CN111768097A - Method, device and system for monitoring task execution state and storage medium - Google Patents
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Abstract
The embodiment of the invention relates to a method, a device, a system and a storage medium for monitoring a task execution state, wherein the method comprises the following steps: periodically acquiring a task data set and task data details corresponding to each piece of task data; counting a task data subset corresponding to each type of label in a preset type of label in a task data set according to preconfigured label information; according to the basic information and the task execution state, respectively carrying out comprehensive summary analysis on the task data subsets corresponding to each type of label to obtain the task execution state analysis result corresponding to each type of label; and displaying the task execution state analysis result according to a preset rule so as to complete monitoring of the task execution state corresponding to each type of label. By the method, the workload and labor cost of enterprises for managing and maintaining the mass tasks are reduced.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a method, a device and a system for monitoring a task execution state and a storage medium.
Background
With the advent of the big data age, a large amount of big data calculation exists in enterprises every day, and is used for supporting enterprise marketing and operation. A large number of hadoop batch processing calculation tasks, Spark real-time calculation tasks, Presto ad hoc inquiry calculation tasks, flight real-time calculation tasks and the like exist in an enterprise big data platform, a data middle station or a data warehouse environment, and the number scales of the tasks are different from ten thousand scales, hundred thousand scales and million scales. Such a huge amount of tasks are often distributed among different subgroups, subsidiaries, business departments, and the like.
The data management department, the operation and maintenance department, the enterprise decision-making department and the like of the platform are difficult to understand the specific execution conditions of different tasks in the mass tasks in detail. That is, the management and maintenance of the enterprise for the massive tasks requires a great amount of work and labor cost.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a system, and a storage medium for monitoring a task execution state to solve the technical problems in the prior art.
In a first aspect, an embodiment of the present invention provides a method for monitoring a task execution state, where the method includes:
the method comprises the steps of periodically acquiring a task data set and task data details corresponding to each piece of task data, wherein the task data details comprise pre-configured label information, basic information and a task execution state corresponding to each piece of task data;
counting a task data subset corresponding to each type of label in a preset type of label in a task data set according to preconfigured label information;
according to the basic information and the task execution state, respectively carrying out comprehensive summary analysis on the task data subsets corresponding to each type of label to obtain a task execution state analysis result corresponding to each type of label, wherein the task execution state analysis result comprises each piece of task execution state information;
and displaying the task execution state analysis result according to a preset rule so as to complete monitoring of the task execution state corresponding to each type of label.
In one possible embodiment, when the first class label includes multiple levels of sub-labels, according to the preconfigured label information, after counting a first task data subset corresponding to the first class label in the task data set, the method further includes:
acquiring preconfigured label information corresponding to each piece of task data in a first task data subset;
and according to the configured label information corresponding to each piece of task data in the first task data subset, counting the task data subsets corresponding to each level sub-label in the first task data subset, so that the execution state analysis result of the task data subset corresponding to each level sub-label is displayed according to a preset rule in the following process.
In one possible embodiment, the preset category labels include, but are not limited to, one or more of the following:
a line of business tag, a mission importance tag, a data application tag, or a bin type tag.
In one possible embodiment, the task execution state analysis result includes: the task execution state statistics method comprises the following steps of task type, total number of tasks, Service-Level agent (SLA) time, current completion progress, each type of task execution state statistics result and total task execution state statistics result.
In one possible embodiment, when detecting that the first task execution state analysis result in the trigger task execution state analysis results, the method further includes:
and displaying a first task detail list corresponding to the first task execution state analysis result, wherein the first task execution state analysis result is any task execution state analysis result of the task execution state analysis results.
In one possible embodiment, the first task detail list comprises: at least one task detail corresponding to a first task execution state analysis result; after displaying the first task detail list corresponding to the first task execution state analysis result, the method further includes:
when triggering of a first task detail in at least one task detail is detected, a second task detail list related to the first task detail is counted and displayed, wherein the second task detail list comprises relevant information corresponding to other tasks affected by the execution state of the first task.
In a second aspect, an embodiment of the present invention provides a task execution state monitoring apparatus, where the apparatus includes:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for periodically acquiring a task data set and task data details corresponding to each piece of task data, and the task data details comprise pre-configured label information, basic information and a task execution state corresponding to each piece of task data;
the counting unit is used for counting the task data subsets corresponding to each type of labels in the preset type of labels in the task data set according to the preconfigured label information;
the processing unit is used for respectively carrying out comprehensive summary analysis on the task data subsets corresponding to each type of label according to the basic information and the task execution state, and acquiring a task execution state analysis result corresponding to each type of label, wherein the task execution state analysis result comprises each piece of task execution state information;
and the display unit is used for displaying the analysis result of the task execution state according to a preset rule so as to monitor the task execution state corresponding to each type of label.
In a possible embodiment, when the first type of tag includes a plurality of levels of sub-tags, the statistics unit is configured to, according to preconfigured tag information, perform statistics on a first task data subset corresponding to the first type of tag in the task data set, and then the acquisition unit is further configured to acquire preconfigured tag information corresponding to each piece of task data in the first task data subset;
the statistical unit is further configured to, according to the configured tag information corresponding to each piece of task data in the first task data subset, perform statistics on the task data subsets corresponding to each level sub-tag in the first task data subset, so that the execution state analysis results of the task data subsets corresponding to each level sub-tag are subsequently and respectively displayed according to a preset rule.
