CN113535157A - Heterogeneous big data resource encapsulation integration system and method capable of being plugged and unplugged during operation - Google Patents
Heterogeneous big data resource encapsulation integration system and method capable of being plugged and unplugged during operation Download PDFInfo
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Abstract
The invention discloses a pluggable heterogeneous big data resource packaging and integrating system and a pluggable heterogeneous big data resource packaging and integrating method during operation, which are used for constructing a universal heterogeneous big data resource model, introducing a corresponding more efficient big data resource packaging mode, completing the fusion and circulation of heterogeneous resources in a big data analysis process in an open combination mode and establishing a loosely-coupled and universal big data resource integrating platform. The scheme combines the characteristics of big data analysis, abstracts a universal heterogeneous resource model containing static information, interactive information, monitoring information and the like based on abstraction and specification of data dimension and incidence relation, constructs a resource encapsulation mechanism containing a registration mechanism, a scheduling mechanism and a plug-in recombination mechanism based on a structural model, completes the fusion and circulation of heterogeneous resources in the big data analysis process in an open combination mode, establishes a loosely-coupled and universal big data resource integration platform, and improves the storage capacity, the calculation capacity and the algorithm capacity of the big data platform in a pluggable mode during operation.
Description
Technical Field
The invention relates to the technical field of big data analysis, in particular to a pluggable heterogeneous big data resource packaging and integrating system and a pluggable heterogeneous big data resource packaging and integrating method during operation.
Background
With the development and the upgrade of big data technology, the fusion innovation with different fields is promoted, the production efficiency of each field is improved, the resource allocation is optimized, and meanwhile, the diversified data analysis requirements of different fields are generated. For a complex and changeable data analysis processing environment in multiple fields, the traditional resource utilization and component operation technology and the like can not meet the requirements, and needs to be improved from the following two aspects:
1. and a universal heterogeneous resource model needs to be constructed, and a big data operation scene is extended. The traditional production flow emphasizes the integrity and the accuracy of the operation, and the production flow is constructed and ensured from the aspects of operation process circulation, processing result transmission, operation environment, execution parameters and the like; the big data analysis process focuses on the processes of data acquisition, treatment, analysis, visualization and application, and due to the fact that data sources and analysis and processing forms are complex and various, abstraction and specification based on data dimensions and incidence relation are not performed on different types of resources such as input data sources, engines providing computing power, operators participating in computing and processing and the like, various interface standards and performance requirements during operation cannot be met, the data analysis process cannot run normally, or the process layout is too complicated, and the productive application of big data cannot be promoted.
2. Resource encapsulation needs to be improved, and big data operation efficiency is improved. The starting point of the traditional resource packaging mode is the associated application, data transmission and the like of resources in a unified operating environment, and the coupling between the resources is strong. Therefore, when a large data analysis multi-field diversified and huge circulation scene is faced, resources providing capabilities of data access, calculation, processing and the like cannot be flexibly fused and operated, the specific business logic is difficult to concentrate on, and the data analysis efficiency is low; if the data analysis scenario changes, various resources of the production flow need to be modified and recombined, resulting in excessive additional duplication.
In order to meet the requirements of multi-field complex and variable big data analysis, a universal heterogeneous resource model needs to be constructed, a corresponding more efficient resource encapsulation mode is introduced, the process of arranging the production flow is decoupled, and the production guarantee capability of various data is improved.
Disclosure of Invention
In view of the above, the present invention provides a pluggable heterogeneous big data resource encapsulation integration system and method during operation, which constructs a universal heterogeneous big data resource model, introduces a correspondingly more efficient big data resource encapsulation manner, completes the fusion flow of heterogeneous resources in the big data analysis process in an open combination manner, and establishes a loosely-coupled and generalized big data resource integration platform.
In order to achieve the purpose, the technical scheme of the invention is as follows: the heterogeneous big data resource encapsulation integrated system capable of being plugged in and pulled out during operation comprises a big data resource pluggable model and a big data analysis subsystem.
The big data resource pluggable model comprises static information, interaction information, monitoring information and scheduling information.
The big data analysis subsystem comprises a heterogeneous big data resource registration module, a big data flow arrangement module, a task scheduling module and a plug-in recombination module during operation.
The heterogeneous big data resource registration module provides a uniform interface for resource registration based on the heterogeneous big data resource model.
The big data flow arrangement module is used for carrying out dragging type layout and connecting line type flow arrangement on big data operators aiming at the registered big data resources to generate a big data business flow; the big data flow arrangement module also supports the display of the flow processing progress in the execution process and the output of the intermediate result.
The task scheduling module constructs a production task based on a big data service flow, performs task scheduling according to scheduling configuration attributes, and supports management, visual monitoring and control execution operation of the scheduling task.
