CN115271473A - Intelligent multidimensional data service index scheduling method - Google Patents
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Abstract
The invention discloses an intelligent multidimensional data service index scheduling method, and relates to the technical field of data processing. The invention comprises the following steps; the system intelligently judges whether the combination relationship of the multidimensional dimension and the dimension index reasonably gives feedback; task scheduling matching; splitting a task into a plurality of small tasks to execute and performing aggregation statistics on the result; writing back task scheduling execution result data; intelligently analyzing feedback data; and visualizing the execution progress of the scheduling task. The invention meets most of service scenes, and fully exerts the effect of executing the brake matching scheduling task; the feasibility of the service index combination is automatically judged, the generation and the scheduling of the tasks are classified, and the coupling and the mutual blocking of the tasks are avoided; a feedback mechanism is added to provide support for subsequent continuous automatic optimization of task scheduling execution, and a visual task scheduling execution stage is added, so that a business party can clearly and visually feel the scheduling execution stage and state of a task.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an intelligent multidimensional data service index scheduling method.
Background
In recent years, due to the influence of epidemic situations and external environments, the service development of the decoration industry is greatly limited, and various indexes of the service development need to be managed more frequently and more finely; at present, the traditional data service report system is used for determining and finally determining required service indexes and statistical forms through feedback of service personnel, demand collection of a product manager and determination of the product manager and the service personnel. The method comprises the steps of arranging requirements by a product manager, gathering synchronous requirements of developers, knowing business requirements by the developers, disassembling the steps of the requirements, primarily evaluating required data, evaluating technical feasibility and finally developing. The execution frequency is determined according to the business requirements of the report, the report requirements with different execution frequencies are executed together in the same batch of tasks, and if the execution of the tasks requires preconditions, developers need to manually associate with the preposed tasks to execute, so that the intelligent performance is not enough. If the business side adjusts the business indexes, the business side executes the business indexes frequently, developers need to develop and adjust again, the business side issues the business indexes online, time and labor are consumed, the period is long, data of the needed dimension indexes cannot be combined freely, dynamic generation task scheduling cannot be performed according to the change of the dimension indexes, and the business requirements of the combination of different indexes can be met only through manual change of the developers.
In the prior art, the previous processing processes all adopt hard coding, business redevelopment is caused if various preconditions are added, if index dimensionality changes, redevelopment is required, online release is realized, the cycle is long, response is not timely enough, the existing scheme does not analyze scheduling execution result data, the execution effect cannot be quantitatively known, the generation effect of the business dimensionality index result is unstable, quantitative analysis is not available, targeted optimization is performed, and the generation strategy of the index data cannot be optimized; in the prior art, all index data, different execution frequencies and different data volumes of scheduling generated by service index data are generally put together for execution, which may cause the index task with larger data to block the execution of a general task, thus affecting the execution of normal scheduling and normal operation of service. The traditional dimension index data task does not support the generation and execution of a dynamic multidimensional service index combined data task, and only a mode of adding functions by re-providing requirements every time is available, so that the mode is not efficient enough and can not meet the capability requirement of aggregation of various service index data of a service.
Disclosure of Invention
The invention provides an intelligent multidimensional data service index scheduling method, which solves the problems.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention discloses an intelligent multidimensional data service index scheduling method, which comprises the following steps;
s1, a service user dynamically configures multidimensional dimension and dimension indexes through service management, and a system intelligently judges whether the combination relationship between the dimension combination and the dimension indexes reasonably gives feedback;
s2, task scheduling matching, namely generating scheduling tasks according to the difference of multidimensional dimensionality and dimensionality indexes, and determining a downstream execution bearer for generating the tasks according to comprehensive algorithm judgment of the combination of the size of the data amount to be executed, the time consumption performance of the previous execution, the execution frequency, the service priority, the tolerance of the blocking of the task execution and the utilization rate of the current system machine resources by the scheduling tasks;
s3, a task scheduling executor automatically judges whether a task needs to be split or not according to the relevant original data of the task from upstream and the expected execution time consumption and business urgency of the multidimensional dimensionality, dimensionality index, date, data range, business relevant data and data size, divides the task into a plurality of small tasks to execute, and finally conducts aggregation statistics on the split task dividing results;
s4, writing back task scheduling execution result data: in order to monitor the execution process of the task, collecting data of each node in the execution process, and storing the data in a database; the data comprises the execution progress of the task, and relates to data volume, time consumption of task execution, and distributed executor information, dimension and dimension indexes;
s5, intelligently analyzing feedback data: performing statistical analysis on the time consumption of task execution, the data quantity of the tasks related to the tasks, the number of executable and adjustable tasks at the same time, associated services, dimensionality and dimensionality index information according to the time consumption influence degree of character execution and by combining with the execution data information of the state network to generate related execution suggestions and suggestions for dynamically combining dimensionality and dimensionality execution;
s6, visualization of the execution progress of the scheduling task: the scheduling task generated by the dimensionality and the multi-dimensionality of the index configured by the business party is displayed in a page form, the circulation state, the splitting and the splitting details of the task and the execution state of the scheduling task on an executor are displayed on the page, and the function of manual intervention including task priority and a data range is modified for an emergency problem by the business party.
