Method and device for adjusting job configuration information and electronic equipment

Abstract

The invention discloses a job configuration information adjusting method and device and electronic equipment. Relates to the distributed field, and the method comprises the following steps: determining whether there is an abnormal job in a historical job set according to a multi-tuple information table, the multi-tuple information table at least comprising a job dependence object, job execution information and job description of each historical job; under the condition that the abnormal job exists in the historical job set, adjusting the job dependence object of the abnormal job according to the job dependence object and the job execution information of each historical job in the multi-tuple information table to obtain first adjustment information; adjusting the job execution information of the abnormal job according to the target job description of the abnormal job to obtain second adjustment information; and adjusting the initial configuration information of the abnormal operation according to the first adjustment information and the second adjustment information to obtain target configuration information. Through the method and the device, the problem of relatively low efficiency of adjusting the operation configuration information in related technologies is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of distributed computing, and in particular, to a method and device for adjusting job configuration information, and an electronic device. BACKGROUND

[0002] In large financial institutions, end-of-day batch job processing is an important link to ensure business continuity and data accuracy. These jobs usually involve processing and integrating large amounts of data to complete key tasks such as financial settlement and account updating. However, in the process of migrating to a distributed platform, most bank systems fail to fully utilize the parallel processing capabilities and resource allocation advantages of new computing architectures, resulting in low efficiency and long execution time of end-of-day batch jobs. Specifically, the complexity of job dependency, unreasonable job scheduling mechanism, and low efficiency of job execution, among other issues, collectively delay the completion time of the entire batch processing.

[0003] To improve job processing efficiency, the execution data of the jobs is usually monitored to determine abnormal data, and the abnormal data is analyzed and processed manually, while the execution parameters or execution methods in the job configuration information are adjusted to improve the efficiency of job execution. However, the manual analysis and adjustment of job configuration information not only consumes time, but also due to the large number of batch jobs and their close interrelation, manual optimization is difficult to cover all optimization points comprehensively, resulting in low adjustment efficiency.

[0004] To address the issue of low efficiency in adjusting job configuration information in the related art, no effective solution has been proposed so far. SUMMARY

[0005] The main purpose of the present application is to provide a method and device for adjusting job configuration information to solve the problem of low efficiency in adjusting job configuration information in the related art.

[0006] To achieve the above purpose, according to one aspect of the present application, a method for adjusting job configuration information is provided. The method comprises: determining whether there is an abnormal job in a historical job set according to a multi-tuple information table, wherein the multi-tuple information table is composed of attribute information of the historical job set, and at least includes job dependency objects, job execution information and job descriptions of each historical job; in the case that there is an abnormal job in the historical job set, adjusting the job dependency objects of the abnormal job according to the job dependency objects and the job execution information of each historical job in the multi-tuple information table, to obtain first adjustment information; adjusting the job execution information of the abnormal job according to the target job description of the abnormal job, to obtain second adjustment information; and adjusting the initial configuration information of the abnormal job according to the first adjustment information and the second adjustment information, to obtain target configuration information.

[0007] Optionally, the tuple information table is constructed as follows: obtain the runtime information of the set of historical jobs executed within the target time period, and obtain the job source code and initial configuration information of each historical job in the set of historical jobs; determine the tuple information of each historical job in the set of historical jobs based on the runtime information, job source code and initial configuration information of the set of historical jobs, and obtain the tuple information table.

[0008] Optionally, determining the tuple information for each historical job in the historical job set based on the runtime information, job source code, and initial configuration information of the historical job set includes: for any historical job, obtaining the execution duration of the historical job from the runtime information, and using a code attribute graph to analyze the job source code and initial configuration information to obtain the job dependencies and job execution information of the historical job; analyzing the job source code and initial configuration information through a graph neural network to obtain the job description of the historical job; and combining the execution duration, job dependencies, job execution information, and job description into the tuple information of the historical job.

[0009] Optionally, determining whether there are abnormal jobs in the historical job set based on the tuple information table includes: obtaining the execution duration of each historical job from the tuple information table and obtaining the duration threshold of each historical job; comparing the execution duration of each historical job with the duration threshold to obtain the comparison result; obtaining the abnormal comparison result from the comparison result of each historical job, and identifying the historical job indicated by the abnormal comparison result as an abnormal job.

[0010] Optionally, the job dependencies of abnormal jobs are adjusted based on the job dependencies and job execution information of each historical job in the tuple information table to obtain the first adjustment information, which includes: determining the preceding dependent objects of the abnormal job and determining the output result of the preceding dependent objects based on their job execution information; determining whether the input information of the abnormal job is related to the output result based on its job execution information; if there is no relationship between the input information and the output result of the abnormal job, determining the target dependent object that is related to the input information based on its job execution information, and determining the first adjustment information based on the preceding dependent object and the target dependent object; if there is a relationship between the input information and the output result of the abnormal job, setting the first adjustment information to null.

[0011] Optionally, adjusting the job execution information of the abnormal job according to the target job description of the abnormal job to obtain the second adjustment information includes: inputting the target job description into the execution information identification model to obtain the standard execution information of the abnormal job, wherein the execution information identification model is used to determine the standard execution information of the job according to the job description; determining the difference information between the standard execution information and the job execution information, and determining the second adjustment information according to the difference information.

[0012] Optionally, adjusting the initial configuration information of the abnormal operation according to the first adjustment information and the second adjustment information to obtain the target configuration information includes: replacing the previous dependency object in the initial configuration information with the target dependency object according to the previous dependency object and the target dependency object carried in the first adjustment information to obtain candidate configuration information; and updating the candidate configuration information according to the difference information carried in the second adjustment information to obtain the target configuration information.

