CN112463798A - Cross-database data extraction method and device, electronic equipment and storage medium - Google Patents
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Abstract
According to the cross-database data extraction method, the cross-database data extraction device, the electronic equipment and the storage medium, provided by one or more embodiments of the specification, aiming at the correlation query upgrading of a cross-physical-database data mart, the automatic merging of data and cross-database joint query are realized by adopting the session-level correlation query among databases, the data extraction and merging processes of different databases are omitted, the development efficiency is improved, and the link flow is reduced.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of data extraction technologies, and in particular, to a method and an apparatus for extracting data across a database, an electronic device, and a storage medium.
Background
At present, data extraction cannot be realized by crossing physical databases, and the conventional mode is to perform advanced extraction and merging on data of different data sources into a single database through an ETL data extraction tool, then perform associated query processing in the single database, and export the data meeting conditions.
ETL: Extract-Transform-Load, a process of extracting (Extract), converting (Transform), and loading (Load) data from a source to a destination.
The inventor finds that in the prior art, data extraction by an ETL data extraction tool has at least the following defects: the early preparation workload of each number extraction (data extraction) is large, and a plurality of rows of data tables need to be built in the merging database in advance to support the table data of the physical database; and the data needs to be merged and concentrated in advance, and the processing link is long, so that the efficiency is low.
Disclosure of Invention
In view of the above, one or more embodiments of the present disclosure are directed to a method, an apparatus, an electronic device, and a storage medium for extracting data across a database, so as to solve technical problems in the prior art.
In view of the above, one or more embodiments of the present specification provide a cross-database data extraction method, including:
acquiring original counting task information;
outputting a counting task list based on the original counting task information;
and responding to target counting task information and data extraction parameters selected from the counting task list by a user, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target counting task information, and storing and outputting the query result subsets in the databases.
As an optional implementation manner, the outputting the scoring task list based on the original scoring task information includes:
extracting feature words from the original counting task information;
performing word segmentation matching on the basis of the characteristic words to calculate information weight, and selecting standard scoring task information with higher matching degree to form a scoring task list;
and outputting the number-extracting task list.
As an optional implementation manner, performing word segmentation matching to calculate information weight based on the feature words, and selecting standard scoring task information with higher matching degree to form a scoring task list, including:
selecting standard number extracting task information containing the feature words as number extracting task information to be selected;
calculating the matching degree of each task information to be selected;
and sequencing all the to-be-selected extracted task information according to the sequence of the matching degree from high to low to obtain an extraction task list.
As an optional implementation, the storing the subset of query results in a database includes:
and storing the subset of the query results into a Gaussian database.
As a second aspect of the present invention, there is provided a cross-database data extraction apparatus comprising:
the acquisition module is used for acquiring original counting task information;
the first output module is used for outputting a counting task list based on the original counting task information;
and the calculation and output module is used for responding to target counting task information and data extraction parameters selected by a user from the counting task list, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target counting task information, and storing and outputting the query result subsets in the databases.
As an optional implementation, the first output module includes:
the extraction unit is used for extracting feature words from the original counting task information;
the calculation unit is used for performing word segmentation matching calculation information weight based on the characteristic words, and selecting standard scoring task information with higher matching degree to form a scoring task list;
and the output unit is used for outputting the number-extracting task list.
As an optional implementation, the computing unit includes:
the selecting subunit is used for selecting standard scoring task information containing the characteristic words as to-be-selected scoring task information;
the calculating subunit is used for calculating the matching degree of each piece of task information to be extracted;
and the sorting subunit is used for sorting all the to-be-selected extracted task information according to the sequence of the matching degree from high to low to obtain the number-extracting task list.
As an optional implementation, the storing the subset of query results in a database includes:
and storing the subset of the query results into a Gaussian database.
As a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method as described above when executing the program.
As a fourth aspect of the present invention, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method as described above.
As can be seen from the foregoing, according to the method, the apparatus, the electronic device, and the storage medium for extracting data across databases provided in one or more embodiments of the present disclosure, for the number increase of the associated queries across the data marts of the physical databases, session-level associated queries among the databases are used to implement automatic merging of data and cross-database joint queries, so that the data extraction and merging processes of different databases are omitted, the development efficiency is improved, and the link flow is reduced.
