CN113220710B - Data query method, device, electronic device and storage medium - Google Patents

Abstract

The present disclosure provides a data query method, device, electronic device and storage medium, which relate to the field of data processing technology, and in particular to the field of intelligent search. The specific implementation scheme is: obtaining initial query information for querying target data; parsing the initial query information to obtain a database query statement suitable for querying the target data, wherein the database query statement is a first query statement or a second query statement; when the database query statement is the first query statement, querying the target data from the first database according to the first query statement; and when the database query statement is the second query statement, querying the target data from the second database according to the second query statement.

Description

Data query method, device, electronic device and storage medium

Technical Field

The present disclosure relates to the field of data processing technology, and in particular to the field of intelligent search.

Background technique

A database, also known as a data management system, is a collection of data that is processed and stored in a certain way so that it can be shared by multiple users and with minimal redundancy. A database can consist of multiple data table spaces, and users can perform operations such as adding, extracting, updating, and deleting data in the file.

Summary of the invention

The present disclosure provides a data query method, device, electronic device, storage medium and computer program product.

According to one aspect of the present disclosure, a data query method is provided, comprising: acquiring initial query information for querying target data; parsing the initial query information to obtain a database query statement suitable for querying the target data, wherein the database query statement is a first query statement or a second query statement; when the database query statement is the first query statement, querying the target data from a first database according to the first query statement; and when the database query statement is the second query statement, querying the target data from a second database according to the second query statement.

According to another aspect of the present disclosure, a data query device is provided, including: a first acquisition module, used to acquire initial query information for querying target data; a parsing module, used to parse the initial query information to obtain a database query statement suitable for querying the target data, wherein the database query statement is a first query statement or a second query statement; a first query module, used to query the target data from a first database according to the first query statement when the database query statement is the first query statement; and a second query module, used to query the target data from a second database according to the second query statement when the database query statement is the second query statement.

According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method as described above.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method described above when executed by a processor.

It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

FIG1 schematically shows an exemplary system architecture to which a data query method and apparatus can be applied according to an embodiment of the present disclosure;

FIG2 schematically shows a flow chart of a data query method according to an embodiment of the present disclosure;

FIG3 schematically shows another flow chart of a data query method according to an embodiment of the present disclosure;

FIG4 schematically shows an example flow chart of a data query method according to an embodiment of the present disclosure;

FIG5 schematically shows a data maintenance flow chart of a data maintenance service according to an embodiment of the present disclosure;

FIG6 schematically shows an architecture diagram of a data query system according to an embodiment of the present disclosure;

FIG7 schematically shows a block diagram of a data query device according to an embodiment of the present disclosure; and

FIG8 shows a schematic block diagram of an example electronic device that can be used to implement embodiments of the present disclosure.

Detailed ways

The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

With the development of science and technology, as well as the popularization of Internet information, artificial intelligence, and big data, more and more data interactions are completed online. Online data interaction requires a huge library of high-quality resources. In order to meet the multi-semantic query of data resources, a large number of fields need to be established for the data to save information in various dimensions of the data. Therefore, each piece of data usually has more than 100 attributes.

A graph database is a database that stores and queries data in a graph data structure. The smallest unit is each piece of data, and there are edges between the data. The difference between a graph database and other databases is that relationships play a primary role in a graph database. A graph database maintains the overall data resources by maintaining the relationships between data.

ES (ElasticSearch, an open source, highly scalable distributed full-text search engine) database maintains multiple data through an inverted index. An index is used as a database, and the smallest unit is each data, and each data is independent of each other. An inverted index means that each item in the index table includes an attribute value and the address of each record with the attribute value. The address of the record is determined by the attribute value, rather than the record determining the attribute value, so it is called an inverted index.

In the process of implementing the concept of the present disclosure, the inventors found that using a graph database to store data is inconvenient to maintain because data can only be queried by traversing the graph, and does not support reverse query, which significantly reduces query performance when the amount of data is large. In addition, using a graph database requires learning a specialized graph query language, which results in excessive learning and usage costs for developers.

