CN112052136A - Data verification method and device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the application discloses a data verification method, which comprises the following steps: reading a preset data loading record table from a database in response to the update of the data in the application program; the data loading record table comprises data loading records of each node when at least two nodes execute the same loading task; determining a data loading parameter of each node according to the data loading record of each node; determining a node with abnormal loading by carrying out logic check on the data loading parameters of the at least two nodes; and sending an alarm notice to the node with the loading exception. The embodiment of the application also provides a data checking device, equipment and a storage medium.
Description
Technical Field
The present application relates to the field of electronic device technology, and relates to, but is not limited to, a data verification method and apparatus, a device, and a storage medium.
Background
Data loading in business engineering is usually multi-machine room multi-instance loading from multi-data sources, and the following problems often exist: when multiple machine rooms are encountered on the line, the potential situation that the data of multiple nodes are inconsistent is not discovered until a fault occurs on the line; the data loading time, time consumption and other loading information of multiple machine rooms and multiple nodes are not checked through a global view, and the checking and the positioning are not easy. Therefore, the business engineering needs to check the data loading conditions of different nodes executing the same loading task at the same time, so as to ensure the normal loading of the business data.
Disclosure of Invention
The embodiment of the application provides a data verification method, a data verification device, data verification equipment and a data verification storage medium.
The technical scheme of the embodiment of the application is realized as follows:
in a first aspect, an embodiment of the present application provides a data verification method, where the method includes:
reading a preset data loading record table from a database in response to the update of the data in the application program; the data loading record table comprises data loading records of each node when at least two nodes execute the same loading task;
determining a data loading parameter of each node according to the data loading record of each node;
determining a node with abnormal loading by carrying out logic check on the data loading parameters of the at least two nodes;
and sending an alarm notice to the node with the loading exception.
In a second aspect, an embodiment of the present application provides a data verification apparatus, including:
the reading module is used for responding to the update of the data in the application program and reading a preset data loading record table from the database; the data loading record table comprises data loading records of each node when at least two nodes execute the same loading task;
the determining module is used for determining the data loading parameters of each node according to the data loading records of each node;
the verification module is used for performing logic verification on the data loading parameters of the at least two nodes to determine the node with abnormal loading;
and the first notification module is used for sending an alarm notification to the node with the abnormal loading.
In a third aspect, an embodiment of the present application provides a data verification device, which includes a memory and a processor, where the memory stores a computer program that is executable on the processor, and the processor implements the steps in the data verification method when executing the program.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the data verification method described above.
The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:
in the embodiment of the application, firstly, in response to the update of data in an application program, reading a preset data loading record table from a database; secondly, determining a data loading parameter of each node according to the data loading record of each node; then, determining a node with abnormal loading by carrying out logic check on the respective data loading parameters of the at least two nodes; finally, sending an alarm notice to the node with abnormal loading; therefore, the loading conditions of a plurality of nodes executing the same loading task are verified and analyzed, and the nodes with inconsistent loading are determined; and the system is connected with a monitoring alarm, and can inform corresponding nodes in time to avoid pit retention on the line.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
fig. 1 is a schematic flowchart of a data verification method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another data verification method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another data verification method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another data verification method according to an embodiment of the present application;
FIG. 5 is a logic flow diagram of a data verification method provided by an embodiment of the present application;
fig. 6A is a schematic structural diagram of a data verification apparatus according to an embodiment of the present disclosure;
fig. 6B is a schematic structural diagram of another data verification apparatus according to an embodiment of the present disclosure;
fig. 7 is a hardware entity diagram of a data verification device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
It should be noted that the terms "first \ second \ third" referred to in the embodiments of the present application are only used for distinguishing similar objects and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may be interchanged under specific ordering or sequence if allowed, so that the embodiments of the present application described herein can be implemented in other orders than illustrated or described herein.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of the present application belong. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The embodiment of the application provides a data verification method which is applied to a terminal. The terminal includes, but is not limited to, a mobile phone, a notebook computer, a tablet computer and a web-enabled device, a multimedia device, a streaming media device, a mobile internet device, a wearable device or other types of terminal devices. The functions implemented by the method can be implemented by calling program codes through a processor in the terminal, and the program codes can be stored in a computer storage medium. The processor may be used for processing of a process for performing data verification, and the memory may be used for storing data required and data generated in the process for performing data verification.
Fig. 1 is a schematic flow chart of a data verification method provided in an embodiment of the present application, and as shown in fig. 1, the method at least includes the following steps:
in step S110, in response to the update of the data in the application program, a preset data loading record table is read from the database.
Here, the data loading record table includes a data loading record of each node when at least two nodes execute the same loading task. The data loading records are formed by writing the data loading information of each node into a database according to a specific data reporting method.
