CN111768097B - Task execution state monitoring method, device, system 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 task data sets and task data details corresponding to each piece of task data; according to the preconfigured label information, counting task data subsets corresponding to each type of labels in the preset type of labels in the task data set; according to the basic information and the task execution state, respectively carrying out comprehensive summarization analysis on the task data subsets corresponding to each type of labels to obtain task execution state analysis results corresponding to each type of labels; and displaying the task execution state analysis result according to a preset rule to complete monitoring of the task execution state corresponding to each type of label. By the mode, the workload and labor cost required by enterprises for managing and maintaining 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, a system and a storage medium for monitoring a task execution state.
Background
With the advent of the big data age, massive big data computation exists in enterprises every day, and the big data computation is used for supporting the marketing and operation of the enterprises. A large number of hadoop batch processing calculation tasks, spark real-time calculation tasks, preston impromptu query calculation tasks, flank real-time calculation tasks and the like exist in an enterprise big data platform, a data middle platform or a data warehouse environment, and the number of tasks is different in scale of ten thousand, hundred thousand and millions. Such massive tasks are often distributed over different sub-clusters, sub-companies, business departments, etc.
The data management department, the operation and maintenance department, the enterprise decision department and the like of the platform are difficult to know the specific execution conditions of different tasks in the mass tasks in detail. That is, the enterprise needs to put a great amount of work and labor cost into the management and maintenance of the massive tasks.
Disclosure of Invention
In view of this, in order to solve the technical problems in the prior art, embodiments of the present invention provide a method, an apparatus, a system, and a storage medium for monitoring a task execution state.
In a first aspect, an embodiment of the present invention provides a method for monitoring a task execution state, where the method includes:
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 tag information, basic information and task execution states corresponding to each piece of task data;
According to the preconfigured label information, counting task data subsets corresponding to each type of labels in the preset type of labels in the task data set;
according to the basic information and the task execution state, respectively carrying out comprehensive summarization analysis on the task data subsets corresponding to each type of labels to obtain task execution state analysis results corresponding to each type of labels, wherein the task execution state analysis results comprise each piece of task execution state information;
and displaying the task execution state analysis result according to a preset rule to complete monitoring of the task execution state corresponding to each type of label.
In one possible implementation manner, when the first type tag includes a multi-level sub-tag, according to the preconfigured tag information, after the first task data subset corresponding to the first type tag in the statistical task data set, the method further includes:
acquiring pre-configured tag information corresponding to each piece of task data in the first task data subset;
and counting task data subsets corresponding to each level of sub-labels 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 of sub-label is 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:
business line labels, task importance labels, data application labels, or bin type labels.
In one possible implementation, the task execution state analysis result includes: task type, total number of tasks, service-Level Agreement (SLA) time, current completion progress, statistics of each type of task execution status, and statistics of all task execution status.
In one possible embodiment, when a first task execution state analysis result of the trigger task execution state analysis results is detected, 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 includes: at least one task detail corresponding to the first task execution state analysis result; after the first task detail list corresponding to the analysis result of the first task execution state is displayed, the method further comprises the following steps:
And when the first task detail in the at least one piece of task details is detected to be triggered, counting and displaying a second task detail list associated with the first task detail, wherein the second task detail list comprises related 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 status monitoring device, including:
the task data acquisition unit is used for periodically acquiring a task data set and task data details corresponding to each piece of task data, wherein the task data details comprise preconfigured label information, basic information and task execution states corresponding to each piece of task data;
the statistics unit is used for counting task data subsets corresponding to each type of labels in the preset type of labels in the task data set according to the preset label information;
the processing unit is used for respectively carrying out comprehensive summarization analysis on the task data subsets corresponding to each type of labels according to the basic information and the task execution state, and obtaining task execution state analysis results corresponding to each type of labels, wherein the task execution state analysis results comprise 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.
