CN116126902A - Method and storage medium for supporting like operation by partition pruning in SQL (structured query language) - Google Patents

Method and storage medium for supporting like operation by partition pruning in SQL (structured query language) Download PDF

Info

Publication number
CN116126902A
CN116126902A CN202310174716.2A CN202310174716A CN116126902A CN 116126902 A CN116126902 A CN 116126902A CN 202310174716 A CN202310174716 A CN 202310174716A CN 116126902 A CN116126902 A CN 116126902A
Authority
CN
China
Prior art keywords
partition
character string
string
sql
operator
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202310174716.2A
Other languages
Chinese (zh)
Inventor
李清源
尹强
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Kingbase Information Technologies Co Ltd
Original Assignee
Beijing Kingbase Information Technologies Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Kingbase Information Technologies Co Ltd filed Critical Beijing Kingbase Information Technologies Co Ltd
Priority to CN202310174716.2A priority Critical patent/CN116126902A/en
Publication of CN116126902A publication Critical patent/CN116126902A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2453—Query optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Computational Linguistics (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Probability & Statistics with Applications (AREA)
  • Software Systems (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention provides a method and a storage medium for supporting like operation by partition pruning in SQL. When a like operator in SQL contains a partition key of a partition table, acquiring a first character string contained in the like operator; judging whether the first character string contains a wild card or not; if yes, obtaining the partition type of the partition table; and processing the first character string in a mode corresponding to the partition type so as to enable the partition table to support like operation. The optimization device can apply the filter condition containing the like operator to the partition pruning, so that the suitability of the filter bar containing the like operator is improved, and the query efficiency is further improved.

