Design Goals & Principles

The design of this job scraping and processing system is guided by the following key principles, ensuring it meets the challenge requirements while adhering to good software and data engineering practices:

• Scalability:

  • The architecture must handle a growing number of target websites, keywords, locations, and roles without significant redesign.
  • It should scale horizontally to accommodate increasing data volume and processing load (e.g., adding more workers, scaling databases and queues).
  • Resource usage should be considered, balancing performance with potential costs.

• Maintainability:

  • Components should be modular and well-defined, allowing for easier updates and bug fixes.
  • Configuration (target sites, keywords, settings) should be managed centrally and easily updatable (e.g., via Django Admin).
  • Code should be clear, well-documented, and potentially testable. This documentation site itself is part of this goal.

• Reliability & Resilience:

  • The system must gracefully handle inevitable failures (network errors, website changes, temporary blocks).
  • Implement robust error handling, logging, and automated retries at appropriate levels (fetching, task execution).
  • Decoupled components (via queues) prevent failures in one part from immediately cascading and halting the entire system.

• Decoupling:

  • Separate distinct concerns: Configuration management, job dispatching, data fetching, data parsing, and data storage should be handled by distinct components or services.
  • This improves maintainability, testability, and allows different parts of the system to scale independently. Queues (like RabbitMQ/SQS) and distinct storage layers (S3, PostgreSQL) are key enablers.

• Efficiency:

  • Optimize scraping processes where possible (e.g., check for APIs before resorting to full browser rendering).
  • Use efficient data formats and storage mechanisms (e.g., HTML compression on S3, appropriate database indexing).
  • Implement targeted scraping using filters (dates, keywords) to avoid unnecessary crawling and reduce load on target sites.

• Data Quality & Integrity:

  • Aim to collect accurate data through careful parsing and validation.
  • Implement processes to handle duplicates, missing values, and inconsistencies during the data processing stage.
  • Store data in a structured, usable format suitable for analysis.

• Monitorability:

  • The system's health and performance must be observable. Implement logging, metrics collection, and potentially alerting to understand throughput, error rates, queue depths, and resource usage.

These principles guide the specific technology choices and architectural decisions detailed in the subsequent sections of this document.