In a possible implementation manner, when the first type tag includes a multi-level sub-tag, the statistics unit is configured to, according to the preconfigured tag information, count, in the task data set, a first task data subset corresponding to the first type tag, and then the obtaining unit is further configured to obtain preconfigured tag information corresponding to each piece of task data in the first task data subset;
the statistics unit is further configured to, according to the configured tag information corresponding to each piece of task data in the first task data subset, count task data subsets corresponding to each level of sub-tags in the first class of tags in the first task data subset, so as to display the execution state analysis result of the task data subset corresponding to each level of sub-tags 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:
business line labels, task importance labels, data application labels, or bin type labels.
In a third aspect, an embodiment of the present invention provides a task execution status monitoring system, including: at least one processor and memory;
the processor is configured to execute a task execution status monitor program stored in the memory, so as to implement the task execution status monitor method as described in any implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium storing one or more programs executable by a task execution status monitoring system as described in the third aspect, to implement a task execution status monitoring method as described in any embodiment of the first aspect.
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 acquired, and then the task data subset corresponding to each type of label in the task data set is 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, summarizing and analyzing each type of task data subset to obtain a task execution state analysis result corresponding to each type of tag, and displaying the task execution state analysis result to complete monitoring of the task execution state corresponding to each type of tag, wherein each type of task execution state analysis result comprises each piece of task execution state information in the type of task data subset. By the method, classified management of all task data can be achieved. Moreover, the worker can understand each piece of 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 label can be focused in a targeted manner. That is, the data management department, the operation and maintenance department, the enterprise decision department and the like of the platform are convenient to know the specific execution conditions of different tasks in the mass tasks in detail. And the workload and labor cost required by enterprises for managing and maintaining mass tasks are greatly reduced.
Drawings
FIG. 1 is a schematic flow chart of a task execution status monitoring method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a label creation process provided by the present invention;
FIG. 3 is a schematic diagram of creating a class 4 tag based on the tag creation process corresponding to FIG. 2 according to the present invention;
FIG. 4 is a schematic diagram of creating a level of multiple sub-labels under a class of labels according to the present invention;
FIG. 5 is a schematic diagram of batch tagging of data provided by the present invention;
FIG. 6 is a schematic diagram of a task execution state analysis result corresponding to financial daily report, wherein the primary label is business application and the secondary label is a financial daily report;
FIG. 7 is a schematic diagram showing the results of analysis of the execution state of corresponding tasks, wherein the primary label is business application and the secondary label is consumer financial daily report;
FIG. 8 is a schematic diagram of other task situations affected by task exe_gdm_m01_ord_da according to the present invention;
FIG. 9 is a schematic diagram of a task and subtask blood margin analysis provided by the present invention;
FIG. 10 is a schematic diagram of a task execution status monitoring device 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
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
For the purpose of facilitating an understanding of the embodiments of the present invention, reference will now be made to the following description of specific embodiments, taken in conjunction with the accompanying drawings, which are not intended to limit the embodiments of the invention.
Fig. 1 is a schematic flow chart of a task execution state monitoring method according to an embodiment of the present invention, as shown in fig. 1. Before introducing the method steps, first the preparation work that needs to be done before performing the method steps is introduced. See in particular the following:
first, tag information is created.
Referring specifically to fig. 2, when creating a tag, the tag includes creating a tag name, a tag description, a tag classification, and a tag status. Tag status may include available, unavailable, etc. For example, tag categories may include, but are not limited to, tag categories as shown in FIG. 2, including, for example, line of business tags, importance, data applications, and bin types, among others. Of course, other labels may be created, and may be specifically set according to the actual situation. Fig. 3 illustrates a schematic diagram of creating the class 4 tag described above.
After creating the tags of the preset categories, a lower level tag may also be set for each category tag. Referring specifically to fig. 3, the option of adding a lower label may be set after each type of label of a preset type, and of course, functions of adding a lower class, editing, deleting, and the like may also be added to each type of label or each level of label.
