CN111949503A - Database management method, database management device, computing equipment and media - Google Patents
Database management method, database management device, computing equipment and media Download PDFInfo
- Publication number
- CN111949503A CN111949503A CN202010822811.5A CN202010822811A CN111949503A CN 111949503 A CN111949503 A CN 111949503A CN 202010822811 A CN202010822811 A CN 202010822811A CN 111949503 A CN111949503 A CN 111949503A
- Authority
- CN
- China
- Prior art keywords
- capacity
- partition
- database
- data
- growth rate
- 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
- 238000007726 management method Methods 0.000 title abstract description 26
- 238000012544 monitoring process Methods 0.000 claims abstract description 61
- 238000000034 method Methods 0.000 claims abstract description 40
- 238000005192 partition Methods 0.000 claims description 102
- 238000004590 computer program Methods 0.000 claims description 16
- 238000004140 cleaning Methods 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000000007 visual effect Effects 0.000 claims description 6
- 238000010586 diagram Methods 0.000 description 12
- 238000012545 processing Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 6
- 238000007405 data analysis Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 238000007781 pre-processing Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000004806 packaging method and process Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 239000000758 substrate Substances 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 108010001267 Protein Subunits Proteins 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- ONUFESLQCSAYKA-UHFFFAOYSA-N iprodione Chemical compound O=C1N(C(=O)NC(C)C)CC(=O)N1C1=CC(Cl)=CC(Cl)=C1 ONUFESLQCSAYKA-UHFFFAOYSA-N 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3452—Performance evaluation by statistical analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/323—Visualisation of programs or trace data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/324—Display of status information
- G06F11/327—Alarm or error message display
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/80—Database-specific techniques
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Hardware Design (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present disclosure provides a database management method, which can be used in the financial field or other fields. The method comprises the following steps: acquiring monitoring index data of a plurality of database tables; respectively counting the monitoring index data of each database table to obtain the statistical data of each database table; calculating the capacity growth rate of each database table according to the statistical data of each database table; and generating alarm information under the condition that the capacity growth rate exceeds a preset growth rate threshold value. The present disclosure also provides a management apparatus of a database, a computing device and a computer storage medium.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for managing a database, a computing device, and a computer storage medium.
Background
In recent years, with popularization and rapid development of banking, the scale of a data center is more and more complex, and the capacity of a database tends to increase rapidly. On one hand, the cost input of the storage equipment is increased due to excessive storage consumption, on the other hand, the pressure of daily operation and maintenance is increased, and how to perform monitoring management and optimization on the capacity of the database table becomes an important subject of database professional research.
The existing database monitoring scheme is limited to monitoring macroscopic indexes such as the whole condition of a database, a table space, a data disk and the like, and cannot track historical capacity and lack a trend evaluation basis.
Disclosure of Invention
One aspect of the present disclosure provides a database management method, including: acquiring monitoring index data of a plurality of database tables; respectively counting the monitoring index data of each database table to obtain the statistical data of each database table; calculating the capacity growth rate of each database table according to the statistical data of each database table; and generating alarm information under the condition that the capacity growth rate exceeds a preset growth rate threshold value.
Optionally, the database table includes a partition table and/or a non-partition table, where the monitoring index data of the partition table includes a table name, partition names of partitions in the partition table, and a capacity size of each partition at each monitoring time point, and the monitoring index data of the non-partition table includes a table name and a capacity size of the non-partition table at each monitoring time point.
Optionally, the performing statistics on the monitoring index data of each database table according to the monitoring index data to obtain statistical data of each database table includes: according to the monitoring index data, determining the statistical data of the partition capacity of each partition in the partition table and the statistical data of the total capacity of all the partitions in the partition table aiming at each partition table; and/or determining statistical data of the table capacity of the non-partitioned table for each non-partitioned table according to the monitoring index data.
Optionally, the calculating a capacity growth rate of each database table according to the statistical data of each database table includes: aiming at each partition table, determining the capacity growth rate of each partition according to the statistical data of the partition capacity of each partition in the partition table, and determining the capacity growth rate of the partition table according to the statistical data of the total capacity of all the partitions in the partition table; and/or determining the capacity growth rate of the non-partition table according to the statistical data of the table capacity of the non-partition table aiming at each non-partition table.
Optionally, the method further comprises: generating a capacity change curve according to the statistical data; and predicting the future capacity change of the database table according to the capacity change curve.
