CN112463798B - Cross-database data extraction method and device, electronic equipment and storage medium - Google Patents
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Abstract
According to the method, the device, the electronic equipment and the storage medium for extracting the cross-database data, which are provided by one or more embodiments of the specification, for the associated query extraction of the cross-physical-database data marts, the session-level associated query among databases is adopted to realize automatic merging data and cross-database joint query, so that 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 technology, and in particular, to a method, an apparatus, an electronic device, and a storage medium for data extraction across databases.
Background
At present, data extraction cannot be carried out across physical libraries, and the conventional mode is that data of different data sources are extracted in advance and integrated into a single database through an ETL data extraction tool, then associated query processing is carried out in the single database, and data meeting the conditions is exported.
ETL: extraction-Transform-Load, the process of extracting (extraction), converting (Transform), loading (Load) data from source to destination.
The inventor finds that in the prior art, the data extraction is performed through an ETL data extraction tool, and at least the following defects exist: the earlier preparation workload of each extraction (data extraction) is large, and a plurality of columns of data tables need to be built in the merging database in advance to accept the table data of the physical database; and the data needs to be integrated and concentrated in advance, and the processing link is long, so that the efficiency is low.
Disclosure of Invention
In view of this, it is an object of one or more embodiments of the present disclosure to provide a method, an apparatus, an electronic device and a storage medium for extracting data across databases, so as to solve the technical problems in the prior art.
In view of the above objects, one or more embodiments of the present disclosure provide a method for extracting data across databases, including:
Acquiring original lifting task information;
Outputting an extraction task list based on the original extraction task information;
and in response to target lifting task information and data extraction parameters selected by a user from the lifting task list, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target lifting task information, and storing and outputting the query result subsets into the databases.
As an optional implementation manner, the outputting the lifting task list based on the original lifting task information includes:
Extracting feature words from the original extraction task information;
performing word segmentation matching calculation information weight based on the feature words, and selecting standard lifting task information with higher matching degree to form a lifting task list;
and outputting the mentioned list of the mentioned tasks.
As an optional implementation manner, performing word segmentation matching calculation on the feature words to calculate information weight, selecting standard extraction task information with higher matching degree to form an extraction task list, including:
selecting standard extraction task information containing the feature words as extraction task information to be selected;
Calculating the matching degree of each to-be-selected lifting task information;
and sequencing all the task information to be extracted according to the sequence of the matching degree from high to low, and obtaining an extraction task list.
As an alternative embodiment, the storing the subset of query results in a database includes:
And storing the query result subset 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 the original lifting task information;
the first output module is used for outputting an extraction task list based on the original extraction task information;
And the calculation and output module is used for responding to target lifting task information and data extraction parameters selected by a user from the lifting task list, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target lifting task information, and storing and outputting the query result subsets into the databases.
As an alternative embodiment, the first output module includes:
The extraction unit is used for extracting feature words from the original extraction task information;
The computing unit is used for carrying out word segmentation matching to compute information weight based on the feature words, and selecting standard lifting task information with higher matching degree to form a lifting task list;
and the output unit is used for outputting the lifting task list.
As an alternative embodiment, the computing unit comprises:
the selecting subunit is used for selecting standard lifting task information containing the feature words as lifting task information to be selected;
the computing subunit is used for computing the matching degree of each to-be-selected lifting task information;
and the sequencing subunit is used for sequencing all the task information to be extracted according to the sequence of the matching degree from high to low to obtain an extraction task list.
As an alternative embodiment, the storing the subset of query results in a database includes:
And storing the query result subset into a Gaussian database.
As a third aspect of the 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.
From the above, it can be seen that, according to the method, the device, the electronic device and the storage medium for extracting data across databases provided in one or more embodiments of the present disclosure, for the associated query number of the data marts across physical libraries, the session-level associated query between databases is used to implement automatic merging data and cross-library joint query, so that the data extraction and merging processes of different databases are omitted, the development efficiency is improved, and the link flow is reduced.
Drawings
For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only one or more embodiments of the present description, from which other drawings can be obtained, without inventive effort, for a person skilled in the art.
