CN117290163B - Data disaster recovery backup system, method and medium based on relational database - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/14—Error detection or correction of the data by redundancy in operation
- G06F11/1402—Saving, restoring, recovering or retrying
- G06F11/1446—Point-in-time backing up or restoration of persistent data
- G06F11/1458—Management of the backup or restore process
- G06F11/1469—Backup restoration techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
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Abstract
The invention relates to the technical field of data disaster recovery processing, and discloses a data disaster recovery backup system, a method and a medium based on a relational database, wherein the data disaster recovery backup system based on the relational database comprises the following components: the priority computing module is used for carrying out data blocking processing on the data to be backed up to obtain backup data blocks and determining the backup priority corresponding to the backup data blocks; the coding packet packaging module is used for carrying out coding processing on the backup data blocks to obtain coding data blocks, and carrying out packaging processing on the coding data blocks to obtain coding data packets; the node setting module is used for carrying out cluster processing on the relational database to obtain a cluster database, and constructing backup protection nodes in the cluster database; and the backup processing module is used for removing the coded data packet to obtain a target coded packet, and executing the backup processing of the target coded packet in the backup protection node to obtain a backup result. The invention aims to improve the reasonability of the data disaster recovery backup of the relational database.
Description
Technical Field
The present invention relates to the field of data disaster recovery processing technologies, and in particular, to a data disaster recovery backup system, method and medium based on a relational database.
Background
The data disaster recovery backup refers to a preventive measure for protecting data from unexpected loss or disastrous events, in which data is copied or backed up in a storage location to ensure that even if data loss, hardware failure, natural disasters or other unexpected situations occur, the data can be quickly recovered and normal operation of a service can be maintained, service interruption caused by the data loss or disastrous events can be reduced to the greatest extent by the data disaster recovery backup system, and the capability of quick and reliable data recovery and continuous operation can be provided.
However, the existing data disaster recovery backup system based on the relational database is mainly a database mirror image system, and the specific process is as follows: the method comprises the steps of constructing a mirror image database corresponding to a relational database, synchronizing data generated by the relational database into the mirror image database in real time, and immediately taking over service when the relational database fails, wherein the method is characterized in that the data are copied in sequence and related data are not subjected to diversified backup processing, and if a certain database in the mirror image database is damaged, the data in the database cannot be recovered, so that the related service of the relational database cannot be executed, and the rationality of the backup processing of the method is reduced.
Disclosure of Invention
The invention provides a data disaster recovery backup system, method and medium based on a relational database, and mainly aims to improve the rationality of the data disaster recovery backup of the relational database.
In order to achieve the above object, the present invention provides a data disaster recovery backup system based on a relational database, which is characterized in that the data disaster recovery backup system based on the relational database includes:
the priority computing module is used for obtaining data to be backed up in the relational database, carrying out data blocking processing on the data to be backed up to obtain backup data blocks, computing the importance degree corresponding to each data block in the backup data blocks, and determining the backup priority corresponding to the backup data blocks according to the importance degree;
the coding packet packaging module is used for calculating forward error correction codes corresponding to the backup data blocks, carrying out coding processing on the backup data blocks according to the forward error correction codes to obtain coded data blocks, calculating associated coefficients corresponding to the coded data blocks, and carrying out packaging processing on the coded data blocks according to the associated coefficients to obtain coded data packets;
the node setting module is used for carrying out cluster processing on the relational database to obtain a cluster database, scheduling disaster log data corresponding to the relational database, and constructing backup protection nodes in the cluster database according to the disaster log data;
And the backup processing module is used for calculating the useful coefficient corresponding to the coded data packet, removing the coded data packet according to the useful coefficient to obtain a target coded packet, and executing the backup processing of the target coded packet in the backup protection node according to the backup priority to obtain a backup result.
Optionally, the calculating the importance corresponding to each data block in the backup data blocks includes:
extracting data target characteristics corresponding to each data in the backup data block;
constructing a target feature matrix corresponding to each data in the backup data block according to the data target features;
calculating a variance coefficient corresponding to the target feature matrix, and carrying out normalization processing on the variance coefficient to obtain a normalized variance value;
determining the data importance of each data in the backup data block according to the normalized variance value, and calculating the data weight corresponding to each data in the backup data block;
and calculating the importance corresponding to each data block in the backup data block by combining the data weight and the data importance.
