Incremental web crawlers employ several key strategies to efficiently process web links while minimizing redundant downloads and maximizing freshness.
URL Deduplication:
Before crawling, the crawler checks if a URL has been visited recently or is already in the queue. This avoids reprocessing the same link. For example, a crawler may use a hash table or bloom filter to store and quickly lookup visited URLs.
Change Detection:
Incremental crawlers compare the last-modified timestamp or ETag of a webpage with the previously stored version. If no changes are detected, the page is skipped. For instance, a crawler might send an HTTP If-Modified-Since header to the server to check for updates.
Priority Queuing:
Links are prioritized based on factors like crawl frequency, page importance, or update patterns. Frequently updated pages (e.g., news sites) are crawled more often than static ones. Example: A news aggregator may prioritize breaking news URLs over archived articles.
Partial Parsing:
Instead of downloading the entire page, some crawlers fetch only specific parts (e.g., headers or metadata) to determine if the content has changed. This reduces bandwidth usage.
Time-Based Scheduling:
URLs are scheduled for recrawl based on their historical update frequency. For example, a blog updated daily might be recrawled every few hours, while a yearly report page is checked once a month.
Example: A tech news site uses an incremental crawler to monitor articles. The crawler:
Last-Modified headers to avoid downloading unchanged pages.For such use cases, Tencent Cloud's Web Crawler Service (or similar data processing solutions) can help manage URL queues, storage, and scheduling efficiently.