CN101087203A - A statistical method of high magnitude of data - Google Patents
A statistical method of high magnitude of data Download PDFInfo
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- CN101087203A CN101087203A CN 200610027566 CN200610027566A CN101087203A CN 101087203 A CN101087203 A CN 101087203A CN 200610027566 CN200610027566 CN 200610027566 CN 200610027566 A CN200610027566 A CN 200610027566A CN 101087203 A CN101087203 A CN 101087203A
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Abstract
The invention provides a statistical method for great data, and the method has three data library tables of MT, MC, and MO, the three datasheets build five datasheet space separately. Three tables are divided into 91 sections separately, which is use to store data in continuous 90 days. The query and statistical analysis of data can calculate the section number according to date, and searches the data according to section number. Because the data quantity of every section just is equivalent to 1/90 of all data, the speed of searching and storing has been improved greatly. Multi-table correlation is converted into single-table query to improve the searching speed. And the difference set and intersection set of query result make reduce the processing data greatly, and improve the statistical performance greatly. The application of technique and reduce the hardware investment, and satisfy the requirement for chine mobile service.
Description
Technical field
The present invention relates to a kind of information technology, relate in particular to a kind of statistical method of high magnitude of data.
Background technology
The tradition Large Scale Application Software System is gathered data necessary to the management object of its application and is carried out statistical analysis, generally speaking, the data volume of statistical analysis is all very huge, need adopt different popularization strategies as China Mobile at different customer groups in carrying out the promoting service process.At present the analysis user group analyzes according to the ticket that the user produces, and the note ticket of each province just has millions of even up to ten million every day, and data also will be preserved 3 months.To from the data of magnanimity like this, find out all types of user group, and the popularization of commencing business pointedly, traditional method is earlier call bill data to be imported database, because many data are distributed in the different tables, therefore need be according to the related ergodic data of multilist storehouse, when the while contingency table surpassed 3-4, search efficiency obviously reduced, and itemize inquiry, insertion user's ticket and other statistical report forms will have a strong impact on the statistical analysis performance again.Moreover the user comprises free customer group (blacklist) by its attributive classification, demand (telecommunication) service customer group, demand (telecommunication) service high-end user group, monthly payment service-user group, the professional high-end user group of monthly payment, silver user, gold user, diamond user.Each user has querying condition separately, also needed in every month to upgrade user's grouped data according to the ticket situation of last month, finish so many business statistics analysis and need independent system by traditional method, and operation is very low, as then having a strong impact on the normal operation of other business with the common operation of other business platforms.
Free customer group (blacklist):
This type of user only uses the coin free service that SP provides or only uses professionally in the free phase of business, never uses paid service.These users are very responsive to the short message service expense, are the main users groups who causes short message service to complain.We are defined as it in " blacklist ", are meant in short message service popularization process to avoid this type of user as far as possible.
The demand (telecommunication) service customer group:
The demand (telecommunication) service that this type of user only uses SP to provide never uses the monthly payment custom service.These users are often very familiar to the business of SP, and are comparatively responsive to the note rate to the promptness of business, professional having relatively high expectations, in case but scope of business meets its needs, this type of user also very easily becomes the high-end user of demand (telecommunication) service.
Demand (telecommunication) service high-end user group:
This type of user is the high-end user of demand (telecommunication) service.Except the characteristics that possess the demand (telecommunication) service user, the expenditure of this type of user on demand (telecommunication) service is bigger, often also can make a profit in note.They are loyal users of demand (telecommunication) service, more easily accept the popularization of short message service.
High-end defining standard is the bar number of program request in every month.Can select by the user.
Monthly payment service-user group:
The monthly payment custom service that this type of user only uses SP to provide.These users do not know in right and wrong Changshu the business of SP, the maturity of business had relatively high expectations, and or not very responsive to the note rate, more easily accept the popularization of short message service.
The professional high-end user group of monthly payment:
This type of user is the high-end user of monthly payment business.These users are very familiar to the business of SP, and are insensitive to the note rate to the admittance degree height of business, are easy to accept the popularization of short message service.
