CN112422635B - Data checking method, device, equipment, system and storage medium - Google Patents
Data checking method, device, equipment, system and storage medium Download PDFInfo
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Abstract
The application discloses a data checking method, a device, equipment, a system and a storage medium, and relates to the field of data processing. The method comprises the following steps: generating and transmitting a data stream comprising data associated with the write operation under the condition that the write operation occurs in each system data pool, wherein the data stream comprises a main key value of the data; dividing the data stream into at least one data area based on the fields of the data stream and a preset area dividing rule, wherein each data area comprises data streams corresponding to at least two system data pools; in each data area, according to the primary key value corresponding to the data flow, checking the data flow corresponding to at least two system data pools in the data area to determine whether the data of at least two system data pools in the data area are consistent. According to the embodiment of the application, the problem of data unevenness of a cross-system can be timely found.
Description
Technical Field
The application belongs to the field of data processing, and particularly relates to a data checking method, a device, equipment, a system and a storage medium.
Background
As business complexity increases, a business may involve multiple systems. Correspondingly, the cross-system check of the business data is needed among a plurality of systems to find the inconsistency of the business data among the cross-systems, so that measures can be conveniently taken for each system, and the stable operation of each system is ensured.
At present, service data can be acquired from two systems participating in data verification. For example, service data within 1 day after the end is acquired from the system a and the system B, and whether the service data in the system a and the system B are consistent or not is compared piece by piece, that is, whether a problem of data irregularity across systems exists or not is solved. However, the data checking method cannot find the problem of cross-system data irregularity in time.
Disclosure of Invention
The embodiment of the application provides a data checking method, a device, equipment, a system and a storage medium, which can timely find the problem of cross-system data unevenness.
In a first aspect, an embodiment of the present application provides a data collation method, including: generating and transmitting a data stream comprising data associated with the write operation under the condition that the write operation occurs in each system data pool, wherein the data stream comprises a main key value of the data; dividing the data stream into at least one data area based on the fields of the data stream and a preset area dividing rule, wherein each data area comprises data streams corresponding to at least two system data pools; in each data area, according to the primary key value corresponding to the data flow, checking the data flow corresponding to at least two system data pools in the data area to determine whether the data of at least two system data pools in the data area are consistent.
In a second aspect, an embodiment of the present application provides a data collation apparatus including: the data stream generating module is used for generating and transmitting a data stream comprising data associated with the write operation under the condition that the write operation occurs in each system data pool, wherein the data stream comprises a main key value of the data; the region dividing module is used for dividing the data stream into at least one data region based on the fields of the data stream and a preset region dividing rule, and each data region comprises data streams corresponding to at least two system data pools; and the checking module is used for checking the data streams corresponding to the at least two system data pools in the data areas according to the primary key values corresponding to the data streams in each data area so as to determine whether the data of the at least two system data pools in the data areas are consistent.
In a third aspect, an embodiment of the present application provides a data collation apparatus, including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the data collation method of the first aspect.
In a fourth aspect, embodiments of the present application provide a data collation system, comprising: the data flow device is used for generating and transmitting a data flow comprising data associated with the write operation under the condition that the write operation occurs in each system data pool, wherein the data flow comprises a main key value of the data; the distribution device is used for dividing the data stream into at least one data area based on the fields of the data stream and a preset area division rule, and each data area comprises data streams corresponding to at least two system data pools; and the checking device is used for checking the data streams corresponding to the at least two system data pools in the data areas according to the primary key values corresponding to the data streams in each data area so as to determine whether the data of the at least two system data pools in the data areas are consistent.
In a fifth aspect, embodiments of the present application provide a computer storage medium having stored thereon computer program instructions that, when executed by a processor, implement the data collation method of the first aspect.
The embodiment of the application provides a data checking method, a device, equipment, a system and a storage medium, and under the condition that write operation occurs in each system data pool, a data stream comprising data associated with the write operation is generated. The data stream is divided into at least one data region, and each data region comprises data streams corresponding to at least two system data pools. The data streams corresponding to the at least two system data pools are checked in the data area to determine whether the data of the at least two system data pools are consistent. The data stream is generated by triggering the write operation without setting the time period for acquiring the data, so that the data stream is divided and checked. The write operation is not limited by the time length, and the data can be checked in real time under the condition that the data is changed, so that the problem of data unevenness across systems can be found in time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
FIG. 1 is a flow chart of an embodiment of a data collation method provided in the first aspect of the present application;
FIG. 2 is a flow chart of another embodiment of the data collation method provided in the first aspect of the present application
FIG. 3 is a flow chart of yet another embodiment of a data collation method provided in the first aspect of the present application;
FIG. 4 is a schematic diagram showing an example of a collation window for a data region in the embodiment of the present application
FIG. 5 is a flow chart of yet another embodiment of a data collation method provided in the first aspect of the present application;
FIG. 6 is a schematic diagram of an embodiment of a data collation apparatus according to a second aspect of the present invention;
FIG. 7 is a schematic view of another embodiment of a data collation apparatus provided in the second aspect of the present application;
fig. 8 is a schematic structural view of still another embodiment of the data collation apparatus provided in the second aspect of the present application;
fig. 9 is a schematic structural view of still another embodiment of the data collation apparatus provided in the second aspect of the present application;
fig. 10 is a schematic structural view of an embodiment of a data collation apparatus provided in a third aspect of the present application;
fig. 11 is a schematic structural diagram of an embodiment of a data collation system provided in the fourth aspect of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application are described in detail below to make the objects, technical solutions and advantages of the present application more apparent, and to further describe the present application in conjunction with the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative of the application and are not intended to be limiting. It will be apparent to one skilled in the art that the present application 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 present application by showing examples of the present application.
