CN104252458A - Data analysis method and device - Google Patents
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- CN104252458A CN104252458A CN201310256819.XA CN201310256819A CN104252458A CN 104252458 A CN104252458 A CN 104252458A CN 201310256819 A CN201310256819 A CN 201310256819A CN 104252458 A CN104252458 A CN 104252458A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/23—Updating
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses a data analysis method and device. The method comprises the following steps: receiving data pushed by an application; performing stream-oriented computation on the data according to a preset statistical target to obtain a statistical result. The device comprises a data receiving module and a statistical module, wherein the data receiving module is used for receiving data pushed by the application; the statistical module is used for performing stream-oriented computation on the data according to the preset statistical target to obtain the statistical result. By adopting the technical scheme of the invention, the statistical result can be obtained rapidly in time, and online Internet application with high requirement on instantaneity is satisfied.
Description
Technical field
The present invention relates to field of computer technology, particularly relate to a kind of data analysing method and device.
Background technology
Data analysis refers to classifies to mass data by suitable statistical method, gathers, in the hope of can the function of development data data substantially.Data analysis is widely used in computer system, such as in game operation field, by carrying out data analysis to game data, the status information of whole game player can be grasped, as login, online amount and wastage etc., thus according to the status information of player, advisory opinion can be provided to the planning of game and management.
Existing data analysing method first the mass data of Water demand is all stored into database, and then obtain statistics according to statistical demand to the data acquisition statistical method stored.Therefore existing data analysing method can not obtain statistics in real time, is not suitable for the internet, applications providing online service.
Summary of the invention
Based on this, be necessary, for above-mentioned technical matters, to provide a kind of data analysing method and device, statistics can be obtained fast, in time, the online internet, applications that requirement of real time is higher.
A kind of data analysing method, described method comprises:
Receive the data that application program pushes;
According to the statistics target preset, described data acquisition streaming is calculated and obtains statistics.
A kind of data analysis set-up, comprising:
Data reception module, for receiving the data that application program pushes;
Statistical module, for according to the statistics target preset, calculates described data acquisition streaming and obtains statistics.
Above-mentioned data analysing method and device, receive the data that application program pushes, and according to the statistics target preset, adopt streaming to calculate to data and obtain statistics, compare and store the technical scheme of adding up again with prior art, calculate owing to adopting streaming and add up, statistics can be obtained fast, in time, the online internet, applications that requirement of real time is higher.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the data analysing method in an embodiment;
Fig. 2 is the schematic flow sheet of the data analysing method in another embodiment;
Fig. 3 is the application scenarios figure of the data analysing method in an embodiment;
Fig. 4 is the schematic flow sheet of the data analysing method in another embodiment;
Fig. 5 is the structured flowchart of the data analysis set-up in an embodiment;
Fig. 6 is structured flowchart and the application scenarios figure of data analysis set-up in an embodiment;
Fig. 7 is structured flowchart and the application scenarios figure of data analysis set-up in another embodiment;
Fig. 8 is the structured flowchart of the real-time statistics module in an embodiment.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.
See Fig. 1, in one embodiment, provide a kind of data analysing method, this method can be, but not limited to be applied to game data analysis, also can be applied to other computer intelligence analysis field.This method comprises:
Step 102, receives the data that application program pushes.
In the present embodiment, the data that analyze are pushed by application program.Application program can be game running relative program or other software program.Carry out Data Analysis Services for ease of this method, can in advance application programs push data define data layout.After receiving the data of application program propelling movement, data can be carried out buffer memory or be stored in database.
Step 104, according to the statistics target preset, adopts streaming to calculate to data and obtains statistics.
In the present embodiment, extract buffer memory or storage data in a database, data layout according to data in advance definition is analyzed, and carries out classifying and gathering according to the statistics target preset, and adopts streaming account form to obtain final statistics with cumulative form.The statistics target wherein preset, with game data analysis for example, can comprise a day any active ues, any active ues IP(IP, Internet Protocol, procotol) statistics such as Area distribution, the distribution of any active ues game ratings target.Employing streaming account form can be that the data receiving application program propelling movement also carry out cumulative mode of adding up simultaneously, also in advance the data of Water demand can be divided into multiple part, one of them part of each reception is also carried out statistics obtain an intermediate result according to statistics target, finally intermediate result is carried out cumulatively obtaining statistics.The final statistics that the present embodiment obtains, follow-uply can be supplied to other analysis software and apply, also can solidify and be updated in database, for inquiring about or calling.
In the present embodiment, receive the data that application program pushes, and according to the statistics target preset, adopt streaming to calculate to data and obtain statistics, compare and store the technical scheme of adding up again with prior art, calculate owing to adopting streaming and add up, statistics can be obtained fast, in time, the online internet, applications that requirement of real time is higher.