In addition, although ES storage supports reverse query and is relatively flexible, because the data stored in ES are independent of each other, when certain relationships need to be queried, a second or even multiple queries are required. For the upstream caller of the service, it is easy for the query request to fail due to the long query time.

FIG1 schematically shows an exemplary system architecture to which a data query method and apparatus according to an embodiment of the present disclosure can be applied.

It should be noted that FIG. 1 is merely an example of a system architecture to which an embodiment of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiment of the present disclosure cannot be used in other devices, systems, environments or scenarios.

As shown in Fig. 1, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and/or wireless communication links, etc.

Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as knowledge reading applications, web browser applications, search applications, instant messaging tools, email clients and/or social platform software, etc. (only as examples).

The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

Server 105 can be a server that provides various services, such as a background management server that provides support for the content browsed by users using terminal devices 101, 102, and 103 (only as an example). The background management server can analyze and process the received data such as user requests, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests, etc.) to the terminal device. The server can also be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system. It solves the problems of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server can also be a server for a distributed system, or a server combined with blockchain.

It should be noted that the data query method provided in the embodiment of the present disclosure can generally be executed by the terminal device 101, 102, or 103. Accordingly, the data query device provided in the embodiment of the present disclosure can also be set in the terminal device 101, 102, or 103.

Alternatively, the data query method provided in the embodiment of the present disclosure may also be generally executed by the server 105. Accordingly, the data query device provided in the embodiment of the present disclosure may generally be arranged in the server 105. The data query method provided in the embodiment of the present disclosure may also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the data query device provided in the embodiment of the present disclosure may also be arranged in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and/or the server 105.

For example, when a user needs to query target data, the user can enter initial query information for querying the target data in the search box of the terminal device 101, 102, 103. The terminal device 101, 102, 103 can obtain the initial query information, and then send the obtained initial query information to the server 105, and the server 105 parses the initial query information to obtain a database query statement for querying the target data, so as to query the target data from the corresponding database according to the database query statement. Alternatively, a server or server cluster that can communicate with the terminal device 101, 102, 103 and/or the server 105 parses the initial query information, and queries the target data according to the database query statement obtained by the parsing.

It should be understood that the number of terminal devices, networks and servers in Figure 1 is only illustrative. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

FIG2 schematically shows a flow chart of a data query method according to an embodiment of the present disclosure.

As shown in FIG. 2 , the method includes operations S210 to S240 .

In operation S210, initial query information for querying target data is acquired.

In operation S220, the initial query information is parsed to obtain a database query statement suitable for querying the target data, wherein the database query statement is a first query statement or a second query statement.

In operation S230 , when the database query statement is a first query statement, target data is queried from a first database according to the first query statement.

In operation S240, when the database query statement is a second query statement, target data is queried from a second database according to the second query statement.

According to an embodiment of the present disclosure, the target data may be data that a user wants to obtain and is stored in a background database, and the background database may include the first database and the second database described above, and the first database and the second database may include any two of various relational databases and non-relational databases. For example, the first database and the second database may include any two of MySQL (a small open source relational database management system), SQL Server (a relational database management system), Oracle (a currently popular relational database management system), DB2 (a relational database platform), Redis (a key-value storage database), HBase (a distributed, column-oriented open source database), MongoDB (a document-oriented open source database), ES, Neo4j (graph database), etc.

According to the embodiments of the present disclosure, the backend database may not be limited to two types of databases. In practical applications, the backend database may also include a combination of more types of databases. At the same time, to ensure the integrity of data storage and the effectiveness of data query, the backend database may be, for example, a combination of a relational database and a non-relational database, for example, a combination of a graph database and a non-graph database, etc.

According to an embodiment of the present disclosure, the initial query information is, for example, information input by a user into a search box, which is used to query target data.

According to an embodiment of the present disclosure, a database query statement is, for example, a statement used by a search engine when performing a data query in response to initial query information. The statement may be, for example, a programmatic statement, including a structured query statement and a graph query statement. For example, in a case where the first database and the second database are a combination of a graph database and a non-graph database, the first query statement and the second query statement may be a graph query statement and a structured query statement.

