CN115033575A - A data query method, device, equipment and storage medium - Google Patents

Abstract

The present application relates to the field of computer technology, and discloses a data query method, device, device and storage medium, including: acquiring a plurality of business data tables with associated relationships from a database of a business system, and storing a plurality of the business data tables Store in the data warehouse; determine the target field from the fields corresponding to a plurality of the business data tables stored in the data warehouse according to the query requirements, and construct the target field and corresponding field data in the database of the statistical system The target width table; wherein, the target field contains at least a field corresponding to the query data; a query instruction is obtained and the target width table is queried according to the query instruction to obtain the query data corresponding to the query instruction. The present application can avoid the problem of query logic involving multiple data tables at the same time, resulting in high query complexity and query timeout, simplifying query logic and improving data query efficiency.

Description

A data query method, device, equipment and storage medium

technical field

The present invention relates to the field of computer technology, and in particular, to a data query method, apparatus, device and storage medium.

Background technique

At present, the data query of the revenue kanban is directly based on the data warehouse. The front-end and the back-end are connected through the Data API, and the API is used to provide data services. The specific process is shown in Figure 1. Write the query statement corresponding to the query item in the Data API. Then use the form of interface calls to obtain the desired data from the data warehouse and provide it to the foreground. When a user searches on a page, the front-end provides the query parameters when calling the API, and then through the back-end processing, the query parameters are added to the Data API query statement to obtain the desired query results.

The above process has the following problems: 1) The query logic is complex, and the interface response timeout problem occurs. Often, a data query in the revenue kanban requires multiple tables to be associated with too many query conditions, and the query speed will be greatly reduced. The response time of the interface often exceeds 10 seconds, resulting in an extremely poor user experience;

2) The docking and maintenance costs are high. Due to the complex query logic, the query statements in the Data API are generally long, and the action is dozens of lines of query statements, and the code is relatively complex. Each iteration and optimization of revenue kanban needs to take over the development of the project and spend a lot of time to get familiar with how to use it. As an internal operation of the company, when they find that the revenue counted on the revenue board is inconsistent with their estimated cumulative revenue, they expect to check which one is not correct. Operations generally do not know much about Data API tools. If the query statement is too complex, it will also cost the operation a certain learning cost and the cost of communication with the testers and developers, which is time-consuming and labor-intensive. In addition, the query statement is stored in the Data API development tool, which requires manual maintenance on a regular basis. Once someone uses it, the query result will be affected; 3) The system stability is insufficient, and it is difficult to troubleshoot problems. Based on the current status of the business promotion of each business division, the revenue of each business, and the lack of system stability during peak trading periods, abnormal revenue kanban data may occur. Problems are not easy to troubleshoot, and it takes a lot of time to check the data.

Therefore, the above technical problems are urgently to be solved by those skilled in the art.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a data query method, device, device and storage medium, which can avoid the problem that query logic involves multiple data tables at the same time, which makes the query complex to generate query timeout, and simplifies the query logic and further Improve data query efficiency. Its specific plan is as follows:

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

Obtain a plurality of business data tables with associated relationships from the database of the business system, and store the plurality of business data tables in a data warehouse;

According to the query requirement, the target field is determined from the fields corresponding to the plurality of the business data tables stored in the data warehouse, and a target wide table containing the target field and the corresponding field data is constructed in the database of the statistical system; wherein , the target field contains at least the field corresponding to the query data;

Obtain a query instruction and query the target wide table according to the query instruction to obtain the query data corresponding to the query instruction.

Optionally, after obtaining a plurality of business data tables with associated relationships from the database of the business system, the method further includes:

The data in the plurality of business data tables are sequentially extracted, cleaned, and converted, so as to store the finally processed plurality of the business data tables in the data warehouse.

Optionally, obtaining a plurality of business data tables with associated relationships from the database of the business system, and storing the plurality of business data tables in a data warehouse, including:

A data synchronization tool is used to synchronize a plurality of the business data tables with associated relationships in the database of the business system to the data warehouse according to a first preset synchronization period.