Description

Method and storage medium for supporting like operation by partition pruning in SQL (structured query language)
Technical Field
The invention relates to database technology, in particular to a method and a storage medium for supporting like operation by partition pruning in SQL.
Background
Currently, the concept of partition tables has been introduced in databases. Specifically, the table may be partitioned so that a plurality of partition tables may be obtained. Each partition table is still a complete table logically, and only the data in the table is physically stored in a plurality of table spaces (on physical files), so that the advantage of this is that when the data is queried, the whole table can be prevented from being scanned every time, and the query efficiency is improved. However, in the current query scheme for the partition table, the optimizer cannot apply the filter condition including the like operator to the partition pruning, so that the suitability of the filter bar including the like operator is low, and the query efficiency cannot be further improved.
Disclosure of Invention
One object of the present invention is to process a first string in a manner corresponding to a partition type of a partition table, so as to apply a filtering condition containing like operators to partition pruning, improve suitability of a filtering strip containing like operators, and further improve query efficiency.
In particular, the invention provides a method for supporting like operation by partition pruning in SQL, which comprises the following steps:
when a like operator in SQL contains a partition key of a partition table, acquiring a first character string contained in the like operator;
judging whether the first character string contains a wild card or not;
if yes, obtaining the partition type of the partition table;
and processing the first character string in a mode corresponding to the partition type so that the partition table supports like operation.
Optionally, when the partition type is a range partition, the processing the first string in a manner corresponding to the partition type includes:
acquiring a second character string before a first wildcard character in the first character string from the beginning of the first character string;
creating a third string, wherein the third string is larger than the second string;
and creating a filtering condition according to the second character string and the third character string.
Optionally, the creating the filtering condition according to the second character string and the third character string includes:
and enabling the partition key to be larger than or equal to the second character string and smaller than the third character string.
Optionally, the creating the third string includes:
and adding 1 to the ASCII of the last bit of the second character string to obtain the third character string.
Optionally, when the partition type is a list partition, the processing the first string in a manner corresponding to the partition type includes:
acquiring a second character string before a first wildcard character in the first character string from the beginning of the first character string;
judging whether the first character string contains "%" wild cards;
if not, judging whether the partition key contains the second character string or not, and judging whether the length of the partition key is equal to the length of the first character string or not;
if the partition key comprises the second character string and the length of the partition key is equal to the length of the first character string, the partition table is reserved.
Optionally, if the first character string includes a "%" wild card, judging whether the partition key includes the second character string;
if yes, reserving the partition table.
Optionally, if the first string does not include a wild card, the like operator is converted into an operator supported by the partition table.
Optionally, the converting the like operator to an operator supported by the partition table includes:
the like operator is converted to an "=" operator.
According to another aspect of the present invention, there is also provided a machine-readable storage medium having stored thereon a machine-executable program which when executed by a processor implements a method of any of the above in-SQL partition pruning support like operations.
According to yet another aspect of the present invention, there is also provided a computer device including a memory, a processor, and a machine executable program stored on the memory and running on the processor, and the processor implementing a method of partitioning pruning support like operation in SQL of any of the above when executing the machine executable program.
In the method for supporting like operation by partition pruning in SQL, when the like operation symbol in SQL comprises the partition key of the partition table, the first character string contained in the like operation symbol is obtained, whether the first character string comprises the wild card symbol is judged, if yes, the partition type of the partition table is obtained, and the first character string is processed in a mode corresponding to the partition type so as to enable the partition table to support like operation, so that the optimizer can apply the filter condition containing the like operation symbol in the partition pruning, the suitability of the filter bar containing the like operation symbol is improved, and the operation efficiency is further improved.
Further, assume that the first string S1 is 1234%567%. The second string may be defined as S2. The second string S2 before the first wildcard in the first string S1 is 1234, and it will be understood that the second string before the first wildcard is acquired from the beginning of the first string S1. The third string is defined as S3. The third string S3 is larger than the second string S2, for example, the third string S3 is 1235. By creating a third string, wherein the third string is larger than the second string, and creating filtering conditions based on the second string and the third string, the concept is very novel and smart, improving the suitability of the filter bar containing like operators.
The above, as well as additional objectives, advantages, and features of the present invention will become apparent to those skilled in the art from the following detailed description of a specific embodiment of the present invention when read in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter by way of example and not by way of limitation with reference to the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts or portions. It will be appreciated by those skilled in the art that the drawings are not necessarily drawn to scale. In the accompanying drawings:
FIG. 1 is a schematic flow diagram of a method for supporting like operations by partition pruning in SQL according to one embodiment of the invention;
FIG. 2 is a schematic flow chart of a method of supporting like operations by partition pruning in SQL according to another embodiment of the invention;
FIG. 3 is a schematic flow chart of a method of supporting like operations by partition pruning in SQL according to another embodiment of the invention;
FIG. 4 is a schematic diagram of a machine-readable storage medium according to one embodiment of the invention;
FIG. 5 is a schematic diagram of a computer device according to one embodiment of the invention.
Detailed Description
FIG. 1 is a schematic flow diagram of a method for supporting like operations by partition pruning in SQL according to one embodiment of the invention. The method of partition pruning to support like operations in SQL may generally include:
step S102: when the like operator in SQL contains the partition key of the partition table, the first character string contained in the like operator is acquired.
