Task re-running method and device, electronic terminal and computer readable storage medium

Abstract

The invention provides a task rerunning method and device, an electronic terminal and a computer readable storage medium. The task rerunning method comprises the following steps: acquiring a historical data volume corresponding to the last task rerunning before a current time node and a current data volume corresponding to the current time node; and in response to the situation that the data increment of the current data volume compared with the historical data volume reaches a threshold value, performing task rerunning on the incremental data of the current data volume compared with the historical data volume. According to the task rerunning method, task rerunning can be automatically triggered according to the data increment, and manual operation and maintenance investment is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a task re-running method and device, an electronic terminal and a computer readable storage medium. BACKGROUND

[0002] With the continuous evolution of perception data acquisition technology and the rapid development of big data technology, how to process a large amount of perception data and effectively analyze and use it has become the top priority in the target field. Based on such needs, more and more data intelligent products for analyzing, processing and mining effective information of perception data have emerged. These products are widely used in government agencies and enterprise parks, automatically scheduling and running, processing and analyzing, and delivering the mined information to customers through visual pages.

[0003] Data intelligent products usually arrange various operation task nodes into a work business flow in a certain order, and set a scheduling and running mode so that the tasks can automatically run in a fixed mode, such as real-time scheduling mode or fixed-interval batch processing scheduling mode. Ideally, the tasks can continue to run without manual management after the setting is completed, but the actual situation is usually more complex. Abnormal source data, or device and network problems, can all cause the tasks to fail to run smoothly, causing blockage of the display and application of data results. For the processing of such abnormal situations, technical operation and maintenance personnel usually need to handle it manually, which consumes a lot of labor costs. SUMMARY

[0004] The technical problem solved by the present application is to provide a task re-running method, device, electronic terminal and computer readable storage medium, which can automatically trigger task re-running and reduce manual operation and maintenance investment.

[0005] To solve the above technical problems, the first technical solution adopted by the present application is to provide a task re-running method, comprising:

[0006] obtaining a historical data amount corresponding to the last time when task re-running was performed before a current time node and a current data amount corresponding to the current time node;

[0007] In response to the data increment of the current data amount compared with the historical data amount reaching a threshold value, the increment data of the current data amount compared with the historical data amount is subjected to task re-running.

[0008] In an embodiment, the current data amount and the historical data amount are data amounts of the same type of data.

[0009] In an embodiment, the increment data of the current data amount compared with the historical data amount is subjected to task re-running, comprising:

[0010] Determine the to-be-re-run data corresponding to the incremental data based on the data dependency relationship of the task node and / or the time corresponding to the incremental data.

[0011] Re-run the task for the to-be-re-run data.

[0012] In an embodiment, the to-be-re-run data includes all data having a dependency relationship with the incremental data; or, the to-be-re-run data includes all data of the same type as the incremental data within the time corresponding to the incremental data; or, the to-be-re-run data includes the union of all data of the same type as the incremental data within the time corresponding to the incremental data and all data having a dependency relationship with the incremental data.

[0013] In an embodiment, determining the to-be-re-run data corresponding to the incremental data based on the data dependency relationship of the task node and / or the time corresponding to the incremental data comprises:

[0014] Marking the state of the to-be-re-run data according to the task node;

[0015] After re-running the task for the to-be-re-run data, comprising:

[0016] Modifying the state of the to-be-re-run data.

[0017] In an embodiment, re-running the task for the to-be-re-run data comprises:

[0018] Re-running the task for the to-be-re-run data according to the time corresponding to the to-be-re-run data.

[0019] In an embodiment, in response to the historical data amount being less than a first threshold value and the data increment being greater than or equal to a second threshold value, it is determined that the data increment reaches the threshold value;

[0020] In response to the historical data amount being greater than or equal to the first threshold value and the data increment being greater than or equal to a third threshold value, it is determined that the data increment reaches the threshold value;

[0021] The second threshold value is greater than the third threshold value.