According to an embodiment of the present disclosure, when the backend database is a first database and a second database, a database query statement suitable for querying the target data can be determined from the first query statement and the second query statement. When the backend database includes multiple databases, a database query statement suitable for querying the target data can be determined from the query statements used to query the multiple databases. The determined database query statement can be a query statement with the fastest query efficiency among all selectable query methods.

It should be noted that the above embodiments are merely exemplary embodiments, but are not limited thereto, and may also include other data query methods known in the art, as long as data query can be implemented.

Through the above-mentioned embodiments of the present disclosure, since a database query statement suitable for querying the target data can be obtained by parsing the initial query information, the database query statement suitable for querying the target data can be used to perform data query from the corresponding database, which can at least partially solve the technical problem of low query efficiency of a single type of data storage method when data query is needed, thereby improving data query efficiency.

The data query method shown in FIG. 2 is further described below in conjunction with specific embodiments and with reference to FIGS. 3 to 6 .

According to the embodiments of the present disclosure, in view of the advantages of the graph database in terms of high performance when performing relational queries, and the advantages of the ES storage in supporting inverted indexes, for example, a data query system can be constructed by combining the graph database and the ES storage. For example, the first database can be a graph database, and the second database can be a distributed search engine database, such as an ES database.

Through the above-mentioned embodiments of the present disclosure, the advantages of the graph database and the ES database can be combined to improve data query efficiency.

According to an embodiment of the present disclosure, the initial query information includes text information. Parsing the initial query information to obtain a database query statement suitable for querying target data includes: performing word segmentation processing on the text information to obtain at least one keyword for characterizing the text information. Obtaining a target query statement that matches the at least one keyword. Using the target query statement as a database query statement.

According to an embodiment of the present disclosure, taking the text information "dynasty of the author of Ding Fengbo" as an example, by performing word segmentation processing on the text information, at least one keyword obtained may be "Ding Fengbo", "author", and "dynasty". The target query statement may be a query statement that matches the three keywords "Ding Fengbo", "author", and "dynasty". For example, the target query statement may be a query statement that contains the three variable information "poem", "author", and "dynasty" at the same time.

Through the above-mentioned embodiments of the present disclosure, a database query statement is determined according to keywords obtained by segmenting text information, and a more appropriate query statement can be selected to implement the query of target data, which is conducive to improving the query performance.

According to an embodiment of the present disclosure, the at least one keyword may include N keywords, where N is an integer greater than 1. Obtaining a target query statement that matches at least one keyword may include: in the case where a full match query statement that matches all N keywords is obtained, using the full match query statement as the target query statement. In the case where a query statement that matches all N keywords is not obtained: selecting i target keywords from the N keywords, and obtaining a query statement that matches all i target keywords as the target query statement, where 1≤i＜N.

According to an embodiment of the present disclosure, taking at least one keyword as "Ding Fengbo", "author", and "dynasty" as an example, it can be determined that the value of N at this time is 3, and the full match query statement can be the above-mentioned query statement that contains the three variable information of "poetry", "author", and "dynasty" at the same time. In the case that the three keywords fail to match the full match query statement, two keywords can be selected from the three keywords to match again. For example, the two words "Ding Fengbo" and "author" can be selected as target keywords, and the full match query statement at this time can be a query statement containing the two variable information of "poetry" and "author". Similarly, in the case that a full match query statement that matches any two target keywords cannot be obtained, the number of keywords can continue to be reduced. For example, "Ding Fengbo" can be selected as the target keyword, and a query statement containing the variable information of "poetry" can be obtained as the target query statement.

Through the above-mentioned embodiments of the present disclosure, in the process of determining query statements based on keywords, the target query statement can be determined by reducing the number of keywords, so that the most appropriate database query statement can be effectively determined, providing effective and reasonable guarantees for the query process of target data.

FIG3 schematically shows another flow chart of the data query method according to an embodiment of the present disclosure.

As shown in FIG. 3 , the method includes operations S310 to S370 .

In operation S310, initial query information for querying target data is acquired.

