Failure task influence and traceability analysis method and device, equipment and storage medium

Abstract

The invention discloses a failure task influence and traceability analysis method. The method comprises the steps of obtaining table data information and table generation rule information corresponding to a currently running HQL task; creating HQL task nodes in a preset graphic database according to the table data information; constructing dependency relationships among different HQL task nodes according to the generation rule information of the table, and constructing reverse dependency relationships among the different HQL task nodes to form an HQL blood relationship map; and when detectingthat a certain HQL task fails, performing influence and traceability analysis on the failed HQL task according to the HQL blood relationship map. The invention also discloses a failure task influenceand traceability analysis device, equipment and a storage medium. According to the invention, influence and traceability analysis can be automatically carried out on failed tasks, and the correctnessand effectiveness of analysis are ensured.

Description

technical field [0001] The present invention relates to the technical field of data analysis, in particular to an analysis method, device, equipment and storage medium for the impact of failed tasks and traceability analysis. Background technique [0002] Big data tasks are composed of many small HQL (Hibernate Query Language, query language) statements, and many data tables will be created. Due to reasons such as table structure and table data errors, each small HQL task is executed. Both are likely to fail. Generally, when a task fails to run, it is necessary to evaluate whether it has an impact on other tasks that depend on the task, and to trace the source of the task error. [0003] At present, the impact of failed tasks and traceability analysis generally use relational databases to store dependencies between tasks. When problems need to be located, according to these dependencies, manually configure and generate dependency views, and then analyze related tasks to det...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that it uses a relational database to store the dependencies between a large number of tasks, which is very complicated to execute and has poor scalability. When performing dependent calculations, a full table scan is required, which is inefficient and limited by the size of the memory, and only Can generate dependent views based on relational model data, and requires manual participation in the analysis, which cannot guarantee the correctness and effectiveness of the analysis

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