CN109408539B - Data operation method, device, server and storage medium - Google Patents
Data operation method, device, server and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a data operation method, a data operation device, a server and a storage medium, wherein the method comprises the following steps: judging whether the data operation plan of the target table meets the optimization condition, wherein the data operation plan is a deletion plan or an update plan; if the data operation plan meets the optimization condition, performing data operation on the target table according to the data operation plan; and if the data operation plan does not meet the optimization condition, caching the data in the target table and then performing data operation on the target table according to the data operation plan. The embodiment of the invention can solve the problem of large occupied space of the cache in the prior art, improve the execution performance under the condition of ensuring the normal execution of data operation and reduce the occupied storage space in the execution process.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a data operation method, a data operation device, a server and a storage medium.
Background
The operation of a Data Manipulation Language (DML) of a database is divided into three cases: INSERT (INSERT), DELETE (DELETE), and UPDATE (UPDATE).
In the plan of deleting operation or updating operation of the database, firstly, the data meeting the conditions in the target table is inquired, then the result set is cached, and then deleting operation or updating operation is carried out on each line of cached data. The purpose of this is to prevent the data after the update operation from meeting the query condition, and then being queried again, and performing the update operation or the delete operation again, resulting in data error. Therefore, the child nodes of the top deletion node or the update node of the target table have a cache node, and the intermediate result set is cached. However, when the data size of the result set is very large, a very large memory space may be occupied, or the result set may be saved to a temporary tablespace disk file, which causes consumption of additional memory or disk space.
Disclosure of Invention
The embodiment of the invention provides a data operation method, a data operation device, a server and a storage medium, which can solve the problem of large occupied space of a cache in the prior art.
In a first aspect, an embodiment of the present invention provides a data operation method, including:
judging whether a data operation plan of a target table meets an optimization condition, wherein the data operation plan comprises a deletion plan or an update plan;
if the data operation plan meets the optimization condition, performing data operation on the target table according to the data operation plan;
if the data operation plan does not meet the optimization condition, caching the data in the target table and then performing data operation on the target table according to the data operation plan.
In a second aspect, an embodiment of the present invention further provides a data operating apparatus, where the apparatus includes:
the judging module is used for judging whether a data operation plan of the target table meets an optimization condition, wherein the data operation plan comprises a deletion plan or an update plan;
the first optimization module is used for executing data operation on the target table according to the data operation plan if the data operation plan meets the optimization condition;
and the second optimization module is used for caching the data in the target table and then performing data operation on the target table according to the data operation plan if the data operation plan does not meet the optimization condition.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of data manipulation as described above.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the data operation method described above.
The data operation plan of the target table is judged whether to meet the optimization condition or not, the data operation plan comprises a deletion plan or an update plan, and if the data operation plan meets the optimization condition, data operation is performed on the target table according to the data operation plan; and if the data operation plan does not meet the optimization condition, caching the data in the target table and then performing data operation on the target table according to the data operation plan. The embodiment of the invention can delete the cache node of the top node in the data operation plan meeting the optimization condition, solves the problem of large occupied space when all the data operation plans are cached in the prior art, improves the execution performance under the condition of ensuring the normal execution of the data operation and reduces the occupied storage space in the execution process.
Drawings
FIG. 1 is a flow chart of a data manipulation method according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a data manipulation method according to a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a data manipulation device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a server in the fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a data operation method in a first embodiment of the present invention, where the present embodiment is applicable to a case of implementing data operation, the method may be executed by a data operation device, and the device may be implemented in a software and/or hardware manner, for example, the device may be configured in a server. As shown in fig. 1, the method may specifically include:
and S110, judging whether the data operation plan of the target table meets the optimization condition, wherein the data operation plan is a deletion plan or an update plan.
The target table is a data table in a database, data can be stored in the target table in a B-tree structure, and the B-tree is a data structure and is a balanced lookup tree designed for a disk or other auxiliary storage devices with direct access. Most database systems employ B-trees or variations of B-trees to store information. The B-tree structure comprises a root node (also called a top node), a middle node, leaf nodes and the like, wherein data in the database is stored on the leaf nodes, each leaf node corresponds to one data page, and the data page comprises a plurality of physical records.
