CN114547034A

CN114547034A - Data query method, device, equipment and storage medium

Info

Publication number: CN114547034A
Application number: CN202210167239.2A
Authority: CN
Inventors: 谢新强; 葛东; 黄治纲; 纪勇
Original assignee: Neusoft Corp
Current assignee: Neusoft Corp
Priority date: 2022-02-23
Filing date: 2022-02-23
Publication date: 2022-05-27

Abstract

The application provides a data query method, a data query device, data query equipment and a storage medium. The method comprises the following steps: analyzing the current query characteristics of the service data in real time; selecting an adaptive index mode from the configured multiple index modes according to the current query characteristics; and executing the query operation of the service data by utilizing the adaptive index mode. The dynamic transformation of the index mode adopted when the data query is realized guarantees the adaptability between the data index and the query requirement, and avoids the problem of data query limitation when a single fixed index strategy is adopted, so that the flexibility of the data query is ensured, the expandability of the data query is increased, and the efficiency of the data query is improved.

Description

Data query method, device, equipment and storage medium

Technical Field

The embodiment of the application relates to the technical field of data processing, in particular to a method, a device, equipment and a storage medium for data query.

Background

With the continuous deepening of the application of big data technology, the real-time query and analysis facing multi-scenario data provides a brand-new challenge for high-speed and accurate query of data. The explosive growth of data scale brings huge challenges to the traditional relational database, and makes the traditional relational database encounter bottlenecks in the aspects of data query expansibility, fault tolerance and the like.

At present, when a corresponding data query operation is executed by an existing large-scale data management system, fast query based on a primary key is usually supported, and efficient multidimensional data query cannot be provided due to lack of mechanisms such as indexes and views. At this moment, a single fixed index strategy is usually adopted for the existing data query, and due to the fact that data storage modes in a multi-scene big data environment are complex and diverse, the single fixed index strategy has certain data query limitations, and the efficiency of the data query is greatly influenced.

Disclosure of Invention

The application provides a data query method, a data query device and a storage medium, which ensure the flexibility of data query, increase the expandability of data query and improve the efficiency of data query.

In a first aspect, an embodiment of the present application provides a method for querying data, where the method includes:

analyzing the current query characteristics of the service data in real time;

selecting an adaptive index mode from the configured multiple index modes according to the current query characteristics;

and executing the query operation of the service data by utilizing the adaptive index mode.

In a second aspect, an embodiment of the present application provides an apparatus for querying data, where the apparatus includes:

the characteristic analysis module is used for analyzing the current query characteristics of the service data in real time;

the mode adaptation module is used for selecting an adaptation index mode from the configured multi-index modes according to the current query characteristics;

and the data query module is used for executing query operation of the service data by utilizing the adaptive index mode.

In a third aspect, an embodiment of the present application provides an electronic device, including:

a processor and a memory, the memory being configured to store a computer program, the processor being configured to invoke and execute the computer program stored in the memory to perform the method of data querying as provided in the first aspect of the application.

In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium for storing a computer program, where the computer program causes a computer to execute the method for querying data as provided in the first aspect of the present application.

In a fifth aspect, embodiments of the present application provide a computer program product, comprising computer programs/instructions, wherein the computer programs/instructions, when executed by a processor, implement the method for data query as provided in the first aspect of the present application.

According to the method, the device, the equipment and the storage medium for data query, the adaptive index mode is selected from the configured multiple index modes by analyzing the current query characteristics of the service data in real time, and then the query operation of the service data is executed by using the adaptive index mode, so that the dynamic transformation of the index mode adopted during data query is realized, the adaptability between the data index and the query requirement is ensured, the problem of data query limitation existing when a single fixed index strategy is adopted is avoided, the flexibility of data query is ensured, the expandability of the data query is increased, and the high efficiency of the data query is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a flowchart illustrating a method for querying data according to an embodiment of the present application;

FIG. 2 is a schematic diagram of a system to which a data query method according to an embodiment of the present application is applied;

FIG. 3 is a flow chart illustrating another method for querying data according to an embodiment of the present application;

FIG. 4 is a schematic block diagram of an apparatus for querying data according to an embodiment of the present disclosure;

fig. 5 is a schematic block diagram of an electronic device provided in an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

For real-time query under multi-scenario service data, a preset single fixed index strategy is usually adopted in a service system to execute, and a certain data query limitation problem exists, so that a certain limitation is caused to high-speed accurate query of multi-scenario service data. Therefore, in consideration of data query characteristics for different service scenarios, there are various indexing modes, such as a multi-layer hierarchical indexing technique, a global distributed indexing technique, a data indexing based on a linear technique, and the like. In order to solve the technical problem, the application designs a method for dynamically selecting an index mode under multi-scenario data query, and dynamically selects a corresponding adaptive index mode from multiple configured index modes to execute corresponding data query operation when service data is queried each time, so that dynamic transformation of the index mode adopted during data query is realized, adaptability between a data index and query requirements is ensured, and flexibility and high efficiency of data query are ensured.

