Data calling method and device, computer equipment and computer readable storage medium

Abstract

The invention relates to the technical field of reports, in particular to a data calling method and device, computer equipment and a computer readable storage medium. The method comprises the steps of obtaining a data query instruction input by a demand side in a retrieval interface provided by a local database; if it is determined that the query objects pointed by the data query instruction are other databases, multi-dimensional database features of the other databases are obtained; wherein the multi-dimensional database features at least comprise query language dimension features and data type dimension features; under the condition that connection is established between the local database and other databases, target data corresponding to the data query instruction is called from other databases according to the conversion relation between the multi-dimensional database features of the other databases and the multi-dimensional database features of the local database; wherein the target data is data conforming to the data type dimension characteristics of the local database.

Description

Technical Field

[0001] The present application relates to the field of report technology, and in particular to a data retrieval method, device, computer equipment and computer-readable storage medium. Background Art

[0002] The reporting system, as a specialized software for reporting data processing, cleverly combines computer technology and reporting methods, aiming to achieve efficient production of statistical reports, accurate data presentation and convenient data filling functions.

[0003] At the beginning of system design, in order to reduce development difficulty and maintenance costs, most reporting systems tend to support one or several widely used data source types rather than pursuing comprehensive coverage of all types of data sources.

[0004] However, the diversity of data source types has led to significant differences in data structure, storage methods, and query statements. These differences have put forward higher requirements on the compatibility of the reporting system. Therefore, when facing a diverse data source environment, the compatibility of the existing reporting system still has a lot of room for improvement. Summary of the invention

[0005] Based on this, it is necessary to provide a data retrieval method, device, computer equipment and computer-readable storage medium that can achieve multi-source data access in response to the above technical problems.

[0006] In a first aspect, the present application provides a data retrieval method, the method comprising:

[0007] Obtaining the data query instruction entered by the demander in the search interface provided by the local database;

[0008] If it is determined that the query object pointed to by the data query instruction is another database, then the multi-dimensional database features of the other database are obtained; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features;

[0009] In the case where a connection has been established between the local database and other databases, according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database, the target data corresponding to the data query instruction is retrieved in the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database;

[0010] Output target data through the search interface.

[0011] In one embodiment, after determining that the query object pointed to by the data query instruction is another database, the method further includes:

[0012] Get the connection status between the local database and other databases;

[0013] When the connection status is not connected, obtain the connection parameters of other databases;

[0014] Establish a connection between the local database and other databases based on the connection parameters of the other databases.

[0015] In one embodiment, according to the conversion relationship between the multi-dimensional database features of other databases and the multi-dimensional database features of the local database, in the other database, the target data corresponding to the data query instruction is retrieved, including:

[0016] Extracting the local query statement carried in the data query instruction; wherein the local query statement is a statement that conforms to the query language dimension characteristics of the local database;

[0017] According to the conversion relationship between the query language dimension characteristics of other databases and the query language dimension characteristics of the local database, the local query statement is converted into other query statements; wherein the other query statement is a statement that conforms to the query language dimension characteristics of other databases;

[0018] Send other query statements to other databases;

[0019] Obtain other data retrieved from other databases according to other query statements; wherein the other data is data that conforms to the data type dimension characteristics of other databases;

[0020] According to the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database, other data is converted into target data.

[0021] In one embodiment, the multi-dimensional database feature further includes a transaction processing dimension feature; when the transaction processing dimension feature of other databases indicates support for transaction processing, the other data is data verified by other databases;

[0022] Accordingly, according to the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database, other data is converted into target data, including:

[0023] When the transaction processing dimension feature of the local database supports transaction processing, other acquired data is verified to obtain valid data that passes the verification among other data;

[0024] According to the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database, the valid data is converted into the target data.

[0025] In one of the embodiments, the multi-dimensional database feature also includes a partition strategy dimension feature;

[0026] After the other data is converted into the target data according to the conversion relationship between the data type dimension characteristics of the other database and the data type dimension characteristics of the local database, it includes:

[0027] If the partition strategy dimension characteristics of other databases indicate that the other databases have partition strategies, and the partition strategy dimension characteristics of the local database indicate that the local database does not have partition strategies, a data partition is established in the local database, and the target data is stored in the data partition.

[0028] In one embodiment, obtaining a data query instruction input by a demander in a search interface provided by a local database includes:

[0029] Displaying a search interface provided by the local database to the demander; wherein the search interface includes at least one query icon; the query icon is obtained by visualizing a standard query statement;

[0030] Determining a target image according to a selection operation of at least one query image by a demander;

[0031] According to the standard query statement corresponding to the target icon, a data query instruction input by the demander in the search interface provided by the local database is generated.

[0032] In a second aspect, the present application further provides a data retrieval device, the device comprising:

[0033] A query module is used to obtain the data query instruction entered by the demander in the search interface provided by the local database;

[0034] A database acquisition module, used for acquiring multi-dimensional database features of other databases if it is determined that the query object pointed to by the data query instruction is other databases; wherein the multi-dimensional database features at least include query language dimension features and data type dimension features;

[0035] A data retrieval module is used to retrieve target data corresponding to the data query instruction in the other database according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database when a connection has been established between the local database and the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database;

[0036] The data output module is used to output target data through the retrieval interface.

[0037] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0038] Obtaining the data query instruction entered by the demander in the search interface provided by the local database;

[0039] If it is determined that the query object pointed to by the data query instruction is another database, then the multi-dimensional database features of the other database are obtained; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features;

[0040] In the case where a connection has been established between the local database and other databases, according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database, the target data corresponding to the data query instruction is retrieved in the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database;

[0041] Output target data through the search interface.

[0042] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0043] Obtaining the data query instruction entered by the demander in the search interface provided by the local database;

[0044] If it is determined that the query object pointed to by the data query instruction is another database, then the multi-dimensional database features of the other database are obtained; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features;

[0045] In the case where a connection has been established between the local database and other databases, according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database, the target data corresponding to the data query instruction is retrieved in the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database;

[0046] Output target data through the search interface.