In this embodiment, the data operation plan includes a deletion plan or an update plan, the data operation plan is a tree structure, and data flows from a leaf node to a root node. The cache nodes in the data operation plan include a sequencing node, an aggregation node, a cache node and the like, and all data transmitted upwards by child nodes are cached and transmitted to an upper layer after being processed. The caching nodes may be distributed at various levels of the data operation plan. For example, a deletion plan may be:
create table t1(c1 int,c2 int,c3 int)；
explain delete from t1 where c1<x；
#DELETE:table(T1),type(select),mpp_opt(0)；
#NTTS2:for_mdis(FALSE)；
#PRJT2:exp_num(1),is_atom(FALSE)；
#SLCT2:T1.C1<x；
#CSCN2:INDEX33555446(T1).
where DELETE is a top-level node, NTTS2 is a cache node of the top-level node, CSCN2 is a leaf node, x represents a data query range of the target table t1, and a value of x may be set as needed, for example, x may be 10. The execution steps of the deletion plan are as follows: 1. CSCN2 scans the target table data; 2. SLCT2 data filtering, and querying the data meeting the conditions; 3. PRJT2 column mapping, returning upper layer required column data for deleting data positioning; 4. the NTTS2 inquires result data and caches to NTTS operator; 5. the DELETE DELETE operator locates DELETEs in the B-tree of the table for each piece of data in the NTTS.
Specifically, a recursive algorithm is adopted to judge whether a data operation plan of the target table does not cause result errors after data operation, if the result errors do not occur, the optimization condition is met, otherwise, the optimization condition is not met.
And S120, if the data operation plan meets the optimization condition, executing data operation on the target table according to the data operation plan.
Specifically, if the data operation plan meets the optimization condition, the cache node of the top node in the data operation plan is deleted, and query and data operation are simultaneously executed on the target table according to the data operation plan. For example, if the data operation plan is a deletion plan, the target table is searched for data to be deleted according to the deletion plan, and deletion of the data is executed.
If the data operation plan does not include the leaf node of the target table, then the optimization condition is considered to be satisfied, for example, if the target table is T1, and the leaf node "# SLCT2: T2.c4< y" includes T2, it can be optimized because it does not include the target table.
If a cache node (a cache node not in the top node) already exists in the data operation plan, the lower layer of the node related to the target table is scanned and completed before the node is deleted or updated, and the result is cached, the cache node plan branches the data of the target table in advance, and the subsequent deletion operation or update operation performed on the data of the target table does not affect the correctness, so that the optimization can be performed, and the upward recursion can be continued. Illustratively, the data operation plan includes cache nodes "# AAGR2: grp _ num (0), sfun _ num (1)" of non-top nodes in addition to the cache nodes "# NTTS2: for _ mdis (false)", which are top nodes, and can be optimized.
And S130, if the data operation plan does not meet the optimization condition, caching the data in the target table and then performing data operation on the target table according to the data operation plan.
And if the data operation plan does not meet the optimization condition, inquiring data needing to be operated on the target table according to the data operation plan, caching the data through the cache node of the top node, and then executing the data operation on the data.
According to the technical scheme of the embodiment, whether the data operation plan of the target table meets the optimization condition is judged, the data operation plan comprises a deletion plan or an update plan, and if the data operation plan meets the optimization condition, data operation is performed on the target table according to the data operation plan; and if the data operation plan does not meet the optimization condition, caching the data in the target table and then performing data operation on the target table according to the data operation plan. According to the embodiment, the cache nodes of the top-level nodes in the data operation plan meeting the optimization conditions can be deleted, the problem that the occupied space is large when all the data operation plans are cached in the prior art is solved, the execution performance is improved under the condition that the data operation is normally executed, and the occupied storage space in the execution process is reduced.
Example two
Fig. 2 is a flowchart of a data operation method in the second embodiment of the present invention. On the basis of the above embodiments, the present embodiment further optimizes the data operation method. Correspondingly, as shown in fig. 2, the method of the embodiment specifically includes:
s210, judging whether the data operation plan of the target table meets the optimization condition, wherein the data operation plan comprises a deletion plan or an update plan.
Specifically, a recursive algorithm is adopted to judge whether the data operation plan of the target table comprises the associated operator from bottom to top. If the data operation plan includes the associated operator, after the data operation is performed, the result may be associated with the following data operation, which may cause an error.
The associated operator comprises at least one of an index operator and an associated binary operator, wherein the associated binary operator is a binary operator which needs to perform at least two operations on the target table, such as nested join and index join. The specific type of the associated operator is not limited in this embodiment.
And S220, if the data operation plan of the target table does not comprise the associated operator, executing data operation on the target table according to the data operation plan.
Specifically, if the data operation plan does not include the associated operator, the optimization condition is satisfied, the cache node of the top node in the data operation plan is deleted, and query and data operation are simultaneously performed on the target table according to the data operation plan.
Illustratively, if an erase plan is:
create table t1(c1 int,c2 int,c3 int)；
explain delete from t1 where c1<x；
#DELETE:table(T1),type(select),mpp_opt(0)；
#NTTS2:for_mdis(FALSE)；
#PRJT2:exp_num(1),is_atom(FALSE)；
#SLCT2:T1.C1<x；
#CSCN2:INDEX33555446(T1).
starting from the CSCN2 containing the target table T1, it is considered to be optimized, and the above judgment is continued until the top node's caching node NTTS2, where no associative operator exists, can determine that the NTTS2 in the deletion plan can be optimized, i.e., deleted. When the method is executed, the CSCN2 scans the B tree of the T1 table in sequence, a batch of line data meeting deletion conditions is inquired and deleted by a DELETE operator, after the batch of line data is deleted, the CSCN2 continues to scan the B tree backwards after the last position scanned originally, and the inquiry result is not influenced.
