CN111382198B - Data recovery method, device, equipment and storage medium - Google Patents

Abstract

The invention discloses a data restoration method, device, equipment and storage medium. The method includes: determining at least one table associated with the predetermined field according to the predetermined field in the predetermined table; The association relationship between the at least one table forms an association relationship template between the tables; according to the association relationship template, table data associated with each value of at least one value of the predetermined field is collected and backed up. ; Obtain table data associated with the target value in the at least one value from the backed up table data to perform data restoration. According to the embodiments of the present invention, the batch synchronization efficiency of the relational database can be improved and the cost can be reduced, and automatic regression testing can be completed regularly and quantitatively without relying on manual intervention.

Description

Data restoration methods, devices, equipment and storage media

Technical field

The invention belongs to the field of computers, and in particular relates to a data restoration method, device, equipment and storage medium.

Background technique

As we all know, the database is an indispensable and important component. During the process of software development, debugging, and testing, programmers need to constantly test the current application to ensure the correctness, accuracy, and robustness of the software. Most of the testing is to verify whether the application can complete the expected addition, deletion, modification and query operations on specific data in the database.

Some of the test data contents are non-renewable resources. For example, after adding a piece of data to the database, the target expected data already exists. It is often restricted by primary keys, unique indexes or business rules, and the current data content cannot be used to add new data. operate. Unless manual operation is used to delete the current data content, or find another piece of data content that meets the test requirements, or restore the data in the database. There are currently two ways to achieve data recovery:

1. Batch recovery of relational database

Mainstream relational databases mainly complete batch application data restoration through batch synchronization of backup data or data snapshots. The data sources are backup libraries and logs. Its purpose is mainly to deal with database information security and retain customer data to the greatest extent in sudden failures such as power outages.

For example, mainstream Oracle and MySQL databases support large-volume data synchronization through their own application functions, supplemented by internal or third-party tools such as SQLLOAD and goodenGate; Oracle itself can respond to data that occurs within minutes or even hours based on its underlying capabilities.

2. Batch recovery of non-relational databases

Mainstream non-relational databases, including Hadoop, Hbase, MongoDB and other databases, mainly use internal active-standby and emergency mechanisms to store data in a 1-standby-N mode. However, when a data storage node fails and the data is unavailable or lost, The management node will automatically enable and execute security policies, synchronize the data of other data nodes, and continuously maintain a data persistence state of one copy of data.

The data source of non-relational databases comes from the storage itself and adopts a high-availability mechanism. Its main purpose is to deal with database information security and retain customer data to the greatest extent possible in sudden failures such as failed nodes and downtime.

3. Manual maintenance of small amounts of data synchronization

Regardless of whether it is a relational database or a non-relational database, you can manually compile data operation scripts, such as Structured Query Language (SQL) scripts, to add, delete, modify, and query a small amount of target data. And by reverse writing rollback scripts and local online script backup, a small amount of target data can be basically restored.

Manual script data maintenance is highly operable, has a wide range of applications, and is extremely flexible. It is generally used for daily data maintenance. This also includes the application data restoration function during application development and testing.

Disadvantages of existing technology:

1. Batch data recovery costs are high and cannot meet the demand for rapid testing at any time: regular data recovery of test databases and R&D databases can solve the problem of unavailability of test data content due to changes in target data, but in the face of large-scale industry applications , a test scenario may involve parallel recovery of multiple databases, which is expensive; at the same time, the recovered data content is still unavailable after test use, and you need to find another target or wait for the next batch recovery.

2. Manual maintenance is inefficient and resources are difficult to share: Restoring test data through manual scripts is a common practice in the software development process. Programmers or testers temporarily write rollback scripts based on their understanding of the application and business scenarios to facilitate next debugging. Temporarily compiled scripts often only target one or a few specific scenarios. Once the test is completed, the script is often scrapped and rewritten next time.

3. The complexity of industry application data: Whether it is manual compilation of reply scripts or batch database data recovery, we are faced with the complexity of industry application data. For example: time issue, the test data that could be used yesterday and last month cannot be used today or this month due to business rules restrictions. For example, if there is a discount for the current month, then the data last month will be restored either manually or by database recovery. , are all unavailable. At this time, you need to manually modify (UPDATE) the order date to a certain day of the month, which is a common "data creation" phenomenon in the software industry.