In operation S320, the initial query information is parsed to obtain a database query statement suitable for querying the target data, wherein the database query statement is a first query statement or a second query statement.

In operation S330, the format of the database query statement is obtained.

In operation S340 , when the format matches the format of the query grammar of the first database, it is determined that the database query statement is a first query statement.

In operation S350, target data is queried from a first database according to a first query statement.

In operation S360, when the format matches the format of the query grammar of the second database, it is determined that the database query statement is a second query statement.

In operation S370, target data is queried from the second database according to the second query statement.

According to an embodiment of the present disclosure, when querying data from different databases, it is necessary to combine compatible query statements to achieve quick query. Therefore, after obtaining the database query statement, the type of database to be queried can be determined by judging the syntax format of the database query statement. For example, if the database query statement is a structured query statement and the syntax format is JSON (a lightweight data exchange format), it can be determined that the database to be queried is, for example, an ES database. For another example, if the database query statement is a graph query statement and the syntax format is gremlin (a graph query language), it can be determined that the database to be queried is a graph database.

It should be noted that the remaining technical features in this embodiment have been described in the previous embodiments and will not be repeated here.

Through the above-mentioned embodiments of the present disclosure, since the initial query information used to query the target data can automatically adapt the query method most suitable for querying the target data during the query process, while realizing intelligent query, it can also effectively improve the query performance.

According to an embodiment of the present disclosure, when the database query statement is a first query statement, querying the target data from the first database according to the first query statement includes: obtaining a first query result having a first data format by querying the first database according to the first query statement. Determining a target data format for displaying the target data. Converting the first query result into a target query result having the target data format. Displaying the target data based on the target query result.

According to an embodiment of the present disclosure, since the formats of query results obtained from queries in different databases are different, in order to ensure that different query results are transparent to the upstream data format, the query results can be converted into results with a unified target data format. For example, taking the first database as a graph database as an example, the data format of the first query result obtained by the query can be a string format. After obtaining the first query result in the string format, the first query result in the string format can be converted into a result in the target data format, and the display of the target data can be realized based on the result in the target data format.

Through the above-mentioned embodiments of the present disclosure, after the query results are obtained, the upstream data format is made transparent by unifying the data format, which can effectively reduce the work of developers when performing upstream design and improve query efficiency.

According to an embodiment of the present disclosure, determining a target data format for displaying target data includes: determining a second data format corresponding to a second query result obtained by querying a second database according to a second query statement, and using the second data format as the target data format.

According to the embodiments of the present disclosure, since the ES database can support storage structures such as strings, key-value, objects, and JSON, while the graph database only supports storage structures such as strings and arrays, and the graph database does not support the JSON storage structure. Taking the first database as a graph database and the second database as an ES database as an example, in order to ensure that the query results obtained from the ES database and the graph database are transparent to the upstream data format, the query results in the string format obtained from the graph database can be converted into results in the JSON format. At the same time, since the ES database supports the JSON structure, in order to simplify the design process, for example, the format of the JSON structure can be used as the target data format for displaying the target data.

Through the above-mentioned embodiments of the present disclosure, the design process can be further simplified while ensuring the effective display of the query results.

FIG. 4 schematically shows an example flow chart of a data query method according to an embodiment of the present disclosure.

As shown in FIG. 4 , the method includes operations S410 to S490 .

In operation S410, initial query information for querying target data is acquired.

In operation S420, the Query parsing module is called to query and parse the initial query information, wherein Query represents a message sent by a search engine or a database to find a specific file, website, record or a series of records in the database.

In operation S430, it is determined whether a database query statement corresponding to Query is obtained. If so, operations S440 to 470 are executed; if not, operation S490 is executed.

In operation S440, the database query statement is analyzed to determine the target database to be queried.

In operation S450, when the database query statement is a first query statement, a first database is queried.

In operation S460, when the database query statement is a second query statement, a second database is queried.

In operation S470, it is determined whether the query result is successfully obtained. If yes, operation S480 is executed; if no, operation S490 is executed.

In operation S480, the data format is unified and the target data is returned.

In operation S490, error information is returned.

