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MongoDB For Online Advertising at AOL

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  • Until yesterday, I was in New York, discussing how we’re going to be using MongoDB moving forward
  • Given an advertiser, a well known budget airlinewhich for legal reasons I cant name… lets call them… BrianAirWho want to retarget – or advertise to – the subset of people who have visited their siteSearched for a particular flight on itBut did not buy
  • First steps, is that we need a way of recording the information handed to us by BrianAirWe have an Event server, written in Erlang, which records the dynamic information from tracking pixels, and stores them in MongoDBThe same applies to the conversion pixels.So that we know, for each visitor, what they looked at, and whether they converted for that productThis is all great – its just means that all your advertisers traffic = all of your trafficHigh number of inserts, again making MongoDB a great choice
  • User 123 (a v4 UUID generated, and stored in the cookie)Searches the BrianAir site, to travel to CopenhagenBut does not purchaseWhich means, that in the tracking pixel call, we’ll see something (more complicated than this) but containing the product IDWe map this to the ID in the cookie
  • This allows us to build a profile of each userAgain, you can easily see how the document model from mongoDB works perfectly – allows completely flexible user profile shapes & sizes
  • When User 123 is browsing the web a day later, she gets an ad from the AOL networkWe need to build that ad for user 123
  • Not all ads will be like this – some simpler, some more complicated, but…Once the creative has been chosen:3 main components of a dynamic adUser Info – ie. User 123 searched for product CPHProduct Info – all the products & prices from the BrianAir siteGeolocation – we need to know which local airport user 123 is most likely to want to fly fromGeopIP LookupSpatial indexes query from MongoDB
  • Internal target of 5ms, to supply other systems with the dynamic information needed for an ad
  • Total transactions per second – events, ad impressions, select queriesSo, what kind of platform do we need to do this?
  • For advertising:Couple of DCs in the USOne in EuropeWith the data being replicated between them using our own Tier 1 Carrier (10gb/s)Currently running 8 shards, with 3 machines in each replica setWe’re using a 4 different divers for this project – again something which makes MongoDB so nice to work withSo server hardware…
  • We use pretty high spec machines – but theyre still available off the shelf(well to order anyway!)Key points of the spec are:A lot of network capacity – we hit network capacity before disk capacity beforeXFS (with noatime) – this can save a matter of seconds when MongoDBSo, why did we choose MongoDB…
  • So, to summarise the use case case for Online AdvertisingThe advertiser traffic == our trafficAs a summary…
  • The benefits really are clear when compared to other possible solutionsI don’t have a large team that can support a big hadoopcliuster, for eg.We’re a small team, that really needs to maximise the use of the time availableMongoDB is first & foremost easy to setup & learnDownload & Run – that’s itPerformance is greatWe keep a huge amount of data in RAMIt’s the best way of guaranteeing the performance we needScaling is really easyOps can add a new shard at the appropriate timeWith no downtimeThe community support is unparalleledOnline docs, forums, groupsInternal forumsShould you want it, there are support contracts available
  • During sharding events, global write-lock was too lowWe werent able to sustain the write performance we neededWe needed local writes, because of the transatlantic performance hit of writing remotelyWhen we pre-chunked, we prefixed the shard key with the datacentre name, so that the front end machines would only write locally

Transcript

  • 1. MongoDB for Online Advertising @ AOLMongoDB UK 2012Jon Reedjon.reed@teamaol.com@jon_reed
  • 2. An Apology
  • 3. Until Yesterday I was here MongoDB UK 2012 Page 3
  • 4. Bear with me… MongoDB UK 2012 Page 4
  • 5. Previously…
  • 6. 100,000 hours of free Internet – I’m set for lifehttp://www.reddit.com/user/Austinja MongoDB UK 2012 Page 6
  • 7. Now…
  • 8. UK Software Engineering @ AOL• Jonathan Reed • jon.reed@teamaol.com • @jon_reed• Fantastic UK Scrum & Product Team MongoDB UK 2012 Page 9
  • 9. We use MongoDB a lot…
  • 10. Page 11
  • 11. MongoDB UK 2012 Page 12
  • 12. A Scenario
  • 13. Advertising Requirement• BrianAir *• Retarget people who visited their site• But did NOT purchase flights MongoDB UK 2012* Stolen from the amazing Rhod Gilbert Page 14
  • 14. Understanding a User• Tracking Pixel • Product Page – what a user has looked at• Conversion Pixel • Confirmation Page – what a user has purchased MongoDB UK 2012 Page 15
  • 15. A Simple Story…• User 123• Searches to travel to Copenhagen• Does not purchase• “?product_id=CPH” MongoDB UK 2012 Page 16
  • 16. A User Profile{ … “_id” : “123”, “products” : [“CPH”], …} MongoDB UK 2012 Page 17
  • 17. User 123 Browses the web MongoDB UK 2012 Page 18
  • 18. Building an example Ad{“from” : “LHR”,“to” : “CPH”,“price” : “£39.99”…} Geolocation$loc = array(40.739037, 73.992964); // lat, long from IP$airports->find(array(“loc” => array( “$near” => $loc))); MongoDB UK 2012 Page 19
  • 19. Internal Component SLA 5ms MongoDB UK 2012 Page 20
  • 20. Scaled To Total of…12,000 transactions / second ~ billions / month MongoDB UK 2012 Page 21
  • 21. Our Platform
  • 22. Our MongoDB Platform• Multiple data centres• Replication across our own Tier 1 Carrier• 8 Shards• 3 Machines in each replica set• Mixture of languages• C++• Erlang• Java• PHP MongoDB UK 2012 Page 23
  • 23. MongoDB Hardware• 16 Core 2.4Ghz• 192Gb RAM• RAID 10• 8 Bonded 1Gb NIC• CentOS 6• XFS • noatime, nodiratime, nouuid• TCP stack tuning MongoDB UK 2012 Page 24
  • 24. Why MongoDB?
  • 25. Online Adverting Use Case• 90% Write, 10% Read• Non-Relational Data• Dynamic Document Size• Horizontal Scale is Required MongoDB UK 2012 Page 26
  • 26. Team: Why we use MongoDB• Easy • Installation, Management• Performance • Linear growth• Flexible • 101 use cases, native drivers• Scalable • Sharding, Replication• Support • Community, Documentation, 10gen contract MongoDB UK 2012 Page 27
  • 27. Lessons Learned
  • 28. Lessons Learned• Pre-chunked high-volume collections• Basic Data Centre Awareness MongoDB UK 2012 Page 29
  • 29. Things we’re lookingforward to
  • 30. • MongoDB as storage for Hadoop• Lower level locking• Map/Reduce Enhancements• V8 MongoDB UK 2012 Page 31
  • 31. MongoDC 2012• “Operationalizing MongoDB at AOL”• Michael DelNegro MongoDB UK 2012 Page 32
  • 32. We are hiring!• corp.aol.com/careers • C++ Engineers • JavaScript Engineers • QA Engineers MongoDB UK 2012 Page 33
  • 33. Thanks!Q&A?Jon Reedjon.reed@teamaol.com@jon_reed