Contents of the invention

Embodiments of the present invention provide a data restoration method, device, equipment and storage medium, which can solve the problems of inefficiency and high cost of batch synchronization of relational databases, and can complete automatic regression testing regularly and quantitatively without relying on manual intervention.

In a first aspect, an embodiment of the present invention provides a data restoration method, which method includes:

Determine at least one table associated with the predetermined field according to the predetermined field in the predetermined table;

Form an association relationship template between tables according to the association relationship between the predetermined table and the at least one table;

According to the association relationship template, collect and back up table data associated with each value of at least one value of the predetermined field;

Obtain table data associated with the target value in the at least one value from the backed up table data to perform data restoration.

In a second aspect, an embodiment of the present invention provides a data restoration device, which includes:

An association table determination module, configured to determine at least one table associated with the predetermined field according to the predetermined field in the predetermined table;

A template forming module, configured to form an association relationship template between tables based on the association relationship between the predetermined table and the at least one table;

A data collection module configured to collect and back up table data associated with each value of at least one value of the predetermined field according to the association relationship template;

A data restoration module, configured to obtain table data associated with the target value in the at least one value from the backed up table data to perform data restoration.

In a third aspect, embodiments of the present invention provide a data restoration device, which includes: a processor and a memory storing computer program instructions;

The processor implements the data restoration method when executing the computer program instructions.

In a fourth aspect, embodiments of the present invention provide a computer storage medium. Computer program instructions are stored on the computer storage medium. When the computer program instructions are executed by a processor, the data restoration method is implemented.

The data restoration method, device, equipment and storage medium of the embodiment of the present invention can proceed from a specific scenario and perform data restoration through table data associated with one or more target values. The data restoration efficiency is high and a data restoration process can be completed in a short time. The data of test cases can be restored and reused after testing, solving the problems of inefficiency and high cost of batch synchronization of relational databases. Moreover, association relationship templates are introduced to solve the complexity problem of industry data restoration. The fields of each table can be customized according to the needs of specific scenarios, and automatic data restoration can be realized, greatly improving reusability. Furthermore, it can support large-scale and massive background regression testing. After the test scenarios and data restoration are established, automatic regression testing can be completed regularly and quantitatively without relying on manual intervention.

Description of drawings

In order to explain the technical solutions of the embodiments of the present invention more clearly, the drawings required to be used in the embodiments of the present invention will be briefly introduced below. For those of ordinary skill in the art, without exerting creative efforts, they can also Additional drawings can be obtained from these drawings.

Figure 1 shows a schematic flow chart of a data restoration method provided by an embodiment of the present invention;

Figure 2 shows a schematic diagram of fields in a data table according to an embodiment of the present invention;

Figure 3 shows a schematic diagram of the association between fields according to an embodiment of the present invention;

Figure 4 shows a schematic diagram of the association between tables according to an embodiment of the present invention;

Figure 5 shows a schematic diagram of fields in a data table according to another embodiment of the present invention;

Figure 6 shows a schematic diagram of the association between fields according to another embodiment of the present invention;

Figure 7 shows a schematic diagram of the association between tables according to another embodiment of the present invention;

Figure 8 shows a simplified schematic diagram of the association between tables according to an embodiment of the present invention;

Figure 9 shows a schematic flow chart of a data restoration method provided by another embodiment of the present invention;

Figure 10 shows a schematic flowchart of configuring an association relationship template according to an embodiment of the present invention;

Figure 11 shows a schematic flow chart of collecting table data according to one embodiment of the present invention;

Figure 12 shows a schematic flow chart of template restoration processing according to an embodiment of the present invention;

Figure 13 shows a schematic structural diagram of a data restoration device according to an embodiment of the present invention;

Figure 14 shows a schematic diagram of the hardware structure of a data restoration device provided by an embodiment of the present invention.

Detailed ways

Features and exemplary embodiments of various aspects of the present invention will be described in detail below. In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are configured only to explain the invention and not to limit the invention. It will be apparent to one skilled in the art that the present invention may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the invention by illustrating examples of the invention.

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that these entities or operations are mutually exclusive. any such actual relationship or sequence exists between them. Furthermore, the terms "comprises," "comprises," or any other variations thereof are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements, but also those not expressly listed other elements, or elements inherent to the process, method, article or equipment. Without further limitation, an element defined by the statement "comprising..." does not exclude the presence of additional identical elements in a process, method, article, or device that includes the stated element.