[0013] To achieve the above objectives, according to another aspect of this application, a device for adjusting job configuration information is provided. The device includes: a judgment module, configured to determine whether there are abnormal jobs in a historical job set based on a tuple information table, wherein the tuple information table is composed of attribute information of the historical job set, and includes at least the job dependency objects, job execution information, and job description of each historical job; a first adjustment module, configured to adjust the job dependency objects of the abnormal job according to the job dependency objects and job execution information of each historical job in the tuple information table when there are abnormal jobs in the historical job set, to obtain first adjustment information; a second adjustment module, configured to adjust the job execution information of the abnormal job according to the target job description of the abnormal job, to obtain second adjustment information; and a third adjustment module, configured to adjust the initial configuration information of the abnormal job according to the first and second adjustment information, to obtain target configuration information.

[0014] To achieve the above objectives, according to another aspect of this application, an electronic device is provided, the electronic device including a memory storing an executable program; and a processor for running the program, wherein the program executes the above-described method for adjusting job configuration information during runtime.

[0015] To achieve the above objectives, according to another aspect of this application, a computer program product is provided, including computer instructions that, when executed by a processor, implement the steps of the above-described method for adjusting job configuration information.

[0016] In this embodiment, the existence of abnormal jobs in the historical job set is determined based on a tuple information table. The tuple information table is composed of attribute information of the historical job set and includes at least the job dependencies, job execution information, and job description for each historical job. If abnormal jobs exist in the historical job set, the job dependencies of the abnormal jobs are adjusted according to the job dependencies and job execution information of each historical job in the tuple information table to obtain first adjustment information. The job execution information of the abnormal jobs is then adjusted according to the target job description to obtain second adjustment information. Finally, the initial configuration information of the abnormal jobs is adjusted based on the first and second adjustment information. The method of adjusting the target configuration information by obtaining a tuple information table of historical job sets, acquiring the features recorded in the tuple information table, determining the adjustment information based on the features, and generating first and second adjustment information from different adjustment dimensions, obtaining total adjustment information based on the first and second adjustment information, and adjusting the initial configuration information based on the total adjustment information to obtain the adjusted target configuration information. This achieves the purpose of automatically determining the adjustment point of the initial configuration information and automatically adjusting the initial configuration information, thereby improving the technical effect of adjusting configuration information and solving the technical problem of low efficiency in adjusting job configuration information in related technologies. Attached Figure Description

[0017] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 A hardware block diagram of a computer terminal for implementing a method for adjusting job configuration information is shown.

[0019] Figure 2 This is a flowchart of a method for adjusting job configuration information according to Embodiment 1 of this application;

[0020] Figure 3 This is a schematic diagram of the job dependency information provided in Embodiment 1 of this application. Figure 1 ;

[0021] Figure 4 This is a schematic diagram of the job dependency information provided in Embodiment 1 of this application. Figure 2 ;

[0022] Figure 5 This is a schematic diagram of the job configuration information adjustment system provided in Embodiment 1 of this application;

[0023] Figure 6This is a schematic diagram of an adjustment device for job configuration information according to Embodiment 2 of this application;

[0024] Figure 7 This is a structural block diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0025] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] It should be noted that the method, apparatus and electronic equipment for adjusting job configuration information as defined in this disclosure can be used in the distributed field, or in any field other than the distributed field, such as the financial technology field. The application fields of the method, apparatus and electronic equipment for adjusting job configuration information as defined in this disclosure are not limited.

[0029] It should be noted that all information, user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, and displayed data) used in this application are information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with the relevant laws, regulations, and standards of the relevant regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse use. If the user chooses to refuse, the process will proceed to the expert decision-making process. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, a request to obtain the information needs to be sent to the aforementioned user or organization through the interface. After receiving consent from the aforementioned user or organization, the relevant information is obtained. Users can view the purpose of data use in real time through the authorization interface and have the right to withdraw authorization or delete data at any time. After authorization is withdrawn, the system will terminate the relevant data processing within 24 hours.

[0030] The embodiments or examples disclosed herein are not exhaustive, but merely illustrative of some embodiments or examples, and are not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment or example can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment or example can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment or example can be arbitrarily interchanged. Furthermore, optional methods or examples in a particular embodiment or example can be arbitrarily combined; moreover, embodiments or examples can be arbitrarily combined. For example, some or all steps of different embodiments or examples can be arbitrarily combined, and a particular embodiment or example can be arbitrarily combined with optional methods or examples of other embodiments or examples.

[0031] Example 1

[0032] According to an embodiment of this application, an embodiment of a method for adjusting job configuration information is also provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a method for adjusting job configuration information is shown. Figure 1As shown, the computer terminal 10 (or mobile device) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, processing devices such as microprocessors or programmable logic devices), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface, a universal serial bus port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0034] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0035] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the job configuration information adjustment method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby realizing the aforementioned job configuration information adjustment method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0037] The display may be, for example, a touchscreen LCD display that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0038] Under the aforementioned operating environment, this application provides the following: Figure 2 The method for adjusting the job configuration information is shown. Figure 2 This is a flowchart of the method for adjusting job configuration information according to Embodiment 1 of this application, such as... Figure 2 As shown, the method includes:

[0039] Step S201: Determine whether there are any abnormal jobs in the historical job set based on the tuple information table. The tuple information table consists of the attribute information of the historical job set and includes at least the job dependency objects, job execution information and job description of each historical job.

[0040] It should be noted that the execution entity in this embodiment can be a job configuration information adjustment system. This system can adjust the job configuration information based on the attribute information recorded in the tuple information table, thereby improving the job execution efficiency.

[0041] It should be noted that the tuple information table is a record table of job execution information, containing key attribute information for each job in the historical job set, such as job dependencies, execution information (start time, end time, duration), and job description (function, type, etc.). The historical job set is a dataset consisting of all batch jobs that have run in the distributed system within the target time period. Abnormal jobs are those with abnormal execution times, excessive resource consumption, or that do not meet expected performance metrics.