[0022] To solve the above technical problems, the second technical solution adopted by the present application is to provide a task re-running device, comprising:

[0023] A data acquisition module is configured to acquire a historical data amount corresponding to the last time when task re-running was performed before a current time node and a current data amount corresponding to the current time node;

[0024] A re-running module is configured to, in response to the data increment of the current data amount compared to the historical data amount reaching a threshold value, re-run the task for the incremental data of the current data amount compared to the historical data amount.

[0025] To solve the above technical problems, a third technical solution adopted by the present application is to provide an electronic terminal, the electronic terminal comprising a memory and a processor coupled with each other, the processor being configured to execute program instructions stored in the memory, and the processor being configured to execute program data to implement steps in the task rerun method as described above.

[0026] To solve the above technical problems, a fourth technical solution adopted by the present application is to provide a computer-readable storage medium, the computer-readable storage medium storing a computer program, the computer program being executed by a processor to implement steps in the task rerun method as described above.

[0027] The present application has the beneficial effect that, unlike the prior art, the provided task rerun method comprises obtaining a historical data amount corresponding to the last time when task rerun was performed before a current time node and a current data amount corresponding to the current time node; and in response to a data increment of the current data amount compared with the historical data amount reaching a threshold value, performing task rerun on the increment data of the current data amount compared with the historical data amount. The task rerun method of the present application can automatically trigger task rerun according to data increment, reducing manual operation and maintenance investment. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, 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 creative effort.

[0029] Figure 1 is a flowchart of a first embodiment of the task rerun method provided by the present application;

[0030] Figure 2 is Figure 1 is a flowchart of a specific embodiment of step S12 in the method;

[0031] Figure 3 is a flowchart of a second embodiment of the task rerun device provided by the present application;

[0032] Figure 4 is a framework diagram of an embodiment of the electronic terminal provided by the present application;

[0033] Figure 5 is a framework diagram of an embodiment of the computer-readable storage medium provided by the present application. DETAILED DESCRIPTION

[0034] The technical solutions of the embodiments of the present application will be described in detail below with reference to the drawings in the specification.

[0035] In the following description, for the purposes of explanation, numerous specific details are set forth in order to thoroughly describe the embodiments of the present application. It should be recognized, however, that the embodiments of the present application are sufficiently flexible to be utilized in a variety of situations, many of which are not described in detail.

[0036] The term "and / or", as used herein, merely describes association between associated objects, and can indicate three cases, for example, A and / or B can indicate three cases of A alone, A and B together, and B alone. In addition, the character " / " in the present application generally indicates an "or" relationship between the front and rear associated objects. In addition, "multiple" in the present application means two or more than two.

[0037] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification of the present application is only for the purpose of describing the embodiments of the present application and is not intended to limit the present application.

[0038] Before the embodiments of the present application are further described in detail, the terms and phrases involved in the embodiments of the present application are explained, and the terms and phrases involved in the embodiments of the present application are applicable to the following explanations.

[0039] The task re-running method provided by the embodiments of the present application can be implemented by a server or a terminal alone, or by a server and a terminal cooperatively. In some embodiments, the terminal or the server can implement the task re-running method provided by the embodiments of the present application by running a computer program. For example, the computer program can be a native program or a software module in an operating system; can be a native application program (APP), that is, a program that needs to be installed in an operating system to run, such as a client supporting a virtual scene, such as a game APP; can also be a small program, that is, a program that only needs to be downloaded into a browser environment to run; and can also be a small program that can be embedded into any APP. In summary, the above computer program can be any form of application program, module or plug-in.

[0040] In order for those skilled in the art to better understand the technical solutions of the present application, a task re-running method provided by the present application is further described in detail below in combination with the drawings and specific embodiments.

[0041] Please refer to Figure 1 is a flowchart of the first embodiment of the task re-running method of the present application, which specifically includes:

[0042] Step S11: Obtain the historical data amount corresponding to the last time when the task re-running was performed before the current time node and the current data amount corresponding to the current time node.