In order to solve the existing technical problems, embodiments of the present invention provide a data restoration method, device, equipment and computer storage medium. The following first introduces the data restoration method provided by the embodiment of the present invention.

Figure 1 shows a schematic flowchart of a data restoration method provided by an embodiment of the present invention. As shown in Figure 1, the method includes:

S101: Determine at least one table associated with the predetermined field based on the predetermined field in the predetermined table.

S102: Form an association relationship template between tables based on the association relationship between the predetermined table and at least one table.

S103: Collect and back up table data associated with each value of at least one value of the predetermined field according to the association relationship template.

S104: Obtain table data associated with at least one target value in the backed-up table data for data restoration.

For example, if testing is performed after S103, if the data changes due to testing or other reasons, S104 can be executed to restore the data.

It should be noted that the embodiment of the present invention realizes automatic restoration of local data based on field associations between data tables in a relational database. When a field value of a data table is known, all related object contents involved in the transaction in the data table affected by the previous transaction in the entire database can be automatically restored based on the field value. For example: in the mobile communications industry, after testing a product change on a mobile phone number, restore the data row contents in a series of tables related to the mobile phone number in the current database environment, so that secondary testing or other tests can be conveniently done. Work.

In the embodiment of the present invention, first, starting from the specific scenario, data restoration is performed through table data associated with one or more target values. The data restoration efficiency is high, and a test case can be completed in a short time (about 2 minutes). Data restoration can be reused after testing, solving the problems of inefficiency and high cost of batch synchronization of relational databases. Secondly, association relationship templates are introduced to solve the complexity problem of industry data restoration. The fields of each table can be customized according to the needs of specific scenarios, and automatic data restoration is realized, which greatly improves reusability and solves the complexity problem of industry application data. Finally, data can be reused and special field characteristics can be customized to support large-scale and massive background regression testing. After the test scenarios and data restoration are established, automatic regression testing can be completed regularly and quantitatively without relying on manual intervention.

In one embodiment of the present invention, S101 includes:

Use the predetermined field as the starting field; find the field associated with the starting field, and use the table where the field is located as the pending table; use each field in the pending table as a new starting field, and return to the steps to perform the search , until the first predetermined condition is met; use all the obtained tables to be processed as at least one table.

The first predetermined condition includes: the field associated with the starting field cannot be found or the obtained table to be processed meets the test scenario requirements.

It should be noted that in actual production and application work of the database, based on factors such as efficiency and business complexity, the design and R&D do not completely use the database's primary and foreign keys to maintain data consistency, but use upper-layer WEB or middleware applications to ensure data consistency in the business. Maintain business data consistency in logical code. Figure 2 shows a schematic diagram of fields in a data table according to an embodiment of the present invention. As shown in Figure 2, the relationship between fields is distinguished by the shape of the outer frame of each field. Of course, the relationship between fields can also be distinguished by the color of each field in the table. In Figure 2, field 1 in data table A and field 2 in data table B are both square frames, that is, field 1 in data table A and field 2 in data table B have an associated relationship. In the same way, field 1 in data table B is related to field 1 in data table C, and field 3 in data table C is related to field 1 in data table D.

According to the field correlation relationships in each table in Figure 2, a schematic diagram of the correlation relationships between fields can be drawn as shown in Figure 3. As shown in Figure 3, A1 is associated with B2, B1 is associated with C1, C3 is associated with D1, A1 represents field 1 in data table A, B2 represents field 2 in data table B, B1, C1 , C3, D1 can be deduced in the same way.

Figure 2 enumerates the relationships between four data tables. That is, after obtaining the instance value of A1 (such as the user's mobile phone number PHONE_NO), you can immediately obtain one or a group of B2 instance values through a SQL statement. One or a group depends on A1. One-to-one, one-to-many or many-to-many relationship with B2.

After obtaining B2 based on A1, you can directly obtain B1 in the data table B where B2 is located, and then obtain one or more groups of C1. In the same way, depending on the list of all C1 fields obtained, the list of all D1 fields can be obtained.

