CN110442629B - Big data multi-center heterogeneous dynamic data conversion method - Google Patents
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Abstract
The invention provides a big data multi-center heterogeneous dynamic data conversion method, which comprises the steps of obtaining first data query request information sent by a subsystem, obtaining a data model of the subsystem from a multi-dimensional data dictionary database according to the information of the subsystem and analyzing the obtained data model; and sending second data query request information to the virtual data source according to the analysis result, judging whether the queried data is data for executing preset operation, if not, sending third data query request information to the actual data source from the virtual data source, converting the received data into a data form corresponding to the subsystem according to the data model and the mapping rule of the subsystem, and sending the converted data to the subsystem. The invention can realize multi-center data conversion operation with low cost.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a big data multi-center heterogeneous dynamic data conversion method.
Background
With the development of big data technology, the big data technology has been widely applied in a plurality of fields such as user information collection, user behavior analysis, personalized customization service, etc., wherein data conversion is a key link of big data processing flow. The currently common big data processing flow is an ETL (Extract-Transform-Load) flow. Wherein data transformation (DataTransform) is an important link in the big data processing flow.
The conventional data conversion adopts a centralized method, and as shown in fig. 1, the existing big data processing method may process data of multiple data sources, for example, data of a data source 11, a data source 12, and a data source 13. The conventional data conversion method generally provides a data center 20, data of a plurality of different data sources need to go through data extraction 14 and conversion operation of data adapter 17 to form standard data, the standard data is stored in the data center 20, for example, data of data source 11 goes through data extraction 14 and conversion operation of data adapter 17 to be stored in the data center 20, data of data source 12 goes through data extraction 15 and conversion operation of data adapter 18 to be stored in the data center 20, data of data source 13 goes through data extraction 16 and conversion operation of data adapter 19 to be stored in the data center 20, and data stored in the data center 20 goes through data cleaning and is static and centralized standard data.
With the continuous development of computer technology, the static centralized data conversion technology can not meet the requirements of many practical application scenarios, and the dynamic multi-centralized data conversion demand is increasing. If the static data adapter approach is still taken to address the multicentralized data transformation requirements, its possible architecture is shown in FIG. 2. It is assumed that three subsystems 21, 22, 23 are to be integrated, each subsystem having its own data modality, for example, the data modality of the subsystem is 24, the data modality of the subsystem 22 is 25, and the data modality of the subsystem 23 is 26. If data interaction is required to be implemented between the subsystems, data adapters are required to perform data conversion, for example, data adapter 27 is provided to implement data form 24 to data form 25, data adapter 28 is provided to implement data form 25 to data form 26, and data adapter 29 is provided to implement data form 24 to data form 26.
Obviously, if this kind of technology is adopted, it will result in that when performing multi-center data conversion, a large number of special purpose data adapters need to be constructed, the complexity of development, operation and maintenance is increased, and the cost of the data system is very high. Also, since each adapter is designed for a specific purpose, the software reusability of each adapter is extremely poor. In addition, once the data and its attributes are changed, all adapters related to the data need to be changed one by one, and the system can be upgraded again for use, which greatly increases the upgrade, update and test costs. In some application scenarios where data changes frequently, the negative impact of this method can greatly affect the usability and reliability of the system. Finally, the adapter of this method can only be used for static data conversion, and when the data characteristics are likely to change dynamically, this method cannot meet the actual requirements.
Disclosure of Invention
The invention mainly aims to provide a large data multi-center heterogeneous dynamic data conversion method which is good in adaptability and low in cost.
In order to achieve the main purpose of the invention, the big data multi-center heterogeneous dynamic data conversion method provided by the invention comprises the steps of obtaining first data query request information sent by a subsystem, obtaining a data model of the subsystem from a multi-dimensional data dictionary database according to the information of the subsystem and analyzing the obtained data model; and sending second data query request information to the virtual data source according to the analyzed result, judging whether the queried data is data for executing preset operation, if not, sending third data query request information to the actual data source by the virtual data source, acquiring data returned by the actual data source, converting the received data into a data form corresponding to the subsystem according to a data model and a mapping rule of the subsystem, and sending the converted data to the subsystem.
According to the scheme, after the virtual data source receives the request of the subsystem, the query request is sent to the actual data source according to the result of data model analysis of the subsystem, and after the data returned by the actual data source is acquired, the data is converted into the data model which can be identified by the subsystem after mapping, and the converted data is returned to the subsystem.
Therefore, by applying the scheme of the invention, a large number of data adapters are not needed to be arranged, and only the data forms of various types are required to be adaptively converted according to the attribute of the data model. In addition, because a plurality of adapters which can independently realize the conversion of certain two data forms are not required to be arranged, the cost of data conversion can be greatly reduced.