[0047] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0048] Obtaining the data query instruction entered by the demander in the search interface provided by the local database;

[0049] If it is determined that the query object pointed to by the data query instruction is another database, then the multi-dimensional database features of the other database are obtained; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features;

[0050] In the case where a connection has been established between the local database and other databases, according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database, the target data corresponding to the data query instruction is retrieved in the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database;

[0051] Output target data through the search interface.

[0052] The above-mentioned data retrieval method, device, computer equipment and computer-readable storage medium, the present application obtains the multi-dimensional database characteristics of other databases (including query language dimension characteristics and data type dimension characteristics, etc.), so that the reporting system can understand and parse the data structure and query language of different databases. This enables the reporting system to interact with a variety of databases that support different query languages ​​and data types, thereby improving the compatibility of the system. The present application allows the reporting system to establish a connection between the local database and other databases, and based on the multi-dimensional database feature conversion relationship between the two, retrieve the target data corresponding to the data query instruction in other databases. This means that the reporting system can achieve unified query and integration of data across different database platforms, providing a richer and more accurate data source for report production.

[0053] Furthermore, since the reporting system can understand and convert the data type dimension characteristics of different databases, it can automatically convert data types during the query process to ensure that the target data obtained meets the data type requirements of the local database. This avoids query errors caused by data type mismatches and improves the accuracy and efficiency of data queries. By directly entering data query instructions in the search interface provided by the local database and acquiring and displaying target data in other databases in real time, the reporting system provides users with a unified and concise query interface. This reduces the complexity of user operations and improves user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0055] FIG1 is an application environment diagram of a data retrieval method in one embodiment;

[0056] FIG2 is a schematic diagram of a flow chart of a data retrieval method in one embodiment;

[0057] FIG3 is a flow chart of the steps of establishing a connection between a local database and another database in one embodiment;

[0058] FIG4 is a schematic diagram of a flow chart of a step of retrieving target data corresponding to a data query instruction in one embodiment;

[0059] FIG5 is a schematic diagram of a flow chart of steps for converting other data into target data in one embodiment;

[0060] FIG6 is a flowchart of the steps of obtaining a data query instruction input by a demander in a search interface provided by a local database in one embodiment;

[0061] FIG7 is a structural block diagram of a data retrieval device in one embodiment;

[0062] FIG. 8 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0064] In an exemplary embodiment, as shown in FIG1 , the present application provides an intelligent reporting system, which includes a front-end function subsystem, a data service subsystem, and a back-end management subsystem;

[0065] Among them, the front-end functional subsystem includes custom query module, interactive dashboard, data modeling module, SQL editor module, data modeling module, question building module and operation definition module;

[0066] Among them, the data service subsystem includes a service interface API module, a query processing engine module and a task scheduling module;

[0067] Among them, the background management subsystem includes data access submodule, permission management submodule, performance management submodule, task and scheduling submodule, audit tracking submodule and log monitoring submodule.

[0068] For example, the data access submodule provides users with flexible and scalable multi-data source access and management functions. Its core technical functions include:

[0069] 11. Multiple data source support function: The intelligent reporting system can access multiple types of databases, not only supporting traditional relational databases (such as MySQL, PostgreSQL, Oracle, Gauss, PolarDB, DAMO, etc.), but also compatible with NoSQL databases (such as MongoDB), and can be seamlessly connected with data warehouses (such as BigQuery, Redshift). This feature is due to the multi-database adapter framework used by the intelligent reporting system, which ensures the efficiency of data query and the convenience of integration.

[0070] 12. Secure Sockets Layer SSL / Secure Sockets Layer TLS encryption support function: During the data access process, the intelligent reporting system fully supports SSL / TLS protocol configuration, providing a solid security guarantee for data transmission.

[0071] 13. Connection pool configuration function: Built-in database connection pool settings for the intelligent reporting system, allowing administrators to customize the number of connections and timeout parameters according to actual business needs. This function helps optimize the allocation of database resources, improve query efficiency, and effectively prevent resource blocking to ensure stable operation of the system.

[0072] 14. Database table and field level control function: Provides fine control functions at the database table and field level, including hiding specific fields, specifying field types and formats, etc. This provides a powerful preliminary preparation tool for data modeling and analysis, making the data more in line with analysis needs.

[0073] 15. Incremental synchronization and data caching function: Advanced data synchronization and caching mechanisms are built into the intelligent reporting system. Administrators can set the frequency of data synchronization and caching strategies as needed, thereby effectively reducing the burden on the production database and speeding up the response speed of data analysis. Through metadata scanning configuration, the system can periodically synchronize the metadata of the source database (including table structure, field information, enumeration values, and value distribution, etc.) to the system database, providing strong support for data query construction and efficient execution.

[0074] In order to realize the above-mentioned multiple data sources supporting this function, in an exemplary embodiment, as shown in FIG. 2 , this embodiment provides a data retrieval method, which is applied to the above-mentioned data access submodule, and the method includes:

[0075] S201, obtaining a data query instruction input by a demander in a search interface provided by a local database.

[0076] Among them, the search interface provided by the local database is the interface provided by the front-end function module, which should include necessary input boxes, drop-down menus, filter conditions, etc., so that users can easily enter and select query parameters.

[0077] Specifically, the demand side (such as data analysts, business users, etc.) enters query instructions through the search interface. These instructions may include the database table name, field name, filter conditions, sorting method, etc. to be queried. The interface can provide real-time feedback, such as displaying a preview of the query statement entered by the user, or dynamically updating the available query options based on the user's selection. The background will monitor user interaction events on the search interface, such as button clicks, changes in input box content, etc. When the user submits a query (such as clicking the "Query" button), the background will capture and parse all query parameters entered by the user on the interface. Based on the captured query parameters, the background will build the corresponding database query statement (such as an SQL statement).

[0078] S202: If it is determined that the query object pointed to by the data query instruction is other databases, then multi-dimensional database features of the other databases are obtained.

[0079] Among them, the multi-dimensional database features at least include query language dimension features and data type dimension features.

[0080] Optionally, determine whether the parsed database object belongs to the current local database. If not, it is determined that the query object points to other databases. Identify the query language used by other databases. Different databases may support different query languages ​​or SQL dialects (such as MySQL's SQL, Oracle's PL / SQL, etc.). Based on the identified query language, extract the corresponding language features, such as SQL syntax, functions, operators, etc. These features will be used to subsequently construct query statements suitable for the database.

