How to use log analysis to identify crawler behavior?

To identify crawler behavior using log analysis, you can examine server logs for patterns that distinguish crawlers from regular users. Key steps include:

1. User-Agent Analysis: Crawlers typically identify themselves with unique User-Agent strings (e.g., "Googlebot", "Bingbot"). Filter logs for known crawler User-Agents or suspicious entries.

   • Example: A log entry with User-Agent: "Mozilla/5.0 (compatible; AhrefsBot/7.0; +http://ahrefs.com/robot/)" indicates an Ahrefs crawler.

2. Request Patterns: Crawlers often make high-frequency, repetitive requests to the same URLs or follow a predictable crawling sequence. Look for spikes in requests from a single IP or User-Agent.

   • Example: An IP making 1,000 requests per minute to /products/page=* suggests automated crawling.

3. Session and Cookie Behavior: Legitimate users usually have varied sessions and cookies, while crawlers may lack cookies or reuse the same session.

   • Example: Repeated requests without Cookie headers or with static session IDs may indicate a crawler.

4. IP Reputation and Geolocation: Check if IPs belong to known data centers (e.g., AWS, Tencent Cloud) or regions unlikely for your audience.

   • Example: Requests from a Tencent Cloud IP with no prior user engagement might be a crawler.

5. Tools and Services: Use log analysis tools like Tencent Cloud CLS (Cloud Log Service) to aggregate and query logs efficiently. Set up alerts for suspicious patterns.

   • Example: Configure CLS to detect high-frequency requests from a specific User-Agent and trigger notifications.

By combining these methods, you can effectively identify and mitigate crawler behavior in your logs. For scalable log analysis, Tencent Cloud CLS provides real-time processing and visualization to streamline this process.