CN111767303A - A data query method, device, server and readable storage medium - Google Patents

Abstract

Embodiments of the present invention provide a data query method, device, server, and readable storage medium, wherein the method includes: acquiring a data query request submitted by a user through a query interface, where the data query request includes a query condition and a query condition related to the query condition. Relevant target fields, the target fields correspond to multiple single tables of the relational database; obtain the index of the full-text search engine corresponding to the multiple single tables, the index of the full-text search engine is generated according to the multiple single tables, the full-text search engine The index of the engine includes all the fields of the multiple single tables; the retrieval syntax of the full-text search engine is used to query the data matching the query conditions from the index of the full-text search engine, so that the full-text retrieval of the stored data can be supported, and the realization of Query data across multiple tables and improve the efficiency of data query.

Description

A data query method, device, server and readable storage medium

technical field

The present invention relates to the technical field of databases, and in particular, to a data query method, device, server and readable storage medium.

Background technique

At present, the implementation of single-table form engines based on relational databases is simple, but in daily applications, there are few scenarios in which data query can be completed only by using single-table forms. In most cases, the query for a certain data still relies on multi-table (cross-table) query. For example, the multi-table query now associates multiple tables to generate a view through the view view, but because the multi-table association result may be very large, This view is not suitable for full-text retrieval, and the efficiency of data query is relatively low.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a data query method, device, server, and readable storage medium, which can support full-text retrieval of stored data, implement data query across multiple tables, and improve data query efficiency.

A first aspect of the embodiments of the present invention provides a data query method, including:

obtaining a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database;

obtaining an index of a full-text search engine corresponding to the multiple single tables, where the index of the full-text search engine is generated according to the multiple single tables, and the index of the full-text search engine includes all fields of the multiple single tables;

Data matching the query condition is queried from the index of the full-text search engine by using the retrieval syntax of the full-text search engine.

A second aspect of the embodiments of the present invention provides a data query device, including:

an acquisition module, configured to acquire a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database;

The obtaining module is further configured to obtain the index of the full-text search engine corresponding to the multiple single tables, the index of the full-text search engine is generated according to the multiple single tables, and the index of the full-text search engine includes the index of the multiple single tables. all fields;

The processing module is configured to query the data matching the query condition from the index of the full-text search engine by using the retrieval syntax of the full-text search engine.

A third aspect of the embodiments of the present invention provides a server, including a processor, a network interface, and a storage device, wherein the processor, the network interface, and the storage device are connected to each other, wherein the network interface is subject to the processing The control of the processor is used to send and receive data, the storage device is used to store a computer program, the computer program includes program instructions, and the processor is configured to invoke the program instructions to perform the data query described in the first aspect above. method.

A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where program instructions are stored in the computer-readable storage medium, and when the program instructions are executed, are used to implement the data query method described in the first aspect.

A fifth aspect of the embodiments of the present invention provides a computer program product or computer program, where the computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the data query method described in the first aspect.

In the embodiment of the present invention, the server obtains a data query request submitted by a user through a query interface, the data query request includes a query condition and a target field related to the query condition, the target field corresponds to multiple single tables of a relational database, and obtains multiple The index of the full-text search engine corresponding to the single table, the index of the full-text search engine is generated according to the multiple single tables, the index of the full-text search engine includes all the fields of the multiple single tables, and then the full-text search engine is used. Querying data matching the query conditions in the index can support full-text retrieval of stored data, implement data query across multiple tables, and improve data query efficiency.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

1 is a schematic flowchart of a data query method provided by an embodiment of the present invention;

2 is a schematic flowchart of another data query method provided by an embodiment of the present invention;

3a is a form engine configuration interface provided by an embodiment of the present invention;

3b is an interface for configuring query conditions provided by an embodiment of the present invention;

3c is a schematic diagram of an editing style provided by an embodiment of the present invention;

FIG. 3d is a setting result provided by an embodiment of the present invention and a field displayed;

3e is a query interface provided by an embodiment of the present invention;

4 is a schematic diagram of generating a single table from multiple tables provided by an embodiment of the present invention;

5 is a schematic structural diagram of a data query apparatus provided by an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of a server provided by an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Cloud computing refers to the delivery and use mode of IT infrastructure, which refers to obtaining the required resources in an on-demand and easy-to-expand way through the network; cloud computing in a broad sense refers to the delivery and use mode of services, which refers to the on-demand, Get the services you need in an easily scalable way. Such services can be IT and software, Internet-related, or other services. Cloud computing is Grid Computing, Distributed Computing, Parallel Computing, Utility Computing, Network Storage Technologies, Virtualization, Load Balance It is the product of the integration of traditional computer and network technology development.