Optionally, after the plurality of business data tables are stored in the data warehouse, the method further includes:

According to the configuration logic stored in the data table format, the data in the plurality of data tables is integrated and calculated to obtain the integrated field data;

Correspondingly, constructing a target wide table including the target field and corresponding field data in the database of the statistical system includes:

The target wide table containing the target field and the corresponding integrated field data is constructed in the database of the statistical system.

Optionally, constructing the target wide table including the target field and the corresponding integrated field data in the database of the statistical system includes:

constructing the target wide table containing the target field in a database of the statistical system;

The integrated field data corresponding to the target field in the data warehouse is updated in the target wide table by setting and running a timed task.

Optionally, after obtaining the query instruction, the method further includes:

Obtain the trigger account of the query instruction; wherein, the trigger account is the first department account currently logged in in the statistics system;

Determine the second department account corresponding to the trigger account in the business system, and query the field data related to the second department account in the target width table according to the query instruction, and obtain a the query data corresponding to the query instruction;

The query data corresponding to the query instruction is output to the manual interactive interface of the revenue kanban for display.

Optionally, the data query method further includes:

Build a mapping relationship between the first department account in the statistics system and the second department account in the business system, so as to determine that the trigger account is in the business system according to the mapping relationship The corresponding account of the second department.

A second aspect of the present application provides a data query device, comprising:

an acquisition storage module for acquiring a plurality of business data tables with associated relationships from the database of the business system, and storing a plurality of the business data tables in a data warehouse;

A determination building module is used to determine a target field from the fields corresponding to a plurality of the business data tables stored in the data warehouse according to query requirements, and construct a database containing the target field and corresponding field data in the statistical system The target wide table; wherein, the target field contains at least the field corresponding to the query data;

An acquisition query module is configured to acquire a query instruction and query the target wide table according to the query instruction to obtain the query data corresponding to the query instruction.

A third aspect of the present application provides an electronic device, which includes a processor and a memory; wherein the memory is used to store a computer program, and the computer program is loaded and executed by the processor to implement the aforementioned data query method.

A fourth aspect of the present application provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are loaded and executed by a processor, the aforementioned data query method is implemented .

In this application, a plurality of business data tables with associated relationships are first obtained from the database of the business system, and a plurality of the business data tables are stored in a data warehouse; A target field is determined from the fields corresponding to the business data table, and a target wide table containing the target field and corresponding field data is constructed in the database of the statistical system; wherein, the target field at least contains the field corresponding to the query data and finally obtain a query instruction and query the target wide table according to the query instruction to obtain the query data corresponding to the query instruction. It can be seen that in this application, multiple business data tables in the business system database are uniformly stored in the data warehouse. Due to the association between multiple business data tables, when querying, the query logic may involve multiple data tables at the same time, and the query logic more complicated. To this end, based on the fields of multiple business data tables in the data warehouse, extract fields as needed, and use the extracted fields and field data to build a new wide table in the statistical system. The logic only involves the newly constructed wide table, which can avoid the problem that the query logic involves multiple data tables at the same time, which makes the query complex and causes query timeout, and simplifies the query logic and improves the 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 It is an embodiment of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the provided drawings without creative work.

1 is a schematic diagram of a method for performing data query through API provided by the present application;

2 is a flowchart of a data query method provided by the application;

3 is a schematic diagram of a specific data query method provided by the application;

4 is a schematic diagram of a method for querying the paid amount of a statistical commission order provided by the present application;

5 is a schematic structural diagram of a data query device provided by the present application;

FIG. 6 is a structural diagram of a data query electronic device provided by the present application.

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.

In the prior art, the data API is used to connect the front-end and the back-end, and the solution of using the API to provide data services has complex query logic, interface response timeout problems, high connection and maintenance costs, insufficient system stability, and difficulty in troubleshooting. technical problem. In view of the above technical defects, the present application provides a data query solution, which can avoid the problem of query logic involving multiple data tables at the same time, resulting in high query complexity and query timeout, simplifying query logic and improving data query efficiency.

FIG. 2 is a flowchart of a data query method provided by an embodiment of the present application. Referring to Figure 2, the data query method includes:

S11: Acquire a plurality of business data tables with associated relationships from the database of the business system, and store the plurality of business data tables in a data warehouse.

