WO2018090557A1

WO2018090557A1 - Method and device for querying data table

Info

Publication number: WO2018090557A1
Application number: PCT/CN2017/081321
Authority: WO
Inventors: 彭贵平; 李士福; 郑爱军
Original assignee: 华为技术有限公司
Priority date: 2016-11-18
Filing date: 2017-04-21
Publication date: 2018-05-24
Also published as: CN108073641A; CN108073641B

Abstract

Disclosed are a method and device for querying a data table. The method comprises: acquiring an initial query condition, wherein the initial query condition comprises a first inequality; performing conversion processing on the first inequality to obtain a target query condition, wherein the target query condition comprises a first equality; and querying a data table according to the target query condition. The present invention can improve the efficiency of data table querying.

Description

Method and device for querying data table

The present application claims priority to Chinese Patent Application No. 201611022774.X filed on Nov. 18, 2016, the entire disclosure of which is incorporated herein by reference. In the application.

Technical field

The embodiments of the present application relate to the field of computers, and in particular, to a method and apparatus for querying a data table in the field of computers.

Background technique

The database management system (DBMS) is a layer of database management software between the user and the operating system. Database tuning can make the database application run faster. The goal is to make the database have higher throughput and more. Short response time.

For the database kernel, it mainly implements the optimization technology of structured query language (SQL), including: query reuse technology, query rewrite rule technology, query algorithm optimization technology, parallel query optimization technology, distributed query. Optimization technology and other optimization techniques, but in the parallel computing database (MPPDB) of parallel computing, the optimizer module estimates the value and the cost of executing the job according to the type of the statement in the execution job when performing job optimization. The data distribution on each data node, etc., generates an optimal execution plan, but the optimal execution plan generated by the optimizer module does not necessarily achieve efficient optimization, for example, the data push service passengers in the airline ticketing system, For example, in the database of the aviation system, passengers A and B who boarded the aircraft at the same time within 60 minutes of time were found, but during the execution, it was found that there were hundreds of millions of passengers boarding records within the same time difference. Efficiency when querying different passengers and time differences in the database Low.

Summary of the invention

The method for querying the data table provided by the embodiment of the present application can improve the efficiency of querying the data table.

In a first aspect, a method for querying a data table is provided, the method comprising: obtaining an initial query condition, the initial query condition including a first inequality; converting the first inequality to obtain a target query condition, The target query condition includes a first equation; and the data table is queried according to the target query condition.

Specifically, the first inequality is converted to obtain a target query condition, and the first inequality may be converted to obtain a first equation.

In the embodiment of the present application, the initial query condition is converted to obtain a target query condition, where the initial query condition includes a first inequality, and the target query condition includes a first equation, such that by converting the first inequality to the first class In the formula, using the first inequality to query, within a certain data range, the data satisfying the inequality is more inefficient, so the query efficiency is lower, but the query is transformed by the transformed equation, and the query scope is narrowed, thereby improving the efficiency of the query.

In some implementations, the target query condition further includes a second inequality different from the first inequality.

In the embodiment of the present application, the target query condition further includes a second inequality, that is, the first inequality in the initial query condition may be converted into the first equation and the second inequality, and may be inquired according to the first equation and the second inequality. The data table, in this way, can further narrow the scope of the query by the second inequality, and improve the query efficiency.

In some implementations, the initial query condition further includes a second equation, wherein the according to the target The query condition query data table includes: querying the data table according to the first equation and the second equation.

Optionally, the query method in the embodiment of the present application may be a hash join or a self-join. When it is a hashjoin, the second query may be included in the initial query condition in the hashjoin query method. Using the second equation of hashjoin and the transformed first equation to query the data table, or using the second equation, the first equation, and the second inequality to query the data table, the second equation can be solved in the hashjoin algorithm processing. After filtering, each hash bucket corresponds to a huge amount of data, and the appropriate data is filtered by the first equation, the second inequality, and the second equation to reduce the amount of data in the hash bucket.

