CN117312319A

CN117312319A - Metadata-based data storage method, device, equipment and storage medium

Info

Publication number: CN117312319A
Application number: CN202311297678.6A
Authority: CN
Inventors: 何亚; 吕晓斌; 王近来; 苏怀强; 周鑫; 许露; 唐光圣; 黄浩森; 唐远泉
Original assignee: Chengdu Zhongke Information Technology Co ltd; Chengdu Information Technology Co Ltd of CAS
Current assignee: Chengdu Zhongke Information Technology Co ltd; Chengdu Information Technology Co Ltd of CAS
Priority date: 2023-10-09
Filing date: 2023-10-09
Publication date: 2023-12-29

Abstract

The application discloses a data storage method, device, equipment and storage medium based on metadata, wherein the data storage method comprises the following steps: acquiring statistical historical storage data and determining corresponding service indexes; constructing a plurality of storage templates for storing actual data based on the business indexes; splitting data to be stored based on the storage template, and acquiring a classification type and storage data of the corresponding data to be stored; converting the classification type of the data to be stored and the stored data, generating an object for storing in the storage template, and storing the object in the storage template. According to the method and the device, the service index is obtained and the storage template is constructed through historical storage data based on statistics, meanwhile, the data to be stored is split and stored to the storage template based on the service index, the probability of numerous and complicated data quantity of the data to be stored can be reduced to a certain extent, and therefore the purpose of meeting the data management requirements of a statistics bureau is achieved.

Description

Metadata-based data storage method, device, equipment and storage medium

Technical Field

The present invention relates to the field of data management statistics, and in particular, to a metadata-based data storage method, apparatus, device, and storage medium.

Background

With the advancement of informatization within the statistical bureau, the current statistical bureau has implemented a plurality of informatization items, each of which stores a vast amount of statistical data. Because a plurality of mutually independent service systems exist in the statistical bureau, statistical data are stored in each service system in a scattered manner, and a plurality of specifications exist on index apertures corresponding to the statistical data stored by different service systems, so that the statistical data stored by a plurality of subsystems form an information island, and the statistical data stored among different subsystems are difficult to share. In the prior art, the data is stored by establishing a metadata pair management mode, so that the data sharing requirement among different subsystems in a statistical bureau is met.

The metadata management method disclosed in the prior art includes: data discovery, data catalogs, data dictionaries, data searching and discovery, and data blood-edge analysis functions.

Because the statistical data industry attribute stored in the statistical bureau is strong, and the emphasis points emphasized by different schemes are different, the data volume is too huge and miscellaneous when the statistical data is managed by the metadata management method disclosed in the prior art, and the data management requirement of the statistical bureau is difficult to meet.

Disclosure of Invention

The main purpose of the application is to provide a metadata-based data storage method, device, equipment and storage medium, and aims to solve the technical problem that the data volume is too huge and miscellaneous when the statistical data is managed by the metadata management method disclosed in the prior art, and the data management requirement of the statistical bureau is difficult to meet.

To achieve the above object, the present application provides a metadata-based data storage method, including the steps of:

acquiring statistical historical storage data and determining corresponding service indexes;

constructing a plurality of storage templates for storing actual data based on the business indexes;

splitting data to be stored based on the storage template, and acquiring a classification type and storage data of the corresponding data to be stored;

converting the classification type of the data to be stored and the stored data, generating a storage template for storing, and storing the object in the storage template.

Optionally, the step of constructing a plurality of storage templates for storing actual data based on the acquired business indexes includes:

acquiring index classification category and category attribute data based on the service index;

and constructing a plurality of storage templates based on the index classification category and the category attribute data.

Optionally, the plurality of stored templates includes: a data storage table and a category storage table;

wherein the data storage table is configured to store category attribute data, the data storage table comprising a metadata data table; the category storage table is configured to store index classification categories, the category storage table comprising: metadata index code tables, directory index tables, grouping index tables, and/or combination index tables.

Optionally, the step of splitting the data to be stored based on the constructed storage template includes:

acquiring data to be stored, and judging the corresponding data type;

and when the data type of the data to be stored is single index data, splitting the data to be stored based on the constructed storage template.

Optionally, the step of splitting the data to be stored based on the constructed storage template further includes:

when the acquired data to be stored is grouping index data, acquiring a combined structure corresponding to the data to be stored, splitting the data to be stored into single index data according to the combined structure, and splitting the data to be stored based on the constructed storage template.