[0043] Specifically, before starting to run the task workflow, all historical data amounts between the current time nodes are counted and stored in the database. Assuming that the unit of each time node is one day, the current time node is recorded as the Tth day, all historical data amounts between the Tth day are counted, and the counting is performed according to time.

[0044] In order to record, read and modify the data amounts of each date partition, the application records the data amounts in the form of a dictionary. Further, different types of data can be classified and counted according to the types of the data, for example, the first type of data is recorded as gc, the second type of data is recorded as rl, the third type of data is recorded as rt, and the fourth type of data is recorded as im. When recording the data, all types of data can be further spliced. Taking the current time node T as an example, the final record format of the data amounts of each time node before T is as follows:

[0045] {“T-n”:“gc1000_rl2000_rt1000_im500”,“T-(n-1)”:“gc1000_rl1000_rt1000_im500”,…,“T-1”:“gc1000_rl2000_rt1000_im500”}. The record indicates that at the T-1 time node, there are 1000 first type data, 2000 second type data, 1000 third type data and 500 fourth type data. The same applies to other time nodes, which will not be described here.

[0046] Further, the data amounts of each time node can be stored in the database in the above final record format. In an embodiment, the database is, for example, a ZooKeeper database, which can store data amounts and subsequent running states. The ZooKeeper database uses a tree structure to store data, which is similar to the directory tree of a file system, and each node can store data and child nodes. The ZooKeeper database can also be replaced by other types of databases such as MySQL to achieve similar functional effects.

[0047] Further, at the current time node T, the corresponding historical data amount of the last time when the task is re-run before the current time node T and the corresponding current data amount of the current time node are obtained. Assuming that the historical data amount is:

[0048] {“T-n”:“gc1000_rl2000_rt1000_im500”,“T-(n-1)”:“gc1000_rl1000_rt1000_im500”,…,“T-1”:“gc1000_rl2000_rt1000_im500”}.

[0049] And the current data amount corresponding to the current time node is:

[0050] {“T-n”:“gc1000_rl2000_rt1000_im500”,“T-(n-1)”:“gc1000_rl2000_rt1000_im500”,…,“T-1”:“gc1000_rl2000_rt1000_im500”}。

[0051] Step S12: in response to the data increment of the current data amount compared with the historical data amount reaching a threshold, the incremental data of the current data amount compared with the historical data amount is re-run.

[0052] The historical data amount and the current data amount are compared, and in response to the data increment of the current data amount compared with the historical data amount reaching a threshold, the incremental data of the current data amount compared with the historical data amount is re-run.

[0053] In an embodiment, the current data amount and the historical data amount are data amounts corresponding to the same type of data, that is, in the task re-run triggering mechanism set by the embodiment, the incremental data corresponds to the same type of data, that is, when the data increment of the type of data reaches a threshold, the task re-run mechanism is triggered.

[0054] Specifically, in the above embodiment, at the T-(n-1) time node, the second type of data rl is newly added by 1000 (in the historical data amount, the second type of data rl is 1000, and in the current data amount, the second type of data rl is 2000) compared with the historical data amount, and the data increment of the current data amount compared with the historical data amount is 1000, and the 1000 data is incremental data.

[0055] Further, it is assumed that the data increment of 1000 can trigger the task re-run, and the incremental data is re-run. In this embodiment, the data increment is for the same type of data, and in other embodiments, if the sum of the data increments of multiple types of data reaches a threshold, the data re-run mechanism can also be triggered. This application takes the data increment of a single type of data reaching a threshold to trigger the data re-run mechanism as an example for description.