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In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a logic diagram of an extraction method in accordance with one or more embodiments of the present disclosure;
FIG. 2 is a logic diagram of an extraction method for obtaining an extraction task list according to one or more embodiments of the present disclosure;
FIG. 3 is a logic diagram of an extraction method according to one or more embodiments of the present disclosure, in which a scoring task list is constructed based on matching degrees;
FIG. 4 is a logic diagram of an extraction device in accordance with one or more embodiments of the disclosure;
FIG. 5 is a logic diagram of a first output module of an extraction device in accordance with one or more embodiments of the present disclosure;
FIG. 6 is a logic diagram of a computing unit of an extraction device in accordance with one or more embodiments of the present disclosure;
FIG. 7 is a logic diagram of an electronic device in accordance with one or more embodiments of the disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present disclosure more apparent, the present disclosure is further described in detail below with reference to specific embodiments.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present specification should have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in one or more embodiments of the specification is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another.
In order to achieve the above object, the present invention provides a method for extracting data across databases, comprising:
acquiring original counting task information;
outputting a counting task list based on the original counting task information;
and responding to target counting task information and data extraction parameters selected from the counting task list by a user, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target counting task information, and storing and outputting the query result subsets in the databases.
In the embodiment of the invention, aiming at the correlation query upgrading of the data mart crossing physical databases, the session-level correlation query among the databases is adopted to realize the automatic merging of data and the cross-database joint query, so that the data extraction and merging processes of different databases are saved, the development efficiency is improved, and the link flow is reduced.
FIG. 1 illustrates a cross-database data extraction method, comprising:
and S100, acquiring original counting task information.
And S200, outputting a counting task list based on the original counting task information.
As shown in fig. 2, S200 includes:
s210, extracting feature words from the original counting task information;
s220, performing word segmentation matching calculation on information weight based on the characteristic words, and selecting standard scoring task information with high matching degree to form a scoring task list;
optionally, as shown in fig. 3, performing word segmentation matching to calculate information weight based on the feature words, and selecting standard scoring task information with a higher matching degree to form a scoring task list, where the method includes:
s221, selecting standard scoring task information containing the feature words as to-be-selected scoring task information;
s222, calculating the matching degree of each task information to be extracted;
and S223, sequencing all the to-be-selected extracted task information according to the sequence of the matching degree from high to low to obtain an extraction task list.
And S230, outputting the number extracting task list.
S300, responding to target counting task information and data extraction parameters selected from the counting task list by a user, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target counting task information, and storing and outputting the query result subsets in the databases.
Optionally, the storing the query result subset into a database includes:
and storing the subset of the query results into a Gaussian database.
For further ease of understanding, the following examples are presented to illustrate the invention:
examples
Obtaining original scoring task information, for example, the original scoring task information input by the user is: "extract the first year payment premium information of individual insurance policy";
extracting feature words from the original counting task information: "individual insurance", "policy" and "premium paid in first year";
selecting standard number-increasing task information containing any one of 'individual insurance', 'insurance policy' and 'first-year payment premium' as number-increasing task information to be selected;
calculating the matching degree of each task information to be selected;
sequencing all the extracted task information to be selected according to the sequence of the matching degrees from high to low to obtain a counting task list, and outputting the counting task list to a user for the user to select;
the user selects the task to be executed, and it is assumed here that the user selects "extract first-year premium service data", and inputs data extraction parameters such as organization number, time period, and corresponding channel (e.g. scene user selection: individual insurance);
respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the scoring target scoring task information;
storing the subset of query results in a Gaussian database;
the data extraction here extracts the insurance policy related information and the user related information from the 'insurance' and the 'client' data marts respectively.
And exporting the data information stored in the Gaussian database in an excel text form to provide the user.
It is to be appreciated that the method can be performed by any apparatus, device, platform, cluster of devices having computing and processing capabilities.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Corresponding to the cross-database data extraction, as shown in fig. 4, the present invention also provides a cross-database data extraction apparatus, including:
the acquisition module 10 is used for acquiring original counting task information;
a first output module 20, configured to output a scoring task list based on the original scoring task information;
and the calculation and output module 30 is configured to respond to target scoring task information and data extraction parameters selected by a user from the scoring task list, load query result subsets satisfying query conditions from different databases according to sql scripts in the target scoring task information, and store and output the query result subsets in the databases.