And the plug-in recombination module during operation is used for performing plug-in adjustment on the big data resources based on the current operation environment when the data analysis scene changes.
Further, the heterogeneous big data resources comprise data sources, computing engines, operators and production tasks.
Further, the static information describes basic attributes and static data of the big data resource.
The interactive information is used for describing interface configuration standards, connection conditions and data circulation when the resources perform service analysis.
The monitoring information includes status information of various resources in service analysis and log information for data analysis processing.
The scheduling information comprises scheduling levels and scheduling algorithm information of various big data resources.
Further, the heterogeneous big data resource registration module comprises a registration application of a data source, a registration application of a calculation engine, a registration application of an operator and a registration application of a production task.
The registration application of the computing engine comprises a registration application of a basic processing engine, a registration application of a spark processing engine, a registration and an application of a TensorFlow deep learning framework.
The registration application of the data source comprises registration applications of various databases.
The registration application of the operator supports the componentization packaging of a data processing algorithm, a feature engineering algorithm and a machine learning algorithm and the expansion of a user-defined operator.
The production task registration application provides for the registration of production tasks based on the flow.
Further, in the runtime plug repetition module, plug adjustment of big data resources is performed based on the current runtime environment, which specifically includes:
and aiming at the newly inserted big data resource, recording the current state by extracting the current state information and the data information of the big data resource, redistributing the storage resource and the computing resource aiming at the production task, and storing and circulating intermediate data generated in the execution process of the production task.
The invention also provides a pluggable heterogeneous big data resource encapsulation and integration method during operation, and the work flow of the encapsulation and integration system is as follows:
step one, after the big data resource pluggable model is inserted into the packaging integrated system, the heterogeneous big data resource registration module performs resource registration aiming at the big data resource pluggable model.
After the resource registration is completed, if the big data resource pluggable model is a data source, a calculation engine or an operator, the big data flow arrangement module is used for carrying out dragging type layout and connecting line type flow arrangement on the calculation engine, the data source and the operator to construct a production task; the big data flow arrangement module also supports the display of the flow processing progress in the execution process and the output of the intermediate result.
And if the large data resource pluggable model is a production task, directly executing the step three.
And step three, aiming at the production tasks in the step two, a task scheduling module is utilized to schedule the tasks according to scheduling configuration attributes, and management, visual monitoring and control execution of the production tasks are supported.
And fourthly, changing a data analysis scene, and performing plug-pull adjustment on the big data resource based on the current operating environment.
Has the advantages that:
1. the invention provides a pluggable heterogeneous big data resource packaging and integrating method in a supporting operation. The heterogeneous big data resources comprise data sources for providing data input, engines for providing computing power, operators for participating in computation and processing, applications for providing multi-dimensional display and the like. The method combines the characteristics of big data analysis, abstracts a universal heterogeneous resource model containing static information, interactive information, monitoring information and the like based on the abstraction and the specification of data dimension and incidence relation, constructs a resource encapsulation mechanism containing a registration mechanism, a scheduling mechanism and a plug-in recombination mechanism based on a structural model, completes the fusion and circulation of heterogeneous resources in the big data analysis process in an open combination mode, establishes a loosely-coupled and universal big data resource integration platform, and improves the storage capacity, the computing capacity and the algorithm capacity of the big data platform in a pluggable mode during operation.
2. In order to complete the fusion circulation of the heterogeneous big data resources in the big data analysis process, a heterogeneous big data resource model is abstracted from the resource type, the incidence relation, the capacity range, the configuration mode and the like. The model contains static information, interactive information, monitoring information, scheduling information, and the like. In the invention, resources such as an input data source, a calculation engine, a basic and self-defined operator, a production task and the like are all constructed according to a unified model, so that the fusion of the resources on the aspects of data dimension, scheduling relation, production application and the like is ensured, and the functions of large data analysis such as dragging type layout, connecting line type flow arrangement, flow processing progress monitoring and display, output of intermediate results, production task monitoring and scheduling and the like are finally supported.
3. The invention constructs a pluggable resource encapsulation integration mechanism (hereinafter referred to as pluggable encapsulation mechanism) in operation based on a heterogeneous big data resource model. The pluggable packaging mechanism comprises a registration mechanism, a scheduling mechanism and a pluggable recombination mechanism. The registration mechanism provides registration of resources of different levels such as a calculation engine, a data source, an operator, a task and the like, and registration management is carried out based on a unified model; the scheduling mechanism introduces an isolation mechanism of a computing engine such as container isolation, Yarn isolation and the like and a corresponding data Volume and ShardCacheManager resource sharing mechanism, and after plugging and unplugging operations are carried out during operation, the system can redistribute a task execution sequence according to a currently configured scheduling algorithm; the plugging recombination mechanism is responsible for process circulation after resource addition reduction during big data analysis running, and comprises recording of states before and after plugging operation, reallocation of storage resources after plugging and unplugging of a data source, adjustment of computing resources after plugging and unplugging of a computing engine, storage and connection of intermediate data after plugging and unplugging of an operator and the like.