In one possible embodiment, the preset category labels include, but are not limited to, one or more of the following:
a line of business tag, a mission importance tag, a data application tag, or a bin type tag.
In a third aspect, an embodiment of the present invention provides a task execution state monitoring system, where the system includes: at least one processor and memory;
the processor is configured to execute the task execution state monitoring program stored in the memory to implement the task execution state monitoring method as described in any embodiment of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium, where one or more programs are stored, and the one or more programs may be executed by the task execution state monitoring system described in the third aspect, so as to implement the task execution state monitoring method described in any implementation manner of the first aspect.
According to the task execution state monitoring method provided by the embodiment of the invention, a task data set and task data details corresponding to each piece of task data are periodically obtained, and then according to preconfigured label information included in each piece of task in the task data set, a task data subset corresponding to each type of label in the task data set is counted. And then, according to the basic information and the task execution state, performing summary analysis on each type of task data subset to obtain a task execution state analysis result corresponding to each type of label, and displaying the task execution state analysis result to complete monitoring on the task execution state corresponding to each type of label, wherein each type of task execution state analysis result comprises information of the execution state of each task in the type of task data subset. By the method, all task data can be classified and managed. Also, the worker can know each task execution state information. The tasks are classified according to the labels, and naturally, the execution condition of each task in the task data subset corresponding to a certain type of labels can be focused. Namely, the data management department, the operation and maintenance department, the enterprise decision-making department and the like of the platform can conveniently know the specific execution conditions of different tasks in the mass tasks in detail. The workload and labor cost of enterprises for managing and maintaining massive tasks are greatly reduced.
Drawings
Fig. 1 is a schematic flowchart of a task execution state monitoring method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a tag creation process provided by the present invention;
FIG. 3 is a schematic diagram of creating 4 types of labels based on the label creating process corresponding to FIG. 2;
FIG. 4 is a schematic diagram of creating a level of sub-tags under a class of tags according to the present invention;
FIG. 5 is a schematic diagram of a batch set tag for data provided by the present invention;
fig. 6 is a schematic diagram of a task execution state analysis result corresponding to a financial daily report, where the primary label is a business application and the secondary label is a financial daily report, according to the present invention;
FIG. 7 is a diagram illustrating a result of analyzing a task execution state, where a first-level tag provided by the present invention is a business application, a second-level tag is a consumption financial journal, and a corresponding task is executed;
FIG. 8 is a schematic diagram of other task scenarios provided by the present invention that are affected by task exe _ gdm _ m01_ ord _ da;
FIG. 9 is a schematic diagram of task and subtask blood relationship analysis according to the present invention;
fig. 10 is a schematic structural diagram of a task execution status monitoring apparatus according to an embodiment of the present invention;
fig. 11 is a schematic structural diagram of a task execution status monitoring system according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For the convenience of understanding of the embodiments of the present invention, the following description will be further explained with reference to specific embodiments, which are not to be construed as limiting the embodiments of the present invention.
Fig. 1 is a schematic flowchart of a task execution state monitoring method according to an embodiment of the present invention, as shown in fig. 1. Before describing the method steps, first the preparation work that needs to be done before the method steps are performed is described. See specifically below:
first, tag information is created.
Specifically, as shown in fig. 2, when creating a tag, the creating includes creating a tag name, a tag description, a tag classification, a tag status, and the like. The tag status may include available, unavailable, and the like. For example, the tag categories may include, but are not limited to, the tag categories shown in FIG. 2, including, for example, line of business tags, importance levels, data applications, and bin types, among others. Of course, other labels can be created, and the specific label can be set according to actual conditions. Fig. 3 illustrates a schematic diagram of creating the above-mentioned category 4 tags.
After creating a preset kind of tag, a lower-level tag may also be set for each kind of tag. Specifically, as shown in fig. 3, a lower level tag adding option may be set after each type of preset tag, and of course, functions such as adding a lower level category to each type of tag or each level of tag, editing, deleting, and the like may also be performed.
For example, as shown in fig. 4, the label is one of the lower-level labels corresponding to a certain type of label. Comprises a plurality of same-level sub-labels. Each sub-tag can set the tag name, tag description, tag status, creation time, modification time, and the like, and can also set basic information (not shown in fig. 4) such as tag type, tag responsible person, tag usage, and the like. Editing, deleting, and the like of the above-mentioned content in each sub-level tag may also be configured. Specifically, a plurality of sub-tags are set for each level of tag, and the configured content in the sub-tags and the like can be set according to the actual situation, which is not limited herein.
After the label is created, the application of the label will be described below.
Specifically, in a big data environment (including a big data platform, a data center or a data warehouse environment), during enterprise decision analysis, various data of a production system and purchased third-party data are required to be collected and extracted into the big data environment, and the data are processed and calculated according to a certain data architecture through a big data calculation task of a task center of the big data environment, so that decision analysis data required by an enterprise are generated. In a specific example, the source of the task data set is from decision analysis data generated by a conventional four-layer architecture in a data warehouse after certain data processing, including cache layer data, overlay layer data, general layer data, and application layer data.