According to the embodiments of the present disclosure, the first database is set as a graph database and the second database is set as an ES database as an example. In view of the learning cost of the graph database and the problem of slow graph query in big data scenarios, it is possible to use the graph database for storage only when the graph query method must be used to query data in a specific scenario according to the query scenario. In other non-essential situations, only ES storage can be used to package the graph database and ES database together to realize data management services. Therefore, the user only needs to enter the initial query information, and the data management service can determine whether ES query or graph query is required through analysis.

According to an embodiment of the present disclosure, when a database query statement corresponding to the Query cannot be parsed according to the Query parsing module, or when a query result cannot be successfully obtained from the database, for example, an error message may be returned.

According to an embodiment of the present disclosure, the error information may include, for example, any at least one of information such as query failure, failure to find relevant data, recommendation of similar words, etc.

Through the above-mentioned embodiments of the present disclosure, different databases are encapsulated into the same service, which can meet the resource recall in more query scenarios, effectively improve the recall rate, reduce the time consumption of multiple queries under a single ES storage, and alleviate the problem of graph database response timeout in the case of big data queries. At the same time, the amount of data in the graph database can be reduced. Moreover, for developers, they only need to know ES, which reduces the learning cost and development cost.

According to the embodiments of the present disclosure, before performing data query, for example, data maintenance can be performed on the data management service, that is, a data maintenance service is provided, and a corresponding data processing program is configured for the data management service, so that any data query process in the embodiments of the present disclosure can be followed when data query is required, and basic data for search is configured for each database in the data management service. This can improve the success rate of querying data using the data management service.

According to an embodiment of the present disclosure, the process of configuring basic data may include, for example: obtaining basic data for providing a query basis for target data, wherein the basic data may have a third data format. In the case where it is determined that the basic data needs to be stored in both the first database and the second database: converting the basic data into a storage format adapted to the first database and storing it in the first database, and at the same time, converting the basic data into a storage format adapted to the second database and storing it in the second database.

According to the embodiments of the present disclosure, the data format of the basic data may be different from the storage format of the first database and the second database, and it only needs to adapt the storage format of the corresponding database when it needs to be stored in the corresponding database.

According to an embodiment of the present disclosure, the data format of the basic data may also be different from the data format of only one of the first database and the second database. For example, taking the first database as a graph database and the second database as an ES database as an example, the basic data may be data in the ES format. After receiving the incoming ES format data, the data maintenance service can determine whether it is necessary to store and maintain data relationships in both databases at the same time as needed. In the case where it is determined that the basic data needs to be stored in the graph database at the same time, the data structure can be adjusted to obtain a data structure that is adapted to the storage format of the graph database, and the storage in the graph database can be completed.

Through the above-mentioned embodiments of the present disclosure, a data maintenance method is provided, which can provide data support for data management services.

According to an embodiment of the present disclosure, the process of configuring basic data may further include, for example: when an exception occurs in a first stored process for storing basic data in a first database, performing a rollback operation on the first stored process. When an exception occurs in a second stored process for storing basic data in a second database, performing a rollback operation on the second stored process.

Through the above embodiments of the present disclosure, by performing a rollback operation on the second stored procedure, the availability and stability of the database can be maintained.

FIG5 schematically shows a data maintenance flow chart of a data maintenance service according to an embodiment of the present disclosure.

As shown in FIG. 5 , the method includes operations S510 to S580 .

In operation S510, basic data in the ES format is acquired.

In operation S520, whether the basic data needs to maintain the graph database, if yes, then execute operations S530 to S550; if no, then execute operations S570 to S580.

In operation S530 , the data structure of the basic data is adjusted to adapt to the storage format of the graph database, and the edge relationship is maintained.

In operation S540 , a graph database is imported.

In operation S550, it is determined whether the graph database is successfully imported. If not, operation S560 is executed; if yes, operation S580 is executed.

In operation S560, rollback is performed.

In operation S570, the ES database is imported.

In operation S580, it is determined whether the ES database is successfully imported. If not, operation S560 is executed; if yes, the process ends.