Optionally, the method further comprises: determining a data cleaning period according to the periodical fluctuation change of the capacity change curve; and performing data cleaning operation on the database table according to the data cleaning period.
Optionally, the method further comprises: generating a visual view according to the statistical data; and displaying the visual view.
Another aspect of the present disclosure provides a database management apparatus, including: the acquisition module is used for acquiring monitoring index data of a plurality of database tables; the statistical module is used for respectively carrying out statistics on the monitoring index data of each database table to obtain statistical data of each database table; the calculation module is used for calculating the capacity growth rate of each database table according to the statistical data of each database table; and the alarm module is used for generating alarm information under the condition that the capacity growth rate exceeds a preset growth rate threshold value.
Another aspect of the disclosure provides a computing device comprising: one or more processors; storage means for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
According to the management method of the database, the meter capacity can be automatically collected and analyzed, personnel are not needed to participate, and a large amount of repeated manual operation is avoided. And the capacity management and monitoring of the table level can be realized, and the defect of default capacity monitoring indexes of the existing database table is overcome.
Drawings
For a more complete understanding of the present disclosure and the advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which:
fig. 1 schematically shows a management method of a database and a system architecture of a management apparatus of a database according to an embodiment of the present disclosure;
FIG. 2 schematically shows a flow chart of a method of managing a database according to an embodiment of the present disclosure;
FIG. 3 schematically shows a flow chart of a method of managing a database according to another embodiment of the present disclosure;
FIG. 4 schematically shows a flow chart of a method of managing a database according to another embodiment of the present disclosure;
fig. 5 schematically shows a block diagram of a management device of a database according to an embodiment of the present disclosure; and
FIG. 6 schematically shows a block diagram of a computer system suitable for implementing the above described method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
The embodiment of the disclosure provides a database management method and a database management device capable of applying the method. The method comprises the steps of acquiring monitoring index data of a database table; respectively counting the monitoring index data of each database table according to the monitoring index data to obtain the statistical data of each database table; calculating the capacity growth rate of each database table according to the statistical data of each database table; and generating alarm information under the condition that the capacity growth rate exceeds a preset growth rate threshold value.
It should be noted that the management method and apparatus for the database of the present disclosure may be used in the financial field, and may also be used in any field other than the financial field.
Fig. 1 schematically shows a management method of a database and a system architecture of a management apparatus of a database according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a scenario in which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, but does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to the embodiment may include a real-time statistic information dynamic validation device D001, a basic information acquisition device D002, a basic information preprocessing device D003, a data analysis processing device D004, a monitoring threshold setting device D005, and a result notification and feedback device D006.
The real-time statistical information dynamic validation device D001 may be configured to collect statistical information in real time to ensure that the system table information is updated in time. The basic information acquiring device D002 can be used to acquire information including database table names, partition names, capacity sizes, monitoring time points, date information, and the like. The basic information preprocessing device D003 can be configured to hold the acquired basic information in the monitoring threshold table of the custom performance database in time and perform grouping processing on the table data through an algorithm. The data analysis processing device D004 may be configured to perform algorithm processing on the monitoring threshold table data of the performance database after the grouping processing by the basic information preprocessing device D003, and determine the growth rate and the change of different time dimensions such as hours, days, and months. The monitoring threshold setting device D005 may be configured to determine whether the growth rate results of different time dimensions obtained by the data analysis processing device D004 exceed a threshold through preset monitoring threshold setting. The result notification and feedback device D006 can be used to display the data exceeding the growth range and the growth threshold and notify the relevant professionals.
Fig. 2 schematically shows a flow chart of a method of managing a database according to an embodiment of the present disclosure.
As shown in fig. 2, the method includes operations S210 to S240.
In operation S210, monitoring index data of a plurality of database tables is acquired.
According to an embodiment of the present disclosure, the database table includes a partitioned table and/or a non-partitioned table. The monitoring index data of the partition table may include, for example, a table name, partition names of the partitions in the partition table, and a capacity size of each partition at each monitoring time point, and the monitoring index data of the non-partition table may include, for example, a table name and a capacity size of the non-partition table at each monitoring time point.
According to the embodiment of the present disclosure, the monitoring time point may be set according to actual needs, for example, in the present embodiment, one monitoring time point is set at each whole time point in each day.
