Abnormal impact analysis method, device, computer equipment and storage medium

Abstract

The embodiment of the present application discloses an abnormal impact analysis method, device, computer equipment and storage medium, wherein the abnormal impact analysis method includes: real-time acquisition of scheduling data in a scheduling platform, the scheduling data including task status data of at least one scheduling task; analyzing and processing the task status data of each scheduling task to determine an abnormal scheduling task in at least one scheduling task; acquiring a scheduling task chain; recursively analyzing the abnormal scheduling task based on the scheduling task chain to obtain an impact scheduling task corresponding to the abnormal scheduling task, the impact scheduling task is the mth layer node in the scheduling task chain, the abnormal scheduling task is the nth layer node in the scheduling task chain, and m is greater than n; outputting prompt information, the prompt information includes a task identifier of the impact scheduling task. The embodiment of the present application can quickly locate the impact scheduling task corresponding to the abnormal scheduling task, so that the user can grasp the impact range of the abnormal scheduling task in real time.

Description

Technical Field

[0001] The present application relates to the field of data analysis, and in particular to an abnormal impact analysis method, device, computer equipment and storage medium. Background Art

[0002] Existing scheduling platforms can usually count the number of scheduling tasks within a certain period of time, so that users can understand the number of scheduling tasks within a certain period of time. However, existing scheduling platforms lack analysis of scheduling tasks affected by abnormal scheduling tasks, which makes it difficult for users to make decisions. Based on this, how to conveniently and quickly obtain scheduling tasks affected by abnormal scheduling tasks has become a research hotspot. Summary of the invention

[0003] The embodiments of the present application provide an abnormal impact analysis method, apparatus, computer equipment and storage medium, which can conveniently determine the impact scheduling tasks of abnormal scheduling tasks, so that users can understand the impact scope of abnormal scheduling tasks in real time.

[0004] On the one hand, an embodiment of the present application provides a method for analyzing abnormal impact, the method comprising:

[0005] Acquire scheduling data in the scheduling platform in real time, wherein the scheduling data includes task status data of at least one scheduling task;

[0006] Analyzing and processing the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task;

[0007] Acquire a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a plurality of scheduling tasks, where the plurality of scheduling tasks include the at least one scheduling task;

[0008] Recursively analyzing the abnormal scheduling task based on the scheduling task chain to obtain the affected scheduling task corresponding to the abnormal scheduling task, the affected scheduling task is the m-th layer node in the scheduling task chain, the abnormal scheduling task is the n-th layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task status of the affected scheduling task is an executing state or a non-executing state;

[0009] Output prompt information, where the prompt information includes the task identifier that affects the scheduled task.

[0010] On the other hand, an embodiment of the present application provides an abnormal impact analysis device, the device comprising:

[0011] A processing unit, used for acquiring scheduling data in the scheduling platform in real time, wherein the scheduling data includes task status data of at least one scheduling task;

[0012] The processing unit is further used to analyze and process the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task;

[0013] The processing unit is further used to obtain a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a plurality of scheduling tasks, where the plurality of scheduling tasks include the at least one scheduling task;

[0014] The processing unit is further used to recursively analyze the abnormal scheduling task based on the scheduling task chain to obtain the affected scheduling task corresponding to the abnormal scheduling task, the affected scheduling task is the m-th layer node in the scheduling task chain, the abnormal scheduling task is the n-th layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task state of the affected scheduling task is an executing state or a non-executing state;

[0015] The output unit is used to output prompt information, wherein the prompt information includes the task identifier that affects the scheduled task.

[0016] In another aspect, an embodiment of the present application provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the following steps are implemented:

[0017] Acquire scheduling data in the scheduling platform in real time, wherein the scheduling data includes task status data of at least one scheduling task;

[0018] Analyzing and processing the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task;

[0019] Acquire a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a plurality of scheduling tasks, where the plurality of scheduling tasks include the at least one scheduling task;

[0020] Recursively analyzing the abnormal scheduling task based on the scheduling task chain to obtain the affected scheduling task corresponding to the abnormal scheduling task, the affected scheduling task is the m-th layer node in the scheduling task chain, the abnormal scheduling task is the n-th layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task status of the affected scheduling task is an executing state or a non-executing state;

[0021] Output prompt information, wherein the prompt information includes the task identifier that affects the scheduled task.

[0022] In another aspect, an embodiment of the present application provides a computer device, the computer device comprising a processor and a memory, the memory being used to store a computer program, and the computer program, when executed by the processor, implements the following steps:

[0023] Acquire scheduling data in the scheduling platform in real time, wherein the scheduling data includes task status data of at least one scheduling task;

[0024] Analyzing and processing the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task;

[0025] Acquire a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a plurality of scheduling tasks, where the plurality of scheduling tasks include the at least one scheduling task;

[0026] Recursively analyzing the abnormal scheduling task based on the scheduling task chain to obtain the affected scheduling task corresponding to the abnormal scheduling task, the affected scheduling task is the m-th layer node in the scheduling task chain, the abnormal scheduling task is the n-th layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task status of the affected scheduling task is an executing state or a non-executing state;

[0027] Output prompt information, wherein the prompt information includes the task identifier that affects the scheduled task.

[0028] In another aspect, an embodiment of the present application provides a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the following steps:

[0029] Acquire scheduling data in the scheduling platform in real time, wherein the scheduling data includes task status data of at least one scheduling task;

[0030] Analyzing and processing the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task;

[0031] Acquire a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a plurality of scheduling tasks, where the plurality of scheduling tasks include the at least one scheduling task;

[0032] Recursively analyzing the abnormal scheduling task based on the scheduling task chain to obtain the affected scheduling task corresponding to the abnormal scheduling task, the affected scheduling task is the m-th layer node in the scheduling task chain, the abnormal scheduling task is the n-th layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task status of the affected scheduling task is an executing state or a non-executing state;

[0033] Output prompt information, wherein the prompt information includes the task identifier that affects the scheduled task.

[0034] The embodiment of the present application can obtain the scheduling data of the scheduling platform in real time, and the scheduling data includes the task status data of at least one scheduling task, so as to facilitate the subsequent real-time analysis of at least one scheduling task in the scheduling platform, so that the user can grasp the task status of at least one scheduling task in real time. Secondly, the abnormal scheduling task can be determined in at least one scheduling task, and based on the acquired scheduling task chain, the abnormal scheduling task can be recursively analyzed to obtain the affected scheduling task corresponding to the abnormal scheduling task, so as to effectively locate the impact range of the abnormal scheduling task; and prompt information can be output, and the prompt information includes the task identifier of the affected scheduling task corresponding to the abnormal scheduling task, that is, the task identifier of the affected scheduling task corresponding to the abnormal scheduling task can be displayed, so that the user can grasp the affected scheduling task corresponding to the abnormal scheduling task in real time, so as to grasp the impact range of the abnormal scheduling task. It can be seen that the embodiment of the present application can determine the abnormal scheduling task in at least one scheduling task and the affected scheduling task corresponding to the abnormal scheduling task in real time, so that the user can quickly make appropriate decisions based on the impact range of the abnormal scheduling task. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0036] Figure 1a A flowchart of an abnormal impact analysis solution provided in an embodiment of the present application;

[0037] Figure 1b A schematic diagram of a task identification provided in an embodiment of the present application;

[0038] Figure 2 A flowchart of an abnormal impact analysis method provided in an embodiment of the present application;

[0039] Figure 3 A schematic diagram of a scheduling task chain provided in an embodiment of the present application;

[0040] Figure 4 A flowchart of another abnormal impact analysis method provided in an embodiment of the present application;

[0041] Figure 5 A schematic diagram of a visual page analysis provided in an embodiment of the present application;

[0042] Figure 6 A schematic diagram of a display interface provided in an embodiment of the present application;

[0043] Figure 7 A schematic diagram of the structure of an abnormal impact analysis device provided in an embodiment of the present application;

[0044] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0046] In view of the above problems, the embodiment of the present application proposes an abnormal impact analysis scheme to conveniently and quickly determine the impact scheduling tasks corresponding to the abnormal scheduling tasks, so as to facilitate users to grasp the impact range of the abnormal scheduling tasks in real time. In a specific implementation, the abnormal impact analysis scheme can be executed by a computer device, which can be a terminal or a server; the terminal mentioned here can include but is not limited to: smart phones, tablet computers, laptops, desktop computers, smart watches, smart TVs, smart car terminals, etc.; various clients (applications, APPs) can be run in the terminal, such as video playback clients, social clients, browser clients, information flow clients, education clients, etc. The server mentioned here can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network, content distribution network), and basic cloud computing services such as big data and artificial intelligence platforms, etc.

[0047] It should be noted that in other embodiments, the abnormal impact analysis scheme can also be jointly executed by the server and the terminal; the server and the terminal are connected through a network (such as a wired network, a wireless network, etc.), which is not limited to this. In addition, the computer device mentioned in the embodiment of the present application can be located outside the blockchain network or inside the blockchain network, which is not limited to this; the so-called blockchain network is a network composed of a peer-to-peer network (P2P network) and a blockchain, and the blockchain refers to a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc., which is essentially a decentralized database, a string of data blocks (or blocks) generated by cryptographic methods.

[0048] See also Figure 1a As shown, the general principle of the abnormal impact analysis solution proposed in the embodiment of the present application is as follows:

[0049] ① The computer device can obtain task status data of at least one scheduling task. The at least one scheduling task here may refer to all scheduling tasks in the scheduling platform, or may refer to all unfinished scheduling tasks in the scheduling platform, or may refer to scheduling tasks in the scheduling platform whose task status is in the executing state or the unexecuted state, and this application does not limit this.

[0050] ② The computer device can analyze and process the task status data of each scheduling task to determine one or more abnormal scheduling tasks in at least one scheduling task.

[0051] ③ The computer device may obtain a scheduling task chain, and determine the affected scheduling tasks corresponding to each abnormal scheduling task from the scheduling task chain. The scheduling task chain may be used to indicate the hierarchical relationship between at least one scheduling task, and the computer device may start from each abnormal scheduling task according to the hierarchical relationship between at least one scheduling task, and recursively analyze each abnormal scheduling task based on the scheduling task chain to obtain the affected scheduling tasks corresponding to each abnormal scheduling task.

[0052] ④ Display the task IDs of the affected scheduling tasks corresponding to each abnormal scheduling task. Figure 1b As shown, assuming that one or more abnormal scheduling tasks include scheduling task A and scheduling task B, and the scheduling tasks corresponding to scheduling task A include scheduling task C and scheduling task D, and the scheduling tasks corresponding to scheduling task B include scheduling task E, then the computer device can display the task identifiers of scheduling task C, scheduling task D, and scheduling task E. It should be noted that Figure 1b The display method of the task identifier that affects the scheduling task is only shown by way of example, and this application does not limit this; for example, the computer device may not display the task identifier of the abnormal scheduling task, but only display the task identifier of the scheduling task that affects each abnormal scheduling task.

[0053] Practice has shown that the abnormal impact analysis solution proposed in the embodiment of the present application can have at least the following beneficial effects:

[0054] First point: based on the scheduling task chain, it is convenient to determine the affected scheduling tasks corresponding to each abnormal scheduling task in one or more abnormal scheduling tasks, so as to conveniently determine the impact scope of each abnormal scheduling task; second point: the task identification of the affected scheduling task corresponding to each abnormal scheduling task is displayed, so that the user can intuitively grasp the affected scheduling task corresponding to each abnormal scheduling task, and thus make appropriate decisions quickly.

[0055] Based on the relevant description of the above abnormal impact analysis scheme, the embodiment of the present application proposes an abnormal impact analysis method, which can be executed by the computer device mentioned above, and the computer device can be a terminal or a server; or, the abnormal impact analysis method can be executed by the terminal and the server together. For the convenience of explanation, the following description will take the execution of the abnormal impact analysis method by a computer device as an example; please refer to Figure 2 The abnormal impact analysis method may include the following steps S201-S205:

[0056] S201, acquiring scheduling data in a scheduling platform in real time, where the scheduling data includes task status data of at least one scheduling task.

[0057] It should be noted that the task status data of the at least one scheduled task may include but is not limited to: the task status of each scheduled task in the at least one scheduled task, the task identifier of each scheduled task, etc. The task status of each scheduled task includes but is not limited to: completed status, uncompleted status, etc.

[0058] Optionally, the computer device can store the above-mentioned scheduling data in the form of a matrix, or in the form of a table. The present application does not limit the storage form of the scheduling data. Correspondingly, if the computer device stores the scheduling data in the form of a table, the computer device can synchronize the scheduling data acquired in real time to Hive (a data warehouse tool based on a distributed system infrastructure), or synchronize the scheduling data acquired in real time to Teradata (a data warehouse tool). The present application does not limit this. It is understandable that after the computer device synchronizes the scheduling data to the data warehouse, the structured data file can be mapped to the form of a table.

[0059] It should be noted that the above-mentioned at least one scheduling task may refer to all scheduling tasks in the scheduling platform, or may refer to an unfinished scheduling task in the scheduling platform, and this application does not limit this. It is understandable that the scheduling platform may include completed scheduling tasks and unfinished scheduling tasks, wherein a completed scheduling task may refer to a scheduling task whose task status is a completed state, and an unfinished scheduling task may refer to a scheduling task whose task status is an unfinished state. Further, unfinished scheduling tasks include but are not limited to: scheduling tasks whose task status is an executing state, scheduling tasks whose task status is an unexecuted state, scheduling tasks whose task status is a suspended execution state, and so on; that is, the unfinished state includes but is not limited to: an executing state, an unexecuted state, a suspended execution state, and so on.

[0060] Correspondingly, the above-mentioned scheduled task with the task status of suspended execution may refer to an unfinished scheduled task whose task status at a certain moment before the current system time was in the executing state and whose task status at the current system time is in the unexecuted state.

[0061] S202: Analyze and process the task status data of each scheduling task to determine an abnormal scheduling task in at least one scheduling task.

[0062] It is understandable that the number of abnormal scheduling tasks in at least one scheduling task can be one or more, and this application does not limit this. Accordingly, if the number of abnormal scheduling tasks in at least one scheduling task is more than one, the computer device can analyze and process the task status data of each scheduling task to determine multiple abnormal scheduling tasks in at least one scheduling task.

[0063] S203, obtaining a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a number of scheduling tasks.

[0064] Among them, the above-mentioned several scheduling tasks include the above-mentioned at least one scheduling task. It should be noted that one node in the scheduling task chain corresponds to one scheduling task, and there is a dependency relationship between any node in the i-th layer node in the scheduling task chain and one or more nodes in the i-1th layer node, that is, any node in the i-th layer node depends on one or more nodes in the i-1th layer node, i is an integer greater than 1; that is, any scheduling task in the i-th layer scheduling task in the scheduling task chain depends on one or more scheduling tasks in the i-1th layer scheduling task.

[0065] For example, if Figure 3 As shown, the scheduling task chain includes four layers of nodes, wherein any node in the second layer of nodes has a dependency relationship with one or more nodes in the first layer of nodes, any node in the third layer of nodes has a dependency relationship with one or more nodes in the second layer of nodes, and any node in the fourth layer of nodes has a dependency relationship with one or more nodes in the third layer of nodes; that is, any node in the second layer of nodes depends on one or more nodes in the first layer of nodes, any node in the third layer of nodes depends on one or more nodes in the second layer of nodes, and any node in the fourth layer of nodes depends on one or more nodes in the third layer of nodes. Exemplary, Figure 3 Node a in 31 and node a 32 Both depend on node a 21 .

[0066] Accordingly, when the above-mentioned several scheduling tasks include a first scheduling task and a second scheduling task, and there is a dependency relationship between the first scheduling task and the second scheduling task, the computer device can determine that the first scheduling task depends on the second scheduling task, that is, the execution process of the first scheduling task depends on the execution of the second scheduling task; that is, the dependency relationship between the first scheduling task and the second scheduling task can refer to the first scheduling task being dependent on the second scheduling task. In this case, if there is a dependency relationship between the first scheduling task and the second scheduling task, the computer device needs to execute the first scheduling task based on a certain execution result of the second scheduling task. Optionally, the dependency relationship between the first scheduled task and the second scheduled task includes but is not limited to: a "finish-start" relationship (that is, the first scheduled task can be started only after the second scheduled task is completed; that is, when the task status of the second scheduled task is in the completed state, the first scheduled task can be started, that is, the task status of the first scheduled task can be updated from the unexecuted state to the executing state), a "start-start" relationship (that is, when the second scheduled task is started, the first scheduled task can be started; that is, when the task status of the second scheduled task is in the executing state or the suspended state, the first scheduled task can be started, that is, the task status of the first scheduled task can be updated from the unexecuted state to the executing state), and the like.

[0067] It is understandable that if any scheduling task depends on multiple dependent scheduling tasks other than the any scheduling task, the computer device can determine the dependency relationship between the any scheduling task and each dependent scheduling task, and start executing the any scheduling task when the dependency relationship between the any scheduling task and each scheduling task is established. For example, assuming that scheduling task A depends on scheduling task B and scheduling task C at the same time, the computer device can only execute scheduling task A when the dependency relationship between scheduling task A and scheduling task B is established, and the dependency relationship between scheduling task A and scheduling task C is established.

[0068] It should be noted that the establishment of a dependency relationship between any scheduled task and the scheduled task it depends on may mean that: at the current system time, the execution condition of any scheduled task has been satisfied under the effect of the dependency relationship; the failure of a dependency relationship between any scheduled task and the scheduled task it depends on may mean that: at the current system time, the execution condition of any scheduled task has not been satisfied under the effect of the dependency relationship. For example, assuming that the dependency relationship between scheduled task A and scheduled task B is a "finish-start" relationship, that is, the start of scheduled task A depends on the completion of scheduled task B, and assuming that the task status of scheduled task B is in the executing state, that is, scheduled task B is an unfinished scheduled task, then at the current system time, the dependency relationship between scheduled task A and scheduled task B is not established; assuming that the task status of scheduled task B is in the completed state, that is, the computer device has completed the execution of scheduled task B, then the dependency relationship between scheduled task A and scheduled task B is established.

[0069] S204, recursively analyzing the abnormal scheduling task based on the scheduling task chain to obtain the scheduling task that affects the abnormal scheduling task.

[0070] Among them, the above-mentioned scheduling-affecting task is the m-th layer node in the scheduling task chain, the abnormal scheduling task is the n-th layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task status of the scheduling-affecting task is the executing state or the unexecuted state. It can be understood that the start or completion of each scheduling-affecting task in the scheduling-affecting task corresponding to the abnormal scheduling task is affected by the above-mentioned abnormal scheduling task.

[0071] It should be noted that if the computer device determines that there are multiple abnormal scheduling tasks in at least one scheduling task, the computer device can recursively analyze each abnormal scheduling task based on the scheduling task chain to obtain the scheduling tasks that affect each abnormal scheduling task.

[0072] S205: Output prompt information, where the prompt information includes a task identifier that affects the scheduled task.

[0073] It should be noted that the above task identifier may refer to the scheduling name of the scheduling task or the scheduling sequence number of the scheduling task, and this application does not limit this. It is understandable that the task identifier of the scheduling task is unique and can be used to indicate the corresponding scheduling task.

[0074] It can be understood that when there are multiple abnormal scheduling tasks in at least one scheduling task, the computer device can display the task identifiers of the scheduling tasks affecting each abnormal scheduling task, that is, the prompt information can include the task identifiers of the scheduling tasks affecting each abnormal scheduling task.

[0075] The embodiment of the present application can obtain the scheduling data of the scheduling platform in real time, and the scheduling data includes the task status data of at least one scheduling task, so as to facilitate the subsequent real-time analysis of at least one scheduling task in the scheduling platform, so that the user can grasp the task status of at least one scheduling task in real time. Secondly, the abnormal scheduling task can be determined in at least one scheduling task, and based on the acquired scheduling task chain, the abnormal scheduling task can be recursively analyzed to obtain the affected scheduling task corresponding to the abnormal scheduling task, so as to effectively locate the impact range of the abnormal scheduling task; and prompt information can be output, and the prompt information includes the task identifier of the affected scheduling task corresponding to the abnormal scheduling task, that is, the task identifier of the affected scheduling task corresponding to the abnormal scheduling task can be displayed, so that the user can grasp the affected scheduling task corresponding to the abnormal scheduling task in real time, so as to grasp the impact range of the abnormal scheduling task. It can be seen that the embodiment of the present application can determine the abnormal scheduling task in at least one scheduling task and the affected scheduling task corresponding to the abnormal scheduling task in real time, so that the user can quickly make appropriate decisions based on the impact range of the abnormal scheduling task.

[0076] See also Figure 4 , which is a flowchart of another abnormal impact analysis method provided in an embodiment of the present application. The abnormal impact analysis method can be executed by the computer device mentioned above, which can be a terminal or a server; or, the abnormal impact analysis method can be executed by the terminal and the server together. For the sake of convenience, the following description will be given by taking the computer device executing the abnormal impact analysis method as an example; please refer to Figure 4 The abnormal impact analysis method may include the following steps S401-S408:

[0077] S401, acquiring scheduling data in a scheduling platform in real time, where the scheduling data includes task status data of at least one scheduling task.

[0078] It should be noted that the task status data of the abnormally scheduled task includes the failure time of the abnormally scheduled task.

[0079] S402: Analyze and process the task status data of each scheduling task to determine an abnormal scheduling task in at least one scheduling task.

[0080] In one embodiment, the task status data of each scheduled task includes the start time of each scheduled task; the specific manner in which the computer device analyzes and processes the task status data of each scheduled task to determine the abnormal scheduled task in at least one scheduled task may include the following two:

[0081] 1. If the task status data of each scheduled task includes the end time of each scheduled task, the target running time of each running scheduled task is determined according to the difference between the start time and the end time of at least one running scheduled task whose task status is being executed; the current running time of each running scheduled task is determined according to the difference between the start time of each running scheduled task and the current system time; the running scheduling task whose current running time is greater than the target running time is regarded as an abnormal scheduling task.

[0082] It is understandable that the computer device can determine a scheduled task whose task status is being executed in the at least one scheduled task mentioned above, and use the determined scheduled task as a running scheduled task; that is, the task status of the running scheduled task is being executed.

[0083] For example, assuming that the at least one scheduling task includes scheduling task A, scheduling task B, and scheduling task C, the start time of scheduling task A is 8:00, the start time of scheduling task B is 11:00, and the start time of scheduling task C is 16:00, and assuming that the task status data of each scheduling task includes the end time of each scheduling task, the end time of scheduling task A is 10:00, the end time of scheduling task B is 13:00, and the end time of scheduling task C is 20:00, then the computer device can determine the target running time of each running scheduling task according to the difference between the start time and the end time of the running scheduling task whose task status is the executing state in at least one scheduling task. Assuming that the task status of scheduling task A and scheduling task B is the executing state, and the task status of scheduling task C is the unexecuted state, that is, the running scheduling task in at least one scheduling task includes scheduling task A and scheduling task B, then the computer device can determine that the target running time of scheduling task A is 2 hours (h), and the target running time of scheduling task B is 2 hours. Further, assuming that the current system time is 12:00, the computer device can determine the current running time of each running scheduled task according to the difference between the start time of each running scheduled task and the current system time, that is, the computer device can determine that the current running time of scheduled task A is 4 hours, and the current running time of scheduled task B is 1 hour. In this case, the current running time of scheduled task A is greater than the corresponding target running time, that is, the running scheduled task whose current running time is greater than the target running time is scheduled task A, and the computer device can treat scheduled task A as an abnormal scheduled task.

[0084] 2. If the task status data of each scheduling task includes the target running time of each scheduling task, the current running time of each scheduling task is determined according to the difference between the start time of the running scheduling task with the task status of being executed in at least one scheduling task and the current system time; the running scheduling task whose current running time is greater than the target running time is regarded as an abnormal scheduling task.

[0085] For example, assuming that the at least one scheduling task includes scheduling task A, scheduling task B, and scheduling task C, the start time of scheduling task A is 8:00, the start time of scheduling task B is 11:00, and the start time of scheduling task C is 16:00, and assuming that the task status data of each scheduling task includes the target running time of each scheduling task, wherein the target running time of scheduling task A is 2 hours, the target running time of scheduling task B is 2 hours, and the target running time of scheduling task C is 4 hours. In this case, the computer device can determine the current running time of each running scheduling task according to the difference between the start time of the running scheduling task with the task status of being executed in at least one scheduling task and the current system time; assuming that the task status of scheduling task A and scheduling task B is being executed, and the task status of scheduling task C is not being executed, then the running scheduling task includes scheduling task A and scheduling task B, and assuming that the current system time is 12:00, then the computer device can determine that the current running time of scheduling task A is 4 hours, and the current running time of scheduling task B is 1 hour. Further, the computer device may determine that the running scheduling task whose current running time is greater than the target running time is the scheduling task A, and regard the scheduling task A as an abnormal scheduling task.

[0086] In another embodiment, the computer device may analyze and process the task status data of each scheduled task, and the specific implementation process of determining the abnormal scheduled task in at least one scheduled task may include: determining the scheduled task whose task status is a suspended execution state in at least one scheduled task; treating the scheduled task whose task status is a suspended execution state as an abnormal scheduled task. It should be noted that the scheduled task whose task status is a suspended execution state may refer to: an unfinished scheduled task whose task status is an executing state at a certain moment before the current system time and whose task status is an unexecuted state at the current system time; that is, the scheduled task whose task status is a suspended execution state refers to an unfinished scheduled task that has been executed in any time period before the current system time.

[0087] For example, assuming that at least one scheduling task includes scheduling task A and scheduling task B, and the task status of scheduling task A is in execution, and the task status of scheduling task B is in suspended execution, then the computer device can treat scheduling task B as an abnormal scheduling task.

[0088] S403, obtaining a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a number of scheduling tasks.

[0089] Among them, the plurality of scheduling tasks include the at least one scheduling task mentioned above.

[0090] In one embodiment, the specific implementation process of a computer device obtaining a scheduling task chain may include: obtaining the dependency relationship between a number of scheduling tasks in a scheduling platform; integrating a number of scheduling tasks according to each dependency relationship to obtain the above-mentioned scheduling task chain. Furthermore, the specific implementation process of a computer device integrating a number of scheduling tasks according to each dependency relationship to obtain the above-mentioned scheduling task chain may include: determining a scheduling task that does not depend on any scheduling task among a number of scheduling tasks as the head task of the scheduling task chain, and using the head task as the current node in the scheduling task chain; determining a scheduling task that depends on the current node, using the scheduling task that depends on the current node as the next layer node of the current node, and using the next layer node as the current node, until there is no scheduling task that depends on the current node among the number of scheduling tasks.

[0091] For example, assuming that several scheduling tasks include scheduling task A, scheduling task B, scheduling task C, scheduling task D and scheduling task E, and there is a dependency relationship between scheduling task B and scheduling task A (that is, scheduling task B depends on scheduling task A), there is a dependency relationship between scheduling task C and scheduling task A (that is, scheduling task C depends on scheduling task A), there is a dependency relationship between scheduling task D and scheduling task B (that is, scheduling task D depends on scheduling task B), and there is a dependency relationship between scheduling task E and scheduling task B (that is, scheduling task E depends on scheduling task B), then the computer device can integrate several scheduling tasks according to the various dependencies. Among them, the specific process of the computer device integrating several scheduling tasks is: taking scheduling task A that does not depend on any scheduling task as the head task of the scheduling task chain, at this time the current node is scheduling task A; in this case, the computer device can use scheduling task B and scheduling task C as the next layer nodes of scheduling task A, at this time the current node includes scheduling task B and scheduling task C, that is, the computer device can determine the next layer nodes of scheduling task B and scheduling task C respectively; correspondingly, since scheduling task D and scheduling task E depend on scheduling task B, the computer device can use scheduling task D and scheduling task E as the next layer nodes of scheduling task B, and since there is no scheduling task that depends on scheduling task C, scheduling task D or scheduling task E among the several scheduling tasks, the computer device can obtain a scheduling task chain containing several scheduling tasks.

[0092] In another embodiment, the scheduling task chain is obtained by integrating a plurality of scheduling tasks by the scheduling platform according to the dependencies between the plurality of scheduling tasks. It is understandable that the process of integrating the plurality of scheduling tasks by the scheduling platform can be seen in the specific description of the integration of the plurality of scheduling tasks by the above-mentioned computer device, and this embodiment of the application will not be repeated.

[0093] S404, recursively analyzing the abnormal scheduling task based on the scheduling task chain to obtain the scheduling task affected by the abnormal scheduling task.

[0094] Among them, the scheduling-affecting task is the m-th layer node in the scheduling task chain, the abnormal scheduling task is the n-th layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task status of the scheduling-affecting task is the executing status or the unexecuted status.

[0095] In one embodiment, a computer device recursively analyzes an abnormal scheduling task based on a scheduling task chain, and obtains a specific implementation process of an impact scheduling task corresponding to the abnormal scheduling task, which may include: determining a child node of the abnormal scheduling task in the scheduling task chain, and using the child node of the abnormal scheduling task as an impact scheduling task, and the child node of the abnormal scheduling task is an n+1th layer node; using the child node of the n+1th layer node determined in the scheduling task chain as an impact scheduling task, and the child node of the determined n+1th layer node is an n+2th layer node; until the determined child node is a leaf node, the determined leaf node is used as an impact scheduling task, and the impact scheduling task corresponding to the abnormal scheduling task includes the determined child nodes.