As business complexity increases, a business may involve multiple systems, e.g., a business is performed cooperatively by multiple systems. In order to ensure normal execution of the service, the systems involved in the service store data of the service, and check the data among a plurality of systems so as to find out the condition of inconsistent data among the systems, namely, find out the problem of uneven data among the cross systems, thereby taking measures and providing guarantee for stable operation of the systems.
The amount of data to be checked across systems is very large, and because the clocks of the systems may have differences, in order to avoid missing the checked data, data of services in a longer period of time is generally acquired, for example, data of respective services of the systems in a day that has been completed are acquired, and data of services of different systems are checked one by one to determine whether a problem of data unevenness exists across systems, but in this case, if the problem of data unevenness exists, the problem of data unevenness can only be discovered after time, but cannot be discovered in time.
The application provides a Data checking method, a device, equipment, a system and a storage medium, which can transmit Data in a Data Stream (namely Stream Data) mode under the condition of writing operation, and check Data of different systems by utilizing a main key value of the Data Stream, so that the problem of Data unevenness among the systems can be found in time.
The specific fields of the service and the data are not limited herein, for example, in the transaction field, the service may be specifically a transaction service, and the data of the service may be specifically transaction flow data; the checking of the data is the checking of transaction flow data of the same transaction service, and the checking of transaction details can be realized through the data checking. However, the application scenario in the embodiment of the present application is not limited to the transaction scenario, and other application scenarios that need to perform data verification are also within the protection scope of the embodiment of the present application.
The first aspect of the present application provides a data collation method which can be executed by a data collation apparatus, a data collation device or a data collation system, that is, the data collation method can be realized by a single apparatus or device, or by a system including a plurality of apparatuses or devices, without being limited thereto.
Fig. 1 is a flowchart of an embodiment of a data collation method provided in the first aspect of the present application. As shown in fig. 1, the data collation method may include steps S101 to S103.
In step S101, in the case where a write operation occurs in each system data pool, a data stream including data associated with the write operation is generated and transmitted.
The system data pool is used for storing data of the system, and can be particularly used for storing business data in the system. For example, in the transaction arts, a system data pool may be used to store the aggregate data of the transaction traffic of the system. The system data pool may be provided in the system, or may exist in a database form independent of the system, and is not limited herein. There may be a plurality of systems participating in the data collation, each system may correspond to one system data pool, i.e., there may be a plurality of system data pools participating in the data collation.
The write operation is an operation that may cause a change in data in the system data pool, for example, and without limitation, the write operation may include an insert operation such as an insert operation, an update operation such as an update operation, a delete operation such as a delete operation and a drop operation, a create operation such as a create operation, a modify operation such as an alter operation, and the like.
The data associated with the write operation includes data that the write operation is active. A data stream is an aggregate of a series of dynamic data that is not limited in time distribution and number. In the embodiment of the application, the data is carried by using the data flow. The data stream includes primary key values of the data. The content of the primary key of the data may be set according to the type of the data, and is not limited herein. For example, the data may include transaction flow data, and the primary key of the data may include a transaction flow number.
In the case where the data collation method is performed by the data collation apparatus or the data collation device, the data stream may be transmitted inside the data collation apparatus or the data collation device. In the case where the data collation method is performed by a data collation system including a plurality of apparatuses or devices, a data stream can be transmitted between the apparatuses or devices in the data collation system.
In step S102, the data stream is divided into at least one data area based on the fields of the data stream and a preset area division rule.
The fields of the data stream may be set according to the content, type, etc. of the data. For example, the fields of the data stream may include, without limitation, a system identification field, a primary key value field, a traffic status field, and the like. The system identification field is used for representing the identification of the system corresponding to the system data pool. The primary key field is used to characterize the primary key of the data. The service status field is used for representing the status of the service corresponding to the data.
According to the data checking purpose, the data flow corresponding to each system data pool is divided into a plurality of groups, namely at least one data area by the area dividing rule. Each data region includes at least two data streams corresponding to the system data pools. In each data region, the data contained in the data stream may be collated. Each data area can be corresponding to an inlet of the data flow, and the division of the data flow is realized by setting an area division rule.
The region dividing rule may be set according to the working scene and the working requirement, and is not limited herein. The data region may be regarded as a set of data streams formed after grouping of the data streams. Fields of the data stream of the same data area satisfy the same area division rule. In some examples, data collation is performed pairwise between systems, i.e., between system data pools, and correspondingly, each data region may include data flows corresponding to two system data pools.
For example, one service involves three systems, namely, a system A1, a system A2 and a system A3, wherein data of the system A1 is stored in a system data pool B1, data of the system A2 is stored in a system data pool B2, and data of the system A3 is stored in a system data pool B3. Under the condition that the data of the same service changes, the data of the service in the system data pool B1, the system data pool B2 and the system data pool B3 all change under normal conditions; however, there may be cases where the data of the service in one or both of the system data pools is unchanged, which is not limited herein. The fields of the data stream can embody the system identifier, the primary key value of the data, the service state and the like, and the data stream corresponding to the system data pool B1 and the data stream corresponding to the system data pool B2 can be divided into the data area C1 and the data stream corresponding to the system data pool B2 and the data stream corresponding to the system data pool B3 can be divided into the data area C2 through the area division rule. In the data area C1, the data flow corresponding to the system data pool B1 and the data flow corresponding to the system data pool B2 can be checked, and in the data area C2, the data flow corresponding to the system data pool B2 and the data flow corresponding to the system data pool B3 can be checked.
