CN108628593B - Big data product platform establishing method and device, electronic equipment and storage medium - Google Patents
Big data product platform establishing method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a big data product platform establishing method, a big data product platform establishing device, electronic equipment and a storage medium, wherein the method comprises the following steps: establishing a basic function module to provide uniform function service, data service and interactive operation for each sub-product in a big data product platform; the basic function module realizes the functions required to be realized by each sub-product; establishing an individualized function module, and providing corresponding function service, data service and interactive operation for sub-products in a big data product platform which needs to realize the functions realized by the individualized function module; and when any sub-product in the big data product platform is established, multiplexing the corresponding functions provided by the basic function module and the personalized function module according to the functions to be realized by the sub-product. The scheme solves the problems of function overlapping, state and data asynchronism among sub-products of a big data product platform.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a method and a device for establishing a big data product platform, electronic equipment and a storage medium.
Background
The big data service comprises the whole process of acquisition, transportation, calculation, storage and application of mass data; and the data range is not limited to the enterprise itself, and the data range can also relate to the upstream and downstream enterprises of the enterprise supply chain, even the data of the whole industry and market. Therefore, enterprises can hardly realize all services of big data by using one product, and a big data product platform consisting of a plurality of sub-products must be constructed. And finally, all the sub-products respectively perform their own functions and cooperate with each other to be competent for the work of the big data.
With the continuous expansion of the field of big data services, the increase of data dependence of enterprises, the proliferation of data application scenarios and the like, the traditional big data product platform can only apply new requirements and new service scenarios which are generated continuously by continuously adding new sub-products.
However, with the continuous increase of sub-products of large data product platforms, not only more cost and personnel need to be invested, but also more importantly, the problems of service overlapping and function repetition among the sub-products cannot be solved. The problem that data and states of the sub-products cannot be synchronized is inevitably caused, and finally the whole large data product platform is broken down.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for establishing a big data product platform, an electronic device, and a storage medium, which solve the problems of function overlapping, state, and data asynchronism among sub-products of the big data product platform.
In order to solve the technical problem, the technical scheme of the application is realized as follows:
a big data product platform establishment method comprises the following steps:
establishing a basic function module to provide uniform function service, data service and interactive operation for each sub-product in a big data product platform; the basic function module realizes the functions required to be realized by each sub-product;
establishing an individualized function module, and providing corresponding function service, data service and interactive operation for sub-products in a big data product platform which needs to realize the functions realized by the individualized function module;
and when any sub-product in the big data product platform is established, multiplexing the corresponding functions provided by the basic function module and the personalized function module according to the functions to be realized by the sub-product.
A big data product platform setup apparatus, the apparatus comprising: a first establishing unit, a second establishing unit and a third establishing unit;
the first establishing unit is used for establishing a basic function module and providing uniform function service, data service and interactive operation for each sub-product in the big data product platform; the basic function module realizes the functions required to be realized by each sub-product;
the second establishing unit is used for establishing an individualized function module and providing corresponding function service, data service and interactive operation for sub-products in a big data product platform which needs to realize the functions realized by the individualized function module;
and the third establishing unit is used for multiplexing the corresponding functions provided by the basic function module in the first establishing unit and the personalized function module in the second establishing unit according to the functions to be realized by any sub-product when the sub-product in the big data product platform is established.
An electronic device, comprising:
at least one central processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one central processor; wherein the content of the first and second substances,
the memory stores instructions executable by the one central processing unit, the instructions being executable by the at least one central processing unit to enable the at least one central processing unit to perform the method described above.
A computer-readable storage medium storing computer instructions for causing the computer to perform the above-described method.
According to the technical scheme, on the basis of the traditional construction of the big data platform taking the service requirement as a leading factor, the reusable and independent functions on the big data product platform are used as basic functions, and the non-reusable functions are used as personalized functions, so that the big data product platform is established, and the problems of function overlapping, state and data asynchronization among sub-products of the big data product platform are solved.