It is worth noting that the data loading record table is created in advance according to the acquired data loading information of the plurality of nodes, and the execution conditions of different nodes of the plurality of computer rooms on the same loading task are reflected.
It should be noted that, the updating of data in the application program may be understood as that the application program loads new data, and may include the following scenarios: when the application program is abnormally closed or is subjected to flash quit and the like and needs to be restarted, data loading is needed; when the application program relates to page jump, the page jump may be a native jump native page, and may also be a native jump to a HyperText Markup Language (html) page; since all pages are jumped, loading new data is inevitable; within a certain time, the pages of the application may be refreshed and data loaded, such as a leaderboard of operational activities 23: 59 update, or some time-limited activity, and push of instant messages. The determination can be performed according to actual conditions in the implementation process, and the embodiment of the application is not limited to this.
Step S120, determining data loading parameters of each node according to the data loading records of each node.
And performing information screening on the data loading records read from the database to determine the data loading parameters to be verified.
Here, the data loading parameter is information indicating whether data loading is abnormal, such as the number of loaded data, loading start time, loading period, and the like. The determination can be performed according to actual conditions in the implementation process, and the embodiment of the application is not limited to this.
Step S130, determining a node with abnormal loading by performing logic check on the data loading parameters of the at least two nodes.
Here, by customizing the check logic in a specific data check service, data loading parameters when a plurality of nodes execute a loading task are checked to determine whether a node with abnormal loading, that is, whether a node with inconsistent loading exists is determined.
Step S140, sending an alarm notification to the node with abnormal loading.
Here, the alarm notification indicates that the number of the loaded data of the corresponding node is inconsistent with the number of the correctly loaded data.
In the embodiment of the application, firstly, in response to the update of data in an application program, reading a preset data loading record table from a database; secondly, determining a data loading parameter of each node according to the data loading record of each node; then, determining a node with abnormal loading by carrying out logic check on the respective data loading parameters of the at least two nodes; finally, sending an alarm notice to the node with abnormal loading; therefore, the loading conditions of a plurality of nodes executing the same loading task are verified and analyzed, and the nodes with inconsistent loading are determined; and the system is connected with a monitoring alarm, and can inform corresponding nodes in time to avoid pit retention on the line.
Fig. 2 is a schematic flow chart of another data verification method provided in an embodiment of the present application, and as shown in fig. 2, the method at least includes the following steps:
step S210, acquiring data loading information of each node when the application program triggers a loading task.
Here, the loading task may be triggered manually by a user, that is, common pull-down refresh and slide-up loading, and also includes click button reloading; the loading task can also be triggered by automatic loading of the system, namely, the next page of content is preloaded after the current content is displayed. The determination can be performed according to actual conditions in the implementation process, and the embodiment of the application is not limited to this.
Step S220, asynchronously call a specific data verification service through a data reporting method in a specific JAR file, and insert each piece of data loading information into the data loading record table.
Here, the specific JAR file is some classes that have been written by others or organizations, and then the classes are packaged. The user can introduce the Jar packages into the corresponding items and then can directly use the classes, the attributes and the methods in the Jar packages.
Here, the data reporting method includes parameters related to the data loading information, so that the data loading information is reported to the database by calling the data reporting method. The data reporting method asynchronously calls a specific data verification service through Http, and then operates a data loading record table of the database through the data verification service.
It should be noted that the asynchronous call here means that, in the process of refreshing a page, that is, loading data, the application program operates the database through the data verification service and performs logic verification, and the data verification service returns the verified result to the application program.
Step S230, in response to the update of the data in the application program, reading the data loading records corresponding to the at least two nodes from the database by asynchronously calling a specific data verification service.
Here, the data loading records of the at least two nodes for the same loading task are read from the data loading record table stored in the database.
It should be noted that the data loading record table may include data loading records for executing all loading tasks by multiple nodes, and in this case, when performing data verification, it is necessary to simultaneously read data loading records of multiple nodes for the same loading task.
Step S240, determining a data loading parameter of each node according to the data loading record of each node.
Step S250, determining a node with abnormal loading by performing logic check on the data loading parameters of the at least two nodes.
And step S260, sending an alarm notice to the node with abnormal loading.
It should be noted here that the above-described process of step S230 to step S260 is similar to that of step S110 to step S140. To avoid repetition, the embodiments of the present application are not described herein again.
