CN106411634B - A kind of data life period monitoring method and device - Google Patents
A kind of data life period monitoring method and device Download PDFInfo
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- CN106411634B CN106411634B CN201610739739.3A CN201610739739A CN106411634B CN 106411634 B CN106411634 B CN 106411634B CN 201610739739 A CN201610739739 A CN 201610739739A CN 106411634 B CN106411634 B CN 106411634B
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000012545 processing Methods 0.000 claims description 15
- 238000012423 maintenance Methods 0.000 claims description 14
- 238000012800 visualization Methods 0.000 claims description 11
- 238000009795 derivation Methods 0.000 claims description 7
- 125000004122 cyclic group Chemical group 0.000 claims 2
- 238000013508 migration Methods 0.000 abstract description 3
- 230000005012 migration Effects 0.000 abstract description 3
- 230000008447 perception Effects 0.000 abstract description 3
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- 238000013500 data storage Methods 0.000 description 5
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- CRFWCCGPRXKZSM-UHFFFAOYSA-N 3,4-methylenedioxy-n-methylphentermine Chemical compound CNC(C)(C)CC1=CC=C2OCOC2=C1 CRFWCCGPRXKZSM-UHFFFAOYSA-N 0.000 description 2
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/06—Generation of reports
- H04L43/067—Generation of reports using time frame reporting
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/04—Processing captured monitoring data, e.g. for logfile generation
- H04L43/045—Processing captured monitoring data, e.g. for logfile generation for graphical visualisation of monitoring data
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Abstract
The present invention provides a kind of data life period monitoring method and device, by obtaining storage time and calling frequency of the data in preset duration, according to the storage time and call frequency, the life cycle phase of the data is divided, to select corresponding data store strategy for each life cycle phase of the data.Data life period monitoring scheme of the invention, data mode can be monitored, real-time, accurate perception data state, to take corresponding storage strategy, guide data migration, the data store strategy determined according to the different life stage characteristic of data are more reasonable.
Description
Technical field
The present invention relates to fields of communication technology, and in particular to a kind of data life period monitoring method and device.
Background technique
The data volume of telecommunications industry keeps growing at top speed all the year round, brings problems to managing and maintaining for data, such as stores
Space, Data Preservation Policy etc..With the increase of data storage capacity, the data of substantial amounts how are saved, and how effectively
Data are quickly obtained to rate from huge data, are that faced one of the storage of current data and data maintenance greatly chooses
War.
The form of data is numerous, such as has text, voice, image, pattern and its mixing pattern, for storing data
Storage medium is numerous, for example, disk, CD, hard disk etc..The capacity of various storage mediums, purchase cost are also different.How basis
The characteristics of data, reasonably selects storage medium, is that data maintenance has to consider the problems of.
Currently, it is usually the significance level, data call situation, the reality of enterprise according to data that user, which carries out data maintenance,
The factors such as situation voluntarily determine data store strategy by user, this data maintenance mode does not account in conjunction with purchase cost
The characteristics of data life period, the experience for each user that places one's entire reliance upon, random larger, science, reasonability are poor.
And the life cycle of data is divided into different phase, performance, availability, preservation of each lifecycle stage data etc. are wanted
Ask also different, at present in terms of data maintenance, formulate data store strategy according only to corporate specification, relative monitor system and
Tool is deficient, is not monitored to each life cycle phase of data, can not also have to the stage of data life period
Effect divides, correspondingly, accurately monitoring data also can not just can not instruct to formulate and close in the storage state of each life cycle phase
The data store strategy of reason.
Therefore, a kind of data life period monitoring scheme is needed to solve the above technical problems.
Summary of the invention
The present invention aiming at the above shortcomings existing in the prior art, provides a kind of data life period monitoring method and dress
It sets, at least partly to solve the problems, such as that existing data store strategy is unreasonable.
The present invention is in order to solve the above technical problems, adopt the following technical scheme that
The present invention provides a kind of data life period monitoring method, comprising:
Obtain storage time and calling frequency of the data in preset duration；
According to the storage time and frequency is called, divides the life cycle phase of the data.