Optionally, the calculating the variance coefficient corresponding to the target feature matrix includes:
Calculating a variance coefficient corresponding to the target feature matrix through the following formula:wherein A represents a variance coefficient corresponding to the target feature matrix, b represents a sequence number of the target feature matrix, < ->Matrix total number representing target feature matrix, +.>Matrix expectation value representing the b-th matrix of the target feature matrices,>representing the matrix value corresponding to the b-th matrix in the target feature matrix, < >>Representing the average value of the target feature matrix.
Optionally, the encoding processing is performed on the backup data block according to the forward error correction coding to obtain an encoded data block, which includes:
and encoding the backup data block by the following formula:wherein,representing the encoded data block obtained after the encoding process of the backup data block +.>Respectively representing the start code data, the second code data and the (r-1) th code data in the code data block, ">Representing the initial uncoded data, the second uncoded data and the r-1 th uncoded data in the backup data block, respectively,respectively representing forward error correction codes corresponding to the original uncoded data, ">Respectively representing the corresponding forward error correction codes of the second uncoded data,/>Respectively representing the forward error correction codes corresponding to the r-1 uncoded data.
Optionally, the calculating the association coefficient corresponding to the encoded data block includes:
mining data information corresponding to each piece of data in the coded data block, and calculating an information entropy value corresponding to each piece of information in the data information;
screening the data information according to the information entropy value to obtain target data information;
and calculating the information association degree between each piece of information in the target data information, and carrying out weighted summation on the information association degree to obtain the association coefficient corresponding to the encoded data block.
Optionally, the calculating the information association degree between each piece of information in the target data information includes:
calculating the information association degree between each piece of information in the target data information by the following formula:wherein G represents the degree of information association between each of the pieces of target data information, ++>Information quantity representing target data information, j representing information sequence number of target data information, +.>Represents the standard deviation corresponding to the j-th information in the target data information,/and>represents the standard deviation corresponding to the j+1th information in the target data information,two-stage minimum difference value representing difference value of standard deviation corresponding to jth information and jth+1th information, +. >The two-stage maximum difference value representing the difference value of the standard deviation corresponding to the jth information and the jth+1th information.
Optionally, the clustering processing is performed on the relational database to obtain a clustered database, including:
inquiring a database server corresponding to the relational database, and extracting server parameters corresponding to the database server;
according to the server parameters, analyzing the functional attributes corresponding to the database server, and calculating the support coefficients among the functional attributes;
according to the support coefficient, carrying out cluster processing on the database server to obtain a cluster server;
determining a cluster architecture corresponding to the relational database according to the cluster server;
and carrying out cluster processing on the relational database according to the cluster architecture to obtain a cluster database.
Optionally, the calculating the useful coefficient corresponding to the encoded data packet includes:
calculating the useful coefficient corresponding to the coded data packet through the following formula:wherein R represents a useful coefficient corresponding to the encoded data packet, T represents a fetch response time corresponding to the encoded data packet, < >>Indicating the number of objects contained in the nth data of the encoded data packet, n indicating the data sequence number in the encoded data packet, " >Representing the number of redundant objects of the nth data in the encoded data packet, a>Representing the cost of data storage for the nth data in the encoded data packet.
In order to solve the above problems, the present invention further provides a data disaster recovery backup method based on a relational database, where the method includes:
obtaining data to be backed up in a relational database, carrying out data blocking processing on the data to be backed up to obtain backup data blocks, calculating the importance degree corresponding to each data block in the backup data blocks, and determining the backup priority corresponding to the backup data blocks according to the importance degree;
calculating forward error correction codes corresponding to the backup data blocks, carrying out coding processing on the backup data blocks according to the forward error correction codes to obtain coded data blocks, calculating association coefficients corresponding to the coded data blocks, and carrying out packaging processing on the coded data blocks according to the association coefficients to obtain coded data packets;
clustering the relational database to obtain a clustered database, scheduling disaster log data corresponding to the relational database, and constructing backup protection nodes in the clustered database according to the disaster log data;
And calculating a useful coefficient corresponding to the coded data packet, removing the coded data packet according to the useful coefficient to obtain a target coded packet, and executing backup processing of the target coded packet in the backup protection node according to the backup priority to obtain a backup result.