According to an embodiment of the present disclosure, for example, when there is a batch of basic data that needs to be imported into a database, all basic data can be analyzed to determine whether the basic data needs to be imported into a graph database at the same time. When it is determined through analysis that the graph database needs to be imported, the basic data can be imported into the graph database as a whole to maintain the relationship between the data, which can be determined by maintaining edge relationships in the graph database. At the same time, the process of importing basic data into the ES database can be implemented in batches, for example.

According to an embodiment of the present disclosure, in the event that an error occurs in the process of importing basic data into a graph database or an ES database, a rollback operation can be performed on the currently executed import process to restore the correct state of the database before the import process is executed.

Through the above embodiments of the present disclosure, when maintaining data, developers only need to know ES to operate the data. Since the data is transparent to the user, it reduces the development cost and also provides rollback guarantee.

FIG6 schematically shows an architecture diagram of a data query system according to an embodiment of the present disclosure.

As shown in FIG. 6 , the data query system 600 includes, for example, a data management module 610 , a data maintenance module 620 , a query parsing module 630 , an ES database 640 , and a graph database 650 .

According to an embodiment of the present disclosure, the data management module 610 is used to call the Query parsing module 630 to parse the initial query information input by the user when receiving a query request for target data, so as to obtain a database query statement suitable for querying the target data. Then, by analyzing the database query statement, it is determined whether to call the ES database 640 or the graph database 650 to query the data, and the query results are processed in a unified data format. Afterwards, the data management module 610 can generate a target visual based on the results in the unified data format and return it to the user.

According to an embodiment of the present disclosure, the data maintenance module 620 can receive data in ES format and determine the database that needs to be maintained by analyzing the data structure, that is, determine whether the data in ES format needs to be imported into the ES database and the graph database at the same time. In the case of determining that the data needs to be imported into the graph database, the data maintenance module 620 can also change the data structure of the data in ES format to adapt it to the storage format of the graph database, and import it into the graph database.

Through the above embodiments of the present disclosure, a reliable, efficient and accurate data query system is implemented in combination with data management services and data maintenance services.

FIG. 7 schematically shows a block diagram of a data query device according to an embodiment of the present disclosure.

As shown in FIG. 7 , the data query device 700 includes: a first acquisition module 710 , a parsing module 720 , a first query module 730 and a second query module 740 .

The first acquisition module 710 is used to acquire initial query information for querying target data.

The parsing module 720 is used to parse the initial query information to obtain a database query statement suitable for querying the target data, wherein the database query statement is a first query statement or a second query statement.

The first query module 730 is used to query target data from the first database according to the first query statement when the database query statement is the first query statement.

The second query module 740 is used to query the target data from the second database according to the second query statement when the database query statement is the second query statement.

Through the above-mentioned embodiments of the present disclosure, since a database query statement suitable for querying the target data can be obtained by parsing the initial query information, the database query statement suitable for querying the target data can be used to perform data query from the corresponding database, which can at least partially solve the technical problem of low query efficiency of a single type of data storage method when data query is required, thereby achieving the technical effect of improving data query efficiency.

According to an embodiment of the present disclosure, the initial query information includes text information. The parsing module includes: a word segmentation unit, an acquisition unit and a definition unit.

The word segmentation unit is used to perform word segmentation processing on the text information to obtain at least one keyword used to represent the text information.

The acquisition unit is used to acquire a target query sentence matching at least one keyword.

A definition unit is used to use a target query statement as a database query statement.

According to an embodiment of the present disclosure, the at least one keyword includes N keywords, where N is an integer greater than 1. The acquisition unit includes: a first definition subunit and a second definition subunit.

The first definition subunit is used to use the full match query statement as the target query statement when a full match query statement matching all N keywords is obtained.

The second definition subunit is used to select i target keywords from the N keywords and obtain query statements matching the i target keywords as target query statements when no query statements matching the N keywords are obtained, wherein 1≦i＜N.

According to an embodiment of the present disclosure, the above-mentioned data query device further includes: a second acquisition module, a first determination module and a second determination module.