First, for data query in a multi-scenario service system, the present application may configure a plurality of existing index modes in the service system in advance, so as to support dynamic transformation selection of an index mode actually adopted during real-time query of service data.

Currently, the existing representative data indexing technologies mainly include: the multi-index mode in the present application is exemplified by the three data indexing techniques described above. It should be noted that the multi-indexing scheme in the present application includes, but is not limited to, the three indexing techniques, and the multi-indexing scheme configured in the business system in the present application may be any one of the existing indexing techniques.

1) Multi-level hierarchical indexing techniques

The index part of the multi-layer hierarchical index technology comprises a local index and a global index, and computing resources and storage resources are provided for users through a large number of computer clusters consisting of cheap computers. User data is divided into data blocks according to a certain rule and distributed to different computer nodes for storage according to the protocol of the distributed file system. In the two-tier indexing scheme, a local index is established for each computer node's data, and the local index is responsible for only the data on the local node. In addition to the local index, each compute node needs to share a portion of the memory space for storing the global index. The global index is composed of partial local indexes. Because of the limitation of storage space and the requirement of query efficiency, it is impossible to publish all local index nodes in the global index, and therefore, a part of the local index nodes needs to be selected according to a certain rule for publication. For the selected inodes, there may be different schemes for organizing them in the global index.

2) Global distributed indexing technique

In order to support large-scale data storage and ensure that a service system has higher throughput, a global distributed index technology of a data structure (namely a B-tree structure) of a fault-tolerant and highly-extensible distributed multi-path search tree is provided. The technical scheme has the general characteristics of the traditional B-tree, and also has some new characteristics: automatic load balancing, atomicity of operations, and dynamic addition or deletion of storage nodes. All data is organized in a B-tree structure, with nodes of the B-tree (including internal nodes and leaf nodes) stored scattered on different nodes. In order to achieve data consistency, a version table (version table) is introduced to record the latest version of the node. In order to improve the query efficiency, the internal nodes of the B-tree are cached at the client and updated in a lazy replica mode. This approach has two major disadvantages: firstly, the method has higher efficiency for simple point query, but has lower efficiency for complex range query and multidimensional query; secondly, the maintenance cost of the server side is high, and the client side needs to consume a large amount of memory space to cache the internal nodes of the B-tree.

3) Efficient data indexing technology based on linear technology

The efficient data indexing technology based on the linear technology is a specific indexing structure, such as a B-tree structure or an R-tree structure developed into a multi-dimensional space. When the service data in the service system is updated very frequently, the cost of index updating and maintenance is very high, so that an index scheme based on a linearization technology is provided for reducing the cost of index updating and maintenance and ensuring the performance of the system. The basic idea is as follows: dividing the whole space range into grids with equal size according to a certain rule, allocating a number to each grid, and generating a group of numbers with representative significance for the space target by using the numbers. The essence is that the entities in k-dimensional space are mapped to one-dimensional space in a certain way, so that the data can be organized by using the relatively mature one-dimensional indexing technology in the existing database management system.

From the above, the existing index modes have their own advantages and disadvantages. For example, the global distributed index technology can support large-scale service data storage and ensure that a service system has higher throughput, and the efficient data index technology based on the linear technology can reduce the maintenance cost of data index when service data is frequently updated.

At this time, in order to ensure the adaptability between the data index and the query requirement and improve the flexibility and the efficiency of data query, the present application explains in detail how to dynamically switch to the appropriate index mode to execute the actual data query operation during each data query.

Fig. 1 is a flowchart illustrating a method for querying data according to an embodiment of the present application. Referring to fig. 1, the method may specifically include the following steps:

and S110, analyzing the current query characteristics of the service data in real time.

