CN115630196A - Data query method, data query device, computer equipment, storage medium and program product - Google Patents
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Abstract
The application relates to a data query method, a data query device, a computer device, a storage medium and a program product. The method comprises the following steps: acquiring a plurality of data to be queried; acquiring associated data corresponding to the data to be queried from a preset database according to preset query conditions aiming at the data to be queried; according to the attribute information of the associated data corresponding to each data to be queried, normalizing the multiple data to be queried to obtain a normalization result of the multiple data to be queried; the preset query condition comprises a preset query depth, the preset query depth is smaller than a preset depth threshold value, and the associated data is data which has an associated relation with the data to be queried in a preset graph database; in other words, in the embodiment of the application, the data structure of the graph database is adopted to perform data association on data of different data sources, the data query speed is improved through a short query depth, the data association efficiency can be improved, the data can be rapidly queried, and the data query efficiency is improved.
Description
Technical Field
The present application relates to the field of big data technologies, and in particular, to a data query method, apparatus, computer device, storage medium, and program product.
Background
With the development of big data and cloud computing, efficient storage, fast analysis and real-time query of data become the current focus of attention. In order to meet the focus, how to perform data normalization processing on data of different data sources to obtain normalized data, so as to implement fast processing of data, which is a technical problem to be solved urgently at present.
In the conventional technology, for data of different data sources, an association information table between the data may be established, and then, when data query is performed, the data query may be performed through the pre-established association information table.
However, in the conventional method of performing data query through the pre-established association information table, as the data size of the association information table is larger and larger, the efficiency of data query is lower and lower.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a data query method, apparatus, computer device, computer readable storage medium and computer program product capable of improving data query efficiency.
In a first aspect, the present application provides a data query method. The method comprises the following steps:
acquiring a plurality of data to be queried;
acquiring associated data corresponding to the data to be queried from a preset database according to preset query conditions aiming at the data to be queried; the preset query condition comprises a preset query depth, the preset query depth is smaller than a preset depth threshold value, and the associated data is data which has an associated relation with the data to be queried in a preset graph database;
and normalizing the plurality of data to be queried according to the attribute information of the associated data corresponding to the data to be queried to obtain a normalization result of the plurality of data to be queried.
In the embodiment, a plurality of data to be queried are obtained; acquiring associated data corresponding to the data to be queried from a preset database according to preset query conditions aiming at the data to be queried; then, according to the attribute information of the associated data corresponding to each data to be queried, normalizing the multiple data to be queried to obtain a normalization result of the multiple data to be queried; the preset query condition comprises a preset query depth, the preset query depth is smaller than a preset depth threshold, and the associated data corresponding to the data to be queried is data in a preset graph database and has an associated relation with the data to be queried; that is to say, in the embodiment of the present application, a data structure of a graph database is used to perform data association on data of different data sources, and when querying the graph database, the preset query depth is limited to implement data query with a shorter query path, and then normalization processing is performed on data to be queried based on a path query result, so that data to be queried with an association relationship can be unified and spliced to obtain data association with a long path.
In one embodiment, the preset query condition further includes a preset relationship type; acquiring associated data corresponding to data to be queried from a preset graph database according to preset query conditions, wherein the method comprises the following steps:
acquiring candidate associated data corresponding to the data to be queried from a preset map database according to a preset query depth;
and screening out associated data with the same type as the preset relationship from the candidate associated data according to the preset relationship type, and taking the associated data as associated data corresponding to the data to be inquired.
In this embodiment, after all candidate associated data associated with the data to be queried is acquired through the preset query depth, further, associated data with the same type as the preset relationship is screened out from the candidate associated data according to the type of the relationship between the data to be queried and the candidate associated data, and is used as associated data corresponding to the data to be queried. By adding query conditions, query results matched with the query conditions are further obtained, the flexibility and the selectivity of data query can be improved, and the efficiency of data query is improved.
In one embodiment, normalizing the plurality of data to be queried according to the attribute information of the associated data corresponding to each data to be queried to obtain a normalization result of the plurality of data to be queried, includes:
acquiring attribute information of associated data corresponding to each data to be queried;
judging whether the similarity between the attribute information of the associated data corresponding to different data to be inquired is greater than a preset similarity threshold value or not;
and under the condition that the similarity between the attribute information of the associated data corresponding to different data to be queried is greater than a preset similarity threshold, taking the associated data corresponding to the attribute information with the similarity greater than the preset similarity threshold as target associated data, and carrying out normalization processing on the target associated data to obtain a plurality of normalization results of the data to be queried.
