├── images └── scruna-wb-readme-cover.png ├── pyproject.toml ├── .gitignore ├── data_exp.py ├── main.py ├── README.md └── poetry.lock /images/scruna-wb-readme-cover.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alexfazio/scruna-wb/main/images/scruna-wb-readme-cover.png -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "python-template" 3 | version = "0.1.0" 4 | description = "" 5 | authors = ["Alex Fazio "] 6 | 7 | [tool.poetry.dependencies] 8 | python = ">=3.11" 9 | aiohttp = ">=3.10.10" 10 | aiosqlite = ">=0.20.0" 11 | beautifulsoup4 = ">=4.12.3" 12 | markdown = ">=3.7" 13 | playwright = ">=1.48.0" 14 | 15 | [build-system] 16 | requires = ["poetry-core>=1.0.0"] 17 | build-backend = "poetry.core.masonry.api" -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | egg-info/ 15 | .eggs/ 16 | 17 | # Installer logs 18 | pip-log.txt 19 | pip-delete-this-directory.txt 20 | 21 | # Unit test / coverage reports 22 | htmlcov/ 23 | .tox/ 24 | .nox/ 25 | .coverage 26 | coverage.xml 27 | *.cover 28 | .hypothesis/ 29 | 30 | # PyCharm specific files 31 | .idea/ 32 | *.iml 33 | *.iws 34 | 35 | # Virtual environments 36 | venv/ 37 | ENV/ 38 | .env/ 39 | .venv/ 40 | 41 | # Jupyter Notebook checkpoints 42 | .ipynb_checkpoints 43 | 44 | # Pytest cache 45 | .pytest_cache/ 46 | 47 | # MyPy 48 | .mypy_cache/ 49 | 50 | # VS Code settings 51 | .vscode/ 52 | 53 | # MacOS files 54 | .DS_Store 55 | 56 | # Windows Thumbnails 57 | Thumbs.db 58 | 59 | # Logs and temporary files 60 | *.log 61 | *.tmp 62 | *.swp 63 | 64 | # Environment variables files 65 | .env 66 | 67 | # Scraped data 68 | scraped_data/ 69 | 70 | # Database files 71 | scraper.db 72 | -------------------------------------------------------------------------------- /data_exp.py: -------------------------------------------------------------------------------- 1 | import sqlite3 2 | import os 3 | import webbrowser 4 | from pathlib import Path 5 | from urllib.parse import urlparse 6 | 7 | def is_content_page(url): 8 | """ 9 | Determine if a URL is likely a content page rather than an asset. 10 | """ 11 | parsed = urlparse(url) 12 | path = parsed.path.lower() 13 | 14 | # List of asset patterns and extensions to filter out 15 | asset_patterns = ['/assets/', '/static/', '/dist/', '/js/', '/css/', '/images/'] 16 | asset_extensions = ['.js', '.css', '.png', '.jpg', '.jpeg', '.gif', '.ico', '.svg'] 17 | 18 | return not ( 19 | any(pattern in path for pattern in asset_patterns) or 20 | any(path.endswith(ext) for ext in asset_extensions) 21 | ) 22 | 23 | def get_file_path(url): 24 | """Get the file path for a given URL.""" 25 | filename = url.replace('/', '_').replace(':', '_').replace('?', '_').replace('&', '_') 26 | return Path('scraped_data') / f'{filename}.html' 27 | 28 | def connect_db(): 29 | """Connect to the SQLite database.""" 30 | return sqlite3.connect('scraper.db') 31 | 32 | def get_scraped_pages(db): 33 | """Get all successfully scraped HTML pages from the database.""" 34 | cursor = db.cursor() 35 | cursor.execute('SELECT url, timestamp, status FROM pages WHERE status = "scraped"') 36 | return [page for page in cursor.fetchall() if is_content_page(page[0])] 37 | 38 | def get_error_pages(db): 39 | """Get all pages that encountered errors during scraping.""" 40 | cursor = db.cursor() 41 | cursor.execute('SELECT url, timestamp, status FROM pages WHERE status = "error"') 42 | return [page for page in cursor.fetchall() if is_content_page(page[0])] 43 | 44 | def main(): 45 | if not os.path.exists('scraper.db'): 46 | print("Error: scraper.db not found. Please run the scraper first.") 