CN105468740A - Game player data storage and analysis method and apparatus - Google Patents
Game player data storage and analysis method and apparatus Download PDFInfo
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- CN105468740A CN105468740A CN201510827157.6A CN201510827157A CN105468740A CN 105468740 A CN105468740 A CN 105468740A CN 201510827157 A CN201510827157 A CN 201510827157A CN 105468740 A CN105468740 A CN 105468740A
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Abstract
The invention discloses a game player data storage and analysis method and apparatus. The analysis method comprises the following steps: 1), storing player data of game role groups in the form of a first table and a second table, wherein in the first table, a row name is date, column names include a column cluster name and sub-column names, the column cluster name is a game behavior class of a player, the sub-column names are specific behaviors in the game behavior class, and cells of the first table store roles participating in the specific behaviors and measurement values; in the second table, a row name is a date and role combination, column names include a column cluster name and sub-column names, and cells of the second table store measurement values of corresponding roles participating in the specific behaviors at corresponding date; 2), extracting a screening condition according to an analysis condition and screening out role groups meeting the condition from the first table; and 3), according to the analysis condition, obtaining measurement value information of the behaviors of the role groups from the second table. According to the method and the apparatus, high screening analysis efficiency can be achieved.
Description
[technical field]
The present invention relates in game the storage of the magnanimity player data of role constellation of playing, analytical approach and device.
[background technology]
In game, such as online game, hand trip etc., relate to a large amount of game roles, the equal corresponding a part of player's data of each game role, such as, participate in the number of times of copy, log in duration, active degree, consumption etc.Correspondingly, player's data of magnanimity can be produced in game.The traditional relevant database of existing player data acquisition stores.Generally speaking, game data needs to preserve player's data of every day, when therefore adopting relational data library storage, generally using sky as the major key of table, then the various actions of the id of game role and role is kept in table as other row, as shown in table 1.
Table 1
| Date | Role id | Behavior 1 | Behavior 2 | … | Behavior n | Behavior n+1 |
| 20150725 | id1 | V1 | V2 | … | Vn | Vn+1 |
| 20150725 | id2 | P1 | P2 | … | Pn | Pn+1 |
In table, V1 ~ Vn+1 represents that role id1 participates in the metric of behaviors different in n+1 respectively, and P1 ~ Pn+1 represents that role id2 participates in the metric of behaviors different in n+1 respectively.When above table stores, when carrying out role analysis, query statement, by screening the row of game behavior, obtains the id of qualified role, again using the qualified role id obtained as screening conditions, inquire about their other behavioral datas.
In said process, there is the problem of the non-constant of analysis efficiency.Reason is, general game has hundreds of up to ten million, and even several game role of ten million, will find out qualified role id from so many magnanimity record, search difficulty, efficiency is lower.Further, after filtering out qualified role id, this part role constellation quantity may be not little yet, with these role id for condition removes other behavioral datas searching them further, and efficiency is also very low.Therefore, traditional storage and the efficiency of analytical approach are non-constants.
[summary of the invention]
Technical matters to be solved by this invention is: make up above-mentioned the deficiencies in the prior art, proposes a kind of storage of game player's data, analytical approach and device, can obtain efficient screening strength efficiency.
Technical matters of the present invention is solved by following technical scheme:
An analytical approach for game player's data, comprises the following steps: 1), stores player's data of game role colony with the first table and the second form shown, and wherein, in the first table, on the row date by name, row name comprises row cluster name and sub-row name; Wherein, row cluster name is a class game behavior of player, the concrete behavior under the class game behavior by name of son row; The role and metric that participate in concrete behavior is stored in the cell of the first table; In second table, the combination of row date by name and role, row name comprises described row cluster name and described sub-row name; Corresponding role participates in concrete behavior metric on the corresponding date is stored in the cell of the second table; 2), extract screening conditions according to analysis condition, from the first table, filter out qualified role constellation; 3), according to analysis condition, from the second table, the metric information of the behavior of described role constellation is obtained.
A storage means for game player's data, stores player's data of game role colony with the first table and the second form shown, wherein, in the first table, on the row date by name, row name comprises row cluster name and sub-row name; Wherein, row cluster name is a class game behavior of player, the concrete behavior under the class game behavior by name of son row; The role and the metric that participate in concrete behavior in one day is stored in the cell of the first table; In second table, the combination of row date by name and role, row name comprises described row cluster name and described sub-row name; The metric that respective corners form and aspect should participate in concrete behavior the date is stored in the cell of the second table.