In the embodiment of the application, firstly, under the condition that an application program triggers a loading task, data loading information of each node is obtained; secondly, asynchronously calling a specific data verification service through a data reporting method in a specific JAR file, and inserting each piece of data loading information into the data loading record table; then, responding to the update of data in the application program, reading data loading records corresponding to the at least two nodes from the database by asynchronously calling a specific data verification service so as to perform logic verification; therefore, the data loading conditions of the multiple nodes are reported to the data loading record table in advance to be summarized, so that the loading conditions of the multiple machine rooms and the multiple nodes of the same project are checked at one global view angle, data verification is further performed, the condition of inconsistent loading can be timely subjected to alarm notification, and the normal loading of the service data is ensured.
In some possible embodiments, the loading type of the loading task is a full loading type, the data loading parameters are a number of loaded data, an initial loading time and a loading period, fig. 3 is a flowchart of another data verification method provided in this embodiment of the present application, and as shown in fig. 3, the step S130 or the step S250 "determining a node with an abnormal loading by performing logic verification on the data loading parameters of each of the at least two nodes" may be implemented by:
step S310, using the number of the loaded data with the largest occurrence number from the respective numbers of the loaded data of the at least two nodes as a first candidate number.
Here, each node in the at least two nodes corresponds to one number of loaded data, for example, N nodes have N numbers of loaded data, and the numbers of the loaded data may be the same or different.
Step S320, determining a first ratio between the number of occurrences of the first candidate load number and the total number of all nodes.
Step S330, taking the first candidate load number as a first target load number when the first ratio is greater than a first threshold.
Here, the first threshold is a preset percentage greater than 0 and less than 1, and may be 80%, for example.
Here, the first target loading number is the number of loaded data corresponding to a node which is normally loaded.
It can be understood that if the same loading task is targeted, the number of the loaded data corresponding to different nodes should be consistent. Therefore, the number of the loaded data which has the same number of the loaded data and the maximum occurrence frequency and accounts for more than a certain percentage of the total number of the nodes is screened out to be used as the first target loaded number, namely the correct loaded data number.
For example, if 90 data are loaded on the nodes 1 to 8, 80 data are loaded on the node 9, and 85 data are loaded on the node 10 for the same loading task, it can be determined that the number of times of the loading data 90 is up to 8, and accounts for 80% of the total (the total of all the nodes is 10), so the number of the loading data 90 is the target loading number. That is, for the same loading task, nodes 1 to 8 which load 90 data are all loaded correctly, and the number of correctly loaded data is 90.
In some possible embodiments, in the absence of the first target load number, an alert notification is sent to all nodes executing the load task.
Step S340, determining the node with the abnormal loading according to the initial loading time and the loading period corresponding to the first target loading number.
Here, in implementation, the node that loads the exception may be determined by:
step S3401, regarding the node whose number of loaded data in the at least two nodes is not corresponding to the first target number of loaded data as a first candidate node.
Here, the first candidate node is a node whose number of loaded data is different from the number of correctly loaded data, that is, a node with an abnormal loading.
Step S3402, determining a first interval between an initial loading time corresponding to the first target loading number and an initial loading time corresponding to the first candidate node.
Here, each node corresponding to the first target loading number is determined, then the initial loading time of each node is determined from the data loading records corresponding to the nodes, and then the first interval is determined by making a time difference with the initial loading time corresponding to the node with the inconsistent loading data number.
Step S3403, in a case that the first interval is smaller than the loading period, determining the first candidate node as the node with the abnormal loading.
Here, whether the first interval is smaller than the loading cycle, that is, the time difference between the initial loading time corresponding to the node with inconsistent loading number and the initial loading time corresponding to the first target loading number is compared, and when the node with inconsistent loading number is in the loading cycle of the loading task, the node with inconsistent loading number is a node with abnormal loading.
Illustratively, the earliest loading time among the first target loading number, i.e., nodes 1 to 8 with 90 pieces of loading data, is 10 am 05 minutes, and the rest nodes less than 20% of the total number are nodes 9 and 10; the loading time of the node 9 is 10: 08 minutes, and the loading time of the node 10 is 10: 07 minutes, so that the time difference between the node 9 and the node loaded normally is 2 minutes (less than 3 minutes of the loading period), that is, the node 9 with the loading number inconsistent with the first target loading number is the node loaded abnormally.
In some possible embodiments, the loading type of the loading task is an incremental loading type, and the data loading parameters are a number of loaded data, an initial loading time, an incremental fault tolerance number, and a loading period. Fig. 4 is a schematic flow chart of another data verification method provided in an embodiment of the present application, and as shown in fig. 4, the step S130 or the step S250 "determining a node with an abnormal loading by performing logic verification on respective data loading parameters of the at least two nodes" may be implemented by:
step S410, determining a minimum value and a maximum value from the respective numbers of the loaded data of the at least two nodes.