In this embodiment, by acquiring attribute information of associated data corresponding to each piece of data to be queried, and determining whether similarity between attribute information of associated data corresponding to different pieces of data to be queried is greater than a preset similarity threshold; then, under the condition that the similarity between the attribute information of the associated data corresponding to different data to be queried is greater than a preset similarity threshold, taking the associated data corresponding to the attribute information with the similarity greater than the preset similarity threshold as target associated data, and carrying out normalization processing on the target associated data to obtain a plurality of normalization results of the data to be queried; in addition, all relevant data of the data to be queried are taken as a normalization result, so that the relevant data of the queried data are displayed for the user, and more query information and query ideas are provided for the user.
In one embodiment, the normalizing the target associated data to obtain a plurality of normalized results of the data to be queried includes:
establishing an incidence relation between different data to be queried corresponding to the target incidence data through the target incidence data;
and obtaining a plurality of normalization results of the data to be queried based on the association relationship among different data to be queried and the association data corresponding to the data to be queried.
In this embodiment, the incidence relation between different data to be queried corresponding to the target incidence data is established through the target incidence data, and the normalization results of the multiple data to be queried are obtained based on the incidence relation between the different data to be queried and the incidence data corresponding to the data to be queried, wherein the obtained normalization results include not only the incidence results between the multiple data to be queried but also other incidence data of each data to be queried, so that the comprehensiveness of the normalization query result can be improved.
In one embodiment, the method further comprises:
acquiring data files of different data sources;
analyzing the data files of the data sources through a file analysis processing component aiming at the data files of the data sources to obtain structured data;
and extracting the incidence relation from the structured data through the incidence relation analysis and storage component, and constructing a preset graph database based on the incidence relation and the structured data.
In the embodiment, data files of different data sources are obtained; analyzing the data files of the data sources through a file analysis processing component aiming at the data files of the data sources to obtain structured data; and then, extracting the incidence relation from the structured data through the incidence relation analysis and storage component, and constructing a preset database based on the incidence relation and the structured data. By adopting the method of the embodiment to construct the graph database, the accuracy and the integrity of the graph database can be improved, the graph database is updated in real time, the speed and the accuracy of data normalization query can be improved, and the efficiency of the data normalization query is improved.
In one embodiment, obtaining data files of different data sources includes:
receiving data files of different data sources from a data platform through a file transfer protocol server;
storing data files of different data sources on a file transfer protocol server, generating a file storage path, and sending the file storage path to a message component through the file transfer protocol server;
and acquiring a file storage path from the message component through the file analysis processing component, and acquiring data files of different data sources from the file transfer protocol server based on the file storage path.
In the embodiment, data files of different data sources are received from a data platform through a file transfer protocol server; storing data files of different data sources on a file transfer protocol server, generating a file storage path, and sending the file storage path to a message component through the file transfer protocol server; and acquiring a file storage path from the message component through the file analysis processing component, and acquiring data files of different data sources from the file transfer protocol server based on the file storage path. The embodiment provides a specific implementation process for acquiring data files of different data sources by the server, improves the implementation of acquiring data by the server, and provides a basis for data normalization by the server.
In a second aspect, the present application further provides a data query apparatus. The device includes:
the first acquisition module is used for acquiring a plurality of data to be inquired;
the second acquisition module is used for acquiring the associated data corresponding to the data to be queried from the preset database according to the preset query conditions aiming at the data to be queried; the preset query condition comprises a preset query depth, the preset query depth is smaller than a preset depth threshold value, and the associated data is data which has an associated relation with the data to be queried in a preset graph database;
and the processing module is used for carrying out normalization processing on the plurality of data to be queried according to the attribute information of the associated data corresponding to each data to be queried so as to obtain the normalization results of the plurality of data to be queried.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of the data query method of the first aspect when executing the computer program.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the data query method in the first aspect described above.
In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the data query method in the first aspect.