An analytical equipment for game player's data, comprises memory module, screening module and analysis module; Described memory module is used for the player's data storing game role colony with the first table and the second form shown, and wherein, in the first table, on the row date by name, row name comprises row cluster name and sub-row name; Wherein, row cluster name is a class game behavior of player, the concrete behavior under the class game behavior by name of son row; The role and the metric that participate in concrete behavior in one day is stored in the cell of the first table; In second table, the combination of row date by name and role, row name comprises described row cluster name and described sub-row name; The metric that respective corners form and aspect should participate in concrete behavior the date is stored in the cell of the second table; Described screening module is used for extracting screening conditions according to analysis condition, and filters out qualified role constellation from the first table; Described analysis module is used for according to analysis condition, obtains the metric information of the behavior of described role constellation from the second table.
The beneficial effect that the present invention is compared with the prior art is:
The storage of game player's data of the present invention, analytical approach and device, adopt different table structures, mass data is stored as simultaneously the form of the first table and the second table.Like this, by the first table, row bunch and the design of sub-row name, using the classification of behavior as row bunch, concrete behavior name is referred to as sub-row name, in cell, preserve all roles and the metric of the behavior of certain specific behavior among a day simultaneously, thus be conducive to the role that rapid screening goes out to satisfy condition.By the second table, using role id and date as line unit, carry out filtering screening based on line unit, can quick position to the row unit at role constellation place, be convenient to other behavioural characteristics of quick obtaining game role, carry out efficient population analysis.By the effect of above-mentioned two aspects, rapid screening, quick position analysis, thus analysis efficiency when improving magnanimity player's data analysis.
[embodiment]
Design of the present invention is: the player's data storing magnanimity in game by row, adopts two list structure during storage, can all reach higher screening effeciency in role constellation screening and two stages of role constellation analysis.The table structure of the first table (mass screening table), is conducive to rapid screening and goes out qualified role constellation; The table structure of the second table (population analysis table), is conducive to the metric of quick position to the appointment concrete behavior of role.Compared in the past, thus analysis efficiency is higher.
Game player's data analysing method of this embodiment is a kind of method of the magnanimity player data analysis to the game role colony in game, first the form of magnanimity player data according to the first table and the second table is stored, be stored as two kinds of forms, be respectively used to role constellation screening and role constellation behavioural analysis.Hereinafter be referred to as mass screening table (the first table) and population analysis table (the second table).This embodiment selects HBase tables of data as storage system.Each row of HBase tables of data are made up of row cluster name and sub-row name, i.e. " row cluster name: row name ".One of them row bunch below can corresponding multiple sub-row name, and a sub-row name can only belong to row bunch.The data of same row bunch are kept in same file.When row bunch have multiple, when the data volume related to is larger, multiple file can be divided into, be kept at respectively in multiple file, and multiple file can distribute on a different server.
In mass screening table (the first table), strong as row using the date, namely the data of every day can correspond in the row in table, can store player's data of multiple role by the form of independent a line, also can store player's data of multiple role respectively by the form of multirow.Each class game behavior in game corresponds to row bunch.The possible concrete behavior (property value) of each class game behavior is as a sub-row name, the metric of the role id and behavior property thereof that take part in this concrete behavior is then all kept in the cell under these row, and the role not participating in this concrete behavior is not just kept in the cell under these row.Such as, as shown in table 2 below:
Table 2
In upper table, for this game behavior of participation copy, the row arranging a FB by name are bunch with it corresponding.In one day, the copy that player participates in may have multiple, then sub-row name comprises the behavior of the concrete different copy of multiple participation, as participated in the behavior FB1 of the first authentic copy, participates in the behavior FB2 of triplicate, participate in the behavior FBN of N copy, then have FB1, FB2 under arranging bunch FB,, N number of sub-row name such as FBN.Metric is then the number of times of the concrete copy behavior of participation of roles.If role id1 has the behavior (FB1) participating in the first authentic copy on July 25th, 2015, participating in number of times is 2 times, when then storing, in the row that on July 25th, 2015 is corresponding, a record is stored as in cell under the FB1 row of row bunch FB, such as id1:2, it is capable in order that role is id1 that expression has the participation first authentic copy, and take part in twice.Similarly, when row cluster name is article consumer behavior item, correspondingly, sub-row name comprises the behavior of the concrete different article of multiple purchase, and metric is the number that role buys concrete article.