47 | return 48 | 49 | # Ensure scraped_data directory exists 50 | Path('scraped_data').mkdir(exist_ok=True) 51 | 52 | db = connect_db() 53 | 54 | while True: 55 | print("\nWeb Archive Explorer") 56 | print("-------------------") 57 | print("1. List all successfully scraped pages") 58 | print("2. List pages with errors") 59 | print("3. Open a specific HTML file") 60 | print("4. Show scraping statistics") 61 | print("5. Exit") 62 | 63 | choice = input("\nEnter your choice (1-5): ") 64 | 65 | if choice == '1': 66 | scraped_pages = get_scraped_pages(db) 67 | print(f"\nSuccessfully scraped pages ({len(scraped_pages)}):") 68 | for i, (url, timestamp, status) in enumerate(scraped_pages, 1): 69 | file_path = get_file_path(url) 70 | exists = "✓" if file_path.exists() else "✗" 71 | print(f"{i}. [{exists}] {url} (archived: {timestamp})") 72 | 73 | if scraped_pages: 74 | view = input("\nEnter number to view page (or press Enter to return): ") 75 | if view.isdigit() and 1 <= int(view) <= len(scraped_pages): 76 | url = scraped_pages[int(view)-1][0] 77 | file_path = get_file_path(url) 78 | if file_path.exists(): 79 | webbrowser.open(f'file://{file_path.absolute()}') 80 | else: 81 | print(f"Error: HTML file not found at {file_path}") 82 | 83 | elif choice == '2': 84 | error_pages = get_error_pages(db) 85 | print(f"\nPages with errors ({len(error_pages)}):") 86 | for url, timestamp, status in error_pages: 87 | print(f"- {url} (timestamp: {timestamp})") 88 | 89 | elif choice == '3': 90 | html_files = list(Path('scraped_data').glob('*.html')) 91 | print(f"\nAvailable HTML files ({len(html_files)}):") 92 | for i, file in enumerate(html_files, 1): 93 | print(f"{i}. {file.stem}") 94 | 95 | if html_files: 96 | view = input("\nEnter number to view file (or press Enter to return): ") 97 | if view.isdigit() and 1 <= int(view) <= len(html_files): 98 | webbrowser.open(f'file://{html_files[int(view)-1].absolute()}') 99 | 100 | elif choice == '4': 101 | cursor = db.cursor() 102 | cursor.execute('SELECT COUNT(*) as total FROM pages') 103 | total = cursor.fetchone()[0] 104 | 105 | scraped_pages = len(get_scraped_pages(db)) 106 | error_pages = len(get_error_pages(db)) 107 | 108 | print("\nScraping Statistics") 109 | print("-------------------") 110 | print(f"Total Content URLs: {total}") 111 | print(f"Scraped Content Pages: {scraped_pages}") 112 | print(f"Error Content Pages: {error_pages}") 113 | if total > 0: 114 | print(f"Success Rate: {(scraped_pages/total*100):.1f}%") 115 | 116 | elif choice == '5': 117 | break 118 | 119 | db.close() 120 | print("\nGoodbye!") 121 | 122 | if __name__ == '__main__': 123 | main() 124 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import asyncio, aiohttp, aiosqlite, os, json, random, datetime, markdown 2 | from playwright.async_api import async_playwright 3 | from datetime import datetime 4 | from bs4 import BeautifulSoup 5 | 6 | # Setup 7 | DOMAIN = 'forum.keyboardmaestro.com' 8 | CDX_API = f'http://web.archive.org/cdx/search/cdx?url={DOMAIN}/*&output=json&collapse=urlkey&filter=statuscode:200' 9 | OUTPUT_DIR = 'scraped_data' 10 | 11 | 12 | def generate_dynamic_headers(url: str, timestamp: str) -> dict: 13 | """ 14 | Generate browser-like HTTP headers for web archive requests. 