The data flow of a service corresponding to a system data pool may be divided into multiple data areas, or may be divided into one data area, which is not limited herein. For example, the region division rule may define that in the case where the value of the field D3 of the data stream is one of 0001, 0002, 0003, the data stream is divided into the data region C3 through the entry 2008. The region division rule may define that in a case where a value of a field D3 of the data stream is one of 0003, 0004, the data stream is divided into the data region C4 through the entry 2009. The data stream with the value of field D3 of 0003 is divided into data area C3 and data area C4; a data stream with a value of 0001 for field D3 would be divided into data regions C3. The value of the field D3 of the data stream of the data area C3 satisfies the area division rule that the value of the field D3 of the data stream is one of 0001, 0002, 0003. The value of the field D3 of the data stream of the data area C4 satisfies the area division rule that the value of the field D3 of the data stream is one of 0003 and 0004.
In step S103, in each data area, the data flows corresponding to at least two system data pools in the data area are checked according to the primary key value corresponding to the data flow, so as to determine whether the data of at least two system data pools in the data area are consistent.
Specifically, in each data area, the data streams corresponding to at least two system data pools with the same primary key value in the data area are checked. The data flow corresponding to one system data pool with a certain value of the primary key value exists in the data area, but the data flow corresponding to the other system data pool with the certain value of the primary key value does not exist, and the data inconsistency of at least two system data pools in the data area can be determined, namely the problem of data inconsistency across systems is determined. In the data area, checking the data of the data streams corresponding to at least two system data pools with the same main key value, and if the data of the data streams corresponding to at least two system data pools with the same main key value are the same, determining that the data of the at least two system data pools in the data area are consistent, namely determining that the problem of data unevenness across systems does not occur; if the data of the data streams corresponding to at least two system data pools with the same data primary key value are different, the data inconsistency of the at least two system data pools in the data area can be determined, namely the problem of data inconsistency across systems is determined.
In some examples, the collation of the data streams in the plurality of data regions is performed in parallel. For example, after division, there are 3 data areas, namely, a data area C1, a data area C2, and a data area C3. The collation of the data stream in the data area C1, the collation of the data stream in the data area C2, and the collation of the data stream in the data area C3 may be executed in parallel. The data flow checking in the data areas is executed in parallel, so that the data checking speed can be increased, and the data checking efficiency can be improved. The verification of the data streams in the different data areas may be performed by different apparatuses, devices or modules, which are not limited herein. The data area can be increased or reduced according to specific requirements, and the flexibility and the expandability of data checking are improved.
In some examples, the checking of the data flow in each data area may be performed in the memory, so as to further increase the speed of data checking, improve the efficiency of data checking, and reduce the resources occupied by data checking.
In an embodiment of the present application, in the event that a write operation occurs to each system data pool, a data stream is generated that includes data associated with the write operation. The data stream is divided into at least one data region, and each data region comprises data streams corresponding to at least two system data pools. The data streams corresponding to the at least two system data pools are checked in the data area to determine whether the data of the at least two system data pools are consistent. The data stream is generated by triggering the write operation without setting the time period for acquiring the data, so that the data stream is divided and checked. The write operation is not limited by the time length, and the data can be checked in real time under the condition that the data is changed, so that the problem of data unevenness across systems can be found in time. The data collation method provided by the embodiment of the present application can shorten the time required to find the data unevenness problem across systems to 1 minute or less, compared to a method that requires a day or more at the present stage.
When the amount of data is large, since the data stream is generated by the write operation trigger, the data is checked in real time, and the performance requirement of the data check can be satisfied higher than that in the case of adopting a mode of accumulating a large amount of data and then checking.
Fig. 2 is a flowchart of another embodiment of the data collation method provided in the first aspect of the present application. Fig. 2 is different from fig. 1 in that step S101 in fig. 1 may be refined to steps S1011 to S1013 in fig. 2, and the data collation method shown in fig. 2 may further include step S104.
In step S1011, a binary log of each system data pool is read, and a write operation of each system data pool is determined from the binary log.
The binary log is a BINLOG file and is used for recording changes of a database table structure and modification of table data. For example, a binary log may record the change to the database table structure and the modified operation statement of the table data. From the contents of the binary log, the write operations that occur to the system data pool may be determined.
In step S1012, a data stream message is generated based on the write operation.
The data stream message is used for carrying a data stream, and the specific format of the data stream message is not limited herein. In some examples, the data stream message may specifically be a JSON message. And the data stream message is used for bearing the data stream, so that the data stream is convenient to transmit. For example, the output format of a JSON message carrying a data stream is as follows:
The sysId can represent a system identifier, the seqNo and the traceId can represent main key values of data at different stages, the busTp can represent transaction types, and the seqSt can represent business states corresponding to the data.
Because the data corresponding to the same service may change, in order to enable the data flow to embody the change condition of the data, in some examples, the data flow message may include the data associated with the current write operation and the data associated with the last write operation, where the primary key value is the same. The data with the same main key value is the data corresponding to the same service. The change condition of the data can be embodied through the data related to the current writing operation and the data related to the last writing operation in the data flow message, the relevance of the front data and the rear data can be ensured to be judged in the subsequent process, and whether the data need to be checked or not is determined according to the change condition of the data. For example, in the output format of the JSON packet carrying the data stream, __ before is used as the node label of the data related to the last write operation and the data related to the current write operation.