In this step, since the objective of the present solution is to enable the partition pruning to support like operations (i.e. support like filtering conditions), it is necessary to perform the partition operations when the partition keys of the partition table are included, and if the like operator does not include the partition keys of the partition table, the present solution may not be performed. The first string may be defined as S1. The partition key may be defined as P1.
Step S104: whether the first character string contains a wild card is judged. Wild cards may generally include% as well as underlines, etc.
If yes, step S106 is executed: the partition type of the partition table is obtained. Partition types of partition tables may generally include range partitions, list partitions, hash partitions, and the like.
Step S108: and processing the first character string in a mode corresponding to the partition type so as to enable the partition table to support like operation. In this step, different partition types may correspond to different processing manners. And processing the first character string in a mode corresponding to the partition type to obtain new filtering conditions so as to be applicable to partition pruning, so that the partition table supports like operation.
In this embodiment, when the like operator in the structured query language (Structured Query Language, abbreviated as SQL) includes the partition key of the partition table, the first string included in the like operator is obtained, whether the first string includes the wild card symbol is determined, if yes, the partition type of the partition table is obtained, and the first string is processed in a manner corresponding to the partition type to obtain a new filtering condition, so that the partition table supports the like operation, which realizes that the optimizer can apply the filtering condition including the like operator to the partition pruning, improves the suitability of the filtering strip including the like operator, further improves the operation (such as query operation) efficiency, for example, the data table of 5 partitions, the execution time before the scheme is applied is 54ms, and the execution time after the scheme is applied is 0.02ms, which is greatly improved.
In one embodiment of the present invention, when the partition type is a range partition, processing the first string in a manner corresponding to the partition type may include:
acquiring a second character string before a first wildcard character in the first character string from the beginning of the first character string;
creating a third string, wherein the third string is larger than the second string;
a filter condition is created based on the second string and the third string.
In the present embodiment, the first string S1 is assumed to be 1234%567%. The second string may be defined as S2. The second string S2 from the beginning of the first string S1 to before the first wildcard is 1234, and it is understood that the second string S1 is acquired from the beginning of the first string S until the second string S before the first wildcard is acquired. The third string is defined as S3. The third string S3 is larger than the second string S2, for example, the third string S3 is 1235. By creating a third string, wherein the third string is larger than the second string, and creating filtering conditions based on the second string and the third string, the concept is very novel and smart, improving the suitability of the filter bar containing like operators. Further, assuming that the first string S1 is% 1234%567%, the length of the second string S2 is 0, and in this case, it cannot be used for the division pruning.
In one embodiment of the present invention, creating the filter condition from the second string and the third string may include:
the partition key is made to be larger than or equal to the second character string and smaller than the third character string.
In the present embodiment, the partition key is greater than or equal to the second character string and smaller than the third character string, i.e. "P1> =s2 and P1< S3".
In this embodiment, the filter condition created according to the second string and the third string is "P1> =s2 and P1< S3", which can be more consistent with the original filter condition in like operation, and has high rationality.
In one embodiment of the present invention, creating the third string may include:
the last ASCII of the second string may be incremented by 1 to obtain a third string.
In this embodiment, assuming that the second string S2 is 1234, the last ASCII of the second string is added to 1 to obtain the third string 1235. The third string S3 is obtained by adding 1 to the ASCII of the last bit of the second string, and the third string obtained in this regular manner can avoid being excessively large compared with the second string, which is beneficial to ensuring the rationality of the obtained filtering condition (e.g. "P1> =s2 and P1< S3"). Of course, the last ASCII of the second string may be added with 2, 3, 4, etc., that is, with a preset threshold.
In one embodiment of the present invention, when the partition type is a list partition, processing the first string in a manner corresponding to the partition type may include:
acquiring a second character string before a first wildcard character in the first character string from the beginning of the first character string;
judging whether the first character string contains "%" wild cards;
if not, judging whether the partition key contains the second character string and whether the length of the partition key is equal to that of the first character string;
if the partition key comprises the second character string and the length of the partition key is equal to that of the first character string, reserving a partition table.
In this embodiment, when the first string does not include the "%" wild card, if the partition key includes the second string and the length of the partition key is equal to the length of the first string, it is indicated that the filtering condition can be applied to partition pruning, the partition table is reserved, so that the suitability of the filtering strip including the like operator is improved, otherwise, the filtering strip is filtered.
In one embodiment of the present invention, if the first string contains "%" wild cards, determining whether the partition key contains the second string;
if yes, reserving a partition table.
In this embodiment, if the first string includes "%" wild cards, it is determined whether the partition key includes the second string; if yes, the filtering condition can be applied to partition pruning, a partition table is reserved, the suitability of the filtering strip containing like operators is improved, and otherwise, the filtering condition is filtered. In addition, when the partition type is a hash partition, if the first string includes a wild card, the filtering condition cannot be used for partition pruning.
In one embodiment of the present invention, if the first string does not include a wild card, the like operator is converted into an operator supported by the partition table.
In this embodiment, if the first string does not include a wild card, the like operator is converted into an operator supported by the partition table, and this method may be applicable to each partition type, such as a range partition, a list partition, and a hash partition, which has a high adaptability, and improves the adaptability of the filter stripe including the like operator.
In one embodiment of the invention, converting like operators to partition table supported operators includes:
the like operator may be converted to a "=" operator.
And processing the first character string S1 in a mode corresponding to the partition type so as to enable the partition table to support like operation.
FIG. 2 is a schematic flow chart of a method for supporting like operations by partition pruning in SQL according to another embodiment of the invention, which can include:
step S202: when the like operator in the SQL contains the partition key P1 of the partition table, a first character string S1 contained in the like operator is acquired.