[0042] Specifically, before adjusting the configuration information, it is necessary to first determine whether the job needs to adjust the configuration information. Therefore, after completing the execution operation of the jobs in the historical job set, it is necessary to read the tuple information table that stores the attribute information of historical jobs, and identify jobs with abnormal time consumption by analyzing the job dependencies, execution time, job description and other data carried in the tuple information table. The attribute information of historical jobs can be the execution data of historical jobs when they were executed, as well as the configuration information of historical jobs.

[0043] For example, suppose the tuple information table records the execution status of jobs A, B, and C. If job B takes a longer time to execute, then job B is considered abnormal.

[0044] It should be noted that the tuple information table can be generated from the information recorded in the execution logs, job codes, and job configuration information of each job. Table 1 is an example of an optional tuple information table.

[0045] Table 1

[0046]

[0047] Step S202: If there are abnormal jobs in the historical job set, adjust the job dependency objects of the abnormal jobs according to the job dependency objects and job execution information of each historical job in the tuple information table to obtain the first adjustment information.

[0048] It should be noted that the first adjustment information refers to the adjustment information for abnormal job dependencies after analyzing the historical job dependencies and execution information.

[0049] Specifically, if it is determined that there are abnormal jobs in the historical job set, the abnormal jobs need to be identified first, and the dependencies of the abnormal jobs need to be reconfigured based on the dependency information and execution data in the tuple information table.

[0050] When configuring dependencies for abnormal jobs, the degree of overlap in data processing and the correlation between preceding and subsequent jobs can be determined by analyzing the data flow between jobs. This allows for adjustments to the execution order or parallel strategies of the jobs, reducing data waiting time and processing redundancy, and generating initial adjustment information.

[0051] It should be noted that when adjusting job dependencies based on dependency information and execution data, there may be situations where the dependencies are already optimal and cannot be further adjusted. In such cases, it is necessary to adjust the configuration information of abnormal jobs from the job execution perspective to improve job execution efficiency.

[0052] Step S203: Adjust the job execution information of the abnormal job according to the target job description of the abnormal job to obtain the second adjustment information.

[0053] It should be noted that the second adjustment information refers to the adjustment strategy for job execution information (such as the number of fragments, parallelism, etc.) based on the functional description of the abnormal job.

[0054] Specifically, when adjusting the configuration information, it is also necessary to determine the target job description of the abnormal job, evaluate the rationality of the job execution parameters based on the target job description, analyze the specific functions and processing logic of the abnormal job, and determine whether the execution efficiency can be optimized by adjusting the job configuration parameters, such as the number of job shards and data sorting fields. Based on the job type and function, preset optimization rules or algorithms are applied, such as increasing the number of shards to improve parallel processing capabilities and adjusting the sorting fields to optimize data access patterns, thereby obtaining the second adjustment information.

[0055] For example, if the job description information of job B is summary data, but its number of shards is set too low, resulting in limited data processing capabilities, the number of shards can be increased to fully utilize the parallel processing capabilities of the distributed system and speed up the data aggregation.

[0056] Step S204: Adjust the initial configuration information of the abnormal operation according to the first adjustment information and the second adjustment information to obtain the target configuration information.

[0057] Specifically, after obtaining the first adjustment information and the second adjustment information, the adjustment content of the first adjustment information and the second adjustment information can be combined to generate the final optimized configuration for the abnormal job. Based on the optimized configuration information, the initial configuration information is adjusted to obtain the adjusted target configuration information, thereby completing the update operation of the job's configuration information. This allows the job to be executed according to the latest configuration information when it is executed again in the future, thereby improving the execution efficiency of the job in subsequent executions.

[0058] For example, for job B, first adjust its dependencies to skip unnecessary data processing steps, then increase the number of shards to improve data processing efficiency, and finally form a new job configuration so that job B can maintain its original functions while shortening execution time and improving execution efficiency.

[0059] This embodiment identifies and locates low-performance jobs, adjusts job dependencies and execution parameters, thereby improving job execution efficiency and resource utilization efficiency while ensuring the integrity of job functions, ultimately achieving the technical effect of improving the processing efficiency of batch jobs.

[0060] The method for adjusting job configuration information provided in this application embodiment determines whether there are abnormal jobs in the historical job set based on a tuple information table. The tuple information table is composed of attribute information of the historical job set and includes at least the job dependency objects, job execution information, and job description for each historical job. If abnormal jobs exist in the historical job set, the job dependency objects of the abnormal jobs are adjusted according to the job dependency objects and job execution information of each historical job in the tuple information table to obtain first adjustment information. The job execution information of the abnormal jobs is adjusted according to the target job description of the abnormal jobs to obtain second adjustment information. The abnormal jobs are then adjusted based on the first and second adjustment information. The method of adjusting the initial configuration information to obtain the target configuration information involves acquiring a tuple information table of historical job sets, extracting the features recorded in the tuple information table, determining the adjustment information based on the features, and generating first and second adjustment information from different adjustment dimensions. The total adjustment information is obtained based on the first and second adjustment information, and the initial configuration information is adjusted accordingly to obtain the adjusted target configuration information. This achieves the goal of automatically determining the adjustment point of the initial configuration information and automatically adjusting the initial configuration information, thereby improving the efficiency of adjusting configuration information and solving the technical problem of low efficiency in adjusting job configuration information in related technologies.

[0061] To ensure the comprehensiveness and accuracy of the content in the tuple information table, optionally, in the job configuration information adjustment method provided in this application embodiment, the tuple information table is constructed in the following manner: obtaining the running information of the historical job set executed within the target time period, and obtaining the job source code and initial configuration information of each historical job in the historical job set; determining the tuple information of each historical job in the historical job set based on the running information, job source code and initial configuration information of the historical job set, and obtaining the tuple information table.

