How does this skill improve database count performance?

It provides 'Fast Count' strategies using metadata estimates which can be over 5000x faster than traditional exact counts on large datasets.

Is this skill specific to a particular programming language?

While the principles apply generally, this skill is specifically tailored for C# implementations using the Koan framework.

Can this skill help with Out of Memory (OOM) errors?

Yes, it implements streaming patterns that process data in manageable batches rather than loading entire datasets into memory at once.

What is the speed advantage of bulk removal in this skill?

The skill utilizes specialized TRUNCATE and DROP strategies that can be up to 225x faster than standard record-by-record deletion loops.

Koan Performance Optimizer

Name: Koan Performance Optimizer
Author: sylin-org

bysylin-org

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Gestión de Bases de Datos

Optimizes data-intensive applications through streaming, bulk operations, and high-speed count strategies for the Koan framework.

Acerca de

This skill empowers Claude to optimize Koan-based applications for high-performance data processing and production-ready scalability. It provides specialized implementation patterns for memory-safe streaming of large datasets, lightning-fast metadata-based counts, and efficient bulk operations that can provide up to a 5000x speedup over naive approaches. By leveraging these patterns, developers can resolve memory issues, eliminate N+1 query problems, and ensure their Web APIs remain responsive even when handling millions of records.

Características Principales

Memory-safe streaming for processing million-record datasets
Optimized bulk create and removal operations for database efficiency
Standardized pagination for high-performance Web APIs
High-speed metadata count strategies for instant UI feedback
Batch retrieval patterns to minimize network round-trips
2 GitHub stars

Casos de Uso

Implementing memory-efficient data processing pipelines for large-scale background tasks
Refactoring inefficient loop-based database updates into high-performance bulk operations
Optimizing dashboard and pagination performance in high-volume web applications

Acerca de

Características Principales

Memory-safe streaming for processing million-record datasets
Optimized bulk create and removal operations for database efficiency
Standardized pagination for high-performance Web APIs
High-speed metadata count strategies for instant UI feedback
Batch retrieval patterns to minimize network round-trips
2 GitHub stars

Casos de Uso

Implementing memory-efficient data processing pipelines for large-scale background tasks
Refactoring inefficient loop-based database updates into high-performance bulk operations
Optimizing dashboard and pagination performance in high-volume web applications