CN116149824A

CN116149824A - Task re-running processing method, device, equipment and storage medium

Info

Publication number: CN116149824A
Application number: CN202310188798.6A
Authority: CN
Inventors: 高伟钦; 赖海滨; 苏超然; 陈守当; 翁世清
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2023-02-27
Filing date: 2023-02-27
Publication date: 2023-05-23

Abstract

The disclosure provides a task re-running processing method, device, equipment and storage medium, which can be applied to the technical field of computers, the technical field of big data or the technical field of finance and technology. The method comprises the following steps: responding to a task re-running processing request, and searching at least one target job required by completing the task to be re-run from a job library according to attribute information of the task to be re-run, wherein the task to be re-run is carried in the processing request; performing dependency analysis on the target job according to the dependency required by executing the target job to obtain a dependency analysis result, wherein the dependency analysis result comprises the target job and a plurality of upstream and downstream jobs associated with the target job; determining a target workflow from the dependency analysis result, and configuring a re-running parameter for the target workflow to obtain the re-running workflow; executing the re-running operation flow to obtain a processing result of the task to be re-run.

Description

Task re-running processing method, device, equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technology, big data technology, or financial technology, and in particular, to a task rerun processing method, apparatus, device, storage medium, and program product.

Background

In a scheduling system, a scenario that a task needs to be re-run generally occurs, for example, due to an upstream data supply error, or other businesses often need to re-run according to a processing link of a certain data, so as to meet the requirement of data re-processing. In the process of implementing the inventive concept of the present disclosure, the inventors found that the following problems generally exist in the related art: in the related technology, in the process of executing task rerun, the executing intellectualization and automation degree are lower, and the processing efficiency of the task rerun event is further reduced.

Disclosure of Invention

In view of the foregoing, the present disclosure provides a processing method, apparatus, device, storage medium, and program product for task rerun.

According to one aspect of the present disclosure, there is provided a task rerun processing method, including: responding to a task re-running processing request, and searching at least one target job required by completing the task to be re-run from a job library according to attribute information of the task to be re-run, wherein the task to be re-run is carried in the processing request; performing dependency analysis on the target job according to the dependency relation required by executing the target job to obtain a dependency analysis result, wherein the dependency analysis result comprises the target job and a plurality of upstream and downstream jobs associated with the target job; determining a target operation flow from the dependency analysis result, and configuring a re-running parameter for the target operation flow to obtain a re-running operation flow; executing the re-running operation flow to obtain the processing result of the task to be re-run.

According to an embodiment of the present disclosure, the job library includes a plurality of jobs, each job is configured with an event table, and the event table includes a dependency relationship required for executing the job; the performing dependency analysis on the target job includes: taking the target as a root node, wherein the root node is used for constructing a job tree; configuring the initial layer number of the operation tree and the analysis type of the dependency analysis; and searching upstream and downstream jobs associated with the target job from the event table according to the initial layer number and the analysis type.

According to an embodiment of the present disclosure, the searching the event table for the upstream and downstream jobs associated with the target job according to the initial layer number and the analysis type includes: responding to the operation of the target user for carrying out dependency analysis on the target operation, and checking the operation authority of the target user and the initial layer number to obtain a checking result; and searching for the upstream and downstream jobs associated with the target job from the event table based on a hierarchical traversal algorithm and according to the analysis type under the condition that the verification result represents verification pass.

According to an embodiment of the present disclosure, an upstream job and a downstream job associated with the above-described target job are regarded as hierarchical leaf nodes; the method further comprises the following steps: constructing the operation tree according to the root node and the hierarchical leaf node, wherein the final hierarchical level of the operation tree is smaller than or equal to the initial hierarchical level, and the final hierarchical level is determined according to the searched upstream and downstream operations; and displaying the job tree through a visualization component.

According to an embodiment of the present disclosure, the determining the target job flow from the dependency analysis result includes: selecting the target job and at least one upstream and downstream job from the job tree to obtain an initial job flow; and filtering the initial workflow based on the operation authority of the target user to obtain the target workflow.

According to an embodiment of the present disclosure, the above method further includes: generating a list according to the target job and the upstream and downstream jobs, wherein the jobs in each row in the list represent a group of jobs with the dependency relationship; and displaying the list chart through the visualization component.

According to an embodiment of the present disclosure, the above method further includes: responsive to executing the re-run workflow, determining a downstream job associated with the re-run workflow in accordance with a dependency relationship of the re-run workflow; and displaying the re-running operation flow to an operation and maintenance person of the downstream operation so that the operation and maintenance person can carry out decision analysis on the downstream operation.

According to an embodiment of the present disclosure, the above method further includes: after the operation and maintenance personnel carry out decision analysis on the downstream operation, a decision analysis result is generated; and according to the decision analysis result, assigning a processed state identifier to the re-running operation flow.

According to embodiments of the present disclosure, the above analysis types include traceability analysis, output analysis, and bi-directional analysis.

According to an embodiment of the disclosure, the re-running parameters include a date of the task to be re-run, a lot number of the task to be re-run, a time zone of the task to be re-run, and a name of a re-running workflow.

According to an embodiment of the present disclosure, the above method further includes: generating a to-be-rerun instance table comprising the rerun operation flow based on a preset task rerun strategy; and executing the re-running operation flow according to the to-be-re-run example table to obtain a processing result of the to-be-re-run task.

Another aspect of the present disclosure also provides a task rerun processing device, including: the first searching module is used for responding to a task re-running processing request, and searching at least one target job required by completing the task to be re-run from the job library according to attribute information of the task to be re-run, wherein the task to be re-run is carried in the processing request; the first analysis module is used for carrying out dependency analysis on the target job according to the dependency required by executing the target job to obtain a dependency analysis result, wherein the dependency analysis result comprises the target job and a plurality of upstream and downstream jobs associated with the target job; the first determining module is used for determining a target workflow from the dependency analysis result, and configuring a re-running parameter for the target workflow to obtain the re-running workflow; the first execution module is used for executing the re-running operation flow to obtain a processing result of the task to be re-run.

Another aspect of the present disclosure also provides an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the processing method of task re-running.

Another aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described task re-running processing method.