In the embodiment of the invention, aiming at the correlation query upgrading of the data mart crossing physical databases, the session-level correlation query among the databases is adopted to realize the automatic merging of data and the cross-database joint query, so that the data extraction and merging processes of different databases are saved, the development efficiency is improved, and the link flow is reduced.
As an alternative embodiment, as shown in fig. 5, the first output module 20 includes:
the extracting unit 21 is configured to extract feature words from the original counting task information;
the calculating unit 22 is configured to perform word segmentation matching calculation on the basis of the feature words, and select standard scoring task information with a higher matching degree to form a scoring task list;
the output unit 23 is configured to output the number-extracting task list.
As an alternative embodiment, as shown in fig. 6, the calculation unit 22 includes:
the selecting subunit 22a is configured to select standard scoring task information including the feature words as to-be-selected scoring task information;
the calculating subunit 22b is configured to calculate, for each piece of task information to be extracted, a matching degree thereof;
and the sorting subunit 22c is configured to sort all the to-be-selected extracted task information in the order from high matching degree to low matching degree, so as to obtain an extraction task list.
As an optional implementation, the storing the subset of query results in a database includes:
and storing the subset of the query results into a Gaussian database.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the modules may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.
In correspondence with the cross-database data extraction, an embodiment of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the method as described above when executing the program.
Fig. 7 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
In correspondence with the cross-database data extraction, embodiments of the present invention provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the spirit of the present disclosure, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present description as described above, which are not provided in detail for the sake of brevity.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. A method of cross-database data extraction, comprising:
acquiring original counting task information;
outputting a counting task list based on the original counting task information;
and responding to target counting task information and data extraction parameters selected from the counting task list by a user, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target counting task information, and storing and outputting the query result subsets in the databases.
2. The method for extracting data across databases according to claim 1, wherein outputting a scoring task list based on original scoring task information comprises:
extracting feature words from the original counting task information;
performing word segmentation matching on the basis of the characteristic words to calculate information weight, and selecting standard scoring task information with higher matching degree to form a scoring task list;
and outputting the number-extracting task list.
3. The method for extracting data across databases according to claim 2, wherein the information weight is calculated by performing word segmentation matching based on the feature words, and standard scoring task information with a high matching degree is selected to form a scoring task list, which includes:
selecting standard number extracting task information containing the feature words as number extracting task information to be selected;
calculating the matching degree of each task information to be selected;
and sequencing all the to-be-selected extracted task information according to the sequence of the matching degree from high to low to obtain an extraction task list.
4. The method for cross-database data extraction according to claim 1, wherein the storing the subset of query results into a database comprises:
and storing the subset of the query results into a Gaussian database.
5. A cross-database data extraction apparatus, comprising:
the acquisition module is used for acquiring original counting task information;
the first output module is used for outputting a counting task list based on the original counting task information;
and the calculation and output module is used for responding to target counting task information and data extraction parameters selected by a user from the counting task list, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target counting task information, and storing and outputting the query result subsets in the databases.
6. The cross-database data extraction apparatus of claim 5, wherein the first output module comprises:
the extraction unit is used for extracting feature words from the original counting task information;
the calculation unit is used for performing word segmentation matching calculation information weight based on the characteristic words, and selecting standard scoring task information with higher matching degree to form a scoring task list;
and the output unit is used for outputting the number-extracting task list.
7. The cross-database data extraction apparatus according to claim 6, wherein the computing unit comprises:
the selecting subunit is used for selecting standard scoring task information containing the characteristic words as to-be-selected scoring task information;
the calculating subunit is used for calculating the matching degree of each piece of task information to be extracted;
and the sorting subunit is used for sorting all the to-be-selected extracted task information according to the sequence of the matching degree from high to low to obtain the number-extracting task list.
8. The apparatus of claim 1, wherein the storing the subset of query results into a database comprises:
and storing the subset of the query results into a Gaussian database.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 4 when executing the program.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 4.
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