[0056] Further, it is assumed that the historical data amount is:

[0057] {“T-n”:“gc1000_rl2000_rt1000_im500”,“T-(n-1)”:“gc1000_rl2000_rt1000_im500”,…,“T-1”:“gc1000_rl2000_rt1000_im500”}。

[0058] And the current data amount corresponding to the current time node is:

[0059] {“T-n”:“gc1000_rl2000_rt1000_im500”,“T-(n-1)”:“gc1000_rl2000_rt1000_im500”,…,“T-1”:“gc1000_rl2000_rt1000_im500”}。

[0060] At this time, the current data amount is compared with the historical data amount, at the T-(n-1) time node, 1000 pieces of second type data rl are added (in the historical data amount, the second type data rl is 1000 pieces, and in the current data amount, the second type data rl is 2000 pieces), and at the T-n time node, 200 pieces of first type data gc are added (in the historical data amount, the first type data gc is 800 pieces, and in the current data amount, the first type data gc is 1000 pieces). The data increment of the first type data is small, and at this time, the task re-running mechanism is not triggered. The data increment of the second type data reaches the threshold, and the task re-running mechanism is triggered.

[0061] Further, if the data increment reaches the threshold, the data re-running mechanism is triggered, and the incremental data of the current data amount compared with the historical data amount is re-run.

[0062] In an embodiment, a percentage parameter a% can be set, and if the data increment reaches a% of the historical data amount or more, it is considered that the data increment reaches the threshold, that is, the threshold is a% of the historical data amount. Specifically, it can also be understood that the current data amount is greater than or equal to (1+a%) * historical data amount, and it is considered that the data increment reaches the threshold.

[0063] Further, the task re-running will affect the performance of the overall system running, in order to improve the system performance, the threshold can be determined in the form of a segmented function. Specifically, when the historical data amount is small, a higher threshold can be set to avoid triggering the task re-running when the data increment is small; when the historical data amount is large, a smaller threshold is set to facilitate timely supplement of the actual data. In a specific embodiment, in response to the historical data amount being less than a first threshold and the current data amount being greater than or equal to a second threshold, it is determined that the data increment reaches the threshold; in response to the historical data amount being greater than or equal to the first threshold and the current data amount being greater than or equal to a third threshold, it is determined that the data increment reaches the threshold; wherein the second threshold is determined based on the historical data amount and a first preset coefficient, the third threshold is determined based on the historical data amount and a second preset coefficient, and the first preset coefficient is greater than the second preset coefficient. Specifically, it is expressed in the formula as:

[0064]

[0065] Wherein, S1 represents the historical data amount, S2 represents the current data amount, x represents the first threshold value, a1%*S1 represents the second threshold value, and a2%*S1 represents the third threshold value.

[0066] In actual application, a task scheduling management module can be set in the system, and a running log is recorded in the module. The current data amount and the historical data amount can be recorded in the running log, so as to visualize the data loss situation.

[0067] In this embodiment, the current data amount corresponding to the current time node is compared with the historical data amount corresponding to the last time when the task is re-run before the current time node, and the data increment is calculated. If the data increment reaches a certain threshold value, it is considered that the data corresponding to the time node when the last time the task is re-run may exist data loss due to access delay, and the task running structure of the time node may exist inaccurate situation due to data loss. At this time, the task re-run mechanism is triggered, the incremental data is re-run, the missing data is completed, the task running result is updated, and the task running result is more accurate.

[0068] Further combined Figure 2 , step S12 further includes:

[0069] Step S21: determining the to-be-re-run data corresponding to the incremental data based on the data dependency relationship of the task node and / or the time corresponding to the incremental data.

[0070] Specifically, a data dependency relationship table is maintained for each task node in the system, such as {“task1”:“gc_rl”,“task2”:“im”}, which indicates that the task node task1 depends on the first type of data gc and the second type of data rl, and the task node task2 depends on the fourth type of data.

[0071] In an embodiment, the to-be-re-run data corresponding to the incremental data is determined based on the data dependency relationship of the task node, and the to-be-re-run data includes all data having a dependency relationship with the data type of the incremental data. Assuming that the second type of data rl corresponding to the time node T-(n-1) has incremental data, and the data increment reaches a threshold value, and the current task node is task1, at this time, according to the data dependency relationship of the task node task1, it is known that the calculation of the task node task1 depends on the first type of data gc and the second type of data rl, at this time, all data having a dependency relationship with the data type of the incremental data can be used as the to-be-re-run data, that is, the data corresponding to“gc1000_rl2000” in the current data amount is used as the to-be-re-run data.