See Fig. 2, in another embodiment, a kind of data analysing method, its flow process comprises:
Step 202, the data that real-time reception application program pushes, and data buffer storage in Redis fifo queue.
The data that real-time reception application program pushes, data have wherein pre-defined data layout.Such as in the present embodiment, for game data analysis, the form of data is JSON data layout (JSON, Java Script Object Notation, java script object format, a kind of data interchange format of lightweight), its field comprised is as shown in table 1:
Table 1
| lmode | Shaping | Game data type, as logon data, log-on data, pipelined data etc. |
| uid | Shaping | Data owner ID |
| ltime | Shaping | Data generation time, timestamp |
| luip | Character string | The IP address of data owner |
| ldata | Array | Detailed game data content, determines according to game data type |
Such as, illustrate with data, these data are:
{“lmode”:1,“uid”:1,“ltime”:1368784546,“luip”:“192.168.100.146”,“ldata”:{“level”:10,“money”:3000,“uplogin”:1368764546,“login”:1368784546}}。
Then " lmode ": 1 field represents that the game data type of these data is 1, corresponding to logon data." uid ": 1 field represents that the possessory user ID of game data (ID, Identity, identify label) is 1." ltime ": 1368784546 fields represent that the timestamp that these data produce is 1368784546." luip ": the IP address of " 192.168.100.146 " field references data owner is 192.168.100.146.Ldata field has different definition according to different game data types, such as define in logon data type and log in grade, logging in game coin, last login time stamp and this login time stamp, therefore in this example, " ldata ": { " level ": 10, " money ": 3000, " uplogin ": 1368764546, " login ": 1368784546} field, represent and log in that grade is 10 grades, logging in game coin is 3000, last login time stamp is 1368764546, and this login time stamp is 1368784546.
In the present embodiment, received the data of JSON form by interface function, the parameter of this interface function is each field (as lmode field, uid field etc.) in JSON data layout, after receiving each field, more each field is assembled into JSON data layout.
After receiving the data of application program propelling movement, by data buffer storage in Redis fifo queue.Wherein Redis is a high performance key-value storage system, and the value type that it is supported is relatively more, comprises character string (string), list (list), set (set), ordered set (zset) and Hash (hashs).These data types are all supported push/pop, add/remove and are got common factor union and difference set and abundanter operation, and these operations are all atomicities.The data of Redis are all be buffered in internal memory, and efficiency is very high.
Data are stored in Redis fifo queue, data can be proposed by interface function, for follow-up statistical study.
Step 204, extracts data from Redis fifo queue.
Step 206, carries out legitimate verification to the data extracted, judges that whether data are legal, if legal, perform step 208, otherwise abandons this data, return step 204, again extract data.
Data layout according to data in advance definition carries out legitimate verification to the data extracted.Such as, when application this method carries out game data analysis, during the data layout of data acquisition with table 1, legitimate verification can comprise to be verified the data type of lmode, uid, ltime, luip, ldata field, such as judge that lmode field is shaping, when game data type is logon data, judge that the grade (level) in ldata is shaping etc.When the data extracted are by legitimate verification, perform step 208, otherwise abandon this data, again extract data, and misregistration daily record.
Step 208, according to the statistics target preset, carries out duplicate removal to data.
In this enforcement, if data are legal, according to the statistics object judgement data preset the need of duplicate removal, if desired duplicate removal, calls Redis interface and carries out duplicate removal.
For the data layout in data acquisition table 1, such as add up the day any active ues amount that target comprises game, and this statistics target is set in advance as and needs to carry out duplicate removal, then according to the uid field of data and the key of ltime field generation Redis, call SETNX(SETNX, the application programming interfaces that Redis provides, for key value is set to value, key is retrieved in internal memory, when key does not exist, rreturn value is true) judge whether these data existed in Redis buffer queue, if do not cross, be effective data, need the cumulative statistics of carrying out ASSOCIATE STATISTICS target, otherwise these data are not added up.
Step 210, calls Redis sum function and carries out accumulated counts to the statistics target preset, obtain statistics.
In the present embodiment, according to data layout and the default statistics target of data, Classifying Sum is carried out to data, adopt Redis sum function to carry out accumulated counts and obtain final statistics.
For the data layout of data acquisition table 1, default statistics target comprises day any active ues amount and enlivens IP Area distribution day.If data are:
{“lmode”:1,“uid”:1,“ltime”:1368784546,“luip”:“192.168.100.146”,“ldata”:{“level”:10,“money”:3000,“uplogin”:1368764546,“login”:1368784546}}。
When then adding up, according to uid field, log in the buffer memory key that the period (the timestamp scope corresponding to this statistics day) generates Redis, the data extracted are monitored, calls Redis sum function and accumulated counts is carried out to day any active ues amount; Accordingly, generate the buffer memory key of Redis according to luip field and login period (the timestamp scope corresponding to this statistics day), the data extracted are monitored, calls Redis sum function and carry out accumulated counts to enlivening IP address day.