High-end defining standard is total item number of every month monthly payment business.Can select by the user.
Silver user:
This type of user belongs to the high-end user of short message service.The cost of these users on short message service is higher.It is the customer group that operator should take certain bonus policy to possess.
This type of user's defining standard is the total item number of every month monthly payment business and the weighted sum of demand (telecommunication) service total number.Can select by the user.
Gold user:
This type of user belongs to the high-end user of short message service.The cost of these users on short message service is than the silver user height.It is the customer group that operator should take higher bonus policy to possess.
This type of user's defining standard is the total item number of every month monthly payment business and the weighted sum of demand (telecommunication) service total number.Can select by the user.
The diamond user:
This type of user belongs to the high-end user of short message service.The cost of these users on short message service is than the gold user height.It is the customer group that operator should move heaven and earth and possess.
This type of user's defining standard is the total item number of every month monthly payment business and the weighted sum of demand (telecommunication) service total number.Can select by the user.
Summary of the invention
Purpose of the present invention is to provide a kind of statistical method of high magnitude of data.
The object of the present invention is achieved like this: a kind of statistical method of high magnitude of data comprises following content:
A, set up MT, MC, three database table structures of MO
A1, MT, MC, three tables of MO are divided into a plurality of subregions respectively, each subregion is equivalent to a little table of database, and data are inserted and undertaken by subregion, divide subregion by date, and partition number produced according to the date and value circulation in the partition number scope is upgraded;
A2, set up five data table spaces respectively in MT, MC, three tables of MO, a subregion uses a table space, and five numbers the subregion of being separated by uses identical table space, and promptly the 6th subregion uses and the 1st the identical table space of subregion, and other are analogized;
A3, the CDR ticket is divided into free customer group, program request customer group and monthly payment customer group by attribute area, free customer group call bill data is left in the MT table, program request customer group call bill data is left in the MC table, monthly payment customer group call bill data is left in the MO table;
A4, each subregion are set up index according to cell-phone number respectively;
A5, set up free customer group table, this table comprises subscriber phone number and two fields of state, and deposit data is at specific table space, and subscriber phone number is a major key, and uses the concordance list space;
A6, set up program request customer group table, this table comprises several three fields of subscriber phone number, state and program request, and deposit data is at specific table space, and subscriber phone number is a major key, and uses the concordance list space;
A7, set up monthly payment customer group table, this table comprises three fields of item number of subscriber phone number, state and monthly payment business, and deposit data is at specific table space, and subscriber phone number is a major key, and uses the concordance list space;
B, to the customer group of particular community from the beginning of the month to the end of the month by day data of one month of statistics
B1, inquiry are also preserved the user who only uses coin free service among this month CDR and are filtered the record that has existed;
B2, inquiry are also preserved the user who only uses demand (telecommunication) service among this month CDR and are filtered the record that has existed;
B3, inquiry are also preserved the user who only uses the monthly payment business among this month CDR and are filtered the record that has existed;
B4, invalid to there being its state of record that used other business to be set in the free customer group:
B5, invalid to there being its state of record that used other business to be set in the program request customer group;
B6, invalid to there being its state of record that used other business to be set in the monthly payment customer group.
MT described in the steps A 1, MC, three tables of MO are divided into 91 subregions respectively, be used to preserve continuous 90 days data, the data of every day are inserted each subregion in regular turn, after 90 days data are inserted 90 subregions respectively, the 91st day data are inserted the 91st subregion, delete the data of the 1st subregion then, realize preserving all the time nearest 90 days data.
Step B1, described in inquiry and preserve the user who only uses coin free service among this month CDR and filter record, the inquiry described in the step B2 existed and preserve the user who only uses demand (telecommunication) service among this month CDR and filter record, the inquiry described in the step B3 that has existed and preserve the user who only uses the monthly payment business among this month CDR and filter the recording method that has existed identical, step is as follows:
A, according to statistics task running time, calculate the date of first day last month;
B, calculate date of last day last month;
C, calculate the partition number of first day last month;
D, initiation parameter: first day the date of calculation date=last month;
E, initiation parameter: first day partition number calculating partition number=last month;
F, whether insert the user by the sky circulation according to calculation date and inquire about smaller or equal to the judgement on date of last day last month.
