MongoDBで作るソーシャルデータ新解析基盤
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This document discusses using MongoDB to analyze user data from a game platform. It includes examples of queries on collections like user_charge, daily_charge, user_trace, and daily_trace to retrieve user activity and purchase data for a given date range. It also shows an example of retrieving user registration information and calculating metrics for user retention and engagement. The document demonstrates how to store and retrieve analytics data from different collections in MongoDB.
![> db.user_registration.find({userId:”7777"}).forEach(printjson)
{
"_id" : "2010-06-29+7777+Registration",
"userId" : “7777"
"actionType" : "Registration",
"category" : {
R1” : “True”, # 1 = True
“T” : “ll” #
…
},
“tag”:[“longTerm”,”highFreq”], #
“firstCharge” : “2010-07-07”, #
“lastLogin” : “2010-09-30”, #
“playTerm” : 94, #
“totalCumlativeCharge” : 50000, #
“totalMonthCharge” : 10000, # 1
…
}](https://image.slidesharecdn.com/mongoanalyzer-110517032109-phpapp02/85/MongoDB-18-320.jpg)
![[08/May/2011:04:35:11 +0900]](https://image.slidesharecdn.com/mongoanalyzer-110517032109-phpapp02/85/MongoDB-59-320.jpg)
MongoDBで作るソーシャルデータ新解析基盤
- 12. ScientificPython
- 14. // 11/10 TOP10 > db.user_charge.find({date:"2010-11-10"}) .sort({totalCharge:-1}).limit(10).forEach(printjson) { "_id" : "2010-11-10+7777+Charge", "date" : "2010-11-10", "lastUpdate" : "2010-11-10", "totalCharge" : 10000, "userId" : ”7777", "actionType" : "Charge", "boughtItem" : { " EX" : 13, " +6000" : 3, " PRO" : 20 } }{…
- 15. > db.daily_charge.find({date:"2010-11-10",T:"all"}) .limit(10).forEach(printjson) { "_id" : "2010-11-10+group+Charge+all+all", "date" : "2010-11-10", "total" : 100000, "UU" : 2000, "group" : { " " : 1000000, " " : 1000000, ... }, "boughtItemNum" : { " EX" : 8, " " : 730, ... }, "boughtItem" : { " EX" : 10000, " " : 100000, ...
- 16. > db.user_trace.find({date:"2010-11-10”,actionType: "a{Make}",userId:”7777"}).limit(10).forEach(printjson) { "_id" : "2010-11-10+7777+a{Make}", "date" : "2010-11-10" "lastUpdate" : "2010-11-11", "userId" : ”7777", "actionType" : "a{Make}", "actionDetail" : { "make item ksutera" : 3, "make item makaron" : 1, "make item huwahuwamimiate" : 1, "make item ringoame" : 3, … } }
- 17. > db.daily_trace.find( {date:{$gte:"2010-11-10”,$lte:”2010-11-20”},actionType:"a{Make}"} ).limit(10).forEach(printjson) { "_id" : "2010-11-10+group+a{Make}", "date" : "2010-11-10", "lastUpdate" : "2010-11-12", "actionType" : "a{Make}", "actionDetail" : { "make item kinnokarakuridokei" : 615, "make item banjo-" : 377, "make item itigoke-ki" : 135904, "make item wadaikoan" : 40, "make item ha-pu" : 11, ... },... }
- 18. > db.user_registration.find({userId:”7777"}).forEach(printjson) { "_id" : "2010-06-29+7777+Registration", "userId" : “7777" "actionType" : "Registration", "category" : { R1” : “True”, # 1 = True “T” : “ll” # … }, “tag”:[“longTerm”,”highFreq”], # “firstCharge” : “2010-07-07”, # “lastLogin” : “2010-09-30”, # “playTerm” : 94, # “totalCumlativeCharge” : 50000, # “totalMonthCharge” : 10000, # 1 … }
- 19. > var cross = new Cross() // // × > MCResign = cross.calc(“2010-10-08”,“MC”,1) / 0 (z) ~1000 (s) ~10000 (m) 10000 ~(l) ~1 (z) 50000 10 5 0 50015 ~1 (s) 50000 100 50 3 50153 ~1 (m) 100000 200 100 1 100301 ~3 (l) 100000 300 50 6 100356 3 ~(ll) 0 0 0 0 0 // × > MCNotResign = cross.calc("2010-10-08","MC",-1) / 0 (z) ~1000 (s) ~10000 (m) 10000 ~(l) ~1 (z) 50000 10 5 0 50015 …
- 20. // > access = getAccessData(“tutorial”,“2010-12-01”) UU PATH 10000 /playshop2-gree/tutorial/FirstTopPage 9500 /playshop2-gree/tutorial/Tutorial01Page 8000 /playshop2-gree/tutorial/Tutorial02Page 7700 /playshop2-gree/tutorial/Tutorial03Page 7000 /playshop2-gree/tutorial/Tutorial04Page 4000 /playshop2-gree/tutorial/make/avatar 3800 /playshop2-gree/tutorial/Tutorial05Page …
- 62. db.collection.find( {date:20110517, userId:”1234”} );
- 63. db.collection.insert( {hour:0, userId:”1234”, actionType:”login”,} );