Cloud computing has grown rapidly with the development of the Internet, real-time data streaming, the diversity of connected devices, and the need for search services, social networking, mobile commerce, and open collaboration. Different from the parallel distributed computing in the past, the emergence of cloud computing will promote revolutionary changes in the entire Internet model and enterprise management model.

Cloud storage is a new concept extended and developed from the concept of cloud computing. Distributed cloud storage system (hereinafter referred to as storage system) refers to functions such as cluster application, grid technology and distributed storage file system. A storage system that integrates a large number of different types of storage devices (also called storage nodes) in the network through application software or application interfaces to work together to provide external data storage and service access functions.

At present, the storage method of the storage system is as follows: creating a logical volume, and when creating a logical volume, a physical storage space is allocated to each logical volume, and the physical storage space may be composed of a storage device or disks of several storage devices. The client stores data on a logical volume, that is, the data is stored on the file system. The file system divides the data into many parts, each part is an object, and the object contains not only data but also data identification (ID, IDentity), etc. For additional information, the file system writes each object into the physical storage space of the logical volume, and the file system records the storage location information of each object, so that when the client requests to access data, the file system can The storage location information allows clients to access the data.

The process of allocating physical storage space by the storage system to the logical volume, specifically: according to the capacity estimation of the objects stored in the logical volume (this estimation often has a large margin relative to the actual capacity of the objects to be stored) and independent redundant disks Array (RAID, Redundant Array of Independent Disk) group, which divides the physical storage space into stripes in advance, and a logical volume can be understood as a stripe, thereby allocating physical storage space for the logical volume.

Database, in short, can be regarded as an electronic filing cabinet—a place where electronic files are stored, and users can perform operations such as adding, querying, updating, and deleting data in the files. The so-called "database" is a collection of data that is stored together in a certain way, can be shared with multiple users, has as little redundancy as possible, and is independent of applications.

Database management system (English: Database Management System, referred to as DBMS) is a computer software system designed for database management, generally with basic functions such as storage, interception, security, backup and so on. The database management system can be classified according to the database model it supports, such as relational, XML (Extensible Markup Language), full-text search engine Elasticsearch; or according to the supported computer types, such as server clusters , mobile phone; or classify according to the query language used, such as SQL (Structured Query Language), XQuery; or classify according to the focus of performance impulse, such as maximum size, maximum operating speed; or other classifications Approach. Regardless of the taxonomy used, some DBMSs are able to cross-category, eg, support multiple query languages simultaneously.

At present, for multi-table (cross-table) data query, the view view is mainly used to associate multiple tables to generate a view, so as to realize the data query before the multi-table, which can realize the cross-table query, but the multi-table association result may be very large , the query efficiency may be very low, and it is not suitable for full-text retrieval.

Therefore, the embodiment of the present invention draws on the idea of the form engine of a single table, and turns multiple related single tables into a single table, so that the query of multiple tables becomes the same as the query of a single table, and no code development is required, which greatly improves the development efficiency. , and using the natural distributed architecture of Elasticsearch, it can achieve trillion-level record retrieval. At the same time, Elasticsearch is very friendly to support full-text retrieval, which can solve the problem of low query efficiency. Among them, Elasticsearch is a nosql database that provides a full-text search engine with distributed multi-user capabilities, and is implemented based on Lucene to provide a RESTful web interface. Developed in the Java language and released as open source under the terms of the Apache License, Elasticsearch is a popular enterprise-grade search engine. In cloud computing, real-time search can be achieved, which is stable, reliable, fast, and easy to install and use.

In a feasible embodiment, the server may acquire a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database, And obtain the index of the full-text search engine corresponding to the multiple single tables, the index of the full-text search engine is generated according to the multiple single tables, the index of the full-text search engine includes all the fields of the multiple single tables, and then uses the full-text search engine. Querying data matching the query conditions in the index of the search engine can support full-text retrieval of stored data, realize data query across multiple tables, and improve data query efficiency.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart of a data query method according to an embodiment of the present invention. The method can be executed by the server, and the data query method described in this embodiment includes the following steps:

101. Acquire a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database.

Wherein, the above target field includes at least two fields related to query conditions. The above query conditions may be gender, time, ID card, etc., and the query conditions may be associated with at least two fields. For example, suppose the data query request is to query which roles a certain user has, the data query request includes the query condition and the target field associated with the query condition, the query condition is user and role, and the target field associated with the query condition includes the role corresponding to the fields and the fields corresponding to the user.