[0081] You need to obtain the data type information of related fields in other databases. The data type may vary from database to database (for example, the integer type may be INT in MySQL and NUMBER in Oracle). You need to map the obtained data type information to local or universal data types so that you can correctly understand and process the data in subsequent processing.

[0082] After obtaining the multi-dimensional features of other databases, you can build query statements that are suitable for the database based on these features. This includes using the correct query language, data types, and specific functions or operators that may be required. Finally, execute the constructed query statements through the database connection and obtain the query results.

[0083] S203, when a connection has been established between the local database and other databases, according to the conversion relationship between the multi-dimensional database features of the other database and the multi-dimensional database features of the local database, the target data corresponding to the data query instruction is retrieved in the other database.

[0084] The target data is data that conforms to the data type dimension characteristics of the local database.

[0085] Optionally, first confirm whether the connection between the local database and other databases has been successfully established and is in an available state. If the connection is not established or an exception occurs, these errors should be captured and appropriate processing measures should be taken, such as retrying the connection, returning error information to the user, etc.

[0086] If the connection has been established, you need to analyze in detail the differences in data type dimension characteristics between the local database and other databases, including the representation, precision, and range of data types. Based on these differences, you need to establish a set of data conversion rules to convert data types in other databases into data types that the local database can understand and process. When constructing query statements for other databases, you need to apply the previously established data conversion rules to ensure that the query statements can correctly request data that conforms to the dimensional characteristics of the local database data type. Send the constructed query statements to other databases through the established database connection. After executing the query, other databases will return the result set to you. You need to receive these results and perform further processing as needed, such as data cleaning and format conversion.

[0087] Before returning the result set to the local database or user, the data type returned by other databases needs to be converted according to the previously established conversion rules to ensure that it matches the data type dimension characteristics of the local database. Before returning the target data, the data needs to be verified to ensure its correctness, completeness and consistency. Finally, the target data that conforms to the data type dimension characteristics of the local database is returned to the user or other parts of the local database for subsequent processing.

[0088] S204, outputting target data through a search interface.

[0089] Optionally, the generated display content is sent to the search interface, which is usually rendered directly on the user interface through a network request. After receiving the data, the search interface will update its display content to display the target data in a user-friendly manner. It is necessary to provide additional interactive functions on the search interface, such as data filtering, sorting, and exporting, to meet the different needs of users. It is necessary to monitor and process user interactions on the search interface, such as clicks and inputs, and perform corresponding processing based on user feedback.

[0090] The above-mentioned data access method, this application obtains the multi-dimensional database characteristics of other databases (including query language dimension characteristics and data type dimension characteristics, etc.), so that the reporting system can understand and parse the data structure and query language of different databases. This enables the reporting system to interact with multiple databases that support different query languages ​​and data types, thereby improving the compatibility of the system. This application allows the reporting system to establish a connection between the local database and other databases, and based on the multi-dimensional database feature conversion relationship between the two, retrieve the target data corresponding to the data query instruction in other databases. This means that the reporting system can achieve unified query and integration of data across different database platforms, providing a richer and more accurate data source for report production.

[0091] Furthermore, since the reporting system can understand and convert the data type dimension characteristics of different databases, it can automatically convert data types during the query process to ensure that the target data obtained meets the data type requirements of the local database. This avoids query errors caused by data type mismatches and improves the accuracy and efficiency of data queries. By directly entering data query instructions in the search interface provided by the local database and acquiring and displaying target data in other databases in real time, the reporting system provides users with a unified and concise query interface. This reduces the complexity of user operations and improves user experience.

[0092] In an exemplary embodiment, as shown in FIG3 , after determining that the query object pointed to by the data query instruction is another database, the method further includes:

[0093] S301, obtaining the connection status between the local database and other databases.

[0094] Specifically, query the connection status between the current local database and other databases. This can usually be achieved by querying the database connection pool, checking the network connection status, or querying a specific table. The connection status may include "connected", "disconnected", "connecting", etc. According to the queried connection status, determine whether a connection with other databases has been established.

[0095] S302: When the connection state is not connected, obtain connection parameters of other databases.

[0096] It is understandable that if the current connection status is judged to be "not connected", the step of obtaining connection parameters is entered. It is necessary to obtain the connection parameters of other databases from the configuration file, database management or user input. These parameters usually include the database address (such as IP address, port number), database name, user name, password, connection timeout, etc. The connection parameters may be stored in plain text or encrypted form, and corresponding decryption processing is required when obtaining them. After obtaining the connection parameters, these parameters may need to be verified to ensure their correctness and validity. For example, you can check whether the format of the IP address and port number is correct, whether the database name exists, etc.

[0097] S303: Establish a connection between the local database and other databases according to the connection parameters of other databases.

[0098] Optionally, a database connection request is constructed based on the obtained connection parameters. This usually includes creating a database connection object and setting the corresponding connection properties.

[0099] Use the constructed connection request to try to establish a connection with other databases. During this process, network requests may be sent to the servers of other databases and authentication and authorization checks may be performed.

[0100] After receiving the connection request, other database servers will respond. You need to process these responses to determine whether the connection is successfully established. If the connection is successful, a connection object or connection identifier will be returned; if the connection fails, you need to catch the exception and return an error message.

[0101] Once the connection is successfully established, it is necessary to add the connection to the connection pool so that it can be used for subsequent data query operations. At the same time, it is necessary to monitor the status of the connection to ensure the stability and security of the connection. During the entire connection process, it is necessary to handle various errors that may occur, such as network failure, identity authentication failure, connection timeout, etc. For these errors, it is necessary to give corresponding error prompts and take corresponding processing measures, such as retrying the connection and returning error information to the user.

[0102] In an exemplary embodiment, as shown in FIG4 , according to the conversion relationship between the multi-dimensional database features of other databases and the multi-dimensional database features of the local database, in other databases, the target data corresponding to the data query instruction is retrieved, including:

[0103] S401, extracting the local query statement carried in the data query instruction.

[0104] The local query statement is a statement that conforms to the query language dimensional characteristics of the local database.

[0105] It is understandable that the received data query instruction is parsed, which is usually a complex data structure containing query requests and parameters. From the parsed query instruction, a local query statement is extracted. This statement is written by the user based on the query language dimensional characteristics of the local database to express their query requirements.