15 | 16 | Args: 17 | url (str): The target URL being requested 18 | timestamp (str): The wayback machine timestamp in format YYYYMMDDHHMMSS 19 | 20 | Returns: 21 | dict: A dictionary of HTTP headers including User-Agent, Referer, and security headers 22 | """ 23 | chrome_version = "130.0.0.0" 24 | return { 25 | "sec-ch-ua-platform": '"Windows"', 26 | "Referer": ( 27 | f"https://web.archive.org/web/{timestamp}/" 28 | f"https://{url}" 29 | ), 30 | "User-Agent": ( 31 | "Mozilla/5.0 (Windows NT 10.0; Win64; x64) " 32 | f"AppleWebKit/537.36 (KHTML, like Gecko) Chrome/{chrome_version} " 33 | "Safari/537.36" 34 | ), 35 | "sec-ch-ua": ( 36 | f'"Chromium";v="{chrome_version.split(".")[0]}", ' 37 | f'"Google Chrome";v="{chrome_version.split(".")[0]}", ' 38 | '"Not?A_Brand";v="99"' 39 | ), 40 | "DNT": "1", 41 | "sec-ch-ua-mobile": "?0" 42 | } 43 | 44 | 45 | async def fetch_archived_urls() -> dict: 46 | """ 47 | Fetch all archived URLs for the specified domain from the Wayback Machine CDX API. 48 | 49 | Returns: 50 | dict: A dictionary mapping URLs to their most recent timestamp 51 | {url: timestamp} where timestamp is in YYYYMMDDHHMMSS format 52 | 53 | Notes: 54 | - Uses the CDX API to get historical snapshots 55 | - Collapses multiple snapshots to keep only the most recent version of each URL 56 | - Prints progress information to stdout 57 | """ 58 | print("Fetching archived URLs from CDX API...") 59 | headers = generate_dynamic_headers(DOMAIN, datetime.now().strftime("%Y%m%d%H%M%S")) 60 | async with aiohttp.ClientSession(headers=headers) as session: 61 | async with session.get(CDX_API) as resp: 62 | data = await resp.json() 63 | print(f"Total URLs fetched: {len(data) - 1}") 64 | url_dict = {} 65 | for row in data[1:]: 66 | url = row[2] 67 | timestamp = row[1] 68 | if url not in url_dict or timestamp > url_dict[url]: 69 | url_dict[url] = timestamp 70 | print(f"Total unique URLs after collapsing: {len(url_dict)}") 71 | return url_dict 72 | 73 | 74 | async def init_db() -> aiosqlite.Connection: 75 | """ 76 | Initialize SQLite database connection and create required tables. 77 | 78 | Returns: 79 | aiosqlite.Connection: Database connection object 80 | 81 | Notes: 82 | Creates a 'pages' table with schema: 83 | - url (TEXT PRIMARY KEY) 84 | - timestamp (TEXT) 85 | - status (TEXT) 86 | """ 87 | db = await aiosqlite.connect('scraper.db') 88 | await db.execute(''' 89 | CREATE TABLE IF NOT EXISTS pages ( 90 | url TEXT PRIMARY KEY, 91 | timestamp TEXT, 92 | status TEXT 93 | ) 94 | ''') 95 | await db.commit() 96 | return db 97 | 98 | 99 | async def save_content(url: str, content: str) -> None: 100 | """ 101 | Save scraped content in multiple formats (HTML, JSON, Markdown). 102 | 103 | Args: 104 | url (str): The source URL of the content 105 | content (str): The raw HTML content to save 106 | 107 | Notes: 108 | Saves three files in the OUTPUT_DIR: 109 | - {filename}.html: Raw HTML content 110 | - {filename}.json: Metadata including title and description 111 | - {filename}.md: HTML converted to Markdown format 112 | 113 | Filenames are generated by replacing URL special characters with underscores 114 | """ 115 | filename = url.replace('/', '_').replace(':', '_').replace('?', '_').replace('&', '_') 116 | os.makedirs(OUTPUT_DIR, exist_ok=True) 117 | 118 | html_path = os.path.join(OUTPUT_DIR, f'{filename}.html') 119 | with open(html_path, 'w', encoding='utf-8') as f: 120 | f.write(content) 121 | print(f"Saved HTML content to {html_path}") 122 | 123 | soup = BeautifulSoup(content, 'html.parser') 124 | 125 | data = { 126 | 'url': url, 127 | 'title': soup.title.string.strip() if soup.title