In order to solve the above problems, the present invention further provides a computer readable storage medium storing a computer program, where the computer program is executed by a processor to perform the above-mentioned data disaster recovery backup method based on a relational database.
In the embodiment of the invention, the data to be backed up can be divided into independent data by carrying out data blocking processing on the data to be backed up, so that the data flexibility of the data to be backed up is improved, the processing transmission efficiency of the subsequent data to be backed up is improved, the importance degree of each data block in the backup data block can be known by calculating the importance degree corresponding to the data block, and the determination of the subsequent backup priority is facilitated. In addition, in the embodiment of the invention, the cluster processing is carried out on the relational database, so that the failover can be realized, when a certain database fails, the system can be switched to other nodes to continue providing services, system interruption and data loss are avoided, so that the disaster recovery efficiency of the relational database is improved. Therefore, the data disaster recovery backup system, method and medium based on the relational database can improve the rationality of the data disaster recovery backup of the relational database.
Drawings
FIG. 1 is a functional block diagram of a relational database-based data disaster recovery backup system according to an embodiment of the present invention;
fig. 2 is a flow chart of a data disaster recovery backup method based on a relational database according to an embodiment of the invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the sequence of steps in the method embodiments described below is only an example and is not strictly limited.
In practice, a server device deployed by a relational database-based data disaster recovery backup system may be composed of one or more devices. The data disaster recovery backup system based on the relational database can be realized as follows: service instance, virtual machine, hardware device. For example, the relational database-based data disaster recovery backup system can be implemented as a service instance deployed on one or more devices in a cloud node. In short, the live broadcast service system can be understood as a software deployed on a cloud node, and is used for providing service of disaster recovery backup of data based on a relational database for each user side. Alternatively, the relational database-based data disaster recovery backup system may be implemented as a virtual machine deployed on one or more devices in the cloud node. The virtual machine is provided with application software for managing each user side. Or, the data disaster recovery backup system based on the relational database can also be realized as a service end formed by a plurality of hardware devices of the same or different types, and one or more hardware devices are arranged for providing data disaster recovery backup service based on the relational database for each user end.
In the implementation form, the data disaster recovery backup system based on the relational database and the user side are mutually adapted. Namely, the data disaster recovery backup system based on the relational database is used as an application installed on the cloud service platform, and the user side is used as a client side for establishing communication connection with the application; or realizing the data disaster recovery backup system based on the relational database as a website, and realizing the user side as a webpage; and then, or the data disaster recovery backup system based on the relational database is realized as a cloud service platform, and the user side is realized as an applet in the instant messaging application.
Referring to fig. 1, a functional block diagram of a data disaster recovery backup system based on a relational database according to an embodiment of the present invention is shown.
The data disaster recovery backup system 100 based on the relational database can be arranged in a cloud server, and in an implementation form, the system can be used as one or more service devices, can be used as an application to be installed on a cloud (such as a server of a live service operator, a server cluster and the like), or can be developed into a website. According to the implemented functions, the relational database-based data disaster recovery backup system 100 includes a priority calculating module 101, a coding packet packaging module 102, a node setting module 103, and a backup processing module 104.
In the embodiment of the invention, in the tracking of the disaster recovery backup of the data based on the relational database, each module can be independently realized and called with other modules. The call can be understood that a certain module can be connected with a plurality of modules of another type and provide corresponding services for the plurality of modules connected with the certain module, and in the data disaster recovery backup system based on the relational database provided by the embodiment of the invention, the application range of the data disaster recovery backup architecture based on the relational database can be adjusted by adding the modules and directly calling the modules without modifying program codes, so that the cluster type horizontal expansion is realized, and the purpose of rapidly and flexibly expanding the data disaster recovery backup system based on the relational database is achieved. In practical applications, the modules may be disposed in the same device or different devices, or may be service instances disposed in virtual devices, for example, in a cloud server.
The following description is made with reference to specific embodiments, respectively, regarding each component of the data disaster recovery backup system based on the relational database and specific workflow:
the priority calculating module 101 is configured to obtain data to be backed up in a relational database, perform data blocking processing on the data to be backed up to obtain backup data blocks, calculate importance corresponding to each data block in the backup data blocks, and determine a backup priority corresponding to the backup data blocks according to the importance.