According to the embodiment of the disclosure, monitoring index data such as meter capacity and the like can be detected and collected at regular time through the monitoring device, and then the data are registered in the monitoring valve value table of the customized performance database. And monitoring index data of the database table can be obtained by reading the monitoring valve table.
In operation S220, statistics are performed on the monitoring index data of each database table respectively to obtain statistical data of each database table.
According to the embodiment of the disclosure, statistics can be performed according to two dimensions of a single partition and a partition population for each partition table. For example, in this embodiment, the partition capacity data of each partition in the monitoring index data may be merged and counted according to the partition table to which the monitoring index data belongs, so as to obtain the partition capacity of each partition in each partition table in each statistical base period, and the total capacity of all partitions in each statistical base period in each partition table. And taking the partition capacity of each partition in each statistical base period and the total capacity of the partition table in each statistical base period in the partition table obtained after statistics as the statistical data of the partition table.
According to an embodiment of the present disclosure, for a non-partitioned table, statistics may be performed according to a full table as a dimension. For example, in this embodiment, for each non-partitioned table, the table capacity of the non-partitioned table in each statistical base period may be determined according to the monitoring index data as the statistical data of the non-partitioned table.
In operation S230, a capacity growth rate of each database table is calculated according to the statistical data of each database table.
According to the embodiment of the disclosure, for each partition table, the capacity growth rate of each partition can be determined according to the statistical data of the partition capacity of each partition in the partition table, and the capacity growth rate of the partition table can be determined according to the statistical data of the total capacity of all the partitions in the partition table.
According to embodiments of the present disclosure, for each non-partitioned table, a capacity growth rate of the non-partitioned table may be determined based on statistics of table capacity of the non-partitioned table.
For the partition table, the capacity increase rate of each partition in the partition table can be calculated by the following formula.
gn＝an-an-1
grn＝gn/an-1
Wherein, gnFor capacity increase of the partition in the nth period, anFor the size of the capacity collected at the nth stage of the partition, an-1Size of volume, gr, collected for the n-1 th stage of the partitionnThe capacity growth rate of the partition in the nth phase.
The capacity growth rate of the total capacity of the partition table can be calculated by the following formula.
Wherein G isnFor the capacity increase of the partition table in the nth period,
is the sum of the capacities of all the subareas in the subarea table collected in the nth period,
the sum of the capacities of all the partitions collected in the n-1 th stage, GRnThe capacity growth rate of the partition table in the nth period is shown.
For non-partitioned tables, the capacity growth rate of the non-partitioned table can be calculated by the following formula.
g′n＝bn-bn-1
gr′n＝g′n/bn-1
Wherein, g'nCapacity increase amount of non-zone table in nth period, bnSize of capacity collected for the nth stage of the non-partitioned table, bn-1The volume size, gr 'of the sample collected in the non-partition table at the n-1 th stage'nThe capacity growth rate of the non-partitioned table at the nth stage is shown.
According to the embodiment of the present disclosure, the capacity increase rate may be based on hours, days, weeks, months, etc., for example, which is not specifically limited in this embodiment. For example, in the present embodiment, the capacity increase rate may include an hourly capacity increase rate and a daily capacity increase rate.
In operation S240, in the case where the capacity increase rate exceeds a preset increase rate threshold, alarm information is generated.
According to the embodiment of the disclosure, the preset increase rate threshold value can be set according to actual needs. For example, in this embodiment, the preset growth rate threshold may include a capacity growth rate threshold based on hour and a capacity growth rate threshold based on day for each table, which correspond to the capacity growth rate per hour and the capacity growth rate per day, respectively.
According to the embodiment of the disclosure, the capacity increment of each period of the database table can be warned. And generating alarm information under the condition that the capacity increment of the database table exceeds a preset increment threshold. For example, in the present embodiment, the period may be day, week, and month. The preset increase threshold value can be set according to actual needs.
According to the management method of the database, the meter capacity can be automatically collected and analyzed, personnel are not needed to participate, and a large amount of repeated manual operation is avoided. And the capacity management and monitoring of the table level can be realized, and the defect of default capacity monitoring indexes of the existing database table is overcome.
Fig. 3 schematically shows a flow chart of a method of managing a database according to another embodiment of the present disclosure.
As shown in fig. 3, the method may further include operations S310 to S320 in addition to operations S210 to S240.