Then, the staff sets labels for the four types of data respectively, the labels can be set for each piece of data individually, or the labels can be set for a plurality of pieces of data in batches. Such as shown in fig. 5. FIG. 5 illustrates a schematic diagram of a batch set tag for data.
First, the name of each piece of data, and attribute information corresponding to one or more types of tags in a preset tag category, for example, attribute information configured in a service line tag for a plurality of pieces of data in fig. 5, attribute information in an importance degree, attribute information in data application, and the like, are set. That is, one piece of data is not only configured with one kind of tag, but a plurality of kinds of tags can be configured according to actual situations.
Further, each type of tag may correspond to a plurality of sub-level tags, and then, further refinement may be performed on each piece of data to determine which level sub-tag of a certain type of tag the piece of data specifically corresponds to, and configure details corresponding to the piece of data in the sub-tag. For example, after a piece of data is subdivided, it is the corresponding cross-border service-external receipt-cross-border B in fig. 4, and the corresponding description, state, and the like may be as shown in fig. 4.
Through the above process, a tag is configured for each piece of decision data. Then, the following method steps of the invention are performed:
and step 110, periodically acquiring a task data set and task data details corresponding to each piece of task data.
Specifically, each piece of task data in the task data set includes preconfigured tag information, basic information, and a task execution state.
And the label information is preconfigured in each piece of task data, namely the label information configured by the method. The tag information may include a plurality of tags corresponding to the piece of task data, or a plurality of sub-tags corresponding to each of the plurality of tags, and the like, which is determined according to actual situations.
The basic information may include information such as task name, task description, task principal, principal contact, and SLA time. The partial data is mainly obtained through a task center of a big data platform and can be specifically extracted from a task basic information table.
And the task execution state can be acquired from a task execution log table of a task center in the big data platform. Information such as task name, task execution time, task completion time, task execution status (including waiting, in execution, success or failure, etc.), and task log production time may be extracted.
And step 120, counting the task data subsets corresponding to each type of label in the preset type of labels in the task data set according to the preconfigured label information.
Specifically, as described above, each piece of task data may include a plurality of tags in the tag information. In other words, a plurality of tag contents. For example, as shown in fig. 5, one piece of task data corresponds to a service line tag, an importance level tag, a data application tag, and the like. Then, the task data cannot be directly classified, but the task data subsets corresponding to each class of tags belonging to the preset class tags can be counted on the basis of the tag information.
Optionally, the predetermined category tags described herein include, but are not limited to, one or more of a line of business tag, a task importance tag, a data application tag, or a bin type tag.
For example, with the service line label as a reference, it is determined whether the preconfigured label information included in each piece of task data includes the service line label, and if it is determined that the label information corresponding to the piece of task data includes the service line label, the piece of task data is classified into the task data subset corresponding to the service line label. Or, with the importance degree label as a reference, it may also be determined that the label information corresponding to the piece of task data includes a label of the importance degree, and then, the piece of task data also needs to be classified into a task data subset corresponding to the importance degree. Namely, the same piece of data can be classified into task data subsets corresponding to different labels.
And step 130, respectively performing comprehensive summary analysis on the task data subsets corresponding to each type of label according to the basic information and the task execution state, and obtaining a task execution state analysis result corresponding to each type of label, wherein the task execution state analysis result comprises each piece of task execution state information.
And 140, displaying the task execution state analysis result according to a preset rule so as to complete monitoring of the task execution state corresponding to each type of label.
Optionally, the task execution state analysis result includes, but is not limited to, one or more of the following, for example, including task type, total number of tasks, SLA time, current completion progress, statistics result of task execution state of each type, and statistics result of all task execution states, and so on.
When the task execution state analysis result is specifically displayed, classified and classified display can be performed. For example, one display area is divided for each level. And then dividing a plurality of sub-areas in the display area, wherein each sub-area is used for displaying a task execution state analysis result corresponding to one sub-label. Alternatively, if the display area is not sufficient, the task execution state analysis results corresponding to different levels of sub-labels that can be displayed periodically, for example, in the same time period. And displaying the task execution state analysis result corresponding to the different levels of the sub-labels of another large class in the next time period. For example, 1 minute, or 2 minutes, or other time periods, etc. In this embodiment, a display area is divided for each sub-label, and a corresponding task execution state analysis result is displayed. Therefore, the task execution state can be monitored by the staff more conveniently.
Further optionally, the task execution state analysis result is also updated periodically, and a specific period is the same as a period for acquiring the task data set.
Specifically, as shown in fig. 6 and 7, it is illustrated in fig. 6 that the primary label is a business application, and the secondary label is a financial daily report, that is, the sub-label is a task execution state analysis result corresponding to the financial daily report. Fig. 7 shows that the primary label is a service application, and the secondary label is a consumption financial journal, that is, the sub-label is a task execution state analysis result corresponding to the consumption financial journal.