Optionally, the step of converting the classification type of the data to be stored and the stored data to generate the storage template includes:

acquiring the classification type and the storage data, and converting the classification type and the storage data into data codes;

transcoding the data codes to generate a database main key;

and combining the database primary keys to generate an object for storing the storage template.

Optionally, the step of transcoding the data code to generate a database primary key includes:

and processing the data code in a UUID generation mode or in a distributed snowflake ID generation mode to generate a database main key.

In addition, to achieve the above object, the present application further provides a metadata-based data storage device, including:

the index acquisition module is configured to acquire statistical historical storage data and determine corresponding service indexes;

the template construction module is configured to construct a plurality of storage templates for storing actual data based on the acquired service indexes;

the data splitting module is configured to split the data to be stored based on the constructed storage template and acquire the classification type and the storage data of the corresponding data to be stored;

the data storage module is configured to convert the classification type of the data to be stored and the stored data, generate a storage template for storing the data to be stored, and store the object to the storage template.

In addition, to achieve the above object, the present application further provides a computer device including a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the steps of:

Furthermore, to achieve the above object, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:

The beneficial effects that this application can realize.

According to the metadata-based data storage method, device and equipment and storage medium, the service index capable of representing the content of statistical data is obtained by analyzing historical storage data counted by a statistical bureau, and then a storage template is constructed according to the obtained service index; simultaneously, each piece of data to be stored is split independently according to the acquired business indexes, and then the split data to be stored is stored in a corresponding storage template; the data to be stored is split through the service index, so that complex objective expression of the data to be stored is conveniently screened out, and the probability of complex data volume of the data to be stored can be reduced to a certain extent; meanwhile, the split statistical data is added to a storage template, so that the obtained statistical data is managed, and the purpose of meeting the data management requirement of a statistical office is achieved.

Drawings

FIG. 1 is a flow chart of a metadata-based data storage method according to an embodiment of the present application;

FIG. 2 is a flow chart of a metadata-based data storage method according to another embodiment of the present application;

fig. 3 is a schematic diagram of a metadata-based data storage device according to an embodiment of the present application.

The realization, functional characteristics and advantages of the present application will be further described with reference to the embodiments, referring to the attached drawings.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

The main solutions of the embodiments of the present application are: acquiring historical storage data counted by a counting office, analyzing to obtain service indexes capable of representing the content of the counting data, and constructing a storage template according to the acquired service indexes; simultaneously, each piece of data to be stored is split independently according to the acquired business indexes, and then the split data to be stored is stored in a corresponding storage template; the data to be stored is split through the service index, so that complex objective expression of the data to be stored is conveniently screened out, and the probability of complex data volume of the data to be stored can be reduced to a certain extent; meanwhile, the split statistical data is added to a storage template, so that the obtained statistical data is managed, and the purpose of meeting the data management requirement of a statistical office is achieved.

The metadata management method disclosed in the prior art includes: data discovery, data catalogs, data dictionaries, data searching and discovery, and data blood-edge analysis functions. The attribute of the statistical data industry stored in the statistical bureau is strong, and the emphasis points emphasized by different schemes are different; therefore, the data amount is too large and complicated to satisfy the data management requirement of the statistics bureau when the statistics data is managed by the metadata management method disclosed in the prior art.

The application provides a solution, which is to obtain historical storage data counted by a counting bureau so as to analyze and obtain service indexes capable of representing the content of the counting data, and then construct a storage template according to the obtained service indexes; simultaneously, each piece of data to be stored is split independently according to the acquired business indexes, and then the split data to be stored is stored in a corresponding storage template; the data to be stored is split through the service index, so that complex objective expression of the data to be stored is conveniently screened out, and the probability of complex data volume of the data to be stored can be reduced to a certain extent; meanwhile, the split statistical data is added to a storage template, so that the obtained statistical data is managed, and the purpose of meeting the data management requirement of a statistical office is achieved.

Referring to fig. 1, the present application first provides a metadata-based data storage method, where the data storage method provided in the present application includes: step S10, step S20, step S30, and step S40.

Step S10, acquiring statistical historical storage data and determining corresponding business indexes.

In a specific implementation process, the historical storage data are statistics data of different fields corresponding to the statistics office in a year-round working process, and are used for screening and obtaining important business indexes in the corresponding fields, and the historical storage data are statistics data of the statistics office in the embodiment.

