CN109710681B - Data output method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a data output method, a data output device, computer equipment and a storage medium. The method comprises the following steps: acquiring a data reading instruction and a plurality of optimized data tables; performing dynamic association on the plurality of optimized data tables according to the data reading instruction to obtain a plurality of target association tables; performing out-of-library translation on the target association tables to obtain association relations among the target association tables and data semantics corresponding to the association relations; and outputting corresponding data semantics according to the data reading instruction. By adopting the method, the data can be taken out in a targeted and dynamic manner for association, the data quantity for association is reduced, and the data output efficiency is improved.
Description
Technical Field
The present application relates to the field of big data technologies, and in particular, to a data output method, an apparatus, a computer device, and a storage medium.
Background
With the development of big data technology, data output technology appears, and the traditional data output technology is to associate all data in a database and output corresponding output results in association results of all data according to reading requirements input by users.
However, the conventional data output method has a problem of low efficiency.
Disclosure of Invention
In view of the above, it is necessary to provide a data output method, an apparatus, a computer device, and a storage medium, which are directed to the problem that the current data output method has low efficiency.
A method of data output, the method comprising:
acquiring a data reading instruction and a plurality of optimized data tables;
performing dynamic association on the plurality of optimized data tables according to the data reading instruction to obtain a plurality of target association tables;
performing out-of-library translation on the target association tables to obtain association relations among the target association tables and data semantics corresponding to the association relations;
and outputting corresponding data semantics according to the data reading instruction.
In one embodiment, the fetch data read instruction and the optimized data table include:
acquiring a data sub-table preset rule;
pre-sorting the data of the database according to the preset rule to obtain a plurality of original data tables;
and obtaining rule optimization information, and re-sorting the original data tables according to the rule optimization information to obtain a plurality of optimized data tables.
In one embodiment, the obtaining the data sublist preset rule includes:
pre-sorting the data of the database according to the fields to obtain a plurality of original data tables; and/or
And pre-sorting the data of the database according to the date to obtain a plurality of original data tables.
In one embodiment, the obtaining of the rule optimization information and the reclassifying the plurality of original data tables according to the rule optimization information to obtain a plurality of optimized data tables includes:
acquiring a historical data reading instruction;
generating the rule optimization information according to the historical data reading instruction;
and according to the rule optimization information, re-sorting the original data tables to obtain a plurality of optimized data tables.
In one embodiment, the dynamically associating the optimized data tables according to the data reading instruction to obtain the target association tables includes:
reading a plurality of target association tables;
and storing a plurality of target association tables to an external memory.
In one embodiment, the out-of-library translation of the plurality of target association tables to obtain the data semantics corresponding to each target association table includes:
reading a plurality of target association tables and semantic translation rules in the out-of-library memory;
and performing out-of-library translation on the plurality of target association tables according to the semantic translation rule to obtain association relations among the target association tables and data semantics corresponding to the association relations.
In one embodiment, the method further comprises:
the external memory of the database comprises the target association tables of a plurality of different databases;
and dynamically associating the target association tables of the different databases to obtain a plurality of secondary association tables.
In one embodiment, the method further comprises:
and sending output reminding information after the data semantics are output.
A data output apparatus, the apparatus comprising:
the acquisition module is used for acquiring a data reading instruction and the optimized data table;
the association table generation module is used for carrying out dynamic association on the plurality of optimized data tables according to the data reading instruction to obtain a plurality of target association tables;
the data semantic association module is used for performing out-of-library translation on the target association tables to obtain association relations among the target association tables and data semantics corresponding to the association relations;
and the semantic output module is used for outputting the corresponding data semantics according to the data reading instruction.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method of any of the above embodiments when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above embodiments.
According to the data output method, the data output device, the computer equipment and the storage medium, a plurality of target association tables are obtained from a plurality of optimized data tables, data semantics corresponding to association relations among the target association tables are obtained through the target association tables, and the corresponding data semantics are output according to the data reading instruction; and data are taken out in a targeted and dynamic manner for association, so that the data volume for association is reduced, and the data output efficiency is improved.