The second acquisition module is used to acquire the format of the database query statement.

The first determination module is used to determine that the database query statement is a first query statement when the format matches the format of the query grammar of the first database.

The second determination module is configured to determine that the database query statement is a second query statement when the format matches the format of the query grammar of the second database.

According to an embodiment of the present disclosure, the first query module includes: a query unit, a determination unit, a conversion unit and a display unit.

The query unit is used to obtain a first query result in a first data format by querying a first database according to a first query statement.

The determination unit is used to determine a target data format for displaying the target data.

The conversion unit is used to convert the first query result into a target query result having a target data format.

The display unit is used to display target data based on the target query result.

According to an embodiment of the present disclosure, the above-mentioned determination unit includes: a determination subunit and a third definition subunit.

The determination subunit is used to determine a second data format, where the second data format corresponds to a second query result obtained by querying the second database according to the second query statement.

The third definition subunit is used to use the second data format as the target data format.

According to an embodiment of the present disclosure, the above-mentioned data query device also includes: a third acquisition module and a storage module.

The third acquisition module is used to acquire basic data for providing a query basis for the target data, wherein the basic data has a third data format.

The storage module is used to convert the basic data into a storage format suitable for the first database and store it in the first database, and convert the basic data into a storage format suitable for the second database and store it in the second database when it is determined that the basic data needs to be stored in the first database and the second database at the same time.

According to an embodiment of the present disclosure, the above-mentioned data query device also includes: a first rollback module and a second rollback module.

A first rollback module is used to perform a rollback operation on the first stored procedure when an exception occurs in the first stored procedure for storing the basic data in the first database; and

The second rollback module is used to perform a rollback operation on the second stored procedure when an exception occurs in the second stored procedure for storing the basic data in the second database.

According to an embodiment of the present disclosure, the first database includes a graph database, and the second database includes a distributed search engine database.

According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method described above.

According to an embodiment of the present disclosure, a computer program product includes a computer program, and when the computer program is executed by a processor, the computer program implements the method as described above.

FIG8 shows a schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and/or required herein.

As shown in Figure 8, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.

A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

The computing unit 801 may be a variety of general and/or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as a data query method. For example, in some embodiments, the data query method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed on the device 800 via ROM 802 and/or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the data query method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the data query method in any other appropriate manner (e.g., by means of firmware).

Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and/or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, implements the functions/operations specified in the flow chart and/or block diagram. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communications network). Examples of communications networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server arises through computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims (16)

