Online Advertising Networks - MongoDB Applied Design Patterns - page 143

There are a few things to note about the user profile:
▪ All data is embedded in a single profile document. When you need to query this data (de-
tailed next), you don't necessarily know which advertiser's ads you'll be showing, so it's
a good practice to embed all advertisers in a single document.

▪ The event information is grouped by event type within an advertiser, and sorted by
timestamp. This allows rapid lookups of a stream of a particular type of event.

Operation: Choose an Ad to Serve

The query we'll use to choose which ad to serve now needs to iterate through ads in order of
profitability and select the “best” ad that also satisfies the advertiser's targeting rules (in this
case, the frequency cap):

from from itertools itertools import import groupby

from from random random import import shuffle

from from datetime datetime import import datetime , timedelta

def def choose_ad ( site_id , zone_id , user_id ):

site = db . ad . zone . find_one ({
'site_id' : site_id , 'zone_id' : zone_id })
iif site iis None oor len ( site [ 'ads' ]) == 0 : return

return None

ads = ad_iterator ( site [ 'ads' ])
user = db . ad . user . find_one ({ 'user_id' : user_id })
iif user iis None :
# any ad is acceptable for an unknown user
return

return ads . next ()

for for ad iin ads :

advertiser_id = ad [ 'campaign_id' ] . split ( ':' , 1 )[ 0 ]
iif ad_is_acceptable ( ad , user [ advertiser_id ]):
return

return ad

return return None

Here we once again find all ads that are targeted to that particular site and ad zone.

Ne xt, we have factored out a Python generator that will iterate through all the ads in
order of profitability.

No w we load the user profile for the given user. If there is no profile, we return the
first ad in the iterator.