In this embodiment, the data of each business constitutes the data source of the business system, that is, the database of the business system. The user transaction is in each business system, and the transaction data generated during the transaction is stored in the database of the respective business system. It constitutes the data source of the business system. First, a plurality of business data tables with associated relationships are obtained from the database of the business system, and the plurality of business data tables are stored in a data warehouse. Understandably, a Data Warehouse (DW or DWH) can be abbreviated as a strategic collection that provides all types of data support for decision-making processes at all levels of an enterprise. It is a single data store created for analytical reporting and decision support purposes. Provides guidance on business process improvement, monitoring time, cost, quality and control for businesses that require business intelligence.

Further, a data synchronization tool may be used to synchronize a plurality of the business data tables with associated relationships in the database of the business system to the data warehouse according to a first preset synchronization period. The data synchronization tool can trigger the synchronization of data to the data warehouse through timed tasks, and the query is in the form of "T+1" days, that is, only data up to yesterday can be found today. Before synchronization, that is, after obtaining a plurality of business data tables with associated relationships from the database of the business system, it is necessary to sequentially extract, clean and transform the data in the plurality of business data tables, so that the final The processed multiple business data tables are stored in the data warehouse. For example, multiple business data tables (business order data, configuration data of the charging system, etc.) are: table1, table2, table3, table4. The above tables are extracted, cleaned and transformed by the data synchronization tool and loaded into the data warehouse and stored. Specifically as shown in Figure 3.

S12: Determine a target field from the fields corresponding to a plurality of the business data tables stored in the data warehouse according to the query requirement, and construct a target wide table including the target field and corresponding field data in the database of the statistical system ; wherein, the target field at least includes a field corresponding to the query data.

In this embodiment, after a plurality of business data tables are stored in the data warehouse, a target field is determined from the fields corresponding to the plurality of business data tables stored in the data warehouse according to query requirements. The target field at least includes a field corresponding to the query data. Then, a target wide table containing the target field and corresponding field data is constructed in the database of the statistical system. A wide table is literally a database table with many fields. It usually refers to a data table that is related to indicators, attributes, and maintenance related to business topics. It reduces table association by placing related fields in multiple tables in the same table, which can improve the efficiency of data calculation. In short, it is the exchange of space for time, which is easy to iterate and reduce the number of table associations. When modifying a small amount of data, you do not need to change multiple tables.

In this embodiment, the configuration logic is stored in the data warehouse, and the data in the plurality of data tables is further integrated and calculated according to the configuration logic stored in the data table format to obtain the integrated field data. The newly created target wide table is mainly used to store the integrated statistical fields, put all the fields used for statistics in the same table, and finally conduct data statistical query directly through the wide table, which reduces the number of table associations and improves the performance. Data query statistical efficiency. The target wide table containing the target field and the corresponding integrated field data is constructed in the database of the statistical system. Specifically, the target wide table containing the target field is first constructed in the database of the statistical system, and then the integrated data corresponding to the target field in the data warehouse is set and run by setting and running a scheduled task. The field data is updated in the target wide table.

For example, after the data warehouse tasks are synchronized every day, the field 1, field 2, field 3, and field 4 required for statistics are extracted from multiple business tables and permission tables such as table1, table2, table3, table4, etc., in the form of timed tasks. , Field 5 and other fields required for query form a temporary wide table table5. Which fields need to be used for data statistics, which fields are synchronized from each table to the temporary wide table table5 at 00:00 every day in the form of timed tasks, so that the wide table already has all the data we need to query. field. As for the index of the wide table, it can be created according to the actual business needs. In addition, the data may be updated every day, and the changed data update in the data warehouse is synchronized to the wide table table5 at 2:00 am every day through a scheduled task. Because table5 already contains all the fields required for statistical data, the data system query can directly filter all the conditions through the temporary wide table, and query all the required fields, which greatly reduces the number of table associations, greatly reducing the number of table associations. To a certain extent, the pressure on the database is reduced, the query speed is accelerated, and the query performance is improved.

S13: Acquire a query instruction and query the target width table according to the query instruction to obtain the query data corresponding to the query instruction.

