CN1747398A - Mass performance data statistical method in network element management system - Google Patents
Mass performance data statistical method in network element management system Download PDFInfo
- Publication number
- CN1747398A CN1747398A CN 200410073894 CN200410073894A CN1747398A CN 1747398 A CN1747398 A CN 1747398A CN 200410073894 CN200410073894 CN 200410073894 CN 200410073894 A CN200410073894 A CN 200410073894A CN 1747398 A CN1747398 A CN 1747398A
- Authority
- CN
- China
- Prior art keywords
- performance
- merger
- data
- granularity
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000007619 statistical method Methods 0.000 title claims description 14
- 238000013500 data storage Methods 0.000 claims description 46
- 238000007726 management method Methods 0.000 claims description 38
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 claims description 27
- 239000002245 particle Substances 0.000 claims description 9
- 238000012986 modification Methods 0.000 claims description 2
- 230000004048 modification Effects 0.000 claims description 2
- 238000000034 method Methods 0.000 abstract description 13
- 235000019580 granularity Nutrition 0.000 description 62
- 241001269238 Data Species 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 230000002776 aggregation Effects 0.000 description 2
- 238000004220 aggregation Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000005192 partition Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000012467 final product Substances 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000013517 stratification Methods 0.000 description 1
- 230000003442 weekly effect Effects 0.000 description 1
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Debugging And Monitoring (AREA)
Abstract
The network element management system includes database server and application server. The performance reporting table is set in the database server for use in saving the reported performance data. The method includes following steps: 1) the table for storing different time grain size data is set in database server, and the grain size presents the data reporting time, and there is inclusion relation between grain sizes; 2) The application server periodically merges the reported performance data according to time grain size, and then saves it into grain size data storing table; 3) when the performance statistics is made for mass performance data in the network element management system, the application server retrieves the data storing table relating to grain size according to the grain size required by statistics.
Description
Technical field
The present invention relates to wireless communication field, relate in particular to the statistical method of mass performance data in the Element management system.
Background technology
Element management system obtains the performance data of each network element in the managed networks usually earlier, because the data volume of performance data is very big, even can reach the data record more than hundreds of millions, generally is referred to as mass performance data thus; Then those mass performance data data are carried out performance statistics, monitor at last and control each network element in the whole managed networks according to statistics.
See also Fig. 1, it is the structure diagram of OMC-R (wireless access network operation maintenance center).OMC-R is an application example of Element management system.The application server that it uses is OMC-R server 11, also comprises: OMC-R control desk 14, network element protocol gateway 15, router one 6, network management interface 17, provincial webmaster 18, far-end OMC-R control desk 19 and RNC system (RNS) 20 in database server 12, Local Area Network 13, the net.
This system can be provided with some network element protocol gateways 15 (number that illustrates in the drawings is 1) on LAN13, and each network element protocol gateway 15 receive by snmp protocol (Simple Network Management Protocol) this gateway each RNS system 20 report the network element performance data, subsequently, network element protocol gateway 15 regularly is uploaded to OMC-R server 11 with the network element performance data of collecting, the OMC-R server 11 real-time performance files with reporting are resolved in the database server 12, and provincial webmaster 18 can carry out operations such as the statistics of performance data and inquiry by the network management interface 17 of standard.
In addition, operating personnel not only can log in the OMC-R system and operate by being arranged on OMC-R control desks 14 in the limitation net 13, and can operate to the OMC-R system by 19 Telnets of far-end OMC-R control desk.
In present Element management system, but because the continuous increase of managed network element quantity, thereby the network element performance data that causes reporting also constantly increases.If 15 RNS systems of an Element management system management, then may exist and carry out network element that performance data reports up to 48000, and each network element in these a few network elements all has the counter of different numbers, 10 at least of the numbers of counter, more than 80 counter can be had at most, the data volume that at every turn reports can be calculated thus; And system also can report the data of gathering according to the predefined time cycle (such as 5 minutes); And, 3gpp is organized in and requires performance sampled data can preserve certain hour in the performance specification, according to the demand of above-mentioned code requirement in conjunction with OMC-R, need to determine 3 months performance data of preservation, can know that according to above-mentioned analysis the performance data amount that needs are preserved in the Element management system is big, therefore this mass performance data being added up not is to be easy thing.
The following two kinds of modes of main employing are added up the mass performance data in the Element management system in the prior art.
First method is that form on the performance is set in database server, form is preserved mass performance data on this performance, and the new performance data that obtains also constantly is added on this performance in the form, and form is set up multiple index by fields such as net element information, performance informations and added up the network element performance on this performance.Though it is fairly simple that this method realizes, the OMC-R server need not carry out any processing just can be directly to be carried out inquiry and adds up the non-constant of execution efficient of this method in the form on the performance.Because the data volume all too on this performance in the form is big, when carrying out performance statistics in the form on performance, the speed of statistics is very slow, when data volume reaches certain division of a ci poem value, and database server even can't respond its query requests.
If improve statistics speed in advance, need to rely on the lifting of hardware system, for example in database server, use disk array, by using disk array that form on the performance is carried out physically division, the number that increases CPU is simultaneously carried out the concurrency of statistics to improve database, but improve statistics speed by increasing extra hardware, increased the cost of statistics greatly.
Second method is to preserve those mass performance datas in the mode of little table on database server, such as:
A: according to calling time on the performance data, periodically set up and go up form, be that unit sets up form on every month performance for example according to the moon, then form just is divided into form on a plurality of months performances on performance, reaches the purpose of the whole mass performance data of statistics during performance statistics by the statistics to form on every month performance.
(2) set up form on the network element performance according to different network elements or NE type, for example NE type is the N kind, then set up form on N the corresponding network element performance, simultaneously form just is divided into the N form on first performance of throwing the net on performance, during performance statistics by different network element performances being reported the purpose of expressing the whole mass performance data of statistics.