In step S1013, a data stream message is transmitted through the data stream component.
The data stream messages may be transmitted by the data stream component one by one. The data streaming component may include, but is not limited to, a component of Kafka et al.
In some examples, before step S102 is performed, the data flow packet may also be converted into a format that is more convenient for data verification, for example, the data flow packet is converted into a Map mapping format, and the data converted into the Map mapping format is used to participate in the performance of the subsequent steps, so that data verification is facilitated by configuration.
In step S104, in the case that a system data pool corresponds to a plurality of data streams with the same primary key value, one data stream in which the fields meet the preset screening conditions is reserved.
Among the data streams obtained based on the system data pool, a plurality of data streams may appear in a service, and the data streams corresponding to the service need to be screened, so that the data checking of the data stream corresponding to the service is participated in, and the confusion of data checking is avoided. The main key values corresponding to the data streams are the same, and the service corresponding to the data streams is the same service. Specifically, the meaning of each field of the data stream and the requirement of data check can be utilized to set a screening condition, and one data stream is screened and reserved in a plurality of data streams with the same primary key value through the screening condition. One data stream whose reserved field meets the screening condition can participate in the subsequent data collation process.
In some examples, the data stream includes a traffic status field. The service status field is used for representing the status of the service corresponding to the data of the data flow. The above-mentioned filtering condition may include that the service status field includes a target value in a preset value set, and the service status field of the data stream is different from the service status field of the data stream corresponding to the last write operation. The set of preset values comprises at least one target value. The preset value set may be set according to the working scenario and the working requirement, which is not limited herein.
For example, the value of the service state field is 01, which indicates that the data of the data stream is temporarily not checked; the value of the traffic state field is 00, indicating that the data of the data stream temporarily needs to be checked. The set of preset values includes a target value 00. In the case where the traffic state field of the data stream L1 includes the target value 00 and the traffic state field of the data stream corresponding to the last write operation is 01, the data stream L1 is reserved. In the case where the traffic state field of the data stream L1 includes the target value 00, but the traffic state field of the data stream corresponding to the last write operation is 00, the data stream L2 is discarded.
The screening conditions are not limited to the above, and screening conditions capable of screening a plurality of data streams having the same primary key value are all within the scope of the embodiments of the present application, and are not illustrated herein.
Fig. 3 is a flowchart of still another embodiment of the data collation method provided in the first aspect of the present application. Fig. 3 is different from fig. 1 in that step S103 in fig. 1 may be specifically subdivided into step S1031 and step S1032 in fig. 3.
In step S1031, in each data area, the data stream is divided into collation windows according to the primary key value corresponding to the data stream.
The data streams in different checking windows have different primary key values, namely, the data streams with the same primary key value are not divided into different checking windows, and the data streams with the same primary key value are divided into the same checking window. The data stream is divided into the checking window, and the hash of the data stream can be realized. In some examples, a data stream with the same primary key value corresponding to each system data pool corresponding to a data area is included in a check window of the data area. For example, the data area C1 includes a data stream corresponding to the system data pool B1 and a data stream corresponding to the system data pool B2, and one check window in the data area C1 may include one data stream corresponding to the system data pool B1 and one data stream corresponding to the system data pool B2 with the same primary key value, that is, a pair of checks of the data streams of the system data pool B1 and the system data pool B2 with the same primary key value is performed in each check window in the data area C1.
In step S1032, the data flow within the collation window is collated.
Specifically, the check checks whether the data carried by the data flows within the check window are identical. The granularity of the collation window is smaller than that of the data region, and in some cases, the data stream in the collation window is triggered in the case where the duration of the data stream in the collation window exceeds the preset trigger duration. In other cases, the data flow within the collation window is triggered in the event that the number of data flows within the collation window reaches a preset trigger number. Because the data stream in the embodiment of the application is generated by triggering the writing operation and is not limited by the time length, the granularity of the checking window can be divided into very fine data streams in time or in number, so that the data checking speed is increased and the data checking efficiency is improved. In addition, because the matching of the data streams is completed in the process of dividing the data streams into the checking windows, the checking of the data streams in the checking windows is not required to be matched, the standardization and the plug-in implementation can be realized, the flexibility of data checking development design is improved, the increase and the decrease of the checking windows are relatively flexible, and the expansion is convenient.
In some examples, where the primary key value of the data stream within the existing collation window is different from the primary key value corresponding to the undivided data stream, a new collation window is generated, and the undivided data stream is divided into the new collation window. And triggering to check the data flow in the new check window under the condition that the time length of dividing the undivided data flow into the new check window exceeds the preset triggering time length.
In the case where the time period during which the undivided data stream is divided into the new collation window exceeds the preset trigger time period, and there is no data stream within the data area that can be collated with the data stream divided into the new collation window, a data unevenness problem may occur. The preset trigger time length can be set according to the working scene and the working requirement, and is not limited herein. The setting of the preset trigger duration may be implemented by a timer, for example, when the timer reaches the preset trigger duration, the verification of the data stream in the new verification window is triggered.