The statistics that the present embodiment obtains, can be buffered in Redis buffer memory, is supplied to user side inquiry for follow-up or be supplied to application program application or solidify in a database.
Step 212, extracts statistics, and is updated in statistics database.
In this enforcement, timing calling interface function, extracts statistics successively according to the statistics target preset, and result is updated in statistics database.
See Fig. 3, provide a kind of application scenarios of data analysing method.By application program 302 to queue system 304 propelling data.The data that application program 302 pushes have pre-defined data form, such as JSON data layout.Queue system 304 can be, but not limited to be adopt to comprise FIFO(FIFO, First Input and First Output, first in first out) hardware system of storage chip.Queue system 304 receives the data pushed and to go forward side by side formatting lines, such as, be JSON form, and add in the fifo queue of Redis queue server 310, and propose data to statistical system 306 according to the order of first in first out.Statistical calculation system 306 can be, but not limited to be adopt the chip with statistical computation function to realize, statistical calculation system 306 carries out legitimate verification to the data extracted, again through duplicate removal process, carry out adding up according to the statistics target preset and obtain statistics, and statistics is buffered in Redis caching server 312.Statistical calculation system 306 additionally provides query function.Storage system 308 extracts statistics stored in statistics database 314, and storage system 308 also provides query function.
See Fig. 4, In yet another embodiment, provide a kind of data analysing method, its flow process comprises:
Step 402, the data that timing receipt application program pushes, and Data Update in propelling data database.
In the present embodiment, regularly (such as can be set to every one hour) data receiving application program propelling movement, data are stored in propelling data database, and this propelling data database can be distributed data base or relevant database.
Step 404, after each propelling data database update, according to the statistics target preset, call propelling data statistics of database function to add up the data be stored in propelling data database, obtain temporary statistics result, and the temporary statistics result at every turn obtained is carried out cumulative obtain statistics.
After data are stored into propelling data database, according to the statistics target preset, call propelling data statistics of database function, as COUNT function (counting function), SUM function (summing function) etc. are added up the current data be stored in propelling data database, obtain temporary statistics result.Afterwards, remove the data in propelling data database and the data of reception application program propelling movement again, add up, the temporary statistics result at every turn obtained being carried out adding up obtains final statistics.
Step 406, is saved to final statistics in statistics database.
In the present embodiment, after each propelling data database update, call propelling data statistics of database function and add up, obtain temporary statistics result, and temporary statistics result is carried out the cumulative final statistics obtained and be updated in statistics database.
See Fig. 5, in one embodiment, provide a kind of data analysis set-up, comprising:
Data reception module 502, for receiving the data that application program pushes.
Statistical module 504, for according to the statistics target preset, adopts streaming to calculate to data and obtains statistics.
See Fig. 6, in one embodiment, a kind of data analysis set-up, wherein, data reception module 502 comprises real-time reception module 602, and statistical module 504 comprises real-time statistics module 604.
Real-time reception module 602, for the data that real-time reception application program 600 pushes, and data buffer storage in Redis fifo queue 608.
See Fig. 8, in the present embodiment, real-time statistics module 604 comprises: data extracting unit 6042, for extracting data from Redis fifo queue 608.Authentication unit 6044, for carrying out legitimate verification to the data extracted.Duplicate removal unit 6046, for according to the statistics target preset, carries out duplicate removal to data.Summing elements 6048, carrying out accumulated counts for calling Redis sum function to the statistics target preset, obtaining statistics, and statistics is buffered in Redis buffer memory 610.
In the present embodiment, this data analysis set-up also comprises statistics extraction module 506, statistics extraction module 506 comprises the first extraction module 606, carries out adding up the statistics obtained, and be updated in the first statistics database 612 for extracting real-time statistics module 604.
See Fig. 7, in one embodiment, a kind of data analysis set-up, wherein data reception module 502 comprises real-time reception module 602 and timing receipt module 702, and statistical module 504 comprises real-time statistics module 604 and timing statistical module 704.Statistics extraction module 506 comprises the first extraction module 606 and the second extraction module 706.
Real-time reception module 602, for the data that real-time reception application program 700 pushes, and data buffer storage in Redis fifo queue 608.
See Fig. 8, in the present embodiment, real-time statistics module 604 comprises: data extracting unit 6042, for extracting data from Redis fifo queue 608.Authentication unit 6044, for carrying out legitimate verification to the data extracted.Duplicate removal unit 6046, for according to the statistics target preset, carries out duplicate removal to data.Summing elements 6048, carrying out accumulated counts for calling Redis sum function to the statistics target preset, obtaining statistics, and statistics is buffered in Redis buffer memory 610.