Here, the minimum value is the number with the smallest number of loaded data, and the maximum value is the data with the largest number of loaded data. Each node of the at least two nodes corresponds to one loaded data number, for example, N loaded data numbers exist for N nodes, and under the condition that the loaded data numbers are different, the number L with the minimum loaded data number and the number H with the maximum loaded data number are determined.
And step S420, taking the number of the loaded data with the largest occurrence frequency as a second candidate loading number under the condition that the difference value between the minimum value and the maximum value is larger than the increment fault-tolerant number.
Here, the increment fault-tolerant number represents an error range in which different nodes load data with different numbers for the same increment loading task.
If the difference value between the number L with the minimum loading number and the number H with the maximum loading data number is larger than the increment fault-tolerant number, the node with the loading abnormality is indicated, and the next screening is needed; otherwise, the next loading task is circulated for judgment.
Step S430, determining a first ratio between the number of occurrences of the first candidate load number and the total number of all nodes.
Step S440, taking the second candidate load number as a second target load number when the second ratio is greater than a second threshold.
Here, the second threshold is a preset percentage greater than 0 and less than 1, and may be, for example, 60%.
Here, the second target loading number is the number of loaded data corresponding to the node which is loaded normally.
It can be understood that if the same loading task is targeted, the number of the loaded data corresponding to different nodes should be consistent. Therefore, the number of the loaded data which has the same number of the loaded data and the maximum occurrence frequency and accounts for more than a certain percentage of the total number of the nodes is screened out to be used as the second target loaded number, namely the correct loaded data number.
For example, if 90 pieces of data are loaded on the nodes 1 to 6, 80 pieces of data are loaded on the nodes 7, 8 and 9, and 88 pieces of data are loaded on the node 10 for the same loading task, it can be determined that the maximum number of loaded data is 90, and the difference between the maximum number of loaded data and the minimum number (80) of loaded data is 10, which is greater than the incremental fault-tolerant number (5). Meanwhile, the number of times of occurrence of the loaded data number 90 is at most 6 times, which accounts for 60% of the total number (the total number of all nodes is 10), so that the loaded data number 90 is the target loaded number. That is, for the same loading task, nodes 1 to 6 which load 90 data are all loaded correctly, and the number of correctly loaded data is 90.
In some possible embodiments, in a case that the difference between the minimum value and the maximum value is smaller than the incremental fault-tolerant number, it may be understood that the number of loaded data of all nodes is consistent within an error range, that is, different nodes load the same loading task normally without performing subsequent operations.
Step S450, determining the node with the abnormal loading according to the initial loading time and the loading period corresponding to the second target loading number.
Here, in the implementation process, the process of determining the node with the loading exception may be implemented by:
step S4501, regarding a node, of the at least two nodes, whose number of loaded data is not corresponding to the second target number of loaded data, as a second candidate node.
Here, the first candidate node is a node whose number of loaded data is different from the number of correctly loaded data, that is, a node with an abnormal loading.
Step S4502, determining a second interval between the initial loading time corresponding to the second target loading number and the initial loading time corresponding to the second candidate node.
Here, each node corresponding to the second target loading number is determined, then the initial loading time of each node is determined from the data loading records corresponding to the nodes, and then the second interval is determined by making a time difference with the initial loading time corresponding to the node with the inconsistent loading data number.
Step S4503, when the second interval is smaller than the load cycle, determining the second candidate node as the node with the abnormal load.
Here, whether the first interval is smaller than the loading cycle, that is, the time difference between the initial loading time corresponding to the node with inconsistent loading number and the initial loading time corresponding to the first target loading number is compared, and when the node with inconsistent loading number is in the loading cycle of the loading task, the node with inconsistent loading number is a node with abnormal loading.
The foregoing data verification method is described below with reference to a specific embodiment, but it should be noted that the specific embodiment is only for better describing the present application and is not to be construed as limiting the present application.
The data are loaded from multiple data sources by multiple machine rooms and multiple instances, even if the data are inconsistent, the checking is complicated, and a global view angle is needed. In view of this, data loading conditions of multiple machine rooms and multiple nodes need to be visualized, monitoring and alarming are carried out at the same time, and corresponding personnel can be timely notified if loading conditions are inconsistent.
Fig. 5 is a logic flow diagram of a data verification method according to an embodiment of the present application. As shown in fig. 5, the method comprises the following steps:
step S501, introducing the dependence of JAR files in business engineering.
Illustratively, the name of the JAR file is a flaab-check-core, and when the third-party reference is introduced, the following dependence is introduced.
Step S502, a data reporting method in the JAR file is called, and the data loading information of the nodes is inserted into a data loading record table in the database.