For example, as shown in fig. 4, one of the lower level labels corresponding to a certain type of label is shown. Including multiple identical level sub-tags. Each sub-tag may be provided with tag name, tag description, tag status, creation time, modification time, etc., and may also be provided with basic information (not shown in fig. 4) such as tag type, tag responsible person, tag use, etc. Editing, deleting, etc. of the above-described contents in each sub-level label may also be configured. In particular, how many sub-tags are set for each level tag, and the content configured in the sub-tags and the like can be set according to actual situations, which is not limited too much.
After creating the tag, the application of the tag will be described below.
Specifically, in a big data environment (including a big data platform, a data center or a data warehouse environment), when an enterprise makes 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 is 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 the enterprise is generated. In a specific example, the task data set is derived from decision analysis data generated by a four-layer architecture commonly used in a data warehouse after a certain data processing, and the decision analysis data includes cache layer data, source layer data, general layer data and application layer data.
Then, the staff sets the labels for the four types of data respectively, wherein the labels can be set for each piece of data independently or set for a plurality of pieces of data in batches. For example, as shown in fig. 5. FIG. 5 shows a schematic diagram of batch tagging of data.
First, the name of each piece of data, attribute information corresponding to one or more types of tags in a preset tag class, such as attribute information configured in a business line tag for a plurality of pieces of data, attribute information in importance degree, attribute information in data application, and the like in fig. 5, are set. That is, one piece of data is not only configured with one type of tag, but a plurality of types of tags may be configured according to actual situations.
Further, since each tag can correspond to a plurality of sub-level tags, each piece of data can be further refined to determine which level of sub-tag in a certain tag the piece of data specifically corresponds to, and the details corresponding to the piece of data are configured in the sub-tags. For example, after a piece of data is subdivided, the corresponding cross-border service-external receipt-cross-border B in fig. 4 is just described, and the state and the corresponding description refer to fig. 4.
Through the above procedure, a tag is configured for each piece of decision data. The following method steps of the invention are then carried out:
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 task execution state.
The label information preconfigured by each piece of task data is the label information configured by the method. The tag information may include a plurality of tags corresponding to the task data, or a plurality of sub-tags corresponding to each of the plurality of tags, and the like, and is specifically determined according to the actual situation.
The basic information can comprise information such as task names, task descriptions, task responsible persons, responsible person contact ways, SLA time and the like. The part of data is mainly obtained through a task center of a large data platform, and can be extracted from a task basic information table.
The task execution state can be obtained 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, executing, success or failure, etc.), and task log production time may be extracted.
Step 120, counting task data subsets corresponding to each type of tags in the preset type of tags in the task data set according to the preset tag information.
Specifically, as described above, each piece of task data may include a plurality of tags in the tag information corresponding to the task data. 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, a importance level tag, a data application tag, and the like. Then, the task data cannot be classified directly, but the task data subset corresponding to each type of tag in the preset type of tags can be counted based on the tag information.
Optionally, the preset category labels described herein include, but are not limited to, one or more of a business wire label, a mission importance label, a data application label, or a bin type label.
For example, with the service line tag as a reference, determining whether the pre-configured tag information included in each piece of task data includes the service line tag, and determining that the tag information corresponding to the piece of task data includes the service line tag, then classifying the piece of task data into a task data subset corresponding to the service line tag. In addition, the importance level label is used as a reference, and it is also possible to determine that the label information corresponding to the task data includes a label such as importance level, and then it is also necessary to classify the task data into a task data subset corresponding to importance level. I.e. the same piece of data, can be categorized into task data subsets corresponding to different tags.
And 130, respectively carrying out comprehensive summarization analysis on the task data subsets corresponding to each type of labels according to the basic information and the task execution state, and obtaining task execution state analysis results corresponding to each type of labels, wherein the task execution state analysis results comprise each piece of task execution state information.
And 140, displaying the task execution state analysis result according to a preset rule to complete monitoring of the task execution state corresponding to each type of label.
Optionally, the task execution state analysis results include, but are not limited to, one or more of, for example, task type, total number of tasks, SLA time, current completion progress, statistics of each type of task execution state, and statistics of all task execution states, etc.