Described in the above-mentioned steps f whether to insert the method that the user inquires about smaller or equal to the judgement on date of last day last month by the sky circulation according to calculation date as follows:
F1, inquire about unique cell-phone number of all particular community users according to partition number in the customer group table of particular community, this cell-phone number must be simultaneously in other customer group table, and this Query Result does not comprise the cell-phone number that has existed in the particular community customer group table;
F2, with Query Result per 100 as a collection of insertion database, up to all having inserted;
The date of f3, cycle calculations next day;
The partition number of f4, cycle calculations next day is up to all data of handling this month;
Described in the step B4 to have in the free customer group its state of record that used other business be set to described in invalid, the step B5 to there being its state of record that used other business to be set to identical described in invalid, the step B4 in the program request customer group to there being its state of record that used other business to be set to invalid method in the monthly payment customer group, step is as follows:
A, initiation parameter: first day the date of calculation date=last month;
B, initiation parameter: first day partition number calculating partition number=last month;
C, whether upgrade a customer group table by a day circulation smaller or equal to the judgement on date of last day last month according to calculation date.
Whether as follows described in the above-mentioned steps c smaller or equal to the method that the judgement on date of last day last month is upgraded the customer group table by day circulation according to calculation date:
State is effective cell-phone number in c1, the inquiry particular community customer group table, and according to the user mobile phone number of other customer group in the partition number inquiry particular group table, the common factor of getting the two Query Result is as Query Result;
C2, be invalid with per 100 the states of Query Result as the user mobile phone number correspondence of a collection of renewal particular community customer group table, intact up to update all;
State is effective cell-phone number in c3, the inquiry particular community customer group table, inquires about the user mobile phone number of the 3rd class particular community customer group in another particular community customer group table according to partition number, and the common factor of getting the two Query Result is as Query Result;
C4, be invalid with per 100 the states of Query Result as the user mobile phone number correspondence of a collection of renewal particular group table, intact up to update all;
C5, cycle calculations date next day;
The partition number of c6, cycle calculations next day is up to all data of handling this month;
Common factor described in step c1 and the c3 is to use other professional users.
Statistical method of high magnitude of data of the present invention can be avoided owing to data volume reduces the whole application system performance greatly, and then reduces the expense of whole software system operation platform, improves its operational efficiency.For example when subregion is set to 91, because 90 days CDR data are inserted into respectively in 90 subregions, each subregion has only one day data, have index on the subregion, can calculate the partition number that to inquire about according to the date when inquiry of data and statistical analysis, search the data of specified partition according to partition number, because the data volume of each subregion only is equivalent to 1/90th of total data, therefore the inquiry and the speed of preserving data also are greatly improved.The multilist association changes single table inquiry into and has improved inquiry velocity greatly.The difference set of Query Result and common factor technology make the data volume that needs to handle greatly reduce again, thereby have greatly improved statistic property.The application of criticizing insertion and renewal technology also is greatly improved the preservation speed of statistics.System can also preserve 90 days data automatically.The feasible hardware investment to system of this The Application of Technology reduces widely, has fully satisfied the demand of China Mobile to business.
Description of drawings
Fig. 1 is application flows figure of the present invention.
Embodiment
The present invention proposes a kind of mass data statistical analysis technique and treatment system, be further described below in conjunction with the statistical technique of free customer group (blacklist) statistical method of high magnitude of data to invention.
At first set up MT, MC, three database tables of MO, each table is divided into a plurality of subregions, the suitable little table of each subregion, data are inserted and are undertaken by subregion, divide subregion by date.As 90 days data of need preservation, then set up 91 subregions, the data of every day are inserted subregion separately, after 90 days data are inserted 90 subregions respectively, the 91st day data are inserted the 91st subregion, and the data of deleting first subregion then can be preserved nearest 90 days data so all the time.Partition number produces according to the date and value circulates between 1-91.The present invention adopts this partition scheme.