According to the data query method, the data query device, the computer equipment, the storage medium and the computer program product, a plurality of data to be queried are obtained; acquiring associated data corresponding to the data to be queried from a preset database according to preset query conditions aiming at the data to be queried; then, according to the attribute information of the associated data corresponding to each data to be queried, normalizing the plurality of data to be queried to obtain a normalization result of the plurality of data to be queried; the preset query condition comprises a preset query depth, the preset query depth is smaller than a preset depth threshold, and the associated data corresponding to the data to be queried is data in a preset graph database and has an associated relation with the data to be queried; that is to say, in the embodiment of the present application, a data structure of a graph database is used to perform data association on data of different data sources, and when querying the graph database, by limiting a preset query depth, data query is implemented by using a shorter query path, and then normalization processing is performed on data to be queried based on a path query result, so that data to be queried having an association relationship can be normalized and spliced to obtain a long-path data association.
Drawings
FIG. 1 is a diagram of an application environment of a data query method in one embodiment;
FIG. 2 is a flow diagram that illustrates a methodology for querying data in one embodiment;
FIG. 3 is a flow chart illustrating a data query method according to another embodiment;
FIG. 4 is a flow chart illustrating a data query method according to another embodiment;
FIG. 5 is a flow chart illustrating a data query method according to another embodiment;
FIG. 6 is a flow chart illustrating a data query method according to another embodiment;
FIG. 7 is a flow diagram illustrating a real-time data binning process in accordance with one embodiment;
FIG. 8 is a block diagram that illustrates a model structure for a data normalization query in one embodiment;
FIG. 9 is a block diagram showing the construction of a data search apparatus according to an embodiment;
FIG. 10 is a diagram showing an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
With the development of information technology, everyone will generate a large amount of data information every day, but because of the difference of various factors such as information system, realization technology, industry and the like generating data information, the data is not restricted by standard specifications, so the relevance between the generated data information is weak. Exemplarily, in some scenes, people corresponding to different data information need to be identified and associated, and the association is enhanced; for example: there are 10 mobile phone numbers, and since there is a possibility that the same person has multiple mobile phone numbers, if the 10 mobile phone numbers are normalized, the number of the obtained person objects should be less than or equal to 10.
Conventionally, when data is normalized, data association needs to be completed before a normalized result is queried, and algorithm processing time is needed from data access to a result which can be queried, so that instantaneity is reduced; particularly, a data association form of an association information table is adopted, and data query is performed after data association is performed, so that a large amount of time is consumed; in addition, if indirect association data exists, the problem of association relation loss is easy to occur; if the data volume is greatly increased to reach billions and billions, the effectiveness problem is more serious, so that a new data normalization model is urgently needed to solve the technical problem and improve the efficiency of data query.
The data query method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The terminal 102 is configured to provide an interactive data query service, and the server receives data query information sent by the terminal, performs a data query operation, and returns a data query result, so that the terminal displays the data query result. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104 or may be placed on the cloud or other network server. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart car-mounted devices, and the like. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, and the like. The server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers.
In one embodiment, as shown in fig. 2, a data query method is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
The data type of the data to be queried may be type data related to a data type included in a preset map database, where the data type of the preset map database may be related to a data type of a related data source, for example: the preset map database can be a database constructed by related data in the financial business field, a database constructed by related data in the medical field, a database constructed by related data in the education field and the like.
Alternatively, the server may receive a plurality of pieces of data to be queried sent by the terminal, may also receive a plurality of pieces of data to be queried sent by other servers, and the like.
The preset query condition may include a preset query depth, the preset query depth is smaller than a preset depth threshold, and the associated data is data in a preset map database, which has an association relationship with the data to be queried. In the graph database, the preset query depth is the depth of the query path, and the deeper the query path is, the more information related to the data is, and the slower the efficiency of data query is. Therefore, in the embodiment, the technical scheme of realizing long-path query by short-path splicing query is adopted, and the data query efficiency of the graph database is improved.
Through theoretical analysis and experimental verification, under the condition that the query path is 3 degrees or more and 3 degrees or more, the efficiency of data query is obviously slowed down, and in one case, the preset depth threshold value may be set to 3 degrees, that is, the preset query depth in this embodiment may be 1 degree or 2 degrees.
It should be noted that the preset query depth may also be set by the user, and the server may perform data query according to the preset query depth input by the user when receiving the preset query depth input by the user and sent by the terminal.
Specifically, when the server queries data, associated data having an association relationship with each data to be queried may be obtained from a preset map database according to a preset query depth for each data to be queried; that is, each data to be queried is taken as a central node, and all associated data of a preset query depth associated with the data to be queried are obtained and taken as associated data corresponding to the data to be queried. Exemplarily, when the preset query depth is 1 degree, all node data associated with the data to be queried by 1 degree are obtained as associated data corresponding to the data to be queried.