and soup.title.string else '', 128 | 'description': soup.find('meta', attrs={'name': 'description'})['content'].strip() if soup.find('meta', attrs={ 129 | 'name': 'description'}) else '' 130 | } 131 | json_path = os.path.join(OUTPUT_DIR, f'{filename}.json') 132 | with open(json_path, 'w', encoding='utf-8') as f: 133 | json.dump(data, f, ensure_ascii=False, indent=2) 134 | print(f"Saved JSON data to {json_path}") 135 | 136 | md_content = markdown.markdown(str(soup.body)) if soup.body else '' 137 | md_path = os.path.join(OUTPUT_DIR, f'{filename}.md') 138 | with open(md_path, 'w', encoding='utf-8') as f: 139 | f.write(md_content) 140 | print(f"Saved Markdown content to {md_path}") 141 | 142 | 143 | async def scrape_page(page, db: aiosqlite.Connection, url: str, timestamp: str) -> None: 144 | """ 145 | Scrape a single page from the Wayback Machine archive. 146 | 147 | Args: 148 | page: Playwright page object 149 | db (aiosqlite.Connection): Database connection 150 | url (str): URL to scrape 151 | timestamp (str): Wayback Machine timestamp in YYYYMMDDHHMMSS format 152 | 153 | Notes: 154 | - Uses dynamic headers for each request 155 | - Implements random delays between requests (10-120 seconds) 156 | - Updates page status in database ('scraped' or 'error') 157 | - Handles timeouts and errors gracefully 158 | """ 159 | archive_url = f'https://web.archive.org/web/{timestamp}id_/{url}' 160 | print(f"Scraping URL: {archive_url}") 161 | 162 | # Set headers for each request dynamically this might issues 163 | # will review 164 | headers = generate_dynamic_headers(url, timestamp) 165 | await page.set_extra_http_headers(headers) 166 | 167 | try: 168 | await page.goto(archive_url, timeout=60000, wait_until='networkidle') 169 | content = await page.content() 170 | await save_content(url, content) 171 | await db.execute('UPDATE pages SET status = ? WHERE url = ?', ('scraped', url)) 172 | await db.commit() 173 | print(f"Successfully scraped: {url}") 174 | except Exception as e: 175 | await db.execute('UPDATE pages SET status = ? WHERE url = ?', ('error', url)) 176 | await db.commit() 177 | print(f"Error scraping {url}: {e}") 178 | 179 | # Random delay between 10 seconds and 2 minutes 180 | delay = random.uniform(10, 120) 181 | print(f"Waiting for {delay:.2f} seconds before next request...") 182 | await asyncio.sleep(delay) 183 | 184 | 185 | async def main() -> None: 186 | """ 187 | Main execution function for the web scraper. 188 | 189 | Process: 190 | 1. Fetches all archived URLs from Wayback Machine 191 | 2. Initializes/updates the SQLite database 192 | 3. Processes all pending URLs sequentially 193 | 4. Saves content and updates status for each URL 194 | 195 | Notes: 196 | - Uses Playwright for browser automation 197 | - Maintains state in SQLite database 198 | - Handles one URL at a time to respect rate limits 199 | - Prints progress information throughout execution 200 | """ 201 | url_dict = await fetch_archived_urls() 202 | db = await init_db() 203 | 204 | print("Inserting URLs into the database...") 205 | for url, timestamp in url_dict.items(): 206 | await db.execute( 207 | 'INSERT OR IGNORE INTO pages (url, timestamp, status) VALUES (?, ?, ?)', 208 | (url, timestamp, 'pending') 209 | ) 210 | await db.commit() 211 | print("Database setup complete.") 212 | 213 | print("Fetching pending URLs from the database...") 214 | async with db.execute( 215 | 'SELECT url, timestamp FROM pages WHERE status = ?', ('pending',) 216 | ) as cursor: 217 | pending_rows = await cursor.fetchall() 218 | pending_urls = [(row[0], row[1]) for row in pending_rows] 219 | 220 | if not pending_urls: 221 | print('No URLs to process.') 222 | await db.close() 223 | return 224 | 225 | print(f"Total pending URLs to process: {len(pending_urls)}") 226 | 227 | async with async_playwright() as p: 228 | browser = await p.chromium.launch(headless=True) 229 | context = await browser.new_context() 230 | page = await context.new_page() 231 | 232 | for url, timestamp in pending_urls: 233 | await scrape_page(page, db, url, timestamp) 234 | 235 | await browser.close() 236 | await db.close() 237 | print("Scraping completed.") 238 | 239 | 240 | if __name__ == '__main__': 241 | asyncio.run(main()) 242 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

2 | Alt text for the image 3 |

4 | 5 |

6 |

scruna-wb

7 |

8 | Tuna's Wayback Scraper 9 |
10 |
11 | @tunahorse21 12 | · 13 | @alxfazio 14 |

15 |

16 | 17 | > This project contains derivative content based on code found in [Tuna's](https://x.com/tunahorse21) article ["Web scraping in 2024: pt2 actually scraping"](https://tuna.cat/posts/web-scraping-2024-pt2/). 18 | 19 | ## Overview 20 | This project is a specialized web scraper designed to preserve and archive historical forum content using the Internet Archive's Wayback Machine. Instead of scraping a live website directly, it leverages the Wayback Machine's extensive archive to: 21 | 1. Access historical versions of web pages that might no longer be available 22 | 2. Retrieve content from a specific domain (``) across different points in time 23 | 3. Ensure data consistency and integrity through verified snapshots 24 | 25 | The Wayback Machine serves as a reliable intermediary, providing: 26 | - Timestamped snapshots of web pages 27 | - A structured API for discovering archived content 28 | - Verified copies of pages as they appeared at specific points in time 29 | - Legal and ethical access to historical web content 30 | 31 | The scraper systematically: 32 | 1. Queries the Wayback Machine's CDX API to discover archived pages 33 | 2. Retrieves the most recent snapshot of each unique URL 34 | 3. Processes and stores the content in multiple formats (HTML, JSON metadata, and Markdown) 35 | 4. Tracks progress using a local SQLite database 36 | 37 | This approach ensures reliable archival of web content while respecting rate limits and maintaining data integrity. 38 | 39 | ## Features 40 | - Retrieves historical snapshots from the Wayback Machine 41 | - Saves content in three formats: HTML, JSON metadata, and Markdown 42 | - Uses a SQLite database to track scraping progress 43 | - Implements polite scraping with random delays 44 | - Handles errors gracefully 45 | - Provides browser-like request headers to avoid blocking 46 | 47 | ## Prerequisites 48 | - Python 3.11 or higher 49 | - Poetry (for dependency management) 50 | 51 | ## Installation 52 | 53 | 1. Clone the repository 54 | ```bash 55 | git clone 56 | cd 57 | ``` 58 | 59 | 2. Install dependencies using Poetry 60 | ```bash 61 | poetry install 62 | ``` 63 | 64 | 3. Install Playwright browsers 65 | ```bash 66 | poetry run playwright install chromium 67 | ``` 68 | 69 | ## Project Structure 70 | 71 | ``` 72 | . 