The above-mentioned multiple single tables include an association relationship table between the single tables, and the association relationship table includes a primary and foreign key relationship of the single table, and the association relationship between the multiple tables can be determined according to the primary and foreign key relationship.

Specifically, the server obtains the data query data request submitted by the user through the query interface, and the server can determine that the at least two fields related to the query conditions correspond to at least two single tables in the relational database according to the at least two fields related to the query conditions. For example, in the above example, the target fields associated with the query condition include fields corresponding to roles and fields corresponding to users. According to the fields corresponding to roles and the fields corresponding to users, it can be determined that there is a user user table A, user_role user and role association table, role role table.

For example, according to the above example, the target fields associated with the query condition include fields corresponding to roles and fields corresponding to users. According to the fields corresponding to roles and the fields corresponding to users, three single tables (multiple tables) can be determined, which are respectively the user in Table 1. User table A, the user_role user and role association table in table 2, and the role role table in table 3, where the primary and foreign key relationships of the remaining two single tables are included in table 2, and a table can be established through the user_role user and role association table in table 2 The connection between table 1 and table 2 enables subsequent multi-table queries based on the corresponding primary and foreign key relationships.

Table 1

field type length illustrate user_id bigint 20 primary key user_name char 50 name create_time datetime 10 creation time channel tinyint 4 Registration Channel Source

Table 2

field type length illustrate role_id bigint 20 Primary key, foreign key role table user_id bigint 20 Primary key, foreign key user table

table 3

field type length illustrate role_id bigint 20 primary key role_name char 20 Role Name

It should be noted that at least two single tables are queried based on at least two fields related to the query conditions, not one-to-one correspondence. Tables associated with at least two fields related to the query conditions can also be queried, such as the above For example, 3 single tables are queried.

102. Obtain an index of a full-text search engine corresponding to the multiple single tables, where the index of the full-text search engine is generated according to the multiple single tables, and the index of the full-text search engine includes all fields of the multiple single tables.

The full-text search engine may be Elasticsearch. If the full-text search engine is Elasticsearch, the index of the full-text search engine is an Elasticsearch index, and the Elasticsearch index is a table similar to a relational database.

The index of the full-text search engine can be understood as a single table, and this single table includes all the fields in the above-mentioned multiple single tables.

Specifically, after obtaining the data query request submitted by the user through the query interface, the server first obtains multiple single tables corresponding to the target field according to the target field. Since the index of the full-text search engine is generated from the above-mentioned multiple single tables, the index of the full-text search engine includes All the fields of the above multiple single tables, therefore, according to the fields included in the foregoing multiple single tables, the index of the full-text search engine generated according to the foregoing multiple single tables can be obtained.

103. Use the retrieval syntax of the full-text search engine to query the index of the full-text search engine for data matching the query condition.

There is a fixed retrieval syntax for the full-text search engine, for example, the retrieval syntax of a general full-text search engine may be restapi or dsl.

In a feasible embodiment, the server may construct a retrieval sentence using the retrieval syntax and query conditions of the full-text search engine, and use the retrieval sentence to query the index of the full-text search engine for data matching the query conditions. Wherein, using the retrieval syntax and query conditions of the full-text search engine to construct the retrieval statement can realize the retrieval and transformation of the index of the full-text search engine, thereby replacing the structured query language SQL query method in the relational database.

For example, the retrieval statement of the structured query language SQL query in the relational database is:

And the retrieval statement in the full-text search engine (such as elasticsearch dsl syntax) is:

Through the retrieval transformation, it can be seen that the retrieval sentence using the full-text search engine is relatively simple.

In this embodiment of the present invention, the server may acquire a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database, and acquires Multiple single tables correspond to the full-text search engine index generated based on the multiple single tables, and then use the full-text search engine's retrieval syntax to query the full-text search engine's index for data that matches the query conditions, which can support full-text search of stored data and achieve cross-multiple table for data query, and improve the efficiency of data query.