Figure 4 shows a schematic diagram of the association between tables in an embodiment of the present invention. The data rows of table A depend on A1 to obtain, and the data rows of table B depend on A1 to obtain. Both of them are obtained directly; the data rows of table C depend on A1 to obtain. B2 data acquisition, D table data rows rely on C3 data acquisition in the C1 data content obtained by B1, both are indirect acquisitions. In Figure 4, the solid line represents the direct acquisition relationship, and the dotted line represents the indirect acquisition relationship.

In actual applications, data model design is designed based on specific application needs and mostly evolves incrementally, so the association results of transaction data tables in general industries will have a graph-like structure. The embodiment of the present invention converts the graph-like association relationship into a binary tree-like association relationship.

Figure 5 shows a schematic diagram of fields in a data table according to another embodiment of the present invention. As shown in Figure 5, there are four tables of user basic information (data table A), user ordering information (data table B), user ordering attribute information (data table C), and user attribute configuration information table (data table D). Based on this, a new business operation rule record table X is added due to business needs, and the correlation between its fields is shown in Figure 6. In Figure 6, the field relationships are complex and redundant. When the instance value of a field in data A1 is known, there are many ways to obtain the data row contents of tables B, C, and D, as shown in Figure 7. If the Knowing that A1 needs to obtain table B data, it can be obtained through more than three methods: A1-B2, A1-X1-B1, A1-X1-C3-B1, etc. If table B is obtained through these three methods, it will result in collection Redundant table data and repeated operations during data restoration increase R&D implementation costs and reduce processing efficiency.

Based on the above problems, in one embodiment of the present invention, the method further includes:

After obtaining multiple to-be-processed tables, determine whether there are duplicate tables in the multiple to-be-processed tables; if there are duplicate tables, retain one of the duplicate tables and delete the other tables in the duplicate tables.

The association relationship template is a binary tree-shaped association relationship template.

In the embodiment of the invention, for duplicate tables, only one table is retained and the other tables are deleted. For example, if table A and table B are duplicated, then one of table A and table B is retained and the other table is deleted. Avoid duplicate tables, which lead to multiple association methods, redundant storage and repeated restoration problems, thereby reducing association complexity and improving recovery efficiency. As shown in Figure 8, eliminating duplicate tables greatly simplifies the relationship between tables.

In one embodiment of the present invention, S103 includes:

For each of the at least one value, predetermined steps are performed to obtain table data associated with each of the at least one value.

Among them, the predetermined steps include:

Use the current value to be processed in at least one value as the starting value; according to the order of the tables in the association relationship template, collect at least one table to be collected associated with the starting value; use the field value in each table to be collected as New starting value, return and execute at least one to-be-collected table associated with the new starting value until the second predetermined condition is met; use all obtained data in the to-be-collected table as related to the current to-be-processed value linked table data.

The second predetermined condition includes: there is no table to be collected associated with the starting value within the predetermined collection range.

As an example, based on the field value of A1, you can obtain the contents of all data rows involved in tables A, B, C, and D. After backing up the data row content records, you can use them for subsequent data restoration operations.

For example: when obtaining table data associated with a specific mobile phone number, use the mobile phone number as the starting value, and first collect the user ordering information associated with the mobile phone number according to the order of the tables in the association template. table; secondly, use the field values in the user ordering information table as the new starting value, and collect the user ordering attribute information table associated with the new starting value; thirdly, use the field values in the user ordering attribute information table as A new starting value, and collecting the user attribute configuration information table associated with the new starting value. Therefore, according to the order of the tables in the association template, data in the user ordering information table, the user ordering attribute information table, and the user attribute configuration information table are collected sequentially.

In one embodiment of the present invention, S104 includes:

When performing data restoration, the table data within the predetermined restoration range is replaced with table data associated with the target value.

In one embodiment of the present invention, S104 includes:

Delete the data of the table within the predetermined restoration range through the delete instruction, and insert the table data associated with the target value through the insert instruction.

Figure 9 shows a schematic flowchart of a data restoration method provided by another embodiment of the present invention. As shown in Figure 9, the method includes:

S201: Configure an association template based on the field association relationship.

Among them, S201 includes:

S2011, according to the needs of business testing and automatic testing, select the reservation table and reservation fields and record them. For example: In the mobile industry, starting from the mobile phone number under the basic user table, the data tables and data rows are directly or indirectly associated. After the data is recorded and restored, it can meet more than 95% of business testing needs.