Further, whether the combination relationship in the step S1 is reasonable is determined by the underlying basic data model and the data relationship, and the intelligent task generating system generates the scheduling task to be executed according to the combination relationship including the dimension, the dimension index, the time range, and the service attribution.
Further, the step S2 is specifically to send the task to be executed, the related multidimensional and dimensional index data, the date, the data range, and the service related data to the downstream by scheduling and matching, and dynamically send the task to different executors according to the current downstream load; and (3) carrying out classified sending on the tasks to be executed, independently classifying the tasks to be executed with large data size and long execution period, giving classification levels after comprehensive evaluation, and sending the tasks to be executed to corresponding level queues.
Furthermore, the multidimensional dimensions include cities, popularization channels, customer service, task types and user categories, the business side can define various dimensions, and indexes meet business requirements and data models.
Furthermore, the dimension indexes comprise various indexes including visitor numbers, daily living numbers, reserved numbers, orders, jumping-out rates, page retention time and new user retention rates, and the service side can customize the various indexes and conform to a data model.
Furthermore, the intelligent multidimensional data service index scheduling method is realized based on a scheduling system, and a user-defined message format is adopted for interaction between service side dynamic combination configuration dimensionality and indexes to the scheduling system.
Further, the custom message format includes fields and corresponding field names, formats and descriptions, the fields include Id, dimensions and Business names, the corresponding field names are serial number Id, dimension and index list and Business name, the corresponding formats are character string, list and character string, and the corresponding descriptions are dynamic combination of instruction serial number, dimension and index and Business name to be executed.
Compared with the prior art, the invention has the following beneficial effects:
(1) The invention can dynamically configure and adjust the multidimensional service index, only needs to select the service dimension of the combination on the configuration page, the system can automatically judge the feasibility of the combination, if the combination is unreasonable, the prompt can be given without secondary development, and the invention meets most service scenes and fully exerts the effect of executing the brake matching scheduling task;
(2) The feasibility of the service index combination is automatically judged, and the generation and scheduling of the tasks are automatically classified according to the execution time consumption, the data size and the execution frequency of the feedback executed in the past, so that the coupling and mutual blocking of the tasks are avoided;
(3) According to the invention, the execution time of the tasks is automatically arranged according to the load condition of the system by combining an intelligent scheduling algorithm, the machines for executing the tasks, the index tasks with different time dimensions, different execution frequencies, different execution time consumptions and different priorities and importance degrees specified by the tasks are automatically distributed to different task execution queues according to an intelligent matching scheduling algorithm, so that the business of priority work can be met to the maximum extent, the execution of the non-important index tasks with long time consumption is prevented, and the index tasks with short time consumption and high priority are blocked;
(4) The invention adds a feedback mechanism for executing the task, can provide a data basis for the subsequent execution scheduling of the task through analysis, provides support for the subsequent scheduling execution of the continuous automatic optimization task, increases the visual stage of the task scheduling execution, and can enable a business party to clearly and intuitively feel the scheduling execution stage and state of the task.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of an intelligent multidimensional data service index scheduling method according to the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The core of the technical scheme of the invention is a multidimensional service index dynamic and intelligent combination function, a multidimensional index task execution metadata feedback mechanism, a multidimensional index combination and task execution metadata based task generation and intelligent matching scheduling system;
multidimensional dimension management, dimension index management and service management;
the multidimensional dimensionalities comprise cities, popularization channels, customer service, task types and user categories, the business side defines various dimensionalities by self, and the indexes meet business requirements and data models;
the dimension indexes comprise various indexes including visitor numbers, daily living numbers, reserved numbers, order numbers, jumping-out rates, page staying time and new user remaining rates, and the business side can customize the various indexes and conform to a data model;
the business management comprises the multi-dimensional management and the management of the dimension indexes, in addition, different services are associated with different dimensions and indexes, and business parties can automatically carry out dynamic combination according to needs, wherein the business parties can count the number of visitors and daily lives according to the dimensions of cities and popularization channels according to the details of the group buying activity flow;
referring to fig. 1, the intelligent multidimensional data service index scheduling method, specifically, a multidimensional index and scheduling task execution matching feedback mechanism and a scheduling process, of the present invention includes the following steps;