The main purpose of the present application is to dynamically select a suitable index mode to perform an actual query operation each time a data query is performed in a business system, so as to improve flexibility, efficiency and accuracy of the data query. Meanwhile, the existing different indexing technologies (i.e., the index modes in the present application) have their own advantages and disadvantages, and are all related to the data change characteristics in the service system, for example, the global distributed index can support large-scale data storage and higher throughput, and the efficient data indexing technology based on the linear technology can reduce the index maintenance cost of the service data when the data is frequently updated. Therefore, in order to accurately select the currently most suitable index mode in each data query, when receiving each data query request, the service system first acquires each service data in the current time period and the historical time period, and then analyzes the current query characteristics of the service data in real time according to the query change condition of the service data.

It should be noted that, in order to match the advantages of each existing index pattern, the current query feature in the present application may include at least three types, i.e., data query update time, data throughput, and query response time, so as to subsequently determine which index pattern the current query feature specifically conforms to, thereby selecting the most suitable index pattern to perform the actual query operation.

And S120, selecting an adaptive index mode from the configured multiple index modes according to the current query characteristics.

In the application, in order to realize dynamic transformation of the index mode adopted during data query and ensure the adaptability between the data index and the query requirement, various existing index technologies can be configured in the service system in advance, so that the configured multi-index mode can be obtained, and the index mode which is most suitable for the query can be screened out from the multi-index mode in each subsequent data query.

Therefore, after analyzing the current query features of the business data, the query performance advantages of each index mode on the data query are analyzed in the configured multi-index mode. Then, the current query feature and the query performance advantage brought by each index mode to the data query are subjected to feature matching so as to judge whether the current query feature specifically meets the requirement of the query performance advantage of which index mode. And further, according to the coincidence condition of the query performance advantages of the current query features to each index mode, screening out the index mode which is most suitable for the query from the configured multi-index modes to serve as the adaptive index mode in the application.

It should be noted that, as shown in fig. 2, on the basis of the original data query scheme, the present application may configure a plurality of existing index modes in advance in the business system, for example, a multi-layer hierarchical index technology, a global distributed index technology, and an efficient data index technology based on a linear technology. Meanwhile, in order to realize dynamic transformation of the index mode adopted during data query, a functional module for intelligent identification of the index mode is correspondingly added in the service system. The function module is mainly used for accessing a service data source in a service system and analyzing the current query characteristics of the service data in real time so as to dynamically select an actual adaptive index mode.

And S130, performing query operation of the service data by using the adaptive index mode.

After the adaptive index mode is selected from the multiple index modes, the adaptive index mode is directly operated in the service system, so that the adaptive index mode is adopted to execute the actual query operation of the service data, the flexibility of data query is ensured by dynamically changing the actually adopted index mode under the query of the multiple scene data, and the efficiency of the query of the multiple scene data is improved.

In addition, in order to ensure fast query of data, as shown in fig. 2, after an adaptive index pattern is selected from configured multiple index patterns, the present application further performs routing update and load balancing configuration on the adaptive index pattern, and performs routing update, load balancing, and other operations on data retrieval and storage within a computing cluster for data query, so as to ensure query performance after data index pattern dynamic transformation.

According to the technical scheme provided by the embodiment of the application, the adaptive index mode is selected from the configured multi-index modes by analyzing the current query characteristics of the service data in real time, and then the query operation of the service data is executed by utilizing the adaptive index mode, so that the dynamic transformation of the index mode adopted during data query is realized, the adaptability between the data index and the query requirement is ensured, the problem of data query limitation existing during the adoption of a single fixed index strategy is avoided, the flexibility of data query is ensured, the expandability of the data query is increased, and the high efficiency of the data query is improved.

As an optional implementation scheme in the embodiment of the present application, in order to ensure flexible transformation of the adaptation index mode, the present application describes in detail a specific selection process and an actual query process of the adaptation index mode.

Fig. 3 is a flowchart illustrating another data query method according to an embodiment of the present application. As shown in fig. 3, the method may specifically include the following steps:

and S310, analyzing the current query characteristics of the service data in real time.

S320, determining the query index of each index mode in the configured multi-index mode.

Optionally, in order to ensure the accuracy of adapting the index patterns, the present application analyzes, for each index pattern in the configured multi-index patterns, a query performance advantage of each index pattern for the data query. Then, according to the query performance advantages of each index mode, a corresponding query index is set for each index mode, the query index is used for representing the query performance upper limit supported by the index mode, and when the performance characteristic of the current query execution in the business system is lower than the query index of a certain index mode, the index mode is required to be used for executing the corresponding query operation to improve the corresponding query performance advantages, so that the high efficiency of data query is ensured.