Drawings
FIG. 1 is a big data resource pluggable model of the present invention;
fig. 2 is a schematic diagram of a big data analysis flow of the runtime pluggable heterogeneous resource encapsulation integration method according to the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The invention provides a heterogeneous big data resource encapsulation integration system and a method supporting pluggable operation, and the scheme is as follows:
constructing a heterogeneous big data resource model: the invention abstracts and standardizes the data dimension and the incidence relation of heterogeneous resources such as a data source, a calculation engine, a basis, a self-defined operator, a production task and the like, constructs a unified model comprising static information, interactive information, monitoring information and scheduling information, ensures the fusion of the resources on the aspects of data dimension, scheduling relation, production application and the like, and provides a structural basis for a pluggable packaging integration mechanism.
FIG. 1 is a big data resource pluggable model structure. Heterogeneous resources are functionally and hierarchically divided into data sources, computing engines, basic and custom operators, production tasks, and the like. The heterogeneous resources abstract a model structure comprising static information, interaction information, monitoring information and scheduling information from the aspects of incidence relation, capacity range, configuration mode and the like. The static information describes basic attributes and static data of the resource; the interactive information describes interface configuration standards, connection conditions, circulation data and the like when the resources are subjected to service analysis; the monitoring information comprises state information of resources in service analysis, log information for data analysis and processing and the like; the scheduling information includes the level of resource participation in scheduling, scheduling algorithm information, and the like. The structural model design standardizes heterogeneous resources in an analysis flow in an abstract mode, ensures the circulation of data information, scheduling information, analysis results, state information and the like, and lays the foundation of a loose-coupling pluggable large data resource packaging integration scheme from an element level.
A pluggable packaging mechanism:
the invention establishes a pluggable resource encapsulation integration mechanism during operation, and establishes a registration mechanism, a scheduling mechanism and a plugging recombination mechanism based on a unified heterogeneous model. By the established encapsulation integration rules of universal model management, resource sharing, task rescheduling, storage and calculation resource reallocation, intermediate data storage and transmission and the like, the fusion and circulation of heterogeneous resources under pluggable operation are completed, the storage capacity, the calculation capacity and the algorithm capacity of a big data analysis processing platform are expanded, and a loosely-coupled and multi-scenario big data resource integration design scheme is formed.
The big data analysis subsystem comprises a heterogeneous big data resource registration module, a big data flow programming module, a task scheduling module and a plug-in recombination module during operation.
And the heterogeneous big data resource registration module provides a uniform interface for resource registration based on the heterogeneous resource model and performs uniform packaging of output, scheduling relation, data format and the like. The method comprises the following steps that a calculation engine is registered and applied to mainstream calculation engines such as a basic processing engine, a spark processing engine and a TensorFlow deep learning framework; the data source registration supports registration application of various databases such as mysql, es, hdfs, hbase, mongodb, Dameng, and Jincang; the operator registration supports the componentization packaging of typical algorithms such as data processing, feature engineering, machine learning and the like and the expansion of self-defined operators; task registration provides registration of process-based production tasks.
And the big data flow arrangement module performs dragging type layout and connecting type flow arrangement of the calculation engine, the data source and the operator after the registration of heterogeneous resources is completed, and supports the display of flow processing progress and the output of intermediate results in the execution process.
And the task scheduling module is used for constructing a production task based on the big data service flow, scheduling the task according to the scheduling configuration attribute, and supporting the management, visual monitoring, control execution and other operations of the scheduling task.
And the plug-in recombination module is used for plugging and recombining the data when the operation is performed, changing the data analysis scene and performing plug-in adjustment on the big data resource based on the current operation environment. The current state of the resources is recorded by extracting the current state information, data information and the like of the resources, the tasks under the container isolation and the Yarn isolation are stored, the resources are calculated and redistributed, and the intermediate calculation results are stored and exported, so that the normal operation of the business process is ensured.
A structured model is formed through abstraction and specification of data dimensions and incidence relation of heterogeneous resources, a resource encapsulation integration mechanism comprising a registration mechanism, a scheduling mechanism and a plug-in recombination mechanism is established through data circulation and scheduling rules which are consistent in the model, and a plug-in big data analysis flow is formed during operation. The storage capacity, the computing capacity and the algorithm capacity of the big data analysis platform are expanded, and the big data analysis processing requirements under various business scenes are supported. Therefore, the heterogeneous big data resource encapsulation integration method capable of supporting pluggable operation is completed.
FIG. 2 is a big data analysis flow of a runtime-based pluggable heterogeneous resource encapsulation integration method.
Step one, after a big data resource pluggable model is inserted into the packaging integrated system, the heterogeneous big data resource registration module performs resource registration aiming at the big data resource pluggable model;
after the resource registration is completed, if the big data resource pluggable model is a data source, a calculation engine or an operator, the big data flow arrangement module is used for carrying out dragging type layout and connecting line type flow arrangement on the calculation engine, the data source and the operator to construct a production task; the big data flow arrangement module also supports the display of flow processing progress and the output of intermediate results in the execution process;
if the big data resource pluggable model is a production task, directly executing the step three;
step three, aiming at the production tasks in the step two, a task scheduling module is utilized to perform task scheduling according to scheduling configuration attributes, and management, visual monitoring and control execution of the production tasks are supported;
and fourthly, changing a data analysis scene, and performing plug-pull adjustment on the big data resource based on the current operating environment.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. The heterogeneous big data resource encapsulation integrated system capable of being plugged in and pulled out during operation is characterized by comprising a big data resource pluggable model and a big data analysis subsystem;
the big data resource pluggable model comprises static information, interaction information, monitoring information and scheduling information;
the big data analysis subsystem comprises a heterogeneous big data resource registration module, a big data flow programming module, a task scheduling module and a plug-in recombination module during operation;
the heterogeneous big data resource registration module provides a uniform interface for resource registration based on a heterogeneous big data resource model;
the big data flow arrangement module is used for carrying out dragging type layout and connecting line type flow arrangement of a big data operator aiming at the registered big data resource to generate a big data service flow; the big data flow arrangement module also supports the display of flow processing progress and the output of intermediate results in the execution process;
the task scheduling module constructs a production task based on a big data service flow, performs task scheduling according to scheduling configuration attributes, and supports management, visual monitoring and control execution operation of the scheduling task;
and the plug-in recombination module during operation is used for performing plug-in adjustment on the big data resource based on the current operation environment when the data analysis scene changes.
2. The runtime pluggable heterogeneous big data resource package integration system of claim 1, wherein the heterogeneous big data resources comprise data sources, compute engines, operators, and production tasks.
3. The runtime pluggable heterogeneous big data resource encapsulation integration system of claim 1 or 2, wherein the static information describes basic properties and static data of big data resources;
the interactive information is used for describing interface configuration standards, connection conditions and circulation data when the resources are subjected to service analysis;
the monitoring information comprises state information of various resources in service analysis and log information for data analysis processing;
the scheduling information comprises scheduling levels and scheduling algorithm information of various big data resources.
4. The runtime pluggable heterogeneous big data resource encapsulation integration system according to claim 3, wherein the heterogeneous big data resource registration module comprises a registration application of a data source, a registration application of a computing engine, a registration application of an operator and a registration application of a production task;
the registration application of the computing engine comprises a registration application of a basic processing engine, a registration application of a spark processing engine, and registration and application of a TensorFlow deep learning framework;
the registration application of the data source comprises registration applications of various databases;
the registered application of the operator supports the componentized packaging of a data processing algorithm, a feature engineering algorithm and a machine learning algorithm and the expansion of a user-defined operator;
the production task registration application provides process-based registration of production tasks.
5. The runtime pluggable heterogeneous big data resource encapsulation integrated system according to claim 4, wherein in the runtime plugging repetition module, the plug adjustment of the big data resource based on the current runtime environment specifically includes:
and aiming at the newly inserted big data resource, recording the current state by extracting the current state information and the data information of the big data resource, redistributing the storage resource and the computing resource aiming at the production task, and storing and circulating intermediate data generated in the execution process of the production task.
6. The method for packaging and integrating the pluggable heterogeneous big data resource during the running is characterized in that the workflow of the packaging and integrating system is as follows for the packaging and integrating system as claimed in claim 1:
step one, after a big data resource pluggable model is inserted into the packaging integrated system, the heterogeneous big data resource registration module performs resource registration aiming at the big data resource pluggable model;
after the resource registration is completed, if the big data resource pluggable model is a data source, a calculation engine or an operator, the big data flow arrangement module is used for carrying out dragging type layout and connecting line type flow arrangement on the calculation engine, the data source and the operator to construct a production task; the big data flow arrangement module also supports the display of flow processing progress and the output of intermediate results in the execution process;
if the big data resource pluggable model is a production task, directly executing the step three;
step three, aiming at the production tasks in the step two, a task scheduling module is utilized to perform task scheduling according to scheduling configuration attributes, and management, visual monitoring and control execution of the production tasks are supported;
and fourthly, changing a data analysis scene, and performing plug-pull adjustment on the big data resource based on the current operating environment.
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