Further, the storage time of the data in preset duration that obtain is with before calling frequency, and the method is also
Include:
Maintenance data are treated to classify；
The storage time that data are obtained in preset duration and calling frequency according to the storage time and call frequency
Rate divides the life cycle phase of the data, specifically includes:
It obtains storage time of all types of data in preset duration and calls frequency, when according to the storage of all types of data
Between and call frequency, respectively all types of data divide life cycle phases.
Preferably, described according to the storage time and calling frequency, the life cycle phase of the data is divided, specifically
Include:
Various dimensions discrimination model is established, to the model derivation, obtains the critical of each life cycle phase of the data
Point；
The life cycle of the data is divided according to the critical point of each life cycle phase of the data；
Wherein, the model are as follows:
T is storage time, and f is to call frequency, b1、c1、b2、c2For constant.
Further, it is described according to the storage time and call frequency, divide the data life cycle phase it
Afterwards, the method also includes:
Show the critical point of each life cycle phase of the data.
Further, it is described according to the storage time and call frequency, divide the data life cycle phase it
Afterwards, the method also includes:
According to the storage time and frequency is called, the life cycle phase of the graphical data obtains the data
Life cycle curve, and show the Life cycle curve of the data.
The present invention also provides a kind of data life period monitoring servers, including obtain module and processing module,
The acquisition module is used for, and obtains storage time and calling frequency of the data in preset duration；
The processing module is used for, and according to the storage time and is called frequency, is divided the life cycle rank of the data
Section.
Further, the data life period monitoring server, further includes categorization module, and the categorization module is used for,
Before the acquisition module obtains storage time and calling frequency of the data in preset duration, treats maintenance data and divided
Class；
The acquisition module is specifically used for, and obtains storage time of all types of data in preset duration and calls frequency；
The processing module is specifically used for, and according to the storage time of all types of data and calls frequency, respectively all types of
Data divide life cycle phase.
Preferably, the processing module is specifically used for, and establishes various dimensions discrimination model, to the model derivation, obtains institute
The critical point of each life cycle phase of data is stated, and according to the critical point of each life cycle phase of data division
The life cycle of data；Wherein, the model are as follows:
T is storage time, and f is to call frequency, b1、c1、b2、c2For constant.
Further, the data life period monitoring server further includes visualization model,
The visualization model is used for, and according to the storage time and frequency is called in the processing module, described in division
After the life cycle phase of data, the critical point of each life cycle phase of the data is shown.
Further, the visualization model is also used to, and according to the storage time and calls frequency, the graphical number
According to life cycle phase, obtain the Life cycle curve of the data, and show the Life cycle curve of the data.
The data of different phase its performances, availability, preservation etc. require different, and the present invention is by obtaining data when default
Storage time and calling frequency in length, according to the storage time and call frequency, divide the life cycle phase of the data,
To select corresponding data store strategy for each life cycle phase of the data.Data life period prison of the invention
Prosecutor case can be monitored data mode, and real-time, accurate perception data state refers to take corresponding storage strategy
Data Migration is led, the data store strategy determined according to the different life stage characteristic of data is more reasonable.
Detailed description of the invention
Fig. 1 is that data life period provided in an embodiment of the present invention monitors flow chart；
Fig. 2 is data life period distribution schematic diagram provided in an embodiment of the present invention；
Fig. 3 is the structural schematic diagram of data life period monitoring server provided in an embodiment of the present invention.
Specific embodiment
Below in conjunction with the attached drawing in the present invention, clear, complete description is carried out to the technical solution in the present invention, is shown
So, described embodiment is a part of the embodiments of the present invention, instead of all the embodiments.Based on the implementation in the present invention
Example, those of ordinary skill in the art's every other embodiment obtained without making creative work, all belongs to
In the scope of protection of the invention.
Below in conjunction with Fig. 1,2, data life period monitoring process of the invention is described in detail.
As shown in Figure 1, data life period monitoring process the following steps are included:
Step 101, storage time and calling frequency of the data in preset duration are obtained.
