CN115271473B - 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 of; the system intelligently judges whether the combination relation of the multidimensional dimension and the dimension index gives feedback reasonably or not; task scheduling matching; splitting a task into a plurality of small tasks for execution and carrying out aggregation statistics on the results; writing back task scheduling execution result data; intelligent analysis of feedback data; and visualizing the execution progress of the scheduled tasks. The invention satisfies most of service scenes and fully exerts the effect of executing the braking 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; the feedback mechanism is added to provide support for the follow-up continuous automatic optimization of the scheduling execution of the task, and the visual stage of the scheduling execution of the task is added, so that a business side can clearly and intuitively sense the scheduling execution stage and state of the 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 epidemic situation and external environment influence, business development in the decoration industry is greatly limited, and statistics of various indexes of the business development is required to be managed more frequently and more finely; at present, the traditional data service report system is characterized in that a service manager gathers requirements through feedback of service personnel, and the product manager and the service personnel determine to finally determine required service indexes and statistical forms. The product manager sorts the requirements, sumps the synchronous requirements of the developer, the developer knows the service requirements, disassembles the required steps, and evaluates the required data preliminarily, evaluates the technical feasibility and finally develops. According to the business requirement of the report, determining the execution frequency, wherein the report requirements of different execution frequencies are executed together in the same batch of tasks, and if the task execution requires a precondition, a developer is also required to manually associate the execution of the precondition, so that the intelligent performance is not realized. If the business party adjusts the business index, the execution frequency is high, the developer needs to redevelop adjustment again, and the online release is time-consuming and labor-consuming, has a longer period, can not freely combine the data of the required dimension index, can not dynamically generate task scheduling according to the change of the dimension index, and can only be manually changed by the developer to meet the business requirements of the combination of different indexes.
In the prior art, the prior processing process adopts hard coding, if various pre-conditions are added, the service is redeveloped, if the index dimension is changed, the redeveloped is required, the online release is also required, the period is long, the response is not timely enough, the result data of the scheduling execution cannot be analyzed, the effect of the execution cannot be known quantitatively, the generation effect of the service dimension index result is unstable, quantitative analysis cannot be carried out, targeted optimization is carried out, and the generation strategy of the index data cannot be optimized; in the prior art, all the index data, different execution frequencies and scheduling of service index data generation with different data volumes are generally put together for execution, so that the index task with larger data can block the execution of a general task, and the normal scheduling execution and the normal service operation are influenced. The traditional dimension index data task does not support the generation and execution of the dynamic multidimensional service index combined data task, and the mode of adding functions can only be carried out through the mode of re-extracting the requirements each time, so that the mode is not efficient enough, and the capability requirement of aggregation of various service index data of services can not be met.
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 relates to an intelligent multidimensional data service index scheduling method, which comprises the following steps of;
s1, a user of a service party dynamically configures multidimensional dimension and dimension indexes through service management, and a system intelligently judges whether a combination relation between a dimension combination and the dimension indexes reasonably gives feedback;
s2, task scheduling matching, namely generating a scheduling task according to different multidimensional dimensions and dimension indexes, judging by a comprehensive algorithm combining the scheduling task according to the size of data quantity to be executed, time consuming performance of past execution, execution frequency, service priority, task execution acceptance blocking tolerance and the utilization rate of current system machine resources, and determining a downstream execution undertaker for generating the task;
s3, a task scheduling executor automatically judges whether a task needs to be split according to the related original data of the task which is passed through upstream, according to the multidimensional dimension and dimension index, the date, the data range, the service related data and the size of the data volume, expects to execute time consuming time and service urgency, splits the task into a plurality of small tasks to execute, and finally makes aggregation statistics on the split sub-task results;
s4, writing back data of the task scheduling execution result: in order to monitor the execution process of the task, collecting the data of each node in the execution process, and storing the data in a database; the data comprise execution progress of the task, and relate to data quantity, time consumption of task execution, distributed executor information, dimension and dimension index;
s5, intelligent analysis of feedback data: the method comprises the steps of carrying out statistical analysis on time consumption of task execution, task related data quantity, executable adjustment task quantity at the same time, associated business, dimension and dimension index information according to the degree of influence on time consumption of character execution, combining with execution data information of a national network, and generating related execution suggestions and suggestions of dynamic combination of dimension and dimension execution;
s6, visualizing the execution progress of the scheduling task: the method comprises the steps of displaying a scheduling task which is generated by the dimensionality of service side configuration and the multidimensional degree of indexes in a page form, displaying the circulation state of the task, the split and split details and the execution state of the scheduling task on an executor on the page, and providing the service side with the function of modifying the manual intervention including the task priority and the data range for emergency problems.