Here, a data loading record table (record) is created in the database in advance, and as long as a data reporting method in the JAR file is called, a data loading record is inserted into the data loading record table, and the content of the record is data loading information, such as information of an application ID, an application name, a node, a full path of a data loading class, the number of loaded data, a loading type, loading time, time consumption, a loading interval, the number of incremental fault tolerance, and the like.
Here, the data reporting method is a tool class method in the JAR file. By way of example, the following is a functional implementation of a data reporting method (reportDatawithMethodDesc), which includes a parameter load type (loadType), a load start time (begin), a time consumption (costTime), a full path of a data load class (classPath), a load method (methodName), a load destination (loadDesc) public static void reportDatawithMethodDesc (loadTypeEnum loadType, loading begin, int size, float costTime, String classPath, String methoddDesc)
It should be noted that, in the data reporting method, it is first determined whether the data verification service is in an open state, and the data loading condition can be directly reported in an asynchronous Http manner when the data verification service is opened. The website (Http Url), the application name, the application ID, and the switch for checking whether the data is checked may be configured through a distributed configuration center (e.g., Apollolo).
Step S503, the data loading record is read from the database through the data verification service, and the logic verification processing is performed.
Here, the data verification service defines the verification logic by self, and determines the abnormal data loading condition and sends out a notification alarm according to the number of the loaded data and the loading time of different nodes of the same task.
Step S504, the read data loading record is displayed on the front page.
The check logic for the full load type task and the incremental load type task in step S503 above is described in detail below.
The full load type of task typically occurs at the very start of the project, or at a fixed time on a periodic basis, such as two early morning hours per day. For this application scenario, the following verification steps are taken:
and step S1, searching all tasks of the full load type from the database according to the grouped by combined index.
And step S2, according to the index field of each task, inquiring the task loading details of each task in all node IPs.
Here, the index field may be a primary key and a foreign key in a data table. The index is a structure for sequencing one or more columns of values in the database table, and the index can be used for quickly accessing specific information in the database table.
Here, the details of task loading are the data loading records, and characterize the condition of task loading.
Step S3, record all node IPs for loading each task and the number of data loaded under each node.
Illustratively, the node IPs loading the same task are 10.17.16.245 and 10.17.16.245, respectively, and the corresponding number of the loaded data is 9630 and 9832, respectively, which indicates that the two node IPs load data are inconsistent.
In step S4, the number of loaded data that appears most frequently in all node IPs and exceeds 80% of the total number of all node IPs is selected as the correct number of loads.
Step S5, determining the earliest loading time in the correct records corresponding to the correct loading number, comparing the earliest loading time with the loading times of the remaining records less than 20% of the total number to obtain a time difference, and sending a loading exception notification to the nodes whose time difference is less than the loading period and whose loading numbers are inconsistent.
In step S6, if the correct number of loads has not occurred in step S4, all nodes that execute the task are notified.
Here, if the number of loaded data of all nodes is analyzed for the same loading task, the correct number of loads selected according to the rule of step S4 cannot be found, which indicates that all the nodes are loaded abnormally, and an alarm needs to be notified to all the nodes.
Incremental load type tasks typically occur after a project is started, loading every 15 minutes or hours. For this application scenario, the following verification steps are taken:
and step S1, querying all incremental loading type tasks from the database according to the grouped and summarized combined index.
And step S2, according to the index field of each task, inquiring the task loading details of each task in all node IPs.
Here, the details of task loading are the data loading records, and characterize the condition of task loading.
Step S3, record all node IPs for loading each task and the number of data loaded under each node.
Illustratively, the node IPs loading the same task are 10.17.16.245 and 10.17.16.245, respectively, and the corresponding number of the loaded data is 9630 and 9832, respectively, which indicates that the two node IPs load data are inconsistent.
Step S4, calculating a loading number difference between the minimum number of loaded data and the maximum number of loaded data, and if the loading number difference is smaller than the incremental fault-tolerant number. The loop continues with the next task and jumps to step S2. Otherwise, go to step S5.
In step S5, the number of loaded data that appears most frequently in all node IPs and exceeds 60% of the total number of all node IPs is selected as the correct number of loads.
Step S6, determining the earliest loading time in the records corresponding to the correct loading number, comparing the earliest loading time with the loading times of the remaining records less than 40% of the total number to obtain a time difference, and sending a loading exception notification to the node whose time difference is less than the loading period.
In step S7, if the correct number of loads has not occurred in step S5, all nodes that execute the task are notified.
Here, if the number of loaded data of all nodes is analyzed for the same loading task, the correct number of loads selected according to the rule of step S5 cannot be found, which indicates that all the nodes are loaded abnormally, and an alarm needs to be notified to all the nodes.