Second method reduces the performance data in individual table, therefore in the time of in the statistics span just in time belongs to certain individual table, the data retrieved amount will reduce, so just can improve the execution speed of statistics, but do so apparent in view shortcoming is also arranged, at first, according to big data volume at present like this, even if performance reported data table is divided into a plurality of little tables according to such aforesaid way, the data volume in each little table is still very big, when carrying out performance statistics, speed is still waiting to improve, the most important thing is that if query statistic need be crossed over many tables, statistical efficiency can be also poorer than form on the performance that only adopts in the first method.Also have, along with the growth of the time span of adding up, the performance of statistics descends rapidly, has usually arrived the degree that the database device can't respond.
Thus, prior art mainly exists the statistics of mass performance data to expend time in very much in the statistic processes of the mass performance data of Element management system, i.e. problem such as statistical efficiency difference.And along with the growth of time span of statistics, the performance of statistics descends rapidly, usually makes the degree that database server can't respond.
Summary of the invention
The object of the invention is to provide the statistical method that does not increase hardware cost in a kind of Element management system and improve the mass performance data of statistical efficiency, exist the statistics of mass performance data to expend time in very much to solve in the prior art, the technical problem of statistical efficiency difference.
For addressing the above problem, the invention discloses the statistical method of mass performance data in a kind of Element management system, described Element management system comprises database server and application server, and described database server is provided with form on the performance, be used to preserve the performance data that reports, comprise:
(1) table data store of different time granularity is set on described database server, described time granularity represent performance data on call time, have inclusion relation between described granularity and the granularity;
(2) described application server will report performance data periodically according to each time granularity require merger after, deposit corresponding granularity data storage list in;
(3) when the mass performance data to described Element management system carries out performance statistics, described application server is retrieved the table data store of corresponding granularity according to the granularity of statistical requirements, obtains the statistics of described performance.
Step (2) specifically comprises:
(21) after described application server will satisfy the performance data merger of not merger of minimum time granularity requirements, be saved to minimum time granularity corresponding data storage list, described performance data is the performance data in the form on the described performance;
(22) after described application server will satisfy the performance data merger of not merger of other each time granularity requirement, be saved in the corresponding granularity data storage list, described performance data is less than the performance data in the table data store of maximum particle size correspondence in this granularity.
Step (1) also is included in a performance timestamp table also is set on the described database server, to preserve the time of the last merger of each time granularity table data store;
Also comprise between step (1) and the step (2) when the system time of described Element management system and the difference of described last merger time during, carry out step (2) at least more than or equal to a predefined time value.Described predefined time value is 1 day.
Step (1) also comprises: (11) are provided with a background task in database, and described background task is regularly carried out the merger of performance data between each granularity data storage list; (12) trigger is set in database;
Step (2) also comprises: when database restarts, activate described trigger, begin timing statistics in the modification background task, so that the merger continuously of described background task.
Step (2) also comprises: the performance data in each time granularity table data store is carried out subregion according to different time ranges preserve.
Described step (3) specifically comprises:
(a) granularity of difference counting statistics time started, statistics concluding time, and find wherein minimum granularity;
(b) take out maximum particle size in the performance timestamp table;
(c) maximum particle size in granularity minimum in the step (a) and the step (b) is compared, find the little granularity of granularity to add up employed granularity as this;
(d) according to granularity that this statistics is used in the step (c), determine to adopt corresponding table data store to add up, obtain statistics.
The described time granularity that is provided with in the step (1) comprises hour, fate, the moon, year.Described step (2) specifically comprises: (A) if the current time in system hour arrived integral point greater than one day or current time in system than the last merger in the performance timestamp table, will in the form on the described performance not the performance data of merger hour being after unit carries out merger, be saved in the corresponding hour data storage list, and the time endDate of the last merger of performance timestamp table is set; (B) fate of judging endDate if carry out step (C), otherwise carries out step (A) whether greater than the fate of the last merger in the described performance timestamp table; (C) will in the described hour data storage list not the performance data of merger be after unit carries out merger with the sky, be saved in the corresponding day data storage list, and the last merger time D of performance timestamp table be set; (D) judge and the moon among the D,, otherwise carry out step (A) if carry out step (E) whether greater than the moon number of the last merger in the described performance timestamp table; (E) will in the described day data storage list not the performance data of merger be after unit carries out merger with the moon, be saved in the corresponding moon table data store, and the last merger time M of performance timestamp table be set; (F) judge and year among the M,, otherwise carry out step (A) if carry out step (K) whether greater than the year number of the last merger in the described performance timestamp table; (K) will in the described month table data store not the performance data of merger be after unit carries out merger with the year, be saved in the corresponding annual data storage list, and the last merger time Y of performance timestamp table be set.
Compared with prior art, the present invention has the following advantages: the mass performance data that the present invention reports according to different time granularity merger periodically, be stored in the corresponding granularity data storage list, when carrying out the performance statistics of mass performance data, requirement according to concrete statistics, select corresponding granularity data storage list to carry out performance statistics, owing to the performance data that has reported is carried out merger in advance in the granularity data storage list, such as each record in the hour meter is one hour performance of statistics, therefore the data in the table become the minimizing of the order of magnitude, both improve the efficient of statistics, need not to increase additional hardware again.
In the present invention, consider the accuracy of statistics, the performance data that reports is saved to the predefined time after, just carry out merger and handle, prevent from that data from lagging behind to bring not statistical uncertainty true situation to take place.In the present embodiment, preset time is one day.
In the present invention, a trigger is set under the unstable situation of restarting of database, reaches the effect that continuous merger appears in reported data by revising the statistics initial time.
Description of drawings
Fig. 1 is the structure diagram of an application example OMC-R of Element management system;
Fig. 2 is the statistics flow chart for mass performance data in the Element management system of the present invention;
Fig. 3 is an enforcement schematic diagram of realizing performance data merger storage among the present invention;
Fig. 4 is the implementing procedure figure of statistic processes among the present invention.