Further, whether the combination relation in the step S1 is reasonably determined by the underlying basic data model and the data relation or not is determined by the intelligent task generating system, and the scheduling task to be executed is generated according to the dimension, the dimension index, the time range and the service attribution.
Further, the step S2 specifically includes sending the task to be executed, the related multidimensional dimension and dimension index data, date, data range and service related data to the downstream through scheduling matching, and dynamically sending the data to different executors according to the current downstream load size; 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 quantity and long execution period, giving classification levels after comprehensive evaluation, and sending the tasks to be executed to corresponding level queues.
Further, the multidimensional dimension comprises city, popularization channel, customer service, task type and user category, the business party self-defines various dimensions, and the index accords with business requirement and data model.
Further, the dimension index comprises various indexes including visitor number, daily activity number, pre-form number, order number, jump-out rate, page stay time and new user retention rate, and the business side can customize various indexes and accord with the data model.
Further, the intelligent multidimensional data service index scheduling method is realized based on a scheduling system, and interaction between service side dynamic combination configuration dimensions and indexes to the scheduling system adopts a custom message format.
Further, the custom message format includes fields and corresponding field names, formats and descriptions, the fields include Id, dimensions, business, the corresponding field names are numbered ID, dimension and index list, service names, the corresponding formats are character strings, list, character strings, and the corresponding descriptions are dynamic combinations of instruction numbers, dimension and index, and service names to be executed.
Compared with the prior art, the invention has the following beneficial effects:
(1) The method can dynamically configure and adjust multidimensional service indexes, only the service dimension of the combination is selected on the configuration page, the system can automatically judge the feasibility of the combination, if unreasonable, the prompt is not needed for secondary development, most of service scenes are met, and the execution effect of the brake matching scheduling task is fully exerted;
(2) The invention automatically judges the feasibility of the service index combination, and automatically classifies the generation and the dispatch of the tasks according to the execution time consumption, the data volume and the execution frequency of the feedback of the previous execution of the tasks, thereby avoiding the coupling and the mutual blocking of the tasks;
(3) According to the invention, the execution time of the task is automatically arranged according to the load condition of the system by combining with an intelligent scheduling algorithm, the machine for executing the task is automatically allocated, the index tasks with different time dimensions, different execution frequencies, different execution time consumption, different priorities assigned by the tasks and importance degrees are allocated to different task execution queues according to the intelligent matching scheduling algorithm, so that the service with high priority can be met to the greatest extent, the execution of the index tasks with unimportant and long time consumption is prevented, and the index tasks with short time consumption and high priority are blocked;
(4) The invention adds feedback mechanism to execute task, which can analyze the task to provide data base to automatically optimize task execution, and increase visual stage to sense task execution stage and state.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above 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 that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an intelligent multidimensional data service index scheduling method according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
The technical scheme core of the invention is a multi-dimensional business index dynamic, intelligent combination function, multi-dimensional index task execution metadata feedback mechanism, multi-dimensional index combination and task execution metadata-based task generation and intelligent matching scheduling system;
multidimensional dimension management, dimension index management and business management;
the multidimensional dimension comprises city, popularization channel, customer service, task type and user category, the business side self-defines various dimensions, and the index accords with business requirement and data model;
the dimension index comprises various indexes including visitor number, daily activity number, pre-formed number, order number, jump-out rate, page stay time and new user retention rate, and the business side can customize various indexes and accord with the data model;
the service management comprises the multidimensional dimension management and the dimension index management, in addition, different services are associated with different dimensions and indexes, the service party can automatically and dynamically combine according to the needs, and the number of visitors and daily activities can be counted according to the dimensions of cities and popularization channels by including the details of the flow of group buying activities;
referring to fig. 1, the method for scheduling intelligent multidimensional data service indexes of the present invention specifically includes a multidimensional index and scheduling task execution matching feedback mechanism and a scheduling flow, and includes the following steps;