The attribute information of the associated data may include attribute values of respective attribute parameters of the associated data. For example: when the associated data is mailbox data, the attribute information of the associated data may include a mailbox account, a mailbox password, mailbox content, and the like.
Optionally, when determining whether there is a correlation between the data to be queried, it may be determined whether there is a correlation between the data to be queried and a correlation corresponding to the correlation according to attribute information of correlation data corresponding to the data to be queried, and when determining that there is a correlation between a plurality of data to be queried, an association is established between the data to be queried having the correlation, and the data to be queried having the correlation is normalized, so as to obtain a normalization result of the data to be queried.
Optionally, when determining whether there is a correlation between each piece of data to be queried according to attribute information of associated data corresponding to each piece of data to be queried, it may be determined whether there is associated data with the same attribute information between attribute information of each piece of associated data of data a to be queried and attribute information of each piece of associated data of data B to be queried for every two pieces of data to be queried, such as data a to be queried and data B to be queried, and if there is associated data with the same attribute information between data a to be queried and data B to be queried, it may be determined that there is a correlation between data a to be queried and data B to be queried, and an association relationship is formed between data a to be queried and data B through the associated data with the same attribute information.
Further, after all the data to be queried are judged, for a plurality of data to be queried with the association relation, normalization processing is performed on the plurality of data to be queried according to the association relation of the plurality of data to be queried, so as to obtain a normalization result of the plurality of data to be queried. Optionally, the normalization result may include at least one group of data to be queried with an association relationship, an association relationship between a plurality of data to be queried with an association relationship in each group of data to be queried, and at least one of the data to be queried without an association relationship.
In the data query method, the server acquires a plurality of data to be queried; acquiring associated data corresponding to the data to be queried from a preset database according to preset query conditions aiming at the data to be queried; then, according to the attribute information of the associated data corresponding to each data to be queried, normalizing the plurality of data to be queried to obtain a normalization result of the plurality of data to be queried; the preset query condition comprises a preset query depth, the preset query depth is smaller than a preset depth threshold, and the associated data corresponding to the data to be queried is data in a preset graph database and has an associated relation with the data to be queried; that is to say, in the embodiment of the present application, a data structure of a graph database is used to perform data association on data of different data sources, and when querying the graph database, the preset query depth is limited to implement data query with a shorter query path, and then normalization processing is performed on data to be queried based on a path query result, so that data to be queried with an association relationship can be unified and spliced to obtain data association with a long path.
In an optional embodiment of the present application, for the preset query condition, the preset query condition may include not only a preset query depth but also a preset relationship type, that is, the preset query condition may include a preset query depth and a preset relationship type, and for each data to be queried, the server may obtain associated data corresponding to the data to be queried from the preset map database according to the preset query depth and the preset relationship type. It should be noted that the preset query condition may also include query conditions of other dimensions, for example: and presetting the association degree, and the like, and the embodiment of the application does not limit the specific content of the preset query condition.
In addition, for the preset query conditions, a user can adaptively set or modify various query conditions in the preset query conditions according to actual query requirements, the user inputs the preset query conditions through a terminal, the terminal sends the obtained preset query conditions to a server, and after the server obtains the preset query conditions sent by the terminal, the server obtains associated data corresponding to the data to be queried from a preset database according to the obtained preset query conditions for the data to be queried.
In one implementation manner, in the case that the preset query condition includes a preset query depth and a preset relationship type, as shown in fig. 3, the step 202 "obtaining associated data corresponding to data to be queried from a preset database according to the preset query condition" may include:
Optionally, in this embodiment, after all candidate associated data associated with the data to be queried are acquired through the preset query depth, further, associated data with the same type as the preset relationship is screened out from the candidate associated data according to the type of the relationship between the data to be queried and the candidate associated data, and is used as associated data corresponding to the data to be queried. By adding query conditions, query results matched with the query conditions are further obtained, the flexibility and the selectivity of data query can be improved, and the efficiency of data query is improved.
Fig. 4 is a flowchart illustrating a data query method in another embodiment. The present embodiment relates to an optional implementation process in which a server performs normalization processing on multiple pieces of data to be queried according to attribute information of associated data corresponding to each piece of data to be queried to obtain a normalization result of the multiple pieces of data to be queried, where, on the basis of the foregoing embodiment, as shown in fig. 4, step 203 includes:
Optionally, for each piece of data to be queried, after the server acquires the associated data corresponding to the piece of data to be queried from the graph database, the server may further acquire attribute information of each piece of associated data corresponding to the piece of data to be queried based on the graph database. Or, the server may further obtain attribute information of each associated data corresponding to the data to be queried from another database or another storage device, which is not specifically limited in this embodiment of the application.