In operation S310, a capacity variation curve is generated according to the statistical data.
According to the embodiment of the disclosure, a coordinate system can be established by taking time and capacity as axes, and a capacity change curve is generated by the coordinate system according to the statistical data of each database table.
Operation S320 predicts a future capacity change of the database table according to the capacity change curve.
According to the embodiment of the disclosure, the future capacity change of the database table can be predicted by analyzing the capacity change curve.
For example, the capacity increase of the next 7 days can be predicted according to the capacity increase of the last 7 days. For another example, the capacity increase in the next month can be predicted from the capacity increase in the last month.
According to the embodiment of the disclosure, the future capacity change of the database table is predicted through the capacity change curve to obtain the prediction result, so that the database table can be pre-expanded in advance based on the prediction result to avoid serious business influence caused by unavailability of the database due to the capacity problem.
Fig. 4 schematically shows a flow chart of a method of managing a database according to another embodiment of the present disclosure.
As shown in fig. 4, the method may further include operations S410 to S420 in addition to the operations S210 to S240 and the operations S310 to S320.
In operation S410, a data cleaning period is determined according to the periodic fluctuation variation of the capacity variation curve.
And operation S420, performing a data cleaning operation on the database table according to the data cleaning cycle.
According to the embodiment of the disclosure, by analyzing the periodic fluctuation change of the capacity change curve, the table cleaning rule and strategy can be adjusted in real time according to the change of data, so that the data life cycle can be managed and optimized better. For example, it can predict when the capacity of the database table will be greatly increased, and perform data cleaning operation on the database table every time the large increase is generated, thereby avoiding the problem of insufficient capacity of the database table.
According to another embodiment of the present disclosure, a visualization view may be generated according to the statistical data, and the visualization view may be presented to the user. The change trend of the database table capacity can be intuitively displayed through the visual view, and if abnormal sudden increase exists, related professional business sudden increase behaviors are notified while the visual view is displayed, so that related professional businesses can be analyzed and processed in time.
Fig. 5 schematically shows a block diagram of a management apparatus of a database according to an embodiment of the present disclosure.
As shown in fig. 5, the database management apparatus 500 includes an acquisition module 510, a statistics module 520, a calculation module 530, and an alarm module 540. The management device 500 of the database may perform the method described above with reference to fig. 2 to 4.
Specifically, the obtaining module 510 may be configured to obtain monitoring index data of a plurality of database tables.
The statistical module 520 may be configured to perform statistics on the monitoring index data of each database table respectively to obtain statistical data of each database table.
A calculation module 530 may be configured to calculate a capacity growth rate of each database table according to the statistical data of each database table.
The alarm module 540 may be configured to generate alarm information if the capacity increase rate exceeds a preset increase rate threshold.
According to the management method of the database, the meter capacity can be automatically collected and analyzed, personnel are not needed to participate, and a large amount of repeated manual operation is avoided. And the capacity management and monitoring of the table level can be realized, and the defect of default capacity monitoring indexes of the existing database table is overcome.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any of the obtaining module 510, the statistics module 520, the calculation module 530, and the alarm module 540 may be combined and implemented in one module, or any one of the modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 510, the statistics module 520, the calculation module 530, and the alarm module 540 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or may be implemented by any one of three implementations of software, hardware, and firmware, or any suitable combination of any of them. Alternatively, at least one of the obtaining module 510, the statistics module 520, the calculation module 530 and the alarm module 540 may be at least partially implemented as a computer program module, which when executed may perform a corresponding function.
FIG. 6 schematically shows a block diagram of a computer system suitable for implementing the above described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 6 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.
As shown in fig. 6, computer system 600 includes a processor 610 and a computer-readable storage medium 620. The computer system 600 may perform a method according to an embodiment of the disclosure.
In particular, the processor 610 may comprise, for example, a general purpose microprocessor, an instruction set processor and/or related chip set and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 610 may also include onboard memory for caching purposes. The processor 610 may be a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
Computer-readable storage medium 620, for example, may be a non-volatile computer-readable storage medium, specific examples including, but not limited to: magnetic storage devices, such as magnetic tape or Hard Disk Drives (HDDs); optical storage devices, such as compact disks (CD-ROMs); a memory, such as a Random Access Memory (RAM) or a flash memory; and so on.