S2012: Sort out the mapping relationship, use the predetermined field as the starting field, search for all tables directly related to the starting field in the database and record (or back up) them. Exclude already logged tables when logging. Define the priority sequence for next data collection while recording.

S2013, use the fields in the data table obtained in step S2012 as new starting fields, and repeat the table lookup action in S2012 until all fields in the data table obtained in step S2012 are searched. Exclude already logged tables when logging.

S2014, use the field in the data table obtained in S2013 as the new starting field, and repeat the lookup table action in S2012 until the data result obtained in step S2013 meets the test scenario requirements. Exclude already recorded data tables during recording.

S2015, persistently stores the results of S2014. For the scheduled fields of the scheduled table, there is no need to perform the S201 operation again.

At this point, the configuration of the association relationship template based on the predetermined fields is completed, and the relationship data instance result between the business test dimension data table and the fields is delivered as a binary tree result, that is, the association relationship template. This association template does not depend on the specific values of the predetermined table or predetermined fields. Any field value that is not empty can be applied to this association template to directly or indirectly locate the data rows in the full data table required for the data restoration scenario.

Figure 10 shows a schematic flowchart of configuring an association relationship template according to an embodiment of the present invention. As shown in Figure 10, the template configuration application is responsible for storing the business relationships in the target database that have been artificially sorted out, and entering the configuration code table of the management database. The configuration at this time is mainly pseudo-SQL, that is, there is a value in the assignment part of the SQL statement. Some variables that need to be dynamically obtained during collection execution or restoration execution, such as operation time, operation flow, etc.

S202: Collect data in the table based on at least one field value and association relationship template, and back up the collected data.

The delivery of S202 is the specific data of the data rows in the full data table required for the data restoration scenario to collect the data records before the operation in the data restoration scenario. Different starting tables and field values in the starting table collect different data.

Among them, S202 includes:

S2021: Input the stock target template and at least one valid field value of the predetermined field to the system application. The target template is used to define the scope of data collection and data restoration. The valid field value is the data starting point for a specific restore operation.

S2022: The system automatically uses the valid field value as the starting value and collects all the data row contents of the table associated with the starting value. According to the order of the tables in the relationship template, the data of each table associated with the starting value is collected in sequence and backed up.

S2023: Use the field value in the table obtained in step S2022 as the new starting value, and repeat the operation in step S2021 until the data content obtained in step S2022 is complete.

S2024: Use the data result obtained in step S2023 as the new starting value, and repeat step S2022 until all configuration relationships in the association relationship template in step S2023 are processed.

S2025: The results of S2024 are stored persistently. There is no need to perform the S202 operation every time the test scene data is restored in the future.

At this point, based on at least one field value and association relationship template, the data in the table is collected, the collected data is backed up, and a result set recorded before the business test operation of a binary tree result is delivered. This result set does not depend on any test scenario and can be reused across all action table scope tests. For example: in the mobile communications industry, after the S202 operation is completed by the mobile phone number under the user's basic table, more than 95% of the business testing needs can be met.

Figure 11 shows a schematic flow chart of collecting table data according to one embodiment of the present invention. As shown in Figure 11, the template collection application is internally mainly composed of two application entities: a general variable pool and a pseudo-SQL execution block. The former serves as a general access area and records the encountered pseudo-SQL variables K-V in the form of key-value pairs. The latter The user can dynamically obtain the current value of the variable in pseudo-SQL based on the configured constants, environment variables or SQL statements of the data model. This cycle continues until all pseudo-SQL values required during collection are obtained. After the collection of pseudo SQL values is completed, the model collection application will delete (DELETE) pseudo SQL and insert (INSERT) pseudo SQL in sequence according to the management data model configuration, and enter the table into the table in the way of template processing data instances.

S203: Perform a database data restoration operation based on the target field value, the association template and the backed up data.

S203 delivers all data content in the pre-test data preparation phase for test data restoration. This step is generated in a relational database, and testing can be carried out based on the data backed up by S202. This step can be repeated any number of times. For example, after executing S203 on a mobile phone number, it can be used for multiple and repeated tests to verify the correctness of the system application in the test environment before going online, or the correctness of the system in the production environment after serious going online.