Referring to fig. 6, in the result of analyzing the task execution state corresponding to the financial daily report, it is shown that the completion progress is 100%, and the SLA time is 6: 00. the task (job) types include: extraction operation, source pasting model operation, general model operation and application model operation.
The statistical result of the execution status of each type of task is represented by, for example, 150 total tasks, 150 successful tasks, 0 running task, 0 failed task, and 0 delayed task for the extraction task. Similarly, the source model job has a total number of 150, 150 successes, and 0 tasks of run, fail, wait, and delay. Other types of operation are shown in fig. 6 and will not be illustrated.
And the statistical results of the execution states of all tasks are shown in the last row of fig. 6, the total number is 380, the number of successful tasks is 380, and the number of running, failed, waiting and delaying tasks is 0.
Referring to fig. 7, in the result of analyzing the task execution state corresponding to the consumption financial daily report, it can be seen that the completion rate is 83% and the SLA time is 8: 00. Task (job) distribution case:
for example, the displaying of the statistics of the execution status of each type of task includes:
the total number of the extraction operations is 100, the number of the extraction operations is 100, and the number of the extraction operations.
General type operation, total amount is 50, succeed 30, run 10, fail 5, wait 5, delay 20. Other types of task scenarios, as shown in FIG. 7, will not be described.
The total result of the statistics of the execution states of all the tasks, as shown in fig. 7, includes a total of 300, 250 successes, 15 runs, 5 failures, 30 waits, and 50 delays.
Optionally, in order to enable the staff to have a deeper understanding of the result of the task execution state analysis, the method may further include:
and when a first task execution state analysis result in the trigger task execution state analysis results is detected, counting a first task detail list corresponding to the first task execution state analysis result, and displaying the first task detail list, wherein the first task execution state analysis result is any task execution state analysis result of the task execution state analysis results.
Specifically, for example, when the worker triggers the running task data corresponding to the general model job in fig. 7 (that is, triggers the item of the type of task execution state statistics result), the server may count the detail list of the task currently running, for example, the detail contents include how many pieces of tasks are running, and the task name, the task description, the task running progress, the task tag, and the like, corresponding to each piece of tasks running.
Further optionally, the first task detail list comprises: at least one task detail corresponding to a first task execution state analysis result; after displaying the first task detail list corresponding to the first task execution state analysis result, the method further includes:
when triggering of a first task detail in at least one task detail is detected, a second task detail list related to the first task detail is counted and displayed, wherein the second task detail list comprises relevant information corresponding to other tasks affected by the execution state of the first task.
For example, when a first task detail is triggered, the first task detail list includes a plurality of tasks, and when any task is triggered, the server can also count other task conditions affected by the task. Referring specifically to FIG. 8, FIG. 8 illustrates other task scenarios that are affected by task exe _ gdm _ m01_ ord _ da.
Including influencing business application quantity and importance degree, influencing sub-dependent task quantity, influencing downstream business application range, influencing sub-task blood relationship analysis condition, etc.
FIG. 9 shows a task and subtask blood-level analysis diagram including a job link and a job relationship list, with different colors representing job execution status. In fig. 9 it is actually shown that the status is all green in color and that both the current task and the subtask have been completed (color not shown). Of course, the task and subtask blood relationship analysis can be embodied in other forms, and this embodiment only exemplifies one case.
According to the task execution state monitoring method provided by the embodiment of the invention, the task data set and the task data details corresponding to each piece of task data are periodically obtained, and then the task data subsets corresponding to each type of labels in the task data set are counted according to the preconfigured label information included in each piece of task in the task data set. And then, according to the basic information and the task execution state, performing summary analysis on each type of task data subset to obtain a task execution state analysis result corresponding to each type of label, and displaying the task execution state analysis result to complete monitoring on the task execution state corresponding to each type of label, wherein each type of task execution state analysis result comprises information of the execution state of each task in the type of task data subset. By the method, all task data can be classified and managed. Also, the worker can know each task execution state information.
Tasks are classified according to labels, for example, as described above, several types of labels and their corresponding sub-labels of different levels are set according to needs, so that a worker can naturally and pointedly focus on the execution condition of each task in a task data subset corresponding to a certain type of label. And under the condition of detailed understanding of specific execution conditions of different tasks in the massive tasks, the importance degree of the tasks, the service application distribution of the tasks, the time appeal of the tasks needing to be completed and the like can be distinguished. By the method, the workload and labor cost of enterprises for managing and maintaining the mass tasks are reduced.
And related departments can adjust and allocate resources to the important tasks according to needs, so that the important tasks are guaranteed to be completed in limited time according to service appeal. Meanwhile, when the task execution is abnormal, such as delay, waiting or failure, the decision data can be rapidly acquired in time, and the business operation decision can be made in time. Moreover, the digital management level of the enterprise can be improved, and the digital transformation of the enterprise is promoted.
Fig. 10 is a task execution status monitoring apparatus according to an embodiment of the present invention, where the apparatus includes: an acquisition unit 1001, a statistics unit 1002, a processing unit 1003 and a presentation unit 1004.