First extraction module 606, carries out adding up the statistics obtained for extracting real-time statistics module 604, and is updated in the first statistics database 612.
In this enforcement, timing receipt module 702, for the data that timing receipt application program 700 pushes, and Data Update in propelling data database 708.
Timing statistical module 704, after upgrading for each propelling data database 708, according to the statistics target preset, call propelling data statistics of database function to add up the data be stored in propelling data database 708, obtain temporary statistics result, and the temporary statistics result at every turn obtained is carried out cumulative obtain statistics.
Second extraction module 706, carries out adding up for extracting timing statistical module 704 the temporary statistics result and final statistics that obtain, and is updated in the second statistics database 712.
The above embodiment only have expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (10)
1. a data analysing method, described method comprises:
Receive the data that application program pushes;
According to the statistics target preset, described data acquisition streaming is calculated and obtains statistics.
2. method according to claim 1, is characterized in that, the data that described reception application program pushes comprise:
The data that real-time reception application program pushes, and described data buffer storage in Redis fifo queue;
The statistics target that described basis is preset, calculates acquisition statistics to described data acquisition streaming and comprises:
From described Redis fifo queue, extract data, and legitimate verification is carried out to described data, if not by checking, then again extract data, if by checking, then call Redis sum function and accumulated counts is carried out to the statistics target preset, obtain described statistics.
3. method according to claim 2, is characterized in that, before the described Redis of calling sum function carries out accumulated counts to the statistics target preset, also comprises:
According to the statistics target preset, duplicate removal is carried out to described data.
4. the method according to any one of claims 1 to 3, is characterized in that, the data that described reception application program pushes also comprise:
The data that timing receipt application program pushes, and described Data Update in propelling data database;
The statistics target that described basis is preset, calculates acquisition statistics to described data acquisition streaming and also comprises:
After each described propelling data database update, according to the statistics target preset, call propelling data statistics of database function to add up the data be stored in propelling data database, obtain temporary statistics result, and the temporary statistics result at every turn obtained is carried out cumulative obtain described statistics.
5. method according to claim 1, is characterized in that, in the statistics target that described basis is preset, after calculating the step obtaining statistics, also comprises described data acquisition streaming:
Extract described statistics, and be updated in statistics database.
6. a data analysis set-up, is characterized in that, comprising:
Data reception module, for receiving the data that application program pushes;
Statistical module, for according to the statistics target preset, calculates described data acquisition streaming and obtains statistics.
7. device according to claim 6, is characterized in that, described data reception module comprises real-time reception module, for the data that real-time reception application program pushes, and described data buffer storage in Redis fifo queue;
Described statistical module comprises real-time statistics module, and described real-time statistics module comprises data extracting unit, for extracting data from described Redis fifo queue; Authentication unit, for carrying out legitimate verification to described data; Summing elements, carrying out accumulated counts for calling Redis sum function to the statistics target preset, obtaining described statistics.
8. device according to claim 8, is characterized in that, described real-time statistics module also comprises duplicate removal unit, for according to the statistics target preset, carries out duplicate removal to described data.
9. the device according to any one of claim 6 to 8, is characterized in that, described data reception module comprises timing receipt module further, for the data that timing receipt application program pushes, and described Data Update in propelling data database;
Described statistical module comprises timing statistical module further, after each described propelling data database update, according to the statistics target preset, calling data storehouse statistical function is added up the data be stored in propelling data database, obtain temporary statistics result, and the temporary statistics result at every turn obtained is carried out cumulative obtain described statistics.
10. device according to claim 6, is characterized in that, described device also comprises statistics extraction module, for extracting described statistics, and is updated in statistics database.
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| CN110837511A (en) * | 2019-11-15 | 2020-02-25 | 金蝶软件（中国）有限公司 | Data processing method, system and related equipment |
| CN110837511B (en) * | 2019-11-15 | 2022-08-23 | 金蝶软件（中国）有限公司 | Data processing method, system and related equipment |
| CN112115382A (en) * | 2020-09-30 | 2020-12-22 | 北京格灵深瞳信息技术有限公司 | Data processing method and device, storage medium and electronic device |
| CN112115382B (en) * | 2020-09-30 | 2023-03-10 | 北京格灵深瞳信息技术股份有限公司 | Data processing method and device, storage medium and electronic device |
| CN112306989A (en) * | 2020-10-26 | 2021-02-02 | 北京健康之家科技有限公司 | Database instance processing method and device, storage medium and electronic device |
| CN112765673A (en) * | 2021-03-16 | 2021-05-07 | 杭州数梦工场科技有限公司 | Sensitive data statistical method and related device |
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