[0072] In another embodiment, the to-be-re-run data corresponding to the incremental data is determined based on the time corresponding to the incremental data, and the to-be-re-run data includes all data of the same type as the incremental data within the time corresponding to the incremental data. Assuming that there is incremental data of the fourth type data im corresponding to the time node T-n, and the data increment reaches a threshold, the union of all fourth type data im corresponding to the time node T-n can be used as the to-be-re-run data. It is worth noting that this method is applied to the task node without dependency relationship.

[0073] In another embodiment of the present application, the to-be-re-run data corresponding to the incremental data is determined based on the data dependency relationship of the task node and the time corresponding to the incremental data, and the to-be-re-run data includes the union of all data of the same type as the incremental data and all data having dependency relationship with the incremental data within the time corresponding to the incremental data.

[0074] Specifically, assuming that there is incremental data of the second type data rl corresponding to the time node T-n, and the data increment reaches a threshold, according to the data dependency relationship table {“task1”:“gc_rl”,“task2”:“im”}, it can be known that the task node task1 needs to trigger the data of the time node T-n to perform task re-run, and since there is no incremental data of the fourth type data im, i.e., the task node task2 does not need to trigger task re-run. Further, assuming that there is a task node task3, the data dependency relationship of the task node task3 is {“task3”:“gc_rl_rt”}, i.e., the task node task3 depends on the first type data gc, the second type data rl and the third type data rt, and the increments of the first type data gc, the second type data rl and the third type data rt are as shown in Table (1):

[0075]

[0076] After the data increment calculation, it is found that the third type data rt has data increment N1 at time node T-4, but the data increment N1 of the third type data rt does not reach the threshold value of triggering the re-run; the first type data gc has data increment N2 at time node T-3, but the data increment N2 of the first type data gc also does not reach the threshold value of triggering the re-run. Then, the first type data gc has data increment N3 at time node T-2, and the second type data rl has data increment N4 at time node T-2, at this time, the data increment N3 of the first type data gc reaches the threshold value of triggering the re-run, and the task re-run needs to be performed. The to-be-re-run data for performing the task re-run includes the union of all the first type data gc in time node T-2 and all the first type data gc, the second type data rl and the third type data rt in time node T-4, time node T-3 and time node T-2, that is, all the first type data gc, the second type data rl and the third type data rt in time node T-4, time node T-3 and time node T-2.

[0077] Step S22: performing the task re-run on the to-be-re-run data.

[0078] Further, after determining the to-be-re-run data, the state of the to-be-re-run data is further marked according to the task node. In the database, each task has a node path for storing the running state, for example, / path / task1, which is used to store the running state of the task node in the past n days. The running state is divided into two kinds, action and pass. Action indicates that the task is to be run, the node needs to be re-run or is not run, run abnormally, and the state will be displayed; pass indicates that the task has successfully completed the running.

[0079] In an embodiment, it is assumed that the task node runs successfully last time before the current time node, and the record of the running state is {“T-(n+1)”:“pass”,“T-n”:“pass”,…,“T-1”:“pass”}. There is data increment at the current time node T, and the data is triggered to be re-run, and according to the judgment result, the data to be re-run in the running of the task node task1 at the current time node T includes the data of the time node T-n and the time node T-1, and the state of the data to be re-run is marked, and specifically {“T-(n+1)”:“pass”,“T-n”:“action”,…,“T-1”:“action”}, which represents that the data of the time node T-n and the time node T-1 is the data to be re-run, and needs to be re-run, and after the task is re-run, the state of the data to be re-run is modified, for example, after the task is re-run, the state is modified to {“T-(n+1)”:“pass”,“T-n”:“pass”,…,“T-1”:“pass”}. The data to be re-run in the running of the task node task2 includes the data of the time node T-1, and the state of the data to be re-run is marked, and specifically {“T-(n+1)”:“pass”,“T-n”:“pass”,…,“T-1”:“action”}, which represents that the data of the time node T-n and the time node T-1 is the data to be re-run, and needs to be re-run, and after the task is re-run, the state of the data to be re-run is modified, for example, after the task is re-run, the state is modified to {“T-(n+1)”:“pass”,“T-n”:“pass”,…,“T-1”:“pass”}.