FIG. 1 is a logical schematic diagram of an extraction method according to one or more embodiments of the present disclosure;
FIG. 2 is a logical schematic diagram of an acquire promotion task list of an extraction method according to one or more embodiments of the present disclosure;
FIG. 3 is a logical schematic diagram of a matching degree-based build extraction task list of an extraction method of one or more embodiments of the present disclosure;
FIG. 4 is a schematic diagram of an extraction device according to one or more embodiments of the present disclosure;
FIG. 5 is a logic diagram of a first output module of an extraction device according to one or more embodiments of the present disclosure;
FIG. 6 is a logic diagram of a computing unit of an extraction device of 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 present disclosure.
Detailed Description
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made in detail to the following specific examples.
It is noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present disclosure should be taken in a general sense as understood by one of ordinary skill in the art to which the present disclosure pertains. The use of the terms "first," "second," and the like in one or more embodiments of the present description does not denote any order, quantity, or importance, but rather the terms "first," "second," and the like are 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 lifting task information;
Outputting an extraction task list based on the original extraction task information;
and in response to target lifting task information and data extraction parameters selected by a user from the lifting task list, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target lifting task information, and storing and outputting the query result subsets into the databases.
In the embodiment of the invention, for the associated query promotion of the data marts crossing the physical library, the session-level associated query among databases is adopted to realize automatic merging data and cross-library joint query, so that the data extraction and merging processes of different databases are omitted, the development efficiency is improved, and the link flow is reduced.
FIG. 1 illustrates a cross-database data extraction method, comprising:
S100, acquiring original extraction task information.
S200, outputting an extraction task list based on the original extraction task information.
As shown in fig. 2, S200 includes:
S210, extracting feature words from the original extraction task information;
S220, performing word segmentation matching calculation information weight based on the feature words, and selecting standard extraction task information with higher matching degree to form an extraction task list;
Optionally, as shown in fig. 3, performing word segmentation matching based on the feature words to calculate information weight, selecting standard extraction task information with higher matching degree to form an extraction task list, including:
S221, selecting standard extraction task information containing the feature words as extraction task information to be selected;
s222, calculating the matching degree of each candidate lifting task information;
S223, sorting all the task information to be extracted according to the sequence of the matching degree from high to low, and obtaining an extraction task list.
S230, outputting the lifting task list.
S300, responding to target lifting task information and data extraction parameters selected by a user from the lifting task list, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target lifting task information, and storing and outputting the query result subsets into the databases.
Optionally, the storing the query result subset in a database includes:
And storing the query result subset into a Gaussian database.
For further ease of understanding, embodiments of the present invention are described below in conjunction with the following examples:
Examples
The original extraction task information is obtained, for example, the original extraction task information input by a user is: extracting first-year premium information of an insurance policy;
extracting feature words from the original extraction task information: "personal insurance", "policy" and "pay premium for first year";
Selecting standard lifting task information containing any one of 'personal insurance', 'policy' and 'first-year premium payment' as lifting task information to be selected;
Calculating the matching degree of each to-be-selected lifting task information;
Sequencing all the task information to be extracted according to the sequence of the matching degree from high to low, obtaining an extraction task list, and outputting the extraction task list to a user for the user to select;
the user selects a task to be executed, and here, it is assumed that the user selects "extract first-year-payment premium service data" and inputs data extraction parameters such as organization number, time period, corresponding channel (e.g., scene user selection: personal insurance);
Respectively loading query result subsets meeting query conditions from different databases according to the sql script in the lifting task information of the lifting target;
Storing the query result subset into a gaussian database;
the data extraction is used for respectively extracting the related information of the insurance policy and the related information of the user from two data marts of 'insurance', 'client'.
The data information stored in the Gaussian database is exported and provided for the user in the form of excel text.
It is understood that the method may be performed by any apparatus, device, platform, cluster of devices having computing, processing capabilities.
It should be noted that the methods of one or more embodiments of the present description 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 is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the methods of one or more embodiments of the present description, the devices interacting with each other to accomplish the methods.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can 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 are also possible or may be advantageous.
Corresponding to the cross-database data extraction, as shown in fig. 4, the present invention further provides a cross-database data extraction device, including:
an acquisition module 10, configured to acquire original extraction task information;
a first output module 20 for outputting an extraction task list based on the original extraction task information;
And the calculation and output module 30 is configured to respond to target lifting task information and data extraction parameters selected from the lifting task list by a user, and load query result subsets meeting query conditions from different databases according to sql scripts in the target lifting task information, store the query result subsets into the databases, and output the query result subsets.