In this embodiment, when a screening query is performed on a page, a query instruction is obtained, and the target wide table is queried according to the query instruction to obtain the query data corresponding to the query instruction. This data query process can be used in all statistical schemes, which can improve the efficiency of data query, is universal, and is conducive to secondary development. In most cases, statistical data will not be obtained from only one table. For complex logical queries, fields in multiple tables can be extracted and integrated into a new wide table in this way as needed. , reduce data association queries, simplify SQL query statements, and improve query efficiency. Generally speaking, data statistics will have some commonalities, such as statistics by time, statistics by region, department, etc., and secondary development statistics can also be carried out based on the statistical wide table extracted from this set of processes. By modifying a small amount of data in the wide table, there is no need to modify multiple tables, which can save time and effort during development.

In this embodiment, after the query results are obtained, the query results are displayed on the revenue kanban, and the revenue kanban uses the data visualization tool to collect various revenue data of the company in a more intuitive and visual way, which is also a kind of kanban. Management tools that can improve efficiency. For the sake of data security, the data of the revenue kanban is not disclosed to everyone, but with different permissions, the range of data that can be seen is different. Based on this, when performing a screening query on a page in this embodiment, the final desired statistical result needs to be filtered by account authority and other screening conditions. Specifically, the triggering account of the query instruction is obtained first; wherein, the triggering account is the first department account currently logged in in the statistics system. Then, determine the second department account corresponding to the trigger account in the business system, and query the field data related to the second department account in the target width table according to the query instruction, and obtain the query data corresponding to the query instruction. Therefore, it is necessary to construct a mapping relationship between the first department account in the statistics system and the second department account in the business system, so as to determine the trigger account in the business according to the mapping relationship The corresponding second department account in the system. Finally, the query data corresponding to the query instruction is output to the manual interactive interface of the revenue kanban for display.

Among them, the first department account can be the DingTalk department account (id), the second department account can be the business department account (id), each employee account has a department to which it belongs, and the DingTalk department id and the revenue in the bill The department id is used for mapping and filtering permissions. For example, an order has a corresponding bill, and there is a revenue department in the bill. Map the revenue department id in the bill through the DingTalk department id of the login account, and then filter the corresponding order through the bill under the revenue department, so as to achieve In order to obtain the desired order statistics by filtering the department permissions of the account. In this way, it is convenient to count the revenue data of each employee and the revenue data of each department. Taking the interface of the prior art for counting the amount paid for commission orders as an example, the response time of the interface before optimization often exceeds 10 seconds, the statistical data of the page is delayed, and users are often complained. When the concurrency is 5, the database CPU utilization reaches more than 90%. Apply a little pressure and the system collapses. After optimization, perform stress testing on this interface. The average response time of this interface can generally be controlled at about 1 second, the database pressure is also reduced a lot, and the database CPU utilization can be controlled below 60%. The optimal number of concurrency has been increased to 30, which can be said to be a significant improvement in interface performance.

It can be seen that the embodiment of the present application first obtains multiple business data tables with associated relationships from the database of the business system, and stores the multiple business data tables in the data warehouse; and then stores the multiple business data tables from the data warehouse according to query requirements. The target field is determined in the corresponding fields of a plurality of the business data tables, and a target wide table containing the target field and corresponding field data is constructed in the database of the statistical system; wherein, the target field at least contains the corresponding query data. Finally, a query instruction is obtained and the target wide table is queried according to the query instruction to obtain the query data corresponding to the query instruction. In this embodiment of the present application, multiple business data tables in the business system database are uniformly stored in the data warehouse. more complicated. To this end, based on the fields of multiple business data tables in the data warehouse, extract fields as needed, and use the extracted fields and field data to build a new wide table in the statistical system. The logic only involves the newly constructed wide table, which can avoid the problem that the query logic involves multiple data tables at the same time, which makes the query complex and causes query timeout, and simplifies the query logic and improves the data query efficiency.

The above process is described in detail by taking the query of the paid amount of the commission order as an example, as shown in Figure 4.