Upper table only lists two class game behaviors---and participate in copy behavior and article consumer behavior, be only exemplary at this, be not restricted to this, also can comprise the game behavior of other classifications.These two kinds of behaviors respective column bunch FB and row bunch Item respectively in table 2, what row bunch FB comprised shows FB1, FB2 etc., and what row bunch Item comprised shows Item1, Item2 etc.Cell under row in row bunch FB saves the number of times of role id and participation of roles respective copies, and the record number on the same day in a cell has many, each correspondence role id.As 20150725 row, the cell under FB1 row contains two records, is respectively the number of times that role id1 and role id2 participates in first authentic copy behavior.Cell under row in row bunch Item saves the number of the respective articles of role id and role's purchase.
The table of design said structure is used for subsequent population screening, can quick position to the row at qualified behavior place, thus just can obtain qualified role id by the data of direct reading unit lattice.The table of this structure, after navigating to the row at concrete behavior place, judges the metric of the role having showed this concrete behavior in cell, whether meets filtercondition.Such as, when needing the number of times filtering out participation first authentic copy behavior FB1 to be greater than the role constellation of 10 times, after navigating to these row of FB1, screen [role id: metric] in cell, namely this part the role id selecting metric >10 is the colony that will screen.And for the conventional store scheme of table 1, then there is various disadvantages: on the one hand, because data store by row in the traditional scheme of table 1, even if just screen for some concrete behaviors, also all behaviors reading out role id are needed, just can screen concrete behavior, its cannot quick position to the row at concrete behavior place.On the other hand, need to judge whether degree of conformity value condition to all role id.This is because can not determine whether certain id has certain concrete behavior of performance.And after navigating to row in this embodiment, just can judge, because only have those roles stored in cell just to have the performance of this concrete behavior, other role id not in cell just not this concrete behavior show, do not need extra judgement yet.Compared to the process of conventional store option screening, the mass screening table of this embodiment stores content based on row name and cell and carries out high frequency zone, and filtration efficiency is higher.
The effect that mass screening table adopts HBase tables of data to have is, all row that may occur can not be built when structure table, but due to the extensibility that HBase tables of data provides, can listing newly-built new row now, therefore without the need to worrying the non-existent situation of row name.
Preferably, utilize the characteristic that HBase tables of data stores by row, player's data of inhomogeneous behavior can be kept in different files.HBase tables of data utilizes the advantage of distributed file system, different files corresponding to inhomogeneous column data also can be made further to be distributed to different servers and store.Utilize this storage characteristics, when the condition of mass screening have multiple and for inhomogeneous game behavior time, can be further preferably, screening task for behavior of all categories is carried out independence, such as, by independent for the screening task of FB behavior and Item behavior, according to the deposit position of file, screening task be distributed to each different server of cluster, make full use of the computing power of cluster.Each screening task obtains a collection of qualified role id, finally merges the role id that Servers-all returns in client, is met the role id of final condition.Utilize the advantage of distributed type assemblies, calculation task is distributed to each server of cluster, can further improve screening and filtering efficiency.
In the table structure of population analysis table (the second table), be good for using date and role id as row.For row bunch and sub-row name, similar with mass screening table, concrete behavior is kept in row name, simultaneously using a class behavior classification at concrete behavior place as row bunch, only preserve the metric of the role under each concrete behavior in cell.Such as, as shown in table 3 below:
Table 3
In table 3, row is strong to be made up of date and role id respectively, also lists to participate in copy and article consume two kinds of game behaviors in table, and the implication that each row and aforementioned first in row bunch and row bunch are shown in (mass screening table, table 2) is identical.Cell under row in row bunch FB only saves role participates in respective copies number of times on the corresponding date, and the record number of a day only has one, cell under row in row bunch Item only saves role buys respective articles quantity on the corresponding date, and the record number of same a day only has one.
The table of design said structure is used for subsequent population analysis.When carrying out population analysis, when specifying behavior list and the concrete behavior attribute thereof of Water demand, the metric of role id under this concrete behavior attribute can be obtained rapidly.
After building above-mentioned two tables, according to various analysis scene during analysis, first in the first table, filter out qualified role, in the second table, then obtain the metric information of the behavior of corresponding role constellation, thus extract analytical information.Be described respectively according to different analysis scenes as follows.