A preferable scheme is that if the queried data is determined to be data requiring execution of a preset operation, the operation rule information of the data is acquired from the data operation rule database, after the virtual data source acquires data returned by the actual data source, the data returned by the actual data source is operated according to the operation rule information, the operated data is converted into a data form corresponding to the subsystem according to the data model and the mapping rule of the subsystem, and the converted data is sent to the subsystem.
Therefore, if the data to be inquired is data which needs to be subjected to specific operation, the data to be operated can be operated according to the operation rule set by operation, and the data after operation is subjected to data form conversion, so that the purposes of data inquiry and data operation are achieved.
And if so, generating complementary data query request information, sending fourth data query request information to the virtual data source, and acquiring the complementary data from the virtual data source.
Therefore, if the supplementary data is required to be acquired in the process of performing the preset operation, the required supplementary data can be acquired from the virtual data source, so that the smooth execution of the preset operation can be ensured, the supplementary data can be quickly acquired and the effective operation can be performed, and the result after the operation can be timely returned to the subsystem.
Further, the first data query request information includes a data name and a query condition, and preferably, the query condition includes a condition name and a condition number list.
Therefore, the information such as the data name, the condition value list and the like is sent to the virtual data source, so that the virtual data can conveniently and quickly acquire the relevant information of the data to be inquired, and the data inquiry efficiency is improved.
Further, each subsystem has a unique identification code; the method for acquiring the data model of the subsystem from the multidimensional data dictionary database according to the information of the subsystem comprises the following steps: and acquiring a data model of the subsystem from the multidimensional data dictionary database according to the identification code of the subsystem.
Therefore, the data module of the subsystem is acquired according to the identification code of the subsystem, which subsystem needs to be inquired can be quickly identified, and the data model of the subsystem can be quickly found out.
Further, the analyzing the acquired data model comprises: resolving at least one of the following parameters from the data model: identification of the data field of the subsystem in the virtual data source, identification of the data field of the subsystem in the actual data source, name of the data field of the subsystem in the actual data source, type of the data field of the subsystem in the actual data source, and mapping rules of the data field of the subsystem.
Therefore, a large amount of attribute information of the data model is obtained by analyzing the data model, and a data form conversion mode can be determined according to the attribute information, so that the data form can be accurately converted.
In a further aspect, the multidimensional data dictionary database stores data models corresponding to a plurality of subsystems, and information of each data model includes at least one of: the data name, the data source and the mapping name of the data in the source system, the data format, the static mapping rule code and the address of the operation rule of the data in the operation rule database.
Therefore, the data source can be quickly determined according to the information of the data model, and the required data can be quickly acquired.
Drawings
Fig. 1 is a block diagram of a data conversion system of a conventional big data processing.
FIG. 2 is an architecture diagram of data transformation during multi-center big data processing.
Fig. 3 is a structural diagram of an embodiment of a big data multi-center heterogeneous dynamic data conversion method according to the present invention.
FIG. 4 is a first portion of a flow chart of a big data multi-center heterogeneous dynamic data conversion method embodiment of the present invention.
FIG. 5 is a second portion of a flow chart of a big data multi-center heterogeneous dynamic data conversion method embodiment of the present invention.
The invention is further described with reference to the following figures and examples.
Detailed Description
The big data multi-center heterogeneous dynamic data conversion method is applied to the big data processing process, and mainly converts data in various different data forms so as to meet the requirement of multi-center data conversion. Preferably, the method of the present invention may be applied to a server cloud, and preferably, the server cloud has a plurality of servers that can communicate with each other, and the servers may be physical servers or virtual servers, the physical servers are provided with a processor and a memory, the memory stores a computer program, and the processor implements the large data multi-center heterogeneous dynamic data conversion method by executing the computer program.
The Processor may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like.
The memory may be used to store computer programs and/or modules, and the processor may implement the methods described above by executing or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The present invention is not limited to the type of storage, which may include high speed random access memory, and may also include non-volatile storage, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), a magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The embodiment is a data-driven and multi-center dynamic data conversion method, and aims to meet the data conversion requirements of non-standard data, more changes and interdependence among data sources. The system architecture applied in the present embodiment is shown in fig. 3, and includes five functional modules, which are a multidimensional data dictionary database 30, a virtual data source 40, a data resource setter 46, a data request manager 55, and a data operation rule database 60.