Embodiment
Below in conjunction with accompanying drawing, specify the present invention.
See also Fig. 2, it is the statistics flow chart of mass performance data in the Element management system of the present invention.This Element management system comprises database server and application server, described database server be provided be used to preserve the performance data that reports report the performance table, may further comprise the steps:
S110: the table data store of different time granularity is set on database server, and granularity is represented the precision that calls time on the performance data to have inclusion relation between granularity and the granularity; Time granularity such as minute, the time, sky, the moon, year, and in Element management system by carrying out data and report to be divided into unit, can set up thus as the time table data store, day data storage list, month table data store and annual data storage list.When for example considering statistic property normally with the performance data in a week be with reference to the time, can set up weekly data storage list, month table data store and annual data storage list etc., the kind that time granularity is set can specifically be provided with according to concrete performance statistics situation.Obviously, inclusion relation is arranged all between each granularity, with the granularity that is provided be branch, the time, sky, moon, year be example, comprised 12 months in 1 year, comprised fixing fate in one month, be i.e. the definite inclusion relation of existence between the granularity;
S120: described application server will report performance data periodically according to each time granularity require merger after, deposit corresponding granularity data storage list in; The network element performance data that reports to database server is dynamic, promptly As time goes on, network element performance data on the performance in the form is increase and dynamic change, there were 24 hours such as one day, become 24 performance datas to be stored in the hour data storage list performance data merger of form on the performance, the all properties data statistics that is about in one hour becomes a data record, certainly the condition of merger can be provided with according to the requirement of performance statistics, becomes a performance data to be stored in the hour data storage list aggregation of data newly-increased in the form on the performance such as per two hours.When merger, system can be that merger is carried out on the basis of merger with form on the performance with described granularity data storage list, but considers the efficient of merger, mainly adopts following method to carry out merger:
(21) after application server will satisfy the performance data merger of not merger of minimum time granularity requirements, be saved to minimum time granularity corresponding data storage list, described performance data is the performance data in the form on the described performance;
(21) after described application server will satisfy the performance data merger of not merger of other each time granularity requirement, be saved in the corresponding granularity data storage list, described performance data is less than the performance data in the table data store of granularity correspondence maximum in this granularity.
Such as: when the granularity of native system setting is, sky, the moon, then:
At first, application server with the performance data of the not merger in the form on the performance hour being that unit carries out merger, as will on all properties data statistics that is positioned at same hour of calling time become a hour data record, and be stored in the hour data storage list;
Then, application server with in the hour data storage list not the hour data of merger record be the day data record with the sky for the unit merger, as merger in the hour data storage list and data record that be positioned at are on the same day added up into a day data record, be kept in the day data storage list;
Subsequently, application server with in the day data storage list not the day data of merger record be a month data record with the moon for the unit merger, be kept in moon table data store, as in the day data storage list not merger and be positioned at the day data in January record and add up into a month data record, be kept in moon table data store;
At last, application server with in the moon table data store not the moon data record of merger be the merger data record of growing up in unit with the year, as in the moon table data store merger and be positioned at the day data in same year and write down and add up into an annual data record, be kept in the annual data storage list.
130: when the mass performance data to described Element management system carried out performance statistics, described application server was retrieved the table data store of corresponding granularity according to the granularity of statistical requirements, obtained the statistics of described performance.Such as a certain performance at magnanimity performance data statistical system in July, a day data storage list that then only needs statistics July is inquired about corresponding performance and it is added up and gets final product, be compared in the prior art and those mass performance datas be kept at one or several performance data tables, the data volume that need inquire about during performance statistics of the present invention is considerably less, has improved the efficient of statistics greatly.
Below in conjunction with the structure example figure of the Element management system of Fig. 1, with the time, sky, the moon, year time granularity be example, specify the present invention.
The table data store of different time granularity at first is set: hour data storage list (being called for short hour meter down), day data storage list (being called for short a day table down), month table data store (being called for short menology down) and year property table data store (being called for short chronology down).
The network element performance data that the network element protocol gateway 15 of Element management system periodically will be managed is uploaded to OMC-R server 11 by LAN13, the OMC-R server 11 real-time performance files with reporting are resolved in the database server 12, and database server 12 is kept at performance data on the performance in the form.This cycle, normally unit reported to be divided into.
Because the accuracy of form has arrived minute on the performance, and has comprised current up-to-date performance data.Field in this table is except performance data and network element data, and most important is the time field, and this time field has clearly represented to report the acquisition time (it be accurate to minute) of performance data.By form on such performance, derive four tables of identical in structure (hour meter, day table, menology and chronology) with it again, but the accuracy branch that reports the time field in these tables is clipped to hour, day, month, year.Hour meter, the field of tables such as day table all is identical with raw data table, is the time granularity difference, therefore, these tables are the same with raw data table, all represent the data of all network element all properties).The difference of form is that they are historical data tables on they and the performance, and their time granularity is different with form on the performance simultaneously.
For instance, suppose that the performance reported data reports since 2003/09/27 12:00, reported once in per 5 minutes, the data break of offering on each like this is 5 minutes, they are stored on the performance in the form, data constantly report like this, after being reported to a certain degree, system adds up 2003/09/2712:00 automatically to the data of 2003/09/27 13:00, generate the statistics (being referred to as a day data record) of this hour, the statistics that produces is saved in the hour meter, after the data of hour meter have arrived to a certain degree, 2003/09/27 00:00 is saved in day table, by that analogy to the data of 2003/09/28 00:00, upwards generate data successively, up to chronology.