s1, a user of a service party dynamically configures multidimensional dimension and dimension indexes through service management, and a system intelligently judges whether a combination relation between a dimension combination and the dimension indexes reasonably gives feedback; whether the combination relation is reasonably determined by the bottom basic data model and the data relation or not, and the intelligent task generating system generates a scheduling task to be executed according to the dimension, the dimension index, the time range and the service attribution;
s2, task scheduling matching, namely generating a scheduling task according to different multidimensional dimensions and dimension indexes, judging by a comprehensive algorithm combining the scheduling task according to the size of data quantity to be executed, time consuming performance of past execution, execution frequency, service priority, task execution acceptance blocking tolerance and the utilization rate of current system machine resources, and determining a downstream execution undertaker for generating the task; the method comprises the steps of specifically sending a task to be executed, related multidimensional dimension and dimension index data, date, data range and service related data to a downstream through scheduling matching, and dynamically sending the data 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 quantity 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 tasks with higher priority are blocked by non-important tasks because the tasks are distributed in a waterfall mode without classification before;
s3, a task scheduling executor automatically judges whether a task needs to be split according to the related original data of the task which is passed through upstream, according to the multidimensional dimension and dimension index, the date, the data range, the service related data and the size of the data volume, expects to execute time consuming time and service urgency, splits the task into a plurality of small tasks to execute, and finally makes aggregation statistics on the split sub-task results; the method has the advantages that on one hand, the service overall pressure 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, real-time alarm is given to abnormal scheduling execution, capacity expansion is automatically carried out on the executor with overlarge load, and the problem that the executor cannot automatically increase capacity expansion in the scheduling process can be solved;
s4, writing back data of the task scheduling execution result: in order to monitor the execution process of the task, collecting the data of each node in the execution process, and storing the data in a database; the data comprise execution progress of the task, and relate to data quantity, time consumption of task execution, distributed executor information, dimension and dimension index; the data are distributed in a matching way by correcting task scheduling in the step S2 for example, and the data can provide scheduling optimization basis for intelligent scheduling;
s5, intelligent analysis of feedback data: the method comprises the steps of carrying out statistical analysis on time consumption of task execution, task related data quantity, executable adjustment task quantity at the same time, associated business, dimension and dimension index information according to the degree of influence on time consumption of character execution, combining with execution data information of a national network, and generating related execution suggestions and suggestions of dynamic combination of dimension and dimension execution; the method provides basis for intelligent dynamic matching on one hand and provides suggestion of good service index combination for a service party on the other hand;
s6, visualizing the execution progress of the scheduling task: the method comprises the steps of displaying a scheduling task which is generated by the dimensionality of service side configuration and the multidimensional degree of indexes in a page form, displaying the circulation state of the task, the split and split details and the execution state of the scheduling task on an executor on the page, and providing the service side with the function of modifying the manual intervention including the task priority and the data range for emergency problems.
The intelligent multidimensional data service index scheduling method is realized based on a scheduling system, and interaction between a service party dynamic combination configuration dimension and an index to the scheduling system adopts a custom message format; the self-defined message format comprises fields and corresponding field names, formats and descriptions, wherein the fields comprise Id, dimensions, business, the corresponding field names are a serial number ID, a dimension and index list and a service name, the corresponding formats are character strings, a list and character strings, and the corresponding descriptions are dynamic combinations of instruction numbers, dimensions and indexes and service names to be executed; the content of the corresponding custom message format is shown in table 1 below;
| fields | Field name | Format of the form | Description of the invention |
| Id | Number ID | Character string | Instruction numbering |
| Dimensions | Dimension and index list | List of list | Dynamic combination of dimensions and metrics |
| Business | Service name | Character string | Service name to be executed |
TABLE 1 custom message formatted content
In the table, id expresses the number of the data transfer instruction and is used for interaction between the traceability modules, and the whole scheduling process has the whole parameters, so that the visualized tracking of the scheduling task is also facilitated; dimensions represent the dynamic combination of dimension specified by a service end and dimension instructions, and a scheduling task is not generated to provide a data source; business represents the specific service data representation of the instruction, and the service party specifies a service name with special meaning for analysis.
