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Reverse Engineering Doubleclick Ad Statistics (Part 1)

One of the projects I’m on seeks to proxy web beacons. Basically, I have a WSGI app that serves a 1x1 px gif, and then triggers a Celery app that goes out and actually “clicks” on the intended web beacon. During preliminary load testing with a Doubleclick beacon (actually a Doubleclick link counter), we discovered that requesting that beacon 1000 times in 5 minutes (one request ever 13 second) only reported around 30 “clicks.” We’ve been throwing tests at Doubleclick to see what it reports under different scenarios:

  • 1000 clicks evenly distributed over 5 minutes: 30 clicks

  • 1000 clicks chunked into 50 groups of 20 (I misunderstood how JMeter works): 50 clicks

  • 300 clicks over an hour (1 per 12 seconds): 300 clicks

  • 500 clicks with spoofed User_agent strings (1 per 12 seconds): 500 clicks

So it appears that Doubleclick has a “cooldown” between clicks and doesn’t really care about the User_agent. How long is that cooldown? We know it’s greater than 13 second and less than 12 seconds.

Today, I’m running a test that looks like this:

Number of clicks

Rate of clicks

Delay between clicks

Total time

10240

240 per minute

.25 seconds

2560 s (42.6m)

5120

120 per minute

.5 seconds

2560 s (42.6m)

2560

80 per minute

.75 seconds

1920 s (32m)

1280

40 per minute

1.5 seconds

1920 s (32m)****

640

20 per minute

3 seconds

1920 s (32m)

320

10 per minute

6 seconds

1920 s (32m)

By taking the total number of clicks recorded today (which I’ll know tomorrow), I’ll be able to start approximating the cooldown (for example, if I see 2240 and change clicks, I’ll know that the cooldown is between 1.5 and .75 seconds).

Will update later.


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