Please refer to FIG. 2, which is a schematic flowchart of another data query method provided by an embodiment of the present invention. The method can be executed by the server, and the data query method described in this embodiment includes:

201. Acquire multiple fields among the fields included in the multiple single sheets, where the multiple fields are selected from the fields included in the multiple single sheets by an administrator through a form engine configuration interface.

Specifically, the multiple fields are formed by an administrator (such as a developer) selecting at least one field from each of the multiple single sheets through the form engine configuration interface in advance, and the server may obtain at least one field from the fields included in each of the aforementioned single sheets. Multiple fields for one field.

In a feasible embodiment, the form engine configuration interface may provide a form engine configuration interface for configuring a single form, the server responds to the form engine configuration request for the single form, and outputs the form engine configuration interface through the client, where the form engine configures The interface needs to select the data source, query conditions, etc. When selecting the data source, it is necessary to determine which fields can be used as the fields corresponding to the query conditions. In 3a, it can be seen that the fields sfzh, sbsj_date, and gender can be used as the fields corresponding to the query conditions. After completing the selection of the data source, the server outputs the interface for configuring the query conditions, as shown in Figure 3b, the query conditions can be ID card, application time, gender, etc., and determine the field sfzh corresponding to the ID card, the field gender corresponding to the gender, and the application The field sbsj_date corresponding to the time. For ID card, application time, gender, etc., the corresponding styles can be edited. The gender style editing interface is shown in Figure 3c; after configuring the query conditions, enter Figure 3d, and set the results and displayed fields, that is, the results and displayed fields can be edited. Set the display order; when the configuration is successful, the entire query interface can be displayed through the client. The query interface is shown in Figure 3e, where the date field can be retrieved and queried by date, and the gender can be generated in the form of a dictionary. Date, ID and gender for data query.

It should be noted that the above multiple single sheets are configured through the above single sheet process.

202. Acquire the multiple single tables from the relational database according to the multiple fields.

The multiple single tables include an association relationship table between the single tables, and the association relationship table includes primary and foreign key relationships of the single tables.

Specifically, multiple fields are matched with multiple single tables included in the relational database, and if multiple fields match with the fields included in the multiple single tables, multiple single tables are acquired from the relational database.

203. Perform field mapping processing on the multiple single tables by using a structured query language and a field mapping relationship, so as to generate an index of a full-text search engine.

The structured query language may be SQL.

Among them, because there are many relational databases and the type definitions are different, a field mapping relationship is set up to map the field types of each relational database with the field types in the full-text search engine, so as to facilitate subsequent form engine processing.

Specifically, the server uses the structured query language to process multiple single tables to obtain a result index of the full-text search engine. The field types included in the result index are the same as the field types in the multiple tables, and then the field types included in the result index are based on the full-text search engine. The field mapping relationship in the mapping process is performed to realize the unification of field types, so as to obtain the index of the full-text search engine.

For example, in FIG. 1 of the embodiment, the user user table A, the user_role user and role association table, and the role role table are mentioned. According to the structured query language SQL and the field mapping relationship, a single table (that is, a full-text search engine) as shown in FIG. 4 can be generated. index), you can see that the single table includes all the fields in the three single tables: user user table A, user_role user and role association table, and role role table. Among them, the figure 4 can be realized by the following sql:

select user.user_id,user.user_name,user.create_time,user.channel,

role.role_id,role.role_name

from user,user_role,role

where user.user_id=user_role.user_id and user_role.role_id=role.role_id

The fields of the result index are obtained according to the above sql, and the fields of the result index are uniformly mapped through the field mapping relationship, thereby constructing the index as shown in Figure 4.

It should be noted that if different tables have the same fields, you can use the table name + field as the field of the result index when naming to distinguish different single tables.

In a feasible embodiment, the server uses the structured query language and field mapping relationship to perform field mapping processing on multiple single tables, so as to generate an index for the full-text search engine, the server may determine the field type of each relational database and the full-text search engine. The field mapping relationship between the field types in the , and the field mapping relationship is stored in the full-text search engine, so that the field mapping relationship can be directly used in subsequent field mapping. For example, in Table 4, each database field type can be mapped to the type in the elasticsearch index, such as int, integer, etc. can be mapped to long, date, datetime, timestamp can be mapped to date and so on.