Drawings
FIG. 1 is a schematic diagram of a process for implementing a big data product platform establishment in an embodiment of the present application;
FIG. 2 is a schematic diagram of a big data product platform architecture implemented in an embodiment of the present application;
FIG. 3 is a platform view of a big data product platform implemented with respect to FIG. 2 in an embodiment of the present application;
FIG. 4 is a schematic diagram of an apparatus for implementing the above technique in an embodiment of the present application;
fig. 5 is a schematic diagram of a hardware structure of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clearly apparent, the technical solutions of the present invention are described in detail below with reference to the accompanying drawings and examples.
The application provides a big data product platform establishment scheme, on the basis of the traditional big data platform established by taking the service requirement as the leading factor, the establishment of the big data product platform is realized by taking the reusable and independent functions on the big data product platform as basic functions and taking the non-reusable functions as personalized functions, and the problems of function overlapping, state and data asynchronization among sub-products of the big data product platform are solved.
Due to differences among enterprises, sub-products of a large data product platform are slightly different in number and function, and can be generally divided into the following six sub-products according to functions:
a scheduling task system for data acquisition and transport; a data warehouse (bazaar) system for data storage; a data computation (model) system for data computation; a data presentation (reporting) system for data presentation; the monitoring system, the server and the cluster management system are used for system operation and maintenance; the flow work order system is used for flow management and personnel examination and approval.
The functions used by all the sub-products are used as basic functions, such as a label function, a log function, an authority function, a dictionary function and the like, and are not limited to the realization of the basic functions; the functions used by one or more products, or different functions implemented by different sub-products using the functional entity for one functional entity, and personalized functions such as a task scheduling function, a data warehouse function, a server function, a cluster function, a flow work order function, etc., are not limited to the implementation of these personalized functions.
Building a sub-product, such as a data computing system, basic functions (a tag function, a log function, a permission function, and a dictionary function) need to be implemented, and only a server function among personalized functions or a computing function among server functions need to be implemented.
In specific implementation, the content included in the basic function and the personalized function can be determined according to long-term accumulated experience, and the corresponding content can also be obtained according to a general big data product platform.
The device for implementing the large data platform product establishment can be 1 or more devices, and when the device is implemented by a plurality of devices, a system formed by the plurality of devices is also referred to as a device hereinafter for convenience of description. The following describes in detail a process for implementing the platform establishment of the big data product according to the present application with reference to the accompanying drawings.
Referring to fig. 1, fig. 1 is a schematic diagram of a process for implementing a big data product platform establishment in an embodiment of the present application. The method comprises the following specific steps:
The function realized by the basic function module is the function which needs to be realized by each sub-product; unique, common, independent services are provided for all children of the big data product platform.
The method for establishing the basic function module by the equipment specifically comprises the following steps:
creating an object base class of a basic function; defining object properties and object operations for the object base class of the basic function;
and expanding on the basis of the object base class of the established basic function, and realizing a tag function, a log function, an authority function and a dictionary function by adopting an abstract factory mode.
In specific implementation, the implementation may be implemented in JAVA, C + +, python, or other languages, and examples of the JAVA implementation are given below:
first, an object base class (Atomic services) of a base function is created.
Call public final Class <? The method of > newAtomicServices (), creates an object base class (Atomic services) of basic functions. Where public represents a public method and final Class represents the return of a unique Class (Class), <? Is a generic proxy applicable to any method.
Second, create object properties of the object base class.
On the basis of the object base classes (Atomic services), object attributes of the underlying functions are defined. The object properties include:
GetID; obtaining ID of basic function
GetType; obtaining basic function classes
GetName; obtaining a base function name
GetCreatTime; obtaining base function creation time
GetCreater; obtaining basic function creators
GetClone; obtaining a base function copy
And thirdly, creating object operation of the basic function.
On the basis of the object base class (Atomic services) of the basic function, the object operation of the basic function is defined. The object operation includes:
add (); adding an operation
Update (); update operations
Delete (); delete operation
Select (); query operations
And fourthly, expanding a label function, a log function, a monitoring function, an authority function and a dictionary function based on the basic function.