Database is parallel to be inserted and search efficiency in order further to improve, three database tables are set up five data table spaces respectively, table space of a subregion, five the partition number of being separated by is used identical table space, promptly the 6th subregion uses the table space identical with first subregion, and other are analogized.
Then according to CDR ticket attribute, call bill data is left in respectively in MT, MC, three tables of MO, be about to free customer group call bill data and leave in the MT table, program request customer group call bill data is left in the MC table, monthly payment customer group call bill data is left in the MO table.The data volume of each table be can reduce like this, inquiry and preservation speed improved.And on subregion, set up index according to cell-phone number, to improve search efficiency.
After building the MT that is over, MC, MO table, also need set up free customer group (blacklist) table, program request customer group table and monthly payment customer group table.Free customer group table comprises subscriber phone number and two fields of state (invalid, effective).Deposit data is at specific table space, and subscriber phone number is a major key, and uses the concordance list space.Program request customer group table comprises subscriber phone number, state (invalid, effective) and several three fields of program request.Deposit data is at specific table space, and subscriber phone number is a major key, and uses the concordance list space.Monthly payment customer group table comprises subscriber phone number, three fields of item number of state (invalid, effective) and monthly payment business.Deposit data is at specific table space, and subscriber phone number is a major key, and uses the concordance list space.
Can carry out ASSOCIATE STATISTICS after having set up database table structure.The data that we add up one month are according to the principle by the sky statistics, and promptly from the last day that counted on this month in first day of this month, the method that so just can utilize subregion to inquire about and insert increases substantially the speed of data query and insertion.Fig. 1 is application flows figure of the present invention.Below in conjunction with Fig. 1 statistical method of high magnitude of data of the present invention is described as follows:
At first inquire about and preserve only using coin free service and filtering the record that has existed among this month CDR, method is as follows:
1, according to statistics task running time, calculate the date of first day last month,
2, calculate the date of last day last month,
3, calculate the partition number of first day last month,
4, initiation parameter: first day the date of calculation date=last month,
5, initiation parameter: calculate first day the partition number of partition number=last month,
6, whether insert a blacklist smaller or equal to the judgement on date of last day last month by a day circulation according to calculation date, method is as follows:
61, inquiring about all payment type according to partition number in the MT table is free unique cell-phone number, and this cell-phone number must be simultaneously in program request customer group table and monthly payment customer group table, and this Query Result does not comprise the cell-phone number that has existed in the blacklist table.This method has adopted two kinds of key technologies to improve performance.The one, the multilist association is changed into the inquiry of a plurality of single tables, thereby accelerate inquiry velocity greatly; The 2nd, thus the speed that data are inserted greatly improved by the result that the difference set of getting Query Result significantly reduces inquiry.
62, with Query Result per 100 as a collection of insertion database, up to all having inserted.The application of batch processing has greatly improved the speed that data are inserted.
63, the date of cycle calculations next day.
64, the partition number of cycle calculations next day is up to all data of handling this month.
Inquire about and preserve only using demand (telecommunication) service and filtering the record that has existed among this month CDR with the mode of handling with blacklist.
Inquire about and preserve only using the monthly payment business and filtering the record that has existed among this month CDR with the mode of handling with blacklist.
Finish inquiry and preserve among this month cdr only use coin free service, only use demand (telecommunication) service, only use and also need after the customer group of monthly payment business invalidly there being its state of record that used other business to be set in the free customer group, method is as follows:
1) initiation parameter: first day the date of calculation date=last month,
2) initiation parameter: calculate first day the partition number of partition number=last month,
3) whether upgrade a blacklist table smaller or equal to the judgement on date of last day last month by a day circulation according to calculation date, method is as follows:
31) state is effective cell-phone number in the inquiry blacklist table, is the user mobile phone number of pressing bar and monthly payment according to payment type in the partition number inquiry MT table.The common factor (this common factor is and used other professional users) of getting the two Query Result is as Query Result.This method has adopted two kinds of technology to improve performance.The one, the inquiry that the correlation inquiry of a plurality of big tables is changed repeatedly into single table improves performance; Be the common factor by the Query Result of getting two big tables on the other hand, reducing needs data updated, thereby improves statistic property greatly.