The acquiring unit 1001 is configured to periodically acquire a task data set and task data details corresponding to each piece of task data, where the task data details include preconfigured tag information, basic information, and a task execution state corresponding to each piece of task data;
the counting unit 1002 is configured to count a task data subset corresponding to each type of tag in a preset type of tag in a task data set according to preconfigured tag information;
the processing unit 1003 is configured to perform comprehensive summary analysis on the task data subsets corresponding to each type of tag according to the basic information and the task execution state, and obtain a task execution state analysis result corresponding to each type of tag, where the task execution state analysis result includes information about the execution state of each task;
the displaying unit 1004 is configured to display the task execution state analysis result according to a preset rule, so as to complete monitoring of the task execution state corresponding to each type of tag.
Optionally, when the first class of tags includes multiple levels of sub-tags, the counting unit 1002 is configured to count, according to the preconfigured tag information, a first task data subset corresponding to the first class of tags in the task data set, and then the obtaining unit 1001 is further configured to obtain preconfigured tag information corresponding to each piece of task data in the first task data subset;
the counting unit 1002 is further configured to count the task data subsets in the first task data subset, which respectively belong to each level sub-label in the first class of labels, according to the configured label information corresponding to each piece of task data in the first task data subset, so that the execution state analysis result of the task data subset corresponding to each level sub-label is subsequently displayed according to a preset rule.
Optionally, the predetermined category labels include, but are not limited to, one or more of the following:
a line of business tag, a mission importance tag, a data application tag, or a bin type tag.
Optionally, the task execution state analysis result includes: the task type, the total number of tasks, the SLA time, the current completion progress, the statistics result of the execution state of each type of task, and the total statistics result of the execution states of all tasks.
Optionally, the apparatus further comprises: a detecting unit 1005, configured to detect whether a trigger instruction for triggering a task execution state analysis result is received;
the statistic unit 1002 is further configured to, when the detecting unit 1005 detects a first task execution state analysis result in the trigger task execution state analysis results, list a first task detail corresponding to the first task execution state analysis result;
the displaying unit 1004 is further configured to display a first task detail list, where the first task execution state analysis result is any task execution state analysis result of the task execution state analysis results.
Optionally, the first task detail list comprises: at least one task detail corresponding to a first task execution state analysis result; the detecting unit 1005 is further configured to detect whether a first task detail of the at least one task detail is triggered.
The counting unit 1002 is further configured to count a second task detail list associated with the first task detail when the triggering of the first task detail in the at least one task detail is detected;
the presentation unit 1004 is further configured to present a second task detail list, where the second task detail list includes relevant information corresponding to other tasks affected by the execution state of the first task.
The functions executed by each functional component in the task execution state monitoring apparatus provided in this embodiment have been described in detail in the embodiment corresponding to fig. 1, and therefore are not described herein again.
The task execution state monitoring device provided by the embodiment of the invention periodically obtains the task data set and the task data details corresponding to each piece of task data, and then counts the task data subsets corresponding to each type of labels in the task data set according to the preconfigured label information included in each piece of task in the task data set. And then, according to the basic information and the task execution state, performing summary analysis on each type of task data subset to obtain a task execution state analysis result corresponding to each type of label, and displaying the task execution state analysis result to complete monitoring on the task execution state corresponding to each type of label, wherein each type of task execution state analysis result comprises information of the execution state of each task in the type of task data subset. By the method, all task data can be classified and managed. Also, the worker can know each task execution state information.
Tasks are classified according to labels, for example, as described above, several types of labels and their corresponding sub-labels of different levels are set according to needs, so that a worker can naturally and pointedly focus on the execution condition of each task in a task data subset corresponding to a certain type of label. And under the condition of detailed understanding of specific execution conditions of different tasks in the massive tasks, the importance degree of the tasks, the service application distribution of the tasks, the time appeal of the tasks needing to be completed and the like can be distinguished. By the method, the workload and labor cost of enterprises for managing and maintaining the mass tasks are reduced.
And related departments can adjust and allocate resources to the important tasks according to needs, so that the important tasks are guaranteed to be completed in limited time according to service appeal. Meanwhile, when the task execution is abnormal, such as delay, waiting or failure, the decision data can be rapidly acquired in time, and the business operation decision can be made in time. Moreover, the digital management level of the enterprise can be improved, and the digital transformation of the enterprise is promoted.
Fig. 11 is a schematic structural diagram of a task execution state monitoring system according to an embodiment of the present invention, where the task execution state monitoring system 1100 shown in fig. 11 includes: at least one processor 1101, memory 1102, at least one network interface 1103, and other user interfaces 1104. Task execution status monitoring the various components in task execution status monitoring system 1100 are coupled together by a bus system 1105. It is understood that the bus system 1105 is used to enable communications among the components. The bus system 1105 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled in fig. 11 as the bus system 1105.
The user interface 1104 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, trackball, touch pad, or touch screen, among others.