For example, fig. 4 is a schematic diagram of an example of a verification window of a data area in an embodiment of the present application. As shown in fig. 4, the collation windows existing in the data region C1 include a collation window D1, a collation window D2, and a collation window D3. The primary key value corresponding to the data stream in the collation window D1 is 000792, the primary key value corresponding to the data stream in the collation window D2 is 000982, and the primary key value corresponding to the data stream in the collation window D3 is 000991. If the data stream E1 in the data area C1 is not already divided into the collation window and the primary key value corresponding to the data stream E1 is 000993, the primary key value of the data stream in the existing collation window of the data area C1 is different from the primary key value corresponding to the data stream E1, and therefore, it is necessary to generate a new collation window D4 for the data stream E1 and divide the data stream E1 into the collation windows D4. And setting the preset triggering time length to be 3 minutes, and correspondingly, triggering the checking of the data stream in the checking window D4 after the data stream E1 is divided into the checking window D4 for 3 minutes.
In other examples, an undivided data stream is partitioned into an existing collation window in the event that the primary key of the data stream within the existing collation window is the same as the primary key corresponding to the undivided data stream. Triggering the checking of the data streams in the existing checking window in case the number of the data streams in the existing checking window reaches a preset trigger number. And continuing waiting in the case that the number of the data flows in the existing checking window does not reach the preset trigger number.
The preset trigger number can be set according to the working scene and the working requirement, and is not limited herein.
For example, as shown in fig. 4, the collation windows existing in the data area C1 include a collation window D1, a collation window D2, and a collation window D3. The primary key value corresponding to the data stream in the collation window D1 is 000792, the primary key value corresponding to the data stream in the collation window D2 is 000982, and the primary key value corresponding to the data stream in the collation window D3 is 000991. If the data stream E2 in the data area C1 is not already divided into the collation window and the primary key value corresponding to the data stream E2 is 000991, the data stream E2 is divided into the collation window D3. And setting the preset triggering number as 2, and correspondingly, triggering the checking of the data streams in the checking window when the number of the data streams in the checking window D3 reaches 2.
The check of the data streams in the above embodiments is not limited herein, and specifically, the value of the field of the data carried by the data stream, the number of data streams in the check window, and the like may be checked.
Fig. 5 is a flowchart of still another embodiment of the data collation method provided in the first aspect of the present application. Fig. 5 is different from fig. 1 in that the data collation method shown in fig. 5 may further include step S105 or step S106.
In step S105, in the case where it is determined that the data of at least two system data pools in the data area agree, the value of the data collation success index is increased.
The data of at least two system data pools in the data area are consistent, namely the problem of data unevenness across systems does not occur, and the value of the data check success index can be increased. The data check success index is used for representing the success rate of data check, and the larger the value of the data check success index is, the higher the success rate of data check is. The data checking success index can provide basis for cross-system data irregularity, alarm, risk pre-judgment and the like, and expands the application scope of data checking.
In step S106, in the case where it is determined that the data of the at least two system data pools in the data area are inconsistent, the inconsistent data in the at least two system data pools in the data area are output.
The data of at least two system data pools in the data area are inconsistent, namely the problem of cross-system data unevenness occurs, and the inconsistent data of at least two system data pools in the data area are the data which causes the problem of cross-system data unevenness. Inconsistent data in at least two system data pools in the data area can provide basis for cross-system data unevenness, alarming, risk prejudgment and the like, and the application scope of data collation is enlarged.
In the data collation method in the above-described embodiment, when executed by the data collation apparatus or the data collation device, functions of generating a data stream, dividing a data area, dividing a collation window, collating data, and the like may be realized by different modules or units. The data collation method in the above-described embodiment may be implemented by different means in the case of being executed by the data collation system, such functions as generating a data stream, dividing a data area, dividing a collation window, data collation, and the like. The specific form of the main body that performs the data collation method is not limited here.
The second aspect of the application also provides a data collation apparatus. Fig. 6 is a schematic structural diagram of an embodiment of a data collation apparatus provided in the second aspect of the present application. As shown in fig. 6, the data collation apparatus 200 may include a data stream generation module 201, a region division module 202, and a collation module 203.
The data stream generation module 201 may be configured to generate and transmit a data stream including data associated with a write operation in the event that the write operation occurs to each system data pool.
Wherein the data stream includes primary key values of the data.
The region division module 202 may be configured to divide the data stream into at least one data region based on the fields of the data stream and a preset region division rule.
Wherein each data region includes at least two data streams corresponding to the system data pools.
In some examples, fields of a data stream of the same data region satisfy the same region division rule.
The checking module 203 may be configured to check, in each data area, the data flows corresponding to the at least two system data pools in the data area according to the primary key value corresponding to the data flow, so as to determine whether the data of the at least two system data pools in the data area are consistent.
In some examples, the collation of the data streams in the plurality of data regions is performed in parallel.
In an embodiment of the present application, in the event that a write operation occurs to each system data pool, a data stream is generated that includes data associated with the write operation. The data stream is divided into at least one data region, and each data region comprises data streams corresponding to at least two system data pools. The data streams corresponding to the at least two system data pools are checked in the data area to determine whether the data of the at least two system data pools are consistent. The data stream is generated by triggering the write operation without setting the time period for acquiring the data, so that the data stream is divided and checked. The write operation is not limited by the time length, and the data can be checked in real time under the condition that the data is changed, so that the problem of data unevenness across systems can be found in time.
In some examples, the data stream generation module 201 may be configured to: reading binary logs of each system data pool, and determining write operation of each system data pool according to the binary logs; generating a data stream message based on the writing operation, wherein the data stream message is used for bearing a data stream; and transmitting the data stream message through the data stream component.
