To optimize crawling speed with incremental web crawlers, focus on reducing redundant data fetching and improving efficiency in identifying changes. Here’s how:
Track Last Modified Dates: Use HTTP headers like Last-Modified or ETag to check if a page has been updated since the last crawl. Only re-fetch pages that have changed.
Example: If a webpage’s Last-Modified date is older than the last crawl timestamp, skip it.
Hash-Based Content Comparison: Store hash values (e.g., MD5 or SHA-1) of previously crawled content. Compare hashes before re-fetching to detect changes.
Example: If the hash of a page’s HTML matches the stored hash, skip crawling it again.
Prioritize High-Change URLs: Use historical data to identify URLs that change frequently and prioritize them in the crawl queue.
Example: A news site’s homepage may change hourly, while an archive page rarely changes—crawl the homepage more often.
Distributed Crawling: Split the crawl workload across multiple machines or threads to increase throughput.
Example: Use Tencent Cloud’s Serverless Cloud Function (SCF) to distribute crawling tasks dynamically, scaling resources as needed.
Rate Limiting and Politeness Policies: Avoid overloading target servers by respecting robots.txt and setting delays between requests.
Example: Configure a crawl delay of 2 seconds per domain to minimize server impact.
Incremental Database Updates: Store crawled data in a database that supports efficient updates (e.g., appending new records instead of full rewrites).
Example: Use Tencent Cloud’s TencentDB for MySQL with partitioning to manage large datasets efficiently.
Crawl Scheduling: Schedule crawls during off-peak hours for target websites to reduce latency and improve response times.
Example: Crawl a site at 2 AM when traffic is low.
For large-scale crawling, Tencent Cloud’s Elastic Compute Service (CVM) and Content Delivery Network (CDN) can help optimize performance and reduce latency.