🌾 Agricultural Stock Data Analysis System Development Guide

📋 Project Collaboration Overview

The Agricultural Stock Data Analysis System consists of four core sub-projects that implement a complete data pipeline from data collection to user presentation through a sophisticated collaboration mechanism. This guide provides detailed explanations of the collaboration relationships, development processes, and operational mechanisms of each sub-project.

🏗️ System Architecture & Data Flow

Overall Data Flow

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│  External APIs  │    │  Data Collector │    │ Spark Processor │    │  Backend API    │
│                 │───▶│  data-collector │───▶│ spark-processor │───▶│    backend      │
│                 │    │                 │    │                 │    │                 │
│ • Tencent API   │    │ • Python Crawler│    │ • Data Cleaning │    │ • RESTful API   │
│ • Sina Finance  │    │ • Real-time Col │    │ • Tech Indicators│    │ • Data Caching  │
│ • East Money    │    │ • Data Validation│    │ • Market Analysis│    │ • WebSocket     │
└─────────────────┘    └─────────────────┘    └─────────────────┘    └─────────────────┘
                                                        │                        │
                                                        ▼                        ▼
                                               ┌─────────────────┐    ┌─────────────────┐
                                               │   MySQL DB      │    │   Frontend UI   │
                                               │    Database     │    │    frontend     │
                                               │                 │    │                 │
                                               │ • Raw Data      │    │ • Vue.js UI     │
                                               │ • Analysis Data │    │ • Chart Display │
                                               │ • Index Opt     │◀───│ • User Interact │
                                               └─────────────────┘    └─────────────────┘

2. Deployment Collaboration Checklist

Database Team Responsibilities

MySQL 8.0 environment setup completed
Database table structure creation completed
Index optimization configuration completed
Database connection pool configuration completed
Backup strategy configuration completed

Data Collector Team Responsibilities

Python 3.8+ environment configuration completed
Dependency package installation completed
Configuration file environment variables setup completed
Scheduled task configuration completed
Log monitoring configuration completed

Spark Processor Team Responsibilities

Java 8 environment configuration completed
Spark 3.4.0 cluster setup completed
Maven dependency management configuration completed
Job scheduling configuration completed
Performance monitoring configuration completed

Backend API Team Responsibilities

Spring Boot application packaging completed
Redis cache service configuration completed
API interface testing passed
WebSocket service configuration completed
Load balancing configuration completed

Frontend UI Team Responsibilities

Node.js environment configuration completed
Vue project build completed
Static resource deployment completed
CDN configuration optimization completed
Browser compatibility testing completed

📋 Key Success Factors for Collaboration

1. Unified Technology Stack

Standardized Development Languages: Python 3.8+, Java 8+, Node.js 16+
Unified Database: MySQL 8.0 + Redis 6.0
Containerized Deployment: Docker + Kubernetes
Unified Monitoring Tools: ELK Stack + Prometheus

2. Documentation Collaboration Standards

API Documentation: Using Swagger/OpenAPI 3.0
Database Documentation: Auto-generated using DbDoc
Deployment Documentation: Using Markdown + Flowcharts
User Manual: Using GitBook or VuePress

Through the above collaborative development process, the four sub-projects can organically combine to form an efficient, stable, and scalable agricultural stock data analysis system. While each team focuses on developing their own modules, they ensure overall system consistency and reliability through standardized interfaces and collaboration mechanisms.

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