It is to be understood that the memory 1102 in embodiments of the present invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a programmable Read-only memory (PROM), an erasable programmable Read-only memory (erasabprom, EPROM), an electrically erasable programmable Read-only memory (EEPROM), or a flash memory. The volatile memory may be a Random Access Memory (RAM) which functions as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (staticiram, SRAM), dynamic random access memory (dynamic RAM, DRAM), synchronous dynamic random access memory (syncronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced synchronous SDRAM (ESDRAM), synchronous link SDRAM (SLDRAM), and direct memory bus SDRAM (DRRAM). The memory 1102 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some embodiments, memory 1102 stores elements, executable units or data structures, or a subset thereof, or an expanded set thereof as follows: an operating system 11021 and application programs 11022.
The operating system 11021 includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application 11022 contains various applications such as a media player (MediaPlayer), a Browser (Browser), and the like for implementing various application services. Programs that implement methods in accordance with embodiments of the invention may be included in application 11022.
In the embodiment of the present invention, by calling a program or an instruction stored in the memory 1102, specifically, a program or an instruction stored in the application 11022, the processor 1101 is configured to execute the method steps provided by the method embodiments, for example, including:
the method comprises the steps of periodically acquiring a task data set and task data details corresponding to each piece of task data, wherein the task data details comprise pre-configured label information, basic information and a task execution state corresponding to each piece of task data;
counting a task data subset corresponding to each type of label in a preset type of label in a task data set according to preconfigured label information;
according to the basic information and the task execution state, respectively carrying out comprehensive summary analysis on the task data subsets corresponding to each type of label to obtain a task execution state analysis result corresponding to each type of label, wherein the task execution state analysis result comprises each piece of task execution state information;
and displaying the task execution state analysis result according to a preset rule so as to complete monitoring of the task execution state corresponding to each type of label.
Optionally, pre-configured tag information corresponding to each piece of task data in the first task data subset is obtained;
and according to the configured label information corresponding to each piece of task data in the first task data subset, counting the task data subsets corresponding to each level sub-label in the first task data subset, so that the execution state analysis result of the task data subset corresponding to each level sub-label is displayed according to a preset rule in the following process.
Optionally, the predetermined category labels include, but are not limited to, one or more of the following:
a line of business tag, a mission importance tag, a data application tag, or a bin type tag.
Optionally, the task execution state analysis result includes: the task type, the total number of tasks, the SLA time, the current completion progress, the statistics result of the execution state of each type of task, and the total statistics result of the execution states of all tasks.
Optionally, when a first task execution state analysis result in the trigger task execution state analysis results is detected, the first task detail list corresponding to the first task execution state analysis result is displayed, where the first task execution state analysis result is any task execution state analysis result of the task execution state analysis results.
Optionally, the first task detail list comprises: at least one task detail corresponding to a first task execution state analysis result; when triggering of a first task detail in at least one task detail is detected, a second task detail list related to the first task detail is counted and displayed, wherein the second task detail list comprises relevant information corresponding to other tasks affected by the execution state of the first task.
The methods disclosed in the embodiments of the present invention described above may be implemented in the processor 1101 or by the processor 1101. The processor 1101 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by instructions in the form of hardware, integrated logic circuits, or software in the processor 1101. The processor 1101 may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, or discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software elements in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in the memory 1102, and the processor 1101 reads the information in the memory 1102 and completes the steps of the above method in combination with the hardware thereof.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or any combination thereof. For a hardware implementation, the processing units may be implemented in one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units configured to perform the functions of the present application, or a combination thereof.
For a software implementation, the techniques herein may be implemented by means of units performing the functions herein. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
The task execution state monitoring system provided in this embodiment may be the task execution state monitoring system shown in fig. 11, and may perform all the steps of the task execution state monitoring method shown in fig. 1, so as to achieve the technical effect of the task execution state monitoring method shown in fig. 1.
The embodiment of the invention also provides a storage medium (computer readable storage medium). The storage medium herein stores one or more programs. Among others, the storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, a hard disk, or a solid state disk; the memory may also comprise a combination of memories of the kind described above.
When one or more programs in the storage medium can be executed by one or more processors, the task execution state monitoring method executed on the task execution state monitoring system side is realized.
The processor is used for executing the task execution state monitoring program stored in the memory so as to realize the following steps of the task execution state monitoring method executed on the task execution state monitoring system side:
the method comprises the steps of periodically acquiring a task data set and task data details corresponding to each piece of task data, wherein the task data details comprise pre-configured label information, basic information and a task execution state corresponding to each piece of task data;
counting a task data subset corresponding to each type of label in a preset type of label in a task data set according to preconfigured label information;
according to the basic information and the task execution state, respectively carrying out comprehensive summary analysis on the task data subsets corresponding to each type of label to obtain a task execution state analysis result corresponding to each type of label, wherein the task execution state analysis result comprises each piece of task execution state information;
and displaying the task execution state analysis result according to a preset rule so as to complete monitoring of the task execution state corresponding to each type of label.
Optionally, pre-configured tag information corresponding to each piece of task data in the first task data subset is obtained;
and according to the configured label information corresponding to each piece of task data in the first task data subset, counting the task data subsets corresponding to each level sub-label in the first task data subset, so that the execution state analysis result of the task data subset corresponding to each level sub-label is displayed according to a preset rule in the following process.
Optionally, the predetermined category labels include, but are not limited to, one or more of the following:
a line of business tag, a mission importance tag, a data application tag, or a bin type tag.