Drawings
FIG. 1 is a diagram of an application environment of a data output method in one embodiment;
FIG. 2 is a flow chart illustrating a data output method according to an embodiment;
FIG. 3 is a flowchart illustrating the steps of obtaining a data read command and the optimized data table in one embodiment;
FIG. 4 is a flowchart illustrating steps for obtaining a plurality of optimized data tables based on rule optimization information according to one embodiment;
FIG. 5 is a flowchart illustrating steps for obtaining multiple target association tables according to a data fetch command in one embodiment;
FIG. 6 is a flowchart illustrating the steps of out-of-library translation of the plurality of target association tables to obtain data semantics, in accordance with an embodiment;
FIG. 7 is a block diagram showing the structure of a data output apparatus according to an embodiment;
FIG. 8 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The data output method provided by the application can be applied to the application environment shown in fig. 1. In which a terminal 102 communicates with a server 103 via a network. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 103 may be implemented by an independent server or a server cluster formed by a plurality of servers. The optimized data table refers to a plurality of data tables which are generated by the server 103 through operation arrangement on the database 104 and are beneficial to query and use according to preset rules. In one embodiment, the terminal 102 converts the request into a data reading instruction, and transmits the data reading instruction to the server 103, and the terminal 102 can receive information transmission of the server 103. Further, the server 103 adds a scheduling task according to the data reading instruction of the terminal 102. The task scheduling refers to the access operation of the server 103 to the database 104 according to the data reading instruction and in combination with the existing task. Further, the server 103 accesses the database 104, and dynamically associates the optimized data tables in the database 104 according to the data reading instruction, so as to obtain a plurality of target association tables. Further, the server 103 performs out-of-library translation on the target association tables to obtain data semantics corresponding to the association relationship between the target association tables. The server 103 outputs the corresponding data semantics according to the data reading instruction, and notifies the terminal 102 of the data output result through the network.
In one embodiment, as shown in fig. 2, a data output method is provided, which is described by taking the application environment in fig. 1 as an example, and includes the following steps:
in step S202, the server 103 obtains a data reading instruction and a plurality of optimized data tables of the terminal 102. The terminal 102 generates a data reading instruction according to the requirement. The data reading instruction refers to a command for performing information interaction between the terminal 102 and the server 103 and providing access requirements, and a specific command language is determined according to an implementation scheme. The optimization data table refers to that the server 103 sorts and optimizes the database 104 into a plurality of data tables according to the previous usage habits and rules.
Step S204, the server 103 performs dynamic association on the multiple optimized data tables in the database 104 according to the data reading instruction to obtain multiple target association tables. The dynamic association refers to extracting tables which need to be actually accessed in the using process of the data tables, and then mutually associating and splicing the tables to form the data tables which need to be accessed for multiple times in the later access process. Specifically, the association relationship of 10 tables is configured in advance, and in actual use, if only fields of 2 tables are used, then only the 2 tables are associated when SQL is spliced. In one embodiment, the server 103 accesses the optimized data table after obtaining the data reading instruction. Further, the actually needed data table is searched from the optimized data table according to the instruction requirement, and correlation is carried out to obtain a plurality of target correlation tables.
Step S206, the server 103 performs out-of-library translation on the plurality of target association tables to obtain an association relationship between the target association tables and a data semantic corresponding to the association relationship. In which out-of-library translation refers to converting dimensional codes into textual descriptions. Wherein, the dimension is a field of a non-numerical class, and the index is a field of a numerical class. Specifically, dimensions such as region (province, city, county, etc.), time, brand type, call type, etc. generally have a parameter table use case to describe the encoding and meaning of the dimensions. In one embodiment, a01 is stored in a local field in the plurality of association tables, which is converted into Nanjing by extralibrary translation, thereby obtaining the corresponding data semantics.
Step S208, the server 103 outputs the corresponding data semantics to the terminal 102 according to the data reading instruction. It can be understood that, a plurality of target association tables are obtained according to the data reading instruction, and then the server 103 sends an output reminding message to the terminal 102 after the fetching operation is completed.
In one embodiment, referring to fig. 3, the step S202 of obtaining the data reading command and the optimized data table includes:
step S2021, obtain the preset rule of the data sub-table. In one embodiment, the server 103 obtains the preset rule of the data sub-table according to daily use conditions.