In some implementations, if the form of the first inequality is: AB>C, the converting the first inequality to obtain a target query condition, including: converting AB>C into trunc (A) /C)=trunc(B/C)+n, where the first equation is specifically trunc(A/C)=trunc(B/C)+n, and trunc(·) is a truncation of the number For the operation, C is a positive integer greater than 1, and n is an integer, that is, it can be a positive integer or a negative integer, A and B are the values in the data table, and A and B are positive integers.

In some implementations, if the form of the first inequality is: AB>C, the first inequality is converted to obtain a target query condition, including: converting AB>C into trunc (A/ C)=trunc(B/C)+n and A>B, wherein the second inequality is specifically A>B, and the first equation is specifically trunc(A/C)=trunc(B/C) +n, trunc(·) is an operation that rounds the digits, C is a positive integer greater than 1, and n is an integer, which can be a positive integer or a negative integer, and A and B are in the data table. Value, and A and B are positive integers.

In some implementations, if the form of the first inequality is: DE<F, the first inequality is converted to obtain a target query condition, including: converting DE<F into trunc (D/F) )=trunc(E/F)+m, the first equation is specifically trunc(D/F)=trunc(E/F)+m, which is a rounding of the number, and F is a positive value greater than 1. An integer, m is an integer, that is, it can be a positive integer or a negative integer, D and E are the values in the data table, and D and E are positive integers.

In some implementations, if the form of the first inequality is: DE<F, the converting the first inequality to obtain a target query condition, including: converting DE<F into trunc(D /F)=trunc(E/F)+m and D>E, wherein the second inequality is specifically D>E, and the first equation is specifically trunc(D/F)=trunc(E/F +m, for the truncation of the number, F is a positive integer greater than 1, m is an integer, that is, it can be a positive integer or a negative integer, D and E are the values in the data table, and D And E is a positive integer.

In some implementations, if the form of the first inequality is: GH<I, the converting the first inequality to obtain a target query condition, including: converting GH<I into trunc (G) /I)=trunc(H/I)+p, where the first equation is specifically trunc(G/I)=trunc(H/I)+p, which is a truncation of the number, I is A positive integer greater than 1, p is an integer, that is, a positive integer or a negative integer, G and H are values in the data table, and G and H are positive integers.

In some implementations, if the form of the first inequality is: GH<I, the converting the first inequality to obtain a target query condition, including: converting GH<I into trunc (G) /I)=trunc(H/I)+p and G<H, wherein the second inequality is specifically G<H, and the first equation is specifically trunc(G/I)=trunc(H/I +p, to round the digits, I is a positive integer greater than 1, p is an integer, ie it can be a positive integer or a negative integer, G and H are the values in the data table, and G and H is a positive integer.

In some implementations, the initial query condition is a self-joined selfjoin initial query condition. The query of selfjoin initial query condition means that a table can be connected with itself, which can simplify the complexity of the query and improve the efficiency of the query.

In some implementations, before converting the first inequality to obtain a target query condition, the method further includes: determining whether the data amount included in the data table is greater than a first threshold; The first inequality is converted to obtain the target query condition, including: when the data amount included in the data table is greater than the first threshold, converting the first inequality to obtain the target query condition.

In this way, for the query of massive data, if only the first inequality is used for querying, it is necessary to traverse all the data in the data table to find the data satisfying the first inequality, so that the larger the amount of data, the more the query time will increase. Converting the first inequality to the second inequality and the first equation, when traversing the data in the data table, finding data satisfying the second inequality and the first equality condition, which reduces the complexity of the query and reduces the query time. To further improve the efficiency of the query.

In a second aspect, an apparatus for querying a data table is provided for performing the method of the first aspect or any possible implementation of the first aspect.

In a third aspect, an apparatus for querying a data table is provided, the apparatus comprising: a receiver, a transmitter, a memory, a processor, and a bus system. Wherein the receiver, the transmitter, the memory and the processor are connected by the bus system, the memory is for storing instructions for executing the instructions stored by the memory to control the receiver to receive signals and control the sending The transmitter transmits a signal, and when the processor executes the memory stored instructions, the execution causes the processor to perform the method of the first aspect or any of the possible implementations of the first aspect.