Among them, S203 includes:

S2031. Input the stock target template and valid target value to the system application. The template is used to define the scope of data collection and restoration. The target value is the starting point of the data for a specific restore operation. The target value is the value in at least one valid field value in S2021. The number of target values can be one or more.

In S2032, the system automatically extracts the table data associated with the target value from the data backed up in S202, replaces the KEY variables in the pseudo SQL for data deletion one by one, generates the deletion SQL and executes it.

The purpose of deleting SQL is to delete historical data generated by the last test or other reasons.

S2033: Extract the table data associated with the target value from the data backed up in S202, replace the KEY variables in the pseudo SQL for data restoration one by one, generate the restoration SQL and execute it.

S2034. After all steps S2033 are executed and are correct, the database is submitted and success information is fed back to the upstream of the system. After all S2033 is executed and an error occurs, the database is rolled back, and failure information is fed back to the upstream of the system to locate vulnerabilities (BUGs).

At this point, based on the target value, relationship template and backed-up data, the database data restoration after the operation is completed, and the relational database content restoration required for the test is delivered. S203 is repeatedly called, executed and delivered for each specific test.

Figure 12 shows a schematic flowchart of template restoration processing according to an embodiment of the present invention. As shown in Figure 12, the template restoration processing application mainly consists of two application entities: a general variable pool and a pseudo-SQL execution block. The former serves as a general access area and records the encountered pseudo-SQL variables K-V in the form of key-value pairs. The latter The user can dynamically obtain the current value of the variable in pseudo-SQL based on the configured constants, environment variables or SQL statements of the data model. This cycle continues until all pseudo-SQL values required during the restore process are obtained.

The embodiments of the present invention first start from the business logic relationship. After template design and record storage, the data required for testing can be selectively and automatically restored. The embodiments of the present invention solve the problem of test environment maintenance under the relational database, especially for mobile communications. The Customer Relationship Management (CRM) system in the industry support system can greatly improve the efficiency of daily development and testing.

Secondly, based on the business logic relationships of data tables and fields in relational databases, the application design, process design and model design of data restoration scenarios are realized, which solves the problem of inefficient data restoration operations and solves the problem of different data tables in different test scenarios. The complex problem of data restoration not only improves test efficiency, but can also be used for automated and large-scale system automatic regression testing.

The embodiments of the present invention include at least the following four advantages:

1. Solve the problem of inefficiency and high cost of batch synchronization of relational databases. The embodiment of the present invention starts from the local data of a single database in a specific test scenario, and has high data restoration efficiency. The full data restoration of a test case can be completed within 2 minutes. Can be reused after testing.

2. To solve the problem of low data maintenance efficiency and difficulty in sharing resources, the embodiment of the present invention adopts an independent data maintenance system to automatically generate and deliver DELETE and INSERT scripts for data restoration in test cases. The same test number can be used on demand. As long as the test time (within 2 minutes) does not occur at the same time, there is no exception.

3. Solve the complexity problem of industry application data. The embodiment of the present invention solves the complexity problem of industry application data through multiple templates, introducing external variables and pseudo-SQL. The special fields of each data table can be customized according to the needs of specific scenarios, and Data generation is automated, greatly improving reusability.

4. Based on the reusability of data in this proposal and the customization of special field characteristics, embodiments of the present invention can support large-scale and massive background regression testing. After the test scenarios and data restoration are established, automatic regression testing can be completed regularly and quantitatively without relying on manual intervention. , which traditional solutions cannot provide.

Figure 13 shows a schematic structural diagram of a data restoration device according to an embodiment of the present invention. As shown in Figure 13, the device 300 includes:

The association table determination module 301 is configured to determine at least one table associated with the predetermined field according to the predetermined field in the predetermined table.

The template forming module 302 is configured to form an association relationship template between tables based on an association relationship between a predetermined table and at least one table.

The data collection and backup module 303 is configured to collect and back up table data associated with each value of at least one value of the predetermined field according to the association relationship template.

The data restoration module 304 is configured to obtain table data associated with the target value in at least one value from the backup table data to perform data restoration.

In one embodiment of the present invention, the association table determination module 301 includes:

The first starting field determination module is used to use the predetermined field as the starting field;

The table lookup module is used to find the field associated with the starting field, and the table where the field is located is used as the to-be-processed table.