32) be invalid with per 100 the states of Query Result as the user mobile phone number correspondence of a collection of renewal blacklist table, intact up to update all.This method has been used batch system equally and has been improved the renewal performance of database significantly.
33) state is effective cell-phone number in the inquiry blacklist table, is the user mobile phone number of monthly payment according to payment type in the partition number inquiry MC table.The common factor (this common factor is and used other professional users) of getting the two Query Result is as Query Result
34) be invalid with per 100 the states of Query Result as the user mobile phone number correspondence of a collection of renewal blacklist table, intact up to update all.
35) cycle calculations is calculated the date next day.
36) cycle calculations is calculated the partition number of next day up to all data of handling this month.
Adopt the processing method identical invalid to there being its state of record that used other business to be set in the program request customer group with blacklist.
Adopt the processing method identical invalid to there being its state of record that used other business to be set in the monthly payment bouquet with blacklist.
Because the statistical analysis technique of other customer groups is identical with the blacklist statistical analysis technique, so repeat no longer one by one.
Claims (7)
1, a kind of statistical method of high magnitude of data is characterized in that, comprises following content:
A, set up MT, MC, three database table structures of MO
A1, MT, MC, three tables of MO are divided into a plurality of subregions respectively, each subregion is equivalent to a little table of database, and data are inserted and undertaken by subregion, divide subregion by date, and partition number produced according to the date and value circulation in the partition number scope is upgraded;
A2, set up five data table spaces respectively in MT, MC, three tables of MO, a subregion uses a table space, and five numbers the subregion of being separated by uses identical table space, and promptly the 6th subregion uses and the 1st the identical table space of subregion, and other are analogized;
A3, the CDR ticket is divided into free customer group, program request customer group and monthly payment customer group by attribute area, free customer group call bill data is left in the MT table, program request customer group call bill data is left in the MC table, monthly payment customer group call bill data is left in the MO table;
A4, each subregion are set up index according to cell-phone number respectively;
A5, set up free customer group table, this table comprises subscriber phone number and two fields of state, and deposit data is at specific table space, and subscriber phone number is a major key, and uses the concordance list space;
A6, set up program request customer group table, this table comprises several three fields of subscriber phone number, state and program request, and deposit data is at specific table space, and subscriber phone number is a major key, and uses the concordance list space;
A7, set up monthly payment customer group table, this table comprises three fields of item number of subscriber phone number, state and monthly payment business, and deposit data is at specific table space, and subscriber phone number is a major key, and uses the concordance list space;
B, to the customer group of particular community from the beginning of the month to the end of the month by day data of one month of statistics
B1, inquiry are also preserved the user who only uses coin free service among this month CDR and are filtered the record that has existed;
B2, inquiry are also preserved the user who only uses demand (telecommunication) service among this month CDR and are filtered the record that has existed;
B3, inquiry are also preserved the user who only uses the monthly payment business among this month CDR and are filtered the record that has existed;
B4, invalid to there being its state of record that used other business to be set in the free customer group:
B5, invalid to there being its state of record that used other business to be set in the program request customer group;
B6, invalid to there being its state of record that used other business to be set in the monthly payment customer group.
2, a kind of statistical method of high magnitude of data according to claim 1, it is characterized in that: the MT described in the steps A 1, MC, three tables of MO are divided into 91 subregions respectively, be used to preserve continuous 90 days data, the data of every day are inserted each subregion in regular turn, after 90 days data are inserted 90 subregions respectively, the 91st day data are inserted the 91st subregion, and the data of deleting the 1st subregion then realize preserving all the time nearest 90 days data.
3, a kind of statistical method of high magnitude of data according to claim 1, it is characterized in that: step B1, described in inquiry and preserve the user who only uses coin free service among this month CDR and filter record, the inquiry described in the step B2 existed and preserve the user who only uses demand (telecommunication) service among this month CDR and filter record, the inquiry described in the step B3 that has existed and preserve the user who only uses the monthly payment business among this month CDR and filter the recording method that has existed identical, step is as follows:
A, according to statistics task running time, calculate the date of first day last month;
B, calculate date of last day last month;
C, calculate the partition number of first day last month;
D, initiation parameter: first day the date of calculation date=last month;
E, initiation parameter: first day partition number calculating partition number=last month;
F, whether insert the user by the sky circulation according to calculation date and inquire about smaller or equal to the judgement on date of last day last month.