In some examples, the data stream message includes data associated with the current write operation and data associated with the last write operation having the same primary key value.
Fig. 7 is a schematic structural view of another embodiment of the data collation apparatus provided in the second aspect of the present application. Fig. 7 is different from fig. 6 in that the data collation apparatus 200 shown in fig. 7 may further include a screening module 204.
The filtering module 204 may be configured to, in a case where a system data pool corresponds to a plurality of data streams with the same primary key value, reserve a data stream in which a field meets a preset filtering condition.
In some examples, the data flow includes a traffic status field that characterizes a status of a traffic to which data of the data flow corresponds. The screening conditions included: the service state field includes a target value in a preset value set, and the service state field of the data stream is different from the service state field of the data stream corresponding to the last write operation.
Fig. 8 is a schematic structural view of still another embodiment of the data collation apparatus provided in the second aspect of the present application. Fig. 8 is different from fig. 6 in that the collation module 203 may include a window dividing unit 2031 and a collation unit 2032.
The window dividing unit 2031 may be configured to divide the data stream into collation windows according to the primary key value corresponding to the data stream in each data area.
The primary key values of the data streams within different collation windows are different.
The collation unit 2032 is operable to collate data streams within the collation window.
In some examples, specifically, the window dividing unit 2031 may be configured to generate a new collation window and divide an undivided data stream into the new collation window in a case where a primary key value of the data stream within an existing collation window is different from a primary key value corresponding to the undivided data stream.
The collation unit 2032 is operable to trigger collation of the data stream in the new collation window in the case where the time period for which the undivided data stream is divided into the new collation window exceeds the preset trigger time period.
In other examples, specifically, the window dividing unit 2031 may be configured to divide an undivided data stream into the existing collation window in the case where the primary key value of the data stream within the existing collation window is the same as the primary key value corresponding to the undivided data stream
The collation unit 2032 is operable to trigger collation of the data streams within the existing collation window in the case where the number of data streams within the existing collation window reaches a preset trigger number.
Fig. 9 is a schematic structural view of still another embodiment of the data collation apparatus provided in the second aspect of the present application. Fig. 9 is different from fig. 6 in that the data collation apparatus 200 shown in fig. 9 may further include a processing module 205.
The processing module 205 may be configured to: increasing the value of the data collation success index under the condition that the data of at least two system data pools in the data area are consistent; in the event that it is determined that the data of the at least two system data pools in the data area are inconsistent, outputting the inconsistent data in the at least two system data pools in the data area.
The third aspect of the present application also provides a data collation apparatus. Fig. 10 is a schematic structural view of an embodiment of a data collation apparatus provided in a third aspect of the present application. As shown in fig. 10, the data collation apparatus 300 includes a memory 301, a processor 302, and a computer program stored on the memory 301 and executable on the processor 302.
In one example, the processor 302 may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
The Memory may include Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), magnetic disk storage media devices, optical storage media devices, flash Memory devices, electrical, optical, or other physical/tangible Memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors) it is operable to perform the operations described with reference to the data collation method according to the application.
The processor 302 executes a computer program corresponding to the executable program code by reading the executable program code stored in the memory 301 for realizing the data collation method in the above-described embodiment.
In one example, data collation device 300 may also include a communication interface 303 and bus 304. As shown in fig. 10, the memory 301, the processor 302, and the communication interface 303 are connected to each other through the bus 304 and perform communication with each other.
The communication interface 303 is mainly used to implement communication between each module, device, unit and/or apparatus in the embodiments of the present application. Input devices and/or output devices may also be accessed through the communication interface 303.
Bus 304 includes hardware, software, or both, that couple the components of data collation apparatus 300 to one another. By way of example, and not limitation, bus 304 may include an accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics Bus, an enhanced industry standard architecture (Enhanced Industry Standard Architecture, EISA) Bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industrial Standard Architecture, ISA) Bus, an Infiniband interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a micro channel architecture (Micro Channel Architecture, MCa) Bus, a peripheral component interconnect (Peripheral Component Interconnect, PCI) Bus, a PCI-Express (PCI-X) Bus, a serial advanced technology attachment (Serial Advanced Technology Attachment, SATA) Bus, a video electronics standards association local (Video Electronics Standards Association Local Bus, VLB) Bus, or other suitable Bus, or a combination of two or more of these. Bus 304 may include one or more buses, where appropriate. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.
The fourth aspect of the present application also provides a data collation system. Fig. 11 is a schematic structural diagram of an embodiment of a data collation system provided in the fourth aspect of the present application. As shown in fig. 11, the data collation system may include a data flow means 41, a distribution means 42 and a collation means 43. The number of each of the data flow device 41, the branching device 42, and the collation device 43 in the data collation system is not limited here.
The data flow means 41 may be adapted to generate and transmit a data flow comprising data associated with a write operation in case the write operation occurs in each system data pool.
The data stream includes primary key values of the data.
The splitting device 42 may be configured to split the data stream into at least one data region based on the fields of the data stream and a preset region splitting rule.
Each data region includes at least two data streams corresponding to the system data pools.
The checking means 43 may be configured to check, in each data area, the data streams corresponding to the at least two system data pools in the data area according to the primary key value corresponding to the data stream, to determine whether the data of the at least two system data pools in the data area are identical.
The data flow device 41, the splitting device 42 and the checking device 43 may also perform other steps in the data checking method in the above embodiment, and specifically, reference may be made to the related description of the data checking method in the above embodiment, which is not repeated herein.