[0106] S402: Convert the local query statement into other query statements according to the conversion relationship between the query language dimension features of other databases and the query language dimension features of the local database.

[0107] Among them, other query statements are statements that conform to the query language dimensional characteristics of other databases.

[0108] Optionally, understand the differences between the local database and other databases in terms of query language characteristics, including differences in syntax, functions, operators, etc. Based on the differences in query languages, establish a set of conversion rules to convert local query statements into query statements that conform to the query language characteristics of other databases. Use the established conversion rules to convert local query statements one by one or part by part into other query statements. This may include replacing specific functions, adjusting syntax structures, converting operators, etc.

[0109] S403: Send other query statements to other databases.

[0110] Optionally, if a connection has not been established, a connection to the database is established according to the connection parameters of the other database. Through the established database connection, other query statements are sent to the other database as query requests.

[0111] S404, obtaining other data retrieved from other databases according to other query statements.

[0112] Among them, other data is data that conforms to the data type dimension characteristics of other databases.

[0113] Optionally, wait for other databases to execute the query and return the results. This usually involves internal processing of the database, such as parsing the query statement, accessing the data store, performing calculations, etc. When other databases return the query results, receive these data. These data conform to the data type dimension characteristics of other databases.

[0114] S405: Convert other data into target data according to the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database.

[0115] Optionally, understand the differences between other databases and the local database in data type dimension characteristics, including data type representation, precision, range, etc. Based on the differences in data types, establish a set of data type conversion rules to convert other data into target data that conforms to the data type dimension characteristics of the local database. Use the established data type conversion rules to convert other data into target data one by one or field by field. This may include data type conversion, format adjustment, precision processing, etc. After the conversion is completed, verify the correctness of the target data to ensure that it conforms to the data type requirements of the local database and is consistent with the original query requirements.

[0116] In an exemplary embodiment, the multi-dimensional database feature also includes a transaction processing dimension feature. When the transaction processing dimension feature of other databases indicates support for transaction processing, the other data is data verified by other databases.

[0117] It is understandable that the transaction processing dimension characteristics of other databases are known to indicate support for transaction processing, which means that other databases will perform transaction management during data processing to ensure data consistency and integrity. The transaction processing dimension characteristics of the local database are also known and indicate support for transaction processing in this scenario.

[0118] Accordingly, as shown in FIG5 , according to the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database, other data is converted into target data, including:

[0119] S501, when the transaction processing dimension feature of the local database indicates that transaction processing is supported, verify the other acquired data to obtain valid data that passes the verification from the other data.

[0120] Optionally, firstly, data is obtained from other databases through the steps described above (such as S504). These data may be verified by the internal transaction processing mechanism of other databases.

[0121] It is necessary to determine the rules for verifying other data based on the business logic and transaction processing requirements of the local database. These rules may include checks on data integrity, consistency, validity, etc.

[0122] Use the determined validation rules to validate other data. This may involve checking each piece of data, logical verification between fields, checking the data format, etc. After the validation is completed, filter out the valid data that has passed the validation. These data meet the validation rules and are considered reliable and usable.

[0123] S502: Convert valid data into target data according to the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database.

[0124] Optionally, as before (the first step of S505), it is necessary to understand the differences in data type dimensional characteristics between other databases and the local database. Based on the data type differences, a set of data type conversion rules are established to convert valid data into target data that conforms to the data type dimensional characteristics of the local database. Use the established data type conversion rules to convert valid data. This may include data type mapping, format adjustment, precision conversion, etc. After the conversion is completed, the correctness of the target data needs to be verified. This includes checking whether the data conforms to the data type requirements of the local database and whether the integrity and consistency of the original valid data are maintained.

[0125] In an exemplary embodiment, the multi-dimensional database feature further includes a partition strategy dimensional feature.

[0126] It is understandable that the partition strategy dimension feature of other known databases indicates that the database has a partition strategy, that is, data is stored and managed in other databases according to a certain partition strategy (such as range partitioning, list partitioning, hash partitioning, etc.). The partition strategy dimension feature of the local database indicates that the database currently does not have a partition strategy, that is, data is uniformly stored in the local database and is not organized according to a specific partition strategy.

[0127] Accordingly, after converting other data into target data according to the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database, it includes: if the partition strategy dimension characteristics of other databases represent that there is a partition strategy in other databases, and the partition strategy dimension characteristics of the local database represent that there is no partition strategy in the local database, then a data partition is established in the local database, and the target data is stored in the data partition.

[0128] Optionally, you need to analyze the partitioning strategies of other databases to understand how data is partitioned and what data each partition contains. Based on business requirements and the characteristics of the local database, you need to determine a partitioning strategy that is suitable for the local database. This strategy should be able to optimize query performance, improve data management capabilities, and be as consistent as possible with the partitioning strategies of other databases (if possible).

[0129] According to the determined local partitioning strategy, create corresponding data partitions in the local database. This may involve steps such as setting partition keys, defining partition ranges, and configuring partition storage. Ensure that other data has been converted to target data based on the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database.

[0130] According to the partitioning strategy of the local database, the converted target data is allocated to the corresponding data partitions for storage. This may involve steps such as reorganization of data and creation of indexes to ensure efficient storage and access of data in the partitions. After storage is completed, it is necessary to verify whether the data is correctly stored in the corresponding partitions and whether the integrity and consistency of the data are maintained. This can be achieved by querying the local database and checking the returned results. The metadata of the local database may need to be updated to reflect the newly created partitions and stored data. This includes updating structures such as the data dictionary and partition table.

[0131] In an exemplary embodiment, as shown in FIG6 , obtaining a data query instruction input by a demander in a search interface provided by a local database includes:

[0132] S601, displaying the search interface provided by the local database to the demander.

[0133] The search interface includes at least one query icon, which is obtained by visualizing the standard query statement.

[0134] Optionally, the designer adds at least one query icon in the search interface. These icons are obtained by visualizing standard query statements, and are intended to represent complex query logic in a graphical way. Query icons can be simple graphic symbols, such as magnifying glasses, filters, date selectors, etc., or more complex combination icons to represent specific query conditions or operations. When the demander accesses the local database, the designed search interface, including all query icons, is displayed to the demander.

[0135] S602: Determine a target image according to a selection operation of at least one query image by a demander.

[0136] Optionally, monitor the operations of the demander on the search interface, especially their selection operations on the query icons. This can be achieved by mouse clicks, touch screen, keyboard shortcuts, etc. When the demander selects one or more query icons, identify these selection operations and determine that the selected icons are target icons.

[0137] S603: Generate a data query instruction input by the demander in the search interface provided by the local database according to the standard query statement corresponding to the target icon.

[0138] Optionally, maintain a mapping relationship between a query icon and a standard query statement. Each query icon corresponds to one or more predefined standard query statements, which express the query logic represented by the icon. According to the target icon selected by the demander, obtain the corresponding standard query statement from the mapping relationship. If the target icon is composed of multiple query icons, the query statements corresponding to these icons may be combined to form a more complex query logic. Convert the obtained standard query statement into a data query instruction. This instruction contains the specific conditions, operations, parameters and other information of the query, which can be parsed and executed by the local database.

[0139] In an exemplary embodiment, the above-mentioned custom query module is used to simplify the data query and analysis report generation process, without requiring users to master complex SQL programming skills. Its core functions include the following aspects:

[0140] 21. Intuitive data filtering function: Users can easily select the required data tables and fields, and use the intuitive drag-and-drop interface to set filtering conditions, such as specific time periods, geographic locations, etc., to accurately filter out data sets that meet the conditions.

[0141] 22. Flexible field aggregation and grouping operations: Provides easy-to-use tools that enable users to easily group, aggregate, sum, count, and process data in order to quickly generate various summary statistical information, such as monthly sales statistics, order volume by region, etc.

[0142] 23. Instant generation of reports and charts: The query builder has built-in multiple types of visualization charts, such as bar charts, line charts, pie charts, and geographic distribution charts. Users can intuitively select the display method according to actual needs, and preview the analysis results in real time to achieve instant synchronization of data and charts.

[0143] 24. Dynamic field calculation capability: Users can dynamically calculate selected fields and easily add custom fields such as profit margin and growth rate to further enrich the dimension and depth of data analysis.

[0144] 25. Efficient joint table query function: supports users to perform joint table query on multiple data tables through the association method of primary key-foreign key. This function greatly improves the comprehensiveness and depth of data analysis.

[0145] In the query construction process, the module obtains the metadata of the query object through the API interface, including key elements such as the table structure, field information, and field type available for query. These metadata provide users with detailed tips and guidance on data source selection, filter condition setting, grouping and aggregation field configuration, etc. The query request constructed by the user is saved in the database in the form of a query card entity through the data service API. Subsequently, the query editor passes the user's query instructions to the query processing engine through the data service API, which is responsible for executing the query and returning data results with visual configuration. Finally, the query editor converts these results into interactive charts and displays them to the user.

[0146] In an exemplary embodiment, the interactive dashboard module integrates a series of core functions to help users achieve real-time monitoring and in-depth exploration of data. The specific functions are as follows:

[0147] 31. Real-time data refresh mechanism: The dashboard has a built-in real-time data update function, and its visual components will automatically refresh at preset time intervals to ensure that users can view the latest data dynamics at any time. This feature is extremely practical for data indicators that need to be tracked frequently, such as sales trends, inventory levels, etc.

[0148] 32. Flexible global filtering system: Users can add global filters to the dashboard according to actual needs, such as time range, geographic location, data category, etc. These filters can realize the linkage filtering of multiple charts, help users dynamically adjust the view, and deeply explore the detailed information under different data dimensions.

[0149] 33. Rich visual interactive experience: The dashboard supports a variety of interactive operations. For example, users can directly click on a specific data point on the chart to further drill down to view detailed data or open the associated data table. This intuitive interactive method greatly improves the convenience and depth of data exploration.

[0150] 34. Convenient sharing and embedding functions: The dashboard provides the function of generating sharing links or embedding codes, which facilitates users to collaborate and share data in teams. At the same time, users can also set access permissions according to their needs to ensure the security and privacy of data during sharing.

[0151] 35. Personalized layout adjustment capability: Through the drag-and-drop function, users can freely adjust the layout of the dashboard, integrate related charts and indicators, and form a more intuitive and easy-to-read data display and analysis interface. This feature enables users to customize the display of data relationships according to their own analysis needs, thereby improving the efficiency and accuracy of data analysis.

[0152] In addition, the dashboard configuration information will be securely stored in the database in the form of dashboard entities through the data service API, providing strong support for users' subsequent use and data analysis.

[0153] In an exemplary embodiment, the above data modeling module provides a powerful platform for data engineers to simplify business data structure and significantly improve analysis efficiency. The core functions of this module cover the following aspects:

[0154] 41. Customized data tables and fields: Users can flexibly define the display name, detailed description and data format of tables and fields. This feature supports the conversion of technical field names into business language, making the meaning of data more intuitive and easy to understand, greatly improving users' data comprehension.

[0155] 42. Set column types precisely:

[0156] Setting clear types for fields (such as categories, numbers, dates, etc.) not only optimizes automatically generated filters and aggregation operations, but also significantly enhances the display effect of charts, making data analysis more accurate and efficient.

[0157] 43. Default aggregation settings: For numeric fields, users can preset default aggregation methods (such as sum, average, etc.). When building queries, these aggregation methods will be automatically applied, thereby simplifying the query process and improving analysis efficiency.

[0158] 44. Define association relationships: The module supports defining foreign key relationships between different data tables. This feature enables users to easily associate data across tables and build more complex analysis models, greatly enriching the dimensions of dashboards and query questions.

[0159] 45. Strict permission management: By setting model-level permissions, users can precisely control the access content of different users, thereby effectively ensuring the security and compliance of data and ensuring that the sensitivity and privacy of data are properly protected.

[0160] 46. ​​Integration of joint data sources: The data model of intelligent reports can integrate data from multiple data sources to form a unified view. This feature is particularly suitable for comprehensive investment management and risk control applications, making cross-system data analysis and monitoring more convenient and efficient.