Another aspect of the disclosure also provides a computer program product, including a computer program, where the computer program, when executed by a processor, implements a method for processing task rerun as described above.

According to the task rerun processing method, device, equipment, storage medium and program product provided by the present disclosure, at least one target job is found according to attribute information of a task to be rerun by responding to a task rerun processing request; performing dependency analysis on the target job according to the dependency relation required by executing the target job to obtain an analysis result comprising the target job and the upstream and downstream jobs; determining a target workflow from the analysis result, and configuring a re-running parameter for the target workflow to obtain the re-running workflow; executing the re-running operation flow to obtain a task re-running processing result. Because the analysis result of the dependency relationship is combined in the task re-running processing process, the re-running operation flow is determined based on the target operation and the upstream and downstream operations in the dependency relationship analysis result, and the re-running operation flow is executed, the whole process can be automatically executed, the upstream and downstream operations can be flexibly combined, the problems of low intelligent and automatic degree of executing the re-running task in the related technology are at least partially overcome, and the technical effect of improving the processing efficiency of the task re-running event is further achieved.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates a system architecture diagram of a method and apparatus for processing task re-run in accordance with an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a method of processing task rerun according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow chart of a method of processing task re-running in accordance with another embodiment of the present disclosure;

FIG. 4 schematically illustrates a processing system for task re-running in accordance with an embodiment of the present disclosure;

FIG. 5 schematically illustrates a page schematic of a job tree generated after dependency analysis in accordance with an embodiment of the present disclosure;

FIG. 6 schematically illustrates a page diagram of configuring a re-run parameter according to an embodiment of the present disclosure;

FIG. 7 schematically illustrates a schematic diagram of a list of instances to be rerun generated in accordance with an embodiment of the present disclosure;

FIG. 8A schematically illustrates a re-run workflow received by a target user in accordance with an embodiment of the present disclosure;

FIG. 8B schematically illustrates a re-run workflow received by a target user according to another embodiment of the disclosure;

FIG. 9 schematically illustrates a block diagram of a processing device for task re-running in accordance with an embodiment of the present disclosure; and

fig. 10 schematically illustrates a block diagram of an electronic device adapted to implement a processing method for task re-running according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

In the batch scheduling of the traditional data line, because the upstream data supply error or other business needs to be frequently selected according to the processing link of certain data, the corresponding batch operation is selected and re-run is performed, so as to meet the requirement of data re-processing. Specifically, the following classes of scenarios are included.

Scene one: if a job is encountered that does not run for a long period of time, it may be due to a dependency condition not being reached, e.g., the job may need to rely on the completion results of other jobs to execute. At this time, since the completion status of the upstream job is unknown, it is necessary to trace back the completion status of the upstream job, which requires dependency analysis.

Scene II: if a batch of jobs is run successfully, but the data used is erroneous, the batch is now re-run. The job rerun may reissue a new completion event, which may have an impact on downstream jobs. If the downstream job uses upstream process data, then the downstream job also requires a rerun operation. In this scenario, it is also contemplated that the rerun operation may have been cross-applied (an application, which may be understood as an item, refers to a series of unique, complex and interrelated activities that have a definite goal or purpose), and the operator of the upstream job may not necessarily have authority to operate the downstream job.

The dependency between the operations is complex, and the people's memory may be difficult to comb. There is therefore a need for a way to expose the dependency hierarchy between jobs for better operation and maintenance management. In the aspect of the safety of the data link, after the upstream application runs the operation again, the downstream application needs to be informed in time, so that the downstream application can evaluate the influence of the operation again in time, and can locate the affected application operation set in time and process the operation set.

The related art has the following problems when performing dependency analysis: the information of the upstream and downstream operations cannot be flexibly checked; only a single target job is supported to be analyzed during analysis, and a group of jobs cannot be analyzed together; the analysis result of the dependency relationship cannot be directly operated on the page, such as initiating operations such as temporary rerun and the like on abnormal operation; after the job is re-run, the affected application downstream cannot be made aware of and locate the affected job set. In addition, the batch processing scheduling system of the related technology lacks a method for carrying out batch re-running aiming at the analysis result of the operation dependency relationship, and meanwhile, after the re-running of the upstream operation, the affected downstream operation set cannot be automatically positioned, so that the related operation and maintenance personnel at the downstream are required to be informed by relying on manual work, the automation and the intellectualization of the task re-running process are lower, and the processing efficiency of the task re-running event is reduced.

In view of this, the present disclosure provides a method, apparatus, device, storage medium and program product for processing task rerun, which are used for improving the level of automation and intelligence in the task rerun process and improving the processing efficiency of task rerun events. Specifically, the method comprises the steps of responding to a processing request of task rerun, and searching at least one target job required by completing the task to be rerun from a job library according to attribute information of the task to be rerun, wherein the task to be rerun is carried in the processing request; performing dependency analysis on the target job according to the dependency required by executing the target job to obtain a dependency analysis result, wherein the dependency analysis result comprises the target job and a plurality of upstream and downstream jobs associated with the target job; determining a target workflow from the dependency analysis result, and configuring a re-running parameter for the target workflow to obtain the re-running workflow; executing the re-running operation flow to obtain a processing result of the task to be re-run.

It should be noted that, the method and the device for processing task rerun determined in the embodiments of the present disclosure may be used in the field of computer technology, the field of big data technology, or the field of financial technology, and may also be used in any field other than the field of computer technology, the field of big data technology, or the field of financial technology.

In the technical scheme of the disclosure, the related data (such as including but not limited to personal information of a user) are collected, stored, used, processed, transmitted, provided, disclosed, applied and the like, all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public welcome is not violated.

Fig. 1 schematically illustrates a system architecture diagram of a task re-running processing method and apparatus according to an embodiment of the present disclosure.

As shown in fig. 1, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium used to provide a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 through the network 104 using at least one of the first terminal device 101, the second terminal device 102, the third terminal device 103 to receive or transmit a processing request for task re-running, or the like. Various communication client applications, such as a financial class application, a shopping class application, a web browser application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only) may be installed on the first terminal device 101, the second terminal device 102, the third terminal device 103.