[0062] It should be noted that the target time period refers to the time period during which batch jobs are executed at the end of the day, and the historical job set refers to the collection of batch jobs executed within the target time period. Execution information can include runtime parameters such as job start time, end time, execution status, and execution time, used to evaluate job execution efficiency and identify potential performance issues. Job source code refers to the original program code of the job, containing the job's logic and algorithm details. Initial configuration information can include configuration settings such as job execution parameters, resource allocation, and dependencies, reflecting the job's setup state before actual execution. The tuple information is an abstract expression of job attributes in the historical job set, typically including fields such as job name, previous and subsequent jobs, job execution time, job type, sorting field, and read / write table, forming the basic unit of the tuple information table.

[0063] Specifically, when generating the tuple information table, the system first needs to retrieve the job logs of batch jobs within the target time period recorded in the database, and extract key runtime data such as start time, end time, and execution status for each job. For example, the system will filter out all batch jobs started between 10 PM and 4 AM the next day from the logs, read their start and end timestamps, and whether they were successfully completed.

[0064] Furthermore, after obtaining the runtime information, the system will retrieve the source code of historical jobs from the version control system and read the initial configuration information from the job configuration file, including but not limited to the job's parallelism settings, dependencies, and number of data shards. For example, for job B, the system will download its latest source code and read the list of jobs B's parallelism, number of shards, and dependent jobs from the configuration file.

[0065] After collecting the runtime information, job source code, and configuration information, it is necessary to further integrate the collected data, parse each job, determine the attribute information of each job, and then generate tuple information containing job attributes. This information is then integrated into a tuple information table to obtain a tuple information table containing the attribute information of each historical job in the historical job set. The tuple information table can be shown in Table 1.

[0066] For example, as shown in Table 1, for job B, the system analyzes the source code of job B and identifies that job B is a data aggregation operation, involving reading the table t_aa_dtl and aggregating the amount field by ZONENO; at the same time, according to the initial configuration information, it records that it depends on job A and the number of shards is 1; finally, combined with the running information, it records that its actual time is 300 seconds, and combines the above information into a tuple information, which is added to the tuple information table as the tuple information of job B. The above process is repeated for each job in the historical job set until the tuple information of all jobs has been entered into the tuple information table, thus obtaining the tuple information table corresponding to the historical job set.

[0067] It should be noted that this embodiment constructs a tuple information table by collecting the running information, source code and configuration data of historical job sets. This allows the basic attributes of the job (such as job name and time consumption), as well as the dependencies between jobs, the functional description of the job and the execution parameters to be determined directly from the tuple information table. This provides comprehensive and accurate data support for subsequent job runtime efficiency optimization operations.

[0068] This embodiment constructs a tuple information table based on job information, laying a data foundation for subsequent batch job optimization.

[0069] To ensure the accuracy of the information in the tuple information table, optionally, in the job configuration information adjustment method provided in this application embodiment, determining the tuple information of each historical job in the historical job set based on the running information, job source code, and initial configuration information of the historical job set includes: for any historical job, obtaining the execution duration of the historical job from the running information, and analyzing the job source code and initial configuration information using a code attribute graph to obtain the job dependency objects and job execution information of the historical job; analyzing the job source code and initial configuration information using a graph neural network to obtain the job description of the historical job; and combining the execution duration, job dependency objects, job execution information, and job description into the tuple information of the historical job.

[0070] It should be noted that a code attribute graph is a graphical data structure used for code analysis. It can express the syntactic structure, control flow, and data flow of code, thereby helping to understand and optimize program behavior. Graph neural networks are a machine learning technique that can be used to process graphical structure data.

[0071] Specifically, when generating tuple information, the start and end times of each job are first extracted from the historical job set, and these two time points are used to calculate the actual execution time of the job. For example, if job B starts at time T1 and ends at time T2, then its execution time is T2-T1.

[0072] Furthermore, code attribute graphing techniques are needed to analyze the source code of historical jobs to identify the job's input-output relationships, call sequences, and data processing flow. Simultaneously, code attribute graphs are used to assist in analyzing initial configuration information, such as resource allocation, concurrency settings, and data sharding strategies, to identify bottlenecks in job execution efficiency. For example, job B may involve read and write operations on multiple data tables. Through code attribute graph analysis of the source code, the dependencies of job B on different data tables and its job execution information can be clearly depicted, such as reading from table t_aa_dtl and summarizing operations on the amount field.

[0073] Furthermore, graph neural networks can be used to extract high-level semantic features from the job source code, and combined with the initial configuration information, to generate job description content, thereby describing the main functions of job B, such as data aggregation, data cleaning, or specific business processing logic.

[0074] Finally, the collected attributes—execution duration, dependent objects, execution information, and job description—can be organized into tuple information for historical jobs. This tuple information can then be used to construct a tuple information table, where each row represents a comprehensive attribute description of a historical job. For example, the tuple information for job B could be: execution duration is 300 seconds, dependent objects include jobs A and C, execution information is processing data aggregation and writing aggregation results, and the job description is "aggregate the amount field in the t_aa_dtl table by the ZONENO field".

[0075] It should be noted that code attribute graphs and graph neural networks can be integrated into a recognition system. The job execution time, job source code, and job configuration information can be input into the recognition system to obtain the tuple information of the job.

[0076] This embodiment combines code attribute graph and graph neural network analysis techniques to identify the comprehensive attribute description of each historical job from the historical job set, thereby obtaining tuple information for each historical job and ensuring the accuracy of the tuple information.

[0077] To accurately determine whether there are abnormal jobs, optionally, in the job configuration information adjustment method provided in this application embodiment, determining whether there are abnormal jobs in the historical job set based on the tuple information table includes: obtaining the execution duration of each historical job from the tuple information table, and obtaining the duration threshold of each historical job; comparing the execution duration of each historical job with the duration threshold to obtain a comparison result; obtaining an abnormal comparison result from the comparison results of each historical job, and determining the historical job indicated by the abnormal comparison result as an abnormal job.

