CN104252458B - Data analysing method and device - Google Patents
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- CN104252458B CN104252458B CN201310256819.XA CN201310256819A CN104252458B CN 104252458 B CN104252458 B CN 104252458B CN 201310256819 A CN201310256819 A CN 201310256819A CN 104252458 B CN104252458 B CN 104252458B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- 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
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- G—PHYSICS
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Abstract
The invention discloses a kind of data analysing method and device, the method includes：Receive the data of application program push；According to preset statistics target, statistical result is obtained using streaming computing to the data.Described device includes：Data reception module, the data for receiving application program push；Statistical module, for according to preset statistics target, statistical result to be obtained using streaming computing to the data.Using technical solution of the present invention, can quickly, obtain statistical result in time, meet the relatively high online the Internet, applications of requirement of real-time.
Description
Technical field
The present invention relates to field of computer technology, more particularly to a kind of data analysing method and device.
Background technology
Data analysis refers to being classified to mass data with statistical method appropriate, being summarized, in the hope of can be maximumlly
Develop the function of data information.Data analysis is widely used in computer system, such as in game operation field, by trip
Data of playing carry out data analysis, will appreciate that the status information of entire game player, such as login, online amount and wastage,
To which according to the status information of player, advisory opinion can be provided to the planning and management of game.
Existing data analysing method is will first to need the mass data analyzed all database is arrived in storage, then further according to
Statistical demand obtains statistical result to stored data using statistical method.Therefore existing data analysing method cannot be real-time
Statistical result is obtained, is not suitable for providing the Internet, applications of online service.
Invention content
Based on this, it is necessary in view of the above technical problems, provide a kind of data analysing method and device, can quickly and
When obtain statistical result, meet the relatively high online the Internet, applications of requirement of real-time.
A kind of data analysing method, the method includes：
Receive the data of application program push；
According to preset statistics target, statistical result is obtained using streaming computing to the data.
A kind of data analysis set-up, including：
Data reception module, the data for receiving application program push；
Statistical module, for according to preset statistics target, statistical result to be obtained using streaming computing to the data.
Above-mentioned data analysing method and device receive the data of application program push, and according to preset statistics target, right
Data obtain statistical result using streaming computing, the technical solution counted again are stored compared to prior art, due to using stream
Formula is calculated and is counted, can quickly, obtain statistical result in time, meet the higher online internet of requirement of real-time and answer
With.
Description of the drawings
Fig. 1 is the flow diagram of the data analysing method in one embodiment；
Fig. 2 is the flow diagram of the data analysing method in another embodiment；
Fig. 3 is the application scenario diagram of the data analysing method in one embodiment；
Fig. 4 is the flow diagram of the data analysing method in another embodiment；
Fig. 5 is the structure diagram of the data analysis set-up in one embodiment；
Fig. 6 is the structure diagram and application scenario diagram of the data analysis set-up in one embodiment；
Fig. 7 is the structure diagram and application scenario diagram of the data analysis set-up in another embodiment；
Fig. 8 is the structure diagram of the real-time statistics module in one embodiment.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Referring to Fig. 1, in one embodiment, a kind of data analysing method is provided, this method can be, but not limited to apply
It is analyzed in game data, other computer intelligence analysis fields can also be applied to.This method includes：
Step 102, the data of application program push are received.
In the present embodiment, the data to be analyzed are pushed by application program.Application program can be game running correlation journey
Sequence or other software programs.Data Analysis Services are carried out for ease of this method, it can be fixed to the data of application program push in advance
The good data format of justice.After the data for receiving application program push, data can be cached or be stored in database.
Step 104, according to preset statistics target, statistical result is obtained using streaming computing to data.
In the present embodiment, the data of buffered or storage in the database, the data pre-defined according to data are extracted
Format is analyzed, and is classified and is summarized according to preset statistics target, and streaming computing mode is used to be obtained in the form of cumulative
To final statistical result.Wherein preset statistics target may include for example with game data analysis actively using day
Family, any active ues IP（IP, Internet Protocol, procotol）The systems such as Area distribution, the distribution of any active ues game ratings
Count target.Can be the mode for receiving the data of application program push and being carried out at the same time cumulative statistics using streaming computing mode,
The data analyzed will can also be needed to be divided into multiple portions in advance, receive every time one of part and according to statistics target into
Row statistics obtains an intermediate result, is finally added up intermediate result to obtain statistical result.The present embodiment obtains final
Statistical result, other analysis softwares can be subsequent supplied to and applied, update can also be cured in database, for looking into
It askes or calls.