By the data verification method provided by the embodiment of the application, data alarm information of a plurality of nodes such as a large family machine room, a head ringing machine room and a cloud machine room can be obtained. By inquiring the data alarm information, the data is gathered and found, and one less record is clicked in a large family of machine rooms than a head ringing machine room and a cloud machine room in a project. Because the loaded data are all from the same table, the data of the tables of the three checked machine rooms are the same, and the number of the tables is also the same. The condition that data synchronization is abnormal due to different database data and different machine rooms is eliminated.
The reason for the troubleshooting is therefore that the timing task (cron) of the large family of rooms has not yet been made to trigger that piece of incremental data urgently. And through a distributed scheduling framework (Elastic Job Lite) console, the task of the large-family machine room is triggered again, and the data are checked again to be recovered to be consistent. Therefore, the data are summarized based on the data reporting, a global preview view of multi-machine room and multi-node data loading is provided for the same project, and the condition of abnormal loading can be effectively checked.
When the statistics of which tasks are reported frequently in a certain time, it is found that 30 to 40 records are loaded and reported in each statistics for the same project. Therefore, the data loading record of one node IP is randomly selected to check the corresponding reporting time, and the source code position of the project can be quickly found according to the reporting time. The analysis schedule (schedule) does not load as fast at the corresponding source code location. Meanwhile, the method checks that no modification operation exists in a distributed timing task management (elastic job) console, can analyze and obtain a frequent call of a certain part of a corresponding function code, further analyzes code logic, and can trace a bug (bug) in a program. Therefore, by the data verification method provided by the embodiment of the application, the data loading conditions of multiple nodes of multiple machine rooms are collected and verified, the loading abnormal conditions can be obtained at the first time, and the abnormal reasons can be further analyzed.
In the embodiment of the application, data summarization is carried out based on data reporting, and a global preview view of multi-machine room and multi-node data loading is provided for the same project; meanwhile, the abnormal data loading condition is alarmed through the self-defined check logic, the abnormal loading condition is obtained, the notification is obtained at the first time, and the pits left on the line are avoided.
Based on the foregoing embodiments, an embodiment of the present application further provides a data checking apparatus, where the control apparatus includes modules and units included in the modules, and may be implemented by a processor in a terminal; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the Processor may be a Central Processing Unit (CPU), a microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.
Fig. 6A is a schematic structural diagram of a data verification apparatus provided in an embodiment of the present application, and as shown in fig. 6A, the verification apparatus 600 includes a reading module 610, a determining module 620, a verifying module 630, and a first notifying module 640, where:
the reading module 610 is configured to read a preset data loading record table from a database in response to updating of data in an application program; the data loading record table comprises data loading records of each node when at least two nodes execute the same loading task;
the determining module 620 is configured to determine a data loading parameter of each node according to the data loading record of each node;
the checking module 630 is configured to perform logic checking on the data loading parameters of the at least two nodes, so as to determine a node with an abnormal loading;
the first notification module 640 is configured to send an alarm notification to the node with the abnormal loading.
In some possible embodiments, as shown in fig. 6B, the verification apparatus 600 further includes an obtaining module 650 and a reporting module 660, where: the obtaining module 650 is configured to obtain data loading information of each node when the application triggers a loading task; the data loading information is used for representing the execution condition of the loading task; the reporting module 660 is configured to report each piece of data loading information to the database.
In some possible embodiments, the reporting module 660 is further configured to asynchronously invoke a specific data checking service through a data reporting method in a specific JAR file, and insert each piece of data loading information into the data loading record table.
In some possible embodiments, the reading module 610 is further configured to read, in response to an update of data in an application program, data loading records corresponding to the at least two nodes from the database by asynchronously calling a specific data checking service.
In some possible embodiments, the loading type of the loading task is a full loading type, the data loading parameters are a number of loaded data, an initial loading time, and a loading period, and the checking module 630 includes a first determining submodule, a second determining submodule, a third determining submodule, and a fourth determining submodule, where: the first determining submodule is configured to use, from the respective numbers of loaded data of the at least two nodes, the number of loaded data with the largest occurrence number as a first candidate number of loaded data; the second determining submodule is used for determining a first proportion between the occurrence number of the first candidate load number and the total number of all nodes; the third determining submodule is configured to, when the first ratio is greater than a first threshold, use the first candidate load number as a first target load number; and the fourth determining submodule is used for determining the node with the abnormal loading according to the initial loading time and the loading period corresponding to the first target loading number.
