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Summary of Design

This document has outlined a proposed architecture for a scalable, reliable, and maintainable system designed to scrape, process, and store publicly available job listing data.

Core Problem Addressed

The system tackles the challenge of collecting diverse job postings from numerous websites, across various sectors, locations, and roles, while handling the complexities inherent in web scraping, such as dynamic content, pagination, rate limits, and anti-scraping measures.

Key Architectural Pillars

  • Event-Driven & Decoupled: Utilizes a message queue (e.g., RabbitMQ/SQS) to decouple job dispatching from execution, enhancing resilience and scalability. Distinct components handle specific tasks (configuration, dispatch, fetching, parsing, storage).
  • Scalable Worker Pool: Employs Celery workers, orchestrated potentially via Kubernetes, allowing horizontal scaling to handle varying scraping loads.
  • Hybrid Fetching Strategy: Leverages lightweight requests with robust retries for simpler sites and incorporates Playwright (or alternatives like Splash/APIs) for JavaScript-heavy dynamic sites, managed within the workers.
  • Dual Storage Layers: Uses cloud object storage (S3) for raw, unprocessed HTML (enabling reprocessing and auditing) and a structured relational database (PostgreSQL with JSONB) for cleaned, queryable data suitable for analysis.
  • Centralized Configuration: Employs Django with its built-in Admin UI for easy and accessible management of target websites, keywords, and scraping parameters.
  • Emphasis on Operations: Incorporates comprehensive monitoring (logging, metrics), alerting, robust error handling (retries, DLQs), and standard deployment practices (Docker, K8s, IaC, CI/CD) for maintainability and reliability at scale.

Alignment with Design Goals

This design directly addresses the core goals set out initially:

  • Scalability: Through worker pools, message queues, and scalable cloud services.
  • Maintainability: Via modular components, centralized configuration, IaC, and CI/CD.
  • Reliability: Through decoupled architecture, comprehensive error handling, retries, and monitoring.
  • Efficiency: By using targeted scraping, appropriate tool selection (requests vs. Playwright), caching considerations, and compression.

In conclusion, the proposed architecture provides a solid foundation for building a powerful job market data collection platform, capable of adapting to new sources and scaling to meet future demands.