For example, for an index mode represented by a multi-layer hierarchical index technology, a query index can be set as an upper limit γ of a data query efficiency index in a service system; for the index mode represented by the global distributed index technology, the query index can be set as the upper limit beta of the data throughput in the service system; for the index mode represented by the efficient data index technology based on the linear technology, the query index can be set as the upper limit alpha of the data query update frequency in the service system.

S330, matching the current query characteristics with the query indexes of each index mode, and determining a corresponding adaptive index mode.

In the application, whether the current query characteristic is lower than the query index of each index mode is judged by sequentially matching the current query characteristic of the service data with the query index of each index mode, so that the query performance advantage of which index mode needs to be improved in a service system during the data query is analyzed, and the corresponding adaptive index mode is determined.

For example, if the index update frequency represented by the data index update time in the current query feature is greater than the preset upper limit α of the data query update frequency, it is described that the data index in the service system is frequently updated, which may affect the data index query efficiency, so that an efficient data index technology based on a linear technology may be automatically selected as the adaptive index mode in the present application, so as to reduce the index maintenance cost when the data is frequently updated. If the data throughput in the current query feature is greater than the preset data throughput upper limit β, it indicates that the throughput performance requirement of data query in the service system is high, and a corresponding global distributed index technology needs to be automatically selected as an adaptive index mode in the present application to perform data query, so as to ensure that the service system has high throughput. If the data query efficiency expressed according to the query response time in the current query feature is greater than the upper limit gamma of the known set data query efficiency index, the multi-layer hierarchical index is automatically selected to serve as the adaptive index mode in the application, so that the high efficiency of data query is improved.

The query index of each index mode in the application is a fixed experiment constant set according to the service requirement of a specific service scene.

It should be noted that, in consideration of the fact that the current query feature in the present application may be successfully matched with the query indexes of the multiple index patterns, at this time, an index pattern most suitable for the current query needs to be screened from the successfully matched index patterns. Therefore, the corresponding priority is set for the multi-index mode in advance according to the advantages and the defects of each index mode. Then, if the matching between the current query feature and the query index of at least two index patterns is successful, determining the corresponding adaptive index pattern according to the priority of each index pattern. That is to say, after the current query feature of the service data is successfully matched with the update indexes of the plurality of index patterns, the index pattern with the highest priority may be selected from the index patterns that are successfully matched, and used as the adaptive index pattern in the present application.

S340, switching the current index mode into the adaptive index mode, and operating the adaptive index mode to complete the query operation of the service data.

After the adaptive index mode is determined, the currently adopted index mode is directly switched to the adaptive index mode in the service system, and then the adaptive index mode is operated, so that the actual query operation of the service data is executed by utilizing the adaptive index mode.

Fig. 4 is a schematic block diagram of an apparatus for querying data according to an embodiment of the present disclosure. As shown in fig. 4, the apparatus 400 may include:

the feature analysis module 410 is used for analyzing the current query features of the service data in real time;

a mode adapting module 420, configured to select an adapted index mode from the configured multiple index modes according to the current query feature;

a data query module 430, configured to perform a query operation on the service data using the adaptive index mode.

Further, the mode adaptation module 420 may include:

the index determining unit is used for determining the query index of each index mode in the configured multi-index mode;

and the mode adapting unit is used for matching the current query characteristics with the query indexes of each index mode and determining the corresponding adaptive index mode.

Further, the mode adaptation unit may be specifically configured to:

and if the current query characteristics are successfully matched with the query indexes of at least two index modes, determining a corresponding adaptive index mode according to the priority of each index mode.

Further, the data query module 430 may be specifically configured to:

and switching the current index mode into the adaptive index mode, and operating the adaptive index mode to complete the query operation of the service data.

Further, the apparatus 400 for querying data may further include:

and the index configuration module is used for executing route updating and load balancing configuration on the adaptive index mode.

Further, the current query characteristics of the service data at least comprise data query update time, data throughput and query response time.

In the embodiment of the application, the adaptive index mode is selected from the configured multi-index modes by analyzing the current query characteristics of the service data in real time, and then the query operation of the service data is executed by using the adaptive index mode, so that the dynamic transformation of the index mode adopted during data query is realized, the adaptability between the data index and the query requirement is ensured, the problem of data query limitation existing when a single fixed index strategy is adopted is avoided, the flexibility of data query is ensured, the expandability of the data query is increased, and the high efficiency of the data query is improved.

