PostgreSQL and MySQL, including detailed EXPLAIN plan analysis and specialized index types.

Can this skill help fix the N+1 query problem?

Yes, it includes specific patterns for identifying N+1 issues in application code and replacing them with efficient JOINs or batch loading strategies to minimize database round-trips.

SQL Optimization Patterns

Name: SQL Optimization Patterns
Author: Activer007

byActiver007

Database Management

Optimizes database performance by implementing efficient indexing, query refactoring, and execution plan analysis.

About

The SQL Optimization Patterns skill empowers developers to transform slow database queries into high-performance operations using systematic analysis and industry-standard patterns. It provides expert guidance on interpreting EXPLAIN query plans, selecting appropriate index types like B-Tree and GIN, and resolving common performance bottlenecks like the N+1 query problem and slow offset-based pagination. Whether you are designing a new schema or troubleshooting a production database under heavy load, this skill offers the technical patterns needed to reduce latency, decrease resource consumption, and ensure long-term scalability.

Key Features

In-depth analysis and interpretation of EXPLAIN and EXPLAIN ANALYZE query plans
Cursor-based pagination patterns for efficient handling of large datasets
Advanced performance techniques like table partitioning and materialized views
Implementation of advanced indexing strategies including composite, partial, and covering indexes
Detection and remediation of common anti-patterns like N+1 queries and SELECT *
0 GitHub stars

Use Cases

Designing high-performance database schemas for rapidly growing applications
Debugging and resolving application latency caused by slow-running database queries
Auditing existing queries to reduce database server load and cloud infrastructure costs

About

Key Features

In-depth analysis and interpretation of EXPLAIN and EXPLAIN ANALYZE query plans
Cursor-based pagination patterns for efficient handling of large datasets
Advanced performance techniques like table partitioning and materialized views
Implementation of advanced indexing strategies including composite, partial, and covering indexes
Detection and remediation of common anti-patterns like N+1 queries and SELECT *
0 GitHub stars

Use Cases

Designing high-performance database schemas for rapidly growing applications
Debugging and resolving application latency caused by slow-running database queries
Auditing existing queries to reduce database server load and cloud infrastructure costs