On the basis of generating the object base class (Atomise rvices) of the basic function (including attribute + operation), the object base class (Atomise rvices) of the basic function is expanded, and an abstract factory mode is adopted to generate a label function, a log function, a monitoring function, an authority function and a dictionary function class.
Taking the tag function generation method as an example:
create tag function class: newTagServices class;
the Tag function class (Tag services) inherits the basic function class (Atomic services), namely: tag services extended Atomic services. The inherited label function class (Tag services) automatically inherits all attributes and operations of the basic service class (Atomic services);
the Tag functions class (Tag services) extends its own private attributes, including:
TagTeam; adding a tag group attribute;
orderNumber; self-numbering labels
The Tag services class (Tag services) extends its own private operations, including:
AddTagTeam () Add tag group Add operation
UpdateTagTeam (); update tag group operation
DeleteTagTeam (); delete tag group operation
SelectTagTeam (); inquiry tag group operation
And taking the set of the realized functions as a basic function module, and establishing the basic function module.
Each sub-product has basic service, and some personalized services, namely private services, exist according to the characteristics of the sub-product, for example, one sub-product uses a calculation function on a server, one sub-product uses a basic information function on the server, and the other product uses a state information synchronization function on the server.
The equipment establishes a personalized function module, comprising:
creating a personalized function base class, and defining object attributes and object operations for the personalized function base class;
and expanding the created personalized function base class, and realizing a task scheduling function, a data warehouse function, a server function, a cluster function and a flow work order function by adopting an abstract factory mode.
During specific implementation, languages such as JAVA, C + +, python, etc. may also be used for implementation, and the implementation process is similar to the implementation process of the basic function module, and a specific implementation process is not given here.
And 103, when the device establishes any sub-product in the big data product platform, multiplexing the corresponding functions provided by the basic function module and the personalized function module according to the functions to be realized by the sub-product.
When the function to be realized by a sub-product exists in the functions which can not be realized by both the basic function module and the personalized function module, the function is added in the personalized function module, and the function is reused to establish the sub-product.
With the development of a big data product platform, when a certain personalized function is a function which all the sub-products need to realize, namely, the personalized service function is evolved into a basic service function, the personalized service function can be updated into the basic function to realize.
Referring to fig. 2, fig. 2 is a schematic diagram of a platform architecture of a big data product implemented in an embodiment of the present application.
The big data product platform in fig. 2 is different from a single business latitude in the existing implementation, and is implemented by two latitudes of business and function. As can be seen from fig. 2, each function realized by the basic function module and each sub-product need to be reused, and each sub-product of the functions realized by the personalized function module needs to be reused according to the actual needs. If the task scheduling system only multiplexes the task scheduling function aiming at the personalized function module; the data computing system can multiplex the data warehouse function and the server function aiming at the personalized function module.
In practical applications, functions implemented by the basic function module may be increased or decreased, functions implemented by multiplexing the personalized function module may also be increased or decreased according to actual needs, and the implemented functions of the personalized function module may also be combined or split according to actual needs, and no limitation is imposed on specific implementation.
When the basic functions are specifically multiplexed, the multiplexing can be realized by two modes of webpage link skipping and API interface calling; when the multiplexing of the personalized function is realized, the method can be realized by calling an API interface.
Aiming at the webpage link jump implementation mode, the address of the corresponding function is called in a product platform needing to implement the corresponding function, and the corresponding service function can be directly jumped to by adopting modes of adding a button, adding a keyword link and the like.
Aiming at the API interface calling implementation mode, each function provides a corresponding API calling interface, and a product platform using the function directly calls the API calling interface.
And generating a big data product platform view for displaying the functions of the currently multiplexed basic function module and the personalized function module of each sub-product aiming at the established big data product platform.
Referring to fig. 3, fig. 3 is a platform view of a big data product platform implemented with respect to fig. 2 in an embodiment of the present application. As can be seen from fig. 3, which sub-product multiplexes which functions are clear at a glance, and the function marked by "√" in fig. 3 is a function realized by multiplexing the sub-product; the implementer can update the functions realized by the basic function module and the personalized function module according to the multiplexing condition of each sub-product.