When the task execution state analysis result is displayed specifically, classification and classification display can be performed. For example, one display area is divided for each level. Then dividing a plurality of subareas in the display area, wherein each subarea is used for displaying a task execution state analysis result corresponding to one sub-label. Or if the display area is insufficient, the task execution state analysis results corresponding to the sub-labels of different levels of one major class can be displayed periodically, for example, the same time period is displayed. And in the next time period, displaying the task execution state analysis results corresponding to the different-level sub-labels of the other major class. For example 1 minute, or 2 minutes, or another period of time, 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 is more convenient for the staff to monitor.
Further optionally, the task execution state analysis result is also updated periodically, and a specific period is the same as a period of acquiring the task data set.
Specifically, referring to fig. 6 and fig. 7, a first-level label is a business application, a second-level label is a financial daily report, that is, a sub-label is a task execution state analysis result corresponding to the financial daily report, which is illustrated in fig. 6. Fig. 7 shows a result of performing state analysis on tasks corresponding to a consumer financial daily report by using a primary label as a business application and using a secondary label as a consumer financial daily report, namely using a sub label as a consumer financial daily report.
Referring to fig. 6, in the analysis result of the task execution state corresponding to the financial daily report, the completion progress is shown to be 100%, and the SLA time is shown to be 6:00. the task (job) types include: extraction operation, source model operation, general model operation and application model operation.
The statistics of the execution state of each type of task are shown, for example, for extraction operation, the total number of tasks is 150, the number of tasks is 150 successfully, the number of tasks is 0 in operation, the number of tasks is 0 in failure, and the number of tasks is delayed by 0. Similarly, the source model is pasted, the total number of the source model is 150, the number of the source model is 150, and the number of tasks in operation, failure, waiting, delay and the like is 0. Other types of job scenarios are shown in fig. 6 and are not illustrated one by one.
And the statistics of all task execution states are shown in the last row of fig. 6, the total number of the tasks is 380, the number of the tasks is 380 successfully, and the number of the tasks in operation, failure, waiting and delay is 0.
Referring to fig. 7, in the task execution status analysis result corresponding to the consumption financial daily report, the completion progress is 83% and the SLA time is 8:00. Task (job) distribution scenario:
for example, exposing each type of task execution state statistics includes:
the total amount of the extracted operation is 100, the successful operation is 100, and the operation, failure, waiting, delay and the like are all 0.
The total amount of the general type operation is 50, 30 is successful, 10 is in operation, 5 is failed, 5 is waited, and 20 is delayed. Other types of task situations, see fig. 7, will not be described.
The total result of all task execution state statistics is shown in fig. 7, and includes 300 total pieces, 250 successful pieces, 15 running pieces, 5 failed pieces, 30 waiting pieces and 50 delaying pieces.
Optionally, in order to make the staff more deeply understand the analysis result of the task execution state, the method may further include:
when a first task execution state analysis result in the trigger task execution state analysis results is detected, a first task detail list corresponding to the first task execution state analysis result is counted and displayed, 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 staff member triggers the running task data corresponding to the general model job in fig. 7 (that is, triggers a type of task execution status statistics result), the server may count the details list of the currently running task, for example, including details of how many running tasks are included, and the task name, task description, task running progress, task label and so on corresponding to each running task.
Further optionally, the first task detail list includes: at least one task detail corresponding to the first task execution state analysis result; after the first task detail list corresponding to the analysis result of the first task execution state is displayed, the method further comprises the following steps:
and when the first task detail in the at least one piece of task details is detected to be triggered, counting and displaying a second task detail list associated with the first task detail, wherein the second task detail list comprises related information corresponding to other tasks affected by the execution state of the first task.
For example, when the first task details are 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 shows other task scenarios affected by task exe_gdm_m01_ord_da.
The method comprises the steps of affecting the number and importance of service applications, affecting the number of sub-dependent tasks, affecting the application range of downstream services, affecting the blood-edge analysis condition of sub-tasks and the like.