In a fourth aspect, an apparatus for querying a data table is provided, the apparatus comprising: a memory and a processor. Wherein the memory is for storing computer executable instructions, the processor is for reading the computer executable instructions and may perform the method of the first aspect or any possible implementation of the first aspect.

In a fifth aspect, a computer readable medium is provided for storing a computer program, the computer program comprising instructions for performing the method of the first aspect or any of the possible implementations of the first aspect.

DRAWINGS

FIG. 1 is a schematic diagram of an application scenario of an embodiment of the present application.

FIG. 2 shows a schematic diagram of a method of querying a data table according to an embodiment of the present application.

FIG. 3 shows a schematic diagram of another method of querying a data table according to an embodiment of the present application.

FIG. 4 shows a schematic diagram of an apparatus for querying a data table according to an embodiment of the present application.

FIG. 5 shows a schematic diagram of another apparatus for querying a data table according to an embodiment of the present application.

detailed description

It should be understood that the technical solutions of the embodiments of the present application can be applied to various database systems, for example, a relational database management system (RDBMS), a non-relational (NoSQL) database system, and a massively parallel processing database (massively Parallel processing database, MPP-DB), and the like, which are not limited by the embodiment of the present application.

It should be understood that the embodiment of the present application is described by taking the SQL language as an example, but the embodiment of the present application may also adopt other languages, for example, an object-oriented query language (HQL), and the like. This is not limited.

Let's first introduce the database system and its structure. The database system generally includes four components: database, hardware, software, and personnel. A database (database) refers to a collection of organized, shareable data that is stored on a storage medium internal or external to a computer. The data in the database is organized and described according to a certain mathematical model. Description and storage, with less redundancy, higher data independence and scalability, and can be shared by various users. Hardware refers to the various physical devices that make up a computer system, that is, the internal devices required for storage, as well as the external devices required for storage. The configuration of the hardware should meet the needs of the entire database system. The software includes the operating system, DBMS, and applications. People include end users who access the database using the system's interface or query language, such as adding data to the database, deleting data, or querying data. DBMS is the core software of the database system. Scientifically organizes, stores data and efficiently acquires and maintains data with the support of the operating system. It can enable users to create, modify, optimize or query data in the database through different methods, for example. The DBMS can include an optimizer module for logical query optimization.

The above description of the database system is only for a better understanding of the technical solution of the embodiment of the present application, and the method for applying the query data table in the embodiment of the present application should not be limited.

Figure 1 shows the process of querying data. For example, taking the open source database Postgres as an example, the query process for describing SQL statements includes the following steps:

S101, start the query.

S102: Analyze the initial query condition of the SQL, and convert it into a query tree (Query Tree) through the lexical analysis, the syntax analysis, and the semantic check to pass to the next stage.

The description of the embodiment of the present application is conveniently described by taking an SQL statement as an example. It should be understood that the embodiment of the present application may be described by using other initial query conditions. The form of the initial query condition may be a SQL-like statement, such as “select name from personal basics. Information where age > 30". Alternatively, the form of the initial query condition may also be a natural language, such as "inquiring the name of a person whose age is greater than 30 years old in the basic information of the individual".

S103, performing view rewriting according to the query tree obtained in S101, for example, rewriting with a base table.

S104. Determine, according to the result of the view rewriting, whether a set operation is required.

S105. When a set operation is required, the set is decomposed into ordinary SQL, and the decomposed ordinary SQL is executed as S106.

S106: Perform logic optimization query when the aggregation operation is not needed, for example, using a query technology to perform an equivalent exchange query.

S107. After performing the logic optimization query, perform a physical optimization query, for example, find the least expensive query path among the multiple query paths.

It should be understood that S106 may be before or after S107, and the embodiment of the present application does not limit this.

S108: group, sort, aggregate, and de-optimize the plan obtained by the logic optimization query and the physical optimization query.

S109, the actuator executes the plan.

S110, returning a query result of the execution plan executed by the actuator.

It should be understood that the process of querying the data described in FIG. 1 is only for a better understanding of the technical solution of the embodiment of the present application, and the method for applying the query data table in the embodiment of the present application is not limited.