[0080] It is worth noting that if the time node T-1 does not exist in the historical data, it is considered that the data of the time node has never been run, and the initial running is performed at the time node T, at this time, it is assumed that all the task nodes of the time node T-1 need to be initially run.

[0081] In the embodiments of the present application, the state of the data to be re-run is marked according to the task node, so that the running state of each task node can be visualized and traced.

[0082] In an embodiment of the present application, when the task is re-run on the data to be re-run, the task is also re-run on the data to be re-run according to the time corresponding to the data to be re-run. For example, for the task node task1, the data to be re-run includes the data of the time node T-n and the time node T-1, at this time, the data of the time node T-n is run first, and then the data of the time node T-1 is run.

[0083] Specifically, according to the dependency relationship between the task nodes, each task node is run in turn. When a certain task node is run, the running state record of the task node is read from the corresponding path of the database, and the data with the state of "action" is found out and run in turn according to the time node. In the running log of the task node, the actual time node required for this running is marked, so that the maintenance end can perceive the specific running condition.

[0084] It should be noted that after the task node is run, the running state record in the database is refreshed again. If the node is successfully run without exception, the running state of the data corresponding to the time node is changed from "action" to "pass". If the node fails to run or is not run due to failure of the upstream node, the running state is not updated and remains "action" state, waiting for the next cycle of running to trigger the recalculation again. Assuming that task1 is successfully run and task2 fails to run, after the running is completed, the running state of task1 is updated to {“T-n”:“pass”,“T-(n-1)”:“pass”,…,“T-1”:“pass”}, and the running state of task2 remains {“T-n”:“pass”,“T-(n-1)”:“pass”,…,“T-1”:“action”}.

[0085] In order to make the development or operation and maintenance personnel more intuitively perceive the task running state, and also facilitate subsequent review and inspection, a "task scheduling management" module is added to display the task scheduling log, including running data volume record, running node name, running success or failure mark, running time consumption and other information.

[0086] Further, the task scheduling management page of the product can also support the function of manually triggering task node recalculation, selecting the corresponding node name and running date, and then clicking "run" to realize specified task recalculation. The manual running scheduling is also reflected in the log record, and the mode is "manual". If the data volume changes after manual running, it will also be updated accordingly.

[0087] It should be noted that the scheme of the present application can automatically trigger the re-run mechanism when the source data delay is large or the task running is abnormal, so that the accumulated data amount that is not running is large, thereby reducing the manual operation and maintenance investment. For the processing and analysis task of perception data, there are usually two modes of real-time task and batch processing task, and the present application is mainly applied to the task re-run scheduling in the batch processing scene. Taking the batch processing scene with a day as a time node as an example, in the normal mode, the workflow arranged in a certain dependent order by a plurality of task nodes will start scheduling at a fixed time of each time node, and each task node is run in turn to process the original data collected in the previous time node. For example, the task will process the data collected in the T-1 day (time node T-1) in the T day (time node T). The defect of this scheduling mode is that if the data of the T-1 day cannot be generated due to the abnormality of the upstream link when the task is running in the T day, these data will not be processed again in the future scheduling even if they are completed later. The scheme of the present application solves the defect, and the method provided by the present application will monitor the source data of the previous n days (the value of n is set according to the overall workflow running time, performance and the like) in sequence when running in the T day, and if a significant data increment is perceived, the corresponding task re-run will be triggered, which can to a certain extent, compatible with the abnormal situation of task empty running or result data missing. At the same time, through the task running state record, the tasks running failed or not running in the previous period can also be checked when running in the T day, and the re-run attempt is automatically performed.