The direction of aggregation of data is that form (perfdata) is integrated into performance and reports hour meter (perfhourtab) on the performance, be integrated into a day table (perfdatetab) from hour meter again, be integrated into menology (perfmonthtab) from the sky table again, be integrated into chronology (perfyeartab) by menology more at last.Data volume in each table is successively decreased according to an order of magnitude.Suppose to have in the form on the performance data 37681950 performances to report record, record number in the performance hour meter has only its about 1/12 so, arrive 3140162 records, record in it table will have only 1/24 hour meter record number, it is 130840, and the like, menology, the record number of chronology also reduce according to such order of magnitude.Because the data volume of preserving is few, reduces the number of times of inquiry when adding up, and then improved the speed of statistics.
When the performance data that gateway protocol gateway 15 is collected reported to database server 12, the situation that exists data to lag behind probably brought the performance statistics accuracy to reduce thus.The problem that the accuracy of bringing performance statistics for fear of lagging behind owing to data reduces, the present invention carries out merger after adopting the performance data that will receive to keep the predefined time again.The predefined time that present embodiment adopts is one day.Below concrete introduction how to realize the merger storage of performance data.
See also Fig. 3, it is for realizing the enforcement schematic diagram of performance data merger storage among the present invention.
Step S210: in system's initial launch, design a performance timestamp table in advance, this table be used for writing down last merger hour, the sky of last merger, the moon of last merger, the year of last merger, for example, when supposing the system is initially installed, current time is 2003/09/1012:30, and the numerical value of four fields in the performance timestamp table is respectively so: 2003/09/10 12:00 (last merger hour), 2003/09/10 00:00 (sky of last merger), 2003/09/01 00:00 (moon of last merger), 2003/01/01 00:00 (year of last merger).And the time started of merger setting is the integral point one day after of current system time, and for this example, the time of merger initial launch is 2003/09/11 13:00.Why will prolong one day after, be can be processed for the reported data that guarantees to postpone, and is merged.
Step S220: if the current time in system hour arrived integral point greater than one day or current time in system than the last merger in the performance timestamp table, will in the form on the described performance not the performance data of merger hour being after unit carries out merger, be saved in the corresponding hour data storage list, and the time endDate of the last merger of performance timestamp table is set.
Under the normal condition, as long as database is moving all the time, the moment that each merger takes place all can with one day current time interval, but at database unusual pent situation takes place, after database restarted, the current time can surpass one day with the last merger time, more in this case, can carry out merger every passing hour, with all not the performance data of merger hour being the direct merger of unit.
Step S230: the fate of judging endDate if carry out step S240, otherwise carries out step S220 whether greater than the fate of the last merger in the described performance timestamp table;
Step S240: will in the described hour meter not the performance data of merger be after unit carries out merger with the sky, be saved in the corresponding day data storage list, and the last merger time D of performance timestamp table be set;
Step S250: judge and the moon among the D,, otherwise carry out step S220 if carry out step S260 whether greater than the moon number of the last merger in the described performance timestamp table;
Step S260: will in the described day data storage list not the performance data of merger be after unit carries out merger with the moon, be saved in the corresponding moon table data store, and the last merger time M of performance timestamp table be set;
Step S270: judge and year among the M,, otherwise carry out step S220 if carry out step S280 whether greater than the year number of the last merger in the described performance timestamp table;
Step S280: will in the described month table data store not the performance data of merger be after unit carries out merger with the year, be saved in the corresponding annual data storage list, and the last merger time Y of performance timestamp table be set.
In order to guarantee that system is provided with a background task in database after database generation exception error is restarted, described background task is regularly carried out the merger of performance data between each granularity data storage list, and a trigger is set in database;
When database restarts, activate described trigger, revise in the background task and begin timing statistics, so that described background task can also continue to carry out merger according to preset time, this trigger can be revised the initial launch time of merger, present embodiment is the integral point one day after that this timing statistics is adjusted into the current time, if do not do like that, system after restarting can move at next 5 minutes at once, these next 5 minutes may not be to begin at integral point, and, those the also untreated performance reported datas before database is restarted, to have little time to report, the merger of Fa Shenging is with meaningless like this.
In addition, for form on the performance, day table, menology, chronology, all carry out subregion according to the time, the time range of each concrete partition spans, the user can oneself be provided with, these tables are adopted subregion, reduced from form on the performance importing data, and shown to the sky, analogized in proper order from hour meter importing data to hour meter.Because on physical file, these tables all have been divided into a plurality of according to the time again, the effective like this speed of having accelerated merger data between each stratification.Simultaneously, these tables are carried out another effect of subregion, be to combine existing technology, the cardinal principle of above mentioned prior art is that data are carried out the submeter storage, the present invention has reached same purpose by using partition table, in the performance queries statistic processes, if the time span of query statistic just in time drops in certain subregion, execution speed will be more many soon than not building subregion.
At above-mentioned carried out the corresponding performance data of classified and stored according to different time granularities after, system is the performance statistics that carries out mass performance data as follows.
When carrying out performance statistics, owing to there are four tables, must come clearly the table of which kind of type of this use to finish statistics by software, if the data of adding up comprise the accuracy of current data or performance statistics and need arrive minute, must on performance, add up in the form, for such statistics, the gain of using this method can not bring performance, but when performance statistics statistics to be historical data and statistical accuracy dividing when above, the speed of statistics just will be accelerated greatly.And in reality system, the minimum particle size of statistics seldom reaches branch, and most statistics all will be historical statistics.The program circuit following (seeing also Fig. 4) of statistics:
S310: the granularity of difference counting statistics time started, statistics concluding time, and find wherein minimum granularity;
S320: take out maximum particle size in the performance timestamp table;
S330: maximum particle size among granularity minimum among the step S310 and the step S320 is compared, find the little granularity of granularity to add up employed granularity as this;
S340: according to granularity that this statistics is used among the step S330, determine to adopt corresponding table data store to add up, obtain statistics.
More than disclosed only be several specific embodiment of the present invention, but the present invention is not limited thereto, any those skilled in the art can think variation, all should fall into protection scope of the present invention.