[0161] During the data modeling process, the module will comprehensively manage the metadata of the relevant data models, including the display name of the field, semantic type, whether to store the model in physical view, and cache update strategy. These data models are securely stored in the database in the form of query card entities through the data service API. At the same time, the data modeling module also sends instructions to the data query processing engine through the query service API, and the detailed model data returned by the engine will be intuitively displayed to the user through this module.

[0162] In addition, the synchronization scheduling module will periodically start ETL (extract, transform, load) jobs based on the user's cache configuration to generate persistent data views. Queries built based on these data models can make full use of cached materialized views to further improve query performance. Through these powerful data modeling capabilities, raw data is transformed into business-friendly information sources, allowing users to more easily explore data and generate valuable insights.

[0163] In an exemplary embodiment, the SQL editor is a flexible query and analysis tool designed for users with SQL skills. Its core functions are comprehensive and powerful, including the following:

[0164] 51. SQL query writing and execution: The editor fully supports users to directly write and execute SQL queries, providing great freedom for users with deep technical backgrounds, enabling them to flexibly query data according to their own needs and build personalized analysis results.

[0165] 52. Real-time result preview function: In the SQL editor, users can preview the query results immediately after writing a query. This feature greatly improves the efficiency of query verification and adjustment, ensuring that users can quickly obtain accurate data feedback.

[0166] 53. Visual presentation of query results: The editor supports direct conversion of SQL query results into charts or visualizations, allowing users to easily create intuitive and easy-to-understand reports or graphical presentations to more effectively convey the value of data.

[0167] 54. Query saving and sharing mechanism: Users can save query results and share them within the organization. This function not only facilitates the reuse of query results, but also promotes team collaboration and knowledge accumulation, and improves the overall work efficiency of the team.

[0168] 55. Parameterized query capability: The editor allows users to use variables and parameters in SQL queries to generate dynamic queries. This feature gives users great flexibility, enabling them to generate different data views by adjusting parameters in the same query to meet diverse analysis needs.

[0169] 56. Dashboard embedding function: SQL query results can be seamlessly integrated into the dashboard, adding customized data views to the dashboard. This function not only enriches the display content of the dashboard, but also meets the user's pursuit of more complex analysis and display needs.

[0170] In summary, with its comprehensive and powerful functions, the SQL Editor provides users with SQL skills with an efficient and flexible query and analysis tool, helping users to deeply explore the value of data and improve decision-making efficiency.

[0171] In an exemplary embodiment, the above-mentioned permission management module provides enterprise-level users with powerful and flexible access control functions, aiming to ensure data security and convenient access. Its core functions cover the following two aspects:

[0172] 61. Role-based access control system: Smart reports use advanced role management, allowing administrators to assign preset roles to different users, such as administrator, analyst, viewer, etc., so as to accurately control their data access and editing permissions in the report. This role division mechanism effectively implements the isolation of permissions between different data sources and business areas, providing a solid guarantee for information security. Administrators can easily assign multi-dimensional permissions such as data, collections, and applications to role groups. Among them, data permissions include data viewing, query creation, data downloading, table metadata management, and database management; collection permissions cover viewing, editing, and access blocking. In addition, administrators can also manage download behaviors in detail according to actual needs, such as limiting the number of download items, table-level download control, and prohibiting downloads. It also supports permission plug-ins related to database and collection registration configuration to meet more complex permission management needs. All permission configurations are securely persisted to the database through service APIs to ensure the stability and reliability of permission settings.

[0173] 62. Hierarchical and detailed permission structure: The permission management module not only supports permission settings at the global level, but also further refines permissions to multiple levels such as databases, tables, and columns, providing administrators with more refined permission control methods. Administrators can set read, edit, hide and other permissions for specific data tables or fields according to specific business needs, thereby achieving fine-grained control over cross-departmental data collaboration. This hierarchical permission structure not only ensures data security, but also promotes efficient collaboration and data sharing between departments.

[0174] 63. Question and dashboard permission management: Administrators have the ability to accurately assign access rights to specific analytical questions and dashboards. This measure ensures that sensitive data is only available to authorized users, effectively maintaining data security. More flexibly, smart reports not only support efficient collaboration among internal users, but also allow these analytical views to be securely shared with external users. In this process, administrators can fully control the visibility of sharing and the degree of restriction on interactive operations, which not only ensures the wide dissemination of data, but also ensures the security of data.

[0175] 64. Audit log and monitoring: It will automatically and comprehensively record every user's access and operation of data to form a detailed audit log. Administrators can view these audit logs at any time to conduct strict security monitoring and compliance review. This function not only helps enterprises effectively track data usage, but also greatly improves the transparency of permission management, providing a strong guarantee for enterprise data security and compliance.

[0176] In summary, the permission management module provides enterprise-level users with a comprehensive, flexible and secure access control solution by building a role-based access control system and a hierarchical and detailed permission structure, effectively ensuring data security and convenient access.

[0177] In an exemplary embodiment, the performance management module provides administrators with comprehensive cache policy configuration capabilities to ensure efficient operation of the system and rapid response to data queries. The following is a detailed description of the core functions of the module:

[0178] Administrators can flexibly configure cache policies for each database and globally in the performance module. These policy options are rich and varied, including duration-based caching, schedule-based caching, adaptive caching, extension-based caching, and no caching, to meet performance requirements in different scenarios. When a database does not set a specific cache policy, the system will intelligently inherit the global cache configuration to ensure consistency and efficiency of data queries. It is worth noting that the data cache here does not cache the detailed data of the database, but focuses on caching the query results initiated against the database to maximize query efficiency. In addition, administrators can also fine-tune the persistence cycle of the model. Based on these configuration policies, the system will periodically start ETL (Extract, Transform, Load) jobs to save the model query results in materialized views, thereby further accelerating data query and presentation. To give users greater flexibility, users with editing permissions can also configure cache policies at the query card and dashboard levels. The system will obtain and apply these cache policies level by level to achieve more refined and efficient performance optimization effects.

[0179] All performance-related configurations are securely stored in the database through the service API to ensure the stability and reliability of the configuration. With these powerful features of the performance management module, administrators can easily cope with various performance challenges and provide strong guarantees for the smooth operation of the system.