The data to be queried may correspond to at least one piece of associated data, and one piece of associated data may correspond to at least one piece of attribute information.
Optionally, the server may sequentially determine, for two different data to be queried, whether a correlation exists between the two different data to be queried, and determine, for the two different data to be queried, correlation data corresponding to the two data to be queried, respectively, and attribute information of each correlation data; the method comprises the steps that associated data corresponding to two data to be inquired respectively can form an associated data pair, the similarity between attribute information of the two associated data in each associated data pair is calculated in sequence, and whether the similarity is larger than a preset similarity threshold value or not is judged; alternatively, in the case that the associated data includes a plurality of attribute information, the similarity between each of the attribute information of the associated data in the associated data pair may be sequentially calculated, or the similarity between the attribute feature vectors of two associated data in the associated data pair may be calculated using the plurality of attribute information of the associated data as one attribute feature vector.
Optionally, in a case that it is determined that the similarity between the attribute information of the associated data pair corresponding to the two data to be queried is greater than a preset similarity threshold, it may be determined that there is a correlation between the two associated data in the associated data pair; further, in the case that it is determined that the similarity between the attribute information of at least one group of associated data pairs between two pieces of data to be queried is greater than a preset similarity threshold, that is, in the case that there is a correlation between at least one group of associated data pairs, it may be determined that there is a correlation between the two pieces of data to be queried.
When data normalization is carried out, for a plurality of data to be queried with relevance, the relevant data with relevance in the plurality of data to be queried is taken as target relevant data, and the target relevant data is used for correlating the data to be queried with relevance; then, the target associated data may be normalized to obtain a plurality of normalized results of the data to be queried.
Optionally, when the server performs data normalization processing, an association relationship between different data to be queried corresponding to the target association data may be established through the target association data, and then, a normalization result of a plurality of data to be queried may be obtained based on the association relationship between the different data to be queried and the association data corresponding to the data to be queried. That is to say, when it is determined that there is a correlation between the associated data corresponding to the plurality of different pieces of data to be queried, the associated data is used as target associated data corresponding to the plurality of different pieces of data to be queried, and an association relationship between the plurality of different pieces of data to be queried is established based on the target associated data, that is, the plurality of different pieces of data to be queried are subjected to data association through the target associated data; furthermore, the server can take the multiple data to be queried and the incidence relation among the multiple data to be queried as the normalization result of the multiple data to be queried; of course, the normalization result may further include other associated data corresponding to the plurality of data to be queried, so as to output more associated information.
In addition, it should be noted that the preset similarity threshold may also be 1 or 100%, that is, in the case that the attribute information of two pieces of associated data is completely the same, it is determined that the two pieces of associated data are the same and have a correlation. When data normalization is performed, for two identical associated data, the two associated data can be merged into one node data, that is, the target associated data, which is used as associated data corresponding to two different data to be queried.
In this embodiment, the server obtains attribute information of associated data corresponding to each piece of data to be queried and determines whether the similarity between the attribute information of the associated data corresponding to different pieces of data to be queried is greater than a preset similarity threshold; then, under the condition that the similarity between the attribute information of the associated data corresponding to different data to be queried is greater than a preset similarity threshold, taking the associated data corresponding to the attribute information with the similarity greater than the preset similarity threshold as target associated data, and carrying out normalization processing on the target associated data to obtain a plurality of normalization results of the data to be queried; in addition, all relevant data of the data to be queried are taken as a normalization result, so that the relevant data of the queried data are displayed for the user, and more query information and query ideas are provided for the user.
Fig. 5 is a flowchart illustrating a data query method according to another embodiment. The present embodiment relates to an optional implementation process for a server to construct the preset graph database based on data of different data sources, where on the basis of the foregoing embodiment, as shown in fig. 5, the foregoing method further includes:
The data source is a client or a data platform for collecting data, and the data file can be a file in any format, such as a text file, a compressed file, and the like.
Optionally, the server may receive data files sent by different data sources in real time, and after acquiring the original data, the data sources may send the original data to the server in real time, or upload the original data to the server according to a preset frequency.
The file parsing processing component may be one processing component in the server, or may be a processing component on another terminal or server that is in communication connection with the server, which is not specifically limited in this application.