Specifically, the data are the historical data in preset duration, data can be obtained from data warehouse default
Storage time and calling frequency in duration.Frequency is called to refer to the read-write frequency of data.Preset duration can be 24 months,
In the embodiment of the present invention, the unit of storage time is the moon, as shown in Fig. 2, the historical data in monitoring 17 months, i.e. preset duration
It is 17 months.
Step 102, according to the storage time and calling frequency, the life cycle phase of the data is divided.
Since data storage capacity is related to data call frequency and time data memory, when can pass through storage
Between, frequency of use two indices reflection data storage capacity the case where.Data in different data life cycle phase are in property
Energy, availability, holding time etc. require difference, and the embodiment of the present invention, will according to the storage time and calling frequency of data
The life cycle of data is divided into multiple stages, for using different storage plans for the different data life period stages
Slightly.
As shown in connection with fig. 2, the life cycle phase of data includes: near line stage, on-line stage and archiving phase.
Under normal conditions, in the initial stage of data life period (i.e. near line stage), data call frequency is lower.It is raw in data
Order the period mid-term (i.e. on-line stage), over time, data growth rate promoted, data call frequency variation compared with
Greatly.In the later period (i.e. archiving phase) of data life period, over time, the variation of data call frequency is not obvious,
Illustrate that the business that data are carried enters the desaturation phase.
Specifically, can differentiate MDMP (Multi-perspective and multi- by establishing various dimensions
Dimensional) model obtains the critical point of each life cycle phase of data to the MDMP model derivation, and according to institute
The critical point for stating each life cycle phase of data divides the life cycle of the data.As shown in Fig. 2, abscissa is data
Storage time, ordinate are the calling frequency of data, and curve is data life period curve.Figure it is seen that the near line stage
Critical point with on-line stage is A, and the critical point of on-line stage and archiving phase is B, and the corresponding storage time of A point is March, B
The corresponding storage time of point is October, and therefore, the 0-2 month is the near line stage, and the 3-9 month is on-line stage, and the 10-16 month is filing rank
Section.
The MPMD model are as follows:
Wherein, t is storage time, and f is to call frequency, and b1, c1, b2, c2 are constant.
The calculative strategy of aforementioned four constant are as follows:
The storing data of (such as 24 months) in the preset duration of homogeneous data is chosen as training data, and with recurrence letter
Number Logistic (x) is respectively to storage time t, and frequency f is called to be analyzed, and finds out the constant value of b1, c1, b2, c2.That is:
The S-type distribution of function curve differentiates to two inflection point data, obtains b1, c1
Linear equation in two unknowns group, i.e. constant b1、c1.Similarly, computational constant b2、c2。
To MPMD model derivation, the inflection point A and B of function v is obtained, the inflection point is the critical of adjacent life cycle phase
Point.
MPMD model can evaluate things from multiple angles and multiple dimensions, so that things be accurately positioned.
It should be noted that after step 102, can be selected for each life cycle phase of the data corresponding
Data store strategy.
Specifically, usually business just starts, and data call is less frequent in the near line stage of data life period,
It can select that purchase cost is relatively low, lower storage medium storing data (the i.e. nearline storage plan of reading data sensitivity
Slightly).In the on-line stage of data life period, the frequency of use of data is higher, amplification is larger, needs sensitive using reading data
Degree is higher, data reading speed is very fast, the preferable storage medium storing data of quality, to ensure that the high availability of data (exists
Line storage strategy).In the archiving phase of data life period, usually the desaturation stage of business, data importance can be gradually
It reduces, calls frequency that can decline therewith, at this point it is possible to the lower storage medium storing data of reading data sensitivity is selected, with
Reduce the resource overhead (i.e. filing storage strategy) of data maintenance cost.
Through the above steps as can be seen that the present invention is by obtaining storage time and calling of the data in preset duration
Frequently, according to the storage time and calling frequency, the life cycle phase of the data is divided, so as to for each of the data
Life cycle phase selects corresponding data store strategy.Data life period monitoring scheme of the invention, can be to data shape
State is monitored, real-time, accurate perception data state, so that corresponding storage strategy is taken, guide data migration, according to data
The data store strategy determined of different life stage characteristic it is more reasonable.
Further, it can also include the following steps 100 before step 101:
Step 100, maintenance data are treated to classify.