In the embodiment of the invention, the data to be backed up can be divided into independent data by carrying out data blocking processing on the data to be backed up, so that the data flexibility of the data to be backed up is improved, the processing transmission efficiency of the subsequent data to be backed up is improved, the importance degree of each data block in the backup data block can be known by calculating the importance degree corresponding to the data block, the determination of the subsequent backup priority is facilitated, wherein the relational database is a database for storing and managing data in a form of a table, the data to be backed up is the data to be backed up in the relational database, the backup data block is the data to be backed up is divided into the independent data, the importance degree represents the importance degree corresponding to each data block in the backup data block, the backup priority is the priority degree corresponding to the backup data block during processing, the data blocking processing on the data to be backed up can be realized by an FSP algorithm, and the corresponding priority of the backup data block can provide the numerical value determined according to the importance degree.
As one embodiment of the present invention, the calculating the importance of each data block in the backup data blocks includes: extracting data target characteristics corresponding to each data in the backup data block, constructing a target characteristic matrix corresponding to each data in the backup data block according to the data target characteristics, calculating a variance coefficient corresponding to the target characteristic matrix, carrying out normalization processing on the variance coefficient to obtain a normalized variance value, determining the data importance of each data in the backup data block according to the normalized variance value, calculating the data weight corresponding to each data in the backup data block, combining the data weight and the data importance, and calculating the importance corresponding to each data block in the backup data block.
The data target feature is a key feature in a data feature corresponding to each data in the backup data block, the target feature matrix is a square matrix constructed by each data in the backup data block according to the data target feature, the variance coefficient represents a change degree corresponding to the target feature matrix, the normalized variance value is a value obtained by mapping the variance coefficient in a 0-1 interval, the data importance represents an importance degree corresponding to each data in the backup data block, and the data weight represents a weight proportion corresponding to each data in the backup data block.
Optionally, extracting the data target feature corresponding to each data in the backup data block may be implemented by using a orb feature extraction algorithm, constructing a target feature matrix corresponding to each data in the backup data block may be implemented by using a matrix function, for example, a zero matrix function, normalizing the variance coefficient may be implemented by using a normalization method, the data importance of each data in the backup data block may be obtained by using a percentage corresponding to the normalized variance value, calculating the data weight corresponding to each data in the backup data block may be implemented by using a weight calculator, obtaining the target importance corresponding to each data by using a product of the data weight and the data importance, and summing the target importance of the data in each data block, so as to obtain the importance corresponding to each data block in the backup data block.
Optionally, as an optional embodiment of the present invention, the calculating a variance coefficient corresponding to the target feature matrix includes:
calculating a variance coefficient corresponding to the target feature matrix through the following formula:wherein A represents a variance coefficient corresponding to the target feature matrix, b represents a sequence number of the target feature matrix, < - >Matrix total number representing target feature matrix, +.>Matrix expectation value representing the b-th matrix of the target feature matrices,>representing the matrix value corresponding to the b-th matrix in the target feature matrix, < >>Representing the average value of the target feature matrix.
The encoding packet packaging module 102 is configured to calculate a forward error correction code corresponding to the backup data block, perform encoding processing on the backup data block according to the forward error correction code to obtain an encoded data block, calculate an association coefficient corresponding to the encoded data block, and perform packaging processing on the encoded data block according to the association coefficient to obtain an encoded data packet.
In the embodiment of the invention, by carrying out coding processing on the backup data block, the corresponding data fault tolerance in the backup data block can be improved, the subsequent data loss in the backup data block can be retrieved from other backup data through the forward error correction coding, so that the data recovery efficiency is improved, wherein the forward error correction coding is the data coding for data recovery, the coded data block is the data block obtained after the backup data block is coded according to the forward error correction coding, and optionally, the calculation of the forward error correction coding corresponding to the backup data block can be realized through an error correction code calculator, and the error correction code calculator is compiled by a script language.