The fifth aspect of the present application further provides a computer readable storage medium, on which a computer program is stored, where the computer program can implement the data checking method in the above embodiment when executed by a processor, and achieve the same technical effects, and for avoiding repetition, a detailed description is omitted herein. The computer readable storage medium may include a non-transitory computer readable storage medium, such as Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, and the like, but is not limited thereto.
It should be understood that, in the present specification, each embodiment is described in an incremental manner, and the same or similar parts between the embodiments are all referred to each other, and each embodiment is mainly described in a different point from other embodiments. For apparatus embodiments, device embodiments, system embodiments, computer readable storage medium embodiments, the relevant points may be found in the description of method embodiments. The present application is not limited to the specific steps and structures described above and shown in the drawings. Those skilled in the art may, after appreciating the spirit of the present application, make various changes, modifications and additions, or change the order between steps. Also, a detailed description of known method techniques is omitted here for the sake of brevity.
Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the above-described embodiments are exemplary and not limiting. The different technical features presented in the different embodiments may be combined to advantage. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in view of the drawings, the description, and the claims. In the claims, the term "comprising" does not exclude other means or steps; the word "a" does not exclude a plurality; the terms "first," "second," and the like, are used for designating a name and not for indicating any particular order. Any reference signs in the claims shall not be construed as limiting the scope. The functions of the various elements presented in the claims may be implemented by means of a single hardware or software module. The presence of certain features in different dependent claims does not imply that these features cannot be combined to advantage.
Claims (15)
1. A data collation method, characterized by comprising:
generating and transmitting a data stream comprising data associated with the write operation under the condition that the write operation occurs in each system data pool, wherein the data stream comprises a main key value of the data, and the data associated with the write operation comprises the data acted by the write operation;
dividing the data stream into at least one data area based on the fields of the data stream and a preset area dividing rule, wherein each data area comprises the data streams corresponding to at least two system data pools;
and checking the data streams corresponding to at least two system data pools in the data area according to the primary key values corresponding to the data streams in each data area so as to determine whether the data of the at least two system data pools in the data area are consistent.
2. The method of claim 1, wherein in the event of a write operation to each system data pool, generating and transmitting a data stream comprising data associated with the write operation comprises:
reading binary logs of each system data pool, and determining the write operation of each system data pool according to the binary logs;
Generating a data stream message based on the writing operation, wherein the data stream message is used for bearing the data stream;
and transmitting the data stream message through a data stream component.
3. The method according to claim 2, wherein the data stream message includes data associated with the current write operation and data associated with the last write operation having the same primary key value.
4. The method of claim 1, further comprising, prior to dividing the data stream into at least one data region based on the fields of the data stream and a preset region division rule:
and under the condition that one system data pool corresponds to a plurality of data streams with the same primary key value, reserving one data stream with fields meeting preset screening conditions.
5. The method of claim 4, wherein the data stream includes a traffic status field for characterizing a status of a traffic to which data of the data stream corresponds,
the screening conditions include: the service state field includes a target value in a preset value set, and the service state field of the data stream is different from the service state field of the data stream corresponding to the last writing operation.
6. The method according to claim 1, wherein in each of the data areas, checking the data streams corresponding to at least two system data pools in the data area according to the primary key value corresponding to the data stream, comprises:
dividing the data stream into check windows according to the corresponding primary key value of the data stream in each data region, wherein the primary key value of the data stream in different check windows is different;
-checking said data stream within said checking window.
7. The method of claim 6, wherein the step of providing the first layer comprises,
the dividing the data stream into the checking window according to the primary key value corresponding to the data stream includes:
generating a new check window and dividing the undivided data stream into the new check window under the condition that the main key value of the data stream in the existing check window is different from the main key value corresponding to the undivided data stream;
the collation of the data streams within the collation window includes:
and triggering to check the new data stream in the check window under the condition that the time length of the undivided data stream divided into the new check window exceeds the preset trigger time length.
8. The method of claim 6, wherein the step of providing the first layer comprises,
the dividing the data stream into the checking window according to the primary key value corresponding to the data stream includes:
dividing the undivided data stream into the existing checking window under the condition that the main key value of the data stream in the existing checking window is the same as the main key value corresponding to the undivided data stream;
the collation of the data streams within the collation window includes:
triggering to check the data streams in the existing check window when the number of the data streams in the existing check window reaches a preset trigger number.
9. The method according to claim 1, further comprising, after said checking said data streams corresponding to at least two system data pools in said data area according to said primary key value corresponding to said data stream:
increasing the value of a data collation success index under the condition that the data of at least two system data pools in the data area are determined to be consistent;
outputting inconsistent data in at least two system data pools in the data area under the condition that the data in at least two system data pools in the data area are inconsistent.
10. The method of claim 1, wherein fields of the data stream for the same data region satisfy the same region partitioning rule.
11. The method of claim 1, wherein the collation of the data streams in a plurality of the data regions is performed in parallel.
12. A data collation apparatus, characterized by comprising:
the data stream generating module is used for generating and transmitting a data stream comprising data associated with the write operation under the condition that the write operation occurs in each system data pool, wherein the data stream comprises a main key value of the data, and the data associated with the write operation comprises the data of which the write operation is effective;
the region dividing module is used for dividing the data stream into at least one data region based on the fields of the data stream and a preset region dividing rule, and each data region comprises the data streams corresponding to at least two system data pools;
and the checking module is used for checking the data streams corresponding to at least two system data pools in the data areas according to the primary key values corresponding to the data streams in each data area so as to determine whether the data of the at least two system data pools in the data areas are consistent.