Step S204: it is determined whether the first string S1 contains a wild card.
If yes, go to step S206, if no, go to step S212.
Step S206: when the partition type is a range partition, a second character string S2 preceding the first wild card in the first character string S1 is acquired from the beginning of the first character string S1.
Step S208: if the second string S2 is greater than 0, a third string S3 is created, wherein the third string S3 is greater than the second string S2.
Step S210: the partition key P1 is made larger than or equal to the second string S2and smaller than the third string S3 as filtering conditions so that the partition table supports like operation.
Step S212: the like operator is converted to "=" operator as a filtering condition so that the partition table supports like operation.
FIG. 3 is a schematic flow chart of a method for supporting like operations by partition pruning in SQL according to another embodiment of the invention, which can include:
step 302: when the like operator in the SQL contains the partition key P1 of the partition table, a first character string S1 contained in the like operator is acquired.
Step S304: it is determined whether the first string S1 contains a wild card.
If yes, go to step S306, if no, go to step S314.
Step S306: when the partition type is a list partition, a second character string S2 preceding the first wild card in the first character string S1 is acquired from the beginning of the first character string S1.
Step S308: it is determined whether "%" wild cards are included in the first string S1.
If not, step S310 is executed, and if yes, step S312 is executed.
Step S310: if the partition key P1 includes the second string S2and the length of the partition key P1 is equal to the length of the first string S1, the filter condition is used to make the partition table support like operation.
Step S312: if the partition key P1 contains the second string S2, a filtering condition is used to make the partition table support like operation.
Step S314: the like operator is converted into an operator of "=".
The above embodiments may be combined arbitrarily, and according to any one of the above preferred embodiments or a combination of a plurality of preferred embodiments, the following beneficial effects can be achieved according to the embodiments of the present invention:
in the method for supporting like operation by partition pruning in SQL, when the like operation symbol in the structured query language (Structured Query Language, abbreviated as SQL) comprises the partition key of the partition table, the first character string contained in the like operation symbol is obtained, whether the first character string contains the wild card symbol is judged, if yes, the partition type of the partition table is obtained, and the first character string is processed in a mode corresponding to the partition type, so that the partition table supports like operation, the filter condition containing the like operation symbol can be applied to the partition pruning by the optimizer, the suitability of the filter bar containing the like operation symbol is improved, and the query efficiency is further improved.
The present embodiment also provides a machine-readable storage medium and a computer device. FIG. 4 is a schematic diagram of a machine-readable storage medium 830 according to one embodiment of the invention; fig. 5 is a schematic diagram of a computer device 900 according to one embodiment of the invention. The machine-readable storage medium 830 has stored thereon a machine-executable program 840, which when executed by a processor, implements a method of computing the number of selection rows in a structured query language of a database of any of the embodiments described above.
Computer device 900 may include a memory 920, a processor 910, and a machine executable program 840 stored on memory 920 and running on processor 910, and processor 910 implements the method of computing the number of selection rows in the structured query language of the database of any of the embodiments described above when executing machine executable program 840.
It should be noted that the logic and/or steps represented in the flow diagrams or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any machine-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
For the purposes of this description of the embodiment, a machine-readable storage medium 830 can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium 40 may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system.
The computer device 900 may be, for example, a server, a desktop computer, a notebook computer, a tablet computer, or a smartphone. In some examples, computer device 900 may be a cloud computing node. Computer device 900 may be described in the general context of computer-system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. Computer device 900 may be implemented in a distributed cloud computing environment where remote processing devices coupled via a communications network perform tasks. In a distributed cloud computing environment, program modules may be located in both local and remote computing system storage media including memory storage devices.
Computer device 900 may include a processor 910 adapted to execute stored instructions, a memory 920 providing temporary storage for the operation of the instructions during operation. Processor 910 may be a single core processor, a multi-core processor, a computing cluster, or any number of other configurations. Memory 920 may include Random Access Memory (RAM), read only memory, flash memory, or any other suitable storage system.
Processor 910 may be connected by a system interconnect (e.g., PCI-Express, etc.) to an I/O interface (input/output interface) adapted to connect computer device 900 to one or more I/O devices (input/output devices). The I/O devices may include, for example, a keyboard and a pointing device, which may include a touch pad or touch screen, among others. The I/O device may be a built-in component of the computer device 900 or may be a device externally connected to the computing device.
The processor 910 may also be linked by a system interconnect to a display interface suitable for connecting the computer device 900 to a display device. The display device may include a display screen as a built-in component of the computer device 900. The display device may also include a computer monitor, television, projector, or the like, that is externally connected to the computer device 900. Further, a network interface controller (network interface controller, NIC) may be adapted to connect the computer device 900 to a network through a system interconnect. In some embodiments, the NIC may use any suitable interface or protocol (such as an internet small computer system interface, etc.) to transfer data. The network may be a cellular network, a radio network, a Wide Area Network (WAN), a Local Area Network (LAN), or the internet, among others. The remote device may be connected to the computing device through a network.
The flowcharts provided by this embodiment are not intended to indicate that the operations of the method are to be performed in any particular order, or that all of the operations of the method are included in all of each case. Furthermore, the method may include additional operations. Additional variations may be made to the above-described methods within the scope of the technical ideas provided by the methods of the present embodiments.
By now it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been shown and described herein in detail, many other variations or modifications of the invention consistent with the principles of the invention may be directly ascertained or inferred from the present disclosure without departing from the spirit and scope of the invention. Accordingly, the scope of the present invention should be understood and deemed to cover all such other variations or modifications.