[0078] Specifically, when identifying abnormal jobs, the main criterion is the execution duration of the job. First, the tuple information table needs to be read to extract the execution duration data for each job. Then, based on historical data and business requirements, a duration threshold is determined for each job. For example, if job B has an execution duration of 300 seconds in the tuple information table, and based on the average execution time (250 seconds) and standard deviation (30 seconds) over the past 30 days, the set duration threshold is 290 seconds.

[0079] Furthermore, the execution time and duration threshold of each job can be compared separately. If the execution time of a job exceeds its duration threshold, the job is determined to be abnormal; if the execution time of a job does not exceed its duration threshold, the job is determined to be normal. For example, the execution time of job B is 300 seconds, which is greater than its duration threshold of 290 seconds. Therefore, the comparison result of job B is marked as abnormal.

[0080] Finally, the comparison results of all historical jobs are summarized, and the abnormal comparison results are filtered out to obtain all abnormal jobs. For example, if jobs B, E and I are marked as abnormal in the comparison results, it indicates that these three jobs need further performance analysis and optimization.

[0081] This embodiment uses structured data from a tuple information table and preset duration thresholds to achieve automated identification of abnormal jobs in a historical job set, thereby ensuring efficient and accurate identification of abnormal jobs.

[0082] To accurately determine the first adjustment information, optionally, in the job configuration information adjustment method provided in this application embodiment, adjusting the job dependency objects of abnormal jobs based on the job dependency objects and job execution information of each historical job in the tuple information table to obtain the first adjustment information includes: determining the preceding dependency object of the abnormal job, and determining the output result of the preceding dependency object based on the job execution information of the preceding dependency object; determining whether the input information of the abnormal job is related to the output result based on the job execution information of the abnormal job; if the input information of the abnormal job is not related to the output result, determining the target dependency object that is related to the input information based on the job execution information of the abnormal job, and determining the first adjustment information based on the preceding dependency object and the target dependency object; if the input information of the abnormal job is related to the output result, determining the first adjustment information as empty.

[0083] It should be noted that job dependencies refer to the preceding and succeeding jobs in a job dependency relationship. Job execution information includes the job's execution time, resource usage, and status information, used to evaluate job efficiency and identify potential performance bottlenecks. The first adjustment information consists of adjustment strategies for the dependencies of abnormal jobs, aiming to optimize the dependency flow between jobs, reduce unnecessary data processing, and improve the overall runtime efficiency of batch jobs.

[0084] Specifically, when determining the first adjustment information, it is first necessary to identify the preceding dependent jobs of the abnormal job from the tuple information table. For example, if abnormal job C depends on the completion of job B, then job B is a preceding dependent job of job C. Subsequently, the system will analyze the job execution information of job B to obtain the output results of the preceding dependent jobs.

[0085] Furthermore, the system needs to check the input requirements of the abnormal job C, that is, whether it needs to obtain data from the preceding dependent job B. If the input information of job C does not depend on the specific output result of job B, then the dependency relationship between these two jobs is not optimal. For example, if the input information of job C is content C, and the output result of job B is content B, and there is no relationship between content B and content C, then it indicates that there is no direct correlation between the input of job C and the output of job B.

[0086] Furthermore, if it is determined that there is no correlation between the input information and output result of the abnormal job, the dependency relationship between job C and job B can be deleted, the dependency relationship can be adjusted, the target dependency object that is related to the input information can be determined, and the abnormal job can be directly dependent on the target dependency object, thereby completing the modification of the information related to the dependency information in the configuration information.

[0087] Figure 3 This is a schematic diagram of the job dependency information provided in Embodiment 1 of this application. Figure 1 , Figure 4 This is a schematic diagram of the job dependency information provided in Embodiment 1 of this application. Figure 2 ,like Figure 3 As shown, the dependency relationship between assignment A and assignment D can be: assignment A - assignment B - assignment C - assignment D. If it is determined that there is no relationship between the output of assignment B and the input of assignment C, and there is a relationship between the output of assignment A and the input of assignment C, then the dependency relationship between assignment A and assignment D can be changed to... Figure 4 The dependencies shown improve the execution efficiency of job D by parallelizing jobs B and C.

[0088] It should be noted that when there is a correlation between the input information and output result of an abnormal job, the representation cannot modify the dependency of the abnormal job. In this case, the first adjustment information can be set to empty, and the representation does not need to set the first adjustment information, that is, the dependency does not need to be adjusted.

[0089] This embodiment improves job execution efficiency by analyzing the job dependencies and execution information in the tuple information table to adjust the dependencies of abnormal jobs.

[0090] To accurately determine the second adjustment information, optionally, in the job configuration information adjustment method provided in this application embodiment, adjusting the job execution information of the abnormal job according to the target job description of the abnormal job to obtain the second adjustment information includes: inputting the target job description into the execution information identification model to obtain the standard execution information of the abnormal job, wherein the execution information identification model is used to determine the standard execution information of the job according to the job description; determining the difference information between the standard execution information and the job execution information, and determining the second adjustment information according to the difference information.

[0091] It should be noted that the target job description refers to the text describing the function and expected execution logic of the job. For example, the function of job B is to summarize data and sum the amount field in table t_aa_dtl according to the ZONENO field. The execution information recognition model can be a machine learning-based model used to identify and predict standardized information for job execution, including but not limited to job execution efficiency, resource requirements, and parallelism. By inputting the job description, the model can generate expected execution information that matches the job function. Standard execution information can be parameter information on how the job should be executed. Job execution information refers to the execution parameters and data of the job during actual operation, including job execution time, resource allocation, parallel processing capabilities, etc. Secondary adjustment information refers to the job configuration and execution parameter adjustment suggestions generated by the system based on the differences between the standard execution information and the job execution information, used to improve the actual execution efficiency of the job and reduce resource consumption and execution time.