The computer-readable storage medium 620 may include a computer program 621, which computer program 621 may include code/computer-executable instructions that, when executed by the processor 610, cause the processor 610 to perform a method according to an embodiment of the disclosure, or any variation thereof.
The computer program 621 may be configured with, for example, computer program code comprising computer program modules. For example, in an example embodiment, code in computer program 621 may include one or more program modules, including 621A, 621B, … …, for example. It should be noted that the division and number of the modules are not fixed, and those skilled in the art may use suitable program modules or program module combinations according to actual situations, so that the processor 610 may execute the method according to the embodiment of the present disclosure or any variation thereof when the program modules are executed by the processor 610.
According to an embodiment of the present invention, at least one of the obtaining module 510, the statistics module 520, the calculation module 530 and the alarm module 540 may be implemented as a computer program module as described with reference to fig. 6, which, when executed by the processor 610, may implement the respective operations described above.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
While the disclosure has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents. Accordingly, the scope of the present disclosure should not be limited to the above-described embodiments, but should be defined not only by the appended claims, but also by equivalents thereof.
Claims (10)
1. A method of managing a database, comprising:
acquiring monitoring index data of a plurality of database tables;
respectively counting the monitoring index data of each database table to obtain the statistical data of each database table;
calculating the capacity growth rate of each database table according to the statistical data of each database table; and
and generating alarm information under the condition that the capacity growth rate exceeds a preset growth rate threshold value.
2. The method of claim 1, wherein the database tables include a partition table and/or a non-partition table,
wherein, the monitoring index data of the partition table comprises a table name, partition names of the partitions in the partition table and the capacity of each partition at each monitoring time point,
the monitoring index data of the non-partition table comprises a table name and the capacity size of the non-partition table at each monitoring time point.
3. The method of claim 2, wherein the separately counting the monitoring index data of each database table according to the monitoring index data to obtain the statistical data of each database table comprises:
according to the table name, the partition names of the partitions in the partition table and the capacity of each partition at each monitoring time point, determining the partition capacity of each partition in the partition table in each statistical base period and the total capacity of all the partitions in the partition table in each statistical base period as statistical data of the partition table aiming at each partition table; and/or
And according to the table name and the capacity size of the non-partition table at each monitoring time point, determining the table capacity of the non-partition table in each statistical base period as the statistical data of the non-partition table for each non-partition table.
4. The method of claim 3, wherein said calculating a capacity growth rate for each database table based on said statistical data for each database table comprises:
aiming at each partition table, determining the capacity growth rate of each partition according to the statistical data of the partition capacity of each partition in the partition table, and determining the capacity growth rate of the partition table according to the statistical data of the total capacity of all the partitions in the partition table; and/or
For each non-partitioned table, determining a capacity growth rate of the non-partitioned table according to statistical data of table capacity of the non-partitioned table.
5. The method of claim 1, further comprising:
generating a capacity change curve according to the statistical data; and
and predicting the future capacity change of the database table according to the capacity change curve.
6. The method of claim 5, further comprising:
determining a data cleaning period according to the periodical fluctuation change of the capacity change curve; and
and performing data cleaning operation on the database table according to the data cleaning period.
7. The method of claim 4, further comprising:
generating a visual view according to the statistical data; and
and displaying the visual view.
8. An apparatus for managing a database, comprising:
the acquisition module is used for acquiring monitoring index data of a plurality of database tables;
the statistical module is used for respectively carrying out statistics on the monitoring index data of each database table to obtain statistical data of each database table;
the calculation module is used for calculating the capacity growth rate of each database table according to the statistical data of each database table; and
and the alarm module is used for generating alarm information under the condition that the capacity growth rate exceeds a preset growth rate threshold value.
