How keyvanarasteh/darkwebcrawler works
<explanation>
Below is a step‑by‑step guide to translating your Python DarkWeb Crawler into a clear, detailed system‑design diagram. This is tailored to the project’s file structure and README. Follow each step, then use your favorite diagramming tool (Visio, draw.io, Lucidchart, etc.) to lay out the boxes, arrows, and annotations.
1. Identify the project type and purpose
- This is a single‑node, multi‑threaded, Python‑based crawler for dark‑web domains.
- It’s essentially a producer‑consumer system: seeds → queue → worker threads → database → reporting.
2. Analyze the file structure
- Top‑level Python modules:
• main.py (orchestrator / entry point)
• crawler.py (crawl logic per page)
• crawler_proc.py (possibly subprocess or helper for crawling)
• database.py (DB connection & CRUD)
• connection.py (Tor / HTTP connections with user‑agents)
• pub_methods.py (shared utilities: filtering, regex, parsing)
• reporting.py (post‑crawl reporting & exports)
• settings.py (configuration loader)
- Data folders:
• assets/user-agents.dat (rotating UA list)
• temp/filters.dat (keyword filters)
• queue/x.txt (disk‑backed queue?)
• db_tables/*.sql (schema & seed scripts)
• log/log.txt (application logs)
- Concurrency layout:
• threads/thread.txt & threads_sub/… imply thread management or logging per thread.
3. Extract architecture hints from README
- Multi‑thread high‑performance crawler
- Keyword & domain filtering (simple + regex)
- MySQL backend
4. Main components to include in the diagram
a. Orchestrator (main.py)
b. Configuration Loader (settings.py + assets/)
c. Queue Manager (in‑memory or disk via queue/x.txt)
d. Thread Pool Manager (spawned by main.py)
e. Crawler Workers (crawler.py + crawler_proc.py + connection.py)
f. Filtering Module (pub_methods.py + temp/filters.dat)
g. Database Layer (database.py → MySQL)
h. Reporting Module (reporting.py)
i. Logger (log/log.txt)
5. Relationships & interactions
- On startup, Orchestrator loads Configuration.
- Seeds initial URLs/domains into Queue Manager.
- Thread Pool Manager polls Queue for new tasks.
- Each Crawler Worker:
1. Grabs next URL/domain from Queue.
2. Uses Connection component to fetch page over Tor (user‑agents.dat).
3. Parses content, extracts links.
4. Sends extracted links through Filtering Module.
5. Accepted links re‑enqueue into Queue.
6. Results (metadata, checksums, filters applied) sent to Database Layer.
- Reporting Module aggregates data from MySQL for analytics/exports.
- Logger receives events across all components.
6. Highlight design patterns & principles
- Producer‑Consumer (Queue + Thread Pool)
- Layered Architecture (config → orchestration → workers → persistence → reporting)
- Single Responsibility (each module has a clear purpose)
- Configuration‑Driven (settings.py + assets/dat files)
7. Technologies & frameworks to annotate
- Python 3.x
- MySQL (schema in db_tables/)
- Tor or socks proxy via connection.py
- Multi‑threading (threading or multiprocessing in crawler_proc.py)
- Regex and simple string filters
8. Diagram construction guidelines
- Use rectangular boxes for each major component.
- Group components into logical zones:
• “Control Plane” (Orchestrator + Config + Logger)
• “Crawling Plane” (Queue Manager + Thread Pool + Workers + Filtering)
• “Data Plane” (Database Layer + MySQL + Reporting)
- Draw directional arrows showing data flows:
• From Orchestrator → Queue Manager
• Queue Manager → Worker Threads
• Worker Threads → Queue Manager (for new tasks)
• Worker Threads → Database Layer
• Database Layer → Reporting Module
• All components → Logger
- Color‑code zones:
• Blue for control, orange for crawling, green for data persistence.
- Label each arrow with the data being passed (e.g., “URL task,” “Filtered link,” “Crawl result record,” “Report query”).
9. Tailor the diagram to this crawler project
- Emphasize the multi‑threaded nature: show a “Thread Pool” icon with multiple worker boxes.
- Indicate disk-backed queue file if queue/x.txt is used for persistence.
- Show the filtering step explicitly between page fetch and database insert.
- Include user‑agents.dat and filters.dat as input assets to the connection & filtering modules.
10. Final touches for clarity
- Ensure every component has a clear, unique label.
- Use consistent shapes (e.g., cylinders for databases, parallelograms for files/assets).
- Add a legend explaining colors and shapes.
- Optionally, include the sequence: Initialize → Crawl Loop → Terminate.
By following this mapping, your resulting diagram will faithfully represent the crawler’s architecture, data flows, and key modules. Focus on clarity, correct labeling, and logical grouping rather than over‑abstracting—capture all the essential elements.