[0096] For example, Figure 3 As shown, assuming that node a in the scheduling task chain 21 is an abnormal scheduling task. At this time, the value of n is 2. Then the computer device can determine the child node of the abnormal scheduling task (i.e., node a 31 and node a 32 ), and the child nodes of the abnormal scheduling task are used as the affected scheduling tasks, and the child nodes of the abnormal scheduling task are used as the third-layer nodes; further, the computer device can determine the third-layer node (i.e., node a 31 and node a 32 )'s child node (i.e. node a 41 Node a 42 and node a 43 ), and node a 41 Node a 42 and node a 43 As an impact scheduling task. Since node a 41 Node a 42 and node a 43If all are leaf nodes, the computer device stops recursive analysis after taking each leaf node as an impact scheduling task; in this case, the impact scheduling task includes node a 31 Node a 32 Node a 41 Node a 42 and node a 43 It can be understood that the tasks affecting scheduling include node a 31 Node a 32 Node a 41 Node a 42 and node a 43 Refers to: the impact of scheduling tasks including node a 31 Node a 32 Node a 41 Node a 42 and node a 43 The corresponding scheduling task.

[0097] Furthermore, the computer device can determine the child nodes in the scheduling task chain whose dependency relationship with the abnormal scheduling task is not established, and use the child nodes whose dependency relationship with the abnormal scheduling task is not established as the impacted scheduling tasks, and the child nodes whose dependency relationship with the abnormal scheduling task is not established are the n+1th layer nodes; determine the child nodes in the scheduling task chain whose dependency relationship with the n+1th layer nodes is not established, and use the child nodes whose dependency relationship with the n+1th layer nodes is not established as the impacted scheduling tasks, and the determined child nodes whose dependency relationship with the n+1th layer nodes is not established are the n+2th layer nodes; until the determined child node is a leaf node, the determined leaf node is used as the impacted scheduling task, and the impacted scheduling task corresponding to the abnormal scheduling task includes the determined child nodes.

[0098] For example, assuming that the value of n is 1, the child nodes of the abnormal scheduling task include scheduling task A and scheduling task B, and the dependency relationship between scheduling task A and the abnormal scheduling task is a "finish-start" relationship, that is, the start of scheduling task A depends on the completion of the abnormal scheduling task, and the dependency relationship between scheduling task B and the abnormal scheduling task is a "start-start" relationship, that is, the start of scheduling task B depends on the start of the abnormal scheduling task, and the computer device is executing the abnormal scheduling task, then the dependency relationship between scheduling task A and the abnormal scheduling task is not established, and the dependency relationship between scheduling task B and the abnormal scheduling task is established, then the computer device can use scheduling task A as an impact scheduling task, and scheduling task A is a second-layer node. Assuming that the child node of scheduling task A is scheduling task D, and the dependency relationship between scheduling task D and scheduling task A is a "start-start" relationship, since the computer device has not started to execute scheduling task A, that is, the task state of scheduling task A is an unexecuted state, then the dependency relationship between scheduling task D and scheduling task A is not established, then the computer device can use scheduling task D as an impact scheduling task, and scheduling task D is a third-layer node. Assume that the scheduling task D is a leaf node, so the affected scheduling tasks corresponding to the abnormal scheduling task include scheduling task A and scheduling task D.

[0099] S405: Output prompt information, where the prompt information includes a task identifier that affects the scheduled task.

[0100] In one embodiment, a computer device can obtain task parameter information of an abnormal scheduling task, and task parameter information of an impact scheduling task corresponding to the abnormal scheduling task, each task parameter information including the data processing layer to which the scheduling task corresponding to each task parameter information belongs; the task parameter information of the abnormal scheduling task is displayed, and the task parameter information of the impact scheduling task corresponding to the abnormal scheduling task is displayed. It can be understood that the scheduling task corresponding to the task parameter information of the abnormal scheduling task is the abnormal scheduling task, and the scheduling task corresponding to any task parameter information that affects the scheduling task is any of the impact scheduling tasks. Optionally, the above-mentioned task parameter information includes but is not limited to: the data processing layer to which the corresponding scheduling task belongs, the person in charge of development, the library name, etc. Among them, the data processing layer includes but is not limited to: the data middle layer, the data detail layer, the data service layer, the dimension table layer, and the like.

[0101] For example, Figure 5 As shown, the computer device can obtain scheduling data, which includes at least one scheduling task, so as to determine the abnormal scheduling task and the affected scheduling task corresponding to the abnormal scheduling task in the at least one scheduling task; further, the computer device can obtain the task parameter information of the abnormal scheduling task and each affected scheduling task to display the task parameter information of the abnormal scheduling task and each affected scheduling task, so as to generate a visualization page.

[0102] Optionally, each task parameter information includes attribute information of the scheduled task corresponding to each task parameter information. The computer device can classify at least one scheduled task according to the attribute information of each scheduled task to obtain a classification result; perform statistical analysis on the classification result to obtain a statistical analysis result, and display the statistical analysis result. The attribute information of any scheduled task may include but is not limited to non-critical tasks, critical tasks, etc. For example, Figure 6 As shown, assuming that the attribute information of each scheduled task includes non-critical tasks and critical tasks, then during the line analysis process, the computer device can display the number of unfinished scheduled tasks whose attribute information is non-critical tasks in each line in the display interface 601, and can also display the number of completed scheduled tasks whose attribute information is non-critical tasks in each line in the display interface 601, and can also display the number of unfinished scheduled tasks whose attribute information is critical tasks in each line in the display interface 601, and can also display the number of completed scheduled tasks whose attribute information is critical tasks in each line in the display interface 601, and so on.

[0103] In this case, the computer device can determine the data processing layer to which the running scheduling task whose task status is in the executing state belongs in at least one scheduling task; obtain the number of running scheduling tasks belonging to the same data processing layer, and take the data processing layer to which the running scheduling tasks whose number meets the first preset number condition belong as the first data processing layer; and display the recommended decision information for the first data processing layer, and the recommended decision information is used to indicate the increase of the running resources occupied by the scheduling tasks of the first data processing layer. It should be noted that the data processing layer to which the running scheduling tasks whose number meets the first preset number condition belong can refer to the data processing layer to which the largest number of running scheduling tasks belongs, that is, the data processing layer containing the largest number of running scheduling tasks; it can also refer to the data processing layer to which the running scheduling tasks whose number is greater than the first preset number threshold belong, that is, the data processing layer containing the number of running scheduling tasks greater than the first preset number threshold; it can also refer to the data processing layer to which the running scheduling tasks whose number is less than or equal to the first preset number threshold belong, that is, the data processing layer containing the number of running scheduling tasks less than or equal to the first preset number threshold, and this application does not limit this. Optionally, the first preset number threshold can be set by the computer device, or it can be set by the user according to his wishes, and this application does not limit this.

[0104] For example, assuming that the running scheduling tasks in at least one scheduling task whose task status is running include scheduling task A, scheduling task B and scheduling task C, and scheduling task A and scheduling task B belong to the data middle layer, and scheduling task C belongs to the data service layer; if the data processing layer to which the above-mentioned number of running scheduling tasks that meet the first preset number condition belongs refers to the data processing layer to which the largest number of running scheduling tasks belongs, then the computer device can use the data middle layer as the first data processing layer to display recommended decision information for the data middle layer.

[0105] It should be noted that the computer device can also obtain the number of scheduling-influencing tasks belonging to the same data processing layer, and use the data processing layer to which the scheduling-influencing tasks whose number meets the second preset number condition belong as the second data processing layer; and display the recommended decision information for the second data processing layer, and the recommended decision information is used to indicate the increase of the operating resources occupied by the scheduling tasks of the second data processing layer. Similarly, the data processing layer to which the scheduling-influencing tasks whose number meets the second preset number condition belong can refer to the data processing layer to which the largest number of scheduling-influencing tasks belong, that is, the data processing layer with the largest number of scheduling-influencing tasks; it can also refer to the data processing layer to which the scheduling-influencing tasks whose number is greater than the second preset number threshold belong, that is, the data processing layer to which the number of scheduling-influencing tasks included is greater than the second preset number threshold; it can also refer to the data processing layer to which the scheduling-influencing tasks whose number is less than or equal to the second preset number threshold belong, that is, the data processing layer to which the number of scheduling-influencing tasks included is less than or equal to the second preset number threshold, and this application does not limit this. Optionally, the second preset number threshold can be set by the computing device, or it can be set by the user according to his wishes, and this application does not limit this.