In the present embodiment, the data of application program push is received, and according to preset statistics target, streaming is used to data
It calculates and obtains statistical result, the technical solution counted again is stored compared to prior art, due to being carried out using streaming computing
Statistics, can quickly, obtain statistical result in time, meet the higher online the Internet, applications of requirement of real-time.
Referring to Fig. 2, in another embodiment, a kind of data analysing method, flow includes：
Step 202, the data of real-time reception application program push, and data buffer storage in Redis fifo queues.
The data of real-time reception application program push, data therein have pre-defined data format.Such as in this reality
It applies in example, by taking game data is analyzed as an example, the format of data is JSON data formats（JSON, Java Script Object
Notation, java script object format, a kind of data interchange format of lightweight）, it includes field it is as shown in table 1：
Table 1
| lmode | Shaping | Game data type, such as logon data, log-on data, pipelined data |
| 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 |
For example, with a data for example, the 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 indicates that the game data type of the data is 1, corresponds to logon data."uid":1 field
Indicate the User ID of the game data owner（ID, Identity, identity）It is 1."ltime":1368784546 field lists
Show that the timestamp that the data generate is 1368784546."luip":" 192.168.100.146 " field references data owner's
IP address is 192.168.100.146.Ldata fields have different definition according to different game data types, such as are stepping on
It recorded defined in data type login grade, logged in game money, last login time stamp and this login time stamp, therefore this
In example, " ldata ":{"level":10,"money":3000,"uplogin":1368764546,"login":
1368784546 } field represents and logs in that grade is 10 grades, to log in game money be 3000, last login time stamp is
1368764546 and this login time stamp be 1368784546.
In the present embodiment, the data of JSON formats are received by interface function, the parameter of the interface function is JSON data
Each field in format（Such as lmode fields, uid fields）, after receiving each field, then each field is assembled into JSON data
Format.
After the data for receiving application program push, by data buffer storage in Redis fifo queues.Wherein Redis
It is a high performance key-value storage system, the value types that it is supported are relatively more, including character string（string）,
List（list）, set（set）, ordered set（zset）And Hash（hashs）.These data types all support push/pop,
Add/remove and intersection union and difference set and more rich operation are taken, and these operations are all atomicities.The number of Redis
According to being all to cache in memory, efficiency is very high.
In data storage to Redis fifo queues, data can be proposed by interface function, for subsequent system
Meter analysis.
Step 204, data are extracted from Redis fifo queues.
Step 206, legitimate verification is carried out to the data of extraction, judges whether data are legal, if legal, execute step
208, the data are otherwise abandoned, return to step 204 extracts data again.
The data format pre-defined according to data carries out legitimate verification to the data of extraction.For example, using this method
Carry out game data analysis when, data use table 1 data format when, legitimate verification may include to lmode, uid,
The data type of ltime, luip, ldata field is verified, such as judges that lmode fields are shaping, works as game data
When type is logon data, the grade in ldata is judged（level）Whether shaping etc..When the data of extraction pass through legitimacy
Verification executes step 208, otherwise abandons the data, extract data, and misregistration daily record again.
Step 208, according to preset statistics target, duplicate removal is carried out to data.
In this embodiment, if data are legal, duplicate removal whether is needed according to preset statistics object judgement data, if desired
Duplicate removal calls Redis interfaces to carry out duplicate removal.
By taking data are using the data format in table 1 as an example, such as statistics target includes the day any active ues amount of game, and is somebody's turn to do
Statistics target is set in advance as needing to carry out duplicate removal, then the key of Redis is generated according to the uid fields of data and ltime fields,
Call SETNX（The application programming interfaces that SETNX, Redis are provided retrieve key for key values to be set as value in memory,
Return value is true in the absence of key）Judge that the data whether there is in Redis buffer queues, if not existing
It is effective data, needs the cumulative statistics for carrying out ASSOCIATE STATISTICS target, otherwise the data are not counted.
Step 210, it calls Redis sum functions to carry out accumulated counts to preset statistics target, obtains statistical result.
In the present embodiment, according to the data format of data and preset statistics target, Classifying Sum is carried out to data, is used
Redis sum functions carry out accumulated counts and obtain final statistical result.
By taking data format of the data using table 1 as an example, preset statistics target includes day any active ues amount and day enlivening IP
Area distribution.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 counting, according to uid fields, the period is logged in（Corresponding to the timestamp scope of the statistics day）Generate Redis's
Key is cached, the data of extraction are monitored, Redis sum functions is called to carry out accumulated counts to day any active ues amount；Accordingly
, according to luip fields and log in the period（Corresponding to the timestamp scope of the statistics day）The caching key for generating Redis, to carrying
The data taken are monitored, and Redis sum functions is called to carry out accumulated counts to enlivening IP address day.