1. A data query method, comprising: Acquire initial query information for querying target data; Parsing the initial query information to obtain a database query statement suitable for querying the target data, wherein the database query statement is a first query statement or a second query statement; In a case where the database query statement is the first query statement, querying the target data from a first database according to the first query statement; and In a case where the database query statement is a second query statement, querying the target data from a second database according to the second query statement; The initial query information includes text information; the initial query information is parsed to obtain a database query statement suitable for querying the target data, including: Performing word segmentation processing on the text information to obtain at least one keyword used to represent the text information; Obtaining a target query sentence matching the at least one keyword; and Using the target query statement as the database query statement; The at least one keyword includes N keywords, where N is an integer greater than 1; and obtaining a target query statement matching the at least one keyword includes: When a full match query statement matching all the N keywords is obtained, the full match query statement is used as the target query statement; If a query statement matching all the N keywords is not obtained, the following operations are performed cyclically with N-1 as the initial value of i until the target query statement is obtained: Select i target keywords from the N keywords; When a query statement matching all the i target keywords is obtained, the obtained query statement is used as the target query statement; and In the case that no query statement matching the i target keywords is obtained, i is set to i-1 to reselect the target keyword, wherein 1≤i<N. 2. The method according to claim 1, further comprising: Obtaining the format of the database query statement; In a case where the format matches the format of the query grammar of the first database, determining that the database query statement is the first query statement; and In a case where the format matches the format of the query grammar of the second database, the database query statement is determined to be the second query statement. 3. The method according to claim 1, wherein, when the database query statement is the first query statement, querying the target data from the first database according to the first query statement comprises: According to the first query statement, obtaining a first query result in a first data format by querying the first database; determining a target data format for displaying the target data; converting the first query result into a target query result having the target data format; and The target data is displayed based on the target query result. 4. The method according to claim 3, wherein determining a target data format for presenting the target data comprises: determining a second data format, where the second data format corresponds to a second query result obtained by querying the second database according to the second query statement; and The second data format is used as the target data format. 5. The method according to claim 1, further comprising: Acquire basic data for providing a query basis for the target data, wherein the basic data has a third data format; When it is determined that the basic data needs to be stored in both the first database and the second database: Converting the basic data into a storage format suitable for the first database and storing the format in the first database; and The basic data is converted into a storage format suitable for the second database and stored in the second database. 6. The method according to claim 5, further comprising: When an exception occurs in a first stored procedure for storing the basic data in the first database, performing a rollback operation on the first stored procedure; and When an exception occurs in the second stored procedure for storing the basic data in the second database, a rollback operation is performed on the second stored procedure. 7. The method according to any one of claims 1 to 6, wherein the first database comprises a graph database, and the second database comprises a distributed search engine database. 8. A data query device, comprising: A first acquisition module, used to acquire initial query information for querying target data; A parsing module, used to parse the initial query information to obtain a database query statement suitable for querying the target data, wherein the database query statement is a first query statement or a second query statement; A first query module, configured to query the target data from a first database according to the first query statement when the database query statement is the first query statement; and A second query module, configured to query the target data from a second database according to the second query statement when the database query statement is a second query statement; Wherein, the initial query information includes text information; the parsing module includes: A word segmentation unit, used to perform word segmentation processing on the text information to obtain at least one keyword used to represent the text information; an acquisition unit, configured to acquire a target query sentence matching the at least one keyword; and A definition unit, configured to use the target query statement as the database query statement; The at least one keyword includes N keywords, where N is an integer greater than 1; and the acquiring unit includes: A first definition subunit is configured to, when a full match query statement matching all N keywords is obtained, use the full match query statement as the target query statement; The second definition subunit is used to, if a query statement matching all the N keywords is not obtained, loop the following operations with N-1 as the initial value of i until the target query statement is obtained: Select i target keywords from the N keywords; When a query statement matching all the i target keywords is obtained, the obtained query statement is used as the target query statement; and In the case that no query statement matching the i target keywords is obtained, i is set to i-1 to reselect the target keyword, wherein 1≤i<N. 9. The apparatus according to claim 8, further comprising: A second acquisition module, used to acquire the format of the database query statement; A first determination module, configured to determine that the database query statement is the first query statement if the format matches the format of the query grammar of the first database; and The second determination module is configured to determine that the database query statement is the second query statement if the format matches the format of the query grammar of the second database. 10. The apparatus according to claim 8, wherein the first query module comprises: A query unit, configured to obtain a first query result in a first data format by querying the first database according to the first query statement; a determining unit, configured to determine a target data format for displaying the target data; A conversion unit, configured to convert the first query result into a target query result having the target data format; and A display unit is used to display the target data based on the target query result. 11. The apparatus according to claim 10, wherein the determining unit comprises: a determining subunit, configured to determine a second data format, wherein the second data format corresponds to a second query result obtained by querying the second database according to the second query statement; and The third definition subunit is used to use the second data format as the target data format. 12. The apparatus according to claim 8, further comprising: A third acquisition module, used to acquire basic data for providing a query basis for the target data, wherein the basic data has a third data format; A storage module is used for, when it is determined that the basic data needs to be stored in both the first database and the second database: Converting the basic data into a storage format suitable for the first database and storing the format in the first database; and The basic data is converted into a storage format suitable for the second database and stored in the second database. 13. The apparatus according to claim 12, further comprising: A first rollback module is used to perform a rollback operation on the first stored procedure when an exception occurs in the first stored procedure for storing the basic data in the first database; and The second rollback module is used to perform a rollback operation on the second stored procedure when an exception occurs in the second stored procedure for storing the basic data in the second database. 14. An electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7. 15. A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 7. 16. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.