Coupons.com chose MongoDB as their database to support their coupon code website due to its flexibility in schema changes and ability to scale. They used MongoDB for storing coupon and merchant data, real-time analytics, user preferences, archiving old data, and their internal CMS. While MongoDB met many of their needs, they found limitations around joins and initial support for some programming languages. Overall, MongoDB proved stable and easy to set up and replicate across data centers.

1. MongoDB at Coupons Inc.
http://www.coupons.com/coupon-codes/
http://www.coupons.com/passbook (for iPhone)
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2. How do coupon codes work?
1. User searches “Kmart
coupons” in Google/Coupons Inc. 2. Sees a Coupons.com
link on search results
page.
5. Applies
coupon code at
checkout.
Saves money,
gets items =
happy
customer! 3. Finds coupon
code for $5 for $50.
4. Clicks to get code and is
redirected to Kmart. Affiliate
“cookie” is dropped.
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3. Why we looked for new DB?
 Needed to build a data driven website for
Coupon Codes in a short amount of time.
 Want one DB to reasonably handle multiple
use cases.
 Want to quickly iterate.
 Only thing we knew about our schema is
that it’s going to change pretty quickly.
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4. Main requirements
 Easy to change our model. Low
operational overhead. Developer
can do the changes herself.
 Wanted a DB to avoid multiple
caching layers as this got us into
trouble earlier.
 Initially data size is not going to be
large, but if we do things right its
expected to grow quickly.
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5. Requirements Contd.
 Main use cases did not require join,
and did not need multi row
transactions.
 Need to support Java, Python, Perl
and PHP – because we want to
develop things quickly.
 Should be easy to set up across
multiple data centers soon after
launch.
 Should work in EC2.
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6. Requirements Contd.
 Ok to have only Linux support ( We
have some windows servers in the
company, but we didn't need to
support multiple OS(for DB clients).
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7. DBs considered
 SQLServer behind cache
[Gemfire/Varnish/memcache/apc]
 Used heavily in company. Existing
expertise with SQL Server.
 Operational overhead(schema
changes)
 MySQL
 Familiarity with the DB in the Coupon
Codes team.
 Could not easily change our model.
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

8. DB Considered Contd.
 MongoDB
 Easy to model, make changes to
“schema”.
 Sharding would let us scale in future.
Easy to go from non sharded to
sharded mode.
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9. 1st Use Case
 Main datastore for all
Coupon/Merchant related data.
 Writes by internal processes,
periodic reads for indexing into
Solr.
 Down time does not impact
availability of live site.
 Important consideration when
deploying a new technology. (If
MongoDB fails, it does not affect
the live site.)
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10. Sample Coupon
{
"category_id" : NumberLong(117),
"category_name" : "Books, News & Magazines",
"code" : "",
"coupon_id" : "53_2",
"created_by" : 5,
"exclusive_offer" : 1,
"passbook_offer" : 1,
"tags" : ['passbook', 'black friday' ]
"merchant" : {
"name" : "Abebooks",
"id" : NumberLong(678),
"image_url_small" : "http://cdn.cpnscdn.com/.../aff/in/small/abebooks_88x31_1.gif",
"image_url_large":"http://cdn.cpnscdn.com/.../in/large/abebooks_234x60_0.gif"
},
"url" : "/coupon-codes/go/rs?k=53_2"
}
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11. Real time analytics for website
Writes from frontend servers, reads
internally.
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12. Serve user preferences data for live site.
• 1st use case where live site depends on
MongoDB.
• Writes happen from internal processes
• Reads from frontend servers.
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13. Archiving data
 Archiving old data feeds: Useful for periodic
analysis.
 Internal writes and reads
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14. Internal CMS
 First version Django and MongoDB. Didn't have
good integration.
 Current version is based on MongoDB, but not
Django. Used extensively internally.
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15. Passbooks/Printable Images
Examples of Blobs.
Passbooks are zip files.
Need tight control on Printable Coupons
Images (These are different than the
Grocery Coupons).
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16. Coming up soon
 User Generated Content from
Mongo
 Both real time writes and reads.
 Schema : Still evolving!
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17. What pleasantly surprised us
 Stability
 Database easy to set up (install packages
and some configuration)
 Good documentation (easy to find stuff you
want to do).
 Multi Data Center replication.
 MMS (Mongo Monitoring Service) - Really
excellent service.
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18. Things we learnt the hard way
 Didn't have the latest Debian, Ubuntu, PHP,
Python client packages on Ubuntu repository.
 No great support for Django(packages were not
marked stable then). We moved away from
Django.
 In JavaScript number 42 becomes number 42.0.
(Hit us in production). This is a common
pitfall/learning item for users but it's an aspect of
importing data into Mongo.
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19. Things we learnt the hard way
contd…
 Our application would benefit from
joins which MongoDB does not
support.
 Need a package to install replica
sets on same machine.
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