The statistical result that the present embodiment obtains can be buffered in Redis cachings, be looked into for being subsequent supplied to user terminal
Ask or be supplied to application program to apply or cure in the database.
Step 212, statistical result is extracted, and is updated into statistical result database.
In this embodiment, timing calling interface function extracts statistical result, and handle successively according to preset statistics target
As a result it updates in statistical result database.
Referring to Fig. 3, a kind of application scenarios of data analysing method are provided.It is pushed away from application program 302 to queue system 304
Send data.The data that application program 302 pushes have pre-defined data format, such as JSON data formats.Queue system
304 can be, but not limited to be using comprising FIFO（FIFO, First Input and First Output, first in first out）Storage
The hardware system of chip.Queue system 304 receives the data of push and is formatted, for example, JSON formats, and is added to
In the fifo queue of Redis queue servers 310, and data are proposed to statistical system according to the order of first in first out
306.Statistical calculation system 306 can be, but not limited to be realized using the chip with statistics computing function, statistical calculation system
The data of 306 pairs of extractions of system carry out legitimate verification and are added up to obtain according to preset statistics target using duplicate removal processing
Statistical result, and statistical result is buffered in Redis cache servers 312.Statistical calculation system 306 additionally provides inquiry work(
Energy.Storage system 308 is extracted in statistical result deposit statistical result database 314, and storage system 308 also provides inquiry work(
Energy.
Referring to Fig. 4, In yet another embodiment, a kind of data analysing method is provided, flow includes：
Step 402, the data of timing receipt application program push, and in data update to propelling data database.
In the present embodiment, timing（Such as it could be provided as per every other hour）The data for receiving application program push, number
According in storage to propelling data database, which can be distributed data base or relevant database.
Step 404, after each propelling data database update, according to preset statistics target, propelling data data are called
Library statistical function counts the data being stored in propelling data database, obtains temporary statistics as a result, and being obtained each
To temporary statistics result added up to obtain statistical result.
After data storage to propelling data database, according to preset statistics target, propelling data database system is called
Function is counted, such as COUNT functions（Counting function）, SUM functions（Summing function）Deng to being currently stored in propelling data database
Data counted, obtain temporary statistics result.Later, the data in propelling data database are removed and receive application again
The data of program push, are counted, are added up the temporary statistics result obtained every time to obtain final statistical result.
Step 406, final statistical result is preserved into statistical result database.
In the present embodiment, after each propelling data database update, propelling data statistics of database function is called to carry out
Statistics obtains temporary statistics as a result, and updating the final statistical result that temporary statistics result is added up to statistics
In result database.
Referring to Fig. 5, in one embodiment, a kind of data analysis set-up is provided, including：
Data reception module 502, the data for receiving application program push.
Statistical module 504, for according to preset statistics target, statistical result to be obtained using streaming computing to data.
Referring to Fig. 6, in one embodiment, a kind of data analysis set-up, wherein data reception module 502 includes real-time
Receiving module 602, statistical module 504 include 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
In fifo queue 608.
Referring to Fig. 8, in the present embodiment, real-time statistics module 604 includes：Data extracting unit 6042 is used for from Redis
Data are extracted in fifo queue 608.Authentication unit 6044 carries out legitimate verification for the data to extraction.Duplicate removal list
Member 6046, for according to preset statistics target, duplicate removal to be carried out to data.Summing elements 6048, for calling Redis to add up letter
It is several that accumulated counts are carried out to preset statistics target, statistical result is obtained, and statistical result is buffered in Redis cachings 610.
In the present embodiment, which further includes statistical result extraction module 506, statistical result extraction module
506 including the first extraction module 606, the statistical result counted for extracting real-time statistics module 604, and updates and arrive
In first statistical result database 612.
Referring to Fig. 7, in one embodiment, a kind of data analysis set-up, wherein data reception module 502 include connecing in real time
Module 602 and timing receipt module 702 are received, statistical module 504 includes real-time statistics module 604 and timing statistical module 704.System
It includes the first extraction module 606 and the second extraction module 706 to count result extraction module 506.
Real-time reception module 602, for the data that real-time reception application program 700 pushes, and data buffer storage in Redis
In fifo queue 608.
Referring to Fig. 8, in the present embodiment, real-time statistics module 604 includes：Data extracting unit 6042 is used for from Redis
Data are extracted in fifo queue 608.Authentication unit 6044 carries out legitimate verification for the data to extraction.Duplicate removal list
Member 6046, for according to preset statistics target, duplicate removal to be carried out to data.Summing elements 6048, for calling Redis to add up letter
It is several that accumulated counts are carried out to preset statistics target, statistical result is obtained, and statistical result is buffered in Redis cachings 610.