In the embodiment of the invention, for the associated query promotion of the data marts crossing the physical library, the session-level associated query among databases is adopted to realize automatic merging data and cross-library joint query, so that the data extraction and merging processes of different databases are omitted, 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:
an extracting unit 21 for extracting feature words from the original extraction task information;
The calculating unit 22 is configured to perform word segmentation matching calculation on the basis of the feature words to obtain information weight, and select standard extraction task information with higher matching degree to form an extraction task list;
an output unit 23 for outputting the mentioned list of the mentioned tasks.
As an alternative embodiment, as shown in fig. 6, the computing unit 22 includes:
a selecting subunit 22a, configured to select standard lifting task information including the feature word as lifting task information to be selected;
A calculating subunit 22b, configured to calculate, for each candidate lifting task information, a matching degree thereof;
The sorting subunit 22c is configured to sort all the candidate extraction task information according to the order of the matching degree from high to low, and obtain an extraction task list.
As an alternative embodiment, the storing the subset of query results in a database includes:
And storing the query result subset into a Gaussian database.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in one or more pieces of software and/or hardware when implementing one or more embodiments of the present description.
In response to the cross-database data extraction, an embodiment of the present invention provides 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.
Fig. 7 is a schematic diagram of a hardware structure of an electronic device according to the embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a 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, etc. for executing 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 ROM (Read Only Memory), RAM (Random Access Memory ), static storage, dynamic storage, etc. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
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.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to 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 storage media for a computer 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, which can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the disclosure, including the claims, is limited to these examples; combinations of features of the above embodiments or in different embodiments are also possible within the spirit of the present disclosure, steps may be implemented in any order, and there are many other variations of the different aspects of one or more embodiments 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 those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present disclosure is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Any omissions, modifications, equivalents, improvements, and the like, which are within the spirit and principles of the one or more embodiments of the disclosure, are therefore intended to be included within the scope of the disclosure.
Claims (4)
1. A method for cross-database data extraction, comprising:
Acquiring original lifting task information;
Outputting an extraction task list based on the original extraction task information;
Responding to target lifting task information and data extraction parameters selected by a user from the lifting task list, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target lifting task information, and storing and outputting the query result subsets into the databases;
the outputting the lifting task list based on the original lifting task information comprises the following steps:
Extracting feature words from the original extraction task information;
performing word segmentation matching calculation information weight based on the feature words, and selecting standard lifting task information with higher matching degree to form a lifting task list;
Outputting the mentioned number-increasing task list;
Based on the feature words, word segmentation matching calculation information weight is carried out, and standard extraction task information with higher matching degree is selected to form an extraction task list, which comprises the following steps:
selecting standard extraction task information containing the feature words as extraction task information to be selected;
Calculating the matching degree of each to-be-selected lifting task information;
sequencing all the task information to be extracted according to the sequence of the matching degree from high to low to obtain an extraction task list;
The storing the subset of query results in a database includes:
And storing the query result subset into a Gaussian database.
2. A cross-database data extraction apparatus, comprising:
the acquisition module is used for acquiring the original lifting task information;
the first output module is used for outputting an extraction task list based on the original extraction task information;
The calculation and output module is used for responding to target lifting task information and data extraction parameters selected by a user from the lifting task list, respectively loading query result subsets meeting query conditions from different databases according to sql scripts in the target lifting task information, and storing and outputting the query result subsets into the databases;
the first output module includes:
The extraction unit is used for extracting feature words from the original extraction task information;
The computing unit is used for carrying out word segmentation matching to compute information weight based on the feature words, and selecting standard lifting task information with higher matching degree to form a lifting task list;
the output unit is used for outputting the lifting task list;
The calculation unit includes:
the selecting subunit is used for selecting standard lifting task information containing the feature words as lifting task information to be selected;
the computing subunit is used for computing the matching degree of each to-be-selected lifting task information;
the sorting subunit is used for sorting all the task information to be extracted according to the sequence of the matching degree from high to low to obtain an extraction task list;
The storing the subset of query results in a database includes:
And storing the query result subset into a Gaussian database.
3. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of claim 1 when executing the program.
4. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of claim 1.
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