In this embodiment, the virtual data source 40 is not a real data store, but is only a data agent, unlike a real data store. Therefore, the virtual data source 40 needs to be connected to a plurality of systems, even to the external data source 50, for example, the virtual data source 40 is connected to the subsystem 51, the subsystem 52, and the subsystem 53, and the data form of each subsystem may be different.
Thus, to the data consumer, the virtual data source 40 appears to be indistinguishable from a centralized data warehouse, but in the actual event of a data query, the virtual data source 40 will query data from the actual data source based on the address and query instructions provided by the data manager 45. Thus, any subsystem can become an external actual data source of the virtual data source 40 during operation as long as it is provided with a mechanism, such as a set of instructions or execution rules, for satisfying the query request of the virtual data source 40. The advantage of using the virtual data source 40 is that the complex data dependency relationship between multiple subsystems can be simplified into the relationship of extracting and querying data from the virtual data source 40, thereby avoiding setting independent data adapters between each subsystem and reducing the number of adapters used.
The multidimensional data dictionary database 30 stores a data list containing information of the data models of all subsystems. Preferably, each data model includes at least one of the following attribute information: the data model also comprises addresses of corresponding operations in the data operation rule database if the data entry is data needing operations. For example, the multidimensional data dictionary database 30 stores a plurality of data models, such as data models 31, 32, 33, and the like.
The data operation rules database 60 also contains a data list, and unlike the conventional data list, the data list of the data operation rules database 60 stores code written in a computer language that can be dynamically compiled at runtime, for example, python. For example, the data manipulation rule database 60 stores a plurality of data manipulation rules 61, 62, 63.
The data request manager 55 is configured to manage data requests, wherein the data requests may be implemented based on an abstract data request protocol, and typically comprise a subset of the plurality of subsystem data models.
The data manager 45 is a core function module for implementing the embodiment, and is mainly used for implementing processing of a data query request, conversion of data form, and the like, for example, receiving a data query request of a certain subsystem, and determining whether data to be queried needs to be subjected to preset operation, if the data to be queried does not need to be subjected to the preset operation, the data manager 45 retrieves an actual source of the data from the multidimensional data dictionary database 30 according to the content of the data request, sends query request information to the virtual data source 40, maps a query result into a format required by a data model corresponding to the requested subsystem, and returns the converted data to the corresponding subsystem. If the data to be queried needs to be subjected to preset operation, a corresponding code compiler needs to be called during running, corresponding data operation rules in the data operation rule database 60 are called according to the requirements of the data model, then compiling execution is carried out, and after operation is carried out according to the operation rules, the operation result is returned to a corresponding subsystem. The data resource setter 46 communicates with the data manager 45 and may set parameters of the data manager 45.
Therefore, in the whole process of data query and conversion, the data manager 45 does not need to know the meaning of the data and the rule, and only calls and executes the required operation rule according to the identification code provided by the data model, so that when the data model changes, the code of the data manager 45 does not need to be changed. Therefore, the later maintenance cost of the system is low, the adaptability is strong, and the conversion requirements among various different types of data forms can be met.
Since the plurality of subsystems 51, 52, 53 and the external data source 50 are connected to the "centralized" virtual data source 40 in the form of actual data sources and satisfy the query requests of the virtual data source 40 according to the respective data models. When the subsystem needs to query data according to a specific data model, data query request information is sent to the virtual data source 40 through the data manager 45, the virtual data source 40 analyzes a query path of real data, sends a query request to an actual data source of the real path, returns a query result to the data manager 45, and finally, the data manager 45 converts the query result into a required data form according to the data model of a data requester.
The operation flow of the present embodiment will be described with reference to fig. 4 and 5. Assuming that a certain subsystem needs query information, the subsystem sends a piece of data query request information to the data manager, so that the data manager first executes step S1 to receive the first data query request information sent by the subsystem. Preferably, the first data query request message includes a data name to be queried and a query condition, where the query condition includes a condition name and a condition number list. Therefore, from the perspective of the subsystem, the data manager and the virtual data source are completely constructed by using the data model of the subsystem, that is, the subsystem does not need to convert the data form, but directly sends the request for querying the data to the data manager.
And after receiving the first data query request information of the subsystem, the data manager executes the step S2 to acquire the identification code of the subsystem. Preferably, in this embodiment, each subsystem is provided with its own unique identification code, and the data model of each subsystem stored in the multidimensional data dictionary database 30 is stored according to the identification code of the subsystem, that is, the multidimensional data dictionary database 30 stores the identification code of each subsystem and the data model corresponding to the subsystem, so that the multidimensional data dictionary database 30 can query the data model corresponding to the subsystem according to the identification code of the subsystem.