Step S2022, pre-sorting the data in the database 104 according to the preset rule to obtain a plurality of original data tables. In one embodiment, the data in the database 104 is simply sorted according to a preset rule to obtain a plurality of original data tables.
Step S2023, obtaining rule optimization information, and reclassifying the plurality of original data tables according to the rule optimization information to obtain a plurality of optimized data tables. In one embodiment, the data table rule optimization information is obtained according to the historical database access operation records. And further, reclassifying the plurality of original data tables according to the rule optimization information, extracting more effective data tables and obtaining a plurality of optimized data tables.
In one embodiment, the obtaining the data sublist preset rule comprises:
pre-sorting the data of the database according to the fields to obtain a plurality of original data tables; and/or pre-sorting the data of the database according to the date to obtain a plurality of original data tables. Specifically, the data table has a plurality of attributes, and the data sub-table preset rule can be obtained according to different attributes. Fields may be placed in different tables by base attribute (phone number, name, gender, etc.), call attribute (call duration, number of calls, etc.), traffic attribute (traffic used on the day, cumulative traffic used, etc.), charge attribute (call charge, traffic charge, etc.), etc. and sorted by day.
In an embodiment, referring to fig. 4, in step S2023, obtaining rule optimization information, and performing reclassification on the plurality of original data tables according to the rule optimization information to obtain a plurality of optimized data tables includes:
in step S2024, a history data reading instruction is acquired. Wherein, the historical data reading instruction can be obtained through statistics of a period of time.
Step S2025, generate the rule optimization information according to the historical data reading instruction. The rule optimization information can be the change of the classification method, the disassembly and combination method of the data table. In one embodiment, the statistical field uses frequency to give model optimization suggestions: fields "XX, YY, ZZ, CC, DD, EE" are used together at 54% and it is recommended to adjust them to the same table.
Step S2026, re-tabulating the plurality of original data tables according to the rule optimization information to obtain a plurality of optimized data tables. The original data table may be obtained by performing preliminary simple rule presetting on the database 104.
In an embodiment, referring to fig. 5, in step S204, dynamically associating the optimized data tables according to the data reading instruction to obtain a plurality of target association tables includes:
step S2041, reading a plurality of target association tables; the target association table is a data table which is selected from the optimized data table according to the data reading instruction and needs to be used.
Step S2042, store a plurality of the target association tables to an external memory. The external memory refers to a memory other than the memory in which the database is located. That is, the storage space where the target association table is located is not the same storage space as the storage space where the database is located.
In an embodiment, referring to fig. 6, in step S2042, the out-of-library translation of the target association tables to obtain the data semantics corresponding to each target association table includes:
step S2043, reading the multiple target association tables and semantic translation rules in the out-of-library memory. Where semantic translation rules may be, but are not limited to, field translations that are non-numeric classes. The translation rule may be preset in the server 104, or may be read from a plurality of association tables.
Step S2044, performing out-of-library translation on the plurality of target association tables according to the semantic translation rule to obtain an association relationship between the target association tables and data semantics corresponding to the association relationship.
In one embodiment, the external memory of the database includes the target association tables of a plurality of different databases, and the target association tables of the different databases are dynamically associated to obtain a plurality of secondary association tables. Specifically, a batch of data is obtained from one database, another batch of data is obtained from another database, and the two batches of data can be associated through secondary association, namely cross-library association. The cross-database association comprises access result association between different databases of the same kind and access result association between different databases of different kinds.
In one example, referring to step S208, after the data semantics are output, an output reminding message is sent. The reminding information can be transmitted in a short message, an email and the like.
In the data output method, a plurality of target association tables are obtained from a plurality of optimized data tables, data semantics corresponding to association relations among the target association tables are obtained through the target association tables, and the corresponding data semantics are output according to a data reading instruction; and data are taken out in a targeted and dynamic manner for association, so that the data volume for association is reduced, and the data output efficiency is improved.
