Web Crawler Software Definition Document

1. Introduction

1.1 Purpose

This Software Definition Document (SDD) outlines the design and implementation details of a web crawler system. The system is designed to efficiently crawl websites, store link information, and provide a flexible framework for content parsing.

1.2 Scope

The web crawler system includes:

• Asynchronous web scraping functionality
• Link management using a Neo4j database
• Flexible content parsing system
• Politeness features including rate limiting and robots.txt compliance
• Incremental crawling capabilities
• Content deduplication

1.3 Definitions, Acronyms, and Abbreviations

• SDD: Software Definition Document
• API: Application Programming Interface
• URL: Uniform Resource Locator
• HTML: Hypertext Markup Language
• HTTP: Hypertext Transfer Protocol
• Neo4j: A graph database management system

2. System Overview

The web crawler system is designed to efficiently crawl websites, extract information, and store link relationships. It consists of several main components:

1. Scraper: Handles the crawling logic, including fetching web pages, parsing HTML, and discovering links.
2. LinkManager: Manages link information storage and retrieval using a Neo4j database.
3. ConfigManager: Manages configuration settings for the system.
4. RobotsHandler: Handles robots.txt parsing and crawling permissions.
5. ParserManager: Manages content parsing with support for custom parsers.

3. System Architecture

3.1 Component Diagram

graph TB
    Main[main.py]
    CM[ConfigManager]
    S[Scraper]
    LM[LinkManager]
    RH[RobotsHandler]
    PM[ParserManager]
    Neo4j[(Neo4j Database)]
    Web((Internet))
    CP[Custom Parsers]

    Main --> CM
    Main --> S
    Main --> LM
    S --> Web
    S --> LM
    S --> RH
    S --> PM
    LM --> Neo4j
    PM --> CP

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3.2 Component Descriptions

3.2.1 main.py

The entry point of the application, responsible for initializing components and starting the crawl process.

3.2.2 ConfigManager

Manages configuration settings loaded from a YAML file.

3.2.3 Scraper

The Scraper is responsible for:

• Asynchronously fetching web pages
• Parsing HTML content
• Discovering internal and external links
• Managing the crawling process
• Implementing politeness features (rate limiting, robots.txt compliance)
• Content deduplication

3.2.4 LinkManager

The LinkManager handles:

• Storing and retrieving link information in the Neo4j database
• Managing relationships between links
• Tracking crawl history and content hashes

3.2.5 RobotsHandler

Manages the fetching, parsing, and caching of robots.txt files, ensuring crawler politeness.

3.2.6 ParserManager

Manages content parsing, including loading and using custom parsers.

3.2.7 Neo4j Database

The Neo4j database is used to store:

• Link information (URL, domain, last checked date, content hash)
• Relationships between links (e.g., parent-child relationships)

4. Data Design

4.1 Neo4j Data Model

graph LR
    A[Link] -->|LINKS_TO| B[Link]
    A --> url
    A --> domain
    A --> lastChecked
    A --> contentHash
    A --> lastModified

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4.2 Key Entities

• Link: Represents a web page with properties such as URL, domain, last checked date, content hash, and last modified date.
• LINKS_TO: Represents the relationship between two links (pages).

5. Error Handling and Logging

• The system uses Python's logging module to log information, warnings, and errors.
• Retry mechanisms are implemented for handling transient network errors.
• Exceptions are caught and logged appropriately throughout the system.

6. Performance Considerations

• Asynchronous programming is used to improve crawling performance.
• Rate limiting is implemented to ensure politeness and avoid overwhelming target servers.
• Content deduplication reduces unnecessary storage and processing.
• Incremental crawling minimizes redundant crawling of unchanged content.

7. Security Considerations

• The system respects robots.txt files and implements rate limiting to avoid being perceived as a malicious bot.
• Configuration settings, including database credentials, are stored in a separate YAML file, improving security and maintainability.

8. Deployment

8.1 Deployment Steps

1. Set up a Neo4j database instance.
2. Install required Python packages: pip install -r requirements.txt
3. Configure settings in config.yaml, including database connection details.
4. Run the main script to start the crawling process: python main.py