Claims (10)

1. A method for supporting like operations by partition pruning in SQL, comprising:
when a like operator in SQL contains a partition key of a partition table, acquiring a first character string contained in the like operator;
judging whether the first character string contains a wild card or not;
if yes, obtaining the partition type of the partition table;
and processing the first character string in a mode corresponding to the partition type so that the partition table supports like operation.
2. The method for supporting like operations in partition pruning in SQL of claim 1 wherein,
when the partition type is a range partition, the processing the first string in a manner corresponding to the partition type includes:
acquiring a second character string before a first wildcard character in the first character string from the beginning of the first character string;
creating a third string, wherein the third string is larger than the second string;
and creating a filtering condition according to the second character string and the third character string.
3. The method for supporting like operations in partition pruning in SQL according to claim 2, wherein,
the creating a filter condition from the second string and the third string includes:
and enabling the partition key to be larger than or equal to the second character string and smaller than the third character string.
4. The method for supporting like operations in partition pruning in SQL according to claim 2, wherein,
the creating a third string includes:
and adding 1 to the ASCII of the last bit of the second character string to obtain the third character string.
5. The method for supporting like operations in partition pruning in SQL of claim 1 wherein,
when the partition type is a list partition, the processing the first string in a manner corresponding to the partition type includes:
acquiring a second character string before a first wildcard character in the first character string from the beginning of the first character string;
judging whether the first character string contains "%" wild cards;
if not, judging whether the partition key contains the second character string or not, and judging whether the length of the partition key is equal to the length of the first character string or not;
if the partition key comprises the second character string and the length of the partition key is equal to the length of the first character string, the partition table is reserved.
6. The method for supporting like operations in partition pruning in SQL of claim 5 wherein,
if the first character string contains a "%" wild card, judging whether the partition key contains the second character string;
if yes, reserving the partition table.
7. The method for supporting like operations in partition pruning in SQL of claim 1 wherein,
and if the first character string does not contain the wild card, converting the like operator into an operator supported by the partition table.
8. The method for supporting like operations in partition pruning in SQL of claim 7 wherein,
the converting the like operator to an operator supported by the partition table includes:
the like operator is converted to an "=" operator.
9. A machine-readable storage medium having stored thereon a machine-executable program which when executed by a processor implements a method of partitioned pruning support like operation in SQL according to any one of claims 1 to 8.
10. A computer device comprising a memory, a processor and a machine executable program stored on the memory and running on the processor, and the processor when executing the machine executable program implements a method of partition pruning support like operation in SQL according to any one of claims 1 to 8.
CN202310174716.2A 2023-02-27 2023-02-27 Method and storage medium for supporting like operation by partition pruning in SQL (structured query language) Pending CN116126902A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310174716.2A CN116126902A (en) 2023-02-27 2023-02-27 Method and storage medium for supporting like operation by partition pruning in SQL (structured query language)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310174716.2A CN116126902A (en) 2023-02-27 2023-02-27 Method and storage medium for supporting like operation by partition pruning in SQL (structured query language)

Publications (1)

Publication Number Publication Date
CN116126902A true CN116126902A (en) 2023-05-16

Family

ID=86306335

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310174716.2A Pending CN116126902A (en) 2023-02-27 2023-02-27 Method and storage medium for supporting like operation by partition pruning in SQL (structured query language)

Country Status (1)

Country Link
CN (1) CN116126902A (en)

Similar Documents

Publication Publication Date Title
CN109739894B (en) Method, device, equipment and storage medium for supplementing metadata description
CN109299157B (en) Data export method and device for distributed big single table
CN109815240A (en) For managing method, apparatus, equipment and the storage medium of index
CN110222046B (en) List data processing method, device, server and storage medium
CN117076491A (en) Data processing method, storage medium and equipment
CN116467310A (en) Lock-free marking method for invalid index, storage medium and computer equipment
CN116719843A (en) Query method, storage medium and device for database system
CN116431672A (en) Predicate logic optimization method, storage medium and equipment of database operation statement
CN116662355A (en) Marking method for invalid index, storage medium and computer device
CN116126902A (en) Method and storage medium for supporting like operation by partition pruning in SQL (structured query language)
CN115757479A (en) Database query optimization method, machine-readable storage medium and computer device
CN116010345A (en) Method, device and equipment for realizing table service scheme of flow batch integrated data lake
CN116089527A (en) Data verification method, storage medium and device
CN114218261A (en) Data query method and device, storage medium and electronic equipment
CN113868138A (en) Method, system, equipment and storage medium for acquiring test data
CN108763498B (en) User identity identification method and device, electronic equipment and readable storage medium
CN112749189A (en) Data query method and device
CN117149821B (en) Query optimization method, storage medium and computer equipment
CN117235118B (en) Query optimization method, storage medium and computer equipment
CN112148710B (en) Micro-service library separation method, system and medium
CN116414866A (en) Optimization method, storage medium and device for database operation statement
CN116595010A (en) Database index optimization method, storage medium and device
CN117453770A (en) Processing method, medium and computer equipment for sequence value in database cluster
CN118035315A (en) Database statistical information processing method, storage medium and device
CN116595045A (en) Query optimization method for database, storage medium and computer equipment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination