CN115858310A - Abnormal task identification method and device, computer equipment and storage medium - Google Patents

Abstract

The embodiment of the specification provides an abnormal task identification method, an abnormal task identification device, computer equipment and a storage medium. The method comprises the following steps: determining a target monitoring scene for monitoring the execution condition of the data extraction task in response to the selection operation of the monitoring scene selection control; acquiring execution condition data of a data extraction task; monitoring the execution condition data of the data extraction task according to a monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to a target monitoring scene; the target monitoring scene comprises at least one of a first monitoring scene with a task execution result of failure, a second monitoring scene with overtime execution in the task execution process, a third monitoring scene with task non-execution in the task execution process and a fourth monitoring scene with execution errors which do not follow the task dependency relationship in the task execution process. The embodiment of the specification improves the flexibility of abnormal task identification in different scenes.

Description

Abnormal task identification method and device, computer equipment and storage medium

Technical Field

Embodiments in this specification relate to the technical field of data processing, and in particular, to an abnormal task identification method and apparatus, a computer device, and a storage medium.

Background

With the development of computer internet technology, information systems of enterprises are more and more, and data of each information system is also increased sharply. A data platform (also referred to as a staging platform) is a system by which an enterprise gathers, aggregates data, and performs data analysis, modeling, and application. Data extraction and integration can be performed on data of each information system through the data platform, so that data gaps of each information system are broken, a uniform data center is formed, and a foundation is provided for mining of subsequent data values. In order to ensure the normal operation of the data extraction process, the data extraction tasks need to be monitored, and abnormal tasks in the data extraction tasks need to be detected and identified.

In the related abnormal task identification method, a threshold set for each data extraction task in a specific scene is generally detected, and the data extraction task of the data extraction task exceeding the threshold is identified as an abnormal task.

However, in the related abnormal task identification method, the flexibility of abnormal task identification in different scenes needs to be improved.

Disclosure of Invention

In view of the above, embodiments of the present disclosure are directed to providing an abnormal task identification method, apparatus, device and storage medium, so as to improve flexibility of abnormal task identification in different scenarios to some extent.

An embodiment of the present specification provides an abnormal task identification method, including: determining a target monitoring scene for monitoring the execution condition of the data extraction task in response to the selection operation of the monitoring scene selection control; wherein, the target monitoring scene corresponds to a monitoring strategy; acquiring execution condition data of the data extraction task; monitoring the execution condition data of the data extraction task according to the monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene; the target monitoring scene comprises at least one of a first monitoring scene with a task execution result of failure, a second monitoring scene with overtime execution in the task execution process, a third monitoring scene with task non-execution in the task execution process and a fourth monitoring scene with execution errors which do not follow the task dependency relationship in the task execution process.

An embodiment of the present specification provides an abnormal task recognition apparatus, including: the monitoring scene determining module is used for responding to the selection operation of the monitoring scene selection control and determining a target monitoring scene for monitoring the execution condition of the data extraction task; wherein, the target monitoring scene corresponds to a monitoring strategy; the task data acquisition module is used for acquiring the execution condition data of the data extraction task; the abnormal task identification module is used for monitoring the execution condition data of the data extraction task according to the monitoring strategy and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene; the target monitoring scene comprises at least one of a first monitoring scene with a task execution result of failure, a second monitoring scene with overtime execution in the task execution process, a third monitoring scene with task non-execution in the task execution process and a fourth monitoring scene with execution errors which do not follow the task dependency relationship in the task execution process.

The present specification provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method of any one of the above embodiments when executing the computer program.

The embodiment of the present specification provides a computer readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the method of any one of the above embodiments.

In a plurality of embodiments provided in this specification, a target monitoring scenario for monitoring the execution condition of a data extraction task is determined by responding to a selection operation of a monitoring scenario selection control, execution condition data of the data extraction task is acquired, the execution condition data is monitored according to a monitoring strategy corresponding to the target monitoring scenario, and whether the data extraction task is an abnormal task corresponding to the target monitoring scenario is identified; the target monitoring scene comprises at least one of a first monitoring scene with a task execution result of failure, a second monitoring scene with an execution overtime in a task execution process, a third monitoring scene with a task not being executed in the task execution process and a fourth monitoring scene with an execution error not according to a task dependency relationship in the task execution process, so that abnormal tasks in different scenes can be identified, and the flexibility of identifying the abnormal tasks in different scenes is improved to a certain extent.

Drawings

Fig. 1 is a schematic diagram of an abnormal task recognition system provided in one embodiment of the present specification.

Fig. 2 is a schematic diagram of a flow of an abnormal task identification method according to an embodiment of the present disclosure.

Fig. 3 is a flowchart illustrating an abnormal task identification method for detecting an execution timeout occurring in a task execution process according to an embodiment of the present disclosure.

Fig. 4 is a schematic diagram of an abnormal task identifying apparatus according to an embodiment of the present disclosure.

FIG. 5 is a schematic diagram of a computer device provided in one embodiment of the present description.

Detailed Description

In order to make the technical solution of the present invention better understood, the technical solution of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments of the present invention. All other embodiments obtained by a person of ordinary skill in the art without any inventive work based on the embodiments in the present specification belong to the protection scope of the present specification.

In the related art, an enterprise may have a plurality of information systems, which may be databases, files, streaming media, etc., and meanwhile, data of the information systems also increases sharply, and the performance of a common business database is insufficient and difficult to meet the requirements. Data extraction and integration can be performed on data of each information system of an enterprise through the data platform, so that data gaps of each information system can be broken, a uniform data center is formed, and a foundation is provided for mining of subsequent data values. The data platform (also called data middle platform) is a system for collecting and summarizing data, analyzing, modeling and applying data, and processing such as data extraction, conversion and loading of data of each information system through the data platform is a trend of information construction of enterprises. Along with the increase of information systems or service lines of a data access center, the number of data extraction tasks is increased, and in order to ensure the normal operation of the data extraction tasks and subsequent product lines, processing tasks such as data extraction, conversion, loading and the like need to be monitored, particularly the data extraction tasks need to be monitored, and abnormal tasks in the data extraction tasks are identified.

In a traditional abnormal task identification method, a threshold value set for each data extraction task in a certain specific scene is usually detected, and the data extraction task exceeding the threshold value is identified as an abnormal task, however, the traditional abnormal task identification method has the problem that the flexibility of identifying the abnormal task is low because the abnormal task in different scenes is difficult to be flexibly identified; in addition, the traditional abnormal task identification method has low identification flexibility, so that the identification capability of abnormal tasks in different scenes is poor, and the problem of low identification accuracy is caused; and because it is difficult to flexibly identify the abnormal tasks in different scenes, enterprises often need to additionally customize the missing abnormal task identification function according to different scenes, or set a full-time operation and maintenance post, and rely on manual real-time monitoring data to extract the execution condition of the task, and along with the increase of labor cost, the timeliness and accuracy of the abnormal task identification method are also low.

Therefore, it is necessary to provide an abnormal task identification method, which can determine at least one target monitoring scenario for monitoring the execution of the data extraction task by responding to the selection operation of the monitoring scenario selection control, acquire the execution data of the data extraction task, monitor the execution data according to a monitoring policy corresponding to the at least one target monitoring scenario, and identify whether the data extraction task is an abnormal task corresponding to the target monitoring scenario, so as to solve the technical problem of low accuracy of abnormal task identification in the data extraction process.

One embodiment of the present description provides an exception task identification system. Referring to fig. 1, the abnormal task recognition system may include an information system 100 and a data platform 200. The data platform 200 can include an execution component 210 and at least one monitoring component 220, the execution component 210 can perform data extraction tasks, obtain data from the information system 100 as an information source, and the at least one monitoring component 220 can monitor the data extraction tasks.

In this embodiment, data platform 200 may provide configuration control 230. The configuration control 230 can configure a monitoring policy for monitoring the data extraction task in the at least one monitoring component 220, and can also configure an execution policy for executing the data extraction task in the component 210.

In this embodiment, the data platform 200 may provide an alert component 240 for alerts. At least one alert information template in the alert component 240 may be configured via the configuration control 230.

One embodiment of the present specification provides an abnormal task identifying method. Specifically, referring to fig. 2, the abnormal task identifying method may include the following steps.

Step S210: determining a target monitoring scene for monitoring the execution condition of the data extraction task in response to the selection operation of the monitoring scene selection control; and the target monitoring scene corresponds to a monitoring strategy.

In some cases, the data platform may provide a monitoring scenario selection control, so that different monitoring scenarios may be flexibly selected according to specific service requirements, and abnormal tasks in different scenarios may be identified, where different monitoring scenarios correspond to different monitoring strategies.

In this embodiment, the execution component may determine a target monitoring scenario for monitoring the execution condition of the data extraction task in response to a selection operation of the monitoring scenario selection control. In particular, the monitoring scenario selection controls that the data platform may provide may include at least one of a first, second, third, and fourth monitoring scenario selection control. The execution component can determine a target monitoring scene for monitoring the execution condition of the data extraction task in response to the selection operation of at least one of the first, second, third and fourth monitoring scene selection controls. The target monitoring scene may include at least one of a first monitoring scene in which a task execution result is failure, a second monitoring scene in which execution overtime occurs in a task execution process, a third monitoring scene in which a task is not executed in the task execution process, and a fourth monitoring scene in which an execution error that does not depend on a task dependency occurs in the task execution process.

Step S220: and acquiring the execution condition data of the data extraction task.

In some cases, the execution result of the data extraction task can be determined by combining the monitoring policy corresponding to the target monitoring scenario and the execution data of the data extraction task, so that the execution data of the data extraction task can be acquired, and the monitoring of the data extraction task can be realized according to the monitoring policy and the execution data.

In this embodiment, the execution component may obtain execution status data of the data extraction task. Specifically, the execution condition data acquired by the execution component may be at least one of log data and status data of the data extraction task. The log data is an execution record of the data extraction task, the task execution result of the data extraction task corresponding to the log data can be detected to be successful or failed based on the log data, and the state data can represent the execution state of the data extraction task. As an example, the execution progress of the data extraction task, whether it has been executed, whether it has timed out, whether it is executed in a task dependency relationship, and the like may be detected based on the state data.

Step S230: and monitoring the execution condition data of the data extraction task according to a monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to a target monitoring scene.

In some cases, after the target monitoring scenario, that is, the monitoring policy corresponding to the target monitoring scenario, is determined, the execution condition data of the data extraction task may be monitored according to the monitoring policy corresponding to the target monitoring scenario.

In this embodiment, the execution component may monitor execution condition data of the data extraction task according to a monitoring policy, and identify whether the data extraction task is an abnormal task corresponding to the target monitoring scenario.

In the embodiment, a target monitoring scene for monitoring the execution condition of the data extraction task is determined by responding to the selection operation of the monitoring scene selection control, the execution condition data of the data extraction task is acquired, the execution condition data is monitored according to a monitoring strategy corresponding to the target monitoring scene, and whether the data extraction task is an abnormal task corresponding to the target monitoring scene is identified; the target monitoring scene comprises at least one of a first monitoring scene with a task execution result of failure, a second monitoring scene with an execution overtime in a task execution process, a third monitoring scene with a task not being executed in the task execution process and a fourth monitoring scene with an execution error not according to a task dependency relationship in the task execution process, so that abnormal tasks in different scenes can be identified, and the flexibility of identifying the abnormal tasks in different scenes is improved to a certain extent.

In some embodiments, after determining a target monitoring scenario for monitoring the execution condition of the data extraction task in response to the selection operation of the monitoring scenario selection control, the abnormal task identification method may further include: and calling a monitoring component corresponding to the target monitoring scene. Correspondingly, monitoring the execution condition data of the data extraction task according to the monitoring policy may include: and monitoring the execution condition data of the data extraction task according to a monitoring strategy through a monitoring component corresponding to the target monitoring scene.

In some cases, the monitoring of the performance data of the data extraction tasks may be accomplished by at least one monitoring component provided by the data platform.

In this embodiment, the monitoring strategy corresponding to the target monitoring scenario may be preset in the monitoring component corresponding to the target monitoring scenario. The execution component may invoke the monitoring component corresponding to the target monitoring scenario to monitor the execution data of the data extraction task. Specifically, the data platform may provide an interface call unit, and the execution component may call a monitoring component corresponding to the target monitoring scenario in the at least one monitoring component through the interface call unit. As an example, the first, second, third, and fourth monitoring scenarios correspond to respective monitoring components.

In this embodiment, the execution component may monitor the execution condition data of the data extraction task according to the monitoring policy through the monitoring component corresponding to the target monitoring scenario. Specifically, the execution component may call the monitoring component corresponding to the target monitoring scenario through the interface call unit, so that the monitoring component corresponding to the target monitoring scenario monitors the execution data according to the monitoring policy.

In the above embodiment, after the target monitoring scene is determined, the execution component monitors the execution condition data of the data extraction task by calling the monitoring component and the monitoring strategy corresponding to the target monitoring scene, so that the abnormal tasks in different scenes can be identified, and the flexibility of identifying the abnormal tasks is improved.

In some embodiments, the first monitoring scenario may include at least one of an offline task monitoring scenario and a real-time task monitoring scenario. If the target monitoring scenario includes the first monitoring scenario, when the monitoring component corresponding to the target monitoring scenario is called, the method may include: calling a monitoring component corresponding to the offline task monitoring scene; and/or calling a monitoring component corresponding to the real-time task monitoring scene.

In some cases, the data platform may further provide at least one of an offline task monitoring scene selection control and a real-time task monitoring scene selection control, and when the execution component responds to the selection operation of the monitoring scene selection control and the determined target monitoring scene includes the first monitoring scene, the execution component may respond to the selection operation of the offline task monitoring scene selection control and/or the real-time task monitoring scene selection control, so as to determine the first monitoring scene including at least one of the offline task monitoring scene and the real-time task monitoring scene.

In this embodiment, if the target monitoring scenario includes the first monitoring scenario, the execution component may call the monitoring component corresponding to the offline task monitoring scenario, may also call the monitoring component corresponding to the real-time task monitoring scenario, and may also call the monitoring component corresponding to the offline task monitoring scenario and the monitoring component corresponding to the real-time task monitoring scenario at the same time.

Specifically, the monitoring policy corresponding to the first monitoring scenario may be preset in the monitoring component corresponding to the first monitoring scenario. The monitoring policy corresponding to the first monitoring scenario may include a specified character. The execution component can call the monitoring component corresponding to the first monitoring scene, and the execution condition data of the data extraction task is monitored according to the designated characters. Specifically, the execution component may call the monitoring component corresponding to the first monitoring scenario through the interface call unit, so that the monitoring component corresponding to the first monitoring scenario scans log data or status data of the data extraction task, and determines that a task execution result of the data extraction task is successful in a case that the log data or status data includes the specified character, and determines that the task execution result of the data extraction task is failed in a case that the log data or status data does not include the specified character.

For example, the monitoring policy corresponding to the first monitoring scenario may include a designated character and a designated value corresponding to the designated character, and accordingly, the execution component may call the monitoring component corresponding to the first monitoring scenario through the interface call unit, and determine that the task execution result of the data extraction task is successful in a case where the log data or the state data includes the designated character and the included designated character is equal to the designated value.

Illustratively, the execution component may be a component that can implement Extract-Transform-Load (ETL), for example, the execution component may include a key component, a Flink component, a horn (Yet other Resource router) component, and so on. The monitoring component corresponding to the first monitoring scene can be formed by packaging the abnormity identification modules in the button component, the Flink component and the Yarn component, and can monitor and scan log data or state data of a data extraction task based on the monitoring component corresponding to the first monitoring scene.

As an example, the key component or the Flink component may determine, in response to a selection operation of the offline task monitoring scene selection control, a first monitoring scene including the offline task monitoring scene, and the key component or the Flink component may execute a data extraction task to obtain data from an information system serving as an information source, and may call, through the interface call unit, the monitoring component corresponding to the first monitoring scene, so that the monitoring component corresponding to the first monitoring scene performs progressive scanning on log data of the data extraction task, and in a case that the log data includes a specified character, it is determined that a task execution result of the data extraction task is successful. For example, the designated character may be "success flag", so that when the log data of the data extraction task includes the designated character "success flag", it may be determined that the task execution result of the data extraction task is successful.

As an example, the Yarn component may determine, in response to a selection operation of the real-time task monitoring scene selection control, a first monitoring scene including the real-time task monitoring scene, where the Yarn component may execute a data extraction task to acquire data from one or more information systems serving as information sources, and may call, through the interface call unit, a monitoring component corresponding to the first monitoring scene, so that the monitoring component corresponding to the first monitoring scene scans state data of the data extraction task line by line, and determines that a task execution result of the data extraction task is successful in a case that the state data includes designated characters and the included designated characters are designated values. For example, the designated character may be "finalStatus" indicating an execution result of an execution process of the data extraction task, and a designated value corresponding to the designated character "finalStatus" may be "SUCCESS", so that it may be determined that the task execution result of the data extraction task is successful when the status data of the data extraction task includes the designated character "finalStatus" and the designated character "finalStatus" is equal to "SUCCESS", and thus, a task exception condition of the data extraction task may be monitored in real time.

In the above embodiment, when the target monitoring scene includes the first monitoring scene, the data extraction task whose task execution result is a failure can be identified by calling the monitoring component corresponding to the offline task monitoring scene and/or the real-time task monitoring scene in the first monitoring scene; and the monitoring component corresponding to the off-line task monitoring scene and/or the monitoring component corresponding to the real-time task monitoring scene can be called according to the service requirement, so that the real-time identification or off-line identification of the abnormal task in the data extraction task is realized, and the flexibility and the accuracy of the abnormal task identification are further improved.

In some embodiments, the monitoring policy corresponding to the second monitoring scenario may include an execution duration threshold. Referring to fig. 3, if the target monitoring scenario includes the second monitoring scenario, the execution data of the data extraction task is monitored according to the monitoring policy, and whether the data extraction task is an abnormal task corresponding to the target monitoring scenario may include the following steps.

Step S310: and determining the actual execution duration of the data extraction task from the execution condition data of the data extraction task.

In this embodiment, the monitoring policy corresponding to the second monitoring scenario may be preset in the monitoring component corresponding to the second monitoring scenario. The execution component may determine an actual execution duration of the data extraction task from the execution data of the data extraction task. In particular, the execution component may determine an actual execution duration of the data extraction task from the state data of the data extraction task.

Step S320: and determining the data extraction task as an abnormal task with execution overtime in the task execution process under the condition that the actual execution duration exceeds the execution duration threshold through the monitoring component corresponding to the second monitoring scene.

In this embodiment, the execution component may determine, by the monitoring component corresponding to the second monitoring scenario, that the data extraction task is an abnormal task whose execution time-out occurs in the task execution process when the actual execution duration exceeds the execution duration threshold. Specifically, the execution component may call the monitoring component corresponding to the second monitoring scenario through the interface call unit, so that the monitoring component corresponding to the second monitoring scenario compares the actual execution duration with the execution duration threshold, and determines that the data extraction task is an abnormal task whose execution time-out occurs in the task execution process when the actual execution duration exceeds the execution duration threshold.

For example, a monitoring policy corresponding to the third monitoring scenario that includes an execution duration threshold may be configured via the configuration control. For example, the execution duration threshold may be configured to be 30 minutes or 14.

Illustratively, the monitoring component corresponding to the second monitoring scenario may be a Quartz distributed scheduling framework, and based on the Quartz distributed scheduling framework, an abnormal task with execution timeout in the task execution process may be identified.

In the foregoing embodiment, when the target monitoring scenario includes the second monitoring scenario, the execution component may determine that the data extraction task is an abnormal task whose execution is overtime in the task execution process by calling the monitoring component corresponding to the second monitoring scenario.

In some implementations, the third monitoring scenario may include a monitoring scenario in which the task was not executed before the execution time threshold. The monitoring strategy corresponding to the third monitoring scenario may be preset in the monitoring component corresponding to the third monitoring scenario. The monitoring policy corresponding to the third monitoring scenario may include an execution time threshold. If the target monitoring scene includes a third monitoring scene, monitoring execution condition data of the data extraction task according to a monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene, which may include: and determining the data extraction task as an abnormal task which is not executed by the task under the condition that the data extraction task is determined not to be executed before the execution time threshold value based on the execution condition data and the execution time threshold value of the data extraction task through the monitoring component corresponding to the third monitoring scene.

In some cases, the data platform may also provide a selection control for selecting a monitoring scenario in which the task was not executed before the execution time threshold. When the execution component responds to the selection operation of the monitoring scene selection control and the determined target monitoring scene includes a monitoring scene in which the task is not executed before the execution time threshold in the third monitoring scene, the execution component may respond to the selection operation of the selection control for selecting the monitoring scene in which the task is not executed before the execution time threshold, so as to determine the third monitoring scene including the monitoring scene in which the task is not executed before the execution time threshold.

In this embodiment, if the target monitoring scenario includes a third monitoring scenario, the execution component may invoke the monitoring component corresponding to the third monitoring scenario, and monitor the execution data of the data extraction task according to the execution time threshold. Specifically, the execution component may call the monitoring component corresponding to the third monitoring scenario through the interface call unit, so that the monitoring component corresponding to the third monitoring scenario judges the state data of the data extraction task according to the execution time threshold, and in a case that it is determined that the data extraction task is not executed before the execution time threshold, it is determined that the data extraction task is an abnormal task that is not executed before the execution time threshold, and in a case that it is determined that the data extraction task is executed before the execution time threshold, it may also be determined that the data extraction task is a task that is already executed before the execution time threshold.

For example, the monitoring policy corresponding to the third monitoring scenario, including the execution time threshold, may be configured by the configuration control. For example, the execution time threshold may be configured to be 15 00, and the execution frequency of the monitoring component corresponding to the third monitoring scenario may also be configured to be 1 hour, so that the monitoring component corresponding to the third monitoring scenario may perform monitoring every 1 hour from 15.

Exemplarily, the monitoring component corresponding to the third monitoring scenario may be a packaged Quartz distributed scheduling framework, and based on the Quartz distributed scheduling framework, the task may be identified as an abnormal task that is not executed before the execution time threshold.

In the foregoing embodiment, when the target monitoring scenario includes the third monitoring scenario, the execution component may determine the abnormal task that is not executed before the execution time threshold by calling the monitoring component corresponding to the third monitoring scenario.

In some implementations, the third monitoring scenario may include a monitoring scenario in which the actual number of concurrencies in the peak time period has not reached the specified number of concurrencies. The monitoring policy corresponding to the third monitoring scenario may include a specified amount of concurrency during peak periods. If the target monitoring scene includes a third monitoring scene, monitoring execution condition data of the data extraction task according to a monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene, which may include: and under the condition that the actual concurrency number in the peak time period determined by the monitoring component corresponding to the third monitoring scene does not reach the specified concurrency number, determining that the data extraction task which is not executed by the task exists in the peak time period.

In some cases, the data platform may also provide a selection control for selecting a monitoring scenario in which the actual number of concurrencies in the peak time period has not reached the specified number of concurrencies. The executing component may be configured to, when responding to the selection operation of the monitoring scene selection control and the determined target monitoring scene includes a monitoring scene in which the actual concurrency number in the peak time period does not reach the specified concurrency number in the third monitoring scene, respond to the selection operation of the selection control used for selecting the monitoring scene in which the actual concurrency number in the peak time period does not reach the specified concurrency number, and thereby determine the third monitoring scene including the monitoring scene in which the actual concurrency number in the peak time period does not reach the specified concurrency number.

In this embodiment, if the target monitoring scenario includes a third monitoring scenario, the execution component may invoke the monitoring component corresponding to the third monitoring scenario, and monitor the execution data of the data extraction task according to the specified concurrent amount. Specifically, the execution component may call, through the interface call unit, the monitoring component corresponding to the third monitoring scenario, so that the monitoring component corresponding to the third monitoring scenario compares the specified concurrency number with the actual concurrency number of the state data of the data extraction task in the peak time period, and determines that the data extraction task that is not executed by the task exists in the peak time period when it is determined that the actual concurrency number in the peak time period does not reach the specified concurrency number.

For example, a monitoring policy corresponding to a third monitoring scenario that includes a specified amount of concurrency during peak periods may be configured via a configuration control. For example, peak time periods of 00: 00, and the specified concurrency number may be configured to be 200, and the monitoring frequency of the monitoring component corresponding to the third monitoring scenario may also be configured to be 30 minutes, so that the monitoring component corresponding to the third monitoring scenario may perform monitoring every 30 minutes.

In the above embodiment, in the case that the target monitoring scenario includes the third monitoring scenario, the execution component may determine whether the data extraction task that is not executed by the task exists in the peak time period by calling the monitoring component corresponding to the third monitoring scenario.

In some implementations, the third monitoring scenario may include a monitoring scenario in which a task waiting queue is accumulated and a task execution queue is free. The monitoring policy corresponding to the third monitoring scenario may include a specified number of tasks for a specified task queue. The designated task number of the designated task queue may include the designated task waiting number of the task waiting queue and the designated task execution number of the task execution queue. If the target monitoring scene includes a third monitoring scene, monitoring execution condition data of the data extraction task according to a monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene, which may include: determining the actual task waiting number of the task waiting queue and the actual task execution number of the task execution queue; and determining that the data extraction task which is not executed by the task exists under the condition that the task waiting queue accumulation and the task execution queue is free according to the specified task waiting number sum of the task waiting queue, the specified task execution number of the task execution queue, the actual task waiting number and the actual task execution number through the monitoring component corresponding to the third monitoring scene.

In some cases, the data platform may also provide a selection control for selecting a monitoring scenario in which the task waiting queue is stacked and the task execution queue is free. When the execution component responds to the selection operation of the monitoring scene selection control and determines that the target monitoring scene includes a monitoring scene in which the task waiting queue is stacked and the task execution queue is free in the third monitoring scene, the execution component may respond to the selection operation of the selection control for selecting the monitoring scene in which the task waiting queue is stacked and the task execution queue is free, so as to determine the third monitoring scene including the monitoring scene in which the task waiting queue is stacked and the task execution queue is free.

In this embodiment, if the target monitoring scenario includes a third monitoring scenario, the execution component may invoke the monitoring component corresponding to the third monitoring scenario, and monitor the execution status data of the data extraction task according to the number of the designated tasks in the designated task queue. Specifically, the execution component may determine the actual task waiting number of the task waiting queue and the actual task execution number of the task execution queue according to the state data of the data extraction task, and the execution component may call the monitoring component corresponding to the third monitoring scene through the interface call unit, so that the monitoring component corresponding to the third monitoring scene determines whether the task waiting queue is stacked and whether the task execution queue is idle according to the specified task waiting number, the actual task waiting number, the specified task execution number, and the actual task execution number, and determines that the data extraction task that is not executed by the task exists under the condition that the task waiting queue is stacked and the task execution queue is idle.

For example, the task wait queue accumulation may be considered when the actual number of task waits is greater than the specified number of task waits, and the task execution queue may be considered free when the actual number of task executions is less than or equal to the specified number of task executions. For example, the designated task waiting number and the designated task execution number may be 0, that is, when the actual task waiting number of the task waiting queue is greater than 0, the task waiting queue is considered to be accumulated, and when the actual task execution number of the task execution queue is less than or equal to 0, the task execution queue is considered to be free.

For example, a monitoring policy corresponding to the third monitoring scenario that includes a specified number of tasks for a specified task queue may be configured via a configuration control. For example, the specified task waiting number of the task waiting queue may be configured to be 0 and the specified task execution number of the task execution queue may be 0, and the monitoring frequency of the monitoring component corresponding to the third monitoring scenario may be configured to be 30 minutes, so that the monitoring component corresponding to the third monitoring scenario may perform monitoring every 30 minutes.

In the foregoing embodiment, when the target monitoring scenario includes the third monitoring scenario, the execution component may determine that an unexecuted data extraction task exists by calling the monitoring component corresponding to the third monitoring scenario according to the situations that the task waiting queue is accumulated and the task execution queue is idle, where the reason for this situation may be that the data extraction task is not executed due to a fault of the task distribution program, or may be that an execution policy configured by the configuration control is unreasonable. Therefore, the unreasonable execution strategy can be modified in time or the task division program can be checked in time.

In some embodiments, the monitoring policy corresponding to the fourth monitoring scenario includes a task dependency. If the target monitoring scene includes a fourth monitoring scene, monitoring execution condition data of the data extraction task according to a monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene, which may include: determining a data extraction task which is not executed according to the task dependency relationship in the plurality of data extraction tasks from the execution condition data of the plurality of data extraction tasks according to the task dependency relationship through the monitoring component corresponding to the fourth monitoring scene; and the data extraction tasks are positioned in the same task group or a plurality of task groups.

In this embodiment, the monitoring policy corresponding to the fourth monitoring scenario may be preset in the monitoring component corresponding to the fourth monitoring scenario. The execution component can identify a data extraction task which is not executed according to the task dependency relationship in the multiple data extraction tasks according to the task dependency relationship from the multiple data extraction tasks in the same task group or the execution condition data of the multiple data extraction tasks in the multiple task groups through the monitoring component corresponding to the fourth monitoring scene.

The task dependency relationship may refer to an execution order between data extraction tasks.

The task group may be a group unit in which a plurality of data extraction tasks are executed. For example, the data extraction tasks may be grouped according to the information systems to which the data extraction tasks correspond.

Specifically, the execution component may call, through the interface call unit, the monitoring component corresponding to the fourth monitoring scenario, so that the monitoring component corresponding to the fourth monitoring scenario identifies, according to the task dependency relationship, a data extraction task that is not executed according to the task dependency relationship, from the execution condition data of the multiple data extraction tasks located in the same task group or the multiple data extraction tasks located in the multiple task groups.

Illustratively, the monitoring component corresponding to the fourth monitoring scenario may be an encapsulated Quartz distributed scheduling framework.

For example, the monitoring policy including the task dependency corresponding to the fourth monitoring scenario may be configured by the configuration control. For example, it may be configured that task a is dependent on task B, that is, during the execution of task a and task B, it is required to ensure that task B is executed before task a, and task a is executed before task B, which may result in an overall data error. The monitoring frequency of the monitoring component corresponding to the fourth monitoring scenario may also be configured to be 2 minutes, so that the monitoring component corresponding to the fourth monitoring scenario may perform monitoring every 2 minutes.

In the foregoing embodiment, when the target monitoring scenario includes the fourth monitoring scenario, the execution component may determine the data extraction task that is not executed according to the task dependency relationship by calling the monitoring component corresponding to the fourth monitoring scenario.

In some embodiments, the abnormal task identifying method may further include: and sending alarm information corresponding to the target monitoring scene under the condition that the identification data extraction task is an abnormal task corresponding to the target monitoring scene.

In some cases, upon identifying an anomalous task of the data extraction tasks, the identified anomalous task of the data extraction tasks may be alerted based on an alert component.

In this embodiment, the alert component may be preset with at least one alert information template. The execution component can call the monitoring component corresponding to the target monitoring scene through the interface calling unit, so that the monitoring component corresponding to the target monitoring scene calls the warning component to enable the warning component to give a warning according to the warning information template corresponding to the target monitoring scene and the identified abnormal task in the at least one warning information template under the condition that the identification data extraction task is the abnormal task corresponding to the target monitoring scene. For example, the alarm component may generate alarm information corresponding to the target monitoring scene according to the identified abnormal task and an alarm information template corresponding to the target monitoring scene, and alarm the abnormal task in the data extraction task by using the alarm information through a mode of enterprise WeChat message, nail, mail, telephone or short message.

In the above embodiment, when the identification data extraction task is an abnormal task corresponding to the target monitoring scene, the alarm component is called to alarm the abnormal task through enterprise WeChat messages, nails, mails, telephones or short messages, so as to remind research and development personnel or operators to process the abnormal task in time. And an Application Programming Interface (API) of enterprise WeChat messages, nails, mails, telephones or short messages can be quickly accessed through the warning component, so that a third-party tool does not need to be accessed or secondary development is not needed, the box-opening use and seamless butt joint are realized, and the execution efficiency and the execution accuracy of data extraction tasks are effectively guaranteed.

One embodiment of the present specification also provides an abnormal task identification method, which may include the steps of:

s401: and determining a target monitoring scene for monitoring the execution condition of the data extraction task in response to the selection operation of the monitoring scene selection control.

And the target monitoring scene corresponds to a monitoring strategy. The target monitoring scenario may include at least one of a first monitoring scenario in which a task execution result is a failure, a second monitoring scenario in which an execution timeout occurs in a task execution process, a third monitoring scenario in which a task is not executed in the task execution process, and a fourth monitoring scenario in which an execution error that does not follow a task dependency relationship occurs in the task execution process.

S403: and calling a monitoring component corresponding to the target monitoring scene.

S405: and acquiring the execution condition data of the data extraction task.

S407, monitoring the execution condition data of the data extraction task according to the monitoring strategy through the monitoring component corresponding to the target monitoring scene, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene.

In particular, the first monitoring scenario may include at least one of an offline task monitoring scenario and a real-time task monitoring scenario. If the target monitoring scene comprises a first monitoring scene, a monitoring component corresponding to the offline task monitoring scene can be called, so that the monitoring component corresponding to the offline task monitoring scene monitors the execution condition data of the data extraction task according to a monitoring strategy, and the data extraction task with a failed task execution result is identified; and/or calling a monitoring component corresponding to the real-time task monitoring scene so that the monitoring component corresponding to the real-time task monitoring scene monitors the execution condition data of the data extraction task according to the monitoring strategy, and identifies that the task execution result is a failed data extraction task.

In particular, the monitoring strategy corresponding to the second monitoring scenario may include an execution duration threshold. If the target monitoring scene comprises a second monitoring scene, the actual execution duration of the data extraction task can be determined from the execution situation data of the data extraction task, and the data extraction task is determined to be an abnormal task with overtime execution in the task execution process under the condition that the actual execution duration exceeds the execution duration threshold through the monitoring component corresponding to the second monitoring scene.

In particular, the third monitoring scenario may include a monitoring scenario in which the task was not executed before the execution time threshold. Accordingly, the monitoring policy corresponding to the third monitoring scenario may include an execution time threshold. If the target monitoring scene includes a third monitoring scene, the data extraction task may be determined to be an abnormal task that is not executed by the task under the condition that the data extraction task is determined not to be executed before the execution time threshold based on the execution condition data of the data extraction task and the execution time threshold by the monitoring component corresponding to the third monitoring scene.

In particular, the third monitoring scenario may include a monitoring scenario in which the actual number of concurrencies in the rush hour period does not reach the specified number of concurrencies. Accordingly, the monitoring policy corresponding to the third monitoring scenario may include a specified amount of concurrency during peak periods. If the target monitoring scene includes a third monitoring scene, it may be determined that a data extraction task that is not executed by the task exists in the peak time period under the condition that the actual concurrency number in the peak time period determined by the monitoring component corresponding to the third monitoring scene does not reach the specified concurrency number.

In particular, the third monitoring scenario may include a monitoring scenario in which a task waiting queue is accumulated and a task execution queue is free. Accordingly, the monitoring policy corresponding to the third monitoring scenario may include a specified number of tasks for a specified task queue. The specified task quantity of the specified task queue comprises the specified task waiting quantity of the task waiting queue and the specified task execution quantity of the task execution queue. If the target monitoring scene comprises a third monitoring scene, the actual task waiting quantity of the task waiting queue and the actual task execution quantity of the task execution queue can be determined, and under the condition that the task waiting queue is determined to be stacked and the task execution queue is free according to the specified task waiting quantity, the specified task execution quantity, the actual task waiting quantity and the actual task execution quantity through the monitoring component corresponding to the third monitoring scene, the data extraction task which is not executed by the task exists.

Specifically, the monitoring policy corresponding to the fourth monitoring scenario may include a task dependency relationship. If the target monitoring scene comprises a fourth monitoring scene, determining a data extraction task which is not executed according to the task dependency relationship in the plurality of data extraction tasks from the execution condition data of the plurality of data extraction tasks according to the task dependency relationship through a monitoring component corresponding to the fourth monitoring scene. And the data extraction tasks are positioned in the same task group or a plurality of task groups.

S409: and sending alarm information corresponding to the target monitoring scene under the condition that the identification data extraction task is an abnormal task corresponding to the target monitoring scene.

One embodiment of the present specification also provides an abnormal task identifying apparatus. Referring to fig. 4, the abnormal task identifying device may include a monitoring scenario determining module 410, a task data acquiring module 420, and an abnormal task identifying module 430.

A monitoring scene determining module 410, configured to determine, in response to a selection operation of the monitoring scene selection control, a target monitoring scene for monitoring an execution condition of the data extraction task; wherein, the target monitoring scene corresponds to a monitoring strategy;

a task data obtaining module 420, configured to obtain execution condition data of the data extraction task;

the abnormal task identification module 430 is configured to monitor execution condition data of the data extraction task according to a monitoring policy, and identify whether the data extraction task is an abnormal task corresponding to a target monitoring scene; the target monitoring scene comprises at least one of a first monitoring scene with a task execution result of failure, a second monitoring scene with overtime execution in the task execution process, a third monitoring scene with task non-execution in the task execution process and a fourth monitoring scene with execution errors which do not follow the task dependency relationship in the task execution process.

The specific functions and effects achieved by the abnormal task identifying device can be explained by referring to other embodiments in this specification, and are not described herein again. The respective modules in the target data sorting apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

The embodiment of the present specification provides a computer device, please refer to fig. 5, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the processor implements the abnormal task identification method according to any one of the above embodiments.

Embodiments of the present specification also provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a computer, the computer executes the abnormal task identifying method in any one of the above embodiments.

The embodiment of the present specification further provides a computer program product containing instructions, and the instructions, when executed by a computer, cause the computer to execute the abnormal task identification method in any one of the above embodiments.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 5. The computer apparatus includes a processor, a memory, a communication interface, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement an exception task identification method. The input device of the computer equipment can be a touch layer covered on a display screen, a key, a track ball or a touch pad arranged on a shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.

It should be understood that the specific examples are included merely for purposes of illustrating the embodiments of the disclosure and are not intended to limit the scope of the disclosure.

It should be understood that, in the various embodiments of the present specification, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic, and should not limit the implementation process of the embodiments of the present specification.

It is to be understood that the various embodiments described in the present specification may be implemented individually or in combination, and the embodiments in the present specification are not limited thereto.

Unless otherwise defined, all technical and scientific terms used in the embodiments of the present specification have the same meaning as commonly understood by one of ordinary skill in the art to which this specification belongs. The terminology used in the description is for the purpose of describing particular embodiments only and is not intended to limit the scope of the description. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. As used in the specification embodiments and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

It is to be understood that the processor of the embodiments of the present description may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present specification may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present specification may be embodied directly in a hardware decoding processor, or in a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.

It will be appreciated that the memory in the embodiments of the specification can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The non-volatile memory may be a read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an electrically Erasable EEPROM (EEPROM), or a flash memory. The volatile memory may be Random Access Memory (RAM). It should be noted that the memory of the systems and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present specification.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in this specification, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

In addition, functional units in the embodiments of the present specification may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present specification may be substantially or partially embodied in the form of a software product stored in a storage medium, and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the methods described in the embodiments of the present specification. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope disclosed in the present disclosure, and all the changes or substitutions should be covered within the scope of the present disclosure. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. An abnormal task identification method, characterized in that the method comprises:

determining a target monitoring scene for monitoring the execution condition of the data extraction task in response to the selection operation of the monitoring scene selection control; wherein, the target monitoring scene corresponds to a monitoring strategy;

acquiring execution condition data of the data extraction task;

monitoring the execution condition data of the data extraction task according to the monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene; the target monitoring scene comprises at least one of a first monitoring scene with a task execution result of failure, a second monitoring scene with overtime execution in the task execution process, a third monitoring scene with task non-execution in the task execution process and a fourth monitoring scene with execution errors which do not follow the task dependency relationship in the task execution process.

