HadoopとMongoDBを活用したソーシャルアプリのログ解析
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The document describes the structure of various user activity log collections stored in different databases. It includes collections for errors, access logs, attribute changes, status changes, and purchase charges with details on the fields captured for each user event.
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HadoopとMongoDBを活用したソーシャルアプリのログ解析
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- 20. • def mapper(key, value):! for word in value.split(): yield word,1! def reducer(key, values):! yield key,sum(values)! if __name__ == "__main__":! import dumbo! dumbo.run(mapper, reducer) dumbo start wordcount.py ! -hadoop /path/to/hadoop ! -input wc_input.txt -output wc_output
- 21. • ‣ ‣ ‣ ‣ python wordcount.py map < wc_input.txt | sort | ! python wordcount.py red > wc_output.txt
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- 25. • -----Change------! ActionLogger a{ChangeP} (Point,1371,1383) ! ActionLogger a{ChangeP} (Point,2373,2423)! ActionLogger a{ChangeMedal} (lucky_star,9,10) ! ActionLogger a{ChangeMedal} (lucky_sea_bream,0,1)! ActionLogger a{ChangeG} ! ActionLogger a{ChangeSubG} (SubGold,13,16) ! ActionLogger a{ChangeWakuwakuP} (buy,0,30)! ActionLogger a{ChangeWakuwakuP} (by gacha,30,0) ! ------Get------! ActionLogger a{GetMaterial} (syouhinnomoto,0,-1) ! ActionLogger a{GetMaterial} usesyouhinnomoto ! ActionLogger a{GetMaterial} (omotyanomotoPRO,1,6)! ActionLogger a{GetMaterial} (sui-tunomoto,5,4)! ActionLogger a{GetInterior} (bakery_counter,0,1)! ActionLogger a{GetAvatarPart} (190167,0,1) ! ActionLogger a{GetAvatarPart} (old_girl_09,0,1) ! -----Trade-----! ActionLogger a{Trade} buy 3 itigoke-kis from gree.jp:xxxxx !
- 26. • ‣ ‣ 2010-07-26 00:00:02,446 INFO catalina-exec-483 ActionLogger – userId a{Make} make item onsenmanjyuu! 2010-07-26 00:00:02,478 INFO catalina-exec-411 ActionLogger – userId a{LifeCycle} Login userId 2010-07-26 00:00:02,446 a{Make} {onsenmanjyuu,1}! userId 2010-07-26 00:00:02,478 a{LifeCycle} {Login,1}! userId 2010-07-26 00:00:02,478 a{GetMaterial} {omotyanomotoPRO,5}!
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- 29. • { ! "_id" : "2010-06-27+xxxxx+a{ChangeP}",! "lastUpdate" : "2010-09-17",! "date" : "2010-06-27" ! "userId" : “xxxxx",! "actionType" : "a{ChangeP}",! "actionDetail" : { "Point" : 600 },! }! { ! "_id" : "2010-06-27+xxxxx+a{LifeCycle}", ! "lastUpdate" : "2010-09-17",! "date" : "2010-06-27" ! "userId" : ”xxxxx",! "actionType" : "a{LifeCycle}",! "actionDetail" : { ”Login" : 3 }! }!
- 30. • { "_id" : "2010-08-31+group+a{PutOn}", ! "date" : "2010-08-31", ! "lastUpdate" : "2010-09-21",! "actionType" : "a{PutOn}",! "actionDetail" : { "a{PutOn}" : 52050 } ! }! {...! "actionType" : "a{Make}",! "actionDetail" : { ! ”syurijyou” : 11,! ”aisukuri-mu” : 378,! ”kinnokarakuridokei” : 103,! ”puramoderu” : 22,! ”guremurinno_n” : 164,! ”kyodaipenginno_n” : 76,! ”patinko” : 67,! “wakizasi” : 250,! “dendendaiko” : 13651,! ... (over 100 items)! }! }!
