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How to weather through DDos on AppEngine (en)

Google AppEngine should scale on demand and let you handle any workload. At least this is, what it says on the packaging. Turns out this is not completely true. This is also a very good thing because probably you don’t want to pay for every workload.

We came across this issue a few monts ago when we got under a Distributed Denial-of-Service (DDoS) Attack. Thousands of computers arround the world where requesting millions of pages from our web presence. I’ll sum up what we have done

The Google Infrastructure scaled up our application untill it was running about 1000 instances and then stopped strating new instances. This resulted in legitimate users experiencing a very bad freformance and timeouts for the site. Until that moment we didn’t notice anything - the joy of cloud computing.

Mitigating the Attack - first steps

We use the Website als for a lot ot internal tools. So we deployed and additional version relief and pointed all our internal users to and for them the problem was solved, work in the office could resume.

Google DDoS Protection Service

The Google AppEngine infrastructure provides a DDoS Protection service. This can be used to blacklist up to 100 IP-Adresses or Networks. Requests from blacklisted IPs will not reach your application.

At the Appengine Administration Console under Administration -> Blacklist you can get a list of most active users. We put them in a ddos.yaml redeployed and thought we where all set.

To make this more easy to handle we generated a text file of network names and a tool to process them into ddos.yaml.

Unfortunately it turned out that while ddos.yaml has a limit of 1000 entries we where attacked by far more hosts than 100. So the first step for us was top get the worst offenders. By downloading the logs from Google and doing some shell scripting this was easy: request_logs --num_days=2 -V production . ./logs.txt
cat logs.txt  | cut -d ' ' -f 1 | sort | uniq -c | sort -nr | \
   perl -npe 's/.*(\d+) ([\d.]+)/\2 # \1/;' > ddos.txt

So how to now get ddos.txt down to 100 Lines? We started manually aggregating networks. So # 7 # 6 # 5 # 7


You might read up on CIDR if you don’t understand what the slashes mean. It turnt out that we where over-blocking.

So wh had to to find a way to find the real network sizes of the offending networks. whois | grep -E 'inetnum|descr' gave us the network size, e.g. - If you can’t convert that to CIDR notation by yourself, tools like ip2cidr help. Enter, and get So we put that into our ddos.txt (and regenerated ddos.yaml and uploaded it).  # Belgacom  # DTAG # DTAG # DTAG

This still was not enough. 100 Entries could still not block all the different providers we where seeing attacks from. We obviously avoided putting european IP ranges in the blacklist - because our customers are from there. We saw that most requests came from Asia - where we practically have no customers. So “block Asia” was the way to go. We began searching for huge (“Calss-A”) Netblocks being exclusively useed in Asia.

By using whois | grep descr we could find out that and 79.* to 95.* where exclusively used “overseas”. Adding this huge blocks together with the most active providers into ddos.txt got our app into a usable state again.

In-App country blocking

Obviously this blocking of huge address spaces was a radical approach but it allowed our website to be accessible again to our users. We where aware that our ddos.txt would need permanent management and supervision. So we looked for a way to automate blocking by country.

The idea was to do that in our webapp but so early the DDoS requests would only need minimal resources. Since Google AppEnginge already provides Geolocation Services at no additional costs this was eary to implement.

Appengine provides also something like X-Appengine-Citylatlong: 51.256213,7.150764 this could be used deny access from certain regions, but we didn’t follow up on that.

Having country blocking in place an seeing the attack ebbing down we startet removing the large blocks from ddos.txt.

Long term solution - rate limiting

We didn’t want to engage in a arms race with the attackers. We could start blocking on user agent strings ans such but the best long term solution would be just to avoid single IP adresses to request too much data. This to a certain degree also has it’s problems, e.g. when many users come from the same IP due to NAT or proxies.

We where ready to implement such a scheme along the lines of Simon Wilson’s Blogpost about using memcache buckets for rate limiting but then the DDoS attacks stopped and we fond more interesting things to do.

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