[0092] Specifically, when determining the second adjustment information, the target job description of the abnormal job can be passed as input to the execution information recognition model. This model is trained based on a large amount of historical data and job descriptions to develop an algorithm for recognizing standard execution information of jobs. By inputting the description, the model can predict the execution information that the abnormal job should have under ideal conditions, such as the ideal number of job shards, data preprocessing steps, and resource allocation strategies.

[0093] Furthermore, after determining the standard execution information for the abnormal job, the system will compare the actual execution information in the abnormal job's configuration information with the standard execution information to obtain the difference information. For example, the actual number of fragments for the abnormal job is 1, while the number of fragments in the standard execution information is 8.

[0094] After obtaining the difference information, a second adjustment information can be generated based on the difference information. Then, the configuration information of the abnormal operation can be adjusted according to the abnormal information based on the second adjustment information to obtain the second adjustment information.

[0095] This embodiment analyzes the target job description of abnormal jobs using an execution information recognition model, generates standard execution information that matches the job function, and then generates specific job configuration and execution parameter adjustment suggestions, i.e., second adjustment information, based on the difference between the standard execution information and the actual job execution information. The configuration information is then adjusted according to the second adjustment information, thereby improving the job execution efficiency.

[0096] To ensure the accuracy of configuration information update operations, optionally, in the job configuration information adjustment method provided in this application embodiment, adjusting the initial configuration information of abnormal jobs according to the first adjustment information and the second adjustment information to obtain target configuration information includes: replacing the previous dependency object in the initial configuration information with the target dependency object according to the previous dependency object and the target dependency object carried in the first adjustment information to obtain candidate configuration information; and updating the candidate configuration information according to the difference information carried in the second adjustment information to obtain target configuration information.

[0097] Specifically, when adjusting the initial configuration information, the dependency information can be adjusted first, that is, the previous dependency object in the initial configuration information can be replaced with the target dependency object to obtain candidate configuration information. Then, the execution information can be further adjusted, and the original execution information in the candidate configuration information can be changed to the standard execution information based on the difference information to obtain the target configuration information. This completes the change operation of the initial configuration information, so that when the job is executed according to the target configuration information, the execution efficiency of the job can be improved.

[0098] For example, the dependency of job B is adjusted from job C to job A. The system will find the dependency description of job C in the initial configuration information of job B and replace it with the dependency description of job A, generating candidate configuration information with optimized dependency. Furthermore, the number of shards of job B is increased from 1 to 8 in order to better utilize the multi-core parallel processing capabilities.

[0099] This embodiment optimizes the batch job execution process and improves job efficiency by adjusting the dependencies and execution parameters of abnormal jobs.

[0100] Figure 5 This is a schematic diagram of the job configuration information adjustment system provided in Embodiment 1 of this application, as shown below. Figure 5 As shown, the device is divided into a monitoring module, a job configuration parsing module, a job scheduling optimization module, a job configuration optimization module, and an optimization strategy integration module, among which:

[0101] The monitoring module is used to collect job operation information, such as job start time and end time, calculate job time, and output the execution time of each job.

[0102] The job configuration parsing module is used to parse the job source code and configuration information using code attribute graphs and graph neural networks, converting the job configuration and functionality into a tuple of information. The inputs to the job configuration parsing module are the monitoring module output, the job source code, and the job configuration information (such as job dependencies and the number of job fragments). The output of the job configuration parsing module is a tuple describing the job configuration information.

[0103] For example, the original job dependency can be as follows Figure 3 As shown, the final generated tuple information table can be represented as Table 2:

[0104] Table 2

[0105]

[0106] The job types are as follows: 0 - indicates a head job with no actual function; 1 - indicates a tail job with no actual function; 2 - indicates a summary job that will summarize a certain field according to conditions; 3 - indicates a normal table read / write job where the source table and the target table are not the same table.

[0107] The job scheduling optimization module generates a job dependency graph based on multiple tuples generated by the job configuration parsing module. For a single node, it adjusts the job dependencies based on the functions of subsequent dependent nodes. For example, when parsing the tuple information in Table 2, this module first parses job node A, finding that A is the head job, and the dependency between A and B remains unchanged. Then, it parses job node B, finding that the subsequent job C does not overlap with the data processed by job node B, and adjusts the job dependencies. The subsequent dependencies of job node B are adjusted to the subsequent dependencies of job node C, and the preceding dependencies of job node C are adjusted to the preceding dependencies of job node B. Simultaneously, the adjusted tuple information is written to a new tuple, as shown in Table 3. Afterwards, it iterates from the head job node to the tail job to optimize the job schedule. The final generated job flow example is shown below. Figure 4 As shown.

[0108] Table 3

[0109]

[0110] The job configuration optimization module is used to adjust the job configuration information based on the multiple tuple information generated by the job configuration parsing module, according to the job function and job configuration. For example, for job B, the function is to summarize the amount of data in t_aa_dtl by ZONENO. Currently, the data is sorted by ZONENO, but the number of shards is 1, which means that all data can only be processed serially. Therefore, the number of shards is adjusted to 8, so that the data is divided into 8 shards by ZONENO (data with the same ZONENO are in the same shard), which improves the job processing efficiency. The final generated new tuple information is shown in Table 4.

[0111] Table 4

[0112]

[0113] The optimization approach integration module integrates the outputs of the job dependency optimization module and the job configuration optimization module, compares the results with the original tuple information, and directly provides the job optimization tuple, as shown in Table 5. Optimization personnel only need to perform a simple review of the results before implementation.

[0114] Table 5

[0115]

[0116]

[0117] The aforementioned device can acquire a tuple information table of historical job sets, obtain the features recorded in the tuple information table, determine adjustment information based on the features, generate first and second adjustment information from different adjustment dimensions, obtain total adjustment information based on the first and second adjustment information, and adjust the initial configuration information based on the total adjustment information to obtain the adjusted target configuration information. This achieves the purpose of automatically determining the adjustment point of the initial configuration information and automatically adjusting the initial configuration information, thereby improving the efficiency of adjusting configuration information and solving the technical problem of low efficiency in adjusting job configuration information in related technologies.