Optionally, the task execution state analysis result includes: the task type, the total number of tasks, the SLA time, the current completion progress, the statistics result of the execution state of each type of task, and the total statistics result of the execution states of all tasks.
Optionally, when a first task execution state analysis result in the trigger task execution state analysis results is detected, the first task detail list corresponding to the first task execution state analysis result is displayed, where the first task execution state analysis result is any task execution state analysis result of the task execution state analysis results.
Optionally, the first task detail list comprises: at least one task detail corresponding to a first task execution state analysis result; when triggering of a first task detail in at least one task detail is detected, a second task detail list related to the first task detail is counted and displayed, wherein the second task detail list comprises relevant information corresponding to other tasks affected by the execution state of the first task.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A task execution state monitoring method is characterized by comprising the following steps:
the method comprises the steps of periodically acquiring a task data set and task data details corresponding to each piece of task data, wherein the task data details comprise pre-configured label information, basic information and a task execution state corresponding to each piece of task data;
according to the preconfigured label information, counting a task data subset corresponding to each type of label in the preset type of label in the task data set;
according to the basic information and the task execution state, respectively carrying out comprehensive summary analysis on the task data subsets corresponding to each type of label to obtain a task execution state analysis result corresponding to each type of label, wherein the task execution state analysis result comprises each piece of task execution state information;
and displaying the task execution state analysis result according to a preset rule so as to complete monitoring of the task execution state corresponding to each type of label.
2. The method according to claim 1, wherein when the first class label includes multiple levels of sub-labels, after counting a first task data subset corresponding to the first class label in the task data set according to the preconfigured label information, the method further includes:
acquiring preconfigured label information corresponding to each piece of task data in the first task data subset;
and according to the configured label information corresponding to each piece of task data in the first task data subset, counting the task data subsets corresponding to each level sub-label in the first task data subset, so that the execution state analysis result of the task data subset corresponding to each level sub-label is displayed according to the preset rule in the following process.
3. The method according to claim 1 or 2, wherein the preset category labels include but are not limited to one or more of the following:
a line of business tag, a mission importance tag, a data application tag, or a bin type tag.
4. The method of claim 1 or 2, wherein the task execution state analysis results comprise: the task type, the total number of tasks, the service level agreement SLA time, the current completion progress, the statistics result of the execution state of each type of task, and the total result of the statistics of the execution states of all tasks.
5. The method of claim 4, wherein when a first task execution state analysis result is detected that triggers the task execution state analysis results, the method further comprises:
and counting a first task detail list corresponding to the first task execution state analysis result, and displaying the first task detail list, wherein the first task execution state analysis result is any task execution state analysis result of the task execution state analysis result.
6. The method of claim 5, wherein the first task detail list comprises: at least one task detail corresponding to the first task execution state analysis result; after displaying the first task detail list corresponding to the first task execution state analysis result, the method further comprises:
when triggering of a first task detail in the at least one task detail is detected, a second task detail list associated with the first task detail is counted and displayed, wherein the second task detail list comprises relevant information corresponding to other tasks affected by the execution state of the first task.
7. A task execution state monitoring apparatus, characterized in that the apparatus comprises:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for periodically acquiring a task data set and task data details corresponding to each piece of task data, and the task data details comprise pre-configured label information, basic information and a task execution state corresponding to each piece of task data;
the counting unit is used for counting the task data subsets corresponding to each type of label in the preset type of label in the task data set according to the preconfigured label information;
the processing unit is used for respectively carrying out comprehensive summary analysis on the task data subsets corresponding to each type of label according to the basic information and the task execution state, and acquiring a task execution state analysis result corresponding to each type of label, wherein the task execution state analysis result comprises each piece of task execution state information;
and the display unit is used for displaying the task execution state analysis result according to a preset rule so as to monitor the task execution state corresponding to each type of label.
8. The apparatus according to claim 7, wherein when the first class label includes multiple levels of sub-labels, the statistics unit is configured to, after counting a first task data subset corresponding to the first class label in the task data set according to the preconfigured label information, obtain preconfigured label information corresponding to each piece of task data in the first task data subset;
the statistical unit is further configured to perform statistics on the task data subsets corresponding to each level sub-label in the first task data subset according to the configured label information corresponding to each piece of task data in the first task data subset, so that the execution state analysis result of the task data subset corresponding to each level sub-label is subsequently displayed according to the preset rule.
9. A task execution status monitoring system, the system comprising: at least one processor and memory;
the processor is used for executing the task execution state monitoring program stored in the memory so as to realize the task execution state monitoring method according to any one of claims 1-6.