The embodiment of the present application mainly describes the logic optimization query in the step S106. For example, the amount of data stored and queried by the massive user data is very large, and the distributed database is usually used to segment the data to improve the query performance of the system. . However, from the current usage situation, the distributed database does not solve the problem of massive user data query. For example, the number of users can reach 1 billion, and the attributes of users can reach 1 million. Such data can be stored in a traditional database, which can reach 10 billion columns and millions of rows. In such a business scenario, even with a distributed database, the amount of data queried is still very large. The embodiment of the present application can optimize the query condition of the massive data. In order to reduce the amount of data in the query, the optimal query plan can be understood as the shortest query technology. The following describes the query optimization method of the embodiment of the present application.

FIG. 2 illustrates a query optimization method 200 of an embodiment of the present application. For example, the method 200 can be performed by an optimizer in a database system. The method 200 includes:

S210. Acquire an initial query condition, where the initial query condition includes a first inequality.

As an optional embodiment, the initial query condition is a self-join initial query condition, and the self-join initial query condition refers to a table that can be connected with itself, so that the complexity of the query can be simplified, and the query efficiency is improved.

S220. Perform conversion processing on the first inequality to obtain a target query condition, where the target query condition includes a first equation.

As an optional embodiment, if the form of the first inequality is: AB>C, the first equation is specifically: trunc(A/C)=trunc(B/C)+n, the first The second inequality is specifically A>B, where trunc(·) is a truncated rounding operation, C is a positive integer greater than 1, and n is an integer, that is, it can be a positive integer or a negative integer, and A and B are The values in the data table, and A and B are positive integers. Optionally, S220, comprising: converting AB>C to trunc(A/C)=trunc(B/C)+n, or S220, including: converting AB>C into trunc(A/C)=trunc( B/C)+n and A>B, wherein the second inequality is A>B, and the first equation is trunc(A/C)=trunc(B/C)+n.

As an optional embodiment, if the form of the first inequality is: DE<F, the first equation is specifically: trunc(D/F)=trunc(E/F)+m, the second inequality Specifically, D>E, where trunc(·) is a truncated rounding operation, F is a positive integer greater than 1, and m is an integer, that is, a positive integer or a negative integer, and D and E are the data tables. a value in , and D and E are positive integers; optionally, S220, including: converting DE<F into trunc(D/F)=trunc(E/F)+m; or S220, including DE<F Converted to trunc(D/F)=trunc(E/F)+m and D>E, wherein the second inequality is D>E, and the first equation is trunc(D/F)=trunc( E/F)+m.

As an optional embodiment, if the form of the first inequality is: GH<I, the first equation is specifically: trunc(D/F)=trunc(E/F)+m, the second inequality Specifically, D>E, where trunc(·) is a truncated rounding operation, F is a positive integer greater than 1, m is an integer, D and E are values in the data table, and D and E are positive integers Optionally, S220, comprising: converting GH<I into trunc(G/I)=trunc(H/I)+p; or S220 comprising: converting GH<I into trunc(G/I)=trunc(H) /I) +p and G<H, wherein the second inequality is G<H, and the first equation is trunc(G/I)=trunc(H/I)+p.

S230. Query the data table according to the target query condition. Specifically, the data table may be queried according to the first equation in the target query condition.

As an optional embodiment, the initial query condition includes a second equation, where the S230 includes: querying a data table according to the first equation and the second equation, or the S230 includes: The data table query is queried according to the first equation, the second equation, and the second inequality. Specifically, the optimizer module generates an optimal execution plan according to the SQL statement, needs the underlying scan data, and then filters the scanned data through the hashjoin to filter the appropriate data, wherein the second equation needs to be included in the hashjoin query process. And when the first inequality is included in the initial query condition, the first inequality needs to be rewritten into the second inequality and the first inequal, the second inequality of the hashjoin, the second inequality after the rewriting, and the first equation are used. The query of the statement can solve the problem that the amount of data corresponding to each hash bucket is huge after filtering by the second equation in the hashjoin algorithm processing, and filtering the appropriate data by the first equation, the second inequality and the second equation, and reducing The amount of data in the hash bucket.

As an example, in the prior art, passengers A and passengers who boarded the aircraft at the same time within 60 minutes are found. Passenger B's initial SQL query conditions are:

Select*a.id, b.id from db a, db b where a.id<>b.id and a.port=b.port and a.time–b.time<60.

After rewriting the initial query conditions of the embodiment of the present application, the SQL target query conditions of passengers A and B who are boarding in the same place within 60 minutes are found as follows:

Select*a.id, b.id from db a, db b where a.id<>b.id and a.port=b.port and trunc(a.time/60)=trunk(b.time/60) And a.time>=b.time.

As an example, FIG. 3 depicts a method 300 of querying a data table, the method 300 including:

S301, start the query.

S302. Determine whether the query method is a selfjoin method. If it is not the selfjoin method, skip to S307.

S303. In S302, if it is a selfjoin method, determine a query cost, and determine whether the number of estimated cost rows is large, for example, determining that the number of rows is greater than a certain preset threshold, when the number of rows is less than a preset threshold, or for example, as described above: When the number of values in the data table is greater than the first threshold, if the amount of data is considered to be large, then the process jumps to S307.

S304. In S303, if the number of rows is greater than a preset threshold, determine whether the initial query condition is a convertible non-equivalent expression. If there is no convertible non-equivalent expression in the initial query condition, skip to S307, for example, the non-equivalent expression is the aforementioned first inequality.

S305. If there is a convertible non-equivalent expression in the initial query condition in S304, the non-equivalent expression is converted into an equivalent expression, and optionally, the non-equivalent expression is converted into another non-equivalent expression. Equivalent expressions and equivalent expressions, for example, the equivalent expression may be the first equation described above, and another non-equivalent expression may be the aforementioned second inequality.

S306, rewriting the target query condition, where the target query condition may include an equivalent expression; or the target query condition may include another non-equivalent expression and an equivalent expression.

S307, physical query optimization stage.

S308, ending the query optimization.

It should be understood that the method shown in FIG. 3 is merely exemplary, and the three judgment processes of S302, S303, and S304 may satisfy at least one condition, and not all need to be satisfied. For example, there may be no S302 or S303, etc., and embodiments of the present application are not limited thereto.

Therefore, when querying a large amount of data, when using the second equation in the hashjoin algorithm and the first inequality in the initial query condition, the query efficiency is low, by converting the first inequality into the second inequality and the first The one-element condition increases the conditions for querying data, which further improves the efficiency of querying data.

The method for querying the data table provided by the embodiment of the present application is described above with reference to FIG. 2 and FIG. 3. The apparatus for querying the data table provided by the embodiment of the present application is described below with reference to FIG. 4 and FIG.

FIG. 4 shows an apparatus 300 for querying a data table provided by an embodiment of the present application. The apparatus 300 includes:

The obtaining module 410 is configured to obtain an initial query condition, where the initial query condition includes a first inequality;

The conversion module 420 is configured to perform conversion processing on the first inequality to obtain a target query condition, where the target query condition includes a first equation;

The query module 430 is configured to query the data table according to the target query condition.

As an optional embodiment, the initial query condition further includes a second equation, and the query module 430 is specifically configured to: query the data table according to the first equation and the second equation.

As an optional embodiment, the target query condition further includes a second unequal difference from the first inequality formula.

As an optional embodiment, if the form of the first inequality is: AB>C, the first equation is specifically: trunc(A/C)=trunc(B/C)+n, the first The second inequality is specifically A>B, where trunc(·) is a truncated rounding operation, C is a positive integer greater than 1, n is an integer, A and B are the values in the data table, and A and B are A positive integer.

As an optional embodiment, if the form of the first inequality is: DE<F, the first equation is specifically: trunc(D/F)=trunc(E/F)+m, the second inequality Specifically, D>E, where trunc(·) is a truncated rounding operation, F is a positive integer greater than 1, m is an integer, D and E are values in the data table, and D and E are positive integers .

As an optional embodiment, if the form of the first inequality is: GH<I, the first equation is specifically trunc(G/I)=trunc(H/I)+p and G<H, The second inequality is G<H, where trunc(·) is a truncated rounding operation, I is a positive integer greater than 1, p is an integer, G and H are values in the data table, and G and H is a positive integer.

As an optional embodiment, the initial query condition is a self-joining selfjoin initial query condition.

As an optional embodiment, the conversion module 420 is further configured to: before converting the first inequality to obtain a target query condition, determining whether the data amount included in the data table is greater than a first threshold; When the data amount included in the data table is greater than the first threshold, the first inequality is converted to obtain the target query condition.

It should be understood that the apparatus 400 herein is embodied in the form of a functional module. The term "module" as used herein may refer to an ASIC, an electronic circuit, a processor (eg, a shared processor, a proprietary processor or a group processor, etc.) and memory, a merge logic, and a processor for executing one or more software or firmware programs. / or other suitable components that support the described functionality. In an alternative example, those skilled in the art may understand that the device 400 may be specifically the optimizer in the foregoing embodiment, and the device 400 may be used to execute various processes and/or steps corresponding to the optimizer in the foregoing method embodiments. To avoid repetition, we will not repeat them here.

FIG. 5 shows an apparatus 500 for querying a data table provided by an embodiment of the present application. The apparatus 500 includes a receiver 510, a processor 520, a transmitter 530, a memory 540, and a bus system 550. The receiver 510, the processor 520, the transmitter 530 and the memory 540 are connected by a bus system 550 for storing instructions for executing instructions stored in the memory 540 to control the receiver 510. A signal is received and the transmitter 530 is controlled to send an instruction. The receiver 510 and the transmitter 530 may be a transceiver interface, etc., which is not limited in this embodiment of the present application.

The receiver 510 is configured to obtain an initial query condition, where the initial query condition includes a first inequality; the processor 520 performs conversion processing on the first inequality to obtain a target query condition, where the target query condition includes a first equation. The processor 520 is further configured to query the data table according to the target query condition.

As an optional embodiment, the initial query condition further includes a second equation, and the processor 520 is specifically configured to: query the data table according to the first equation and the second equation.

As an optional embodiment, the target query condition further includes a second inequality different from the first inequality.

As an optional embodiment, the processor 520 is further configured to: before converting the first inequality to obtain a target query condition, determining whether the data amount included in the data table is greater than a first threshold; When the data amount included in the data table is greater than the first threshold, the first inequality is converted to obtain the target query condition.

It should also be understood that the apparatus 500 may be specifically the optimizer in the above embodiments, and may be used to perform various steps and/or processes corresponding to the optimizer in the above method embodiments. Optionally, the memory 540 can include read only memory and random access memory and provides instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory can also store information of the device type. The processor 520 can be configured to execute instructions stored in the memory, and when the processor executes the instructions, the processor 520 can perform the various steps corresponding to the optimizer in the above method embodiments.

It should be understood that, in the embodiment of the present application, the numbers "first" and "second" are only used to distinguish different objects, for example, in order to distinguish different user identifiers or different attribute identifiers, etc., the protection of the embodiments of the present application should not be The scope constitutes any limitation.

Those skilled in the art will appreciate that the various method steps and elements described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both, in order to clearly illustrate hardware and software. Interchangeability, the steps and composition of the various embodiments have been generally described in terms of function in the foregoing description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

A person skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working process of the system, the device and the unit described above can refer to the corresponding process in the foregoing method embodiment, and details are not described herein again.

In the several embodiments provided by the embodiments of the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other manners. For example, the device embodiments described above are merely illustrative. For example, the division of the unit is only a logical function division. In actual implementation, there may be another division manner, for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, or an electrical, mechanical or other form of connection.

The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the embodiments of the present application.

In addition, each functional unit in the embodiment of the present application may be integrated into one processing unit, or may be each Units exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be substantially or partially contributed to the prior art, or all or part of the technical solution may be embodied in the form of a software product stored in a The storage medium includes a plurality of instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like, which can store program code. .