FIG. 9 shows a task and subtask blood edge analysis schematic including a list of job links and job relationships, with different colors representing job execution status. The actual display in fig. 9 is that the status colors are all green, and that both the current task and the subtask have been completed (colors not shown). Of course, the task and subtask blood-edge analysis can also 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 acquired, and then the task data subset corresponding to each type of label in the task data set is 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, summarizing and analyzing each type of task data subset to obtain a task execution state analysis result corresponding to each type of tag, and displaying the task execution state analysis result to complete monitoring of the task execution state corresponding to each type of tag, wherein each type of task execution state analysis result comprises each piece of task execution state information in the type of task data subset. By the method, classified management of all task data can be achieved. Moreover, the worker can understand each piece of task execution state information.
The tasks are classified according to the labels, for example, the above-mentioned labels of several types and the corresponding sub-labels of different levels are set according to the needs, so that the staff can pay attention to the execution condition of each task in the task data subset corresponding to a certain type of label in a targeted manner naturally. The method can also distinguish the importance degree of the tasks, the service application distribution of the tasks, the time requirements of the tasks needing to be completed and the like under the condition of knowing the specific execution conditions of different tasks in a large number of tasks in detail. By the mode, the workload and labor cost required by enterprises for managing and maintaining mass tasks are reduced.
Moreover, related departments can also adjust and allocate resources to important tasks according to the needs, and the important tasks are guaranteed to be completed in limited time according to service requirements. Meanwhile, when abnormal task execution is found, such as delay, waiting or failure, decision data can be timely and rapidly acquired, and service operation decisions can be timely made. Furthermore, the enterprise digital management level can be improved, and enterprise digital transformation is promoted.
Fig. 10 is a device for monitoring a task execution state, which is provided by an embodiment of the present invention, and includes: an acquisition unit 1001, a statistics unit 1002, a processing unit 1003, and a presentation unit 1004.
An obtaining unit 1001, configured to periodically obtain 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 task execution status corresponding to each piece of task data;
a statistics unit 1002, configured to, according to the preconfigured tag information, count task data subsets corresponding to each type of tags in the task data set, where the task data subsets belong to preset types of tags;
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 each piece of task execution state information;
the display unit 1004 is configured to display the task execution state analysis result according to a preset rule, so as to monitor the task execution state corresponding to each type of tag.
Optionally, when the first type tag includes a multi-level sub-tag, the statistics unit 1002 is configured to, according to the preconfigured tag information, count, in the task data set, a first task data subset corresponding to the first type tag, 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 statistics unit 1002 is further configured to, according to the configured tag information corresponding to each piece of task data in the first task data subset, count the task data subsets corresponding to each level of sub-tags in the first class of tags in the first task data subset, so as to display the execution state analysis result of the task data subset corresponding to each level of sub-tag according to a preset rule.
Optionally, the preset category labels include, but are not limited to, one or more of the following:
business line labels, task importance labels, data application labels, or bin type labels.
Optionally, the task execution state analysis result includes: task type, total number of tasks, SLA time, current completion progress, statistics of each type of task execution status, and statistics of all task execution status.
Optionally, the apparatus further comprises: a detection unit 1005, configured to detect whether a trigger instruction triggering a task execution state analysis result is received;
the statistics unit 1002 is further configured to, when the detection unit 1005 detects a first task execution state analysis result from the trigger task execution state analysis results, list a first task detail corresponding to the first task execution state analysis result;
The display unit 1004 is further configured to display the 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 includes: at least one task detail corresponding to the first task execution state analysis result; the detection unit 1005 is further configured to detect whether to trigger a first task detail of the at least one task detail.
The statistics unit 1002 is further configured to, when detecting that a first task detail in the at least one task detail is triggered, count a second task detail list associated with the first task detail;
the display unit 1004 is further configured to display a second task detail list, where the second task detail list includes related 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 status monitoring device provided in this embodiment are described in detail in the embodiment corresponding to fig. 1, so that the details are not repeated here.
