What is the primary benefit of the Entity-First pattern?

It eliminates the need for manual repository boilerplate by making entities self-persisting and self-aware, allowing for direct Save() and Get() operations.

Does this skill support different database providers?

Yes, the pattern is provider-agnostic and works consistently across SQL, NoSQL, Vector, and JSON data stores.

How does it handle performance for large datasets?

It includes specialized features like batch operations to prevent N+1 queries, streaming for memory-efficient processing, and fast-count metadata estimates.

Why should I avoid the manual repository pattern in Koan?

Manual repositories in the Koan Framework duplicate existing features, break provider transparency, and increase the maintenance burden of the codebase.

Koan Entity-First Data Access

Name: Koan Entity-First Data Access
Author: sylin-org

bysylin-org

•

데이터베이스 관리

Simplifies data persistence by replacing traditional repository patterns with self-aware, self-persisting entities.

소개

The Koan Entity-First skill implements an Active Record-inspired pattern within the Koan Framework, allowing developers to bypass the boilerplate of manual repositories. It provides built-in support for chronological GUID v7 generation, static query methods, and efficient batch operations across SQL, NoSQL, and JSON stores. This skill is ideal for teams looking to streamline their C# data access layer while maintaining provider transparency and high-performance streaming capabilities for large datasets.

주요 기능

Efficient batch operations and provider-specific bulk persistence
2 GitHub stars
Automated GUID v7 ID generation for native chronological ordering
Built-in pagination and memory-efficient data streaming for large datasets
Self-persisting entity methods and static query helpers for rapid CRUD
Optimized metadata-based 'Fast Count' operations for high-performance dashboards

사용 사례

Scaffolding CRUD operations without creating repetitive repository interfaces
Implementing memory-efficient ETL pipelines using background data streaming
Eliminating N+1 query issues through efficient batch retrieval patterns

소개

주요 기능

Efficient batch operations and provider-specific bulk persistence
2 GitHub stars
Automated GUID v7 ID generation for native chronological ordering
Built-in pagination and memory-efficient data streaming for large datasets
Self-persisting entity methods and static query helpers for rapid CRUD
Optimized metadata-based 'Fast Count' operations for high-performance dashboards

사용 사례

Scaffolding CRUD operations without creating repetitive repository interfaces
Implementing memory-efficient ETL pipelines using background data streaming
Eliminating N+1 query issues through efficient batch retrieval patterns