As an embodiment of the present invention, the encoding the backup data block according to the fec encoding to obtain an encoded data block includes:
and encoding the backup data block by the following formula:wherein,representing the encoded data block obtained after the encoding process of the backup data block +.>Respectively representing the start code data, the second code data and the (r-1) th code data in the code data block, ">Representing the initial uncoded data, the second uncoded data and the r-1 th uncoded data in the backup data block, respectively,respectively representing forward error correction codes corresponding to the original uncoded data, ">Respectively representing the corresponding forward error correction codes of the second uncoded data,/>Respectively representing the forward error correction codes corresponding to the r-1 uncoded data.
In the embodiment of the invention, the association relation between each data block in the encoded data blocks can be obtained by calculating the association coefficient corresponding to the encoded data blocks, so that the encoded data blocks with the association relation can be packed together later, and the subsequent data recovery processing can be quickly performed, wherein the association coefficient represents the association degree between each data block in the encoded data blocks, and the encoded data packet is a data packet obtained after the encoded data blocks are packed according to the numerical value of the association coefficient, and optionally, the packing processing of the encoded data blocks can be realized by a frame packing method.
In the embodiment of the present invention, the calculating the association coefficient corresponding to the encoded data block includes: mining data information corresponding to each piece of data in the coded data block, calculating an information entropy value corresponding to each piece of information in the data information, screening the data information according to the information entropy value to obtain target data information, calculating information association degree between each piece of information in the target data information, and carrying out weighted summation on the information association degree to obtain association coefficients corresponding to the coded data block.
The data information is attribute description information of each data in the coded data block, the information entropy value represents the information quantity contained in each information in the data information, the target data information is information with the largest information quantity contained in the data information, and the information association degree represents the association degree between each information in the target data information.
Optionally, mining the data information corresponding to each data in the encoded data block may be implemented by a genetic algorithm, filtering the data information may be implemented by a filter filtering function, and weighting and summing the information association may be implemented by a weighted sum algorithm.
Optionally, as an optional embodiment of the present invention, the calculating an information entropy value corresponding to each piece of the data information includes:
by the following formulaCalculating an information entropy value corresponding to each piece of information in the data information:wherein H represents the information entropy value corresponding to each information in the data information, i represents the information serial number corresponding to the data information, t represents the information quantity of the data information, +.>Representing the probability of occurrence of the i-th information in the data information, is->The occurrence probability of the characteristic information h of the i-th information in the data information is represented.
Optionally, as an optional embodiment of the present invention, the calculating an information association degree between each information in the target data information includes:
calculating the information association degree between each piece of information in the target data information by the following formula:wherein G represents the degree of information association between each of the pieces of target data information, ++>Information quantity representing target data information, j representing information sequence number of target data information, +.>Represents the standard deviation corresponding to the j-th information in the target data information,/and>represents the standard deviation corresponding to the j+1th information in the target data information, Two-stage minimum difference value representing difference value of standard deviation corresponding to jth information and jth+1th information, +.>The two-stage maximum difference value representing the difference value of the standard deviation corresponding to the jth information and the jth+1th information.
The node setting module 103 is configured to perform cluster processing on the relational database to obtain a clustered database, schedule disaster log data corresponding to the relational database, and construct a backup protection node in the clustered database according to the disaster log data.
In the embodiment of the invention, the cluster processing is carried out on the relational database, so that the fault transfer can be realized, when a certain database fails, the system can be switched to other nodes to continue providing services, the system interruption and the data loss are avoided, and the disaster recovery efficiency of the relational database is improved, wherein the cluster database is a database obtained after a server in the relational database constructs a logic relationship.
In the embodiment of the present invention, the clustering processing is performed on the relational database to obtain a clustered database, including: inquiring a database server corresponding to the relational database, extracting server parameters corresponding to the database server, analyzing functional attributes corresponding to the database server according to the server parameters, calculating support coefficients among the functional attributes, carrying out cluster processing on the database server according to the support coefficients to obtain a cluster server, determining a cluster architecture corresponding to the relational database according to the cluster server, and carrying out cluster processing on the relational database according to the cluster architecture to obtain the cluster database.
The database servers are processing servers of each database in the relational databases, the server parameters are introduction parameter information corresponding to the database servers, the functional attributes are functional properties corresponding to each server in the database servers, such as functions of master-slave replication or shared storage, the support coefficients represent the degree of mutual support between the functional attributes, the cluster servers are servers obtained after combination between the database servers, and the cluster architecture is a cluster framework corresponding to the relational databases when combination is performed.