Claims (9)
1, the statistical method of mass performance data in a kind of Element management system, described Element management system comprises database server and application server, and described database server is provided with form on the performance, is used to preserve the performance data that reports, it is characterized in that, comprising:
(1) table data store of different time granularity is set on described database server, described time granularity represent performance data on call time, have inclusion relation between described granularity and the granularity;
(2) described application server will report performance data periodically according to each time granularity require merger after, deposit corresponding granularity data storage list in;
(3) when the mass performance data to described Element management system carries out performance statistics, described application server is retrieved the table data store of corresponding granularity according to the granularity of statistical requirements, obtains the statistics of described performance.
2, the statistical method of mass performance data in a kind of Element management system as claimed in claim 1 is characterized in that step (2) specifically comprises:
(21) after described application server will satisfy the performance data merger of not merger of minimum time granularity requirements, be saved to minimum time granularity corresponding data storage list, described performance data is the performance data in the form on the described performance;
(22) after described application server will satisfy the performance data merger of not merger of other each time granularity requirement, be saved in the corresponding granularity data storage list, described performance data is less than the performance data in the table data store of maximum particle size correspondence in this granularity.
3, the statistical method of mass performance data in a kind of Element management system as claimed in claim 1 or 2 is characterized in that,
Step (1) also is included in a performance timestamp table also is set on the described database server, to preserve the time of the last merger of each time granularity table data store;
Also comprise between step (1) and the step (2) when the system time of described Element management system and the difference of described last merger time during, carry out step (2) at least more than or equal to a predefined time value.
4, the statistical method of mass performance data in a kind of Element management system as claimed in claim 3 is characterized in that described predefined time value is 1 day.
5, the statistical method of mass performance data in a kind of Element management system as claimed in claim 1 or 2 is characterized in that,
Step (1) also comprises:
(11) background task is set in database, described background task is regularly carried out the merger of performance data between each granularity data storage list;
(12) trigger is set in database;
Step (2) also comprises: when database restarts, activate described trigger, begin timing statistics in the modification background task, so that the merger continuously of described background task.。
6, the statistical method of mass performance data in a kind of Element management system as claimed in claim 1 or 2 is characterized in that,
Step (2) also comprises: the performance data in each time granularity table data store is carried out subregion according to different time ranges preserve.
7, the statistical method of mass performance data in a kind of Element management system as claimed in claim 3 is characterized in that described step (3) specifically comprises:
(a) granularity of difference counting statistics time started, statistics concluding time, and find wherein minimum granularity;
(b) take out maximum particle size in the performance timestamp table;
(c) maximum particle size in granularity minimum in the step (a) and the step (b) is compared, find the little granularity of granularity to add up employed granularity as this;
(d) according to granularity that this statistics is used in the step (c), determine to adopt corresponding table data store to add up, obtain statistics.
As the statistical method of mass performance data in claim 1 or the 4 described a kind of Element management systems, it is characterized in that 8, the described time granularity that is provided with in the step (1) comprises hour, fate, the moon, year.
9, the statistical method of mass performance data in a kind of Element management system as claimed in claim 8 is characterized in that described step (2) specifically comprises:
(A) if the current time in system hour arrived integral point greater than one day or current time in system than the last merger in the performance timestamp table, will in the form on the described performance not the performance data of merger hour being after unit carries out merger, be saved in the corresponding hour data storage list, and the time endDate of the last merger of performance timestamp table is set;
(B) fate of judging endDate if carry out step (C), otherwise carries out step (A) whether greater than the fate of the last merger in the described performance timestamp table;
(C) will in the described hour data storage list not the performance data of merger be after unit carries out merger with the sky, be saved in the corresponding day data storage list, and the last merger time D of performance timestamp table be set;
(D) judge and the moon among the D,, otherwise carry out step (A) if carry out step (E) whether greater than the moon number of the last merger in the described performance timestamp table;
(E) will in the described day data storage list not the performance data of merger be after unit carries out merger with the moon, be saved in the corresponding moon table data store, and the last merger time M of performance timestamp table be set;