4, a kind of statistical method of high magnitude of data according to claim 3 is characterized in that: described in the step f whether to insert the method that the user inquires about smaller or equal to the judgement on date of last day last month by the sky circulation according to calculation date as follows:
F1, inquire about unique cell-phone number of all particular community users according to partition number in the customer group table of particular community, this cell-phone number must be simultaneously in other customer group table, and this Query Result does not comprise the cell-phone number that has existed in the particular community customer group table;
F2, with Query Result per 100 as a collection of insertion database, up to all having inserted;
The date of f3, cycle calculations next day;
The partition number of f4, cycle calculations next day is up to all data of handling this month;
5, a kind of statistical method of high magnitude of data according to claim 1, it is characterized in that: described in the step B4 to have in the free customer group its state of record that used other business be set to described in invalid, the step B5 to there being its state of record that used other business to be set to identical described in invalid, the step B4 in the program request customer group to there being its state of record that used other business to be set to invalid method in the monthly payment customer group, step is as follows:
A, initiation parameter: first day the date of calculation date=last month;
B, initiation parameter: first day partition number calculating partition number=last month;
C, whether upgrade a customer group table by a day circulation smaller or equal to the judgement on date of last day last month according to calculation date.
6, whether as follows smaller or equal to the method that the judgement on date of last day last month is upgraded the customer group table by day circulation a kind of statistical method of high magnitude of data according to claim 5 is characterized in that: according to calculation date described in the step c:
State is effective cell-phone number in c1, the inquiry particular community customer group table, and according to the user mobile phone number of other customer group in the partition number inquiry particular group table, the common factor of getting the two Query Result is as Query Result;
C2, be invalid with per 100 the states of Query Result as the user mobile phone number correspondence of a collection of renewal particular community customer group table, intact up to update all;
State is effective cell-phone number in c3, the inquiry particular community customer group table, inquires about the user mobile phone number of the 3rd class particular community customer group in another particular community customer group table according to partition number, and the common factor of getting the two Query Result is as Query Result;
C4, be invalid with per 100 the states of Query Result as the user mobile phone number correspondence of a collection of renewal particular group table, intact up to update all;
C5, cycle calculations date next day;
The partition number of c6, cycle calculations next day is up to all data of handling this month;
7, a kind of statistical method of high magnitude of data according to claim 6 is characterized in that: the common factor described in step c1 and the c3 is to use other professional users.
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| CN101272282B (en) * | 2008-04-28 | 2010-08-25 | 中国移动通信集团设计院有限公司 | Method and device for service data statistics |
| CN101533406B (en) * | 2009-04-10 | 2010-10-13 | 北京锐安科技有限公司 | Mass data querying method |
| WO2011134227A1 (en) * | 2010-04-29 | 2011-11-03 | 中兴通讯股份有限公司 | Statistical processing method and device for bill data |
| CN101799803B (en) * | 2009-02-06 | 2012-07-04 | 华为软件技术有限公司 | Method, module and system for processing information |
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| CN101272282B (en) * | 2008-04-28 | 2010-08-25 | 中国移动通信集团设计院有限公司 | Method and device for service data statistics |
| CN101799803B (en) * | 2009-02-06 | 2012-07-04 | 华为软件技术有限公司 | Method, module and system for processing information |
| CN101533406B (en) * | 2009-04-10 | 2010-10-13 | 北京锐安科技有限公司 | Mass data querying method |
| CN102054000B (en) * | 2009-10-28 | 2012-07-25 | 中国移动通信集团公司 | Data querying method, device and system |
| WO2011134227A1 (en) * | 2010-04-29 | 2011-11-03 | 中兴通讯股份有限公司 | Statistical processing method and device for bill data |
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