According to the task execution state monitoring device 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 acquired, and then the task data subset corresponding to each type of label in the task data set is 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, summarizing and analyzing each type of task data subset to obtain a task execution state analysis result corresponding to each type of tag, and displaying the task execution state analysis result to complete monitoring of the task execution state corresponding to each type of tag, wherein each type of task execution state analysis result comprises each piece of task execution state information in the type of task data subset. By the method, classified management of all task data can be achieved. Moreover, the worker can understand each piece of task execution state information.
The tasks are classified according to the labels, for example, the above-mentioned labels of several types and the corresponding sub-labels of different levels are set according to the needs, so that the staff can pay attention to the execution condition of each task in the task data subset corresponding to a certain type of label in a targeted manner naturally. The method can also distinguish the importance degree of the tasks, the service application distribution of the tasks, the time requirements of the tasks needing to be completed and the like under the condition of knowing the specific execution conditions of different tasks in a large number of tasks in detail. By the mode, the workload and labor cost required by enterprises for managing and maintaining mass tasks are reduced.
Moreover, related departments can also adjust and allocate resources to important tasks according to the needs, and the important tasks are guaranteed to be completed in limited time according to service requirements. Meanwhile, when abnormal task execution is found, such as delay, waiting or failure, decision data can be timely and rapidly acquired, and service operation decisions can be timely made. Furthermore, the enterprise digital management level can be improved, and enterprise digital transformation is promoted.
Fig. 11 is a schematic structural diagram of a task execution status monitoring system according to an embodiment of the present invention, and a task execution status monitoring system 1100 shown in fig. 11 includes: at least one processor 1101, memory 1102, at least one network interface 1103, and other user interface 1104. Task execution status monitoring the various components in the task execution status monitoring system 1100 are coupled together by a bus system 1105. It is appreciated that bus system 1105 is used to implement the connected communications between these components. The bus system 1105 includes a power bus, a control bus, and a status signal bus in addition to a data bus. But for clarity of illustration, the various buses are labeled as bus system 1105 in fig. 11.
The user interface 1104 may include, among other things, a display, a keyboard, or a pointing device (e.g., a mouse, a trackball, a touch pad, or a touch screen, etc.).
It will be appreciated that memory 1102 in embodiments of the 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 (ProgrammableROM, PROM), an erasable programmable Read-only memory (ErasablePROM, EPROM), an electrically erasable programmable Read-only memory (ElectricallyEPROM, EEPROM), or a flash memory, among others. The volatile memory may be a random access memory (RandomAccessMemory, RAM) that acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic random access memory (DynamicRAM, DRAM), synchronous dynamic random access memory (SynchronousDRAM, SDRAM), double data rate synchronous dynamic random access memory (ddr SDRAM), enhanced Synchronous Dynamic Random Access Memory (ESDRAM), synchronous link dynamic random access memory (SynchlinkDRAM, SLDRAM), and direct memory bus random access memory (DirectRambusRAM, DRRAM). The memory 1102 described herein is intended to comprise, without being limited to, these and any other suitable types of memory.
In some implementations, the memory 1102 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: 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 programs 11022 include various application programs such as a media player (MediaPlayer), a Browser (Browser), and the like for implementing various application services. A program for implementing the method of the embodiment of the present invention may be included in the application program 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 perform the method steps provided by the method embodiments, for example, including:
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 tag information, basic information and task execution states corresponding to each piece of task data;
According to the preconfigured label information, counting task data subsets corresponding to each type of labels in the preset type of labels in the task data set;
according to the basic information and the task execution state, respectively carrying out comprehensive summarization analysis on the task data subsets corresponding to each type of labels to obtain task execution state analysis results corresponding to each type of labels, wherein the task execution state analysis results comprise each piece of task execution state information;
and displaying the task execution state analysis result according to a preset rule to complete monitoring of the task execution state corresponding to each type of label.