Analyze scene one: the number of times participating in copy FB1 on July 25th, 2015 need be analyzed and be greater than 2 and the number of times participating in copy FB2 this crowd of players being greater than 2, the number summation of purchase product Item1 during July 26 to 27 days July in 2015 in 2015.
A1. the mass screening stage:
With 20150725 for condition filter is capable strong, filter out the record that the date is on July 25th, 2015, then row corresponding to first authentic copy behavior FB1 are participated in row cluster name FB and row name FB1 for filtercondition navigates to, take out and participate in the role id of the first authentic copy and the number of times of participation, then filter out the role id set set1 participating in number of times and be greater than 2.
Then participate in row corresponding to triplicate behavior FB2 with row cluster name FB and row name FB2 for filtercondition navigates to, take out and participate in the role id of triplicate and the number of times of participation, then filter out the role id set set2 participating in number of times and be greater than 2.
Sought common ground by set set1 and set set2, obtain qualified role constellation set, terminate to this mass screening stage.
A2. the population analysis stage:
First the day part filtering trip strong is 20150726 to 20150727, and role id part be positioned at the mass screening stage terminate after row among the set set that obtains.Then navigate to row corresponding to article Item1 by row cluster name Item and row name Item1, the content of retrieval unit lattice, be the number that each role id buys article Item1.To the summation of all contents, be the objective result finally will analyzed.
Analyze scene two: other behavioural characteristic that the number of times sum participating in the first authentic copy and triplicate on July 25th, 2015 is greater than this crowd of players of 2 need be analyzed.In this analysis scene, relative to analysis scene one, the condition of mass screening is more complicated, needs to filter according to the result after each behavior combination.
B1: role constellation screening stage:
With 20150725 for condition filter is capable strong, filter out the record that the date is on July 25th, 2015, then participate in row corresponding to first authentic copy behavior with row cluster name FB and row name FB1 for filtercondition navigates to, take out and participate in the role id of the first authentic copy and the number of times of participation, save as one and map map1.
Then participate in row corresponding to triplicate behavior with row cluster name FB and row name FB2 for filtercondition navigates to, take out and participate in the role id of triplicate and the number of times of participation, save as another one and map map2.
Map1 will be mapped and map map2 and combine, after obtaining the number of times of participation of roles two copies, then filter out the set set that number of times sum is greater than the role id of 2.Terminate to this mass screening stage.
B2: the population analysis stage is similar with the population analysis stage analyzed in scene one, in this not repeated description.
Analyze scene three: the number of times participating in the first authentic copy on July 25th, 2015 need be analyzed and be greater than 2, and the number of times buying article Item1 is greater than other behavioural characteristics of this crowd of players of 0.This analysis scene is the situation that role's screening conditions contain different classes of behavior.
C1: role constellation screening stage:
Build two screening tasks, participate in row corresponding to first authentic copy behavior FB1 with row cluster name FB and row name FB1 for filtercondition navigates to respectively, and with row cluster name Item and row name Item1 for filtercondition navigates to the row at article Item1 place.Two screening tasks are sent to server corresponding to HBase tables of data simultaneously, carry out inquiry screening simultaneously, and separately the role id set be filled into is turned back to client, then carry out gathering merging, get common factor, the role id finally satisfied condition gathers.
C2: the population analysis stage is similar with the population analysis stage analyzed in scene one, in this not repeated description.
To sum up, the analytical approach in this embodiment, stores according to two kinds of forms, is respectively used to mass screening and population analysis, can analyze scene complete analysis task for difference.During analysis, rapid screening can go out qualified role constellation, simultaneously can quick position to the metric of the appointment concrete behavior of role, the effectively efficiency of raising analytic process.
A kind of analytical equipment of game player's data is also provided in this embodiment, comprises memory module, screening module and analysis module.
Wherein, memory module is used for the player's data storing game role colony with the first table and the second form shown, and wherein, in the first table, on the row date by name, row name comprises row cluster name and sub-row name; Wherein, row cluster name is a class game behavior of player, the concrete behavior under the class game behavior by name of son row; The role and the metric that participate in concrete behavior in one day is stored in the cell of the first table; In second table, the combination of row date by name and role, row name comprises row cluster name and sub-row name; The metric of corresponding participation of roles concrete behavior is stored in the cell of the second table.
Screening module is used for extracting screening conditions according to analysis condition, and filters out qualified role constellation from the first table.
Analysis module is used for according to analysis condition, obtains the metric information of the behavior of role constellation from the second table.