According to the technical scheme, the problems of repeated function development and resource waste among the sub-products are solved by multiplexing the basic function module; the problems of function information isolated islands, different states and the like of each sub-product are solved by multiplexing functions realized by the personalized function module; the whole technical scheme realizes the consistency of a big data product platform and improves the performance of the product.
When a new sub-product needs to be added in the established big data product platform or a new service requirement is met, the functions realized by the basic function module and the personalized function module can be reused, and the new functions can be realized through the combined function; therefore, the complexity of the new service is reduced, and the consistency of information and state is ensured.
Based on the same inventive concept, the application also provides a big data product platform establishing device. Referring to fig. 4, fig. 4 is a schematic structural diagram of an apparatus applied to the above technology in the embodiment of the present application. The device includes: a first establishing unit, a second establishing unit and a third establishing unit;
the first establishing unit is used for establishing a basic function module and providing uniform function service, data service and interactive operation for each sub-product in the big data product platform; the basic function module realizes the functions required to be realized by each sub-product;
the second establishing unit is used for establishing an individualized function module and providing corresponding function service, data service and interactive operation for sub-products in a big data product platform which needs to realize the functions realized by the individualized function module;
and the third establishing unit is used for multiplexing the corresponding functions provided by the basic function module in the first establishing unit and the personalized function module in the second establishing unit according to the functions to be realized by any sub-product in the big data product platform when the sub-product is established.
Preferably, the first and second liquid crystal films are made of a polymer,
the first establishing unit is specifically used for establishing a basic function base class when the basic function module is established; defining object properties and object operations for the basic function base class; and expanding on the basis of the established basic function base class, and realizing the basic function by adopting an abstract factory mode.
Preferably, the first and second liquid crystal films are made of a polymer,
the second establishing unit is specifically used for establishing a personalized function base class when the personalized function module is established, and defining object attributes and object operations for the personalized function base class; and expanding on the basis of the created personalized function base class, and realizing the personalized function by adopting an abstract factory mode.
Preferably, the first and second liquid crystal films are made of a polymer,
the functions realized by the basic function module comprise: a tag function, a log function, an authority function, and a dictionary function;
the functions realized by the personalized function module comprise: a scheduling task function, a data warehouse function, a server function, a cluster function, and a flow work order function.
Preferably, the first and second liquid crystal films are made of a polymer,
and the second establishing unit is further used for adding the function in the personalized function module when the function which cannot be realized by both the basic function module and the personalized function module exists in the functions to be realized by the sub-product, and multiplexing the function through the third establishing unit to establish the sub-product.
Preferably, the apparatus further comprises: a display unit;
and the display unit is used for generating a large data product platform view aiming at the established large data product platform and displaying the functions of the basic function module and the personalized function module which are currently multiplexed by each sub-product.
The units of the above embodiments may be integrated into one body, or may be separately deployed; may be combined into one unit or further divided into a plurality of sub-units.
Based on the same inventive concept, the application also provides a system for deploying the electronic equipment. Referring to fig. 5, fig. 5 is a schematic diagram of a hardware structure of an electronic device in the embodiment of the present application.
The electronic device shown in fig. 5 comprises at least a central processor 501, a memory 502. The memory 502 and the at least one central processing unit 501 are connected by a bus, the memory 502 is used for storing computer instructions, and when the electronic device is operated, the at least one central processing unit 501 executes the computer instructions stored in the memory 502, so that the electronic device executes the specific processes of the method described above.
Based on the same inventive concept, the present application also proposes a computer-readable storage medium storing computer instructions for causing the computer to perform the above-mentioned method.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
In summary, according to the application, on the basis of the traditional construction of the big data platform mainly based on the service requirement meeting, the establishment of the big data platform is realized by taking the reusable and independent functions on the big data platform as basic functions and taking the non-reusable functions as personalized functions, and the problems of function overlapping, state and data asynchronization among sub-products of the big data platform are solved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (14)
1. A big data product platform establishing method is characterized by comprising the following steps:
establishing a basic function module to provide uniform function service, data service and interactive operation for each sub-product in a big data product platform; the basic function module realizes the functions required to be realized by each sub-product;
establishing an individualized function module, and providing corresponding function service, data service and interactive operation for sub-products in a big data product platform which needs to realize the functions realized by the individualized function module;
and when any sub-product in the big data product platform is established, multiplexing the corresponding functions provided by the basic function module and the personalized function module according to the functions to be realized by the sub-product.