[0088] The task re-run method of the present application increases the monitoring of the source access data amount, in addition to retrying the failed or not executed tasks through the running state record, it can also be compatible to a certain extent for the serious data delay situation, and when the originally missing data is completed in the subsequent period, the task re-calculation is automatically detected and triggered, which can greatly improve the stability of the program running and reduce the on-site manual operation and maintenance investment. Moreover, the method of the present application adds the function of task re-run in the framework of normal task running process and running period, and the overall workflow framework does not need to be adjusted, only by passing in the date assignment, the task originally only running in the time node T-1 can automatically trigger one or more time nodes of the running history, so as to achieve the purpose of task re-calculation.

[0089] Referring to Figure 3Fig. 1 is a structural schematic diagram of an embodiment of a task heavy running device according to the present application. The task heavy running device 30 specifically comprises a data acquisition module 31 and a heavy running module 32. The data acquisition module 31 is configured to acquire a historical data amount corresponding to a last time of task heavy running before a current time node and a current data amount corresponding to the current time node. The heavy running module 32 is configured to perform task heavy running on the incremental data of the current data amount compared with the historical data amount, in response to the data increment of the current data amount compared with the historical data amount reaching a threshold value.

[0090] In an embodiment, the current data amount and the historical data amount are data amounts corresponding to data of a same type.

[0091] In an embodiment, the heavy running module 32 determines the to-be-heavy-running data corresponding to the incremental data based on a data dependency relationship of the task node and / or a time corresponding to the incremental data, and performs task heavy running on the to-be-heavy-running data.

[0092] In an embodiment, the to-be-heavy-running data comprises all data having a dependency relationship with the incremental data, or the to-be-heavy-running data comprises all data of a same type as the incremental data within a time corresponding to the incremental data, or the to-be-heavy-running data comprises a union set of all data of a same type as the incremental data within the time corresponding to the incremental data and all data having a dependency relationship with the incremental data.

[0093] In an embodiment, the heavy running module 32 is further configured to mark a state of the to-be-heavy-running data according to the task node, and modify the state of the to-be-heavy-running data.

[0094] In an embodiment, the task heavy running is performed on the to-be-heavy-running data according to a time corresponding to the to-be-heavy-running data.

[0095] In an embodiment, in response to the historical data amount being less than a first threshold value and the data increment being greater than or equal to a second threshold value, it is determined that the data increment reaches the threshold value; in response to the historical data amount being greater than or equal to the first threshold value and the data increment being greater than or equal to a third threshold value, it is determined that the data increment reaches the threshold value; wherein the second threshold value is greater than the third threshold value.

[0096] Fig. 2 is a structural schematic diagram of an embodiment of a task heavy running device according to the present application. The task heavy running device 30 specifically comprises a data acquisition module 31 and a heavy running module 32. The data acquisition module 31 is configured to acquire a historical data amount corresponding to a last time of task heavy running before a current time node and a current data amount corresponding to the current time node. The heavy running module 32 is configured to perform task heavy running on the incremental data of the current data amount compared with the historical data amount, in response to the data increment of the current data amount compared with the historical data amount reaching a threshold value. Figure 4 Figure 4 Fig. 3 is a framework schematic diagram of an embodiment of an electronic terminal according to the present application. The electronic terminal 80 comprises a memory 81 and a processor 82 coupled with each other. The processor 82 is configured to execute program instructions stored in the memory 81, so as to implement the steps of any one of the task heavy running method embodiments. In a specific implementation scenario, the terminal 80 can include but is not limited to a microcomputer, a server, and in addition, the terminal 80 can also include a notebook computer, a tablet computer and other mobile devices, which are not limited herein.