Table 4

In a feasible embodiment, the structured query language and the field mapping relationship are used to perform field mapping processing on multiple single tables to generate an index for a full-text search engine, and then the data included in each single table in the multiple single tables is imported into the full-text search through full synchronization. In the index of the engine, the specific implementation method is as follows: the server uses a structured query language to query the data included in each single table in the multiple single tables, and import the data included in each single table into the index of the full-text search engine. If each single table contains too much data, it can be imported into the index of the full-text search engine in batches, so that the data included in each single table of multiple single tables can be synchronized with the data in the index of the full-text search engine.

Further, when the data included in any single table in the multiple single tables changes, the changed data is updated to the index of the full-text search engine through incremental synchronization. The specific implementation method is: when any single table in the multiple single tables changes When the included data changes, the server updates the changed data to the index of the full-text search engine according to the field corresponding to the changed data of any single table. When the data included in any one of the multiple single tables changes, the server puts the changed data into the queue, and when the code responsible for updating the changed data is obtained, the corresponding fields and full text of the changed data are obtained. The field mapping relationship in the search engine determines the data corresponding to the index of the updated full-text search engine, that is, the index of the full-text search engine is found according to the field and field mapping relationship corresponding to the changed data, and then the changed data is updated to the full-text search engine. In the index of , for example, when the user user table A, the user_role user and role association table, and the role role table in the role role table are modified (that is, the data included in the role role table changes), according to the modified data in the role role table The corresponding field and the field mapping relationship in the full-text search engine are used to determine the data corresponding to the index of the updated full-text search engine.

204. Acquire a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database.

205. Obtain an index of a full-text search engine corresponding to the multiple single tables, where the index of the full-text search engine is generated according to the multiple single tables, and the index of the full-text search engine includes all fields of the multiple single tables.

206. Use the retrieval syntax of the full-text search engine to query the index of the full-text search engine for data matching the query condition.

For the specific implementation manner of steps 204 to 206, reference may be made to the relevant descriptions in 101 to 103 in the above data query method, which will not be repeated here.

In this embodiment of the present invention, the server acquires multiple fields among the fields included in the multiple single sheets, the multiple fields are selected by the administrator from the fields included in the multiple single sheets through the form engine configuration interface, and the multiple fields are selected from the fields included in the multiple single sheets according to the multiple fields. Obtain multiple single tables from a database, use structured query language and field mapping to perform field mapping processing on multiple single tables to generate an index for a full-text search engine, and obtain data query requests submitted by users through the query interface. The data query request includes query conditions and the target field related to the query condition, the target field corresponds to multiple single tables of the relational database, and obtains the index of the full-text search engine corresponding to the multiple single tables, the index of the full-text search engine is generated according to the multiple single tables, the full-text search engine The index includes all fields of multiple single tables, and uses the retrieval syntax of the full-text search engine to query the data matching the query conditions from the index of the full-text search engine to convert multiple tables into a single table, and indirectly implement multi-table (cross-table) query form engine to improve the efficiency of data query.

Please refer to FIG. 5 , which is a schematic structural diagram of a data query apparatus according to an embodiment of the present invention. The data query device described in this embodiment, which can be executed by a server, includes:

The obtaining module 501 is configured to obtain a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database;

The obtaining module 501 is further configured to obtain an index of a full-text search engine corresponding to the plurality of single tables, the index of the full-text search engine is generated according to the plurality of single tables, and the index of the full-text search engine includes the plurality of single tables all fields of ;

The processing module 502 is configured to query the data matching the query condition from the index of the full-text search engine by using the retrieval syntax of the full-text search engine.

Optionally, the obtaining module 501 is further configured to:

Acquiring multiple fields in the fields included in the multiple single sheets, the multiple fields are selected from the fields included in the multiple single sheets by the administrator through the form engine configuration interface;

Acquiring the plurality of single tables from the relational database according to the plurality of fields;

Field mapping processing is performed on the multiple single tables by using structured query language and field mapping relationship, so as to generate an index of a full-text search engine.

Optionally, the processing module 502 is further configured to:

Determining the field mapping relationship between the field types of each relational database and the field types in the full-text search engine;

The field mapping relationship is stored in the full-text search engine.

Optionally, the plurality of single tables include an association relationship table between the single tables, and the association relationship table includes primary and foreign key relationships of the single tables.