Specifically, data to be safeguarded can classify according to following classification standard part: client domain, public domain, service-domain, product
Domain, resource domains, marketing domain, affiliate domain, business administration domain；The detailed data of supporting type data, using data,
Prototype data summarizes data；Program software, daily record data, process data, the ephemeral data of system operation data.
Correspondingly, being monitored respectively in subsequent step 101-102 for all types of data, that is, obtain all types of
Storage time of the data in preset duration and calling frequency according to the storage time of all types of data and call frequency, respectively
Life cycle phase is divided for all types of data.
Further, after step 102, data life period monitoring process can with the following steps are included:
Step 103, the critical point of each life cycle phase of the data is shown.
Specifically, by storage time and calling the two parameters of frequency in the form of a two-dimensional array, data are indicated
The critical point A and B of life cycle phase, and showed by visualization model.
Step 104, according to the storage time and calling frequency, the life cycle phase of the graphical data is obtained
The Life cycle curve of the data, and show the Life cycle curve of the data.
Specifically, visualization model is obtained by the storage time of all types of data and after calling frequency graphical treatment
The Life cycle curve (as shown in Figure 2) of the type data, and show.The Life cycle curve of graphics data it is specific
Implementation belongs to the prior art, and details are not described herein.
It should be noted that step 103 can be individually performed or step 104 is individually performed, step 103 can also be both executed
Step 104 is executed again, and the execution sequence of step 103 and step 104 is unlimited, can also synchronize execution.
Data life period monitoring scheme of the invention chooses storage time and calls frequency two indices to mass data
It is monitored, in conjunction with MPMD model, is handled using MPMD algorithm analysis, the trend and shape of the life cycle phase of visualized data
State changes inflection point.Relative to according to the scheme of corporate specification maintenance data, the present invention is using information-based means, in conjunction with O&M at present
System actual conditions, it is more flexible, improve the treatment effeciency of data maintenance.
Data life period monitoring scheme of the invention monitors the growth trends of core data in operational system, by obtaining
It takes storage time, call two key indexes of frequency, show that data in the distribution situation of different times, realize data life period
Automatic management.
Based on the same technical idea, the embodiment of the present invention also provides a kind of data life period monitoring server, such as Fig. 3
Shown, which may include obtaining module 31 and processing module 32.
It obtains module 31 to be used for, obtains storage time and calling frequency of the data in preset duration.
Processing module 32 is used for, and according to the storage time and is called frequency, is divided the life cycle phase of the data.
Further, the data life period monitoring server further includes categorization module 33, and categorization module 33 is used for,
It obtains module 31 and obtains storage time of the data in preset duration with before calling frequency, treat maintenance data and classify.
It obtains module 31 to be specifically used for, obtain storage time of all types of data in preset duration and calls frequency.
Processing module 32 is specifically used for, and according to the storage time of all types of data and calls frequency, respectively all types of numbers
According to division life cycle phase.
Preferably, processing module 32 is specifically used for, and establishes various dimensions discrimination model, to the model derivation, obtains described
The critical point of each life cycle phase of data, and the number is divided according to the critical point of each life cycle phase of the data
According to life cycle；Wherein, the model are as follows:t
For storage time, f is to call frequency, b1、c1、b2、c2For constant.
Further, the data life period monitoring server further includes visualization model 34, and visualization model 34 is used
In, according to the storage time and frequency is called in processing module 32, after the life cycle phase for dividing the data, display
The critical point of each life cycle phase of the data.
Further, visualization model 34 is also used to, and according to the storage time and calls frequency, the graphical data
Life cycle phase, obtain the Life cycle curve of the data, and show the Life cycle curve of the data.
It is understood that the principle that embodiment of above is intended to be merely illustrative of the present and the exemplary implementation that uses
Mode, however the present invention is not limited thereto.For those skilled in the art, essence of the invention is not being departed from
In the case where mind and essence, various changes and modifications can be made therein, these variations and modifications are also considered as protection scope of the present invention.