The first terminal device 101, the second terminal device 102, the third terminal device 103 may be various electronic devices supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (merely an example) providing support for processing requests for the user to re-run the tasks transmitted by the first terminal apparatus 101, the second terminal apparatus 102, and the third terminal apparatus 103. The background management server may analyze and process the received data such as the processing request, and feed back the processing result (for example, the rerun workflow obtained or generated according to the processing request, the result obtained by executing the rerun workflow, information, or data, etc.) to the terminal device.

For example, the server 105 may respond to a processing request of task rerun, and find at least one target job required for completing the task to be rerun from the job library according to attribute information of the task to be rerun, where the task to be rerun is carried in the processing request; performing dependency analysis on the target job according to the dependency required by executing the target job to obtain a dependency analysis result, wherein the dependency analysis result comprises the target job and a plurality of upstream and downstream jobs associated with the target job; determining a target workflow from the dependency analysis result, and configuring a re-running parameter for the target workflow to obtain the re-running workflow; executing the re-running operation flow to obtain a processing result of the task to be re-run.

It should be noted that, the task re-running processing method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the processing device for task rerun provided in the embodiments of the present disclosure may be generally disposed in the server 105. The processing method for task rerun provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105. Accordingly, the processing apparatus for task rerun provided in the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and/or the server 105.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The task re-running processing method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 8B based on the scenario described in fig. 1.

Fig. 2 schematically illustrates a flow chart of a processing method of task re-running according to an embodiment of the present disclosure.

As shown in fig. 2, the task rerun processing method of this embodiment includes operations S201 to S204.

In operation S201, in response to a processing request for task rerun, at least one target job required for completing the task to be rerun is found from the job library according to attribute information of the task to be rerun, where the task to be rerun is carried in the processing request.

In operation S202, a dependency analysis is performed on the target job according to a dependency required for executing the target job, and a dependency analysis result is obtained, where the dependency analysis result includes the target job and a plurality of upstream and downstream jobs associated with the target job.

In operation S203, a target workflow is determined from the dependency analysis result, and a re-run parameter is configured for the target workflow to obtain a re-run workflow.

In operation S204, the re-running job flow is executed, and a processing result of the task to be re-run is obtained.

According to the embodiment of the disclosure, task re-running may be understood as link re-running, which may refer to a process of taking a certain job or job flow as an initial node, locating a dependency relationship of the node according to the dependency relationship and the dependency hierarchy, and selecting a job/job flow node in the result set to assemble the job flow for re-running.

According to embodiments of the present disclosure, a job flow may be understood as an entity in a scheduling system for organizing and managing several business-related jobs. Briefly, a job flow is a running set of jobs. Each job node may implement certain processing logic according to configuration. The job nodes may be unassociated or dependent by directed edges, but loops cannot be formed when there is association. It is understood that in job flow scheduling, a job flow may be a scheduling unit, and a job node therein may be an execution unit of minimum granularity.

According to embodiments of the present disclosure, a job may be a basic configuration unit of a scheduling system, may be a logical unit defined by a person using the scheduling system to accomplish a certain job, and may include an executed program and its parameters. In task scheduling, a job (or task) is the smallest unit of execution. The same program, different parameter forms, can be different job configurations for the scheduling system. The job is a basic unit of scheduling, and the job flow is an organization unit of the job, and a group of jobs with similar functions and frequencies are generally organized under the same job flow.

According to embodiments of the present disclosure, the processing request for task re-run may be triggered when tracing an upstream job or upstream task; the method can also be triggered when the re-running data is required to be obtained; but also in case the correct data need to be re-run if the incorrect data is used. A task to be rerun can be formed while a task rerun processing request is triggered, for example, a task tracing an upstream job; a task of re-running according to the current data is needed; a re-run task using the correct data, etc.

According to an embodiment of the present disclosure, the attribute information of the task to be rerun may be determined according to the rerun task, for example, information such as a field, an identifier, etc., for identifying the type of the task to be rerun. Specifically, different identifiers may be given to each condition of triggering the task to re-run the processing request, when the processing request is responded, fields in the processing request, such as "tracing", "xx service", etc., may be extracted, it is determined according to the fields that the task to be re-run is triggered when the upstream task needs to be traced, and according to the fields, a job related to the "xx service" is found from the job library, where the target job may be the related job. It is understood that a plurality of jobs may be recorded in the job library, and a plurality of target jobs found from the job library may constitute one job set as a job set to be analyzed.

According to embodiments of the present disclosure, dependency analysis required to execute a target job may be understood as the upstream and downstream dependencies of the job/job flow. For example, the execution condition of the target job needs to depend on the upstream job, and the target job can be executed only after the upstream job is completed. By analyzing the target job, it is possible to analyze the upstream and downstream jobs for which the target job is found, and based on the result, generate an analysis result including the target job and a plurality of upstream and downstream job compositions associated with the target job.

According to the embodiment of the disclosure, it may be understood that when the dependency analysis is implemented, an event table may be acquired first, and data of the event table may be a static table generated by initializing after the job configuration is completed. The event table stores the event related to the job, and the direction of the event may be stored, for example, an input event, an output event, or the like may be represented by (I/O). The event table may store information such as event identification, service date, batch number, object identification, object type, etc., and may also store a dependency relationship between jobs, for example, when job a needs to complete execution of job B, or when an execution result of job B is obtained, job a is executed; the job a may be executed only when the execution of the job B and/or the job C is completed.

According to the embodiment of the disclosure, since in the analysis result, part of the upstream and downstream jobs may not need to be re-run, but only a few of the jobs are re-run, the target job flow may be a temporary job flow assembled from the target job in the analysis result and the upstream and downstream jobs that need to be re-run.

According to an embodiment of the present disclosure, the re-running parameters include a date of the task to be re-run, a lot number of the task to be re-run, a time zone of the task to be re-run, and a name of a re-running workflow.

According to the embodiment of the disclosure, when the re-running parameters are configured for the target workflow, the date, the batch number, the time zone, the automatic naming and the like can be automatically captured, and the task date, the batch number, the name of the target workflow, the time zone of the task to be re-run and the like can also be input into the page, so that the re-running parameters and the target workflow can be combined into a temporary re-running workflow.