(F) judge and year among the M,, otherwise carry out step (A) if carry out step (K) whether greater than the year number of the last merger in the described performance timestamp table;
(K) will in the described month table data store not the performance data of merger be after unit carries out merger with the year, be saved in the corresponding annual data storage list, and the last merger time Y of performance timestamp table be set.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2004100738943A CN100484017C (en) | 2004-09-08 | 2004-09-08 | Method for statistics of mass performance data in network element management system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2004100738943A CN100484017C (en) | 2004-09-08 | 2004-09-08 | Method for statistics of mass performance data in network element management system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1747398A true CN1747398A (en) | 2006-03-15 |
| CN100484017C CN100484017C (en) | 2009-04-29 |
Family
ID=36166735
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2004100738943A Expired - Fee Related CN100484017C (en) | 2004-09-08 | 2004-09-08 | Method for statistics of mass performance data in network element management system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100484017C (en) |
Cited By (37)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101197712B (en) * | 2007-10-19 | 2010-06-09 | 中兴通讯股份有限公司 | Method for capturing performance object data in telecommunication network management system |
| CN101819586A (en) * | 2010-03-31 | 2010-09-01 | 中兴通讯股份有限公司 | Intelligent sorting server and method and system for data statistics |
| CN101155380B (en) * | 2006-09-29 | 2010-09-08 | 中国移动通信集团北京有限公司 | Integrating system and method for wireless network test data |
| CN101866361A (en) * | 2010-06-30 | 2010-10-20 | 山东中创软件工程股份有限公司 | Heterogeneous data integration method and device |
| CN101404590B (en) * | 2008-11-19 | 2011-04-13 | 杭州华三通信技术有限公司 | Method for junior webmaster reporting data to superior webmaster, and webmaster equipment |
| CN102045203A (en) * | 2010-12-30 | 2011-05-04 | 华为技术有限公司 | Network management method, device and system |
| CN102104950A (en) * | 2009-12-17 | 2011-06-22 | 中兴通讯股份有限公司 | Method and base station for managing collected property data |
| CN101202656B (en) * | 2006-12-15 | 2011-12-07 | 中兴通讯股份有限公司 | Method and apparatus for resource supervising |
| CN102291269A (en) * | 2011-09-29 | 2011-12-21 | 迈普通信技术股份有限公司 | Data merging processing method |
| CN102413498A (en) * | 2011-11-22 | 2012-04-11 | 大唐移动通信设备有限公司 | Methods and devices for reporting and acquiring performance data files |
| CN103051472A (en) * | 2012-12-17 | 2013-04-17 | 中兴通讯股份有限公司 | Method and device for analyzing key performance indexes of network |
| CN103095516A (en) * | 2011-11-03 | 2013-05-08 | 中国移动通信集团福建有限公司 | Monitoring method and device of network element measurement index delayed uploading |
| WO2013075486A1 (en) * | 2011-11-24 | 2013-05-30 | 中兴通讯股份有限公司 | Method and device for counting in-service rate of network object |
| CN103258009A (en) * | 2013-04-16 | 2013-08-21 | 北京京东尚科信息技术有限公司 | Method and system for acquiring and analyzing method performance data |
| CN103473271A (en) * | 2013-08-20 | 2013-12-25 | 苏州迈科网络安全技术股份有限公司 | Optimized storing method for mass data |
| CN103490949A (en) * | 2013-09-06 | 2014-01-01 | 京信通信系统（中国）有限公司 | Method and device for collecting data information and detecting data information anomalies |
| CN103678402A (en) * | 2012-09-21 | 2014-03-26 | 厦门雅迅网络股份有限公司 | Real-time data statistics method of mass data |
| CN103942287A (en) * | 2014-04-10 | 2014-07-23 | 大唐移动通信设备有限公司 | Mass data processing method, database server and application server |
| CN103955508A (en) * | 2014-04-29 | 2014-07-30 | 安徽中科大国祯信息科技有限责任公司 | Method for layering, storing and inquiring data of curve chart of project control system |
| CN104199945A (en) * | 2014-09-10 | 2014-12-10 | 北京国双科技有限公司 | Data storing method and device |
| CN101997701B (en) * | 2009-08-10 | 2014-12-17 | 中兴通讯股份有限公司 | Data processing method and a device thereof |
| CN104462166A (en) * | 2014-08-28 | 2015-03-25 | 大唐移动通信设备有限公司 | Report generation method and system |
| CN103560921B (en) * | 2013-11-19 | 2017-01-18 | 中国科学院计算机网络信息中心 | Method for merging network streaming data |
| CN107516199A (en) * | 2017-08-01 | 2017-12-26 | 携程旅游信息技术（上海）有限公司 | Container charging method, apparatus and system, equipment, the storage medium of cloud platform |
| CN107644033A (en) * | 2016-07-21 | 2018-01-30 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus that data query is carried out in non-relational database |
| CN107729412A (en) * | 2017-09-26 | 2018-02-23 | 郑州云海信息技术有限公司 | A kind of method and system of the historical performance statistics based on tyre communication |
| CN107885791A (en) * | 2017-10-19 | 2018-04-06 | 北京工业大数据创新中心有限公司 | A kind of sensing data Hierarchical storage apparatus and method |
| CN108228685A (en) * | 2017-06-12 | 2018-06-29 | 深圳市创梦天地科技股份有限公司 | Obtain the method and device of the statistic behavior of data |
| CN108733839A (en) * | 2018-05-25 | 2018-11-02 | 北京神州绿盟信息安全科技股份有限公司 | A kind of statistical method and device of mass data |
| CN108959442A (en) * | 2018-06-13 | 2018-12-07 | 福建天晴数码有限公司 | Based on the streaming real-time computing technique of predefined computation rule, storage medium |
| CN104424236B (en) * | 2013-08-26 | 2018-12-07 | 腾讯科技（深圳）有限公司 | Storage method and device in data acquisition |
| CN109274545A (en) * | 2017-07-17 | 2019-01-25 | 北京航空航天大学 | A kind of cycle storage method based on mixed cloud |
| CN109389022A (en) * | 2017-08-09 | 2019-02-26 | 宏碁股份有限公司 | Method and device for processing image data |
| CN109492008A (en) * | 2018-11-13 | 2019-03-19 | 上海理想信息产业（集团）有限公司 | A kind of network big data design methods and system based on HBase |