[0118] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0119] Example 2

[0120] This application also provides a device for adjusting job configuration information. It should be noted that this device can be used to execute the job configuration information adjustment method provided in the above embodiments. The following describes the device for adjusting job configuration information provided in this application.

[0121] According to an embodiment of this application, an apparatus for implementing the above-described method for adjusting job configuration information is also provided. Figure 6 This is a schematic diagram of a device for adjusting job configuration information according to Embodiment 2 of this application, as shown below. Figure 6 As shown, the device includes:

[0122] The judgment module 61 is used to determine whether there are abnormal jobs in the historical job set based on the tuple information table. The tuple information table is composed of the attribute information of the historical job set and includes at least the job dependency objects, job execution information and job description of each historical job.

[0123] The first adjustment module 62 is used to adjust the job dependency objects of the abnormal job according to the job dependency objects and job execution information of each historical job in the tuple information table when there are abnormal jobs in the historical job set, so as to obtain the first adjustment information.

[0124] The second adjustment module 63 is used to adjust the job execution information of the abnormal job according to the target job description of the abnormal job, so as to obtain the second adjustment information.

[0125] The third adjustment module 64 is used to adjust the initial configuration information of the abnormal operation according to the first adjustment information and the second adjustment information to obtain the target configuration information.

[0126] The job configuration information adjustment device provided in this application embodiment determines whether there are abnormal jobs in the historical job set by the judgment module 61 based on the tuple information table. The tuple information table is composed of attribute information of the historical job set and includes at least the job dependency objects, job execution information, and job descriptions of each historical job. If abnormal jobs exist in the historical job set, the first adjustment module 62 adjusts the job dependency objects of the abnormal jobs according to the job dependency objects and job execution information of each historical job in the tuple information table to obtain first adjustment information. The second adjustment module 63 adjusts the job execution information of the abnormal job according to the target job description of the abnormal job to obtain second adjustment information. The third adjustment module 64 adjusts the job execution information of the abnormal job according to the first adjustment information. The initial configuration information and second adjustment information are used to adjust the initial configuration information of abnormal operations to obtain target configuration information. By obtaining the tuple information table of the historical operation set and acquiring the features recorded in the tuple information table, adjustment information is determined based on the features. At the same time, first adjustment information and second adjustment information are generated from different adjustment dimensions. The total adjustment information is obtained based on the first adjustment information and the second adjustment information, and the initial configuration information is adjusted based on the total adjustment information to obtain the adjusted target configuration information. This achieves the purpose of automatically determining the adjustment point of the initial configuration information and automatically adjusting the initial configuration information, thereby improving the technical effect of adjusting configuration information and solving the technical problem of low efficiency in adjusting operation configuration information in related technologies.

[0127] Optionally, in the job configuration information adjustment device provided in this application embodiment, the tuple information table is constructed by the following device: an acquisition unit, used to acquire the operation information of the historical job set executed within the target time period, and acquire the job source code and initial configuration information of each historical job in the historical job set; and a determination unit, used to determine the tuple information of each historical job in the historical job set based on the operation information, job source code and initial configuration information of the historical job set, to obtain the tuple information table.

[0128] Optionally, in the job configuration information adjustment device provided in this application embodiment, the determining unit includes: a first acquisition module, used to acquire the execution time of any historical job from the running information, and analyze the job source code and initial configuration information using a code attribute graph to obtain the job dependency objects and job execution information of the historical job; an analysis module, used to analyze the job source code and initial configuration information through a graph neural network to obtain the job description of the historical job; and a combination module, used to combine the execution time, job dependency objects, job execution information and job description into tuple information of the historical job.

[0129] Optionally, in the job configuration information adjustment device provided in this application embodiment, the judgment module 61 includes: a second acquisition module, used to acquire the execution duration of each historical job from the tuple information table, and acquire the duration threshold of each historical job; a comparison module, used to compare the execution duration of each historical job with the duration threshold to obtain a comparison result; and a first determination module, used to acquire abnormal comparison results from the comparison results of each historical job, and determine the historical job indicated by the abnormal comparison results as an abnormal job.

[0130] Optionally, in the job configuration information adjustment device provided in this application embodiment, the first adjustment module 62 includes: a second determining module, used to determine the preceding dependent object of the abnormal job, and determine the output result of the preceding dependent object based on the job execution information of the preceding dependent object; a judging module, used to judge whether the input information of the abnormal job is related to the output result based on the job execution information of the abnormal job; a third determining module, used to determine the target dependent object related to the input information based on the job execution information of the abnormal job when there is no relationship between the input information and the output result of the abnormal job, and determine the first adjustment information based on the preceding dependent object and the target dependent object; and a fourth determining module, used to determine the first adjustment information as empty when there is a relationship between the input information and the output result of the abnormal job.

[0131] Optionally, in the job configuration information adjustment device provided in the embodiments of this application, the second adjustment module 63 includes: an input module, used to input the target job description into the execution information identification model to obtain the standard execution information of the abnormal job, wherein the execution information identification model is used to determine the standard execution information of the job based on the job description; and a fifth determination module, used to determine the difference information between the standard execution information and the job execution information, and determine the second adjustment information based on the difference information.

[0132] Optionally, in the job configuration information adjustment device provided in the embodiments of this application, the third adjustment module 64 includes: a replacement module, used to replace the previous dependency object in the initial configuration information with the target dependency object according to the previous dependency object and the target dependency object carried in the first adjustment information, so as to obtain candidate configuration information; and an update module, used to update the candidate configuration information according to the difference information carried in the second adjustment information, so as to obtain target configuration information.

[0133] It should be noted that the aforementioned judgment module 61, first adjustment module 62, second adjustment module 63, and third adjustment module 64 correspond to steps S201 to S204 in Embodiment 1. The instances and application scenarios implemented by each of these units and their corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the aforementioned modules or units can be hardware or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). These modules can also run as part of a device in the computer terminal 10 provided in Embodiment 1.

[0134] Example 3

[0135] Embodiments of this application may provide an electronic device. Figure 7 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 7 As shown, the electronic device may include: one or more ( Figure 7 (Only one is shown) processor 1002, memory 1004, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0136] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0137] Those skilled in the art will understand that Figure 7 The structure shown is for illustrative purposes only. Electronic devices can also be smartphones, tablets, handheld computers, mobile internet devices (MIDs), PADs, and other terminal devices. Figure 7 This does not limit the structure of the aforementioned electronic device. For example, electronic devices may also include components that are more... Figure 7 The more or fewer components shown (such as network interfaces, display devices, etc.), or having the same Figure 7 The different configurations shown.

[0138] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0139] Example 4

[0140] Embodiments of this application also provide a storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the job configuration information adjustment method provided in Embodiment 1.

[0141] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0142] Embodiments of this application also provide a computer program product, which, when executed on a data processing device, is adapted to perform the steps of a method for adjusting job configuration information.

[0143] Embodiments of this application also provide a computer-readable storage medium, which includes a stored executable program, wherein the method for adjusting the job configuration information described above is used to control the device where the computer-readable storage medium is located to execute the above-described method when the executable program is running.

[0144] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0145] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0147] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0148] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0149] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0150] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for adjusting job configuration information, characterized in that, include: The system determines whether there are any abnormal jobs in the historical job set based on the tuple information table. The tuple information table is composed of the attribute information of the historical job set, and the tuple information table includes at least the job dependency objects, job execution information and job description of each historical job. If the abnormal job exists in the historical job set, the job dependency object of the abnormal job is adjusted according to the job dependency object and job execution information of each historical job in the tuple information table to obtain the first adjustment information; The job execution information of the abnormal job is adjusted according to the target job description of the abnormal job to obtain the second adjustment information; The initial configuration information of the abnormal operation is adjusted according to the first adjustment information and the second adjustment information to obtain the target configuration information.

2. The method according to claim 1, characterized in that, The tuple information table is constructed in the following manner: Obtain the execution information of the set of historical jobs executed within the target time period, and obtain the job source code and initial configuration information of each historical job in the set of historical jobs; Based on the runtime information of the historical job set, the job source code, and the initial configuration information, the tuple information of each historical job in the historical job set is determined, and the tuple information table is obtained.

3. The method according to claim 2, characterized in that, The tuple information for each historical job in the historical job set is determined based on the runtime information of the historical job set, the job source code, and the initial configuration information, including: For any historical job, the execution time of the historical job is obtained from the running information, and the job source code and the initial configuration information are analyzed using the code attribute graph to obtain the job dependency objects and job execution information of the historical job; The job description of the historical job is obtained by analyzing the job source code and the initial configuration information using a graph neural network. The execution duration, the job dependent objects, the job execution information, and the job description are combined into a tuple of information for the historical jobs.

4. The method according to claim 1, characterized in that, Determining whether there are abnormal jobs in the historical job set based on the tuple information table includes: The execution duration of each historical job is obtained from the tuple information table, and the duration threshold of each historical job is obtained. The execution time and duration threshold of each historical job are compared to obtain the comparison results; Obtain the abnormal comparison results from the comparison results of each historical task, and identify the historical task indicated by the abnormal comparison results as the abnormal task.

5. The method according to claim 1, characterized in that, Based on the job dependency objects and job execution information of each historical job in the tuple information table, the job dependency objects of the abnormal job are adjusted to obtain the first adjustment information, which includes: Identify the preceding dependent objects of the abnormal job, and determine the output result of the preceding dependent objects based on the job execution information of the preceding dependent objects; Based on the job execution information of the abnormal job, determine whether there is a correlation between the input information of the abnormal job and the output result; If there is no correlation between the input information and the output result of the abnormal job, a target dependency object that is related to the input information is determined based on the job execution information of the abnormal job, and the first adjustment information is determined based on the preceding dependency object and the target dependency object. If there is a correlation between the input information and the output result of the abnormal operation, the first adjustment information is determined to be empty.

6. The method according to claim 1, characterized in that, The job execution information of the abnormal job is adjusted according to the target job description of the abnormal job to obtain the second adjustment information, which includes: The target job description is input into the execution information identification model to obtain the standard execution information of the abnormal job, wherein the execution information identification model is used to determine the standard execution information of the job based on the job description; The difference information between the standard execution information and the job execution information is determined, and the second adjustment information is determined based on the difference information.

7. The method according to claim 1, characterized in that, The initial configuration information of the abnormal operation is adjusted based on the first adjustment information and the second adjustment information to obtain the target configuration information, including: Based on the preceding dependency object and the target dependency object carried in the first adjustment information, the preceding dependency object in the initial configuration information is replaced with the target dependency object to obtain candidate configuration information; Based on the difference information carried in the second adjustment information, the candidate configuration information is updated using the difference information to obtain the target configuration information.

8. A device for adjusting job configuration information, characterized in that, include: The judgment module is used to determine whether there are abnormal jobs in the historical job set based on the tuple information table. The tuple information table is composed of the attribute information of the historical job set, and the tuple information table includes at least the job dependency object, job execution information and job description of each historical job. The first adjustment module is used to adjust the job dependency objects of the abnormal job according to the job dependency objects and job execution information of each historical job in the tuple information table when the abnormal job exists in the historical job set, so as to obtain the first adjustment information. The second adjustment module is used to adjust the job execution information of the abnormal job according to the target job description of the abnormal job to obtain the second adjustment information; The third adjustment module is used to adjust the initial configuration information of the abnormal operation according to the first adjustment information and the second adjustment information to obtain the target configuration information.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they implement the steps of the job configuration information adjustment method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the method for adjusting job configuration information as described in any one of claims 1 to 7.

Images