Optionally, after acquiring data files of different data sources, the server may send the data files to the file analysis processing component to instruct the file analysis processing component to analyze the data files of the different data sources, so as to obtain structured data corresponding to each data file; the structured data may be a data group with an association, such as: the structured data may be json (JavaScript Object Notation) data.
Optionally, the server may also call the file parsing component to parse the data files of different data sources to obtain the structured data corresponding to each data file. It should be noted that one data file may correspond to at least one structured data.
The association relationship analysis and storage component may be a processing component in the server, or may also be a processing component on another terminal or the server that is in communication connection with the server, which is not specifically limited in this application. For an association relationship, it may include a relationship type, a relationship attribute, and the like.
Optionally, the server may send the structured data corresponding to each acquired data file to the association relationship analysis and warehousing component, and the association relationship analysis and warehousing component extracts the association relationship of the data from the structured data and constructs the preset graph data based on the association relationship and the structured data. Of course, the server may also invoke the association analysis and warehousing component to analyze the structured data to extract the association from the structured data, and construct the preset database based on the association and the structured data.
It should be noted that the server may obtain data files of different data sources in real time, and after obtaining new incremental data, the server only needs to perform association analysis processing on the incremental data, and adds an association relationship corresponding to the incremental data to a preset map database that has been constructed, so as to implement real-time update of the preset map database; when the data normalization query is carried out, the server can directly adopt the latest preset graph database to carry out the data query without carrying out the correlation processing operation on the data in the database, and the efficiency of the data normalization query can be greatly improved.
In the embodiment, the server acquires data files of different data sources; analyzing the data files of the data sources through a file analysis processing component aiming at the data files of the data sources to obtain structured data; and then, extracting the incidence relation from the structured data through the incidence relation analysis and storage component, and constructing a preset database based on the incidence relation and the structured data. By adopting the method of the embodiment to construct the graph database, the accuracy and the integrity of the graph database can be improved, the graph database is updated in real time, the speed and the accuracy of data normalization query can be improved, and the efficiency of the data normalization query is improved.
Fig. 6 is a flowchart illustrating a data query method according to another embodiment. Based on the foregoing embodiment, as shown in fig. 6, the foregoing step 501 includes:
A File Transfer Protocol Server (FTP Server for short) is a computer that provides File storage and access services on the internet, and provides services according to the FTP Protocol. In short, the server supporting the FTP protocol is an FTP server.
In this embodiment, the data platform may be connected to a plurality of different data sources, the data files collected by the data sources are sent to the data platform, and the FTP server may acquire the data files of the different data sources from the data platform.
The message component may be a processing component in another server or terminal in communication connection with the server.
In this embodiment, the FTP server stores the acquired data files of different data sources, so as to obtain file storage paths corresponding to the data files of different data sources, and then the FTP server may send the file storage paths corresponding to the data files of different data sources to the message component.
Optionally, the file parsing processing component may monitor the message component, obtain a file storage path corresponding to the data file from the message component, and obtain the data file corresponding to the file storage path from the FTP server according to the file storage path, so as to obtain the data files of different data sources.
In the embodiment, a server receives data files of different data sources from a data platform through a file transfer protocol server; storing data files of different data sources on a file transfer protocol server, generating a file storage path, and sending the file storage path to a message component through the file transfer protocol server; and acquiring a file storage path from the message component through the file analysis processing component, and acquiring data files of different data sources from the file transfer protocol server based on the file storage path. The embodiment provides a specific implementation process for acquiring data files of different data sources by the server, improves the implementation of acquiring data by the server, and provides a basis for data normalization by the server.
In an alternative embodiment of the present application, a complete flow of a data query method is provided, which specifically includes the following steps:
(1) The server acquires data files of different data sources in real time, and constructs and updates the graph database based on the data files of the different data sources.
Referring to fig. 7, the process flow of the server acquiring and warehousing data in real time may include: the data platform pushes data files (such as compressed files zip) of different data sources to an FTP server in real time, the FTP server sends a file storage path to a message component (such as kafka) while uploading the data files is completed, a file analysis processing component (such as flink) acquires the file storage path from the message component (kafka), acquires the data files based on the file storage path, analyzes the data files into structured json data and pushes the structured json data to the message component (kafka), an association relation analysis and storage component (such as flink) acquires the structured json data from the message component (kafka), and the data files are stored in a database after an association relation is extracted.
(2) The method comprises the steps that a server obtains a plurality of data to be queried, and obtains associated data corresponding to the data to be queried from a graph database according to a preset query depth and a preset relationship type aiming at each data to be queried; illustratively, the preset query depth may be 1 degree, and the preset relationship type may be the same person.
(3) And acquiring attribute information of associated data corresponding to each data to be queried, and judging whether the same associated data exists between different data to be queried or not based on the attribute information of the associated data.
(4) If the same associated data exist among different data to be queried, the same associated data are subjected to normalization processing to obtain the association relation among the different data to be queried; exemplarily, referring to fig. 8, where a node 1 and a node 2 represent two different data to be queried, if the same associated data exists in the associated data of the node 1 and the associated data of the node 2, that is, the same node 3 exists, the same node 3 may be merged to obtain a merged node 3, and the merged node 3 is used as an associated node of the node 1 and also as an associated node of the node 2.
(5) And generating a plurality of normalization results of the data to be queried according to the associated data of each data to be queried and the associated relation between the data to be queried. Exemplarily, referring to fig. 8, for node 1 and node 2, the input is two different objects, and after performing a data query, it is determined that there are associated nodes in node 1 and node 2, then node 1 and node 2 may be normalized into one object and output, and the output object includes all associated nodes in node 1 and node 2.
(6) And outputting a plurality of normalization results of the data to be queried.
The method in the embodiment can solve the problem of fast access of mass data of different data sources, the complex association relationship is formed by the simple association relationship, the result set of the long-path query effect is realized after the short-path query of the specified node on the specified path is realized through the association relationship, the effect of normalizing the real-time query result is realized, the complexity of data processing is reduced, the data storage capacity is improved, the long-path query effect is realized by using the short-path query time, and the efficiency of data processing is improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not limited to being performed in the exact order illustrated and, unless explicitly stated herein, may be performed in other orders. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a data query device for realizing the data query method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, so specific limitations in one or more embodiments of the data query device provided below can be referred to the limitations of the data query method in the foregoing, and details are not described here.
In one embodiment, as shown in fig. 9, there is provided a data query apparatus including: a first obtaining module 901, a second obtaining module 902 and a processing module 903, wherein:
a first obtaining module 901, configured to obtain multiple data to be queried.
A second obtaining module 902, configured to obtain, for each data to be queried, associated data corresponding to the data to be queried from a preset map database according to a preset query condition; the preset query condition comprises a preset query depth, the preset query depth is smaller than a preset depth threshold value, and the associated data is data which has an associated relation with the data to be queried in a preset graph database.
The processing module 903 is configured to perform normalization processing on the multiple pieces of data to be queried according to the attribute information of the associated data corresponding to each piece of data to be queried, so as to obtain a normalization result of the multiple pieces of data to be queried.
In one embodiment, the preset query condition further includes a preset relationship type; the second obtaining module 902 includes a first obtaining unit and a second obtaining unit; the first acquisition unit is used for acquiring candidate associated data corresponding to the data to be queried from a preset map database according to a preset query depth; and the second obtaining unit is used for screening out the associated data with the same type as the preset relationship from the candidate associated data according to the preset relationship type and taking the associated data as the associated data corresponding to the data to be inquired.
In one embodiment, the processing module 903 includes a third obtaining unit, a determining unit, and a processing unit; the third acquiring unit is used for acquiring attribute information of associated data corresponding to each data to be queried; the judging unit is used for judging whether the similarity between the attribute information of the associated data corresponding to different data to be inquired is greater than a preset similarity threshold value or not; and the processing unit is used for taking the associated data corresponding to the attribute information with the similarity larger than the preset similarity threshold value as the target associated data and carrying out normalization processing on the target associated data to obtain the normalization results of the plurality of data to be queried under the condition that the similarity between the attribute information of the associated data corresponding to different data to be queried is larger than the preset similarity threshold value.
In one embodiment, the processing unit is specifically configured to establish an association relationship between different data to be queried, which correspond to the target associated data, through the target associated data; and obtaining a plurality of normalization results of the data to be queried based on the association relationship among different data to be queried and the associated data corresponding to the data to be queried.
In one embodiment, the apparatus further comprises: the device comprises a third acquisition module, a fourth acquisition module and a construction module; the third acquisition module is used for acquiring data files of different data sources; the fourth acquisition module is used for analyzing the data files of the data sources through the file analysis processing component aiming at the data files of the data sources to obtain structured data; and the construction module is used for extracting the incidence relation from the structured data through the incidence relation analysis and storage component and constructing a preset graph database based on the incidence relation and the structured data.