Optionally, obtaining preconfigured label information corresponding to each piece of task data in the first task data subset;
and counting task data subsets corresponding to each level of sub-labels 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 of sub-label is displayed according to a preset rule.
Optionally, the preset category labels include, but are not limited to, one or more of the following:
Business line labels, task importance labels, data application labels, or bin type labels.
Optionally, the task execution state analysis result includes: task type, total number of tasks, SLA time, current completion progress, statistics of each type of task execution status, and statistics of all task execution status.
Optionally, when a first task execution state analysis result in the trigger task execution state analysis results is detected, a first task detail list corresponding to the first task execution state analysis result is displayed, wherein 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 includes: at least one task detail corresponding to the first task execution state analysis result; and when the first task detail in the at least one piece of task details is detected to be triggered, counting and displaying a second task detail list associated with the first task detail, wherein the second task detail list comprises related information corresponding to other tasks affected by the execution state of the first task.
The method disclosed in the above embodiment of the present invention may be applied to the processor 1101 or implemented 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 integrated logic circuitry in hardware in the processor 1101 or instructions in software. The processor 1101 described above may be a general purpose processor, a digital signal processor (DigitalSignalProcessor, DSP), an application specific integrated circuit (application specific IntegratedCircuit, ASIC), an off-the-shelf programmable gate array (FieldProgrammableGateArray, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. The disclosed methods, steps, and logic blocks 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 embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software elements in a decoding processor. The software elements may be located in a random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 1102, and the processor 1101 reads information in the memory 1102 and performs the steps of the method in combination with its hardware.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more application specific integrated circuits (ApplicationSpecificIntegratedCircuits, ASIC), digital signal processors (DigitalSignalProcessing, DSP), digital signal processing devices (dspev), programmable logic devices (ProgrammableLogicDevice, PLD), field programmable gate arrays (Field-ProgrammableGateArray, FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions of the application, or a combination thereof.
For a software implementation, the techniques herein may be implemented by means of units that perform 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 a task execution state monitoring system as shown in fig. 11, and may perform all steps of the task execution state monitoring method as shown in fig. 1, so as to achieve the technical effects of the task execution state monitoring method as shown in fig. 1, and the detailed description with reference to fig. 1 is omitted herein for brevity.
The embodiment of the invention also provides a storage medium (computer readable storage medium). The storage medium here stores one or more programs. Wherein the storage medium may comprise volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid state disk; the memory may also comprise a combination of the above types of memories.
When one or more programs in the storage medium are executable by one or more processors, the task execution state monitoring method executed on the task execution state monitoring system side is implemented.
The processor is used for executing the task execution state monitoring program stored in the memory to realize the following steps of the task execution state monitoring method executed on the task execution state monitoring system side:
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 tag information, basic information and task execution states corresponding to each piece of task data;
according to the preconfigured label information, counting task data subsets corresponding to each type of labels in the preset type of labels in the task data set;
According to the basic information and the task execution state, respectively carrying out comprehensive summarization analysis on the task data subsets corresponding to each type of labels to obtain task execution state analysis results corresponding to each type of labels, wherein the task execution state analysis results comprise each piece of task execution state information;
and displaying the task execution state analysis result according to a preset rule to complete monitoring of the task execution state corresponding to each type of label.
Optionally, obtaining preconfigured label information corresponding to each piece of task data in the first task data subset;
and counting task data subsets corresponding to each level of sub-labels 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 of sub-label is displayed according to a preset rule.
Optionally, the preset category labels include, but are not limited to, one or more of the following:
business line labels, task importance labels, data application labels, or bin type labels.
Optionally, the task execution state analysis result includes: task type, total number of tasks, SLA time, current completion progress, statistics of each type of task execution status, and statistics of all task execution status.