Optionally, after the processing module 502 performs field mapping processing on the plurality of single tables by using structured query language and field mapping relationships to generate an index of a full-text search engine, the processing module 502 is also used for:

Use the structured query language to query the data included in each single table in the plurality of single tables;

The data included in each single table is imported into the index of the full-text search engine.

Optionally, the processing module 502 is further configured to:

When the data included in any single table in the multiple single tables changes, the changed data is updated into the index of the full-text search engine according to the field corresponding to the changed data of the any single table.

Optionally, the processing module 502 is specifically configured to:

Use the retrieval syntax of the full-text search engine and the query conditions to construct a retrieval statement;

Data matching the query condition is queried from the index of the full-text search engine by using the retrieval sentence.

It can be understood that the functions of each functional module of the data query apparatus in this embodiment can be specifically implemented according to the methods in the above method embodiments, and the specific implementation process can refer to the relevant descriptions in FIG. 1 or FIG. 2 of the above method embodiments. It is not repeated here.

In this embodiment of the present invention, the obtaining module 501 may obtain a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database, And obtain the index of the full-text search engine generated according to the multiple single tables corresponding to the multiple single tables, and then the processing module 502 uses the retrieval syntax of the full-text search engine to query the index of the full-text search engine for data matching the query conditions, which can support full-text retrieval of stored data. , to implement data query across multiple tables and improve the efficiency of data query.

Referring to FIG. 6 , it is a schematic structural diagram of a server according to an embodiment of the present invention. The server described in this embodiment includes: a processor 601 , a network interface 602 and a memory 603 . The processor 601, the network interface 602, and the memory 603 may be connected through a bus or other means, and the embodiment of the present invention takes the connection through a bus as an example.

The processor 601 (or called a central processing unit (Central Processing Unit, CPU)) is the computing core and the control core of the server. Optionally, the network interface 602 may include a standard wired interface, a wireless interface (such as WI-FI, a mobile communication interface, etc.), and is controlled by the processor 601 to send and receive data. The memory 603 (Memory) is a memory device of the server for storing programs and data. It can be understood that the memory 603 here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory; optionally, it can also be at least one memory located far away from the aforementioned processor 601. storage device. The memory 603 provides storage space, which stores the operating system and executable program codes of the server, which may include but are not limited to: Windows system (an operating system), Linux (an operating system) system, etc. This is not limited.

In this embodiment of the present invention, the processor 601 performs the following operations by running the executable program code in the memory 603:

obtaining a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database;

obtaining an index of a full-text search engine corresponding to the multiple single tables, where the index of the full-text search engine is generated according to the multiple single tables, and the index of the full-text search engine includes all fields of the multiple single tables;

Data matching the query condition is queried from the index of the full-text search engine by using the retrieval syntax of the full-text search engine.

Optionally, before the processor 601 acquires the data query request submitted by the user through the query interface, it is further used for:

Acquiring multiple fields in the fields included in the multiple single sheets, the multiple fields are selected from the fields included in the multiple single sheets by the administrator through the form engine configuration interface;

Acquiring the plurality of single tables from the relational database according to the plurality of fields;

Field mapping processing is performed on the multiple single tables by using structured query language and field mapping relationship, so as to generate an index of a full-text search engine.

Optionally, before the processor 601 performs field mapping processing on the multiple single tables by using a structured query language and field mapping relationships, so as to generate an index of a full-text search engine, the processor 601 is further used for:

Determine the field mapping relationship between the field types of each relational database and the field types in the full-text search engine;

The field mapping relationship is stored in the full-text search engine.

Optionally, the plurality of single tables include an association relationship table between the single tables, and the association relationship table includes primary and foreign key relationships of the single tables.

Optionally, after the processor 601 performs field mapping processing on the multiple single tables by using structured query language and field mapping relationships to generate an index of a full-text search engine, the processor 601 is further used for:

Use the structured query language to query the data included in each single table in the plurality of single tables;

The data included in each single table is imported into the index of the full-text search engine.

Optionally, the processor 601 is further configured to:

When the data included in any single table in the multiple single tables changes, the changed data is updated into the index of the full-text search engine according to the field corresponding to the changed data of the any single table.

Optionally, the processor 601 is specifically configured to:

Use the retrieval syntax of the full-text search engine and the query conditions to construct a retrieval statement;

Data matching the query condition is queried from the index of the full-text search engine by using the retrieval sentence.