73 | ├── main.py # Main scraper implementation 74 | ├── data_exp.py # Data exploration utilities 75 | ├── scraper.db # SQLite database (auto-generated) 76 | ├── scraped_data/ # Output directory for scraped content 77 | │ ├── *.html # Raw HTML files 78 | │ ├── *.json # Metadata files 79 | │ └── *.md # Markdown versions 80 | └── pyproject.toml # Project dependencies and configuration 81 | ``` 82 | 83 | ## How It Works 84 | 85 | ### Process Flow 86 | ```mermaid 87 | flowchart TD 88 | A["Start: main()"] --> B["fetch_archived_urls(): 89 | Fetch Archived URLs from CDX API"] 90 | B --> C["init_db(): 91 | Initialize SQLite Database"] 92 | 93 | subgraph DB_Setup["Database Setup"] 94 | C --> D["init_db(): 95 | Create pages table"] 96 | D --> E["main(): 97 | Insert/Update URLs with pending status"] 98 | end 99 | 100 | E --> F["main(): 101 | Fetch pending URLs from database"] 102 | F --> G{"main(): 103 | Any pending URLs?"} 104 | 105 | G -->|No| H["main(): 106 | Close DB & Exit"] 107 | G -->|Yes| I["main(): 108 | Launch Headless Browser"] 109 | 110 | subgraph Scraping["Scraping Process"] 111 | I --> J["main(): 112 | Create new browser context & page"] 113 | J --> K["scrape_page(): 114 | Get next pending URL"] 115 | 116 | K --> L["generate_dynamic_headers(): 117 | Generate browser headers"] 118 | L --> M["scrape_page(): 119 | Navigate to Wayback Machine URL"] 120 | 121 | M --> N{"scrape_page(): 122 | Successful?"} 123 | 124 | N -->|Yes| O["save_content(): 125 | Save Content"] 126 | O --> P["scrape_page(): 127 | Update DB status: scraped"] 128 | 129 | N -->|No| Q["scrape_page(): 130 | Update DB status: error"] 131 | 132 | P --> R["scrape_page(): 133 | Random delay 10-120s"] 134 | Q --> R 135 | 136 | R --> S{"main(): 137 | More URLs?"} 138 | S -->|Yes| K 139 | S -->|No| T["main(): 140 | Close browser"] 141 | end 142 | 143 | T --> H 144 | 145 | subgraph Saving["Content Saving"] 146 | O --> O1["save_content(): 147 | Save HTML"] 148 | O --> O2["save_content(): 149 | Save JSON metadata"] 150 | O --> O3["save_content(): 151 | Save Markdown"] 152 | end 153 | ``` 154 | 155 | ### 1. URL Collection 156 | - Queries the Wayback Machine's CDX API to get all archived URLs for the domain (`fetch_archived_urls`) 157 | - Filters for successful responses (HTTP 200) (`fetch_archived_urls`) 158 | - Keeps only the most recent version of each URL (`fetch_archived_urls`) 159 | 160 | ### 2. Database Management 161 | - Uses SQLite to track scraping progress (`init_db`) 162 | - Schema: 163 | - `url`: The target URL (PRIMARY KEY) 164 | - `timestamp`: Wayback Machine timestamp 165 | - `status`: Current status (pending/scraped/error) 166 | 167 | ### 3. Scraping Process 168 | The scraper follows this workflow: 169 | 170 | 1. **Initialization** 171 | - Fetches archived URLs (`main`) 172 | - Sets up database (`main`) 173 | - Marks all URLs as 'pending' (`main`) 174 | 175 | 2. **Browser Setup** 176 | - Launches headless Chromium browser (`main`) 177 | - Creates new context and page (`main`) 178 | 179 | 3. **For Each URL** 180 | - Generates dynamic browser-like headers (`generate_dynamic_headers`) 181 | - Navigates to Wayback Machine URL (`scrape_page`) 182 | - Implements random delays (10-120 seconds) (`scrape_page`) 183 | - Updates status in database (`scrape_page`) 184 | 185 | 4. **Content Saving** 186 | For each successfully scraped page: 187 | - Saves raw HTML (`save_content`) 188 | - Extracts and saves metadata (title, description) as JSON (`save_content`) 189 | - Converts content to Markdown (`save_content`) 190 | 191 | ### 4. Error Handling 192 | - Implements timeouts for page loads (`scrape_page`) 193 | - Marks failed URLs with 'error' status (`scrape_page`) 194 | - Continues processing remaining URLs after