It is to be understood that apparatus embodiments and method embodiments may correspond to one another and that similar descriptions may refer to method embodiments. To avoid repetition, further description is omitted here. Specifically, the apparatus 400 shown in fig. 4 may perform any method embodiment provided in the present application, and the foregoing and other operations and/or functions of each module in the apparatus 400 are respectively for implementing corresponding processes in each method of the embodiment of the present application, and are not described herein again for brevity.

The apparatus 400 of the embodiments of the present application is described above in connection with the figures from the perspective of functional modules. It should be understood that the functional modules may be implemented by hardware, by instructions in software, or by a combination of hardware and software modules. Specifically, the steps of the method embodiments in the present application may be implemented by integrated logic circuits of hardware in a processor and/or instructions in the form of software, and the steps of the method disclosed in conjunction with the embodiments in the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. Alternatively, the software modules may be located in random access memory, flash memory, read only memory, programmable read only memory, electrically erasable programmable memory, registers, and the like, as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps in the above method embodiments in combination with hardware thereof.

Fig. 5 is a schematic block diagram of an electronic device 500 provided in an embodiment of the present application.

As shown in fig. 5, the electronic device 500 may include:

a memory 510 and a processor 520, the memory 510 being configured to store a computer program and to transfer the program code to the processor 520. In other words, the processor 520 may call and run a computer program from the memory 510 to implement the method in the embodiment of the present application.

For example, the processor 520 may be configured to perform the above-described method embodiments according to instructions in the computer program.

In some embodiments of the present application, the processor 520 may include, but is not limited to:

general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like.

In some embodiments of the present application, the memory 510 includes, but is not limited to:

volatile memory and/or non-volatile memory. The non-volatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

In some embodiments of the present application, the computer program may be partitioned into one or more modules, which are stored in the memory 510 and executed by the processor 520 to perform the methods provided herein. The one or more modules may be a series of computer program instruction segments capable of performing certain functions, the instruction segments describing the execution of the computer program in the electronic device.

As shown in fig. 5, the electronic device may further include:

a transceiver 530, the transceiver 530 being connectable to the processor 520 or the memory 510.

The processor 520 may control the transceiver 530 to communicate with other devices, and in particular, may transmit information or data to the other devices or receive information or data transmitted by the other devices. The transceiver 530 may include a transmitter and a receiver. The transceiver 530 may further include one or more antennas.

It should be understood that the various components in the electronic device are connected by a bus system that includes a power bus, a control bus, and a status signal bus in addition to a data bus.

Embodiments of the present application also provide a computer storage medium having a computer program stored thereon, where the computer program, when executed by a computer, enables the computer to execute the method of the above method embodiments. In other words, the present application also provides a computer program product containing instructions, which when executed by a computer, cause the computer to execute the method of the above method embodiments.

When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions described in accordance with the embodiments of the present application occur, in whole or in part, when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a Digital Video Disk (DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.

Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the module is merely a logical division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.

Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. For example, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method of data querying, comprising:

analyzing the current query characteristics of the service data in real time;

2. The method of claim 1, wherein selecting an adapted index pattern from the configured multiple index patterns according to the current query feature comprises:

determining a query index of each index mode in the configured multi-index mode;

and matching the current query characteristics with the query indexes of each index mode to determine a corresponding adaptive index mode.

3. The method of claim 2, wherein the matching the current query feature and the query indicator of each index pattern to determine a corresponding adapted index pattern comprises:

4. The method of claim 1, wherein the performing the query operation of the traffic data using the adaptive index mode comprises:

5. The method of claim 1, further comprising, after selecting the adapted index mode from the configured multiple index modes:

and performing routing update and load balancing configuration on the adaptive index mode.

6. The method of claim 1, wherein the current query characteristics of the traffic data comprise at least a data query update time, a data throughput, and a query response time.

7. An apparatus for querying data, comprising:

8. An electronic device, comprising:

a processor and a memory, the memory for storing a computer program, the processor for invoking and executing the computer program stored in the memory to perform the method of data querying of any one of claims 1-6.

9. A computer-readable storage medium for storing a computer program which causes a computer to execute the method of data query according to any one of claims 1-6.

10. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the method of data query according to any of claims 1-6.