What indexing strategies are included?

The skill covers single-column, composite (order-sensitive), covering, and full-text indexes, along with guidance on when to add or avoid them based on cardinality.

How does this skill help with N+1 query problems?

It provides specific patterns for eager loading, nested relationship loading, and conditional eager loading to reduce the number of database roundtrips.

Can it assist with database monitoring?

It includes implementation guides for slow query logging, connection pooling configuration, and building health check controllers to monitor database latency.

Does it support large-scale data migrations or updates?

Yes, it includes patterns for chunking large datasets, using cursors for memory efficiency, and performing mass updates or upserts to handle high-volume data safely.

Database Performance & Optimization

Name: Database Performance & Optimization
Author: omanjaya

byomanjaya

Gestión de Bases de Datos

Optimizes database schemas, queries, and caching strategies to improve application speed and resource efficiency.

Acerca de

The Database Performance skill provides Claude with specialized knowledge to audit and enhance your application's data layer. It focuses on resolving common bottlenecks such as N+1 query issues, missing indexes, and inefficient data retrieval patterns. By implementing advanced indexing strategies, sophisticated caching patterns like cache-aside and write-through, and efficient dataset processing techniques like chunking and cursors, this skill helps developers build scalable, high-performance systems that handle large volumes of data with minimal latency.

Características Principales

0 GitHub stars
Performance Monitoring (Slow query detection and connection pool optimization)
Advanced Indexing (Composite, Covering, and Full-text strategies)
Efficient Data Processing (Chunking, lazy collections, and cursor iteration)
Sophisticated Caching (Redis tags, cache-aside, and write-through patterns)
Query Optimization (N+1 resolution, eager loading, and selective column retrieval)

Casos de Uso

Identifying and fixing slow database queries in production environments.
Implementing scalable caching layers for complex dashboards and aggregate reports.
Designing high-performance database schemas with optimized primary keys and relationships.

Acerca de

Características Principales

0 GitHub stars
Performance Monitoring (Slow query detection and connection pool optimization)
Advanced Indexing (Composite, Covering, and Full-text strategies)
Efficient Data Processing (Chunking, lazy collections, and cursor iteration)
Sophisticated Caching (Redis tags, cache-aside, and write-through patterns)
Query Optimization (N+1 resolution, eager loading, and selective column retrieval)

Casos de Uso

Identifying and fixing slow database queries in production environments.
Implementing scalable caching layers for complex dashboards and aggregate reports.
Designing high-performance database schemas with optimized primary keys and relationships.