[0106] For example, assuming that the scheduling tasks affecting the abnormal scheduling task include scheduling task A, scheduling task B and scheduling task C, scheduling task A and scheduling task B belong to the data intermediate layer, and scheduling task C belongs to the data service layer; if the data processing layer to which the scheduling tasks affecting the number that meet the second preset number condition belong refers to the data processing layer to which the largest number of scheduling tasks affecting the number belong, then the computer device can use the data intermediate layer as the second data processing layer and display recommended decision information for the second data processing layer.

[0107] Similarly, if there are multiple abnormal scheduling tasks, the computer device can also obtain the number of abnormal scheduling tasks belonging to the same data processing layer, and use the data processing layer to which the abnormal scheduling tasks whose number meets the third preset number condition belong as the third data processing layer; and display the recommended decision information for the third data processing layer, where the recommended decision information is used to indicate the increase of the operating resources occupied by the scheduling tasks of the third data processing layer. It can be understood that the data processing layer to which the abnormal scheduling tasks whose number meets the third preset number condition belong may refer to the data processing layer to which the largest number of abnormal scheduling tasks belongs, or may refer to the data processing layer to which the abnormal scheduling tasks whose number is greater than the third preset number threshold belong, or may refer to the data processing layer to which the abnormal scheduling tasks whose number is less than or equal to the third preset number threshold belong, and this application does not limit this. Optionally, the third preset number threshold may be set by the computer device, or may be set by the user as desired, and this application does not limit this.

[0108] S406, obtaining historical record data, where the historical record data includes the historical recovery duration of each scheduled task.

[0109] It should be noted that the computer device can obtain historical record data from the scheduling platform, or download historical record data from the cloud according to user instructions, or obtain historical record data from a mobile storage device provided by the user, which mobile storage device can be a mobile hard disk or a USB flash disk (USB flash disk), etc. The computer device can also obtain historical record data from the storage space of the computer device. For example, when the historical record data exists in the read-only memory (ROM) of the computer device, the computer device can obtain the historical record data from the read-only memory.

[0110] S407: Taking the average of all historical recovery durations of the abnormally scheduled tasks as the predicted recovery duration of the abnormally scheduled tasks.

[0111] S408: The time point whose difference from the failure time is the predicted recovery time is used as the recovery time of the abnormal scheduling task, and the recovery time of the abnormal scheduling task is displayed.

[0112] It should be noted that if the above-mentioned abnormal scheduling task is a scheduling task in a suspended execution state, the failure time may refer to the suspended execution time of the abnormal scheduling task; if the above-mentioned abnormal scheduling task is a scheduling task whose current running time is greater than the target running time, the failure time may refer to the end time of the abnormal scheduling task, or refers to the time point whose difference with the start time of the abnormal scheduling task is the target running time. This application does not limit this.

[0113] Furthermore, the task status data of each scheduled task includes the start time of each scheduled task. The computer device can use the time point whose difference from the start time of each task affecting the scheduled task is the predicted recovery time as the recovery time of each task affecting the scheduled task; and display the recovery time of each task affecting the scheduled task.

[0114] For example, assuming that the predicted recovery time is 3 hours, the affected scheduling tasks include scheduling task A and scheduling task B, and the start time of scheduling task A is 9:00, and the start time of scheduling task B is 11:00, then the computer device can determine that the recovery time of scheduling task A is 12:00, and can determine that the recovery time of scheduling task B is 14:00.

[0115] The embodiment of the present application can analyze and process the task status data of each scheduling task in the scheduling platform to obtain an abnormal scheduling task in at least one scheduling task; and recursively analyze the abnormal scheduling task based on the scheduling task chain to obtain the affected scheduling task corresponding to the abnormal scheduling task, thereby displaying the task identifier of each affected scheduling task, so that the user can grasp the affected scheduling task corresponding to the abnormal scheduling task in the scheduling platform in real time and intuitively, as well as the impact range of the abnormal scheduling task. In addition, the embodiment of the present application can determine the predicted recovery time of the abnormal scheduling task through the historical recovery time of each scheduling task in the historical record data, and can determine the recovery time of the abnormal scheduling task according to the predicted recovery time and the fault time, so as to conveniently predict the recovery time of the abnormal scheduling task and the affected scheduling task corresponding to the abnormal scheduling task; further, the recovery time of the abnormal scheduling task and the recovery time of each affected scheduling task can be displayed, so that the user can intuitively grasp the recovery time of the abnormal scheduling task and each affected scheduling task. The embodiment of the present application can also display the task parameter information of abnormal scheduling tasks and each task that affects the scheduling task. Each task parameter information includes the data processing layer to which the scheduling task corresponding to each task parameter information belongs, and based on the relationship between each running scheduling task and each data processing layer, or based on the relationship between the scheduling task that affects the scheduling task and each data processing layer, etc., the recommended decision information for the corresponding data processing layer is displayed to facilitate users to make appropriate decisions quickly.

[0116] Based on the description of the above-mentioned abnormal impact analysis method, the present application also proposes an abnormal impact analysis device, which can be a computer program (including program code) running in a computer device. Figure 2 or Figure 4 The abnormal impact analysis method shown in Figure 7 , the abnormal impact analysis device can run the following units:

[0117] The processing unit 701 is used to obtain the scheduling data in the scheduling platform in real time, wherein the scheduling data includes task status data of at least one scheduling task;

[0118] The processing unit 701 is further configured to analyze and process the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task;

[0119] The processing unit 701 is further configured to obtain a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a plurality of scheduling tasks, where the plurality of scheduling tasks include the at least one scheduling task;

[0120] The processing unit 701 is further configured to recursively analyze the abnormal scheduling task based on the scheduling task chain to obtain an impacted scheduling task corresponding to the abnormal scheduling task, wherein the impacted scheduling task is an m-th layer node in the scheduling task chain, and the abnormal scheduling task is an n-th layer node in the scheduling task chain, where m is greater than n, m and n are both positive integers, and the task status of the impacted scheduling task is an executing state or a non-executing state;

[0121] The output unit 702 is used to output prompt information, where the prompt information includes the task identifier that affects the scheduled task.

[0122] In one implementation, when the processing unit 701 recursively analyzes the abnormal scheduling task based on the scheduling task chain to obtain the scheduling-affecting task corresponding to the abnormal scheduling task, it can be specifically used to:

[0123] Determine a child node of the abnormal scheduling task in the scheduling task chain, and use the child node of the abnormal scheduling task as the affected scheduling task, wherein the child node of the abnormal scheduling task is a node at the n+1th layer;

[0124] The child node of the n+1th layer node determined in the scheduling task chain is used as the scheduling-affecting task, and the child node of the n+1th layer node determined is the n+2th layer node;

[0125] When the determined child node is a leaf node, the determined leaf node is used as the affected scheduling task, and the affected scheduling task corresponding to the abnormal scheduling task includes each of the determined child nodes.