The historical storage data are data in the economic field, and the business index is a basic attribute of actual effective data in the economic field, and specifically comprises time, region, index, unit and data attribute; when the data storage corresponding to the economic field is processed by the statistical bureau, splitting is performed based on main basic attributes of the service data storage, wherein the basic attributes of the corresponding data in the economic field comprise time, region, index, unit and data attribute, so that the data corresponding to the economic field can be split based on the basic attributes, the length of actual data is greatly reduced, and the convenience for extracting effective attributes in the actual data is improved.

And step S20, constructing a plurality of storage templates for storing actual data based on the acquired business indexes.

In the specific implementation process, the storage template is used for storing the split data to be stored, and the convenience of data management can be improved by constructing the storage template corresponding to the acquired service index; the method has the advantages that the economic field data corresponding to the statistical data are obtained, the storage template for facilitating the user to store the actual data can be conveniently constructed to a certain extent, so that the data to be stored are split and then added into the corresponding storage template, the purpose of storing the actual data is achieved, and meanwhile, the efficiency of managing the data can be improved by constructing the storage template.

The service index set is also called a service index library, and the storage module is constructed based on the service index in the service index library, so that the management after splitting the corresponding data is realized through the storage module, the convenience of managing the actual data can be improved to a certain extent, the complicated degree of storing the actual data can be reduced, and the management of the actual statistical data in the statistical bureau is facilitated.

And step S30, independently splitting each piece of data to be stored based on the constructed storage template, and acquiring the classification type and the storage data corresponding to each piece of data to be stored.

The method has the advantages that the service indexes corresponding to the data are stored based on the statistics history, the data to be stored are split according to the service indexes, the actual data to be stored are split into a plurality of classification types and storage data only comprising basic attributes, the memory capacity occupied by the data to be stored is greatly shortened, and meanwhile the efficiency of managing the data to be stored can be improved.

Step S40, converting the acquired classification type and the storage data, generating a storage template for storing, and storing the object in the storage template.

The data to be stored is split into the classification type and the storage data, and then the generated object is stored in the storage template, so that the convenience of data management is improved, the indexes of the statistical data can be managed based on a unified standard format, and the convenience of actual data management can be improved.

According to the method, historical storage data counted by a counting office are analyzed, so that service indexes capable of representing the content of the counting data are obtained, and then a storage template is constructed according to the obtained service indexes; simultaneously, each piece of data to be stored is split independently according to the acquired business indexes, and then the split data to be stored is stored in a corresponding storage template; the data to be stored is split through the service index, so that complex objective expression of the data to be stored is conveniently screened out, and the probability of complex data volume of the data to be stored can be reduced to a certain extent; meanwhile, the split statistical data is added to a storage template, so that the acquired statistical data is convenient to manage, and the data management requirement of a statistical bureau is met; simultaneously, the service index related to the data to be stored is obtained, and the purpose of reducing the data quantity of the data to be stored is achieved; the method and the device can be used for conveniently managing the data, and meanwhile, the probability of omission in the data storage process can be reduced.

As an optional implementation manner, based on the embodiment shown in fig. 1, to further reduce convenience for managing data to be stored, when performing the step of constructing a plurality of storage templates for storing actual data based on the acquired traffic indexes, the data storage method provided in the present application includes: acquiring index classification categories and category attribute data based on the acquired service indexes; and constructing a plurality of storage templates based on the index classification category and the category attribute data.

The method comprises the steps of obtaining index classification category and category attribute data based on the obtained service index, and then constructing a storage template according to the category attribute data, so that the efficiency of splitting actual data based on the service index can be greatly improved, and the convenience of managing the actual data is improved by adding the split data to a storage module to construct an actual table for counting the data to be stored.

As an alternative embodiment, the constructing a plurality of storage templates includes: a data storage table and a category storage table; wherein the data storage table is configured to store category attribute data, the data storage table comprising a metadata data table; the category storage table is configured to store index classification categories, the category storage table comprising: metadata index code tables, directory index tables, grouping index tables, and/or combination index tables.

The metadata data table includes a column name, a type, and an annotation, the column name including: MACRODATAID, COMBINEID, YEAR, MONTH, TIMEFRAMECODE, UNITCODE, INDCTATTR, DATA, OPERUSERID, OPERDATE, MODIFYUSERID and MODIFYDATE; the type includes a string and time; the annotation comprises: primary key, combined index ID, year, month, time type code, unit, data attribute, data, operator, operation time, modifier, modification time. The constructed metadata data table is shown in table 1.