According to the embodiment of the disclosure, the temporary re-running operation flow described above is executed, and corresponding processing results, such as traced upstream operation, data formed by the upstream operation, re-running data, data obtained after re-running by using correct data, and the like, can be obtained.

According to an embodiment of the present disclosure, a plurality of jobs are included in a job library, each job is configured with an event table including dependency relationships required to execute the job. Operation S202 may further include the following operations: taking the target as a root node, wherein the root node is used for constructing a job tree; configuring an initial layer number of a job tree and an analysis type of dependency analysis; and searching upstream and downstream jobs associated with the target job from the event table according to the initial layer number and the analysis type.

According to the embodiment of the disclosure, the above operation may be performed in a page, for example, in response to an operation of selecting a job set to be analyzed, N target jobs to be analyzed are selected from the job set, the N target jobs are respectively used as N root nodes, and N job trees may be constructed according to the N root nodes. Where N may be a positive integer, preferably N may be less than or equal to 5, and N is set to be less than or equal to 5 because of considering resources in the running process and the related traffic data volume, if N is greater than 5, a plurality of traffic data may be related, the data volume is large, and meanwhile, the consumption of the running resources is increased, but the processing efficiency of the running event is reduced, so N is set to be less than or equal to 5. However, it should also be understood that the N and N thresholds may be adaptively adjusted according to actual needs for different operating environments or re-running situations. It will be appreciated that after analyzing the N jobs, the analysis of the remaining jobs in the set of jobs to be analyzed may be continued until each job in the set of jobs is analyzed. By analyzing the jobs in the job set in batches, the operation resources can be reduced, and the processing efficiency of the re-running event can be improved.

According to embodiments of the present disclosure, after the root node is determined, the initial layer number M may be configured for the job tree in response to the operation of the page. The hierarchy of the embodiments of the present disclosure may be understood as a hierarchy in a job tree graph, the initial hierarchy number M may be a positive integer, preferably M may not exceed 30, i.e. the job tree may not exceed 30 layers. Since a plurality of service data are involved in each layer in consideration of resources in the running process and the related service data amount, in the case that M exceeds 30, a plurality of service data may be involved in the running process, the data amount is large, meanwhile, the consumption of the running resources is increased, and the processing efficiency of the re-running event is reduced, so that M is set to be not more than 30. However, it should also be understood that the thresholds of M and M may be adaptively adjusted according to actual needs for different running environments or running conditions. It should be understood that the initial hierarchical level is not the final hierarchical level of the job tree, and due to the difference of data, the final hierarchical level is smaller than the initial hierarchical level.

According to embodiments of the present disclosure, the job tree may also be configured with analysis types, which may include traceability analysis, output analysis, and bi-directional analysis. The analysis type may refer to an analysis direction when the dependency analysis is performed. For example, trace-source analysis may refer to forward trace-source calculation of upstream nodes/jobs; output analysis may refer to reckoning downstream nodes/jobs backward; bi-directional analysis may refer to the simultaneous computation of upstream and downstream nodes/jobs. After the configuration of the root node, the initial layer level and the analysis type is completed, searching from the event table according to the set parameters in response to the searching operation on the click page.

According to embodiments of the present disclosure, looking up the upstream and downstream jobs associated with the target job from the event table may include the following operations, depending on the initial layer number and analysis type: responding to the operation of the target user for carrying out dependency analysis on the target operation, and checking the operation authority and the initial layer number of the target user to obtain a checking result; and searching for the upstream and downstream jobs associated with the target job from the event table based on the hierarchical traversal algorithm and according to the analysis type under the condition that the verification result represents that the verification passes.

According to embodiments of the present disclosure, a target user may refer to a person using a dispatch system. The user account can be created for the target user at the user management module, the relationship between the target user and the application can be many-to-many, and one user can have the operation authority of one or more applications. An application may also belong to multiple users.

According to the embodiment of the disclosure, under the condition that the set root node, the initial layer number and the analysis type are received, the operation authority of the target user, whether the setting of the initial layer number exceeds a threshold value, the integrity of service data, the correctness of the service data and the like can be checked first, and a response check result can be obtained. In the case where the above-mentioned tests are passed, different analysis branches may be taken according to the analysis type. And searching out the upstream or downstream operation of the operation according to the mapping relation of the operation input event and the operation output event in the event table or the dependency relation between the operation and the operation. In the searching process, a hierarchical traversal algorithm can be used to search the target operation layer by layer according to the set initial layer number until the service data is searched or the set initial layer number is traversed, so that the dependency analysis of the target operation is completed.

According to the embodiment of the present disclosure, when setting the threshold value of N or M, it is also necessary to consider the algorithm rate, response speed, and the like of the hierarchical traversal algorithm.

According to embodiments of the present disclosure, the dependency analysis result may be displayed in the form of a job tree, for example, the found upstream and downstream jobs associated with the target job may be used as leaf nodes; constructing a job tree according to the root node and the hierarchical leaf node, wherein the final hierarchical level of the job tree is smaller than or equal to the initial hierarchical level, and the final hierarchical level is determined according to the searched upstream and downstream operations; the job tree is exposed through the visualization component.

According to the embodiment of the disclosure, a job tree is constructed according to the actually found upstream and downstream jobs and the target job, the job tree has an actual number of levels, and the final number of levels may be the actual number of levels. It will be appreciated that the final number of layers will be less than the initial number of layers. When the visual component is utilized to display the operation tree, if the final layer level is smaller than the initial layer level, displaying the operation tree to the final layer level; if the final number of levels is equal to the initial number of levels, the job tree is presented to the initial number of levels. Optionally, when the job tree is displayed, there are N root nodes, N job tree graphs may be displayed, and the instance state (such as ready, running success, running failure, etc. states) of each job may be displayed, and different states of the job instance may be represented by different colors.

According to the embodiment of the disclosure, N target jobs are selected as root nodes, and N final job tree graphs are displayed, so that it can be understood that the embodiment of the disclosure can support a plurality of jobs to perform dependency analysis together, the problem that only a single target is supported as a request for analysis in the related art is solved, and the processing efficiency of task re-running events is improved.

According to embodiments of the present disclosure, the dependency analysis results may be presented in the form of a list, such as generating a list from the target job and the upstream and downstream jobs, where the jobs of each row in the list represent a set of jobs having a dependency; the list diagram is presented by a visualization component.

In accordance with embodiments of the present disclosure, when the dependency analysis results are presented using a list, each row of data/jobs in the list may be a set of data/jobs having a dependency. Based on this, the target job and the upstream and downstream jobs can be generated and presented in this manner. Alternatively, different states of the job instance may be represented by different colors in the list.

According to the embodiment of the disclosure, the visualization of the dependency analysis result is realized by displaying the job tree or the list, and the job tree or the list can clearly display the dependency or the processing logic among the jobs and display the instance state condition of the jobs, so that an operator can flexibly look up the information of the upstream job and the downstream job and make a decision based on the looked up information.

According to an embodiment of the present disclosure, operation S203 may further include the following operations: selecting a target job and at least one upstream and downstream job from the job tree to obtain an initial job flow; and filtering the initial workflow based on the operation authority of the target user to obtain the target workflow.

According to embodiments of the present disclosure, the process of obtaining the initial workflow may be obtained in response to a target user's hooking operation on the page. Specifically, a target job and an upstream job and a downstream job which need to be run again can be checked from the displayed list or the job tree, and the checked jobs are assembled, so that an initial job flow can be obtained. However, since the operation authority of each target user is not the same, and some target users may not have authority to actually operate a certain job, only the job can be checked or checked, and the initial job flow cannot be triggered to be executed with authority. Therefore, after the initial workflow is obtained, the initial workflow is filtered according to the actual operation authority of the target user, and the target workflow obtained after the filtering can be triggered to be executed by the target user.

According to the embodiment of the disclosure, the authority filtering is performed before the target workflow is formed, so that the safety of data can be improved, and the target user without operation authority is prevented from performing wrong selection on the job, so that the operation of the whole link is influenced.

According to the embodiment of the disclosure, after the re-running parameters are configured for the target workflow, the re-running workflow can be obtained, and when the re-running workflow is executed specifically, an interface for executing the re-running workflow can be set on the back-end server, so that the execution of the re-running workflow is realized. Alternatively, when the target user initiates execution of the re-run workflow, the backend server may analyze downstream jobs affected by the re-run workflow and notify the operation and maintenance personnel of the downstream jobs.

According to an embodiment of the present disclosure, when executing the rerun workflow may further include the following operations: generating a to-be-rerun instance table comprising rerun operation flows based on a preset task rerun strategy; and executing the rerun operation flow according to the to-be-rerun instance table to obtain the processing result of the to-be-rerun task.

According to an embodiment of the present disclosure, the preset task rerun strategy may be determined according to the degree of parallelism of rerun (K rerun job flows may be executed simultaneously, K is a positive integer), the scope of rerun (such as a service date scope), the dependency relationship between jobs, and the like. For example, in consideration of the running resources of the backend server, it may be set to execute K running job flows simultaneously; or according to the dependency relationship, whether the re-running workflow D depends on the re-running workflow E and/or the re-running result of the re-running workflow F, etc., thereby setting the execution sequence of the re-running workflow.

According to an embodiment of the present disclosure, the to-be-rerun instance table may be obtained according to the rerun workflow after instantiation, and the instantiation process may be understood as converting the configured workflow into a workflow that can be executed.

According to the embodiment of the disclosure, it can be understood that whether the operation tree or the list can show a plurality of target nodes and associated upstream and downstream operations, and a plurality of re-running operation flows can be obtained by checking the upstream and downstream operations for each target node; in another embodiment, multiple rerun workflows may also be obtained by hooking multiple upstream and downstream jobs in one target node according to traffic conditions. Based on the above, when the rerun task is specifically executed, the rerun task can be split to obtain a plurality of rerun operation flows, and according to a preset task rerun strategy (the rerun parallelism, the rerun range (such as the service date range), etc.), the sequence of executing the rerun operation flows is determined, and all to-be-rerun instance lists corresponding to the task are generated, and the rerun of the operation flows is triggered at fixed time.

According to an embodiment of the present disclosure, the above method may further include the following operations: responsive to performing the operation of the re-run workflow, determining a downstream job associated with the re-run workflow in accordance with the dependency relationship of the re-run workflow; and displaying the re-running workflow by an operation and maintenance personnel of the downstream operation so that the operation and maintenance personnel can carry out decision analysis on the downstream operation.

According to the embodiment of the disclosure, when the downstream operation is automatically found according to the dependency relationship of the re-running operation flow, the service personnel of the downstream operation can be informed of the backlog through the message label of the page. Optionally, the list, the job tree, the running operation flow being executed or completed being executed and the like can be displayed through the page, so that the operation and maintenance personnel of the downstream operation can be informed of the information of the running operation, and the affected downstream operation list can be displayed. The operation and maintenance personnel can conduct decision analysis to determine that the affected downstream operations are to be re-run. Alternatively, the notification can be performed by means of a short message, an internal message, etc. For example, the affected downstream operation, service date, batch number and other information and the message notification template set in advance are subjected to content splicing, and relevant operation and maintenance personnel are notified by alarm short messages, alarm internal messages and the like.

According to the embodiment provided by the disclosure, the affected downstream operation can be automatically searched, and the operation and maintenance personnel of the downstream operation can be automatically notified, so that the operation and maintenance personnel of the downstream operation can timely perform decision analysis on the affected downstream operation, and the loss caused by data re-running to the service is reduced.

According to the embodiment of the disclosure, after the operation and maintenance personnel performs decision analysis on the downstream operation, a decision analysis result is generated; and according to the decision analysis result, the processed state identification can be given to the heavy running workflow.

According to the embodiment of the disclosure, the unprocessed rerun operation flows can be distinguished by giving the processed rerun operation flows with the processed state identifiers, so that the rerun operation flows are prevented from being repeatedly processed by operation and maintenance personnel, and the processing efficiency of the rerun operation flows is improved.

Fig. 3 schematically illustrates a flow chart of a processing method of task re-running according to another embodiment of the present disclosure.

As shown in fig. 3, the method includes operations S301 to S306.

In operation S301, an analysis node is added to a page. It is understood that the root node is added to the page.

In operation S302, an analysis type and a layer number are selected.

In operation S303, a request is initiated and a dependency analysis is performed in the background.