The second starting field determination module is used to use each field in the table to be processed as a new starting field.

The table search module is also used to repeatedly perform the search steps until the first predetermined condition is met.

The first table determination module is used to treat all the obtained tables to be processed as at least one table.

In one embodiment of the present invention, the first predetermined condition includes: a field associated with the starting field cannot be found or the obtained table to be processed meets the test scenario requirements.

In one embodiment of the invention, the device 300 further includes:

The judgment module is used to judge whether there are duplicate tables in the multiple to-be-processed tables after obtaining multiple to-be-processed tables;

The deduplication module is used to retain one of the duplicate tables and delete other duplicate tables when there are duplicate tables.

In one embodiment of the present invention, the association relationship template is a binary tree-shaped association relationship template.

In one embodiment of the present invention, the data collection and backup module 303 includes:

The data collection module is configured to perform predetermined steps on each of the at least one value to obtain table data associated with each of the at least one value.

Among them, the data collection module includes:

The first starting value determination module is configured to use the current value to be processed in at least one value as the starting value.

The table collection module is used to sequentially collect at least one to-be-collected table associated with the starting value according to the order of the tables in the association relationship template.

The second starting value determination module is used to use the field value in each table to be collected as a new starting value.

The table collection module is also configured to repeatedly execute at least one table to be collected associated with the new starting value until the second predetermined condition is met.

The second table determination module is used to use the obtained data in all tables to be collected as table data associated with the current value to be processed.

In one embodiment of the present invention, the second predetermined condition includes: there is no table to be collected associated with the starting value within the predetermined collection range.

In one embodiment of the present invention, the data restoration module 304 includes:

The data replacement module is used to replace table data within a predetermined restore range with table data associated with the target value.

In one embodiment of the invention, the data replacement module includes:

The data deletion module is used to delete the data of the table within the predetermined restoration range through deletion instructions.

Data insertion module is used to insert table data associated with target values through insert instructions.

In one embodiment of the present invention, the data collection and backup module is configured to collect table data associated with each value of at least one value of a predetermined field in a relational database.

The data restoration module is used to restore data in the relational database based on the table data associated with the target value.

Figure 14 shows a schematic diagram of the hardware structure of a data restoration device provided by an embodiment of the present invention.

The data restoration device may include a processor 401 and a memory 402 storing computer program instructions.

Specifically, the above-mentioned processor 401 may include a central processing unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more integrated circuits according to embodiments of the present invention.

Memory 402 may include bulk storage for data or instructions. By way of example and not limitation, the memory 402 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive or two or more A combination of many of the above. Memory 402 may include removable or non-removable (or fixed) media, where appropriate. Where appropriate, the memory 402 may be internal or external to the integrated gateway disaster recovery device. In certain embodiments, memory 402 is non-volatile solid-state memory. In certain embodiments, memory 402 includes read-only memory (ROM). Where appropriate, the ROM may be a mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically rewritable ROM (EAROM) or flash memory or A combination of two or more of these.

The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any of the data restoration methods in the above embodiments.

In one example, the data restoration device may also include a communication interface 403 and a bus 410. Among them, as shown in Figure 14, the processor 401, the memory 402, and the communication interface 403 are connected through the bus 410 and complete communication with each other.

The communication interface 403 is mainly used to implement communication between modules, devices, units and/or equipment in the embodiment of the present invention.

Bus 410 includes hardware, software, or both, coupling the components of the data recovery device to one another. By way of example, and not limitation, the bus may include Accelerated Graphics Port (AGP) or other graphics bus, Enhanced Industry Standard Architecture (EISA) bus, Front Side Bus (FSB), HyperTransport (HT) interconnect, Industry Standard Architecture (ISA) Bus, Infinite Bandwidth Interconnect, Low Pin Count (LPC) Bus, Memory Bus, Micro Channel Architecture (MCA) Bus, Peripheral Component Interconnect (PCI) Bus, PCI-Express (PCI-X) Bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus or other suitable bus or a combination of two or more of these. Where appropriate, bus 410 may include one or more buses. Although embodiments of the invention describe and illustrate a particular bus, the invention contemplates any suitable bus or interconnection.

The data restoration device can execute the data restoration method in the embodiment of the present invention, thereby realizing the data restoration method and device described in conjunction with FIG. 1 and FIG. 13 .