First extraction module 606, the statistical result counted for extracting real-time statistics module 604, and update
Into the first statistical result database 612.
In this implementation, timing receipt module 702, for the data that timing receipt application program 700 pushes, and more data
Newly in propelling data database 708.
Timing statistical module 704, according to preset statistics target, is adjusted after the update of each propelling data database 708
The data being stored in propelling data database 708 with propelling data statistics of database function pair are counted, and are united temporarily
Meter is as a result, and added up the temporary statistics result obtained every time to obtain statistical result.
Second extraction module 706, for extracting temporary statistics result that timing statistical module 704 is counted and most
Whole statistical result, and update into the second statistical result database 712.
Several embodiments of the invention above described embodiment only expresses, the description thereof is more specific and detailed, but simultaneously
Cannot the limitation to the scope of the claims of the present invention therefore be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the guarantor of the present invention
Protect range.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (8)
1. a kind of data analysing method, the method includes：
The data of application program push are received, including：The data of real-time reception application program push, the format of the data are
The field of JSON formats, the JSON formats includes data type, data owner ID, data generation time, timestamp, data
The IP address and detailed data content of the owner, and the data buffer storage in Redis fifo queues；
According to preset statistics target, statistical result is obtained using streaming computing to the data, including：From Redis elder generations
Into extracting data in first dequeue, and legitimate verification is carried out to the data and extracts data again if not verified,
If by verification, calls Redi s sum functions to carry out accumulated counts to preset statistics target, obtains the statistical result,
Wherein, streaming computing further includes that the data analyzed will be needed to be divided into multiple portions in advance, receives one of part number every time
It is counted to obtain intermediate result according to and according to statistics target, is added up the intermediate result to obtain statistical result.
2. according to the method described in claim 1, it is characterized in that, in the calling Redis sum functions to preset statistics
Before target carries out accumulated counts, further include：
According to preset statistics target, duplicate removal is carried out to the data.
3. according to claim 1 to 2 any one of them method, which is characterized in that the data for receiving application program push
Further include:
The data of timing receipt application program push, and in the data update to propelling data database；
It is described according to preset statistics target, obtaining statistical result using streaming computing to the data further includes：
After each propelling data database update, according to preset statistics target, propelling data statistics of database letter is called
It is several that the data being stored in propelling data database are counted, temporary statistics are obtained as a result, and interim by what is obtained every time
Statistical result is added up to obtain the statistical result.
4. according to the method described in claim 1, it is characterized in that, described according to preset statistics target, to the data
After the step of obtaining statistical result using streaming computing, further include：
The statistical result is extracted, and is updated into statistical result database.
5. a kind of data analysis set-up, which is characterized in that including：
Data reception module, the data for receiving application program push, the data reception module includes real-time reception module,
For the data of real-time reception application program push, the format of the data is JSON formats, the field packet of the JSON formats
Containing data type, data owner ID, data generation time, timestamp, the IP address of data owner and detailed data content,
And the data buffer storage in Redis fifo queues；
Statistical module, for according to preset statistics target, statistical result, the system to be obtained using streaming computing to the data
It includes real-time statistics module to count module, and the real-time statistics module includes data extracting unit, is used for from the advanced elder generations of the Redis
Data are extracted in dequeue；Authentication unit, for carrying out legitimate verification to the data；Summing elements, for calling Redis
Sum function carries out accumulated counts to preset statistics target, obtains the statistical result, wherein streaming computing further includes advance
The data analyzed will be needed to be divided into multiple portions, receive one of partial data every time and counted according to statistics target
Intermediate result is obtained, is added up the intermediate result to obtain statistical result.
6. device according to claim 5, which is characterized in that the real-time statistics module further includes duplicate removal unit, is used for
According to preset statistics target, duplicate removal is carried out to the data.
7. according to claim 5 to 6 any one of them device, which is characterized in that the data reception module further comprises
Timing receipt module, for the data of timing receipt application program push, and the data update to propelling data database
In；
The statistical module further comprises timing statistical module, after each propelling data database update, according to
Preset statistics target is called the data that statistics of database function pair is stored in propelling data database to be counted, is obtained
Temporary statistics are as a result, and added up the temporary statistics result obtained every time to obtain the statistical result.
8. device according to claim 5, which is characterized in that described device further includes statistical result extraction module, is used for
The statistical result is extracted, and is updated into statistical result database.
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