Table 1 metadata data table

Column name	Type(s)	Annotating
			MACRODATAID	Character string	Main key
COMBINEID	Character string	Combination index ID
			YEAR	Character string	Year of life
MONTH	Character string	Month of moon
			TIMEFRAMECODE	Character string	Time type coding
UNITCODE	Character string	Unit (B)
			INDCTATTR	Character string	Data attributes
DATA	Character string	Data
			OPERUSERID	Character string	Operator
OPERDATE	Time	Time of operation
			MODIFYUSERID	Character string	Modifier person
MODIFYDATE	Time	Modification time

The metadata index code table includes a column name, a type, and a comment, the column name including: INDCTID, INDCTNAME, INDCTCODE, INDCTTYPE, UNITCODE, INDCTEXPLAN, FILLEXPLAIN, STATUS, DATATYPE, OPERUSERID, OPERDATE, MODIFYUSERID and MODIFYDATE; the type includes a string and time; the annotation comprises: a primary key, an index name, an index code, an index type, a unit code, an index interpretation, a fill instruction, an enablement status, a data type, an operator, an operation time, a modifier, and a modification time. The constructed metadata index code table is shown in table 2.

Table 2 metadata index code table

Column name	Type(s)	Annotating
			INDCTID	Character string	Main key
INDCTNAME	Character string	Index name
			INDCTCODE	Character string	Index code
INDCTTYPE	Character string	Index type
			UNITCODE	Character string	Unit code
INDCTEXPLAN	Character string	Index interpretation
			FILLEXPLAIN	Character string	Fill-in instruction
STATUS	Character string	Enable state
			DATATYPE	Character string	Data type
OPERUSERID	Character string	Operator
			OPERDATE	Time	Time of operation
MODIFYUSERID	Character string	Modifier person
			MODIFYDATE	Time	Modification time

The directory index table includes a column name, a type, and an annotation, the column name including: CATALOGID, CATALOGNAME, STATUS, CATALOGCODE, OPERUSERID, OPERDATE, MODIFYUSERID and MODIFYDATE; the type includes a string and time; the annotation comprises: a home key, a directory name, a directory state, a directory code, an operator, an operation time, a modifier, and a modification time. The constructed directory index table is shown in table 3.

Table 3 directory index table

Column name	Type(s)	Annotating
			CATALOGID	Character string	Main key
CATALOGNAME	Character string	Directory name
			STATUS	Character string	Directory state
CATALOGCODE	Character string	Directory code
			OPERUSERID	Character string	Operator
OPERDATE	Time	Time of operation
			MODIFYUSERID	Character string	Modifier person
MODIFYDATE	Time	Modification time

The grouping index table includes a column name, a type, and an annotation, the column name including: GROUPID, GROUPNAME, STATUS, ORGANIZEID, OPERUSERID, OPERDATE, MODIFYUSERID and MODIFYDATE; the type includes a string and time; the annotation comprises: a primary key, a group name, a group status, an organization ID, an operator, an operation time, a modifier, and a modification time. The constructed grouping index table is shown in table 4.

TABLE 4 grouping indicator table

Column name	Type(s)	Annotating
			GROUPID	Character string	Main key
GROUPNAME	Character string	Packet name
			STATUS	Character string	Packet status
ORGANIZEID	Character string	Tissue ID
			OPERUSERID	Character string	Operator
OPERDATE	Time	Time of operation
			MODIFYUSERID	Character string	Modifier person
MODIFYDATE	Time	Modification time

The combined index table includes a column name, a type, and an annotation, the column name including: COMBINEID, TOWNCODE, MACROINDCTCODE, DATATYPE, GROUPGROUPID and catalogcatalog id; the type includes a character string; the annotation comprises: primary key, region code, pointer code, data type, group type, and directory type. The constructed combination index table is shown in table 5.

Table 5 combined index table

Column name	Type(s)	Annotating
			COMBINEID	Character string	Main key
TOWNCODE	Character string	Region code
			MACROINDCTCODE	Character string	Index code
DATATYPE	Character string	Data type
			GROUPGROUPID	Character string	Packet type
CATALOGCATALOGID	Character string	Directory type

As an alternative implementation manner, referring to fig. 2, based on the embodiment shown in fig. 1, before the step of splitting each data to be stored separately based on the constructed storage template, the data storage method provided in the present application further includes: acquiring data to be stored, and judging the data type of the corresponding actual data; splitting the data to be stored based on the service index of the corresponding data to be stored when the acquired data to be stored is single index data; when the acquired data to be stored is grouping index data, acquiring a combined structure corresponding to the data to be stored, splitting the data to be stored into single index data based on the combined structure, and splitting the split data to be stored.