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- 35. • MySQL: select * from things where x=3 and y="foo"! MongoDB: db.things.find( { x : 3, y : "foo" } );! MySQL: select z from things where x=3! MongoDB: db.things.find( { x : 3 }, { z : 1 } ); db.collection.find({ "field" : { $gt: value } } ); ! // : field > value ! db.collection.find({ "field" : { $lt: value } } ); ! // : field < value ! db.collection.find({"field”: {$gt: value1, $lt: value2}});! // value1 <= field <= value2
- 36. MySQL: select * from things where x in (b,a,c)! MongoDB: db.collection.find( { "field" : { $in : array } } ); ! db.things.find({j:{$in: [2,4,6]}});! db.customers.find( { name : /acme.*corp/i } ); ! db.myCollection.find().sort( { ts : -1 } ); // ts ! > m = function() { emit(this.user_id, 1); } ! > r = function(k,vals) { return 1; } ! > res = db.events.mapreduce(m, r, { query : {type:'sale'} }); ! > db[res.result].find().limit(2) ! { "_id" : 8321073716060 , "value" : 1 } ! { "_id" : 7921232311289 , "value" : 1 } !
- 37. • { ! "_id" : "2010-06-27+xxxxx+a{ChangeP}",! "lastUpdate" : "2010-09-17",! "date" : "2010-06-27" ! "userId" : “xxxxx",! "actionType" : "a{ChangeP}",! "actionDetail" : { "Point" : 600 },! }! { ! "_id" : "2010-06-27+xxxxx+a{LifeCycle}", ! "lastUpdate" : "2010-09-17",! "date" : "2010-06-27" ! "userId" : ”xxxxx",! "actionType" : "a{LifeCycle}",! "actionDetail" : { ”Login" : 3 }! }!
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- 42. • {! "_id" : "2010-06-28+xxxx+Charge",! "lastUpdate" : "2010-09-20",! "userId" : ”xxxx",! "date" : "2010-06-28",! "actionType" : "Charge",! "totalCharge" : 1210,! "boughtItem" : { " EX 5 " : 1,! " 5 " : 1,! " 5 " : 1,! " " : 1,! " " : 2 }! }!
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- 48. • {! "_id" : "2010-07-11+xxxxx+Registration",! "lastUpdate" : "2010-09-25",! "actionType" : "Registration",! "userId" : ”xxxxx",! "date" : "2010-07-11",! "firstCharge" : "2010-07-12",! "lastCharge" : "2010-09-02",! "lastLogin" : "2010-09-02",! "firstChargeTerm" : 1,! "playTerm" : 50,! "totalMonthCharge" : 1000,! "totalMonthChargeDetail" : {! "1th" : 74.3! "2th" : 17.1,! "3th" : 8.6,! i.e. "4th" : 0,! },! "totalCumlativeCharge" : 10000,! "totalCumlativeChargeDetail" : {! "1th" : 2,! "2th" : 0.5,! "3th" : 0.2,! "4th" : 0,! "5th" : 0.1,! "6th" : 27.5,! "7th" : 1.2,! "8th" : 49! "9th" : 19.5,! 2.7% }! }!
- 49. • topMonthCharge = function(n){! return db.user_registration.find({},{! totalMonthCharge:true,! totalMonthChargeDetail:true,! userId:true! }).sort({totalMonthCharge:-1}).limit(n);! }! > topMonthCharge(20) ! { ! "_id" : "2010-07-10+9999+Registration",! Top20 "userId" : ”9999”,! "totalMonthCharge" : 10000,! "totalMonthChargeDetail" : { "5th" : 13.7, "4th" : 27.6, "3th" : 21, "2th" : 16.2, "1th" : 21.5 }! }! …!
- 50. findUser = function(x){ ! return db.user_charge.find({userId:x},{! userId:true,! totalCharge:true,! boughtItem:true}).sort({date:-1})! }! > findUserCharge("9999")! {! "_id" : "2010-09-08+9223458+Charge",! "totalCharge" : 2000,! "userId" : ”9999",! "boughtItem" : {! Top " 110 " : 2! }! }! {! "_id" : "2010-09-07+9223458+Charge",! "totalCharge" : 5000,! "userId" : ”9999",! "boughtItem" : {! " 350 " : 1,! " 110 " : 2! }! }! …!
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- 53. db.user_error! db.user_access! ( )! db.user_trace! (from )! (from )! db.user_attr! ( )! db.user_status! db.user_charge! (from Cassandra)! (from MySQL)!
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