Alternatively, querying the database server corresponding to the relational database may be implemented by a station length tool, extracting the server parameter corresponding to the database server may be implemented by a parameter extracting tool, the parameter extracting tool is compiled by JAVA language, the functional attribute corresponding to the database server may be obtained by analyzing the text semantics of the parameter related to the use in the server parameter, calculating the support coefficient between the functional attributes may be implemented by a COUNTIF function, and performing cluster processing on the database server may be implemented by a master-slave cluster method, and the cluster architecture corresponding to the relational database may be determined according to the architecture corresponding to the cluster server.
According to the method, the backup protection node is constructed in the cluster database according to the disaster log data, the data in the cluster database can be protected when a disaster occurs, so that the disaster tolerance rate corresponding to the cluster database is improved, the disaster log data are relevant data of disaster records of the relational database, the backup protection node is a server for protecting the data in the cluster database, optionally, the disaster log data corresponding to the relational database can be scheduled to be realized through a priority scheduling algorithm, and the backup protection node can be constructed in the cluster database through virtual software, such as VMware, hyper-V or KVM.
The backup processing module 104 is configured to calculate a useful coefficient corresponding to the encoded data packet, reject the encoded data packet according to the useful coefficient to obtain a target encoded packet, and execute backup processing of the target encoded packet in the backup protection node according to the backup priority to obtain a backup result.
According to the embodiment of the invention, the data value in the coded data packet can be known through the useful coefficient by calculating the useful coefficient corresponding to the coded data packet, so that the coded data packet can be subjected to rejection processing, and the backup processing of the user with the data value can be conveniently performed, and the waste of storage resources is avoided, wherein the useful coefficient represents the data value corresponding to the data in the coded data packet, the target coded packet is the coded packet obtained by subjecting the data without the value in the coded data packet to the rejection processing, and optionally, the rejection processing of the coded data packet can be realized through a box-line diagram method.
As one embodiment of the present invention, the calculating the useful coefficient corresponding to the encoded data packet includes:
calculating the useful coefficient corresponding to the coded data packet through the following formula:wherein R represents a useful coefficient corresponding to the encoded data packet, T represents a fetch response time corresponding to the encoded data packet, < >>Indicating the number of objects contained in the nth data of the encoded data packet, n indicating the data sequence number in the encoded data packet, ">Representing the number of redundant objects of the nth data in the encoded data packet, a>Representing the cost of data storage for the nth data in the encoded data packet.
According to the backup priority, the backup processing of the target coding packet is executed in the backup protection node, so that important data can be stored preferentially, and unnecessary loss caused by loss of the important data is avoided.
In the embodiment of the invention, the data to be backed up can be divided into independent data by carrying out data blocking processing on the data to be backed up, so that the data flexibility of the data to be backed up is improved, the processing transmission efficiency of the subsequent data to be backed up is improved, the importance degree of each data block in the backup data block can be known by calculating the importance degree corresponding to the data block, and the determination of the subsequent backup priority is facilitated. In addition, in the embodiment of the invention, the cluster processing is carried out on the relational database, so that the failover can be realized, when a certain database fails, the system can be switched to other nodes to continue providing services, system interruption and data loss are avoided, so that the disaster recovery efficiency of the relational database is improved. Therefore, the data disaster recovery backup method based on the relational database can improve the rationality of the data disaster recovery backup of the relational database.
Referring to fig. 2, a flow chart of a data disaster recovery backup method based on a relational database according to an embodiment of the invention is shown. In this embodiment, the data disaster recovery backup method based on the relational database includes:
obtaining data to be backed up in a relational database, carrying out data blocking processing on the data to be backed up to obtain backup data blocks, calculating the importance degree corresponding to each data block in the backup data blocks, and determining the backup priority corresponding to the backup data blocks according to the importance degree;
calculating forward error correction codes corresponding to the backup data blocks, carrying out coding processing on the backup data blocks according to the forward error correction codes to obtain coded data blocks, calculating association coefficients corresponding to the coded data blocks, and carrying out packaging processing on the coded data blocks according to the association coefficients to obtain coded data packets;
clustering the relational database to obtain a clustered database, scheduling disaster log data corresponding to the relational database, and constructing backup protection nodes in the clustered database according to the disaster log data;
and calculating a useful coefficient corresponding to the coded data packet, removing the coded data packet according to the useful coefficient to obtain a target coded packet, and executing backup processing of the target coded packet in the backup protection node according to the backup priority to obtain a backup result.