Illustratively, when the single data index "2022 a city district production total is XXX hundred million" is split, the split may be "time" according to time, district, index, unit, data attribute: 2022; region: market A; the index is as follows: regional production total; units: billions of yuan; data attributes: an object of absolute value ".

And the total value of the regional production in the A city of 2022 is XXX hundred million yuan; the first industry production total in 2022, market a, is XXX hundred million yuan; the second industry production total for the 2022 market a is XXX hundred million yuan; when the total production value of the third industry in 2022A is XXX hundred million yuan, the first industry, the second industry and the third industry are divided into one group, and the regional total production value is formed together. Thus, it can be split into four objects as follows:

time: 2022; region: market A; the index is as follows: regional production total; units: billions of yuan; data attributes: absolute value ";

time: 2022; region: market A; the index is as follows: a first industry; units: billions of yuan; data attributes: absolute value ";

time: 2022; region: market A; the index is as follows: a second industry; units: billions of yuan; data attributes: absolute value ";

time: 2022; region: market A; the index is as follows: a third industry; units: billions of yuan; data attributes: absolute value).

By judging the data type of the data to be stored, when the corresponding data type to be stored is single-index data, the data amount of the single-index data is smaller, and the occupied memory is smaller, so that the difficulty of directly splitting the single-index data is low, and meanwhile, metadata with smaller memory for describing the data can be obtained by splitting; when the corresponding data to be stored is the grouping index data, the corresponding grouping index data is split according to the combination structure, and then the split grouping index data is further split, so that the probability of losing the data in the splitting process can be reduced.

As an alternative implementation manner, based on the embodiment shown in fig. 1, the step of converting the acquired classification type and the stored data includes: acquiring the classification type and the storage data, and converting the classification type and the storage data into data codes; transcoding the data codes to generate a database main key; and combining the database primary keys to generate an object for storing the storage template.

Optionally, the step of transcoding the obtained data code to generate a database primary key includes: processing the obtained data codes in a UUID generation mode to generate a database main key; the UUID is Universally Unique Identifier, a universally unique identification code.

Optionally, the step of transcoding the obtained data code to generate a database primary key includes: processing the obtained data codes in a distributed snowflake ID generation mode to generate a database main key; the distributed snowflake ID is an ID generation algorithm that generates globally unique and increasing trends in the distributed system.

Exemplary, "time: 2022; region: market A; the index is as follows: regional production total; units: billions of yuan; data attributes: the absolute value "can be converted into the following format by data encoding: time: 2022- > year 2022; region: a is a city- > area A; the index is as follows: regional production total- > indicator: GDP; units: yiyuan- > unit: yuan_100000000";

then generating an object in a UUID generation mode, wherein the object is expressed as { "id: "," ad20e6b3-3bdd-777f-779f-7c746d3ba3bf "," year ": "2022", "area": "A", "indicator": "GDP", "unit": "yuan_100000000", "value" means "XXX".

In addition, in order to achieve the above object, referring to fig. 3, the present application further provides a metadata-based data storage device, including: an index acquisition module 310, a template construction module 320, a data splitting module 330, and a data storage module 340.

The index acquisition module 310 is configured to acquire statistical historical data and determine corresponding business indexes.

Because the statistical bureau has a plurality of subsystems and the business indexes corresponding to the historical data counted in different subsystems are different, analysis is needed based on the historical storage data acquired by the statistical bureau, the business index data corresponding to the historical storage data is acquired based on the index acquisition module 310, and a business index library is constructed based on the acquired business index, so that the data to be split in the business index library can be conveniently split, and the aim of reducing the redundancy of the actual data is fulfilled.

Illustratively, when the statistical office processes the data storage corresponding to the economic domain, the index obtaining module 310 splits the data based on the main attribute of the service data storage, and the basic attribute of the corresponding data in the economic domain includes time, region, index, unit and data attribute, so that the corresponding economic domain data can be split based on the basic attribute, thereby greatly reducing the length of the actual data and improving the convenience of extracting the effective attribute in the actual data.