| CN110489477A (en) * | 2019-08-27 | 2019-11-22 | 山东浪潮通软信息科技有限公司 | A kind of method of configurable data source superior system automatic butt different from more |
| CN110515952A (en) * | 2019-08-28 | 2019-11-29 | 广州高谱技术有限公司 | A kind of storage method of electric power data |
| CN111241087A (en) * | 2020-03-10 | 2020-06-05 | 天津市普迅电力信息技术有限公司 | Energy data automatic processing method based on distributed computing |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5504873A (en) * | 1989-11-01 | 1996-04-02 | E-Systems, Inc. | Mass data storage and retrieval system |
| CN1275137C (en) * | 2001-09-07 | 2006-09-13 | 中兴通讯股份有限公司 | Mass data processing method and system |
-
2004
- 2004-09-08 CN CNB2004100738943A patent/CN100484017C/en not_active Expired - Fee Related
Cited By (48)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101155380B (en) * | 2006-09-29 | 2010-09-08 | 中国移动通信集团北京有限公司 | Integrating system and method for wireless network test data |
| CN101202656B (en) * | 2006-12-15 | 2011-12-07 | 中兴通讯股份有限公司 | Method and apparatus for resource supervising |
| CN101197712B (en) * | 2007-10-19 | 2010-06-09 | 中兴通讯股份有限公司 | Method for capturing performance object data in telecommunication network management system |
| CN101404590B (en) * | 2008-11-19 | 2011-04-13 | 杭州华三通信技术有限公司 | Method for junior webmaster reporting data to superior webmaster, and webmaster equipment |
| CN101997701B (en) * | 2009-08-10 | 2014-12-17 | 中兴通讯股份有限公司 | Data processing method and a device thereof |
| CN102104950A (en) * | 2009-12-17 | 2011-06-22 | 中兴通讯股份有限公司 | Method and base station for managing collected property data |
| CN101819586A (en) * | 2010-03-31 | 2010-09-01 | 中兴通讯股份有限公司 | Intelligent sorting server and method and system for data statistics |
| CN101866361A (en) * | 2010-06-30 | 2010-10-20 | 山东中创软件工程股份有限公司 | Heterogeneous data integration method and device |
| CN102045203B (en) * | 2010-12-30 | 2013-01-30 | 华为技术有限公司 | Network management method, device and system |
| CN102045203A (en) * | 2010-12-30 | 2011-05-04 | 华为技术有限公司 | Network management method, device and system |
| CN102291269A (en) * | 2011-09-29 | 2011-12-21 | 迈普通信技术股份有限公司 | Data merging processing method |
| CN103095516A (en) * | 2011-11-03 | 2013-05-08 | 中国移动通信集团福建有限公司 | Monitoring method and device of network element measurement index delayed uploading |
| CN103095516B (en) * | 2011-11-03 | 2015-11-25 | 中国移动通信集团福建有限公司 | The monitoring method that the time delay of a kind of network element measurement index is uploaded and equipment |
| CN102413498A (en) * | 2011-11-22 | 2012-04-11 | 大唐移动通信设备有限公司 | Methods and devices for reporting and acquiring performance data files |
| CN102413498B (en) * | 2011-11-22 | 2014-04-02 | 大唐移动通信设备有限公司 | Methods and devices for reporting and acquiring performance data files |
| WO2013075486A1 (en) * | 2011-11-24 | 2013-05-30 | 中兴通讯股份有限公司 | Method and device for counting in-service rate of network object |
| CN103139807A (en) * | 2011-11-24 | 2013-06-05 | 中兴通讯股份有限公司 | Method and device for calculating network object service rates |
| CN103139807B (en) * | 2011-11-24 | 2017-11-10 | 中兴通讯股份有限公司 | Statistical method and device of the network object in the rate that takes |
| CN103678402B (en) * | 2012-09-21 | 2018-10-16 | 厦门雅迅网络股份有限公司 | A kind of method of data real-time statistics under mass data |
| CN103678402A (en) * | 2012-09-21 | 2014-03-26 | 厦门雅迅网络股份有限公司 | Real-time data statistics method of mass data |
| CN103051472A (en) * | 2012-12-17 | 2013-04-17 | 中兴通讯股份有限公司 | Method and device for analyzing key performance indexes of network |
| CN103051472B (en) * | 2012-12-17 | 2016-09-07 | 中兴通讯股份有限公司 | The analysis method and device of network key performance indications |
| CN103258009B (en) * | 2013-04-16 | 2016-05-18 | 北京京东尚科信息技术有限公司 | Obtain the method and system with analytical method performance data |
| CN103258009A (en) * | 2013-04-16 | 2013-08-21 | 北京京东尚科信息技术有限公司 | Method and system for acquiring and analyzing method performance data |
| CN103473271A (en) * | 2013-08-20 | 2013-12-25 | 苏州迈科网络安全技术股份有限公司 | Optimized storing method for mass data |
| CN104424236B (en) * | 2013-08-26 | 2018-12-07 | 腾讯科技（深圳）有限公司 | Storage method and device in data acquisition |
| CN103490949A (en) * | 2013-09-06 | 2014-01-01 | 京信通信系统（中国）有限公司 | Method and device for collecting data information and detecting data information anomalies |
| CN103560921B (en) * | 2013-11-19 | 2017-01-18 | 中国科学院计算机网络信息中心 | Method for merging network streaming data |
| CN103942287A (en) * | 2014-04-10 | 2014-07-23 | 大唐移动通信设备有限公司 | Mass data processing method, database server and application server |
| CN103955508B (en) * | 2014-04-29 | 2017-04-12 | 安徽中科大国祯信息科技有限责任公司 | Method for layering, storing and inquiring data of curve chart of project control system |
| CN103955508A (en) * | 2014-04-29 | 2014-07-30 | 安徽中科大国祯信息科技有限责任公司 | Method for layering, storing and inquiring data of curve chart of project control system |
| CN104462166A (en) * | 2014-08-28 | 2015-03-25 | 大唐移动通信设备有限公司 | Report generation method and system |
| CN104199945A (en) * | 2014-09-10 | 2014-12-10 | 北京国双科技有限公司 | Data storing method and device |
| CN107644033B (en) * | 2016-07-21 | 2021-08-17 | 阿里巴巴集团控股有限公司 | Method and equipment for querying data in non-relational database |
| CN107644033A (en) * | 2016-07-21 | 2018-01-30 | 阿里巴巴集团控股有限公司 | A kind of method and apparatus that data query is carried out in non-relational database |
| CN108228685A (en) * | 2017-06-12 | 2018-06-29 | 深圳市创梦天地科技股份有限公司 | Obtain the method and device of the statistic behavior of data |
| CN109274545A (en) * | 2017-07-17 | 2019-01-25 | 北京航空航天大学 | A kind of cycle storage method based on mixed cloud |
| CN107516199A (en) * | 2017-08-01 | 2017-12-26 | 携程旅游信息技术（上海）有限公司 | Container charging method, apparatus and system, equipment, the storage medium of cloud platform |