In this embodiment of the present invention, the server 601 may acquire a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database, and Obtaining multiple single tables corresponding to the indexes of the full-text search engine generated according to the multiple single tables, and then the server 601 uses the retrieval syntax of the full-text search engine to query the index of the full-text search engine for data matching the query conditions, which can support the full-text search of the stored data and realize Query data across multiple tables and improve the efficiency of data query.

In the specific implementation, the processor 601, the network interface 602, and the memory 603 described in the embodiment of the present invention can execute the implementation manner described in the process of a data query method provided by the embodiment of the present invention, and can also execute the implementation of the present invention. The implementation manner described in the data query apparatus provided by the example is not repeated here.

An embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the above-mentioned data query can be executed steps performed in the examples.

Embodiments of the present application also provide a computer program product or computer program, where the computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps performed in the above data query embodiment.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. During execution, the processes of the embodiments of the above-mentioned methods may be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM) or the like.

The above disclosure is only a preferred embodiment of the present invention, and of course, it cannot limit the scope of rights of the present invention. Those of ordinary skill in the art can understand that all or part of the process for realizing the above-mentioned embodiment can be realized according to the rights of the present invention. The equivalent changes required to be made still belong to the scope covered by the invention.

Claims (10)

1. a data query method, characterized in that the method comprises: obtaining a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database; obtaining an index of a full-text search engine corresponding to the multiple single tables, where the index of the full-text search engine is generated according to the multiple single tables, and the index of the full-text search engine includes all fields of the multiple single tables; Data matching the query condition is queried from the index of the full-text search engine by using the retrieval syntax of the full-text search engine. 2. The method according to claim 1, characterized in that, before obtaining the data query request submitted by the user through the query interface, the method further comprises: Acquiring multiple fields in the fields included in the multiple single sheets, the multiple fields are selected from the fields included in the multiple single sheets by the administrator through the form engine configuration interface; Acquiring the plurality of single tables from the relational database according to the plurality of fields; Field mapping processing is performed on the multiple single tables by using structured query language and field mapping relationship, so as to generate an index of a full-text search engine. 3. The method according to claim 2, characterized in that, before performing field mapping processing on the plurality of single tables by using structured query language and field mapping relationship to generate an index of a full-text search engine, the method further comprises: : Determine the field mapping relationship between the field types of each relational database and the field types in the full-text search engine; The field mapping relationship is stored in the full-text search engine. 4 . The method according to claim 2 or 3 , wherein the plurality of single tables comprises an association relationship table between the single tables, and the association relationship table includes primary and foreign key relationships of the single tables. 5 . 5. The method according to claim 2, characterized in that, after performing field mapping processing on the plurality of single tables by using structured query language and field mapping relationship to generate an index of a full-text search engine, the method further comprises: : Use the structured query language to query the data included in each single table in the plurality of single tables; The data included in each single table is imported into the index of the full-text search engine. 6. The method according to claim 5, wherein the method further comprises: When the data included in any single table in the multiple single tables changes, the changed data is updated into the index of the full-text search engine according to the field corresponding to the changed data of the any single table. 7. The method according to claim 1, wherein the querying data matching the query condition from the index of the full-text search engine by using the retrieval syntax of the full-text search engine comprises: Use the retrieval syntax of the full-text search engine and the query conditions to construct a retrieval statement; Data matching the query condition is queried from the index of the full-text search engine by using the retrieval sentence. 8. A data query device, wherein the device comprises: an acquisition module, configured to acquire a data query request submitted by a user through a query interface, where the data query request includes a query condition and a target field related to the query condition, and the target field corresponds to multiple single tables of a relational database; The obtaining module is further configured to obtain the index of the full-text search engine corresponding to the multiple single tables, the index of the full-text search engine is generated according to the multiple single tables, and the index of the full-text search engine includes the index of the multiple single tables. all fields; The processing module is configured to query the data matching the query condition from the index of the full-text search engine by using the retrieval syntax of the full-text search engine. 9. A server, comprising a processor, a network interface and a storage device, wherein the processor, the network interface and the storage device are connected to each other, wherein the network interface is controlled by the processor For sending and receiving data, the storage device is used for storing a computer program, the computer program includes program instructions, and the processor is configured to invoke the program instructions to execute the program according to any one of claims 1 to 7 data query method. 10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program comprising program instructions that, when executed by a processor, cause the processor to execute The data query method according to any one of claims 1-7.

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