The template construction module 320 is communicatively connected to the index acquisition module 320, the template construction module 320 being configured to construct a plurality of storage templates for storing actual data based on the acquired business index.

The economic field data corresponding to the statistical data is obtained through the template construction module 320, so that a storage template for facilitating the user to store the actual data can be conveniently constructed to a certain extent, the data to be stored is split and then added into the corresponding storage template, the purpose of storing the actual data is achieved, and meanwhile, the efficiency of managing the data can be improved through constructing the storage template.

The storage module is constructed by the index construction module 320 in the service index library based on the acquired service index library, and the storage module is used for realizing management after splitting corresponding data, so that convenience in managing actual data can be improved to a certain extent, the tedious degree of storing actual data can be reduced, and the management of actual statistical data in the statistical bureau is facilitated.

The data splitting module 330 is communicatively connected to the template construction module 320, and the data splitting module 330 is configured to split the data to be stored based on the constructed storage template, and obtain the classification type and the storage data corresponding to the data to be stored.

The data splitting module 330 is used for splitting the to-be-stored data based on the business indexes corresponding to the statistical historical stored data according to the business indexes, and splitting the to-be-stored actual data into a plurality of classification types and stored data only containing basic attributes, so that the memory quantity occupied by the to-be-stored data is greatly shortened, and the efficiency of managing the to-be-stored data can be improved.

The data storage module 340 is communicatively coupled to the data splitting module 330, the data storage module 340 being configured to translate the obtained classification type and the stored data, generate an object for storage to the storage template, and store the object to the storage template.

The data storage module 340 splits the data to be stored into the classification type and the storage data, and then stores the generated object into the storage template so as to improve the convenience of managing the data, so that the index of the statistical data can be managed based on a unified standard format, and the convenience of managing the actual data can be improved.

In some embodiments, the present application also provides a computer device including a memory storing a computer program and a processor implementing the metadata-based data storage method of the above various embodiments when the processor executes the computer program.

In some embodiments, the present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the metadata-based data storage method of the various embodiments described above.

According to the method provided by the embodiment of the application, the historical storage data counted by the counting bureau is analyzed, so that the service index capable of representing the content of the counting data is obtained, and then a storage template is constructed according to the obtained service index; simultaneously, each piece of data to be stored is split independently according to the acquired business indexes, and then the split data to be stored is stored in a corresponding storage template; the data to be stored is split through the service index, so that complex objective expression of the data to be stored is conveniently screened out, and the probability of complex data volume of the data to be stored can be reduced to a certain extent; meanwhile, the split statistical data is added to a storage template, so that the obtained statistical data is managed, and the purpose of meeting the data management requirement of a statistical office is achieved.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile memory can include read-only memoryOnly Memory, ROM), magnetic tape, floppy disk, flash Memory, optical storage, etc. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.

The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.

Claims

1. A metadata-based data storage method, characterized in that the data storage method comprises the steps of:

converting the classification type of the data to be stored and the stored data, generating an object for storing in the storage template, and storing the object in the storage template.

2. The metadata-based data storage method of claim 1, wherein the step of constructing a plurality of storage templates for storing actual data based on the acquired traffic metrics comprises:

3. The metadata-based data storage method of claim 2, wherein the plurality of storage templates comprises: a data storage table and a category storage table;

4. The metadata-based data storage method of claim 1, wherein the splitting of the data to be stored based on the constructed storage template comprises:

acquiring data to be stored, and judging the corresponding data type;

5. The metadata-based data storage method of claim 4, wherein the splitting the data to be stored based on the constructed storage template further comprises:

6. The metadata-based data storage method of claim 1, wherein the step of converting the classification type of the data to be stored and the stored data to generate an object for storage in the storage template comprises:

transcoding the data codes to generate a database main key;

7. The metadata-based data storage method of claim 6, wherein the step of transcoding the data encoding to generate a database primary key comprises:

8. A metadata-based data storage device, the data storage device comprising:

an index acquisition module (310) configured to acquire statistical historical stored data and determine a corresponding business index;

a template construction module (320) configured to construct a plurality of storage templates for storing actual data based on the traffic metrics;

a data splitting module (330) configured to split data to be stored based on the storage template, and acquire a classification type and storage data corresponding to the data to be stored;

and the data storage module (340) is configured to convert the classification type of the data to be stored and the stored data, generate an object for storing in the storage template, and store the object in the storage template.

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.