It should be understood that although the various steps in the flow charts of fig. 2-6 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-6 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 7, there is provided a data output apparatus including:
an obtaining module 701, configured to obtain a data reading instruction and the optimized data table;
an association table generating module 702, configured to perform dynamic association on the multiple optimized data tables according to the data reading instruction, so as to obtain multiple target association tables;
a data semantic association module 703, configured to perform out-of-library translation on the multiple target association tables to obtain an association relationship between the target association tables and a data semantic corresponding to the association relationship;
and the semantic output module 704 is configured to output the corresponding data semantic according to the data reading instruction.
For specific limitations of the data output device, reference may be made to the above limitations of the data output method, which are not described herein again. The modules in the data output device can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data reading instructions and a plurality of optimized data tables. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data output method.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method of any of the above embodiments when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any of the above embodiments.
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 hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of outputting data, the method comprising:
acquiring a data reading instruction and a plurality of optimized data tables; the optimized data tables are obtained by obtaining historical data reading instructions, generating rule optimization information according to the historical data reading instructions, and reclassifying a plurality of original data tables according to the rule optimization information; the original data tables are obtained by obtaining data sub-table preset rules and pre-dividing data of the database according to the data sub-table preset rules;
performing dynamic association on the plurality of optimized data tables according to the data reading instruction to obtain a plurality of target association tables; storing a plurality of target association tables to an off-library memory; reading a plurality of target association tables and semantic translation rules in the out-of-library memory; performing out-of-library translation on the plurality of target association tables according to the semantic translation rule to obtain association relations among the target association tables and data semantics corresponding to the association relations; the external memory refers to a memory outside a memory where the database is located, and the storage space where the target association table is located and the storage space where the database is located are not the same storage space; the out-of-library translation is to convert dimension codes into character descriptions, and the dimensions are non-numerical fields;
and outputting corresponding data semantics according to the data reading instruction.
2. The method of claim 1, wherein the obtaining the data sub-table preset rule comprises:
pre-sorting the data of the database according to the fields to obtain a plurality of original data tables; and/or
And pre-sorting the data of the database according to the date to obtain a plurality of original data tables.
3. The method of claim 1, wherein the optimized data table is obtained by performing a sorting optimization on the database according to previous usage habits and rules.
4. The method of claim 1, further comprising:
the out-of-library memory comprises the target association tables of a plurality of different databases;
and dynamically associating the target association tables of the different databases to obtain a plurality of secondary association tables.
5. The method of claim 1, further comprising:
and sending output reminding information after the data semantics are output.
6. The method of claim 1, wherein the pre-set rules are derived from attributes of tables in the database.
7. The method according to claim 1, wherein the dynamic association refers to extracting tables actually required to be accessed in a process of using a plurality of optimized data tables, and further associating and splicing the tables into a plurality of target association tables.
8. A data output apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring a data reading instruction and a plurality of optimized data tables;
the association table generation module is used for carrying out dynamic association on the plurality of optimized data tables according to the data reading instruction to obtain a plurality of target association tables; storing a plurality of target association tables to an off-library memory; the optimized data tables are obtained by obtaining historical data reading instructions, generating rule optimization information according to the historical data reading instructions, and reclassifying a plurality of original data tables according to the rule optimization information; the original data tables are obtained by obtaining data sub-table preset rules and pre-dividing data of the database according to the data sub-table preset rules;
the data semantic association module is used for reading a plurality of target association tables and semantic translation rules in the external memory; performing out-of-library translation on the plurality of target association tables according to the semantic translation rule to obtain association relations among the target association tables and data semantics corresponding to the association relations; the external memory refers to a memory outside a memory where the database is located, and the storage space where the target association table is located and the storage space where the database is located are not the same storage space; the out-of-library translation is to convert dimension codes into character descriptions, and the dimensions are non-numerical fields;
and the semantic output module is used for outputting the corresponding data semantics according to the data reading instruction.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN112241410A (en) * | 2020-10-22 | 2021-01-19 | 曙光信息产业(北京)有限公司 | Data storage method, data index construction method and device and computer equipment |
| CN112732715B (en) * | 2020-12-31 | 2023-08-25 | 星环信息科技(上海)股份有限公司 | Data table association method, device and storage medium |
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