[0180] In an exemplary embodiment, the above-mentioned log monitoring module provides system administrators with comprehensive user behavior monitoring functions. Administrators can easily view user query records, configuration changes, and management operations to ensure that all system activities are under control. In addition, the module also supports the generation of system usage analysis reports to help administrators gain in-depth understanding of system operation status and user behavior patterns. This information is obtained in real time from the audit log table through the API to ensure the accuracy and timeliness of the data.

[0181] In an exemplary embodiment, the above-mentioned log monitoring module provides system administrators with comprehensive user behavior monitoring functions. Administrators can easily view user query records, configuration changes, and management operations to ensure that all system activities are under control. In addition, the module also supports the generation of system usage analysis reports to help administrators gain in-depth understanding of system operation status and user behavior patterns. This information is obtained in real time from the audit log table through the API to ensure the accuracy and timeliness of the data.

[0182] In an exemplary embodiment, the functional modules of the above data service include the following core components:

[0183] Service API submodule: This module provides a series of RESTful services, including but not limited to cards, dashboards, collections, permissions, metadata, user management and other related functions. As a bridge for data interaction, it can not only efficiently read and operate data from the database, but also meet complex query requirements by calling the query engine to ensure accurate data transmission and processing.

[0184] Query processing engine submodule: The query processing engine is the core power of data services. It is responsible for converting the user's query instructions through a series of middleware processes such as pre-processing, compilation, execution and post-processing, and converting them into data results that are easy to display on the front end. This engine can not only accurately control and return data according to the system's preset permissions and cache configuration, but also has a high degree of flexibility, allowing middleware plug-ins to expand system functions and override default behaviors. Whether calling external interfaces or reading local extension modules, the query processing engine can easily cope with it and meet diverse query processing needs.

[0185] Task scheduling submodule: Based on the powerful Quarz framework, the task scheduling module can periodically synchronize key information such as data source metadata and field values ​​according to a predetermined schedule. At the same time, it can also periodically trigger ETL jobs to achieve cache persistence of data models, providing solid support for query construction, processing, and system performance optimization. The efficient operation of this module ensures the stability of data services and the timeliness of data processing.

[0186] Furthermore, based on the above intelligent reporting system, a data synchronization process is provided. The data synchronization process is a core link in the intelligent reporting system, which ensures that the system can obtain data from multiple data sources in real time and accurately, and effectively manage and analyze the data.

[0187] Specifically, the data synchronization process mainly includes the following steps: adding a database, obtaining metadata, processing and storing metadata, scanning after synchronization (including field scanning and data analysis, fingerprint recognition), and scheduling and periodic synchronization. The entire process is designed to ensure that the system can securely and efficiently connect to data sources, obtain and store metadata, and facilitate subsequent data analysis and query processing.

[0188] 71. Add a database

[0189] Information filling: The administrator needs to fill in the relevant information of the business database in the system, including database type, connection address, port number, user name, password, etc.

[0190] Connect and Synchronize: After filling in the database connection information, the administrator clicks Save. The system then uses JDBC (Java Database Connectivity) or the configured database driver to start connecting to the database through the DatabaseConnector component and starts the metadata synchronization process.

[0191] 72. Metadata Acquisition

[0192] First synchronization scan: When you connect to the database for the first time, the system will perform a full synchronization, that is, scan and obtain all metadata in the database.

[0193] Metadata extraction: The system retrieves the database metadata by calling the DatabaseMetaData interface. This information includes table name, column name, data type, primary key, foreign key relationship, etc.

[0194] Metadata processing and storage

[0195] Storing metadata: After the system extracts metadata, it will store it in the system's database, such as v_table (table details table) and v_field (field details table), for subsequent analysis and query use.

[0196] 73. Scan after synchronization is completed

[0197] Field scanning and data analysis: The system will perform an in-depth scan of the fields of each table in the database, identify the field type (such as text, numeric, date, etc.) and field value, and cache this information in the v_fieldvalue table. This step helps optimize query performance because the system can perform more efficient filtering and aggregation operations based on field type and value.

[0198] Fingerprint recognition: The system also performs fingerprint recognition, which is a deep scan of the first N rows of data (such as 10,000 rows) in the table or view in the database to obtain more accurate field information and data distribution. This helps the system better understand the data structure and optimize query processing strategies.

[0199] 74. Scheduling and periodic synchronization

[0200] Task scheduling: Based on the Quarz task scheduling framework, the system will periodically synchronize the scanned data of data source metadata and field values ​​according to the preset schedule. This ensures the timeliness and accuracy of the data.

[0201] Periodic ETL jobs: The system periodically initiates ETL (Extract, Transform, Load) jobs to persist the cache of the data model. This helps improve query performance because the system can read data directly from the cache without having to retrieve it from the data source every time.

[0202] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0203] Based on the same inventive concept, the embodiment of the present application also provides a data retrieval device for implementing the data retrieval method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more data retrieval device embodiments provided below can refer to the limitations on the data retrieval method above, and will not be repeated here.

[0204] In an exemplary embodiment, as shown in FIG7 , a data retrieval device is provided, the device comprising:

[0205] The query module 11 is used to obtain the data query instruction input by the demander in the search interface provided by the local database;

[0206] The database acquisition module 12 is used to acquire multi-dimensional database features of other databases if it is determined that the query object pointed to by the data query instruction is other databases; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features;

[0207] The data retrieval module 13 is used to retrieve the target data corresponding to the data query instruction in the other database according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database when the connection between the local database and the other database has been established; wherein the target data is data that conforms to the data type dimensional characteristics of the local database;

[0208] The data output module 14 is used to output target data through the search interface.

[0209] In one of the embodiments, the device includes a connection verification module for obtaining a connection status between the local database and other databases;

[0210] When the connection status is not connected, obtain the connection parameters of other databases;

[0211] Establish a connection between the local database and other databases based on the connection parameters of the other databases.

[0212] In one embodiment, the data retrieval module 11 is further used to: extract the local query statement carried in the data query instruction; wherein the local query statement is a statement that conforms to the query language dimension characteristics of the local database;

[0213] According to the conversion relationship between the query language dimension characteristics of other databases and the query language dimension characteristics of the local database, the local query statement is converted into other query statements; wherein the other query statement is a statement that conforms to the query language dimension characteristics of other databases;

[0214] Send other query statements to other databases;

[0215] Obtain other data retrieved from other databases according to other query statements; wherein the other data is data that conforms to the data type dimension characteristics of other databases;

[0216] According to the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database, other data is converted into target data.