In operation S304, a temporary task re-run is initiated on the page according to the dependency analysis result.

In operation S305, the background calculates the affected job and notifies the target user of the downstream application.

In operation S306, the user of the downstream application views the affected job information on the page, making a decision.

According to the embodiment of the disclosure, after the job configuration is completed, an event table is generated, the table only needs to record basic information such as the object and the direction of the event, and the subsequent job dependency analysis is data of the table. The method can realize flexible dependency analysis, and the dependency only needs to record the execution result of the dependent operation, and the rest information of the dependent operation does not need to be recorded, so that the data redundancy is low.

According to the embodiment of the disclosure, using the event table data described above, upstream or downstream information of a target job is analyzed to obtain job data.

According to the embodiment of the present disclosure, based on the above-described job data, temporary job flow rerun is performed. The target user selects the operation to form a temporary operation flow, and the batch running can be carried out according to the original dependency relationship. And simultaneously deducing the application affected at the first stage downstream, and storing the temporary workflow information related downstream application into a database.

According to the embodiment of the disclosure, notification is initiated to a target user (operation and maintenance personnel) of a downstream application according to the data obtained in the above process. After logging in the related page, the user of the downstream application can look up the workflow which has already been rerun on the page, and meanwhile, the link to be run is displayed, and the user judges whether to rerun the affected job.

According to the embodiment of the disclosure, a downstream application user sets a processing state of an upstream initiation task according to a result of analysis processing.

According to the embodiment of the present disclosure, the content in operations S301 to S306 may refer to the content related to operations S201 to S204, which is not described herein.

Fig. 4 schematically illustrates a processing system for task re-running according to an embodiment of the present disclosure.

As shown in fig. 4, the processing system 400 for task re-running may include a dependency analysis module 410, a temporary workflow re-run initiation module 420, an automatic re-run module 430, and a link re-run impact analysis module 440. The processing system 400 for task rerun may be used to implement the content related in operations S201 to S204 or operations S301 to S306.

FIG. 5 schematically illustrates a page schematic of a job tree generated after dependency analysis in accordance with an embodiment of the present disclosure.

The dependency analysis module 410 may specifically limit up to 5 in response to a page operation, such as selecting a set of jobs to be analyzed, as a root node. The number of levels of analysis is chosen, where the number of levels is limited, as is the concept of levels of the tree graph, and typically no more than 30 levels. The type of analysis is selected, and dependency analysis can be performed according to three directions (upstream nodes are calculated according to dependency, namely forward tracing, downstream nodes are calculated according to output, namely backward calculation, and bidirectional calculation, namely calculation of upstream nodes and downstream nodes is performed simultaneously). After these configurations are completed, click look-up. The background calculates and returns data, and a page segmentation node diagram (a job tree diagram) and a list display mode. The node map presentation, i.e. like the tree map, may be as shown in fig. 5. With several root nodes, several tree graphs are shown and job instance states are shown, with different states of the job instance being represented by different colors. The list shows that each row of data is a set of dependencies.

The backend implementation may be through an event table. First there is an event table, stored job events, event directed, input events and output events are represented by (I/O). Information such as event identification, business date, lot number, object identification, object type, etc. is stored. The data of this table is a static table that is initialized after job configuration is completed.

After the back end receives the data of the page, the user authority, the data integrity and the data correctness are checked. Depending on the type of analysis, different branches are taken. And according to the job input event and the mapping relation of the output event in the event table, searching out an upstream or downstream object of the job. A hierarchical traversal algorithm is used here, looking up layer by layer. The final result data is tree-structured, and the number of tree levels is less than or equal to the number of page input levels.

Fig. 6 schematically illustrates a page diagram of configuring a re-run parameter according to an embodiment of the present disclosure.

The temporary workflow restart initiation module 420 may specifically respond to a page operation, such as checking a job in a data list, where the list job is found according to a dependency relationship between jobs, and the user may not have permission to apply the job, and only can view the job at this time. The selected operation is controlled by the authority, and the operation can not be rerun. The business date, the batch number, the temporary operation flow name and the temporary operation flow group are input to form a temporary operation flow for the running operation again, and the configuration process can be shown in figure 6.

When the back end is realized, an interface for the re-running of the temporary operation flow can be opened at the back end, and the re-running of the group of operations can be performed. When the user initiates the temporary flow rerun, the background analyzes the downstream jobs affected by the set of jobs and notifies the application project set through the rerun notification module.

Fig. 7 schematically illustrates a schematic diagram of a list of instances to be rerun generated according to an embodiment of the present disclosure.

The automatic re-running module 430 may split the task flow included in the task according to the task information initiated by the temporary task flow re-running initiation module 420, generate all the to-be-re-run instance lists corresponding to the task according to the re-running parallelism and the re-running scope (such as the service date scope), and trigger the re-running of the task flow at fixed time. The launched to-be-rerun instance list may be as shown in fig. 7.

FIG. 8A schematically illustrates a re-run workflow received by a target user in accordance with an embodiment of the present disclosure; fig. 8B schematically illustrates a schematic diagram of a re-run job flow received by a target user according to another embodiment of the present disclosure.

The link re-running impact analysis module 440 is configured to notify an operator of the downstream job. The user may be informed of the event that needs to be handled, for example by a message tag in the upper left hand corner. Or a page notification, such as setting a temporary re-running influence page of the job, displaying re-running task information to a downstream application user, and displaying an influenced job list. The user may autonomously decide whether to re-run these affected jobs. The page on which the rerun workflow is received may be as shown in fig. 8A to 8B. In an embodiment, an alarm notification may also be performed, for example, notifying the affected job, service date, batch number in a manner of an in-station message, a short message, etc.

The processing system 400 for task rerun may be used to implement the content related to operations S201 to S204 or operations S301 to S306, which will not be described herein.

The embodiment of the disclosure can visually select nodes on a link by combining the analysis result of the operation dependency relationship, and assemble the nodes into tasks for the operation stream to run again; the analysis result of the operation dependency relationship can check the complete operation link, but only can operate the operation/operation flow with the operation authority; the embodiment of the disclosure can also realize analysis of the application at the downstream of the running task and timely notification.