In addition, combined with the data restoration method in the above embodiment, the embodiment of the present invention can be implemented by providing a computer storage medium. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the data restoration methods in the above embodiments is implemented.

It is to be understood that this invention is not limited to the specific arrangements and processes described above and illustrated in the drawings. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications and additions, or change the order between steps after understanding the spirit of the present invention.

The functional blocks shown in the above structural block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, or the like. When implemented in software, elements of the invention are programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine-readable medium or transmitted over a transmission medium or communications link via a data signal carried in a carrier wave. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. Code segments may be downloaded via computer networks such as the Internet, intranets, and the like.

It should also be noted that the exemplary embodiments mentioned in the present invention describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above steps. That is to say, the steps may be performed in the order mentioned in the embodiments, or may be different from the order in the embodiments, or several steps may be performed simultaneously.

The above are only specific implementations of the present invention. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, modules and units can be referred to the foregoing method embodiments. The corresponding process will not be described again here. It should be understood that the protection scope of the present invention is not limited thereto. Any person familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should be covered. within the protection scope of the present invention.

Claims (13)

1. A data restoration method, characterized by including: Determine at least one table associated with the predetermined field according to the predetermined field in the predetermined table; Form an association relationship template between tables according to the association relationship between the predetermined table and the at least one table; According to the association relationship template, collect and back up table data associated with each value of at least one value of the predetermined field; Obtain table data associated with the target value in the at least one value from the backed up table data to perform data restoration. 2. The method of claim 1, wherein determining at least one table associated with the predetermined field according to the predetermined field in the predetermined table includes: Use the predetermined field as a starting field; Find the field associated with the starting field, and use the table where the field is located as the pending table; Use each field in the to-be-processed table as a new starting field, and return to the steps of performing the search until the first predetermined condition is met; Use all the obtained tables to be processed as the at least one table. 3. The method according to claim 2, wherein the first predetermined condition includes: a field associated with a starting field cannot be found or the obtained table to be processed meets the test scenario requirements. 4. The method of claim 2, further comprising: After obtaining multiple to-be-processed tables, determine whether there are duplicate tables among the multiple to-be-processed tables; If the duplicate tables exist, one of the duplicate tables is retained and the other tables in the duplicate tables are deleted. 5. The method according to claim 1, characterized in that the association relationship template is a binary tree-shaped association relationship template. 6. The method according to claim 1, characterized in that, according to the association relationship template, collecting table data associated with each value of at least one value of the predetermined field, including: For each of the at least one value, perform predetermined steps to obtain table data associated with each of the at least one value; Wherein, the predetermined steps include: Use the current value to be processed in the at least one value as the starting value; According to the order of the tables in the association relationship template, sequentially collect at least one to-be-collected table associated with the starting value; Use the field value in each of the tables to be collected as a new starting value, and return to execute at least one table to be collected associated with the new starting value until the second predetermined condition is met; All obtained data in the table to be collected are used as table data associated with the current value to be processed. 7. The method according to claim 6, wherein the second predetermined condition includes: there is no table to be collected associated with the starting value within the predetermined collection range. 8. The method of claim 1, wherein, When performing the data restoration, table data within a predetermined restoration range is replaced with table data associated with the target value. 9. The method of claim 8, wherein, The data of the table within the predetermined restoration range is deleted through the delete instruction, and the table data associated with the target value is inserted through the insert instruction. 10. The method of claim 1, wherein, Collecting table data associated with each of the at least one value of the predetermined field in the relational database; and, The data in the relational database is restored according to the table data associated with the target value. 11. A data restoration device, characterized in that it includes: An association table determination module, configured to determine at least one table associated with the predetermined field according to the predetermined field in the predetermined table; A template forming module, configured to form an association relationship template between tables based on the association relationship between the predetermined table and the at least one table; A data collection module configured to collect and back up table data associated with each value of at least one value of the predetermined field according to the association relationship template; A data restoration module, configured to obtain table data associated with the target value in the at least one value from the backed up table data to perform data restoration. 12. A data restoration device, characterized in that the device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the data restoration method according to any one of claims 1-10 is implemented. 13. A computer storage medium, characterized in that computer program instructions are stored on the computer storage medium, and when the computer program instructions are executed by a processor, the data restoration method according to any one of claims 1-10 is implemented. .