The foregoing is only a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto, and any person skilled in the art can easily think of various kinds within the technical scope disclosed in the embodiments of the present application. Modifications or substitutions are intended to be included within the scope of the present application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

Claims

A method for querying a data table, the method comprising:

Obtaining an initial query condition, where the initial query condition includes a first inequality;

Performing conversion processing on the first inequality to obtain a target query condition, where the target query condition includes a first equation;

The data table is queried according to the target query condition.
The method of claim 1 wherein said initial query condition further comprises a second equation,

The querying the data table according to the target query condition includes:

The data table is queried according to the first equation and the second equation.
The method of claim 1 or 2, wherein the target query condition further comprises a second inequality different from the first inequality.
The method according to claim 3, wherein if the form of the first inequality is: AB>C, the first equation is specifically: trunc(A/C)=trunc(B/C) +n, the second inequality is specifically A>B, wherein trunc(·) is a truncation rounding operation, C is a positive integer greater than 1, n is an integer, and A and B are values in the data table. And A and B are positive integers.
The method according to claim 3 or 4, wherein if the form of the first inequality is: DE<F, the first equation is specifically: trunc(D/F)=trunc(E/ F)+m, the second inequality is specifically D>E, wherein trunc(·) is a truncated rounding operation, F is a positive integer greater than 1, m is an integer, and D and E are in the data table. The value, and D and E are positive integers.
The method according to claim 3 or 4, wherein if the form of the first inequality is: GH < I, the first equation is specifically trunc (G / I) = trunc (H / I +p and G<H, the second inequality is G<H, where trunc(·) is a truncated rounding operation, I is a positive integer greater than 1, p is an integer, and G and H are the data The values in the table, and G and H are positive integers.
The method according to any one of claims 1 to 6, wherein the initial query condition is a self-joining selfjoin initial query condition.
The method according to any one of claims 1 to 7, wherein before the converting the first inequality to obtain a target query condition, the method further comprises:

Determining whether the amount of data included in the data table is greater than a first threshold;

Performing conversion processing on the first inequality to obtain target query conditions, including:

When the data amount included in the data table is greater than the first threshold, the first inequality is converted to obtain the target query condition.
An apparatus for querying a data table, the apparatus comprising:

An obtaining module, configured to obtain an initial query condition, where the initial query condition includes a first inequality;

a conversion module, configured to perform conversion processing on the first inequality to obtain a target query condition, where the target query condition includes a first equation;

The query module is configured to query the data table according to the target query condition.
The apparatus according to claim 9, wherein said initial query condition further comprises a second equation,

The query module is specifically configured to: query the data table according to the first equation and the second equation.
The apparatus of claim 9 or 10, wherein the target query condition further comprises a second inequality different from the first inequality.
The apparatus according to claim 11, wherein if the form of the first inequality is: AB>C, the first equation is specifically: trunc(A/C)=trunc(B/C) +n, the second inequality is specifically A>B, wherein trunc(·) is a truncation rounding operation, C is a positive integer greater than 1, n is an integer, and A and B are values in the data table. And A and B are positive integers.
The apparatus according to claim 11 or 12, wherein if the form of the first inequality is: DE<F, the first equation is specifically: trunc(D/F)=trunc(E/ The second inequality of F)+m is specifically D>E, wherein trunc(·) is a truncated rounding operation, F is a positive integer greater than 1, m is an integer, and D and E are in the data table. Value, and D and E are positive integers.
The apparatus according to claim 11 or 12, wherein if the form of the first inequality is: GH < I, the first equation is specifically trunc (G / I) = trunc (H / I +p and G<H, the second inequality is G<H, where trunc(·) is a truncated rounding operation, I is a positive integer greater than 1, p is an integer, and G and H are the data The values in the table, and G and H are positive integers.
The apparatus according to any one of claims 9 to 14, wherein the initial query condition is a self-joining selfjoin initial query condition.
The device according to any one of claims 9 to 15, wherein the conversion module is further used to:

Before performing the conversion processing on the first inequality to obtain the target query condition, determining whether the data amount included in the data table is greater than a first threshold;

When the data amount included in the data table is greater than the first threshold, the first inequality is converted to obtain the target query condition.
An apparatus for querying a data table, comprising:

Memory for storing programs;

A processor for executing the program stored by the memory, the processor for performing the method of any one of claims 1-8 when the program is executed.
A computer readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of any of claims 1-8.