The task re-running processing method provided by the embodiment of the disclosure can improve the automation and intelligent level of the batch intelligent scheduling platform according to the requirement scene of the user. Specifically, page operations may be supported; the analysis of the dependency relationship of the operation can be supported, the upstream and downstream operation information can be flexibly checked, and the instance state condition of the operation can be displayed; supporting a group of jobs to perform dependency analysis together, wherein each target job serves as a root node; on the basis of the operation dependency relationship, a temporary task can be initiated to perform link re-running, so that a user can conveniently trace back reasons of operation abnormality, and nodes in the temporary task comprise two types of operation nodes and operation flow nodes; when the task re-running is initiated, the node-affected downstream operation/workflow nodes in the task can be automatically positioned, the affected first-stage downstream is automatically deduced, the influence on the downstream application is automatically deduced, and the operation and maintenance personnel of the downstream application are timely and automatically notified according to various forms such as in-station messages, short messages and the like; the affected downstream application may link to the upstream already rerun workflow while locating the affected jobs of the application, further dependency analysis is performed by the downstream application based on the jobs, and link rerun is performed, with the downstream application determining the jobs that need to be rerun.

In the authority control aspect, the application operation without the authority can be checked by performing the dependency analysis, the operation information of the upstream and downstream operations/operation flows of the complete link can be checked according to the dependency analysis, but the operation of the application operation cannot be performed, the operations such as link re-running and the like of the authorized operation/operation flow can only be checked, and only the user with the corresponding operation authority has the authorized application operation. The downstream application may set a processing state for the upstream initiated task and may set the processing state to "processed" after the downstream has performed a corresponding impact analysis and operation for the re-run task.

It should be noted that, unless there is an execution sequence between different operations or an execution sequence between different operations in technical implementation, the execution sequence between multiple operations may be different, and multiple operations may also be executed simultaneously in the embodiment of the disclosure.

Based on the task rerun processing method, the disclosure also provides a task rerun processing device. The device will be described in detail below in connection with fig. 9.

Fig. 9 schematically illustrates a block diagram of a processing apparatus for task re-running according to an embodiment of the present disclosure.

As shown in fig. 9, the processing apparatus 900 for task rerun of this embodiment includes a first search module 910, a first analysis module 920, a first determination module 930, and a first execution module 940.

The first search module 910 is configured to search, in response to a processing request for task rerun, at least one target job required for completing the task to be rerun from a job library according to attribute information of the task to be rerun, where the task to be rerun is carried in the processing request.

The first analysis module 920 is configured to perform dependency analysis on the target job according to a dependency required for executing the target job, to obtain a dependency analysis result, where the dependency analysis result includes the target job and a plurality of upstream and downstream jobs associated with the target job.

The first determining module 930 is configured to determine a target workflow from the dependency analysis result, and configure a re-running parameter for the target workflow to obtain a re-running workflow.

The first execution module 940 is configured to execute the re-running job flow to obtain a processing result of the task to be re-run.

According to an embodiment of the disclosure, the first analysis module further comprises a first determination unit, a first configuration unit, a first search unit.

And the first determining unit is used for taking the target as a root node, wherein the root node is used for constructing a job tree.

The first configuration unit is used for configuring the initial layer number of the job tree and the analysis type of the dependency analysis.

And the first searching unit is used for searching the upstream and downstream jobs associated with the target job from the event table according to the initial layer number and the analysis type.

According to an embodiment of the present disclosure, the first lookup unit may further include a check subunit, a lookup subunit.

And the verification subunit is used for responding to the operation of the target user for carrying out the dependency analysis on the target operation, verifying the operation authority of the target user and the initial layer number and obtaining a verification result.

And the searching subunit is used for searching the upstream and downstream jobs associated with the target job from the event table based on a hierarchical traversal algorithm and according to the analysis type under the condition that the verification result represents that the verification passes.

According to the embodiment of the disclosure, the processing device for task rerun can further comprise a construction module and a first display module.

The construction module is used for constructing the operation tree according to the root node and the hierarchical leaf node, wherein the final layer level of the operation tree is smaller than or equal to the initial layer level, and the final layer level is determined according to the searched upstream and downstream operation.

And the first display module is used for displaying the job tree through the visualization component.

According to an embodiment of the present disclosure, the first determining module may further include a selecting unit, a filtering unit.

And the selection unit is used for selecting the target job and at least one of the upstream and downstream jobs from the job tree to obtain an initial job flow.

And the filtering unit is used for filtering the initial workflow based on the operation authority of the target user to obtain the target workflow.

According to the embodiment of the disclosure, the processing device for task rerun can further comprise a first generating module and a second displaying module.

A first generation module, configured to generate a list according to the target job and the upstream and downstream jobs, where the jobs in each row in the list represent a set of jobs having the dependency relationship;

and the second display module is used for displaying the list graph through the visualization component.

According to the embodiment of the disclosure, the processing device for task rerun can further comprise a second determining module and a third displaying module.

And the second determining module is used for determining a downstream job associated with the re-running workflow according to the dependency relationship of the re-running workflow in response to the operation of executing the re-running workflow.

And the third display module is used for displaying the re-running operation flow to the operation and maintenance personnel of the downstream operation so that the operation and maintenance personnel can carry out decision analysis on the downstream operation.

According to the embodiment of the disclosure, the processing device for task rerun can further comprise a second generating module and a giving module.

The second generation module is used for generating a decision analysis result after the operation and maintenance personnel carry out decision analysis on the downstream operation.

And the giving module is used for giving the processed state identification to the rerun operation flow according to the decision analysis result.

According to the embodiment of the disclosure, the processing device for task rerun can further comprise a third generating module and a second executing module.

And the third generation module is used for generating a to-be-rerun instance table comprising the rerun operation flow based on a preset task rerun strategy.

And the second execution module is used for executing the re-running operation flow according to the to-be-re-run instance table to obtain a processing result of the to-be-re-run task.

According to an embodiment of the present disclosure, any of the plurality of modules of the first search module 910, the first analysis module 920, the first determination module 930, and the first execution module 940 may be combined in one module to be implemented, or any of the plurality of modules may be split into a plurality of modules. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the first lookup module 910, the first analysis module 920, the first determination module 930, and the first execution module 940 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or in hardware or firmware, such as any other reasonable manner of integrating or packaging the circuitry, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the first lookup module 910, the first analysis module 920, the first determination module 930, and the first execution module 940 may be at least partially implemented as computer program modules that, when executed, may perform the corresponding functions.

