MongoDB for Analytics

Transcript

1. GitHub John Nunemaker MongoSF 2012 May 4, 2012 MongoDB for

   Analytics A loving conversation with @jnunemaker
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3. Background How hernias can be good for you

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6. 1 month Of evenings and weekends

7. 1 year Since public launch

8. 13 tiny servers 2 web, 6 app, 3 db, 2

   queue

9. 7-8 Million Page views per day

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14. Implementation Imma show you how we do what we do

    baby

15. Doing It (mostly) Live No aggregate querying

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18. get('/track.gif') do track_service.record(...) TrackGif end

19. class TrackService def record(attrs) message = MessagePack.pack(attrs) @client.set(@queue, message) end

    end

20. class TrackProcessor def run loop { process } end def

    process record @client.get(@queue) end def record(message) attrs = MessagePack.unpack(message) Hit.record(attrs) end end

21. http://bit.ly/rt-kestrel

22. class Hit def record site.atomic_update(site_updates) Resolution.record(self) Technology.record(self) Location.record(self) Referrer.record(self) Content.record(self)

    Search.record(self) Notification.record(self) View.record(self) end end

23. class Resolution def record(hit) query = {'_id' => "..."} update

    = {'$inc' => {}} update['$inc']["sx.#{hit.screenx}"] = 1 update['$inc']["bx.#{hit.browserx}"] = 1 update['$inc']["by.#{hit.browsery}"] = 1 collection(hit.created_on) .update(query, update, :upsert => true) end end end

24. Pros

25. Pros Space

26. Pros Space RAM

27. Pros Space RAM Reads

28. Pros Space RAM Reads Live

29. Cons

30. Cons Writes

31. Cons Writes Constraints

32. Cons Writes Constraints More Forethought

33. Cons Writes Constraints More Forethought No raw data

34. http://bit.ly/rt-counters http://bit.ly/rt-counters2

35. Time Frame Minute, hour, month, day, year, forever?

36. # of Variations One document vs many

37. Single Document Per Time Frame

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39. { "t" => 336381, "u" => 158951, "2011" => {

    "02" => { "18" => { "t" => 9, "u" => 6 } } } }

40. { '$inc' => { 't' => 1, 'u' => 1,

    '2011.02.18.t' => 1, '2011.02.18.u' => 1, } }

41. Single Document For all ranges in time frame

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43. { "_id" =>"...:10", "bx" => { "320" => 85, "480"

    => 318, "800" => 1938, "1024" => 5033, "1280" => 6288, "1440" => 2323, "1600" => 3817, "2000" => 137 }, "by" => { "480" => 2205, "600" => 7359,

44. "600" => 7359, "768" => 4515, "900" => 3833, "1024"

    => 2026 }, "sx" => { "320" => 191, "480" => 179, "800" => 195, "1024" => 1059, "1280" => 5861, "1440" => 3533, "1600" => 7675, "2000" => 1279 } }

45. { '$inc' => { 'sx.1440' => 1, 'bx.1280' => 1,

    'by.768' => 1, } }

46. Many Documents Search terms, content, referrers...

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48. [ { "_id" => "<oid>:<hash>", "t" => "ruby class variables",

    "sid" => BSON::ObjectId('<oid>'), "v" => 352 }, { "_id" => "<oid>:<hash>", "t" => "ruby unless", "sid" => BSON::ObjectId('<oid>'), "v" => 347 }, ]

49. Writes {'_id' => "#{sid}:#{hash}"}

50. Reads [['sid', 1], ['v', -1]]

51. Growth Don’t say shard, don’t say shard...

52. Partition Hot Data Currently using collections for time frames

53. Bigger, Faster Server More CPU, RAM, Disk Space

54. Users Sites Content Referrers Terms Engines Resolutions Locations Users Sites

    Content Referrers Terms Engines Resolutions Locations

55. Partition by Function Spread writes across a few servers

56. Users Sites Content Referrers Terms Engines Resolutions Locations

57. Partition by Server Spread writes across a ton of servers,

    way down the road, not worried yet

58. GitHub Thank you! [email protected] John Nunemaker MongoSF 2012 May 4,

    2012 @jnunemaker