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chuangkevin
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houseprice-backend-architecture

Document MSSQL to Elasticsearch sync architecture with Hangfire jobs and cache strategies.

Updated Mar 16, 2026
Search Tags:#cache#elasticsearch#rabbitmq#backend-architecture#data-sync#mssql#hangfire
One-click install
npx skills add https://github.com/chuangkevin/project-bridge --skill houseprice-backend-architecture
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: houseprice-backend-architecture
Source: https://github.com/chuangkevin/project-bridge/tree/main/skill/houseprice/common/houseprice-backend-architecture
Command: npx skills add https://github.com/chuangkevin/project-bridge --skill houseprice-backend-architecture

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a clear, discoverable explanation of the HousePrice backend architecture so engineers and new team members can quickly understand how data moves from MSSQL to Elasticsearch, how scheduled jobs and events are structured, and where cache or direct SQL exceptions apply.

Core Features & Use Cases

  • Data Flow Mapping: Describes MSSQL as source-of-truth, batch or incremental sync patterns, and Elasticsearch as the query store.
  • Job & Scheduling Patterns: Explains Hangfire-based recurring jobs, the Job → TransformService → Repository layering, and centralized job registration.
  • Integration & Exceptions: Notes RabbitMQ domain events, Evertrust framework responsibilities, and management-platform exceptions that query MSSQL directly.
  • Use Cases: Onboarding new backend developers, cross-repo development handoffs, code review context for sync logic, and troubleshooting sync or cache-related issues.

Quick Start

Summarize the HousePrice backend flow from MSSQL to Elasticsearch, list recurring Hangfire jobs, and highlight caching and event-driven exceptions.

Frequently Asked Questions about houseprice-backend-architecture

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does MSSQL to Elasticsearch data synchronization work in a backend architecture?▼

MSSQL to Elasticsearch synchronization uses batch or incremental patterns where MSSQL serves as the source-of-truth and Elasticsearch acts as the query store. Scheduled jobs and domain events drive the data flow through a structured service layer.

How do I structure recurring scheduled jobs using Hangfire for data sync?▼

Structure Hangfire scheduled jobs using a centralized registration approach that follows a Job to TransformService to Repository layering pattern. This separates job execution logic from data transformation and storage operations.

When should I query MSSQL directly instead of using Elasticsearch?▼

Query MSSQL directly for management platform exceptions where operational or administrative tasks require accessing the source-of-truth immediately. Elasticsearch remains the primary query store for standard application reads.

Can I use RabbitMQ domain events to trigger Elasticsearch incremental sync?▼

Yes, RabbitMQ domain events integrate with the backend architecture to trigger incremental sync to Elasticsearch. The Evertrust framework manages these event-driven flows alongside Hangfire recurring jobs for batch updates.

What is the best way to map backend data flows for developer onboarding?▼

Map backend data flows by documenting the MSSQL source, Hangfire job scheduling, TransformService patterns, and Elasticsearch query store. Highlight cache strategies and RabbitMQ event-driven exceptions to provide complete architectural context.

Why do I need a TransformService layer between scheduled jobs and the repository?▼

A TransformService layer separates data transformation logic from repository storage operations during scheduled job execution. This pattern ensures sync jobs process MSSQL source data consistently before writing to Elasticsearch.

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