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
obtaining data to be backed up in a relational database, carrying out data blocking processing on the data to be backed up to obtain backup data blocks, calculating the importance degree corresponding to each data block in the backup data blocks, and determining the backup priority corresponding to the backup data blocks according to the importance degree;
calculating forward error correction codes corresponding to the backup data blocks, carrying out coding processing on the backup data blocks according to the forward error correction codes to obtain coded data blocks, calculating association coefficients corresponding to the coded data blocks, and carrying out packaging processing on the coded data blocks according to the association coefficients to obtain coded data packets;
clustering the relational database to obtain a clustered database, scheduling disaster log data corresponding to the relational database, and constructing backup protection nodes in the clustered database according to the disaster log data;
and calculating a useful coefficient corresponding to the coded data packet, removing the coded data packet according to the useful coefficient to obtain a target coded packet, and executing backup processing of the target coded packet in the backup protection node according to the backup priority to obtain a backup result.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. The data disaster recovery backup system based on the relational database is characterized by comprising the following components:
The priority computing module is used for obtaining data to be backed up in the relational database, carrying out data blocking processing on the data to be backed up to obtain backup data blocks, computing the importance degree corresponding to each data block in the backup data blocks, and determining the backup priority corresponding to the backup data blocks according to the importance degree;
the coding packet packaging module is configured to calculate a forward error correction code corresponding to the backup data block, perform coding processing on the backup data block according to the forward error correction code to obtain a coded data block, calculate an association coefficient corresponding to the coded data block, and perform packaging processing on the coded data block according to the association coefficient to obtain a coded data packet, where calculating the association coefficient corresponding to the coded data block includes:
mining data information corresponding to each piece of data in the coded data block, and calculating an information entropy value corresponding to each piece of information in the data information;
screening the data information according to the information entropy value to obtain target data information;
calculating the information association degree between each piece of information in the target data information by the following formula: Wherein G represents the degree of information association between each of the pieces of target data information, ++>Information quantity representing target data information, j representing information sequence number of target data information, +.>Represents the standard deviation corresponding to the j-th information in the target data information,/and>represents the standard deviation corresponding to the j+1th information in the target data information,/for>Two-stage minimum difference value representing difference value of standard deviation corresponding to jth information and jth+1th information, +.>A two-stage maximum difference value representing a difference value of standard deviations corresponding to the jth information and the (j+1) th information;
carrying out weighted summation on the information association degree to obtain association coefficients corresponding to the encoded data blocks;
the node setting module is used for carrying out cluster processing on the relational database to obtain a cluster database, scheduling disaster log data corresponding to the relational database, and constructing backup protection nodes in the cluster database according to the disaster log data;
the backup processing module is used for calculating the useful coefficient corresponding to the coded data packet through the following formula:wherein R represents a useful coefficient corresponding to the encoded data packet, T represents a fetch response time corresponding to the encoded data packet, < > >Indicating the number of objects contained in the nth data of the encoded data packet, n indicating the data sequence number in the encoded data packet, ">Representing the number of redundant objects of the nth data in the encoded data packet, a>Representing the cost of data storage of the nth data in the encoded data packet;
and removing the coded data packet according to the useful coefficient to obtain a target coded packet, and executing backup processing of the target coded packet in the backup protection node according to the backup priority to obtain a backup result.
2. The relational database-based data disaster recovery backup system as set forth in claim 1 wherein said calculating a corresponding importance for each of said backup data blocks comprises:
extracting data target characteristics corresponding to each data in the backup data block;
constructing a target feature matrix corresponding to each data in the backup data block according to the data target features;
calculating a variance coefficient corresponding to the target feature matrix, and carrying out normalization processing on the variance coefficient to obtain a normalized variance value;
determining the data importance of each data in the backup data block according to the normalized variance value, and calculating the data weight corresponding to each data in the backup data block;
And calculating the importance corresponding to each data block in the backup data block by combining the data weight and the data importance.