[0126] In another implementation manner, the task status data of each scheduled task includes the start time of each scheduled task; when the processing unit 701 analyzes and processes the task status data of each scheduled task to determine an abnormal scheduled task in the at least one scheduled task, it can be specifically used to:

[0127] If the task status data of each scheduled task includes the end time of each scheduled task, then determine the target running time of each running scheduled task according to the difference between the start time and the end time of the running scheduled task whose task status is being executed in at least one scheduled task; determine the current running time of each running scheduled task according to the difference between the start time of each running scheduled task and the current system time; treat the running scheduled task whose current running time is greater than the target running time as the abnormal scheduling task; or,

[0128] If the task status data of each scheduled task includes the target running time of each scheduled task, the current running time of each scheduled task is determined according to the difference between the start time of the running scheduled task with the task status of being executed in at least one scheduled task and the current system time; the running scheduled task whose current running time is greater than the target running time is regarded as the abnormal scheduled task.

[0129] In another implementation manner, when the processing unit 701 analyzes and processes the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task, it can be specifically used to:

[0130] Determine, among the at least one scheduled task, a scheduled task whose task status is a suspended execution status;

[0131] The scheduled task whose task status is the suspended execution status is taken as the abnormal scheduled task.

[0132] In another implementation, the processing unit 701 may also be used to: obtain task parameter information of the abnormal scheduling task, and task parameter information of the scheduling task affected by the abnormal scheduling task, wherein each task parameter information includes a data processing layer to which the scheduling task corresponding to each task parameter information belongs;

[0133] The output unit 702 may also be used to: display the task parameter information of the abnormal scheduling task, and display the task parameter information of the scheduling task affected by the abnormal scheduling task.

[0134] In another implementation manner, the processing unit 701 may also be used to: determine the data processing layer to which the running scheduling task whose task status is in the executing state belongs among the at least one scheduling task; obtain the number of running scheduling tasks belonging to the same data processing layer, and use the data processing layer to which the running scheduling tasks whose number meets the first preset number condition belong as the first data processing layer;

[0135] The output unit 702 may also be used to: display recommended decision information for the first data processing layer, where the recommended decision information is used to indicate an increase in the operating resources occupied by the scheduled tasks of the first data processing layer.

[0136] In another implementation manner, the processing unit 701 may also be used to: obtain the number of scheduling-influencing tasks belonging to the same data processing layer, and use the data processing layer to which the scheduling-influencing tasks whose number meets a second preset number condition belong as the second data processing layer;

[0137] The output unit 702 may also be used to: display recommended decision information for the second data processing layer, where the recommended decision information is used to indicate an increase in the operating resources occupied by the scheduled tasks of the second data processing layer.

[0138] In another implementation manner, the task status data of the abnormal scheduling task includes the failure time of the abnormal scheduling task, and the processing unit 701 can also be used to: obtain historical record data, the historical record data including the historical recovery time of each scheduling task; use the average of all historical recovery time of the abnormal scheduling task as the predicted recovery time of the abnormal scheduling task; use the time point whose difference from the failure time is the predicted recovery time as the recovery time of the abnormal scheduling task;

[0139] The output unit 702 may also be used to display the recovery time of the abnormally scheduled task.

[0140] In another implementation, the task status data of each scheduled task includes the start time of each scheduled task, and the processing unit 701 may also be used to: use the time point whose difference from the start time of each scheduling task is the predicted recovery time as the recovery time of each scheduling task;

[0141] The output unit 702 may also be used to display the recovery time of each task affecting the scheduling.

[0142] According to one embodiment of the present application, Figure 2 or Figure 4 Each step involved in the method shown can be Figure 7 The abnormal impact analysis device shown is performed by each unit. For example, Figure 2 The steps S201-S204 shown in FIG. 1 can be performed by Figure 7 The processing unit 701 shown in FIG. 1 is executed, and step S205 can be performed by Figure 7 The output unit 702 shown in is executed. For example, Figure 4 The steps S401-S404 and steps S406-S407 shown in FIG. 4 can be performed by Figure 7 The processing unit 701 shown in FIG. 1 is executed, and step S405 can be performed by Figure 7 The output unit 702 shown in FIG. 1 is executed, and step S408 can be performed by Figure 7 The processing unit 701 and the output unit 702 shown in FIG. 7 are executed together, and so on.

[0143] According to another embodiment of the present application, Figure 7 The various units in the abnormal impact analysis device shown can be separately or all combined into one or several other units to constitute, or some of the units (some) can also be split into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the function of one unit can also be realized by multiple units, or the function of multiple units can be realized by one unit. In other embodiments of the present application, the abnormal impact analysis device can also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0144] According to another embodiment of the present application, the program can be executed by running on a general computing device such as a computer including a central processing unit (CPU), a random access memory medium (RAM), a read-only memory medium (ROM), and other processing elements and storage elements. Figure 2 or Figure 4 A computer program (including program code) for each step involved in the corresponding method shown in Figure 7 The abnormal impact analysis device shown in the embodiment of the present application is implemented by the abnormal impact analysis method. The computer program can be recorded on a computer storage medium, for example, and loaded into the above-mentioned computing device through the computer storage medium and run therein.

[0145] The embodiment of the present application can obtain the scheduling data of the scheduling platform in real time, and the scheduling data includes the task status data of at least one scheduling task, so as to facilitate the subsequent real-time analysis of at least one scheduling task in the scheduling platform, so that the user can grasp the task status of at least one scheduling task in real time. Secondly, the abnormal scheduling task can be determined in at least one scheduling task, and based on the acquired scheduling task chain, the abnormal scheduling task can be recursively analyzed to obtain the affected scheduling task corresponding to the abnormal scheduling task, so as to effectively locate the impact range of the abnormal scheduling task; and prompt information can be output, and the prompt information includes the task identifier of the affected scheduling task corresponding to the abnormal scheduling task, that is, the task identifier of the affected scheduling task corresponding to the abnormal scheduling task can be displayed, so that the user can grasp the affected scheduling task corresponding to the abnormal scheduling task in real time, so as to grasp the impact range of the abnormal scheduling task. It can be seen that the embodiment of the present application can determine the abnormal scheduling task in at least one scheduling task and the affected scheduling task corresponding to the abnormal scheduling task in real time, so that the user can quickly make appropriate decisions based on the impact range of the abnormal scheduling task.

[0146] Based on the description of the above method embodiment and device embodiment, the present application embodiment also provides a computer device. Figure 8 The computer device at least includes a processor 801, an input interface 802, an output interface 803, and a computer storage medium 804. The processor 801, the input interface 802, the output interface 803, and the computer storage medium 804 in the computer device may be connected via a bus or other means.

[0147] The computer storage medium 804 may be stored in a memory of a computer device. The computer storage medium 804 is used to store a computer program. The computer program includes program instructions. The processor 801 is used to execute the program instructions stored in the computer storage medium 804 . The processor 801 (or CPU (Central Processing Unit)) is the computing core and control core of a computer device, which is suitable for implementing a computer program, specifically suitable for loading and executing a computer program to implement a corresponding method flow or a corresponding function; in one embodiment, the processor 801 described in the embodiment of the present application can be used to perform a series of abnormal impact analyses, specifically including: real-time acquisition of scheduling data in a scheduling platform, the scheduling data including task status data of at least one scheduling task; analyzing and processing the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task; acquiring a scheduling task chain, the scheduling task chain is used to indicate a hierarchical relationship between a plurality of scheduling tasks, the plurality of scheduling tasks including the at least one scheduling task; recursively analyzing the abnormal scheduling task based on the scheduling task chain to obtain an impact scheduling task corresponding to the abnormal scheduling task, the impact scheduling task is an m-th layer node in the scheduling task chain, the abnormal scheduling task is an n-th layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task status of the impact scheduling task is an executing state or an unexecuted state; outputting prompt information, the prompt information including a task identifier of the impact scheduling task, and the like.