2. The method of claim 1, wherein the monitoring strategy corresponding to the second monitoring scenario comprises an execution duration threshold; the second monitoring scene corresponds to a monitoring component;

if the target monitoring scene comprises the second monitoring scene, monitoring the execution condition data of the data extraction task according to the monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene, wherein the steps of:

determining the actual execution duration of the data extraction task from the execution condition data of the data extraction task;

and determining that the data extraction task is an abnormal task with execution overtime in the task execution process under the condition that the actual execution duration exceeds the execution duration threshold through the monitoring component corresponding to the second monitoring scene.

3. The method of claim 1, wherein the third monitoring scenario comprises a monitoring scenario in which a task is not executed before a time threshold is executed; accordingly, the monitoring strategy corresponding to the third monitoring scenario includes an execution time threshold; the third monitoring scene corresponds to a monitoring component;

if the target monitoring scene comprises the third monitoring scene, monitoring the execution condition data of the data extraction task according to the monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene, wherein the monitoring comprises the following steps:

and determining that the data extraction task is an abnormal task which is not executed under the condition that the data extraction task is determined not to be executed before the execution time threshold value on the basis of the execution condition data of the data extraction task and the execution time threshold value through a monitoring component corresponding to the third monitoring scene.

4. The method of claim 1, wherein the third monitoring scenario comprises at least one of a monitoring scenario in which an actual concurrency number in a peak time period does not reach a specified concurrency number, a monitoring scenario in which a task waiting queue is piled up and a task execution queue is idle; correspondingly, the monitoring strategy corresponding to the third monitoring scene comprises at least one of a specified concurrency quantity in a peak time period and a specified task quantity of a specified task queue; the specified task quantity of the specified task queue comprises the specified task waiting quantity of the task waiting queue and the specified task execution quantity of the task execution queue; the third monitoring scene corresponds to a monitoring component;

if the target monitoring scene comprises the third monitoring scene, monitoring the execution condition data of the data extraction task according to the monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene, wherein the steps of:

determining that a data extraction task which is not executed by the task exists in the peak time period under the condition that the actual concurrency number in the peak time period determined by the monitoring component corresponding to the third monitoring scene does not reach the specified concurrency number;

and/or the presence of a gas in the atmosphere,

determining the actual task waiting number of the task waiting queue and the actual task execution number of the task execution queue; and determining that a data extraction task which is not executed by the task exists under the condition that the task waiting queue is determined to be accumulated and the task execution queue is free according to the designated task waiting number, the designated task execution number, the actual task waiting number and the actual task execution number through a monitoring component corresponding to the third monitoring scene.

5. The method according to claim 1, wherein the monitoring policy corresponding to the fourth monitoring scenario includes a task dependency relationship; the fourth monitoring scene corresponds to a monitoring component;

if the target monitoring scene comprises the fourth monitoring scene, monitoring the execution condition data of the data extraction task according to the monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene, wherein the monitoring method comprises the following steps:

determining a data extraction task which is not executed according to the task dependency relationship in the data extraction tasks from the execution condition data of the data extraction tasks through a monitoring component corresponding to the fourth monitoring scene according to the task dependency relationship; and a plurality of data extraction tasks are positioned in the same task group or a plurality of task groups.

6. The method of claim 1, wherein the first monitoring scenario comprises at least one of an offline task monitoring scenario and a real-time task monitoring scenario; the first monitoring scene corresponds to a monitoring component;

if the target monitoring scene comprises the first monitoring scene, monitoring the execution condition data of the data extraction task according to the monitoring strategy, and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene, wherein the steps of:

calling a monitoring component corresponding to the offline task monitoring scene so that the monitoring component corresponding to the offline task monitoring scene monitors the execution condition data of the data extraction task according to the monitoring strategy, and identifying that the task execution result is a failed data extraction task;

and/or the presence of a gas in the gas,

and calling a monitoring component corresponding to the real-time task monitoring scene so that the monitoring component corresponding to the real-time task monitoring scene monitors the execution condition data of the data extraction task according to the monitoring strategy, and identifying that the task execution result is a failed data extraction task.

7. The method according to any one of claims 1 to 6, further comprising:

and sending alarm information corresponding to the target monitoring scene under the condition that the data extraction task is identified to be an abnormal task corresponding to the target monitoring scene.

8. An abnormal task identifying apparatus, characterized in that the apparatus comprises:

the monitoring scene determining module is used for responding to the selection operation of the monitoring scene selection control and determining a target monitoring scene for monitoring the execution condition of the data extraction task; the target monitoring scene corresponds to a monitoring strategy;

the task data acquisition module is used for acquiring the execution condition data of the data extraction task;

the abnormal task identification module is used for monitoring the execution condition data of the data extraction task according to the monitoring strategy and identifying whether the data extraction task is an abnormal task corresponding to the target monitoring scene; the target monitoring scene comprises at least one of a first monitoring scene with a task execution result of failure, a second monitoring scene with overtime execution in the task execution process, a third monitoring scene with task non-execution in the task execution process and a fourth monitoring scene with execution errors which do not follow the task dependency relationship in the task execution process.

9. A computer device comprising a memory storing a computer program and a processor implementing the method of any one of claims 1 to 7 when the processor executes the computer program.

10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 7.

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