9. A computing device, comprising:
one or more processors;
a memory for storing one or more computer programs,
wherein the one or more computer programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010822811.5A CN111949503B (en) | 2020-08-14 | 2020-08-14 | Database management method, device, computing equipment and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010822811.5A CN111949503B (en) | 2020-08-14 | 2020-08-14 | Database management method, device, computing equipment and medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111949503A true CN111949503A (en) | 2020-11-17 |
| CN111949503B CN111949503B (en) | 2023-11-14 |
Family
ID=73343515
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010822811.5A Active CN111949503B (en) | 2020-08-14 | 2020-08-14 | Database management method, device, computing equipment and medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111949503B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103412911A (en) * | 2013-08-02 | 2013-11-27 | 中国工商银行股份有限公司 | Method and device for monitoring performance of database system |
| CN104636450A (en) * | 2015-01-26 | 2015-05-20 | 上海新炬网络信息技术有限公司 | Database table space monitoring method |
| CN105183614A (en) * | 2015-11-03 | 2015-12-23 | 华夏银行股份有限公司 | Database failure prediction method and device |
| CN105743706A (en) * | 2016-03-31 | 2016-07-06 | 武汉虹信技术服务有限责任公司 | Disk array performance monitoring and early-warning method and system based on HTTP |
| CN108920324A (en) * | 2018-06-08 | 2018-11-30 | 广东轩辕网络科技股份有限公司 | The method of the trend analysis of information technoloy equipment memory capacity and early warning, system and electronic device |
| CN110287082A (en) * | 2019-07-02 | 2019-09-27 | 中国工商银行股份有限公司 | Database table spatial content monitoring method and device |
| US20190340260A1 (en) * | 2018-05-03 | 2019-11-07 | Sap Se | Data partitioning based on estimated growth |
-
2020
- 2020-08-14 CN CN202010822811.5A patent/CN111949503B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103412911A (en) * | 2013-08-02 | 2013-11-27 | 中国工商银行股份有限公司 | Method and device for monitoring performance of database system |
| CN104636450A (en) * | 2015-01-26 | 2015-05-20 | 上海新炬网络信息技术有限公司 | Database table space monitoring method |
| CN105183614A (en) * | 2015-11-03 | 2015-12-23 | 华夏银行股份有限公司 | Database failure prediction method and device |
| CN105743706A (en) * | 2016-03-31 | 2016-07-06 | 武汉虹信技术服务有限责任公司 | Disk array performance monitoring and early-warning method and system based on HTTP |
| US20190340260A1 (en) * | 2018-05-03 | 2019-11-07 | Sap Se | Data partitioning based on estimated growth |
| CN108920324A (en) * | 2018-06-08 | 2018-11-30 | 广东轩辕网络科技股份有限公司 | The method of the trend analysis of information technoloy equipment memory capacity and early warning, system and electronic device |
| CN110287082A (en) * | 2019-07-02 | 2019-09-27 | 中国工商银行股份有限公司 | Database table spatial content monitoring method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111949503B (en) | 2023-11-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9245248B2 (en) | Data metric resolution prediction system and method | |
| US10557840B2 (en) | System and method for performing industrial processes across facilities | |
| US20150294246A1 (en) | Selecting optimal training data set for service contract prediction | |
| EP3514700A1 (en) | Dynamic outlier bias reduction system and method | |
| US20130238399A1 (en) | Computer-Implemented Systems and Methods for Scenario Analysis | |
| US20150309964A1 (en) | Dynamic outlier bias reduction system and method | |
| US8380654B2 (en) | General market prediction using position specification language | |
| Tsai et al. | A critical review of laboratory performance indicators | |
| Chen et al. | Optimal variability sensitive condition-based maintenance with a Cox PH model | |
| CN106257473A (en) | Population estimation method and population speculate equipment | |
| JP2009086896A (en) | Failure prediction system and failure prediction method for computer | |
| Bai et al. | Managing admission and discharge processes in intensive care units | |
| CN110659998A (en) | Data processing method, data processing apparatus, computer apparatus, and storage medium | |
| RU2632124C1 (en) | Method of predictive assessment of multi-stage process effectiveness | |
| US20140207504A1 (en) | System and method of calculating task efforts and applying the results for resource planning | |
| CN111949502A (en) | Database early warning method and device, computing equipment and medium | |
| CN111949503A (en) | Database management method, database management device, computing equipment and media | |
| CN111861757A (en) | Financing matching method, system, equipment and storage medium | |
| Hay et al. | Evidence for predictive trends in TRL transition metrics | |
| US20160283878A1 (en) | System and method to use multi-factor capacity constraints for product-based release and team planning | |
| CN113065797B (en) | Method, device, terminal equipment and medium for optimizing execution period of multitasking | |
| US20210256447A1 (en) | Detection for ai-based recommendation | |
| CN112308344A (en) | Method and device for predicting reservation value of non-departure flight and electronic equipment | |
| US20150149239A1 (en) | Technology Element Risk Analysis | |
| Lau | eHealth economic evaluation framework |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |