CN115292580A

CN115292580A - Data query method and device, computer equipment and storage medium

Info

Publication number: CN115292580A
Application number: CN202210996994.1A
Authority: CN
Inventors: 刘凡; 向旗
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2022-08-19
Filing date: 2022-08-19
Publication date: 2022-11-04

Abstract

The embodiment of the application belongs to the field of big data, and relates to a data query method, which comprises the following steps: receiving an index data query request; converting the index data query request into a query statement in a preset language; acquiring the query frequency of a query statement based on a preset query statement searcher, and judging whether the query frequency is smaller than a preset frequency threshold value; if the frequency is not less than the frequency threshold, redirecting the query statement to a preset index table, and judging whether a target index matched with the query statement exists in the index table or not; if the target index matched with the query statement exists, target index data corresponding to the target index are queried from the index table; and returning the target index data to the client of the user. The application also provides a data query device, computer equipment and a storage medium. In addition, the application also relates to a block chain technology, and the target index data can be stored in the block chain. The method and the device can improve the processing efficiency of index data query and reduce the resource consumption of repeated query operation.

Description

Data query method and device, computer equipment and storage medium

Technical Field

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

Background

The current big data service is mainly divided into three service links: data acquisition, data processing, data analysis and display. Each business department can generate data in the business activity, and the big data department collects the data of each department and processes the data according to business requirements and related business fields to meet the business requirements of the business departments, wherein a larger proportion of the business requirements is to track business indexes. The business indexes are often mature and fixed indexes in the industry, for example, the number of active users per month, can quickly reflect the behavior trend in business activities, and business departments can analyze and display the indexes so as to provide targeted services for user behaviors.

In the existing query behavior of index data, different business personnel can perform a large amount of repeated query analysis on fixed indexes at any time and any place according to business scenes and requirements. However, a large amount of repeated queries consume a large amount of platform computing resources, which results in a large amount of computing resources being wasted and the query efficiency of index data is low.

Disclosure of Invention

An embodiment of the application aims to provide a data query method, a data query device, computer equipment and a storage medium, so as to solve the technical problems that a large amount of repeated queries exist in query behaviors of existing index data, a large amount of computing resources are wasted easily, and query efficiency of the index data is low.

In order to solve the above technical problem, an embodiment of the present application provides a data query method, which adopts the following technical solutions:

receiving an index data query request input by a user;

converting the index data query request into a query statement in a preset language;

acquiring the query frequency of the query statement based on a preset query statement searcher, and judging whether the query frequency is smaller than a preset frequency threshold value;

if the frequency threshold value is not smaller than the frequency threshold value, the query statement is redirected to a preset index table, and whether a target index matched with the query statement exists in the index table is judged;

if a target index matched with the query statement exists, querying target index data corresponding to the target index from the index table;

and returning the target index data to the client of the user.

Further, before the step of redirecting the query statement to a preset index table, the method further includes:

acquiring a pre-stored historical query sentence corresponding to a historical index data query request; wherein the number of the historical query statements comprises a plurality;

acquiring current time, and judging whether the current time is in a service idle time period or not;

if so, performing disassembly analysis processing on the historical query statement to obtain repeated data in the historical query statement;

classifying and sorting the repeated data based on a preset algorithm to obtain intermediate data;

pre-calculating the intermediate data to obtain corresponding index data;

and constructing the index table by using the intermediate data and the indexes based on the one-to-one correspondence relationship between the intermediate data and the index data.

Further, before the step of obtaining the current time and judging whether the current time is in the service idle time period, the method further includes:

acquiring a preset division value, and dividing the time of a day into a plurality of processing time periods based on the division value;

respectively acquiring the total resource consumption of each processing time period in a preset time period based on a preset statistical database;

screening out a first resource consumption total amount which is larger than a first preset resource threshold value from all the resource consumption total amounts, and determining a first processing time period corresponding to the first resource consumption total amount;

for each first processing time period, acquiring all resource consumption corresponding to the first appointed processing time period in the preset time period; wherein the first designated processing time period is any one of the first processing time periods;

judging whether each resource consumption is larger than a second preset resource threshold value;

if so, marking the first appointed processing time period as a second processing time period;

removing the second processing time periods from all the processing time periods to obtain third processing time periods;

and taking the third processing time period as the service idle time period.

Further, after the step of constructing the index table by using the intermediate data and the index based on the one-to-one correspondence between the intermediate data and the index data, the method further includes:

acquiring an occupied capacity value of the index table and acquiring local available memory space;

calculating a difference between the available storage capacity and the occupied capacity value;

judging whether the difference value is larger than a preset threshold value or not;

if the index table is larger than the preset threshold value, storing the index table locally;

and if the index table is not larger than the preset threshold, storing the index table in a block chain.

Further, after the step of determining whether the query frequency is smaller than a preset frequency threshold, the method further includes:

if the frequency threshold value is smaller than the frequency threshold value, translating the query statement into an application program language;

sending the application program language to a preset data platform, and performing instant calculation on the basis of the application program language through the data platform to generate corresponding first index data;

receiving the first index data fed back by the data platform;

and returning the first index data to the client of the user.

Further, after the step of determining whether a target index matching the query statement exists in the index table, the method further includes:

if the target index matched with the query statement does not exist, sending the query statement to a preset computing platform, and performing instant computation on the basis of the query statement through the computing platform to generate corresponding second index data;

receiving the second index data fed back by the computing platform;

and returning the second index data to the client of the user.

Further, the index data query request carries user information of the user, and the step of converting the index data query request into a query statement in a preset language specifically includes:

acquiring the user information from the index data query request;

performing identity authentication on the user based on the user information, and judging whether the identity authentication passes;

and if the identity authentication is passed, executing the step of converting the index data query request into a query statement in a preset language.

In order to solve the above technical problem, an embodiment of the present application further provides a data query device, which adopts the following technical solutions:

the first receiving module is used for receiving an index data query request input by a user;

the conversion module is used for converting the index data query request into a query statement in a preset language;

the first judgment module is used for acquiring the query frequency of the query statement based on a preset query statement searcher and judging whether the query frequency is smaller than a preset frequency threshold value;

the second judging module is used for redirecting the query statement to a preset index table and judging whether a target index matched with the query statement exists in the index table or not if the frequency is not less than the frequency threshold;

the query module is used for querying target index data corresponding to the target index from the index table if the target index matched with the query statement exists;

and the first returning module is used for returning the target index data to the client of the user.

In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:

receiving an index data query request input by a user;

if a target index matched with the query statement exists, target index data corresponding to the target index are queried from the index table;

and returning the target index data to the client of the user.

In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:

receiving an index data query request input by a user;

and returning the target index data to the client of the user.

Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:

in the embodiment of the application, after an index data query request input by a user is received, the index data query request is converted into a query statement in a preset language, then the query frequency of the query statement is obtained based on a preset query statement searcher, whether the query frequency is smaller than a preset frequency threshold value or not is judged, if the query frequency is not smaller than the frequency threshold value, the query statement is redirected to a preset index table, whether a target index matched with the query statement exists in the index table or not is judged, if the target index matched with the query statement exists, target index data corresponding to the target index is queried from the index table, and finally the target index data is returned to a client of the user. According to the index data query method, the query frequency corresponding to the index data query request is judged, and when the query statement is judged to belong to the high-frequency query statement, the target index data corresponding to the query statement can be rapidly queried by using the preset index table, so that multiple links of the whole query process can be skipped, the resource consumption of repeated index query operation is reduced, and the processing efficiency of index data query is effectively improved.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram to which the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of a data query method according to the present application;

FIG. 3 is a schematic block diagram of one embodiment of a data query device according to the present application;

FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein may be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. Network 104 is the medium used to provide communication links between

terminal devices

101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to a smart phone, a tablet computer, an e-book reader, an MP3 player (Moving Picture expert group Audio Layer III, motion Picture expert compression standard Audio Layer 3), an MP4 player (Moving Picture expert group Audio Layer IV, motion Picture expert compression standard Audio Layer 4), a laptop portable computer, a desktop computer, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that, the data query method provided in the embodiments of the present application is generally executed by a server/terminal device, and accordingly, the data query apparatus is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for an implementation.

With continued reference to FIG. 2, a flow diagram of one embodiment of a data query method in accordance with the present application is shown. The data query method comprises the following steps:

step S201, an index data query request input by a user is received.

In this embodiment, an electronic device (for example, the server/terminal device shown in fig. 1) on which the data query method operates may receive an index data query request input by a user through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G/5G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, an UWB (ultra wideband) connection, and other wireless connection means now known or developed in the future. The user sends an index data query request to the electronic equipment through the client to obtain the required index data.

Step S202, converting the index data query request into a query statement in a preset language.

In this embodiment, the preset language is a DSL language, and the query statement is an SQL statement. The process of converting the index data query request into a query statement in a preset language may be performed by an application related to statement conversion.

Step S203, obtaining the query frequency of the query statement based on a preset query statement searcher, and determining whether the query frequency is smaller than a preset frequency threshold.

In this embodiment, when the query statement searcher obtains each query statement, the query statement searcher records a query state of the query statement, where the query state specifically refers to a query frequency of the query statement. If the query frequency of the query statement is greater than the frequency threshold, the index data corresponding to the query statement is judged to belong to the repeated calculation index. The value of the frequency threshold is not particularly limited, and may be set according to actual use requirements, for example, 5 times per week.

Step S204, if the frequency threshold is not less than the frequency threshold, the query statement is redirected to a preset index table, and whether a target index matched with the query statement exists in the index table is judged.

In this embodiment, if the query frequency of the query statement is not less than the preset frequency threshold, it indicates that the query indicator corresponding to the query statement belongs to the high-frequency query indicator, so that the indicator data corresponding to the query statement can be quickly found through the pre-constructed index table. The index table is generated after processing a pre-stored historical query statement corresponding to a historical index data query request, and in a non-business peak time period, namely under the condition that computing resources of the electronic equipment are idle, the electronic equipment can perform computation in advance for business personnel to query requirements in business time according to an off-line computing request initiated by a program and prepare corresponding indexes and index data so as to perform subsequent index table construction processes. Specifically, the high-frequency query statement and the low-frequency query statement can be classified by judging the query frequency of the query statement, so that in order to avoid repeated calculation brought by the high-frequency query statement and instant calculation pressure brought to a calculation platform by instant calculation, the high-frequency query statement is subjected to one-time whole-process offline precomputation in an off-peak service period, and the offline precomputation comprises query statement storage, analysis, intermediate data classification and finally forming an index table of indexes, so that when follow-up service personnel query repeated indexes, rapid query can be performed through the index table.

In step S205, if there is a target index matching the query statement, target index data corresponding to the target index is queried from the index table.

In this embodiment, if a target index matching the query statement exists in the index table, it indicates that the to-be-queried index corresponding to the query statement belongs to the already-proposed calculated index data, and the target index data corresponding to the target index can be directly queried through the index table and can be displayed.

And step S206, returning the target index data to the client of the user.

According to the method and the device, after an index data query request input by a user is received, the index data query request is converted into a query statement in a preset language, then the query frequency of the query statement is obtained based on a preset query statement searcher, whether the query frequency is smaller than a preset frequency threshold value or not is judged, if not, the query statement is redirected to a preset index table, whether a target index matched with the query statement exists in the index table or not is judged, if the target index matched with the query statement exists, target index data corresponding to the target index are queried from the index table, and finally the target index data are returned to a client of the user. According to the index data query method, the query frequency corresponding to the index data query request is judged, and when the query statement is judged to belong to the high-frequency query statement, the target index data corresponding to the query statement can be rapidly queried by using the preset index table, so that multiple links of the whole query process can be skipped, the resource consumption of repeated index query operation is reduced, and the processing efficiency of index data query is effectively improved.

In some optional implementations, before step S204, the electronic device may further perform the following steps:

acquiring a pre-stored historical query statement corresponding to the historical index data query request; wherein the number of the historical query statements comprises a plurality.

In this embodiment, service personnel often perform a large number of index query operations on respective application systems according to different service scenarios and changing service requirements, so as to generate corresponding historical index data query requests, and the historical index data query requests can be analyzed into a unified DSL language through application to obtain historical query statements, which are generally SQL-like query statements and transmitted to electronic equipment through corresponding interfaces, so as to store the historical query statements.

And acquiring the current time, and judging whether the current time is in the service idle time period.

In this embodiment, the data processing for generating the index table is performed in the business idle time period of the electronic device, so that the influence on the normal operation of the electronic device can be effectively reduced, and the reasonable utilization of system resources can be ensured.

If so, carrying out disassembly analysis processing on the historical query statement to obtain repeated data in the historical query statement.

In this embodiment, the repeated data refers to the same query syntax and object in the historical query statement, and includes query operators, filter conditions, and query sub-table combinations.

And classifying and sorting the repeated data based on a preset algorithm to obtain intermediate data.

In this embodiment, the preset algorithm is specifically a shuffle algorithm. The shuffle algorithm, also called shuffle algorithm, aims exactly opposite to the various sort algorithms, i.e. to shuffle an ordered (or unordered) series of elements to meet the demand.

And carrying out pre-calculation processing on the intermediate data to obtain corresponding index data.

In this embodiment, the pre-calculation processing on the intermediate data refers to pre-calculation processing on a query operator, a filter condition, and a query sub-table combination in the intermediate data, so as to obtain corresponding index data.

In this embodiment, for all the obtained intermediate data and index data, the intermediate data may be stored in a preset data table as an index of the index data having a corresponding relationship, so as to generate the index table.

According to the index table establishing method and device, the index table is established based on the pre-stored historical index query request, the follow-up operation of query behavior of high-frequency repeated indexes by service personnel can be facilitated, the required index data can be rapidly and automatically queried from the index table, the query of the high-frequency indexes can skip multiple links of the whole query process, and the corresponding index data can be directly obtained through the index table, so that the resource consumption of the repeated operation can be reduced, the resource utilization rate is improved, and the processing efficiency of index data query is effectively improved.

In some optional implementation manners of this embodiment, before the step of obtaining the current time and determining whether the current time is within the service idle time period, the electronic device may further perform the following steps:

the method comprises the steps of obtaining a preset division value, and dividing the time of day into a plurality of processing time periods based on the division value.

In this embodiment, the division value is not particularly limited, and may be set to 6 hours, for example, that is, 4 hours is used as the time length of one processing period, 24 hours included in one day may be divided into 6 processing periods, that is, 0 to 4; 4; 20:00-24:00.

And respectively acquiring the total resource consumption amount of each processing time period in a preset time period based on a preset statistical database.

In this embodiment, the statistical database is a database storing resource consumption data of the electronic device. The resource consumption data may refer to memory consumption, CPU consumption, and the like. In addition, the preset time period is not specifically limited, and may be set according to actual service use requirements. For example, the preset time period may be the last week adjacent to the current time. For example, if any one of the processing periods is 12-00.

Screening out a first resource consumption total amount larger than a first preset resource threshold value from all the resource consumption total amounts, and determining a first processing time period corresponding to the first resource consumption total amount.

In this embodiment, the value of the first preset resource threshold is not specifically limited, and may be set according to actual requirements, and if the total resource consumption is CPU consumption, the first preset resource threshold may be 70%.

For each first processing time period, acquiring all resource consumption corresponding to the first appointed processing time period in the preset time period; wherein the first designated processing time period is any one of the first processing time periods.

And judging whether each resource consumption is larger than a second preset resource threshold value.

In this embodiment, the value of the second preset resource threshold is not specifically limited, and may be set according to actual requirements, and if the total resource consumption is CPU consumption, the first preset resource threshold may be 60%.

If so, marking the first appointed processing time period as a second processing time period.

And eliminating the second processing time periods from all the processing time periods to obtain third processing time periods.

And taking the third processing time period as the service idle time period.

According to the method and the device, the resource consumption data of the electronic equipment in the preset time period are subjected to statistical analysis processing, the business idle time period of the electronic equipment is intelligently determined based on the obtained analysis result, the accuracy of the generated business idle time period is effectively guaranteed, the index table generation processing cannot be carried out in the business idle time period in the follow-up process, but the index table generation processing is carried out in the business idle time period in which the business idle time period is avoided, the influence on the normal operation of the electronic equipment is effectively reduced, the reasonable utilization of system resources is guaranteed, and the processing speed and the efficiency of the index table generation are improved.

In some optional implementations, after the step of constructing the index table using the intermediate data and the index based on the one-to-one correspondence between the intermediate data and the index data, the electronic device may further perform the following steps:

and acquiring the occupied capacity value of the index table and acquiring the local available storage capacity.

Calculating a difference between the amount of available memory and the amount of occupied capacity.

In this embodiment, the difference is obtained by subtracting the occupied capacity value from the available storage.

And judging whether the difference value is larger than a preset threshold value or not.

In this embodiment, the preset threshold is a value corresponding to normal operation of the electronic device, that is, if the current available storage capacity of the electronic device is greater than the preset threshold, it indicates that the electronic device can operate normally. In addition, the value of the preset threshold is not particularly limited, and may be set according to actual use requirements.

And if the index table is larger than the preset threshold value, storing the index table locally.

In the embodiment, the index table is stored in a local storage mode, so that the problem of low success rate of data query of the index table caused by unstable or disconnected network and other conditions can be effectively avoided, and the storage adaptability of the index table is improved.

In this embodiment, by storing and managing the quote data list using a block chain, the security and non-tamper property of the index table can be effectively ensured.

According to the method and the device, the occupied capacity value of the index table is compared with the local available storage amount, and then the index table is correspondingly stored in the local or block chain based on the obtained comparison result, so that the intelligence and the storage adaptability of the storage of the index table are improved, and the data safety of the index table is also guaranteed.

In some optional implementations, after step S203, the electronic device may further perform the following steps:

if the frequency is smaller than the frequency threshold value, translating the query statement into an application program language.

In this embodiment, the query statement is generally an SQL-like statement. If the query frequency of the query statement is less than the frequency threshold, the query statement searcher only records the query state of the query statement, namely the query frequency, and does not perform redirection processing on the index table.

And sending the application program language to a preset data platform so as to perform instant calculation based on the application program language through the data platform and generate corresponding first index data.

In this embodiment, the data platform may be a big data platform having an index data calculation capability corresponding to an application program language.

And receiving the first index data fed back by the data platform.

And returning the first index data to the client of the user.

After the query frequency of the query statement is obtained, if the query frequency is judged to be smaller than the preset frequency threshold value, the application program language corresponding to the query statement is calculated in real time intelligently based on the preset data platform to generate corresponding index data, and therefore the processing intelligence of index data query is effectively improved.

In some optional implementation manners of this embodiment, after step S204, the electronic device may further perform the following steps:

and if the target index matched with the query statement does not exist, sending the query statement to a preset computing platform, and performing instant computation on the basis of the query statement through the computing platform to generate corresponding second index data.

In this embodiment, the query statement is an SQL-like statement, and the data platform may be a big data platform having an index data calculation capability corresponding to the query statement.

And receiving the second index data fed back by the computing platform.

And returning the second index data to the client of the user.

According to the index data query method and device, after the query statement is redirected to the preset index table, if the target index corresponding to the query statement does not exist in the index table, the query statement can be intelligently calculated in real time subsequently based on the preset calculation platform so as to generate corresponding index data, and the processing intelligence of index data query is effectively improved.

In some optional implementation manners of this embodiment, the index data query request carries user information of the user, and step S202 includes the following steps:

and acquiring the user information from the index data query request.

In this embodiment, the user information may include identity information of the user, such as name information or user ID information of the user. Account and password information or biometric information of the user.

And performing identity authentication on the user based on the user information, and judging whether the identity authentication is passed or not.

In this embodiment, there are two ways of performing authentication on a user, and the user can select a corresponding authentication way according to actual use requirements. Specifically, the first authentication method is to authenticate the user by checking an account password input by the user, obtain a valid account password corresponding to the user information based on the user information, compare the valid account password with the account password input by the user, determine that the user passes authentication if the valid account password is the same as the user input account password, and determine that the user does not pass authentication if the valid account password is not the same as the user input account password. The second authentication method is to verify whether the biometric information of the user is legal or not, obtain the legal biometric information corresponding to the user information based on the user information, compare the legal biometric information with the biometric information input by the user, determine that the user passes the authentication if the two are the same, and otherwise determine that the user does not pass the authentication.

And if the identity authentication is passed, executing a step of converting the index data query request into a query statement in a preset language.

According to the method and the device, after the index data query request input by the user is received, the user can be authenticated firstly, and only when the user passes the authentication, the index data query request input by the user can be responded subsequently, so that the condition that an illegal user steals important data is avoided, and the compliance and the intelligence of index data query request processing are ensured.

It is emphasized that, in order to further ensure the privacy and security of the target index data, the target index data may also be stored in a node of a block chain.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a string of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence base technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, the processes of the embodiments of the methods described above can be included. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a data query apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which can be applied to various electronic devices.

As shown in fig. 3, the data query apparatus 300 according to the present embodiment includes: a first receiving module 301, a converting module 302, a first judging module 303, a second judging module 304, a querying module 305 and a first returning module 306. Wherein:

a first receiving module 301, configured to receive an index data query request input by a user;

a conversion module 302, configured to convert the index data query request into a query statement in a preset language;

a first judging module 303, configured to obtain a query frequency of the query statement based on a preset query statement searcher, and judge whether the query frequency is smaller than a preset frequency threshold;

a second determining module 304, configured to redirect the query statement to a preset index table if the frequency threshold is not less than the frequency threshold, and determine whether a target index matching the query statement exists in the index table;

a query module 305, configured to query, if a target index matching the query statement exists, target index data corresponding to the target index from the index table;

a first returning module 306, configured to return the target index data to the client of the user.

In this embodiment, the operations executed by the modules or units respectively correspond to the steps of the data query method in the foregoing embodiment one to one, and are not described herein again.

In some optional implementations of this embodiment, the data query apparatus further includes:

the first acquisition module is used for acquiring a pre-stored historical query statement corresponding to the historical index data query request; wherein the number of the historical query statements comprises a plurality;

the third judging module is used for acquiring the current time and judging whether the current time is in the service idle time period or not;

the analysis module is used for carrying out disassembly analysis processing on the historical query statement if the historical query statement exists, so as to obtain repeated data existing in the historical query statement;

the classification module is used for classifying and sorting the repeated data based on a preset algorithm to obtain intermediate data;

the first calculation module is used for carrying out pre-calculation processing on the intermediate data to obtain corresponding index data;

and the construction module is used for constructing the index table by using the intermediate data and the indexes based on the one-to-one correspondence relationship between the intermediate data and the index data.

In some optional implementations of this embodiment, the data querying device further includes:

the dividing module is used for acquiring a preset dividing value and dividing the time of day into a plurality of processing time periods based on the dividing value;

the second acquisition module is used for respectively acquiring the total resource consumption of each processing time period in a preset time period based on a preset statistical database;

the screening module is used for screening out a first resource consumption total amount which is larger than a first preset resource threshold value from all the resource consumption total amounts, and determining a first processing time period corresponding to the first resource consumption total amount;

a third obtaining module, configured to obtain, for each first processing time period, all resource consumption amounts corresponding to the first specified processing time period within the preset time period; wherein the first designated processing time period is any one of the first processing time periods;

the fourth judging module is used for judging whether each resource consumption is larger than a second preset resource threshold value;

the marking module is used for marking the first appointed processing time period as a second processing time period if the first appointed processing time period is the second appointed processing time period;

the removing module is used for removing the second processing time periods from all the processing time periods to obtain third processing time periods;

and the determining module is used for taking the third processing time period as the business idle time period.

the fourth acquisition module is used for acquiring the occupied capacity value of the index table and acquiring the local available storage capacity;

the second calculation module is used for calculating the difference value between the available storage capacity and the occupied capacity value;

a fifth judging module, configured to judge whether the difference is greater than a preset threshold;

the first storage module is used for storing the index table locally if the index table is larger than the preset threshold value;

and the second storage module is used for storing the index table in the block chain if the index table is not larger than the preset threshold.

the translation module is used for translating the query statement into an application program language if the frequency threshold is smaller than the frequency threshold;

the first generation module is used for sending the application program language to a preset data platform so as to perform instant calculation on the basis of the application program language through the data platform and generate corresponding first index data;

the second receiving module is used for receiving the first index data fed back by the data platform;

and the second returning module is used for returning the first index data to the client of the user.

In this embodiment, the operations performed by the modules or units are in one-to-one correspondence with the steps of the data query method in the foregoing embodiment, and are not described herein again.

the second generation module is used for sending the query statement to a preset computing platform if a target index matched with the query statement does not exist, so that instant computation is performed on the basis of the query statement through the computing platform, and corresponding second index data are generated;

the third receiving module is used for receiving the second index data fed back by the computing platform;

and the third returning module is used for returning the second index data to the client of the user.

In some optional implementation manners of this embodiment, the index data query request carries user information of the user, and the conversion module 302 includes:

the acquisition sub-module is used for acquiring the user information from the index data query request;

the verification submodule is used for performing identity verification on the user based on the user information and judging whether the identity verification passes;

and the execution sub-module is used for executing the step of converting the index data query request into a query statement in a preset language if the identity authentication is passed.

In order to solve the technical problem, the embodiment of the application further provides computer equipment. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system and various application software installed in the computer device 4, such as computer readable instructions of a data query method. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, for example, execute computer readable instructions of the data query method.

The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.

The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the data query method as described above.

Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method of the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present application or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (such as a ROM/RAM, a magnetic disk, and an optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. A data query method, comprising the steps of:

receiving an index data query request input by a user;

and returning the target index data to the client of the user.

2. The data query method of claim 1, further comprising, before the step of redirecting the query statement to a preset index table:

acquiring a pre-stored historical query statement corresponding to the historical index data query request; wherein the number of the historical query statements comprises a plurality;

pre-calculating the intermediate data to obtain corresponding index data;

3. The data query method according to claim 2, before the steps of obtaining the current time and determining whether the current time is in a service idle period, further comprising:

if yes, marking the first appointed processing time period as a second processing time period;

and taking the third processing time period as the service idle time period.

4. The data query method according to claim 2, after the step of constructing the index table using the intermediate data and the index based on the one-to-one correspondence between the intermediate data and the index data, further comprising:

acquiring an occupied capacity value of the index table and acquiring a local available storage amount;

5. The data query method according to claim 1, after the step of determining whether the query frequency is less than a preset frequency threshold, further comprising:

receiving the first index data fed back by the data platform;

and returning the first index data to the client of the user.

6. The data query method of claim 1, after the step of determining whether a target index matching the query statement exists in the index table, further comprising:

receiving the second index data fed back by the computing platform;

and returning the second index data to the client of the user.

7. The data query method according to claim 1, wherein the index data query request carries user information of the user, and the step of converting the index data query request into a query statement in a preset language specifically includes:

acquiring the user information from the index data query request;

8. A data query apparatus, comprising:

the conversion module is used for converting the index data query request into a query statement of a preset language;

the second judgment module is used for redirecting the query statement to a preset index table and judging whether a target index matched with the query statement exists in the index table or not if the frequency is not smaller than the frequency threshold;

9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of the data query method of any one of claims 1 to 7.

10. A computer readable storage medium, having computer readable instructions stored thereon, which when executed by a processor implement the steps of the data query method of any one of claims 1 to 7.