And the data manager acquires the subsystem from which the information is sent according to the received first data query request information, acquires the identification code corresponding to the subsystem, and then executes the step S3 to acquire the corresponding data model from the multidimensional data dictionary database according to the identification code of the subsystem. Preferably, after the data model is obtained, the obtained data model is analyzed, and specifically, at least one of the following parameters is analyzed from the data model: identification of the data field of the subsystem in the virtual data source, identification of the data field of the subsystem in the actual data source, name of the data field of the subsystem in the actual data source, type of the data field of the subsystem in the actual data source, and data field mapping rule of the subsystem.
And if one subsystem relates to a plurality of data models, acquiring the plurality of data models, analyzing the plurality of data models and acquiring all condition parameter fields.
Next, step S4 is executed to send a second data query request message to the virtual data source, at this time, step S5 is also executed to determine whether the queried data needs to undergo a preset operation, that is, whether the queried data needs to undergo a certain operation after acquiring data from the actual data source before returning to the subsystem, for example, whether the data to be queried is the sum of a certain set of data, or the inverse of a certain data, and if the data needs to undergo the preset operation, step S11 is executed, and if the data does not need to undergo the preset operation, step S6 is executed.
In step S6, the virtual data source sends the third data query request information to the actual data source. For example, if the actual data source is a subsystem, the data manager runs the query protocol of the subsystem and sends the request information of the data query to the subsystem through the query protocol. Since the virtual data source determines the path of the actual data source, it is also determined to which subsystem the third data query request information is transmitted.
Then, step S7 is executed, and the data manager receives the data returned by the actual data source, that is, the query result, which is the data sent by the subsystem as the actual data source, and therefore, the data form of the data is the data form of the subsystem as the actual data source, and therefore, the data form of the returned data is not necessarily consistent with the data form of the subsystem that issued the query request, and if the acquired data is directly sent to the subsystem that issued the query request, the subsystem that issued the query request cannot recognize the data.
Therefore, the data manager executes step S8 to convert the received data into the data form corresponding to the subsystem that issued the query request, and specifically, the data manager converts the data obtained by the query into the data form of the subsystem according to the data model and the field mapping rule of the subsystem that issued the query request. Finally, step S9 is executed to send the converted data to the subsystem sending the query request, so that the subsystem sending the query request can identify the received data.
If the determination result in step S5 is yes, the data indicating the query can be returned to the subsystem that issued the query request only through the preset operation, so step S11 is executed to obtain the operation rule information of the data from the data operation rule database, for example, obtain the type of the preset operation or the name of the preset operation from the second data request information, and then obtain the code of the corresponding operation rule from the data operation rule database.
Next, step S12 is executed to determine whether to execute the preset operation, and whether to acquire the supplementary data, for example, whether to acquire the relevant data from other subsystems to complete the preset operation, if so, step S17 is executed, otherwise, step S13 is executed. If the supplementary data does not need to be acquired, the data returned by the actual data source is acquired, and then step S14 is executed to perform budgeting, such as accumulation operation or reciprocal operation, on the acquired data according to the data operation rule acquired in step S11.
Then, step S15 is executed to perform data form conversion on the data obtained by the operation, for example, to obtain information about the data form of the subsystem issuing the query request, and convert the data obtained by the operation into the data form of the subsystem according to the data model and the field mapping rule of the subsystem issuing the query request. Finally, step S16 is executed to send the converted data to the subsystem sending the query request, so that the subsystem sending the query request can identify the received data.
If the judgment result in the step S12 is yes, it indicates that the preset operation is implemented, and the supplementary data needs to be acquired from other subsystems or external data sources. At this time, step S17 is performed, the data manager generates the supplementary data query request information, and then step S18 is performed, transmits the fourth data query request information to the virtual data source, and acquires the supplementary data from the actual data source. In general, the supplemental data that needs to be obtained has an explicit data source from which it can be determined from which subsystem or external data source the supplemental data can be obtained. After determining the source of the supplementary data, the supplementary data may be acquired through the methods of steps S1 to S9.
After acquiring the supplementary data, step S19 is performed to acquire the supplementary data, in which case the data manager serves as a receiver of the supplementary data, receives the acquired supplementary data, and then, step S13 is performed to perform a preset operation on the acquired supplementary data and data acquired from the actual data source, and acquire the operated result.
Of course, if there are a plurality of supplementary data to be acquired, it is necessary to perform steps S1 to S9 a plurality of times until all supplementary data are acquired.
It can be seen that, by applying the method of the embodiment, in the process of data conversion, each subsystem only needs to adopt the data model mentioned above, and does not need to know other systems and data forms, so that the relative independence of the subsystems is favorably maintained, and in many practical application scenarios of multiple data centers, the dependency among the subsystems can be greatly reduced, thereby reducing the development and maintenance cost.
In addition, in the embodiment, the virtual data source is adopted to change the serial connection of the data among the subsystems into the parallel connection of the centralized data between the subsystems and the virtual data source, so that the data query sequence, the data state and the like do not need to be maintained, and the maintenance of the data flow among the subsystems is simplified.
In addition, in the embodiment, a data adapter is not needed, and the data model is used for defining and maintaining the relationship between data and between data and a system to realize data conversion, so that the data conversion process is changed from code driving to data driving, and the system expansion, data expansion and change processes are greatly simplified. For example, if an integrated system needs to add or change an updated data source, all subsystems can use the data without updating the data manager and all subsystems by adding or changing the data model of the data source in the multidimensional data dictionary database and registering the agents in the virtual data source.
In summary, in the big data era, how to change original data into usable information, data conversion is an essential step, the same piece of data has different usages in different application scenarios and different systems, and an efficient and universal data conversion framework can greatly improve the use efficiency and use value of the data. Likewise, in the application scenario of large-scale software integration, a centralized data standardization method is not the best choice from the perspective of implementation and use, and multi-center data transformation is a necessary trend. The method has the innovation points that the method can be used for data conversion between departments in an organization, can also be used for platform software integration data conversion between cross-departments and cross-industries, and more importantly can be used for multi-center big data mining.
Finally, it should be emphasized that the present invention is not limited to the above embodiments, for example, the change of specific attribute information of the data model, or the change of information contained in the data query request information, etc., and these changes should also be included in the protection scope of the claims of the present invention.
Claims (8)
1. The big data multi-center heterogeneous dynamic data conversion method is characterized by comprising the following steps:
acquiring first data query request information sent by a subsystem, acquiring a data model of the subsystem from a multidimensional data dictionary database according to the information of the subsystem, and analyzing the acquired data model;
and sending second data query request information to a virtual data source according to the analyzed result, and judging whether the queried data is data for executing preset operation, if not, sending third data query request information to an actual data source by the virtual data source, and after the virtual data source acquires data returned by the actual data source, converting the received data into a data form corresponding to the subsystem according to a data model and a mapping rule of the subsystem, and sending the converted data to the subsystem.
2. The big data multi-center heterogeneous dynamic data conversion method according to claim 1, wherein:
if the inquired data is determined to be data needing to execute preset operation, acquiring operation rule information of the data from a data operation rule database, after acquiring the data returned by the actual data source, performing operation on the data returned by the actual data source according to the operation rule information, converting the operated data into a data form corresponding to the subsystem according to a data model and a mapping rule of the subsystem, and sending the converted data to the subsystem.
3. The big data multi-center heterogeneous dynamic data conversion method according to claim 2, wherein:
and after acquiring the operation rule information of the data from the data operation rule database, judging whether the operation on the inquired data needs to acquire supplementary data, if so, generating supplementary data inquiry request information, sending fourth data inquiry request information to the virtual data source, and acquiring the supplementary data from the virtual data source.
4. The big data multi-center heterogeneous dynamic data conversion method according to any one of claims 1 to 3, wherein:
the first data query request information includes a data name and a query condition.
5. The big data multi-center heterogeneous dynamic data conversion method according to claim 4, wherein:
the query condition includes a condition name and a condition number list.
6. The big data multi-center heterogeneous dynamic data conversion method according to any one of claims 1 to 3, wherein:
each subsystem has a unique identification code;
the obtaining of the data model of the subsystem from the multidimensional data dictionary database according to the information of the subsystem comprises: and acquiring a data model of the subsystem from the multidimensional data dictionary database according to the identification code of the subsystem.
7. The big data multi-center heterogeneous dynamic data conversion method according to any one of claims 1 to 3, wherein:
the analyzing the acquired data model comprises: resolving at least one of the following parameters from the data model: identification of the data field of the subsystem in the virtual data source, identification of the data field of the subsystem in the actual data source, name of the data field of the subsystem in the actual data source, type of the data field of the subsystem in the actual data source, and mapping rules of the data field of the subsystem.
8. The big data multi-center heterogeneous dynamic data conversion method according to any one of claims 1 to 3, wherein:
the multidimensional data dictionary database stores a plurality of data models corresponding to the subsystems, and the information of each data model at least comprises one of the following data models: the name of the data, the source of the data, the mapping name of the data in the source system, the format of the data, the static mapping rule code, and the address of the operation rule of the data in the operation rule database.
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