| CN109389022A (en) * | 2017-08-09 | 2019-02-26 | 宏碁股份有限公司 | Method and device for processing image data |
| CN107729412A (en) * | 2017-09-26 | 2018-02-23 | 郑州云海信息技术有限公司 | A kind of method and system of the historical performance statistics based on tyre communication |
| CN107885791A (en) * | 2017-10-19 | 2018-04-06 | 北京工业大数据创新中心有限公司 | A kind of sensing data Hierarchical storage apparatus and method |
| CN108733839A (en) * | 2018-05-25 | 2018-11-02 | 北京神州绿盟信息安全科技股份有限公司 | A kind of statistical method and device of mass data |
| CN108959442A (en) * | 2018-06-13 | 2018-12-07 | 福建天晴数码有限公司 | Based on the streaming real-time computing technique of predefined computation rule, storage medium |
| CN109492008A (en) * | 2018-11-13 | 2019-03-19 | 上海理想信息产业（集团）有限公司 | A kind of network big data design methods and system based on HBase |
| CN110489477A (en) * | 2019-08-27 | 2019-11-22 | 山东浪潮通软信息科技有限公司 | A kind of method of configurable data source superior system automatic butt different from more |
| CN110489477B (en) * | 2019-08-27 | 2023-03-28 | 浪潮通用软件有限公司 | Method for automatically butting configurable data source with multiple different superior systems |
| CN110515952A (en) * | 2019-08-28 | 2019-11-29 | 广州高谱技术有限公司 | A kind of storage method of electric power data |
| CN111241087A (en) * | 2020-03-10 | 2020-06-05 | 天津市普迅电力信息技术有限公司 | Energy data automatic processing method based on distributed computing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN100484017C (en) | 2009-04-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN1747398A (en) | Mass performance data statistical method in network element management system | |
| CN110555012B (en) | Data migration method and device | |
| CN1095120C (en) | Computer system having client-server architecture | |
| US20180285414A1 (en) | Database management system cluster node subtasking data query | |
| US9703824B2 (en) | Managing a distributed database | |
| US20110153662A1 (en) | Managing queries | |
| WO2020134609A1 (en) | Data storage method and apparatus | |
| EP3285187B1 (en) | Optimized merge-sorting of data retrieved from parallel storage units | |
| CN109271435A (en) | A kind of data pick-up method and system for supporting breakpoint transmission | |
| CN101042673A (en) | Software testing system and testing method | |
| CN1858735A (en) | Method for processing mass data | |
| CN1655513A (en) | Method for realizing configuration data real-time synchronization for network management system and network element device | |
| CN101059810A (en) | System and method for implementing automatic optimization of data base system | |
| CN101937474A (en) | Mass data query method and device | |
| CN1901568A (en) | Method for realizing historical property collection in net managing system | |
| CN1929488A (en) | Method and device for operation in multiple pages based on server paging | |
| CN1866251A (en) | Method and apparatus for reducing paging data retrieve time | |
| CN110647542B (en) | Data acquisition method and device | |
| CN113568931A (en) | Route analysis system and method for data access request | |
| CN112181498B (en) | Concurrency control method, device and equipment | |
| KR101830504B1 (en) | In-Memory DB Connection Support Type Scheduling Method and System for Real-Time Big Data Analysis in Distributed Computing Environment | |
| CN101388790B (en) | Method for importing radio network performance data | |
| CN110825732A (en) | Data query method and device, computer equipment and readable storage medium | |
| CN1913448A (en) | Method of synchronous operating network element by network management and its system | |
| CN1770798A (en) | Method for traffic data gathering and analysis statistic |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| ASS | Succession or assignment of patent right |
Owner name: INST OF TELECOMMUNICATION SCIENCE AND TECHNOLGOY Free format text: FORMER OWNER: DATANG MOBILE COMMUNICATION EQUIPMENT CO., LTD. Effective date: 20110509 Free format text: FORMER OWNER: DATANG MOBILE COMMUNICATION EQUIPMENT CO., LTD., SHANGHAI |
|
| C41 | Transfer of patent application or patent right or utility model | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20110509 Address after: 100083 No. 40, Haidian District, Beijing, Xueyuan Road Patentee after: CHINA ACADEMY OF TELECOMMUNICATIONS TECHNOLOGY Address before: 100083 No. 40, Haidian District, Beijing, Xueyuan Road Co-patentee before: SHANGHAI DATANG MOBILE COMMUNICATIONS EQUIPMENT Co.,Ltd. Patentee before: DATANG MOBILE COMMUNICATIONS EQUIPMENT Co.,Ltd. |
|
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20060315 Assignee: TENTH INSTITUTE OF TELECOMMUNICATIONS TECHNOLOGY Assignor: CHINA ACADEMY OF TELECOMMUNICATIONS TECHNOLOGY Contract record no.: 2015990000346 Denomination of invention: Mass performance data statistical method in network element management system Granted publication date: 20090429 License type: Exclusive License Record date: 20150520 |
|
| LICC | Enforcement, change and cancellation of record of contracts on the licence for exploitation of a patent or utility model | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 100083 No. 40, Haidian District, Beijing, Xueyuan Road Patentee after: CHINA ACADEMY OF TELECOMMUNICATIONS TECHNOLOGY Address before: 100083 No. 40, Haidian District, Beijing, Xueyuan Road Patentee before: CHINA ACADEMY OF TELECOMMUNICATIONS TECHNOLOGY |
|
| CP01 | Change in the name or title of a patent holder | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20210607 Address after: 100085 1st floor, building 1, yard 5, Shangdi East Road, Haidian District, Beijing Patentee after: DATANG MOBILE COMMUNICATIONS EQUIPMENT Co.,Ltd. Address before: 100083 No. 40, Haidian District, Beijing, Xueyuan Road Patentee before: CHINA ACADEMY OF TELECOMMUNICATIONS TECHNOLOGY |
|
| TR01 | Transfer of patent right | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20090429 |