Claims (8)
1. a kind of data life period monitoring method, which is characterized in that the described method includes:
Obtain storage time and calling frequency of the data in preset duration；
According to the storage time and frequency is called, divides the life cycle phase of the data；
It is described according to the storage time and call frequency, divide the life cycle phase of the data, specifically include:
Various dimensions discrimination model is established, to the model derivation, obtains the critical point of each life cycle phase of the data；
The life cycle of the data is divided according to the critical point of each life cycle phase of the data；
Wherein, the model are as follows:
T is storage time, and f is to call frequency, b1、c1、b2、c2For constant.
2. the method as described in claim 1, which is characterized in that the storage time and tune for obtaining data in preset duration
Before frequency, the method also includes:
Maintenance data are treated to classify；
The storage time that data are obtained in preset duration and calling frequency according to the storage time and call frequency,
The life cycle phase for dividing the data, specifically includes:
Obtain storage time of all types of data in preset duration and call frequency, according to the storage time of all types of data and
Frequency is called, respectively all types of data divide life cycle phase.
3. the method as described in claim 1, which is characterized in that it is described according to the storage time and calling frequency, divide institute
After the life cycle phase for stating data, the method also includes:
Show the critical point of each life cycle phase of the data.
4. method as claimed in claim 3, which is characterized in that it is described according to the storage time and calling frequency, divide institute
After the life cycle phase for stating data, the method also includes:
According to the storage time and frequency is called, the life cycle phase of the graphical data obtains the life of the data
Cyclic curve is ordered, and shows the Life cycle curve of the data.
5. a kind of data life period monitoring server, which is characterized in that including obtaining module and processing module,
The acquisition module is used for, and obtains storage time and calling frequency of the data in preset duration；
The processing module is used for, and according to the storage time and is called frequency, is divided the life cycle phase of the data；
The processing module is specifically used for, and establishes various dimensions discrimination model, to the model derivation, obtains each life of the data
The critical point in the phase of the cycles is ordered, and divides the Life Cycle of the data according to the critical point of each life cycle phase of the data
Phase；Wherein, the model are as follows:T is storage
Time, f are to call frequency, b1、c1、b2、c2For constant.
6. data life period monitoring server as claimed in claim 5, which is characterized in that it further include categorization module, it is described
Categorization module is used for, and before the acquisition module obtains storage time and calling frequency of the data in preset duration, is treated
Maintenance data are classified；
The acquisition module is specifically used for, and obtains storage time of all types of data in preset duration and calls frequency；
The processing module is specifically used for, and according to the storage time of all types of data and calls frequency, respectively all types of data
Divide life cycle phase.
7. data life period monitoring server as claimed in claim 5, which is characterized in that it further include visualization model,
The visualization model is used for, and according to the storage time and is called frequency in the processing module, is divided the data
Life cycle phase after, show the critical point of each life cycle phase of the data.
8. data life period monitoring server as claimed in claim 7, which is characterized in that the visualization model is also used
According to the storage time and calling frequency, the life cycle phase of the graphical data obtains the life of the data
Cyclic curve, and show the Life cycle curve of the data.
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| CN107844275A (en) * | 2017-11-22 | 2018-03-27 | 郑州云海信息技术有限公司 | A kind of moving method of data, device and medium |
| CN110068767B (en) * | 2019-04-17 | 2024-06-21 | 上海蔚来汽车有限公司 | Power battery data monitoring method, system and device |
| CN110716926B (en) * | 2019-09-06 | 2022-09-20 | 未鲲(上海)科技服务有限公司 | Periodic view data generation method and device, computer equipment and storage medium |
| CN110929983B (en) * | 2019-10-17 | 2023-10-20 | 辽宁中医药大学 | Management method and system for reading resources and service life cycle |
| CN112035404B (en) * | 2020-08-28 | 2023-02-10 | 康键信息技术（深圳）有限公司 | Medical data monitoring and early warning method, device, equipment and storage medium |
| CN112365244B (en) * | 2020-11-27 | 2024-04-26 | 深圳前海微众银行股份有限公司 | Data life cycle management method and device |
| CN114857067B (en) * | 2022-05-20 | 2023-08-11 | 北京一诺先科装备技术有限公司 | Full life cycle monitoring and management method and system for large turbine compressor |
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