[0148] The embodiment of the present application also provides a computer storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer storage medium provides a storage space, which stores the operating system of the computer device. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage; optionally, it can also be at least one computer storage medium located away from the aforementioned processor. In one embodiment, the processor can load and execute the computer program stored in the computer storage medium to implement the above-mentioned related Figure 2 or Figure 4 The illustrated abnormality affects each method step in an embodiment of the method for analyzing the abnormality.

[0149] The embodiment of the present application can obtain the scheduling data of the scheduling platform in real time, and the scheduling data includes the task status data of at least one scheduling task, so as to facilitate the subsequent real-time analysis of at least one scheduling task in the scheduling platform, so that the user can grasp the task status of at least one scheduling task in real time. Secondly, the abnormal scheduling task can be determined in at least one scheduling task, and based on the acquired scheduling task chain, the abnormal scheduling task can be recursively analyzed to obtain the affected scheduling task corresponding to the abnormal scheduling task, so as to effectively locate the impact range of the abnormal scheduling task; and prompt information can be output, and the prompt information includes the task identifier of the affected scheduling task corresponding to the abnormal scheduling task, that is, the task identifier of the affected scheduling task corresponding to the abnormal scheduling task can be displayed, so that the user can grasp the affected scheduling task corresponding to the abnormal scheduling task in real time, so as to grasp the impact range of the abnormal scheduling task. It can be seen that the embodiment of the present application can determine the abnormal scheduling task in at least one scheduling task and the affected scheduling task corresponding to the abnormal scheduling task in real time, so that the user can quickly make appropriate decisions based on the impact range of the abnormal scheduling task.

[0150] It should be noted that according to one aspect of the present application, a computer program product or a computer program is also provided, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer storage medium. The processor of the computer device reads the computer instructions from the computer storage medium, and the processor executes the computer instructions, so that the computer device performs the above Figure 2 ,or Figure 4 The methods shown are provided in various optional aspects of the abnormal impact analysis method embodiment.

[0151] Furthermore, it should be understood that what is disclosed above is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for analyzing abnormal impact, characterized in that: include: Acquire scheduling data in the scheduling platform in real time, wherein the scheduling data includes task status data of at least one scheduling task; Analyzing and processing the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task; Acquire a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a plurality of scheduling tasks, where the plurality of scheduling tasks include the at least one scheduling task; Determine the child node of the abnormal scheduling task in the scheduling task chain, and use the child node of the abnormal scheduling task as the affected scheduling task, the child node of the abnormal scheduling task is the n+1th layer node, the affected scheduling task is the mth layer node in the scheduling task chain, the abnormal scheduling task is the nth layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task state of the affected scheduling task is an executing state or a non-executing state; The child node of the n+1th layer node determined in the scheduling task chain is used as the scheduling-affecting task, and the child node of the n+1th layer node determined is the n+2th layer node; When the determined child node is a leaf node, the determined leaf node is used as the affected scheduling task, and the affected scheduling task corresponding to the abnormal scheduling task includes each determined child node; Output prompt information, where the prompt information includes the task identifier that affects the scheduled task.

2. The method according to claim 1, characterized in that The task status data of each scheduled task includes the start time of each scheduled task; and the analyzing and processing the task status data of each scheduled task to determine an abnormal scheduled task in the at least one scheduled task includes: If the task status data of each scheduled task includes the end time of each scheduled task, then determine the target running time of each running scheduled task according to the difference between the start time and the end time of the running scheduled task whose task status is being executed in at least one scheduled task; determine the current running time of each running scheduled task according to the difference between the start time of each running scheduled task and the current system time; treat the running scheduled task whose current running time is greater than the target running time as the abnormal scheduling task; or, If the task status data of each scheduled task includes the target running time of each scheduled task, the current running time of each scheduled task is determined according to the difference between the start time of the running scheduled task with the task status of being executed in at least one scheduled task and the current system time; the running scheduled task whose current running time is greater than the target running time is regarded as the abnormal scheduled task.

3. The method according to claim 1, characterized in that The analyzing and processing the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task includes: Determine, among the at least one scheduled task, a scheduled task whose task status is a suspended execution status; The scheduled task whose task status is the suspended execution status is taken as the abnormal scheduled task.

4. The method according to claim 1, characterized in that: The method further comprises: Acquire task parameter information of the abnormal scheduling task, and task parameter information of the scheduling task affected by the abnormal scheduling task, wherein each task parameter information includes a data processing layer to which the scheduling task corresponding to each task parameter information belongs; The task parameter information of the abnormal scheduling task is displayed, and the task parameter information of the scheduling task affected by the abnormal scheduling task is displayed.

5. The method according to claim 4, characterized in that The method further comprises: Determine the data processing layer to which the running scheduled task whose task status is being executed among the at least one scheduled task belongs; Acquire the number of running scheduling tasks belonging to the same data processing layer, and use the data processing layer to which the running scheduling tasks whose number meets the first preset number condition belong as the first data processing layer; Recommended decision information for the first data processing layer is displayed, where the recommended decision information is used to indicate an increase in the running resources occupied by the scheduled tasks of the first data processing layer.

6. The method according to claim 4, characterized in that The method further comprises: Acquire the number of scheduling-influencing tasks belonging to the same data processing layer, and use the data processing layer to which the scheduling-influencing tasks whose number meets a second preset number condition belong as the second data processing layer; Recommended decision information for the second data processing layer is displayed, where the recommended decision information is used to indicate an increase in the operating resources occupied by the scheduled tasks of the second data processing layer.

7. The method according to claim 1, characterized in that The task status data of the abnormally scheduled task includes the failure time of the abnormally scheduled task, and the method further includes: Acquire historical record data, the historical record data including the historical recovery duration of each scheduling task; The average of all historical recovery durations of the abnormal scheduling task is used as the predicted recovery duration of the abnormal scheduling task; The time point whose difference from the failure time is the predicted recovery time is used as the recovery time of the abnormal scheduling task, and the recovery time of the abnormal scheduling task is displayed.

8. The method according to claim 7, characterized in that The task status data of each scheduled task includes the start time of each scheduled task, and the method further includes: The time point whose difference from the start time of each task influencing the scheduling is the predicted recovery time is used as the recovery time of each task influencing the scheduling; The recovery time of each task affecting the scheduling is displayed.

9. An abnormal impact analysis device, characterized in that: include: A processing unit, used for acquiring scheduling data in the scheduling platform in real time, wherein the scheduling data includes task status data of at least one scheduling task; The processing unit is further used to analyze and process the task status data of each scheduling task to determine an abnormal scheduling task in the at least one scheduling task; The processing unit is further used to obtain a scheduling task chain, where the scheduling task chain is used to indicate a hierarchical relationship between a plurality of scheduling tasks, where the plurality of scheduling tasks include the at least one scheduling task; The processing unit is further used to determine the child node of the abnormal scheduling task in the scheduling task chain, and use the child node of the abnormal scheduling task as the affected scheduling task, the child node of the abnormal scheduling task is the n+1th layer node, the affected scheduling task is the mth layer node in the scheduling task chain, the abnormal scheduling task is the nth layer node in the scheduling task chain, m is greater than n, m and n are both positive integers, and the task state of the affected scheduling task is an executing state or an unexecuted state; use the child node of the n+1th layer node determined in the scheduling task chain as the affected scheduling task, and the child node of the determined n+1th layer node is the n+2th layer node; until the determined child node is a leaf node, use the determined leaf node as the affected scheduling task, and the affected scheduling task corresponding to the abnormal scheduling task includes the determined child nodes; The output unit is used to output prompt information, wherein the prompt information includes the task identifier that affects the scheduled task.

10. A computer device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 8 is implemented.

11. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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