s1, a service user dynamically configures multidimensional dimension and dimension indexes through service management, and a system intelligently judges whether the combination relationship between the dimension combination and the dimension indexes reasonably gives feedback; whether the combination relation is reasonable or not is determined by the bottom basic data model and the data relation, and the intelligent task generating system generates a scheduling task to be executed according to the dimensionality, the dimensionality index, the time range and the service attribution;
s2, task scheduling matching, namely generating scheduling tasks according to the difference of multidimensional dimensionality and dimensionality indexes, and determining a downstream execution undertaker for generating the tasks according to comprehensive algorithm judgment combining the size of the data volume to be executed, the time consumption performance of the past execution, the execution frequency, the service priority, the tolerance of the blocking of the task execution and the utilization rate of the current system machine resources by the scheduling tasks; the method comprises the steps that tasks to be executed, relevant multidimensional dimension and dimension index data, dates, data ranges and service relevant data are sent to downstream through scheduling matching, and are dynamically sent to different executors according to the current downstream load; the method comprises the steps of carrying out classified sending on tasks to be executed, independently classifying the tasks to be executed with large data volume and long execution period, giving classification levels after comprehensive evaluation, and sending the tasks to be executed to corresponding level queues; the scheduling can solve the problem that the prior tasks are not classified in a grading way and are distributed in a waterfall way manner to a great extent, so that the tasks with higher priority are blocked by the unimportant tasks;
s3, a task scheduling executor automatically judges whether a task needs to be split or not according to the relevant original data of the task from upstream and the expected execution time consumption and business urgency of the multidimensional dimensionality, dimensionality index, date, data range, business relevant data and data size, divides the task into a plurality of small tasks to execute, and finally conducts aggregation statistics on the split task dividing results; the method has the advantages that on one hand, the overall pressure of service is reduced, on the other hand, the overall efficiency is improved, the execution time of tasks is reasonably optimized, the health condition of an executor is monitored in real time in the execution process, the abnormal scheduling execution is alarmed in real time, the executor with overlarge load is automatically expanded, and the problem that the executor cannot automatically increase the expansion in the scheduling process can be solved;
s4, writing back task scheduling execution result data: in order to monitor the execution process of the task, collecting data of each node in the execution process, and storing the data in a database; the data comprises the execution progress of the task, and relates to data volume, time consumption of task execution, distributed executor information, dimensionality and dimensionality indexes; the data serve as the matching distribution of task scheduling in the step S2 of subsequent correction, and the data can provide a scheduling optimization basis for intelligent scheduling;
s5, intelligently analyzing feedback data: performing statistical analysis on the time consumption of task execution, the data quantity of the tasks related to the tasks, the number of executable and adjustable tasks at the same time, associated services, dimensionality and dimensionality index information according to the time consumption influence degree of character execution and by combining with the execution data information of the state network to generate related execution suggestions and suggestions for dynamically combining dimensionality and dimensionality execution; on one hand, a basis is provided for intelligent dynamic matching, and on the other hand, a good suggestion of service index combination is provided for a service party;
s6, visualization of the execution progress of the scheduling task: the scheduling task generated by the dimensionality and the multi-dimensionality of the index configured by the service party is displayed in a page form, the circulation state, the splitting and splitting details of the task and the execution state of the scheduling task on an executor are displayed on the page, and the function of manual intervention including task priority and data range modification by the service party for emergency is provided.
The intelligent multidimensional data service index scheduling method is realized based on a scheduling system, and a user-defined message format is adopted for interaction between service side dynamic combination configuration dimensionality and indexes to the scheduling system; the self-defined message format comprises fields and corresponding field names, formats and descriptions, wherein the fields comprise Id, dimensions and Business, the corresponding field names are serial number ID, dimension and index lists and Business names, the corresponding formats are character strings, lists and character strings, and the corresponding descriptions are instruction serial numbers, dynamic combinations of the Dimensions and indexes and Business names to be executed; the content of the corresponding custom message format is shown in table 1 below;
| field(s) | Name of field | Format | Description of the invention |
| Id | Number ID | Character string | Instruction numbering |
| Dimensions | Dimension and index List | Lists | Dynamic combination of dimensions and indices |
| Business | Name of service | Character string | Name of service to be executed |
TABLE 1 custom message Format content
In the table, id represents the number of the data transmission instruction, and is used for tracing the interaction between modules, and the whole scheduling process has whole parameters, so that the visual tracking of the scheduling task is facilitated; dimensions represent the dynamic combination of the dimensionality and the dimensionality instruction specified by a service end, and a scheduling task is not generated to provide a data source; business represents the Business name of special meaning assigned by the Business party for analysis and instructing the concrete Business data representation.