In some possible embodiments, the fourth determination submodule comprises a first determination unit, a second determination unit and a third determination unit, wherein: the first determining unit is configured to use a node, as a first candidate node, where the number of loaded data in the at least two nodes is not corresponding to the first target loaded number; the second determining unit is configured to determine a first interval between an initial loading time corresponding to the first target loading number and an initial loading time corresponding to the first candidate node; the third determining unit is configured to determine the first candidate node as the node with the abnormal load if the first interval is smaller than the load cycle.
In some possible embodiments, the loading type of the loading task is an incremental loading type, the data loading parameters are a number of loaded data, an initial loading time, an incremental fault tolerance number, and a loading period, and the checking module 630 includes a fifth determining sub-module, a sixth determining sub-module, a seventh determining sub-module, an eighth determining sub-module, and a ninth determining sub-module, where: the fifth determining submodule is used for determining a minimum value and a maximum value from the respective number of the loaded data of the at least two nodes; the sixth determining submodule is used for taking the number of the loaded data with the largest occurrence frequency as a second candidate loading number under the condition that the difference value between the minimum value and the maximum value is larger than the increment fault-tolerant number; the seventh determining submodule is configured to determine a first ratio between the number of occurrences of the first candidate load number and the total number of all nodes; the eighth determining submodule is configured to use the second candidate load number as a second target load number when the second ratio is greater than a second threshold; and the ninth determining submodule is configured to determine the node with the abnormal loading according to the initial loading time and the loading period corresponding to the second target loading number.
In some possible embodiments, the ninth determining submodule includes a fourth determining unit, a fifth determining unit, and a sixth determining unit, wherein: the fourth determining unit is configured to use a node, as a second candidate node, where the number of loaded data in the at least two nodes is not corresponding to the second target number of loaded data; the fifth determining unit is configured to determine a second interval between an initial loading time corresponding to the second target loading number and an initial loading time corresponding to the second candidate node; the sixth determining unit is configured to determine the second candidate node as the node with the abnormal load if the second interval is smaller than the load cycle.
In some possible embodiments, the verification apparatus 600 further includes a second notification module, configured to send an alarm notification to all nodes executing the loading task when the first target loading number does not exist; or sending an alarm notification to all nodes executing the loading task under the condition that the second target loading number does not exist.
Here, it should be noted that: the above description of the apparatus embodiments, similar to the above description of the method embodiments, has similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the apparatus of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
It should be noted that, in the embodiment of the present application, if the data verification method is implemented in the form of a software functional module and is sold or used as an independent product, the data verification method may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes several instructions to enable a terminal (which may be a smartphone with a camera, a tablet computer, or the like) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Correspondingly, the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps in the data verification method in any of the above embodiments.
Correspondingly, in an embodiment of the present application, a chip is further provided, where the chip includes a programmable logic circuit and/or a program instruction, and when the chip runs, the chip is configured to implement the steps in any of the data verification methods in the foregoing embodiments.
Correspondingly, in an embodiment of the present application, there is further provided a computer program product, which is used to implement the steps in the data verification method in any of the foregoing embodiments when the computer program product is executed by a processor of a terminal.
Based on the same technical concept, the embodiment of the present application provides a data verification device, which is used for implementing the data verification method described in the above method embodiment. The data verification device includes, but is not limited to, a mobile phone, a notebook computer, a tablet computer, a handheld internet device, a multimedia device, a streaming media device, a mobile internet device, a wearable device, or other types of terminal devices. Fig. 7 is a hardware entity diagram of a data verification apparatus according to an embodiment of the present application, as shown in fig. 7, the apparatus 700 includes a memory 710 and a processor 720, the memory 710 stores a computer program that can be executed on the processor 720, and the processor 720 executes the computer program to implement steps in any data verification method according to the embodiment of the present application.
The Memory 710 is configured to store instructions and applications executable by the processor 720, and may also buffer data (e.g., image data, audio data, voice communication data, and video communication data) to be processed or already processed by the processor 720 and modules in the terminal, and may be implemented by a FLASH Memory (FLASH) or a Random Access Memory (RAM).
The steps of any of the above-described data verification methods are implemented by processor 720 when executing the program. Processor 720 generally controls the overall operation of device 700.
The Processor may be at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a Central Processing Unit (CPU), a controller, a microcontroller, and a microprocessor. It is understood that the electronic device implementing the above-mentioned processor function may be other electronic devices, and the embodiments of the present application are not particularly limited.
The computer storage medium/Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a magnetic Random Access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read-Only Memory (CD-ROM), and the like; but may also be various terminals such as mobile phones, computers, tablet devices, personal digital assistants, etc., that include one or any combination of the above-mentioned memories.
Here, it should be noted that: the above description of the storage medium and device embodiments is similar to the description of the method embodiments above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and apparatus of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.