First, the business system generates transaction data based on transactions. These transaction-related data are stored in different databases, constituting the data source of the business system, and these data originally stored in the database are not processed by logical operations. Due to the special business scenarios and system performance factors, the logic of commission calculation is put into the big data platform for processing. This requires the table data to be synchronized to the data warehouse through timed tasks before operation. Then, synchronize multiple tables such as table1, table2, table3, table4, and table5 in the original database to the database of the data warehouse. Then some fields required for statistics are extracted from these tables, such as bill type, bill status, billing amount, total amount, billing strategy, payee (company), revenue department, and the division to which the bill belongs. Use the extracted fields to create a new temporary wide table data new table and synchronize the data updates in the data warehouse to the new table table through scheduled tasks every day. In this way, the data that originally needs to be related to multi-table query can be queried only through a single table, which greatly reduces the pressure on the database and speeds up the data query speed.

Often due to the company's data security considerations, the data on the revenue dashboard is not disclosed to everyone, but with different permissions and different scopes of data that can be seen. In addition to the filtering of account permissions, the data of the revenue kanban also has the filtering of business data permissions. The filtering of account permissions is realized by mapping between the DingTalk department and the revenue department of the bill. You can find the corresponding revenue department id through each DingTalk department id. The order exists in the bill, and there is a revenue department in the bill, so that the viewable order data can be filtered by the department permissions of the account. The page is queried by the filter conditions, the back-end undergoes logical operations and processing, and is returned in the interface to finally obtain the paid amount of the order, which is displayed on the front-end.

In the above process, the first aspect simplifies the query and reduces the cost of docking learning. The query statement in the original solution is stored on the DATA API development tool. The query statement on the DATA API development tool is different from the statement queried directly from the database, and the query SQL is associated with too many database tables, and the statement is too complicated. , development requires a certain learning cost. The new solution realizes the conversion from multi-table association query to single-table query, the SQL statement is more concise, the query efficiency is improved, it is easier for newbies to take over the business, and the cost of learning time is reduced. It has been proved by practice that it took at least 0.5 days for development and testing to be familiar with the original design before using the original solution, but after adopting the new solution, it took less than 0.5 days for development and testing to get started, saving money. Time costs.

The second aspect improves query efficiency, user experience and system performance. The original solution directly calls the DATAAPI to query from the data warehouse, which is subject to the data warehouse, and problems such as interface timeout and slow SQL often occur. The new solution is optimized in combination with the data warehouse. By preprocessing the data, the data of dozens of tables is unified and integrated into a temporary wide table, which realizes the conversion of multi-table association query to single-table query, which simplifies the Query SQL statements to improve query efficiency. Slow SQL and interface timeouts have been resolved, improving user experience and significantly improving system performance.

In the third aspect, the fault tolerance rate of the system is also improved, and the support for business expansion is smoother. Due to the continuous expansion of the business, if you want to add, delete or modify some content for the revenue kanban in the future, you only need to judge whether you need to add new fields in the temporary wide table according to the statistical fields, and then update the query SQL. The SQL for the new scheme is more minimalistic and easier to modify. And if the data in the data warehouse is lost for some reason, the temporary wide table will also be synchronized to the wide table through a scheduled task before the data is lost, so the entire Kanban board will not be unavailable. . At the same time, you can also add system monitoring alarms, and re-pull and calculate for lost and unsynchronized data.

The fourth aspect is to improve the versatility, reduce the cost of secondary development, and shorten the project delivery cycle. In most cases, statistics will not be available in just one table. For similar logically complex queries, fields in multiple tables can be extracted and integrated on demand into a new wide table in this way, thereby improving query efficiency. This method is suitable for data statistics queries of various businesses. In addition, based on the statistical wide table extracted by this set of processes, secondary statistics can be performed, such as statistics by time, statistics by region, department, etc., which is convenient for upward compatibility with different types of statistics. The original plan takes 1 day to develop and design the data statistics kanban, 5 days for development, and at least 3 days for data testing; the new plan can be modified on the basis of the original wide table, and only a small number of data fields need to be modified according to the needs of iteration No need to change multiple tables. The development design hardly needs to be changed, the development only takes 1 day, and the test only takes 2 days to complete. The original delivery took 9 days, but it only takes 3 days after optimization, which greatly reduces the time cost of secondary development and shortens the delivery cycle.