2. The method of claim 1, wherein the building a base function module comprises:
creating a basic function base class; defining object properties and object operations for the basic function base class;
and expanding on the basis of the established basic function base class, and realizing the basic function by adopting an abstract factory mode.
3. The method of claim 1,
the establishing of the personalized function module comprises the following steps:
creating a personalized function base class, and defining object attributes and object operations for the personalized function base class;
and expanding on the basis of the created personalized function base class, and realizing the personalized function by adopting an abstract factory mode.
4. The method of claim 1,
the functions realized by the basic function module comprise: a tag function, a log function, an authority function, and a dictionary function;
the functions realized by the personalized function module comprise: a scheduling task function, a data warehouse function, a server function, a cluster function, and a flow work order function.
5. The method according to any one of claims 1-4, wherein the method further comprises:
and when the functions to be realized by the sub-product exist the functions which can not be realized by both the basic function module and the personalized function module, adding the functions into the personalized function module, and reusing the functions to establish the sub-product.
6. The method according to any one of claims 1-4, wherein the method further comprises:
and generating a big data product platform view for displaying the functions of the currently multiplexed basic function module and the personalized function module of each sub-product aiming at the established big data product platform.
7. A big data product platform establishing device is characterized by comprising: a first establishing unit, a second establishing unit and a third establishing unit;
the first establishing unit is used for establishing a basic function module and providing uniform function service, data service and interactive operation for each sub-product in the big data product platform; the basic function module realizes the functions required to be realized by each sub-product;
the second establishing unit is used for establishing an individualized function module and providing corresponding function service, data service and interactive operation for sub-products in a big data product platform which needs to realize the functions realized by the individualized function module;
and the third establishing unit is used for multiplexing the corresponding functions provided by the basic function module in the first establishing unit and the personalized function module in the second establishing unit according to the functions to be realized by any sub-product when the sub-product in the big data product platform is established.
8. The apparatus of claim 7,
the first establishing unit is specifically used for establishing a basic function base class when the basic function module is established; defining object properties and object operations for the basic function base class; and expanding on the basis of the established basic function base class, and realizing the basic function by adopting an abstract factory mode.
9. The apparatus of claim 7,
the second establishing unit is specifically used for establishing a personalized function base class when the personalized function module is established, and defining object attributes and object operations for the personalized function base class; and expanding on the basis of the created personalized function base class, and realizing the personalized function by adopting an abstract factory mode.
10. The apparatus of claim 7,
the functions realized by the basic function module comprise: a tag function, a log function, an authority function, and a dictionary function;
the functions realized by the personalized function module comprise: a scheduling task function, a data warehouse function, a server function, a cluster function, and a flow work order function.
11. The apparatus according to any one of claims 7 to 10,
the second establishing unit is further configured to add the function to the personalized function module when the function that cannot be realized by both the basic function module and the personalized function module exists in the functions to be realized by the sub-product, and multiplex the function through the third establishing unit to establish the sub-product.
12. The apparatus of any of claims 7-10, further comprising: a display unit;
and the display unit is used for generating a large data product platform view aiming at the established large data product platform and displaying the functions of the basic function module and the personalized function module which are currently multiplexed by each sub-product.
13. An electronic device, comprising:
at least one central processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one central processor; wherein the content of the first and second substances,
the memory stores instructions executable by the one central processor to enable the at least one central processor to perform the method of any one of claims 1-6.
14. A computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-6.
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| US10643181B2 (en) * | 2015-08-18 | 2020-05-05 | Satish Ayyaswami | System and method for a big data analytics enterprise framework |
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| CN101853160A (en) * | 2010-05-18 | 2010-10-06 | 上海动量软件技术有限公司 | Platform system and method for realizing framework configuration based on cloud components in computer software system |
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