[0217] In one embodiment, the multi-dimensional database feature further includes a transaction processing dimension feature; when the transaction processing dimension feature of other databases indicates support for transaction processing, the other data is data verified by other databases;

[0218] The data retrieval module 11 is used to verify other acquired data and obtain valid data that passes the verification from other data when the transaction processing dimension feature representation of the local database supports transaction processing;

[0219] According to the conversion relationship between the data type dimension characteristics of other databases and the data type dimension characteristics of the local database, the valid data is converted into the target data.

[0220] In one embodiment, the data output module 14 is further used to display a search interface provided by the local database to the demander; wherein the search interface includes at least one query icon; the query icon is obtained by visualizing the standard query statement;

[0221] Determining a target image according to a selection operation of at least one query image by a demander;

[0222] According to the standard query statement corresponding to the target icon, a data query instruction input by the demander in the search interface provided by the local database is generated.

[0223] Each module in the above data retrieval device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0224] In an exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG8. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a data retrieval method is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0225] Those skilled in the art will understand that the structure shown in FIG. 8 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0226] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0227] Obtaining the data query instruction entered by the demander in the search interface provided by the local database;

[0228] If it is determined that the query object pointed to by the data query instruction is another database, then the multi-dimensional database features of the other database are obtained; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features;

[0229] In the case where a connection has been established between the local database and other databases, according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database, the target data corresponding to the data query instruction is retrieved in the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database;

[0230] Output target data through the search interface.

[0231] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0232] Obtaining the data query instruction entered by the demander in the search interface provided by the local database;

[0233] If it is determined that the query object pointed to by the data query instruction is another database, then the multi-dimensional database features of the other database are obtained; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features;

[0234] In the case where a connection has been established between the local database and other databases, according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database, the target data corresponding to the data query instruction is retrieved in the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database;

[0235] Output target data through the search interface.

[0236] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0237] Obtaining the data query instruction entered by the demander in the search interface provided by the local database;

[0238] If it is determined that the query object pointed to by the data query instruction is another database, then the multi-dimensional database features of the other database are obtained; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features;

[0239] In the case where a connection has been established between the local database and other databases, according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database, the target data corresponding to the data query instruction is retrieved in the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database;

[0240] Output target data through the search interface.

[0241] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0242] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0243] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0244] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A data retrieval method, characterized in that: The method comprises: Obtaining the data query instruction entered by the demander in the search interface provided by the local database; If it is determined that the query object pointed to by the data query instruction is another database, then the multi-dimensional database features of the other database are obtained; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features; In the case where a connection has been established between the local database and the other database, according to the conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database, the target data corresponding to the data query instruction is retrieved in the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database; The target data is outputted through the search interface.

2. The method according to claim 1, characterized in that After determining that the query object pointed to by the data query instruction is another database, the method further includes: Acquire the connection status between the local database and the other databases; When the connection state is not connected, obtaining connection parameters of the other database; A connection is established between the local database and the other database according to the connection parameters of the other database.

3. The method according to claim 1, characterized in that The retrieving, in the other database, target data corresponding to the data query instruction according to the conversion relationship between the multi-dimensional database features of the other database and the multi-dimensional database features of the local database includes: Extracting a local query statement carried in the data query instruction; wherein the local query statement is a statement that conforms to the query language dimension characteristics of the local database; According to the conversion relationship between the query language dimensional features of the other database and the query language dimensional features of the local database, the local query statement is converted into other query statements; wherein the other query statement is a statement that conforms to the query language dimensional features of the other database; Sending the other query statements to the other databases; Acquire other data retrieved by the other database according to the other query statement; wherein the other data is data that conforms to the data type dimension characteristics of the other database; The other data is converted into target data according to the conversion relationship between the data type dimensional characteristics of the other database and the data type dimensional characteristics of the local database.

4. The method according to claim 3, characterized in that The multi-dimensional database feature also includes a transaction processing dimension feature; when the transaction processing dimension feature of the other database indicates support for transaction processing, the other data is data verified by the other database; Accordingly, the converting of the other data into target data according to the conversion relationship between the data type dimension feature of the other database and the data type dimension feature of the local database includes: In the case where the transaction processing dimension feature of the local database indicates that it supports transaction processing, verifying the other acquired data to obtain valid data that passes the verification among the other data; The valid data is converted into target data according to the conversion relationship between the data type dimension characteristics of the other database and the data type dimension characteristics of the local database.

5. The method according to claim 3, characterized in that: The multi-dimensional database features also include partition strategy dimensional features; After converting the other data into target data according to the conversion relationship between the data type dimension feature of the other database and the data type dimension feature of the local database, the method further includes: If the partition strategy dimension characteristics of the other databases indicate that the other databases have partition strategies, and the partition strategy dimension characteristics of the local database indicate that the local database does not have partition strategies, a data partition is established in the local database, and the target data is stored in the data partition.

6. The method according to any one of claims 1 to 5, characterized in that The step of obtaining the data query instruction input by the demander in the search interface provided by the local database includes: Displaying a search interface provided by the local database to the demander; wherein the search interface includes at least one query icon; the query icon is obtained by visualizing a standard query statement; Determining a target image according to a selection operation of the demander on the at least one query image; According to the standard query statement corresponding to the target icon, a data query instruction input by the demander in the search interface provided by the local database is generated.

7. A data retrieval device, characterized in that: The device comprises: A query module is used to obtain the data query instruction entered by the demander in the search interface provided by the local database; A database acquisition module, configured to acquire multi-dimensional database features of other databases if it is determined that the query object pointed to by the data query instruction is other databases; wherein the multi-dimensional database features at least include query language dimensional features and data type dimensional features; A data retrieval module, configured to retrieve, in the other database, target data corresponding to the data query instruction, according to a conversion relationship between the multi-dimensional database characteristics of the other database and the multi-dimensional database characteristics of the local database, when a connection has been established between the local database and the other database; wherein the target data is data that conforms to the data type dimensional characteristics of the local database; A data output module is used to output the target data through the search interface.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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