13. A data collation apparatus, characterized by comprising: a processor and a memory storing computer program instructions;
the processor, when executing the computer program instructions, implements a data collation method according to any one of claims 1 to 11.
14. A data collation system, comprising:
a data stream device, configured to generate and transmit a data stream including data associated with a write operation in a case where the write operation occurs in each system data pool, where the data stream includes a primary key value of the data, and the data associated with the write operation includes data that is acted on by the write operation;
the splitting device is used for dividing the data stream into at least one data area based on the fields of the data stream and a preset area division rule, and each data area comprises the data streams corresponding to at least two system data pools;
and the checking device is used for checking the data streams corresponding to at least two system data pools in the data areas according to the primary key values corresponding to the data streams in each data area so as to determine whether the data of the at least two system data pools in the data areas are consistent.
15. A computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement a data collation method according to any one of claims 1 to 11.
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| TW110139362A TWI802056B (en) | 2020-10-27 | 2021-10-22 | Data verification method, device, equipment, system and storage medium |
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106326219A (en) * | 2015-06-16 | 2017-01-11 | 阿里巴巴集团控股有限公司 | Business system data check method, apparatus and system |
| CN109684350A (en) * | 2018-12-15 | 2019-04-26 | 平安证券股份有限公司 | Registration of securities verification of data method, apparatus, computer equipment and storage medium |
| WO2020207014A1 (en) * | 2019-04-09 | 2020-10-15 | 平安科技（深圳）有限公司 | Automated regression testing method and apparatus for big data, test center server and storage medium |
Family Cites Families (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9063657B2 (en) * | 2011-04-19 | 2015-06-23 | International Business Machines Corporation | Virtual tape systems using physical tape caching |
| CN103136276A (en) * | 2011-12-02 | 2013-06-05 | 阿里巴巴集团控股有限公司 | System, method and device of verification of data |
| CN102571617B (en) * | 2012-03-22 | 2015-04-01 | 中国科学院上海高等研究院 | Streaming data server, streaming media transmission method and data transmission system |
| CN103645963B (en) * | 2013-12-26 | 2016-06-29 | 深圳市迪菲特科技股份有限公司 | A kind of storage system and data consistency verification method thereof |
| US10191956B2 (en) * | 2014-08-19 | 2019-01-29 | New England Complex Systems Institute, Inc. | Event detection and characterization in big data streams |
| TWI607340B (en) * | 2015-01-09 | 2017-12-01 | Chunghwa Telecom Co Ltd | Privacy data flow security and storage protection method and system |
| CN106454767A (en) * | 2015-08-05 | 2017-02-22 | 中兴通讯股份有限公司 | Business data synchronization method, device and system |
| US11550632B2 (en) * | 2015-12-24 | 2023-01-10 | Intel Corporation | Facilitating efficient communication and data processing across clusters of computing machines in heterogeneous computing environment |
| CN109840837B (en) * | 2017-11-27 | 2022-09-20 | 财付通支付科技有限公司 | Financial data processing method and device, computer readable medium and electronic equipment |
| CN110196844B (en) * | 2018-04-16 | 2024-01-30 | 腾讯科技（深圳）有限公司 | Data migration method, system and storage medium |
| CN110213071B (en) * | 2018-04-16 | 2021-11-02 | 腾讯科技（深圳）有限公司 | Data checking method, device, system, computer equipment and storage medium |
| TW201947492A (en) * | 2018-05-14 | 2019-12-16 | 玉山商業銀行股份有限公司 | System and method for operational data convergence |
| CN108647353A (en) * | 2018-05-16 | 2018-10-12 | 口碑(上海)信息技术有限公司 | A kind of method, apparatus of real-time core to data |
| CN113553313B (en) * | 2018-07-10 | 2023-12-05 | 创新先进技术有限公司 | Data migration method and system, storage medium and electronic equipment |
| US10795913B2 (en) * | 2018-10-11 | 2020-10-06 | Capital One Services, Llc | Synching and reading arrangements for multi-regional active/active databases |
| CN110046202B (en) * | 2019-03-07 | 2023-05-26 | 中国人民解放军海军工程大学 | Real-time data management method for integrated power system based on memory key value database |
| CN110716813A (en) * | 2019-09-17 | 2020-01-21 | 贝壳技术有限公司 | Data stream processing method and device, readable storage medium and processor |
| CN112422635B (en) * | 2020-10-27 | 2023-05-23 | 中国银联股份有限公司 | Data checking method, device, equipment, system and storage medium |
-
2020
- 2020-10-27 CN CN202011167710.5A patent/CN112422635B/en active Active
-
2021
- 2021-09-14 WO PCT/CN2021/118146 patent/WO2022089063A1/en active Application Filing
- 2021-10-22 TW TW110139362A patent/TWI802056B/en active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106326219A (en) * | 2015-06-16 | 2017-01-11 | 阿里巴巴集团控股有限公司 | Business system data check method, apparatus and system |
| CN109684350A (en) * | 2018-12-15 | 2019-04-26 | 平安证券股份有限公司 | Registration of securities verification of data method, apparatus, computer equipment and storage medium |
| WO2020207014A1 (en) * | 2019-04-09 | 2020-10-15 | 平安科技（深圳）有限公司 | Automated regression testing method and apparatus for big data, test center server and storage medium |
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