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import csv
import scrapy
from scrapy.crawler import CrawlerProcess
from scrapy.spiders import CrawlSpider, Rule
from scrapy.linkextractors import LinkExtractor
from scrapy.exceptions import CloseSpider
import os
class ExtendedEduSpider(CrawlSpider):
name = 'extended_education_spider'
custom_settings = {
'CONCURRENT_REQUESTS': 16, # Increase concurrency with multiple threads
'CONCURRENT_REQUESTS_PER_DOMAIN': 8, # Limit concurrency per domain
'DOWNLOAD_DELAY': 0.5, # Reduce delay between requests
'AUTOTHROTTLE_ENABLED': True, # Automatically adjust crawling speed
'AUTOTHROTTLE_START_DELAY': 1, # Initial delay for autothrottle
'AUTOTHROTTLE_TARGET_CONCURRENCY': 16, # Target concurrency level
'HTTPCACHE_ENABLED': True, # Enable HTTP caching
'HTTPCACHE_EXPIRATION_SECS': 3600, # Cache expiration time (in seconds)
}
def __init__(self, *args, **kwargs):
super(ExtendedEduSpider, self).__init__(*args, **kwargs)
self.scraped_urls = set()
def parse_item(self, response):
if response.url in self.scraped_urls:
return
self.scraped_urls.add(response.url)
content = ' '.join(response.xpath('//p/text()').extract()).strip()
image_urls = [response.urljoin(url) for url in response.css('img::attr(src)').extract()]
yield {
'Domain': response.url.split('/')[2],
'URL': response.url,
'Title': response.css('title::text').get(),
'Content': content,
'Image URLs': '|'.join(image_urls),
}
# Check the size of the output file
file_size = os.path.getsize('educational_data.csv') / (1024 * 1024 * 1024) # Convert bytes to gigabytes
if file_size >= 0.5: # Stop spider after collecting 1GB of data
raise CloseSpider(reason='Reached 1GB limit')
# Define rules to follow links
rules = (
Rule(LinkExtractor(allow=()), callback='parse_item', follow=True),
)
# Configure the output and other settings
process = CrawlerProcess(settings={
'USER_AGENT': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.99 Safari/537.36',
'ROBOTSTXT_OBEY': True,
'FEEDS': {
'educational_data.csv': {
'format': 'csv',
'fields': ['Domain', 'URL', 'Title', 'Content', 'Image URLs'],
},
},
})
uni_urls = []
with open('universities.csv', 'r') as file:
reader = csv.reader(file)
for row in reader:
uni_urls.append(row[1])
# Start the crawling process for each domain
process.crawl(ExtendedEduSpider, start_urls=[
'https://www.geeksforgeeks.org/data-structures/',
'https://en.wikipedia.org/wiki/Artificial_intelligence',
'https://www.w3schools.com/js/default.asp',
'https://www.w3schools.com/sql/default.asp',
'https://www.w3schools.com/python/default.asp',
'https://www.w3schools.com/java/default.asp',
'https://www.w3schools.com/php/default.asp',
'https://www.w3schools.com/c/index.php',
'https://www.geeksforgeeks.org/machine-learning/',
'https://www.geeksforgeeks.org/python-mongodb-tutorial/',
'https://www.geeksforgeeks.org/system-design-tutorial/',
'https://www.geeksforgeeks.org/web-design/',
'https://en.wikipedia.org/wiki/Data_mining',
'https://en.wikipedia.org/wiki/Information_retrieval',
'https://en.wikipedia.org/wiki/Natural_language_processing',
'https://www.geeksforgeeks.org/wikipedia-module-in-python',
'https://www.geeksforgeeks.org/how-to-extract-wikipedia-data-in-python',
'https://www.geeksforgeeks.org/web-scraping-from-wikipedia-using-python-a-complete-guide',
] + uni_urls)
process.start()
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