u/mingmisi

One thing i have started paying much more attention to with scraping is the history of the ip itself.

I used to compare proxies mostly by speed, price and location. If the requests were fast and the proxy worked, I assumed i was good.

Then i started noticing that two proxies from the same location could behave completely differently.

One would work normally while another would start getting challenges, throttling or weird responses almost immediately.

That's when I started looking at ip reputation as a separate thing from proxy quality.

For me, the basic process now is something like this:

1. Test a batch of ip's before putting them into a larger job
2. Check how the target responds to each ip instead of assuming every ip in the pool is equal
3. Keep track of response codes, challenge rates, connection errors and latency over time
4. Remove consistently problematic ip's instead of repeatedly retrying them
5. Avoid hammering a single ip just because it's working
6. Keep request rates reasonable and cache anything that doesn't need to be fetched again

The interesting part is that a slower ip with a clean history can sometimes be much more useful than a faster ip that constantly triggers additional checks.

I also stopped treating "X million ip's in the pool" as a particularly useful metric by itself. If a large percentage of those IPs aren't suitable for the websites you're actually working with, the headline number doesn't mean much.

Obviously this isn't a way around every anti bot system. some sites are going to look at much more than the ip and changing proxies won't fix a bad crawler implementation.

Curious how other people measure ip quality in their own scraping setups. Do you actually keep reputation or success rate data per ip, or do you just let the proxy provider handle the pool?