10. A computer storage medium storing one or more programs executable by the task execution status monitoring system according to claim 9 to implement the task execution status monitoring method according to any one of claims 1 to 6.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112487282A (en) * | 2020-11-04 | 2021-03-12 | 山东中创软件商用中间件股份有限公司 | Business processing condition monitoring method and device, electronic equipment and storage medium |
| CN112597166A (en) * | 2020-12-28 | 2021-04-02 | 恩亿科(北京)数据科技有限公司 | Data stream task management method, system, computer device and readable storage medium |
| CN112837134A (en) * | 2021-03-10 | 2021-05-25 | 拉扎斯网络科技（上海）有限公司 | Task display device |
| CN113672418A (en) * | 2021-08-02 | 2021-11-19 | 北京每日优鲜电子商务有限公司 | Data processing task detail page display method and device, electronic equipment and medium |
| CN114579401A (en) * | 2022-02-28 | 2022-06-03 | 北京字节跳动网络技术有限公司 | Task detection method and device, electronic equipment and storage medium |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150012330A1 (en) * | 2013-07-05 | 2015-01-08 | Fujitsu Limited | Work management method |
| US20180121311A1 (en) * | 2016-10-28 | 2018-05-03 | Linkedin Corporation | Identifying request-level critical paths in multi-phase parallel tasks |
| CN109685349A (en) * | 2018-12-14 | 2019-04-26 | 拉扎斯网络科技（上海）有限公司 | Task monitoring method and system, electronic equipment and computer readable storage medium |
| US20190163821A1 (en) * | 2016-09-26 | 2019-05-30 | Splunk Inc. | Data Fabric Service System Architecture |
| CN110288193A (en) * | 2019-05-23 | 2019-09-27 | 中国平安人寿保险股份有限公司 | Mission Monitor processing method, device, computer equipment and storage medium |
| CN110427295A (en) * | 2019-06-26 | 2019-11-08 | 苏州浪潮智能科技有限公司 | A kind of monitoring method, equipment and the readable medium of cloud platform resource |
| CN110597858A (en) * | 2019-08-30 | 2019-12-20 | 深圳壹账通智能科技有限公司 | Task data processing method and device, computer equipment and storage medium |
| CN110704484A (en) * | 2019-09-09 | 2020-01-17 | 华迪计算机集团有限公司 | Method and system for processing mass real-time data stream |
| CN110839055A (en) * | 2018-08-17 | 2020-02-25 | 菜鸟智能物流控股有限公司 | Task processing method, device and system |
| CN110912757A (en) * | 2019-12-24 | 2020-03-24 | 聚好看科技股份有限公司 | Service monitoring method and server |
| CN111177489A (en) * | 2019-12-26 | 2020-05-19 | 贝壳技术有限公司 | Object data display method and system |
-
2020
- 2020-06-28 CN CN202010600259.5A patent/CN111768097B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150012330A1 (en) * | 2013-07-05 | 2015-01-08 | Fujitsu Limited | Work management method |
| US20190163821A1 (en) * | 2016-09-26 | 2019-05-30 | Splunk Inc. | Data Fabric Service System Architecture |
| US20180121311A1 (en) * | 2016-10-28 | 2018-05-03 | Linkedin Corporation | Identifying request-level critical paths in multi-phase parallel tasks |
| CN110839055A (en) * | 2018-08-17 | 2020-02-25 | 菜鸟智能物流控股有限公司 | Task processing method, device and system |
| CN109685349A (en) * | 2018-12-14 | 2019-04-26 | 拉扎斯网络科技（上海）有限公司 | Task monitoring method and system, electronic equipment and computer readable storage medium |
| CN110288193A (en) * | 2019-05-23 | 2019-09-27 | 中国平安人寿保险股份有限公司 | Mission Monitor processing method, device, computer equipment and storage medium |
| CN110427295A (en) * | 2019-06-26 | 2019-11-08 | 苏州浪潮智能科技有限公司 | A kind of monitoring method, equipment and the readable medium of cloud platform resource |
| CN110597858A (en) * | 2019-08-30 | 2019-12-20 | 深圳壹账通智能科技有限公司 | Task data processing method and device, computer equipment and storage medium |
| CN110704484A (en) * | 2019-09-09 | 2020-01-17 | 华迪计算机集团有限公司 | Method and system for processing mass real-time data stream |
| CN110912757A (en) * | 2019-12-24 | 2020-03-24 | 聚好看科技股份有限公司 | Service monitoring method and server |
| CN111177489A (en) * | 2019-12-26 | 2020-05-19 | 贝壳技术有限公司 | Object data display method and system |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112487282A (en) * | 2020-11-04 | 2021-03-12 | 山东中创软件商用中间件股份有限公司 | Business processing condition monitoring method and device, electronic equipment and storage medium |
| CN112597166A (en) * | 2020-12-28 | 2021-04-02 | 恩亿科(北京)数据科技有限公司 | Data stream task management method, system, computer device and readable storage medium |
| CN112837134A (en) * | 2021-03-10 | 2021-05-25 | 拉扎斯网络科技（上海）有限公司 | Task display device |
| CN113672418A (en) * | 2021-08-02 | 2021-11-19 | 北京每日优鲜电子商务有限公司 | Data processing task detail page display method and device, electronic equipment and medium |
| CN114579401A (en) * | 2022-02-28 | 2022-06-03 | 北京字节跳动网络技术有限公司 | Task detection method and device, electronic equipment and storage medium |
| CN114579401B (en) * | 2022-02-28 | 2024-07-09 | 北京字节跳动网络技术有限公司 | Task detection method and device, electronic equipment and storage medium |
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