The analytical equipment of this embodiment, magnanimity player data are stored according to two kinds of forms, be respectively used to mass screening and population analysis, during analysis, rapid screening can go out qualified role constellation, simultaneously can quick position to the metric of the appointment concrete behavior of role, the effectively efficiency of raising analytic process.
Above content is in conjunction with concrete preferred implementation further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For general technical staff of the technical field of the invention, make some substituting or obvious modification without departing from the inventive concept of the premise, and performance or purposes identical, all should be considered as belonging to protection scope of the present invention.
Claims (10)
1. an analytical approach for game player's data, is characterized in that: comprise the following steps:
1), store player's data of game role colony with the first table and the second form shown, wherein, in the first table, on the row date by name, row name comprises row cluster name and sub-row name; Wherein, row cluster name is a class game behavior of player, the concrete behavior under the class game behavior by name of son row; The role and metric that participate in concrete behavior is stored in the cell of the first table; In second table, the combination of row date by name and role, row name comprises described row cluster name and described sub-row name; Corresponding role participates in concrete behavior metric on the corresponding date is stored in the cell of the second table;
2), extract screening conditions according to analysis condition, from the first table, filter out qualified role constellation;
3), according to analysis condition, from the second table, the metric information of the behavior of described role constellation is obtained.
2. the analytical approach of game player's data according to claim 1, is characterized in that: described step 1) in, player's data of different lines cluster name are kept in different files.
3. the analytical approach of game player's data according to claim 2, is characterized in that: described different file stores on a different server respectively.
4. the analytical approach of game player's data according to claim 3, is characterized in that: described step 2) in, screening conditions comprise multiple, and relate to inhomogeneous game behavior.
5. the analytical approach of game player's data according to claim 4, it is characterized in that: described step 2) in screening time, according to the memory location of file, the screening task for inhomogeneous game behavior is distributed on corresponding server and screens simultaneously.
6. the analytical approach of game player's data according to claim 1, is characterized in that: described step 1) in, the first table and/or the second table are HBase tables of data.
7. the analytical approach of game player's data according to claim 1, it is characterized in that: described step 2) in screening time, navigate in row corresponding to concrete behavior with the row cluster name in described first table and son row filtercondition by name, role in acquiring unit lattice and metric information, then filter out the qualified role of metric according to screening conditions.
8. the analytical approach of game player's data according to claim 1, it is characterized in that: described step 3) in attainment degree magnitude information time, the date of first filtering trip strong in the second table meets analysis condition, and role is arranged in described step 2) row among the role constellation that obtains, then row corresponding to concrete behavior to be analyzed are navigated to by row cluster name and row name, the content of acquiring unit lattice, is the metric information of the behavior need analyzing acquisition.
9. a storage means for game player's data, is characterized in that: the player's data storing game role colony with the first table and the second form shown, and wherein, in the first table, on the row date by name, row name comprises row cluster name and sub-row name; Wherein, row cluster name is a class game behavior of player, the concrete behavior under the class game behavior by name of son row; The role and the metric that participate in concrete behavior in one day is stored in the cell of the first table; In second table, the combination of row date by name and role, row name comprises described row cluster name and described sub-row name; The metric that respective corners form and aspect should participate in concrete behavior the date is stored in the cell of the second table.
10. an analytical equipment for game player's data, is characterized in that: comprise memory module, screening module and analysis module;
Described memory module is used for the player's data storing game role colony with the first table and the second form shown, and wherein, in the first table, on the row date by name, row name comprises row cluster name and sub-row name; Wherein, row cluster name is a class game behavior of player, the concrete behavior under the class game behavior by name of son row; The role and the metric that participate in concrete behavior in one day is stored in the cell of the first table; In second table, the combination of row date by name and role, row name comprises described row cluster name and described sub-row name; The metric that respective corners form and aspect should participate in concrete behavior the date is stored in the cell of the second table;
Described screening module is used for extracting screening conditions according to analysis condition, and filters out qualified role constellation from the first table;
Described analysis module is used for according to analysis condition, obtains the metric information of the behavior of described role constellation from the second table.
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| CN107038179A (en) * | 2016-08-23 | 2017-08-11 | 平安科技（深圳）有限公司 | Item of information storage method and system |
| CN109364473A (en) * | 2018-09-29 | 2019-02-22 | 杭州电魂网络科技股份有限公司 | Analysis method and system are reported in game |
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| CN105468740B (en) | 2019-03-08 |
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