In addition, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing an automatic test line of a device to perform all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The methods disclosed in the several method embodiments provided in the present application may be combined arbitrarily without conflict to obtain new method embodiments.
The features disclosed in the several method or apparatus embodiments provided in the present application may be combined arbitrarily, without conflict, to arrive at new method embodiments or apparatus embodiments.
The above description is only for the embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (12)
1. A method for data verification, the method comprising:
reading a preset data loading record table from a database in response to the update of the data in the application program; the data loading record table comprises data loading records of each node when at least two nodes execute the same loading task;
determining a data loading parameter of each node according to the data loading record of each node;
determining a node with abnormal loading by carrying out logic check on the data loading parameters of the at least two nodes;
and sending an alarm notice to the node with the loading exception.
2. The method of claim 1, wherein the method further comprises:
under the condition that the application program triggers a loading task, acquiring data loading information of each node; the data loading information is used for representing the execution condition of the loading task;
reporting each data loading information to the database.
3. The method of claim 2, wherein said reporting each of said data load information into said database comprises:
and asynchronously calling a specific data verification service through a data reporting method in a specific JAR file, and inserting each piece of data loading information into the data loading record table.
4. The method of claim 1, wherein reading the pre-defined data load record table from the database in response to the update of the data in the application program comprises:
and responding to the update of the data in the application program, and reading the data loading records corresponding to the at least two nodes from the database by asynchronously calling a specific data verification service.
5. The method of claim 1, wherein the load type of the load task is a full load type, the data load parameters are a number of loaded data, an initial load time, and a load cycle,
the determining a node with abnormal loading by performing logic check on the data loading parameters of the at least two nodes includes:
taking the number of the loaded data with the largest occurrence frequency from the respective numbers of the loaded data of the at least two nodes as a first candidate loaded number;
determining a first ratio between a number of occurrences of the first candidate load number and a total number of all nodes;
taking the first candidate loading number as a first target loading number under the condition that the first ratio is larger than a first threshold value;
and determining the node with the abnormal loading according to the initial loading time and the loading period corresponding to the first target loading number.
6. The method as claimed in claim 5, wherein said determining the node with the abnormal loading according to the starting loading time and the loading cycle corresponding to the first target loading number comprises:
taking the node with the loading data number not corresponding to the first target loading number in the at least two nodes as a first candidate node;
determining a first interval between an initial loading time corresponding to the first target loading number and an initial loading time corresponding to the first candidate node;
determining the first candidate node as the node with the load exception if the first interval is smaller than the load cycle.
7. The method of claim 1, wherein the loading type of the loading task is an incremental loading type, the data loading parameters are a loading data number, an initial loading time, an incremental fault tolerance number and a loading period,
the determining a node with abnormal loading by performing logic check on the data loading parameters of the at least two nodes includes:
determining a minimum value and a maximum value from the respective number of the loaded data of the at least two nodes;
taking the number of the loaded data with the largest occurrence frequency as a second candidate loading number under the condition that the difference value between the minimum value and the maximum value is larger than the increment fault-tolerant number;
determining a first ratio between a number of occurrences of the first candidate load number and a total number of all nodes;
taking the second candidate loading number as a second target loading number under the condition that the second proportion is larger than a second threshold value;
and determining the node with the abnormal loading according to the initial loading time and the loading period corresponding to the second target loading number.
8. The method of claim 7, wherein determining the node with the abnormal loading according to the loading cycle and the starting loading time corresponding to the second target loading number comprises:
taking the node with the number of the loaded data in the at least two nodes which is not corresponding to the second target loaded number as a second candidate node;
determining a second interval between the initial loading time corresponding to the second target loading number and the initial loading time corresponding to the second candidate node;
determining the second candidate node as the node with the load exception if the second interval is smaller than the load cycle.
9. The method of claim 7, wherein the method further comprises:
sending an alarm notification to all nodes executing the loading task under the condition that the first target loading number does not exist; or
And sending an alarm notification to all nodes executing the loading task under the condition that the second target loading number does not exist.
10. A data checking device is characterized in that the checking device comprises a reading module, a determining module, a checking module and a first notification module, wherein:
the reading module is used for responding to the update of the data in the application program and reading a preset data loading record table from the database; the data loading record table comprises data loading records of each node when at least two nodes execute the same loading task;
the determining module is used for determining the data loading parameters of each node according to the data loading records of each node;
the checking module is used for performing logic checking on the data loading parameters of the at least two nodes to determine the node with abnormal loading;
and the first notification module is used for sending an alarm notification to the node with the abnormal loading.
11. A data verification device comprising a memory and a processor, said memory storing a computer program operable on the processor, wherein the processor implements the steps of the method of any one of claims 1 to 9 when executing said program.
12. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 9.
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