The invention can automatically match multidimensional indexes as required, automatically judge matching feasibility, automatically distribute and classify according to the execution time of multidimensional service indexes, the size of data volume and the execution frequency, avoid task coupling and avoid the execution of time-consuming tasks blocking important tasks, automatically arrange the execution time of the tasks according to the load condition of a system and an intelligent scheduling algorithm, and automatically distribute machines for executing the tasks. The system can automatically judge the feasibility of the combination by intelligently combining a plurality of dimensions without redevelopment and only selecting the combined service dimension on the configuration page, and can prompt that the actually necessary multidimensional service index is separately customized and developed in this way if the system is unreasonable, so that the system can meet the requirements of most service scenes in general conditions. The method comprises the steps of dividing different services into different dimensions, having service indexes and dimension indexes, automatically generating optimal task scheduling through matching of the services and the dimensions, finishing execution of multi-dimensional task data as soon as possible, judging whether the multi-dimensional task data are required to be executed in batches for many times according to the magnitude of the data and the number of the index dimensions, and subsequently summarizing results.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. An intelligent multidimensional data service index scheduling method is characterized by comprising the following steps;
s1, a service user dynamically configures multidimensional dimension and dimension indexes through service management, and a system intelligently judges whether the combination relation between the dimension combination and the dimension indexes reasonably gives feedback;
s2, task scheduling matching, namely generating scheduling tasks according to the difference of multidimensional dimensionality and dimensionality indexes, and determining a downstream execution undertaker for generating the tasks according to comprehensive algorithm judgment combining the size of the data volume to be executed, the time consumption performance of the past execution, the execution frequency, the service priority, the tolerance of the blocking of the task execution and the utilization rate of the current system machine resources by the scheduling tasks;
s3, a task scheduling executor automatically judges whether a task needs to be split or not according to relevant original data of the task from upstream, the expected execution time consumption and the business urgency degree of multidimensional dimensionality, dimensionality index, date, data range, business relevant data and data size, divides the task into a plurality of small tasks to be executed, and finally conducts aggregation statistics on the split task division results;
s4, writing back task scheduling execution result data: in order to monitor the execution process of the task, collecting data of each node in the execution process, and storing the data in a database; the data comprises the execution progress of the task, and relates to data volume, time consumption of task execution, and distributed executor information, dimension and dimension indexes;
s5, intelligently analyzing feedback data: performing statistical analysis on the time consumption of task execution, the data quantity of the tasks related to the tasks, the number of executable and adjustable tasks at the same time, associated services, dimensionality and dimensionality index information according to the time consumption influence degree of character execution and by combining with the execution data information of the state network to generate related execution suggestions and suggestions for dynamically combining dimensionality and dimensionality execution;
s6, visualization of the execution progress of the scheduling task: the scheduling task generated by the dimensionality and the multi-dimensionality of the index configured by the service party is displayed in a page form, the circulation state, the splitting and splitting details of the task and the execution state of the scheduling task on an executor are displayed on the page, and the function of manual intervention including task priority and data range modification by the service party for emergency is provided.
2. The method according to claim 1, wherein whether the combination relationship in step S1 is reasonable is determined by a bottom layer basic data model and a data relationship, and the intelligent task generating system generates the scheduling task to be executed according to the combination relationship including the dimension, the dimension index, the time range, and the service attribution.
3. The intelligent multidimensional data service index scheduling method according to claim 1, wherein the step S2 is to send the task to be executed, the relevant multidimensional dimension and dimension index data, the date, the data range, and the service-related data to downstream by scheduling matching, and dynamically send the task to different executors according to the current downstream load; and (3) carrying out classified sending on the tasks to be executed, independently classifying the tasks to be executed with large data size and long execution period, giving classification levels after comprehensive evaluation, and sending the tasks to be executed to corresponding level queues.
4. The method of claim 1, wherein the multidimensional dimensions include cities, promotion channels, customer service, task types, user categories, and business-side customized dimensions.
5. The method according to claim 1, wherein the dimension index includes various indexes including visitor number, daily number, reserved number, order number, jump-out rate, page stay time, and new user stay rate, and the service side defines various indexes by user.
6. The method according to claim 1, wherein the intelligent multidimensional data service index scheduling method is implemented based on a scheduling system, and a custom message format is adopted for interaction between a service party dynamic combination configuration dimension and an index to the scheduling system.
7. The method as claimed in claim 6, wherein the custom packet format includes fields and corresponding field names, formats and descriptions, the fields include Id, dimensions and Business names, the corresponding field names include Id, dimension and index list, and the Business names include character string, list and character string, and the corresponding descriptions include instruction number, dynamic combination of dimension and index, and Business name to be executed.
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| CN116611710A (en) * | 2023-06-21 | 2023-08-18 | 深圳传世智慧科技有限公司 | Visual dynamic display method and system |
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