errors (`scrape_page`) 195 | 196 | ## Output Formats 197 | 198 | 1. **HTML Files** 199 | - Raw HTML content exactly as archived 200 | - Filename: `{url_safe_name}.html` 201 | 202 | 2. **JSON Metadata** 203 | - URL 204 | - Page title 205 | - Meta description 206 | - Filename: `{url_safe_name}.json` 207 | 208 | 3. **Markdown** 209 | - Converted HTML content 210 | - Preserves basic formatting 211 | - Filename: `{url_safe_name}.md` 212 | 213 | ## Data Exploration 214 | The project includes a data exploration script (`data_exp.py`) that allows you to: 215 | - View database tables 216 | - Check scraping status 217 | - Open scraped HTML files in browser 218 | - Review successful and failed scrapes 219 | 220 | ## Configuration 221 | Key settings in `main.py`: 222 | ```python 223 | DOMAIN = '' 224 | OUTPUT_DIR = 'scraped_data' 225 | ``` 226 | 227 | ## Rate Limiting 228 | To respect server resources and avoid blocking: 229 | - Random delays between requests (10-120 seconds) 230 | - Browser-like headers 231 | - Single-threaded processing 232 | - Proper user agent strings 233 | 234 | ## Error States 235 | The scraper handles several error conditions: 236 | - Network timeouts 237 | - Invalid HTML content 238 | - Missing pages 239 | - Server errors 240 | 241 | ## Notes 242 | - All scraped data is stored in the `scraped_data` directory 243 | - The SQLite database (`scraper.db`) maintains state between runs 244 | - The scraper can be safely stopped and resumed 245 | - Progress is logged to stdout 246 | 247 | ## Limitations 248 | - Single-domain focused 249 | - Sequential processing only 250 | - Requires stable internet connection 251 | - May be affected by Wayback Machine availability 252 | 253 | ## Contributing 254 | 1. Fork the repository 255 | 2. Create a feature branch 256 | 3. Commit changes 257 | 4. Push to the branch 258 | 5. Create a Pull Request 259 | 260 | ## License 261 | 262 | This project is licensed under the [MIT](https://opensource.org/licenses/MIT) License - see the [LICENSE](LICENSE) file for details. 263 | 264 | ## Disclaimer 265 | 266 | *It is the sole responsibility of the end users to respect websites' policies when scraping, searching, and crawling with Web Archive Scraper. Users are advised to adhere to the applicable privacy policies and terms of use of the websites prior to initiating any scraping activities. By default, Web Archive Scraper respects the directives specified in the websites' robots.txt files when crawling. By utilizing Web Archive Scraper, you expressly agree to comply with these conditions.* 267 | 268 | [↑ Back to Top ↑](#Web-Archive-Scraper) 269 | 270 | Copyright (c) 2024-present, Alex Fazio -------------------------------------------------------------------------------- /poetry.lock: -------------------------------------------------------------------------------- 1 | # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 2 | 3 | [[package]] 4 | name = "aiohappyeyeballs" 5 | version = "2.4.3" 6 | description = "Happy Eyeballs for asyncio" 7 | optional = false 8 | python-versions = ">=3.8" 9 | files = [ 10 | {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = "sha256:8a7a83727b2756f394ab2895ea0765a0a8c475e3c71e98d43d76f22b4b435572"}, 11 | {file = "aiohappyeyeballs-2.4.3.tar.gz", hash = "sha256:75cf88a15106a5002a8eb1dab212525c00d1f4c0fa96e551c9fbe6f09a621586"}, 12 | ] 13 | 14 | [[package]] 15 | name = "aiohttp" 16 | version = "3.10.10" 17 | 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