In one embodiment, the third obtaining module includes a receiving unit, a generating unit, and a fourth obtaining unit; the receiving unit is used for receiving data files of different data sources from the data platform through the file transfer protocol server; the generating unit is used for storing data files of different data sources on the file transfer protocol server, generating a file storage path and sending the file storage path to the message component through the file transfer protocol server; and the fourth acquisition unit is used for acquiring the file storage path from the message component through the file analysis processing component and acquiring the data files of different data sources from the file transfer protocol server based on the file storage path.
The modules in the data query device can be wholly or partially implemented by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 10. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data of a preset database. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data query method.
It will be appreciated by those skilled in the art that the configuration shown in fig. 10 is a block diagram of only a portion of the configuration associated with the present application, and is not intended to limit the computing device to which the present application may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the data query method in any one of the above embodiments when executing the computer program.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the data query method of any of the above embodiments.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps of the data query method of any of the above embodiments.
It should be noted that, the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases involved in the embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (10)
1. A method of data query, the method comprising:
acquiring a plurality of data to be queried;
aiming at each data to be queried, acquiring associated data corresponding to the data to be queried from a preset database according to preset query conditions; the preset query conditions comprise preset query depths, the preset query depths are smaller than a preset depth threshold value, and the associated data are data in the preset graph database and have an associated relation with the data to be queried;
and carrying out normalization processing on the plurality of data to be queried according to the attribute information of the associated data corresponding to each data to be queried to obtain the normalization results of the plurality of data to be queried.
2. The method according to claim 1, wherein the preset query condition further comprises a preset relationship type; the acquiring the associated data corresponding to the data to be queried from a preset map database according to the preset query condition comprises the following steps:
acquiring candidate associated data corresponding to the data to be queried from a preset database according to the preset query depth;
and screening out associated data with the same type as the preset relationship from the candidate associated data according to the preset relationship type, wherein the associated data is used as associated data corresponding to the data to be inquired.
3. The method according to claim 1 or 2, wherein the normalizing the plurality of data to be queried according to the attribute information of the associated data corresponding to each data to be queried to obtain the normalization result of the plurality of data to be queried comprises:
acquiring attribute information of associated data corresponding to each piece of data to be queried;
judging whether the similarity between the attribute information of the associated data corresponding to different data to be inquired is greater than a preset similarity threshold value or not;
if yes, the associated data corresponding to the attribute information with the similarity larger than the preset similarity threshold value is used as target associated data, and normalization processing is carried out on the target associated data to obtain normalization results of the data to be queried.
4. The method according to claim 3, wherein the normalizing the target-associated data to obtain the normalized result of the plurality of data to be queried comprises:
establishing an incidence relation between different data to be queried corresponding to the target associated data through the target associated data;
and obtaining the normalization results of the data to be queried based on the association relationship among different data to be queried and the associated data corresponding to the data to be queried.
5. The method according to claim 1 or 2, characterized in that the method further comprises:
acquiring data files of different data sources;
analyzing the data files of the data sources through a file analysis processing component aiming at the data files of the data sources to obtain structured data;
and extracting the incidence relation from the structured data through an incidence relation analysis and storage component, and constructing the preset graph database based on the incidence relation and the structured data.
6. The method according to claim 5, wherein the obtaining data files of different data sources comprises:
receiving data files of different data sources from a data platform through a file transfer protocol server;
storing data files of different data sources on the file transfer protocol server, generating a file storage path, and sending the file storage path to a message component through the file transfer protocol server;
and acquiring the file storage path from the message component through the file analysis processing component, and acquiring the data files of the different data sources from the file transfer protocol server based on the file storage path.
7. A data query apparatus, characterized in that the apparatus comprises:
the first acquisition module is used for acquiring a plurality of data to be inquired;
the second acquisition module is used for acquiring the associated data corresponding to the data to be queried from a preset database according to preset query conditions aiming at the data to be queried; the preset query conditions comprise preset query depths, the preset query depths are smaller than a preset depth threshold value, and the associated data are data in the preset graph database and have an associated relation with the data to be queried;
and the processing module is used for carrying out normalization processing on the plurality of data to be queried according to the attribute information of the associated data corresponding to the data to be queried so as to obtain the normalization results of the plurality of data to be queried.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, realizes the steps of the method of any one of claims 1 to 6.
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