Optionally, when a first task execution state analysis result in the trigger task execution state analysis results is detected, a first task detail list corresponding to the first task execution state analysis result is displayed, wherein 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 includes: at least one task detail corresponding to the first task execution state analysis result; and when the first task detail in the at least one piece of task details is detected to be triggered, counting and displaying a second task detail list associated with the first task detail, wherein the second task detail list comprises related information corresponding to other tasks affected by the execution state of the first task.
Those of skill would further appreciate that the various illustrative elements 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 elements and steps are described above generally in terms of function in order to clearly illustrate the 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 solution. 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, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed 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 foregoing detailed description of the invention has been presented for purposes of illustration and description, and it should be understood that the invention is not limited to the particular embodiments disclosed, but is intended to cover all modifications, equivalents, alternatives, and improvements within the spirit and principles of the invention.
Claims (10)
1. A method for monitoring a task execution state, the method comprising:
periodically acquiring a task data set and task data details corresponding to each piece of task data, wherein the task data details comprise preconfigured label information, basic information and task execution states corresponding to each piece of task data;
Counting task data subsets corresponding to each type of tags in the preset types of tags in the task data set according to the preset tag information;
according to the basic information and the task execution state, respectively carrying out comprehensive summarization analysis on task data subsets corresponding to each type of tag to obtain task execution state analysis results corresponding to each type of tag, wherein the task execution state analysis results comprise each piece of task execution state information;
and displaying the task execution state analysis result according to a preset rule 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 type tag includes a multi-level sub-tag, according to the preconfigured tag information, counting the first subset of task data belonging to the first type tag in the task data set, and then the method further includes:
acquiring pre-configured tag information corresponding to each piece of task data in the first task data subset;
and counting task data subsets corresponding to each level of 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 of sub-label is displayed according to the preset rule.
3. The method according to claim 1 or 2, wherein the pre-set category labels include, but are not limited to, one or more of the following:
business line labels, task importance labels, data application labels, or bin type labels.
4. The method according to claim 1 or 2, wherein the task execution state analysis result includes: task type, total number of tasks, service level agreement SLA time, current completion progress, per-type task execution status statistics, and total results of all task execution status statistics.
5. The method of claim 4, wherein when a first one of the task execution state analysis results is detected as triggered, the method further comprises:
and counting 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.
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 the first task detail list corresponding to the first task execution state analysis result is displayed, the method further comprises:
And when the triggering of the first task detail in the at least one task detail is detected, counting and displaying a second task detail list associated with the first task detail, wherein the second task detail list comprises related information corresponding to other tasks influenced by the execution state of the first task.
7. A task execution status monitoring device, the device comprising:
the task data acquisition unit is used for periodically acquiring a task data set and task data details corresponding to each piece of task data, wherein the task data details comprise preconfigured label information, basic information and task execution states corresponding to each piece of task data;
the statistics unit is used for counting task data subsets corresponding to each type of labels in the preset type of labels in the task data set according to the preset label information;
the processing unit is used for respectively carrying out comprehensive summarization analysis on the task data subsets corresponding to each type of labels according to the basic information and the task execution state, and obtaining task execution state analysis results corresponding to each type of labels, wherein the task execution state analysis results comprise 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 type tag includes a multi-level sub-tag, the statistics unit is configured to, according to the preconfigured tag information, count, in the task data set, a first subset of task data corresponding to the first type tag, and the obtaining unit is further configured to obtain preconfigured tag information corresponding to each piece of task data in the first subset of task data;
the statistics unit is further configured to, according to the configured tag information corresponding to each piece of task data in the first task data subset, count task data subsets corresponding to each level of sub-tags in the first class of tags in the first task data subset, so as to display the execution state analysis result of the task data subset corresponding to each level of sub-tags according to the preset rule.
9. A task execution status monitoring system, the system comprising: at least one processor and memory;
The processor is configured to execute a task execution status monitor program stored in the memory, so as to implement the task execution status monitor method according to any one of claims 1 to 6.
10. A computer storage medium storing one or more programs executable by the task execution state monitoring system according to claim 9 to implement the task execution state monitoring method according to any one of claims 1 to 6.
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| 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 |
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