3. The relational database-based data disaster recovery backup system as set forth in claim 2, wherein said calculating a coefficient of variance corresponding to said target feature matrix comprises:
calculating a variance coefficient corresponding to the target feature matrix through the following formula:wherein A represents a variance coefficient corresponding to the target feature matrix, b represents a sequence number of the target feature matrix, < ->Matrix total number representing target feature matrix, +.>Matrix expectation value representing the b-th matrix of the target feature matrices,>representing the matrix value corresponding to the b-th matrix in the target feature matrix, < >>Representing the average value of the target feature matrix.
4. The relational database-based data disaster recovery backup system as set forth in claim 1, wherein said encoding the backup data block according to the forward error correction code to obtain an encoded data block comprises:
and encoding the backup data block by the following formula:wherein,representing the encoded data block obtained after the encoding process of the backup data block +. >Respectively representing the start code data, the second code data and the (r-1) th code data in the code data block,respectively represent the preparationStarting uncoded data, second uncoded data and (r-1) th uncoded data in the partial data block, ">、/>、/>Respectively representing forward error correction codes corresponding to the original uncoded data, ">、/>、/>Respectively representing the corresponding forward error correction codes of the second uncoded data,/>、/>、/>Respectively representing the forward error correction codes corresponding to the r-1 uncoded data.
5. The relational database-based data disaster recovery backup system as set forth in claim 1, wherein the clustering the relational database to obtain a clustered database comprises:
inquiring a database server corresponding to the relational database, and extracting server parameters corresponding to the database server;
according to the server parameters, analyzing the functional attributes corresponding to the database server, and calculating the support coefficients among the functional attributes;
according to the support coefficient, carrying out cluster processing on the database server to obtain a cluster server;
determining a cluster architecture corresponding to the relational database according to the cluster server;
And carrying out cluster processing on the relational database according to the cluster architecture to obtain a cluster database.
6. The data disaster recovery backup method based on the relational database is characterized by comprising the following steps:
obtaining data to be backed up in a relational database, carrying out data blocking processing on the data to be backed up to obtain backup data blocks, calculating the importance degree corresponding to each data block in the backup data blocks, and determining the backup priority corresponding to the backup data blocks according to the importance degree;
calculating the forward error correction code corresponding to the backup data block, performing coding processing on the backup data block according to the forward error correction code to obtain a coded data block, calculating an association coefficient corresponding to the coded data block, and performing packaging processing on the coded data block according to the association coefficient to obtain a coded data packet, wherein the calculating the association coefficient corresponding to the coded data block comprises the following steps:
mining data information corresponding to each piece of data in the coded data block, and calculating an information entropy value corresponding to each piece of information in the data information;
screening the data information according to the information entropy value to obtain target data information;
Calculating the information association degree between each piece of information in the target data information by the following formula:wherein G represents the degree of information association between each of the pieces of target data information, ++>Information quantity representing target data information, j representing information sequence number of target data information, +.>Represents the standard deviation corresponding to the j-th information in the target data information,/and>represents the standard deviation corresponding to the j+1th information in the target data information,/for>Two-stage minimum difference value representing difference value of standard deviation corresponding to jth information and jth+1th information, +.>A two-stage maximum difference value representing a difference value of standard deviations corresponding to the jth information and the (j+1) th information;
carrying out weighted summation on the information association degree to obtain association coefficients corresponding to the encoded data blocks;
clustering the relational database to obtain a clustered database, scheduling disaster log data corresponding to the relational database, and constructing backup protection nodes in the clustered database according to the disaster log data;
calculating the useful coefficient corresponding to the coded data packet through the following formula:wherein R represents a useful coefficient corresponding to the encoded data packet, T represents a fetch response time corresponding to the encoded data packet, < > >Indicating the number of objects contained in the nth data of the encoded data packet, n indicating the data sequence number in the encoded data packet, ">Representing the number of redundant objects of the nth data in the encoded data packet, a>Representing the cost of data storage of the nth data in the encoded data packet;
and removing the coded data packet according to the useful coefficient to obtain a target coded packet, and executing backup processing of the target coded packet in the backup protection node according to the backup priority to obtain a backup result.
7. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the relational database-based data disaster recovery backup method according to claim 6.
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| CN111679939A (en) * | 2020-06-16 | 2020-09-18 | 江苏超流信息技术有限公司 | Data backup method and system |
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