Referring to FIG. 5 , an embodiment of the present application also discloses a data query device, including:

an acquisition storage module 11, configured to acquire a plurality of business data tables with associated relationships from the database of the business system, and store the plurality of business data tables in a data warehouse;

Determine the construction module 12, which is used to determine the target field from the fields corresponding to the plurality of the business data tables stored in the data warehouse according to the query requirements, and construct the database containing the target field and the corresponding field in the statistical system A target wide table of data; wherein, the target field at least includes a field corresponding to the query data;

The acquiring query module 13 is configured to acquire a query instruction and query the target width table according to the query instruction to obtain the query data corresponding to the query instruction.

It can be seen that the embodiment of the present application first obtains multiple business data tables with associated relationships from the database of the business system, and stores the multiple business data tables in the data warehouse; and then stores the multiple business data tables from the data warehouse according to query requirements. The target field is determined in the corresponding fields of a plurality of the business data tables, and a target wide table containing the target field and corresponding field data is constructed in the database of the statistical system; wherein, the target field at least contains the corresponding query data. Finally, a query instruction is obtained and the target wide table is queried according to the query instruction to obtain the query data corresponding to the query instruction. In this embodiment of the present application, multiple business data tables in the business system database are uniformly stored in the data warehouse. more complicated. To this end, based on the fields of multiple business data tables in the data warehouse, extract fields as needed, and use the extracted fields and field data to build a new wide table in the statistical system. The logic only involves the newly constructed wide table, which can avoid the problem that the query logic involves multiple data tables at the same time, which makes the query complex and causes query timeout, and simplifies the query logic and improves the data query efficiency.

In some specific embodiments, the acquisition and storage module 11 is specifically configured to use a data synchronization tool to synchronize a plurality of the business data tables with associated relationships in the database of the business system to a first preset synchronization period. in the data warehouse.

In some specific embodiments, the data query apparatus further includes:

a processing module, configured to sequentially perform extraction processing, cleaning processing and conversion processing on the data in a plurality of the business data tables, so as to store the final processed plurality of the business data tables in the data warehouse;

an integration module, configured to perform integrated calculation on the data in a plurality of the data tables according to the configuration logic stored in the data table format, to obtain the field data after integration;

In some specific embodiments, the determining the building module 12 specifically includes:

a determining unit, configured to determine a target field from fields corresponding to a plurality of the business data tables stored in the data warehouse according to query requirements;

A construction unit, configured to construct the target wide table including the target field and the corresponding integrated field data in the database of the statistical system.

In some specific embodiments, the constructing unit is specifically configured to construct the target wide table including the target field in the database of the statistical system, and configure the data warehouse by setting and running a scheduled task. The integrated field data corresponding to the target field is updated in the target wide table.

In some specific embodiments, the data query apparatus further includes:

an account acquisition module, configured to acquire a trigger account of the query instruction; wherein, the trigger account is the first department account currently logged in in the statistics system;

an account number determination module, configured to determine the second department account corresponding to the trigger account in the business system, and perform the search for the field related to the second department account in the target width table according to the query instruction querying the data to obtain the query data corresponding to the query instruction;

an output module, configured to output the query data corresponding to the query instruction to the manual interactive interface of the revenue kanban for display;

A relationship building module for constructing a mapping relationship between the first department account in the statistical system and the second department account in the business system, so as to determine the trigger account according to the mapping relationship the corresponding second department account in the business system.

Further, the embodiments of the present application also provide an electronic device. FIG. 6 is a structural diagram of an electronic device 20 according to an exemplary embodiment, and the contents in the diagram should not be considered as any limitation on the scope of use of the present application.

FIG. 6 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21 , at least one memory 22 , a power supply 23 , a communication interface 24 , an input and output interface 25 and a communication bus 26 . Wherein, the memory 22 is used for storing a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the data query method disclosed in any of the foregoing embodiments.

In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows is applicable Any communication protocol in the technical solution of the present application is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, here No specific limitation is made.

In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc. The resources stored on it can include an operating system 221, a computer program 222, and data 223, etc., and the storage method can be short-term storage. or permanent storage.

The operating system 221 is used to manage and control each hardware device and computer program 222 on the electronic device 20, so as to realize the operation and processing of the massive data 223 in the memory 22 by the processor 21, which can be Windows Server, Netware, Unix, Linux etc. The computer program 222 may further include a computer program that can be used to complete other specific tasks in addition to the computer program that can be used to complete the data query method executed by the electronic device 20 disclosed in any of the foregoing embodiments. Data 223 may include data sheet data collected by electronic device 20 .

