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Data exception processing method and device, storage medium and electronic equipment

A technology of data anomalies and processing methods, which is applied in the field of big data, can solve problems such as slow query speed, waste of manpower, time and equipment resources, and reduce computer use efficiency, so as to simplify the recognition process and improve recognition accuracy and efficiency.

Pending Publication Date: 2021-05-04
BEIJING WODONG TIANJUN INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] However, in the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art: when it is determined that the execution of the overall data task of big data fails or is delayed, it is necessary to manually identify the individual scheduling tasks for the overall data task one by one Whether it is the source of data abnormality, the query speed is slow, consumes a lot of manpower, time and equipment resources, reduces the efficiency of computer use, and affects the execution efficiency of data tasks

Method used

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  • Data exception processing method and device, storage medium and electronic equipment
  • Data exception processing method and device, storage medium and electronic equipment
  • Data exception processing method and device, storage medium and electronic equipment

Examples

Experimental program
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Embodiment 1

[0024] figure 1 It is a schematic flow chart of a data anomaly processing method provided in Embodiment 1 of the present invention. This embodiment is applicable to quickly determine the source task that causes data anomalies. This method can be executed by the data anomaly processing device provided in the embodiment of the present invention. The device can be realized by means of software and / or hardware. Specifically include the following steps:

[0025] S110. When it is determined that the data is abnormal, invoke the task relationship tree of each scheduling task used to complete the data. The task relationship tree includes the parent-child relationship of each scheduling task and the completion status of each scheduling task.

[0026] S120. Determine the bottom-level abnormal scheduling task in the task relationship tree.

[0027] S130. Based on the task relationship tree, determine the exception source task according to the completion status of at least one parent ta...

Embodiment 2

[0041] Figure 4 It is a schematic flow chart of a data exception processing method provided by the embodiment of the present invention, which is optimized on the basis of the above embodiments, and the method specifically includes:

[0042] S210. Monitor the completion status of each scheduling task, and update the task relationship tree according to the completion status of each scheduling task.

[0043] S220. When it is determined that the data is abnormal, invoke the task relationship tree of each scheduling task used to complete the data. The task relationship tree includes the parent-child relationship of each scheduling task and the completion status of each scheduling task.

[0044] S230. Determine the bottom-level abnormal scheduling task in the task relationship tree.

[0045] S240. Based on the task relationship tree, determine the exception source task according to the completion status of at least one parent task of the underlying exception scheduling task.

[0...

Embodiment 3

[0053] Figure 5 It is a schematic structural diagram of a data exception processing device provided in Embodiment 3 of the present invention. The device includes a task relationship tree calling module 310, a bottom-level exception scheduling task determination module 320, and an exception source task determination module 330, wherein:

[0054] The task relationship tree calling module 310 is used to call the task relationship tree of each scheduling task used to complete the data when it is determined that the data is abnormal, the task relationship tree includes the parent-child relationship of each scheduling task, and each of the scheduling tasks the completion status of the task;

[0055] The underlying abnormal scheduling task determination module 320 is configured to determine the underlying abnormal scheduling task in the task relationship tree;

[0056] The exception source task determination module 330 is configured to determine the exception source task based on t...

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Abstract

The invention discloses a data exception processing method and device, a storage medium and electronic equipment. The method comprises steps of, when it is determined that data exception occurs, calling a task relation tree used for completing all scheduling tasks of the data, wherein the task relation tree comprises the set membership of all the scheduling tasks and a completion state of all the scheduling tasks; determining a bottom layer abnormal scheduling task in the task relation tree; and on the basis of the task relation tree, according to the completion state of at least one parent task of the underlying exception scheduling task, determining an exception source task. According to the method, the abnormal source task is automatically and quickly determined, each scheduling task does not need to be manually identified, the identification process of the abnormal source task is simplified, and identification precision and efficiency of the abnormal source task are improved.

Description

technical field [0001] Embodiments of the present invention relate to the field of big data technology, and in particular, to a method, device, storage medium, and electronic device for processing abnormal data. Background technique [0002] With the continuous development of big data technology, storing and managing large amounts of data through databases has become a common means of data management. Data management tasks such as reading, querying, and editing data are realized on the database by creating tasks, and each data management task can be executed sequentially by multiple scheduling tasks. [0003] However, in the process of implementing the present invention, the inventors found that there are at least the following problems in the prior art: when it is determined that the execution of the overall data task of big data fails or is delayed, it is necessary to manually identify the individual scheduling tasks for the overall data task one by one Whether it is the ...

Claims

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Application Information

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IPC IPC(8): G06F11/07G06F16/25G06F9/48
CPCG06F11/079G06F16/252G06F9/4881
Inventor 于涵
Owner BEIJING WODONG TIANJUN INFORMATION TECH CO LTD