It should be noted that, in the embodiment of the present disclosure, the portion of the processing apparatus for task rerun corresponds to the portion of the processing method for task rerun in the embodiment of the present disclosure, and the description of the portion of the processing apparatus for task rerun specifically refers to the portion of the processing method for task rerun, which is not described herein again.

As shown in fig. 10, an electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage section 1008 into a Random Access Memory (RAM) 1003. The processor 1001 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 1001 may also include on-board memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.

In the RAM 1003, various programs and data necessary for the operation of the electronic apparatus 1000 are stored. The processor 1001, the ROM 1002, and the RAM 1003 are connected to each other by a bus 1004. The processor 1001 performs various operations of the method flow according to the embodiment of the present disclosure by executing programs in the ROM 1002 and/or the RAM 1003. Note that the program may be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 may also perform various operations of the method flow according to the embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the disclosure, the electronic device 1000 may also include an input/output (I/O) interface 1005, the input/output (I/O) interface 1005 also being connected to the bus 1004. The electronic device 1000 may also include one or more of the following components connected to the I/O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output portion 1007 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage portion 1008 including a hard disk or the like; and a communication section 1009 including a network interface card such as a LAN card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the internet. The drive 1010 is also connected to the I/O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed as needed in the drive 1010, so that a computer program read out therefrom is installed as needed in the storage section 1008.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 1002 and/or RAM1003 and/or one or more memories other than ROM 1002 and RAM1003 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. When the computer program product runs in a computer system, the program code is used for enabling the computer system to realize the task re-running processing method provided by the embodiment of the disclosure.

The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 1001. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of signals on a network medium, distributed, and downloaded and installed via the communication section 1009, and/or installed from the removable medium 1011. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 1009, and/or installed from the removable medium 1011. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 1001. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims

1. A task rerun processing method comprises the following steps:

responding to a task re-running processing request, and searching at least one target job required by completing the task to be re-run from a job library according to attribute information of the task to be re-run, wherein the task to be re-run is carried in the processing request;

performing dependency analysis on the target job according to the dependency relation required by executing the target job to obtain a dependency relation analysis result, wherein the dependency relation analysis result comprises the target job and a plurality of upstream and downstream jobs associated with the target job;

Determining a target workflow from the dependency analysis result, and configuring a re-running parameter for the target workflow to obtain a re-running workflow;

executing the re-running operation flow to obtain a processing result of the task to be re-run.

2. The method of claim 1, wherein the job library comprises a plurality of jobs, each job configured with an event table comprising dependencies required to execute the job;

the performing dependency analysis on the target job includes:

taking the target as a root node, wherein the root node is used for constructing a job tree;

configuring an initial layer number of the job tree and an analysis type of the dependency analysis;

and searching upstream and downstream jobs associated with the target job from the event table according to the initial layer level and the analysis type.

3. The method of claim 2, wherein the looking up the upstream and downstream jobs associated with the target job from the event table according to the initial layer number and the analysis type comprises:

responding to the operation of the target user for carrying out dependency analysis on the target operation, and checking the operation authority of the target user and the initial layer number to obtain a checking result;

And searching for an upstream job and a downstream job which are associated with the target job from the event table based on a hierarchical traversal algorithm and according to the analysis type under the condition that the verification result represents that verification passes.

4. A method according to claim 3, wherein an upstream and downstream job associated with the target job acts as a hierarchical leaf node;

the method further comprises the steps of:

constructing the job tree according to the root node and the hierarchical leaf node, wherein the final level of the job tree is smaller than or equal to the initial level, and the final level is determined according to the searched upstream and downstream jobs;

the job tree is exposed through a visualization component.

5. The method of claim 4, wherein the determining a target job flow from the dependency analysis results comprises:

selecting the target job and at least one upstream and downstream job from the job tree to obtain an initial job flow;

and filtering the initial workflow based on the operation authority of the target user to obtain the target workflow.

6. The method of claim 4, further comprising:

generating a list according to the target job and the upstream and downstream jobs, wherein the jobs in each row in the list represent a group of jobs with the dependency relationship;

And displaying the list graph through the visualization component.

7. The method of claim 1, further comprising:

responsive to performing the operation of the re-run workflow, determining a downstream job associated with the re-run workflow in accordance with a dependency relationship of the re-run workflow; and

and displaying the re-running operation flow to an operation and maintenance person of the downstream operation so that the operation and maintenance person can carry out decision analysis on the downstream operation.

8. The method of claim 7, further comprising:

after the operation and maintenance personnel carry out decision analysis on the downstream operation, a decision analysis result is generated;

and according to the decision analysis result, assigning a processed state identifier to the rerun operation flow.

9. The method of claim 2, wherein the analysis types include traceability analysis, output analysis, and bi-directional analysis.

10. The method according to any one of claims 1 to 9, wherein the re-running parameters include a date of the task to be re-run, a lot number of the task to be re-run, a time zone of the task to be re-run, a name of a re-run workflow.

11. The method of any one of claims 1-9, further comprising:

Generating a to-be-rerun instance table comprising the rerun workflow based on a preset task rerun strategy;

and executing the rerun operation flow according to the to-be-rerun instance table to obtain a processing result of the to-be-rerun task.

12. A task rerun processing device, comprising:

the first searching module is used for responding to a task re-running processing request, and searching at least one target job required by completing the task to be re-run from a job library according to attribute information of the task to be re-run, wherein the task to be re-run is carried in the processing request;

the first analysis module is used for carrying out dependency analysis on the target job according to the dependency required by executing the target job to obtain a dependency analysis result, wherein the dependency analysis result comprises the target job and a plurality of upstream and downstream jobs associated with the target job;

the first determining module is used for determining a target workflow from the analysis result of the dependency relationship, and configuring a re-running parameter for the target workflow to obtain a re-running workflow;

the first execution module is used for executing the re-running operation flow to obtain a processing result of the task to be re-run.

13. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-11.

14. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-11.

15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 11.