[0097] ​Specifically, the processor 82 is configured to control itself and the memory 81 to implement the steps of any of the above-described task re-running method embodiments. The processor 82 can also be referred to as a CPU (Central Processing Unit). The processor 82 can be an integrated circuit chip including a processing core. The processor 82 can also be a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array) or other programmable logic device, discrete gate or transistor logic device, or a discrete hardware component. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. In addition, the processor 82 can be jointly implemented by an integrated circuit chip.

[0098] Referring to Figure 5 , Figure 5 is a schematic diagram of a framework of an embodiment of the computer-readable storage medium provided by the present application. The computer-readable storage medium 90 stores program instructions 901 capable of being executed by a processor, and the program instructions 901 are used to implement the steps of any of the above-described task re-running method embodiments.

[0099] In some embodiments, the apparatus provided by the embodiments of the present disclosure has functions or includes modules that can be used to execute the methods described in the above method embodiments, and the specific implementation can refer to the description of the above method embodiments. For brevity, details are not described here.

[0100] The above description of various embodiments tends to emphasize the differences between various embodiments, and the same or similar parts can be mutually referred to. For brevity, details are not described here.

[0101] In several embodiments provided in the present application, it should be understood that the disclosed method and device can be implemented in other ways. For example, the above-described device embodiment is only schematic, for example, the division of the module or unit is only a logical function division, and actual implementation can have another division manner, for example, a unit or component can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual elements can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0102] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0103] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor execute all or part of the steps of the method of each embodiment of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0104] The above is only an embodiment of the present application, and does not limit the patent protection scope of the present application. Any equivalent structure or equivalent flow transformation, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A method for rerunning a task, characterized in that, The method comprises: acquiring historical data volume corresponding to the last time when task re-run is performed before a current time node and current data volume corresponding to the current time node; in response to data increment of the current data volume compared with the historical data volume reaching a threshold, performing task re-run on the increment data of the current data volume compared with the historical data volume.

2. The method of claim 1, wherein, The current data volume and the historical data volume are data volumes of the same type of data.

3. The method of claim 2, wherein, Performing task re-run on the increment data of the current data volume compared with the historical data volume comprises: determining to-be-re-run data corresponding to the increment data based on data dependency relationship of a task node and / or time corresponding to the increment data; performing task re-run on the to-be-re-run data.

4. The method of claim 3, wherein, The to-be-re-run data comprises all data having dependency relationship with the increment data, or the to-be-re-run data comprises all data of the same type as the increment data within the time corresponding to the increment data, or the to-be-re-run data comprises the union of all data of the same type as the increment data within the time corresponding to the increment data and all data having dependency relationship with the increment data.

5. The method of claim 3, wherein, Determining to-be-re-run data corresponding to the increment data based on data dependency relationship of a task node and / or time corresponding to the increment data comprises: marking state of the to-be-re-run data according to a task node; after performing task re-run on the to-be-re-run data, comprising: modifying the state of the to-be-re-run data.

6. The method of claim 3, wherein, Performing task re-run on the to-be-re-run data comprises: performing task re-run on the to-be-re-run data according to time corresponding to the to-be-re-run data.

7. The method of claim 1, wherein, in response to the historical data volume being less than a first threshold and the data increment being greater than or equal to a second threshold, determining that the data increment reaches the threshold; in response to the historical data volume being greater than or equal to the first threshold and the data increment being greater than or equal to a third threshold, determining that the data increment reaches the threshold; wherein the second threshold is greater than the third threshold.

8. A task re-run apparatus, characterized by comprising: The method comprises: a data acquisition module, configured to acquire historical data volume corresponding to the last time when task re-run is performed before a current time node and current data volume corresponding to the current time node; a re-run module, configured to, in response to data increment of the current data volume compared with the historical data volume reaching a threshold, perform task re-run on the increment data of the current data volume compared with the historical data volume.

9. An electronic terminal, characterized in that The electronic terminal comprises a memory and a processor coupled with each other, the processor is configured to execute program instructions stored in the memory, and the processor is configured to execute program data to implement steps in the task re-run method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement steps in the task re-run method according to any one of claims 1-7.

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