Further, an embodiment of the present application also discloses a storage medium, where a computer program is stored in the storage medium, and when the computer program is loaded and executed by a processor, the steps of the data query method disclosed in any of the foregoing embodiments are implemented.

The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments may be referred to each other. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method.

Finally, it should also be noted that in this document, relational terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply these entities or that there is any such actual relationship or sequence between operations. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

The data query method, device, device and storage medium provided by the present invention have been introduced in detail above. Specific examples are used in this paper to illustrate the principles and implementations of the present invention. The method of the invention and its core idea; at the same time, for those skilled in the art, according to the idea of the present invention, there will be changes in the specific implementation and application scope. In summary, the content of this description should not be understood to limit the present invention.

Claims (10)

1. A method for querying data, comprising:

acquiring a plurality of service data tables with incidence relations from a database of a service system, and storing the plurality of service data tables into a data warehouse;

determining target fields from fields corresponding to a plurality of business data tables stored in the data warehouse according to query requirements, and constructing a target width table containing the target fields and corresponding field data in a database of a statistical system; wherein, the target field at least comprises a field corresponding to the query data;

and acquiring a query instruction and querying the target wide table according to the query instruction to obtain the query data corresponding to the query instruction.

2. The data query method of claim 1, after obtaining a plurality of business data tables with association relationships from a database of a business system, further comprising:

and sequentially carrying out extraction processing, cleaning processing and conversion processing on the data in the plurality of business data tables so as to store the finally processed plurality of business data tables into the data warehouse.

3. The data query method of claim 1, wherein the obtaining a plurality of business data tables with association relationships from a database of a business system and storing the plurality of business data tables in a data warehouse comprises:

and synchronizing a plurality of business data tables with incidence relations in a database of the business system to the data warehouse according to a first preset synchronization period by using a data synchronization tool.

4. The data query method of claim 3, after storing the plurality of business data tables in a data warehouse, further comprising:

integrating and calculating data in the data tables according to configuration logic stored in a data table format to obtain integrated field data;

correspondingly, the constructing a target width table containing the target fields and the corresponding field data in the database of the statistical system includes:

and constructing the target wide table containing the target fields and the corresponding integrated field data in a database of the statistical system.

5. The method according to claim 4, wherein said constructing the target broad table containing the target fields and the corresponding integrated field data in the database of the statistical system comprises:

constructing the target width table containing the target field in a database of the statistical system;

and updating the integrated field data corresponding to the target field in the data warehouse in the target wide table in a mode of setting and running a timing task.

6. The data query method according to any one of claims 1 to 5, wherein after the obtaining the query instruction, the method further comprises:

acquiring a trigger account of the query instruction; the triggering account is a first department account which is logged in the statistical system at present;

determining a second department account corresponding to the trigger account in the service system, and inquiring the field data related to the second department account in the target broad list according to the inquiry instruction to obtain the inquiry data corresponding to the inquiry instruction;

and outputting the query data corresponding to the query instruction to a manual interaction interface of a revenue billboard for display.

7. The data query method of claim 6, further comprising:

and constructing a mapping relation between the first department account number in the statistical system and the second department account number in the business system so as to determine the second department account number corresponding to the trigger account number in the business system according to the mapping relation.

8. A data query apparatus, comprising:

the acquisition and storage module is used for acquiring a plurality of business data tables with incidence relations from a database of a business system and storing the business data tables into a data warehouse;

the determining and constructing module is used for determining a target field from fields corresponding to a plurality of business data tables stored in the data warehouse according to query requirements, and constructing a target width table containing the target field and corresponding field data in a database of a statistical system; wherein, the target field at least comprises a field corresponding to the query data;

and the acquisition query module is used for acquiring a query instruction and querying the target wide table according to the query instruction to obtain the query data corresponding to the query instruction.

9. An electronic device, comprising a processor and a memory; wherein the memory is for storing a computer program that is loaded and executed by the processor to implement the data query method of any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions which, when loaded and executed by a processor, implement the data query method of any one of claims 1 to 7.

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