The invention can automatically match the multidimensional index according to the need, automatically judge the matching feasibility, automatically allocate and classify according to the time consumption of the multidimensional service index execution, the data volume and the execution frequency, avoid the task coupling, avoid the time-consuming task from blocking the execution of important tasks, automatically combine the intelligent scheduling algorithm according to the load condition of the system, automatically schedule the execution time of the task and automatically allocate the machine for executing the task. The system can automatically judge the feasibility of combination without redevelopment and only selecting the combined service dimension on the configuration page, if unreasonable, the system can prompt that the necessary multidimensional service index is actually customized and developed independently, and the system meets the requirements of most service scenes in general cases. According to the method, the optimal task scheduling is automatically generated through matching of the service and the dimension, multi-dimensional task data is completed as soon as possible, the generated data is executed in batches according to the magnitude of the data, the number of the dimension is indicated, whether the data need to be executed in batches from a plurality of times is judged, and then the result is summarized, so that the problem of blockage is solved, the importance of the dimension index is distinguished, more resource is automatically allocated for execution with high priority, the task is split into the smallest executable task, the tasks are executed in batches for a plurality of times, and the summary combination is carried out subsequently, so that the efficiency is greatly improved, the load is reduced, and the execution time of the task is reasonably optimized.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form 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 and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. An intelligent multidimensional data service index scheduling method is characterized by comprising the following steps of;
s1, a user of a service party dynamically configures multidimensional dimension and dimension indexes through service management, and a system intelligently judges whether a combination relation between a dimension combination and the dimension indexes reasonably gives feedback;
s2, task scheduling matching, namely generating a scheduling task according to different multidimensional dimensions and dimension indexes, judging by a comprehensive algorithm combining the scheduling task according to the size of data quantity to be executed, time consuming performance of past execution, execution frequency, service priority, task execution acceptance blocking tolerance and the utilization rate of current system machine resources, and determining a downstream execution undertaker for generating the task;
s3, a task scheduling executor automatically judges whether a task needs to be split according to the related original data of the task which is passed through upstream, according to the multidimensional dimension and dimension index, the date, the data range, the service related data and the size of the data volume, expects to execute time consuming time and service urgency, splits the task into a plurality of small tasks to execute, and finally makes aggregation statistics on the split sub-task results;
s4, writing back data of the task scheduling execution result: in order to monitor the execution process of the task, collecting the data of each node in the execution process, and storing the data in a database; the data comprise execution progress of the task, and relate to data quantity, time consumption of task execution, distributed executor information, dimension and dimension index;
s5, intelligent analysis of feedback data: the method comprises the steps of carrying out statistical analysis on time consumption of task execution, task related data quantity, executable adjustment task quantity at the same time, associated business, dimension and dimension index information according to the degree of influence on time consumption of character execution, combining with execution data information of a national network, and generating related execution suggestions and suggestions of dynamic combination of dimension and dimension execution;
s6, visualizing the execution progress of the scheduling task: the method comprises the steps of displaying a scheduling task which is generated by the dimensionality of service side configuration and the multidimensional degree of indexes in a page form, displaying the circulation state of the task, the split and split details and the execution state of the scheduling task on an executor on the page, and providing the service side with the function of modifying the manual intervention including the task priority and the data range for emergency problems.
2. The intelligent multidimensional data service index scheduling method according to claim 1, wherein whether the combination relation in the step S1 is reasonably determined by a bottom basic data model and a data relation is determined by an intelligent task generating system, and the intelligent task generating system generates scheduling tasks to be executed according to the dimensions, the dimension indexes, 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 specifically that the task to be executed, the multidimensional dimension and dimension index data, date, data range and service related data are sent to the downstream through scheduling matching, and are dynamically sent to different executors according to the current downstream load size; 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 quantity and long execution period, giving classification levels after comprehensive evaluation, and sending the tasks to be executed to corresponding level queues.
4. The intelligent multidimensional data service index scheduling method according to claim 1, wherein the multidimensional dimensions comprise cities, popularization channels, customer service, task types and user categories, and service parties define various dimensions.
5. The intelligent multidimensional data service index scheduling method according to claim 1, wherein the dimension index includes various indexes including visitor number, daily activity number, pre-form number, order number, jump-out rate, page stay time and new user retention rate, and the service side defines various indexes.
6. The intelligent multidimensional data service index scheduling method according to claim 1, wherein the intelligent multidimensional data service index scheduling method is realized based on a scheduling system, and interaction between service side dynamic combination configuration dimensions and indexes to the scheduling system adopts a custom message format.
7. The intelligent multidimensional data service index scheduling method according to claim 6, wherein the custom message format includes fields and corresponding field names, formats and descriptions, the fields include Id, dimensions, business, the corresponding field names are numbered IDs, dimension and index lists, service names, the corresponding formats are character strings, lists, character strings, and the corresponding descriptions are dynamic combinations of instruction numbers, dimensions and indexes, service names to be executed.
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