And S230, if the data operation plan of the target table comprises the associated operator, caching the data in the target table and then performing data operation on the target table according to the data operation plan.
Specifically, if the data operation plan includes the associated operator, the data operation on the data is executed after the data operation plan queries the data to be operated on the target table and the data is cached by the cache node of the top node.
If the data operation is planned as an update plan and the update column contains index operators used by the query, then optimization cannot be achieved. The reason is that if the query uses an index operator, the scanned index B-tree is updated, and the updated data may be scanned while the query is continued, which may cause query data errors. If the update plan re-inserts the index operator for deletion, for example, inserts the cluster index, the physical storage location of the data in the target table B-tree may be changed, and the updated data may be scanned while continuing the query, which may cause query data errors, and thus the optimization is not possible. The database index is divided into a primary index and a secondary index, wherein the clustered index belongs to the primary index.
If the data operation plan includes associated binary operators, such as nested links and index links, after one of the child nodes deletes or updates a portion of the data, the other child node may rescan the target table, which may involve the updated data and cause a result error, and therefore, the optimization cannot be performed in this case. Illustratively, if the data operation plan includes a left child node "# HASH2 INNER JOIN: KEY _ NUM (1)" and a right child node "# CSCN2: INDEX33555448(T1 as C)" nested together with NEST LOOP INNER JOIN2, the target table T1 is executed multiple times in the right child node, and thus cannot be optimized.
According to the technical scheme of the embodiment, whether the data operation plan of the target table meets the optimization condition is judged, the data operation plan comprises a deletion plan or an update plan, and if the data operation plan of the target table does not comprise the associated operator, data operation is performed on the target table according to the data operation plan; and if the data operation plan of the target table comprises the associated operator, caching the data in the target table and then performing data operation on the target table according to the data operation plan. According to the embodiment, the cache nodes of the top-level nodes in the data operation plan meeting the optimization conditions can be deleted, the problem that the occupied space is large when all the data operation plans are cached in the prior art is solved, the execution performance is improved under the condition that the data operation is normally executed, and the occupied storage space in the execution process is reduced.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a data operation device according to a third embodiment of the present invention, which is applicable to a data operation. The data operation device provided by the embodiment of the invention can execute the data operation method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. The apparatus specifically includes a determining module 310, a first optimizing module 320, and a second optimizing module 330, wherein:
a judging module 310, configured to judge whether a data operation plan of the target table meets an optimization condition, where the data operation plan includes a deletion plan or an update plan;
a first optimization module 320, configured to perform a data operation on the target table according to the data operation plan if the data operation plan meets the optimization condition;
the second optimization module 330 is configured to, if the data operation plan does not meet the optimization condition, cache the data in the target table and perform data operation on the target table according to the data operation plan.
The data operation plan of the target table is judged whether to meet the optimization condition or not, the data operation plan comprises a deletion plan or an update plan, and if the data operation plan meets the optimization condition, data operation is performed on the target table according to the data operation plan; and if the data operation plan does not meet the optimization condition, caching the data in the target table and then performing data operation on the target table according to the data operation plan. The embodiment of the invention can delete the cache node of the top node in the data operation plan meeting the optimization condition, solves the problem of large occupied space when all the data operation plans are cached in the prior art, improves the execution performance under the condition of ensuring the normal execution of the data operation and reduces the occupied storage space in the execution process.
Optionally, the data operation plan satisfies the optimization conditions as follows: the data operation plan of the target table does not include an associative operator;
the data operation plan not meeting the optimization conditions is as follows: the data operation plan of the target table includes associative operators.
Optionally, the associated operator comprises at least one of an index operator and an associated binary operator.
The data operation device provided by the embodiment of the invention can execute the data operation method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 4 is a schematic structural diagram of a server in the fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary server 412 suitable for use in implementing embodiments of the present invention. The server 412 shown in fig. 4 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present invention.
As shown in FIG. 4, server 412 is in the form of a general purpose server. Components of server 412 may include, but are not limited to: one or more processors 416, a storage device 428, and a bus 418 that couples the various system components including the storage device 428 and the processors 416.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for instance, in storage 428, such program modules 442 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 442 generally perform the functions and/or methodologies of the described embodiments of the invention.
The server 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing terminal, display 424, etc.), with one or more terminals that enable a user to interact with the server 412, and/or with any terminals (e.g., network card, modem, etc.) that enable the server 412 to communicate with one or more other computing terminals. Such communication may occur via input/output (I/O) interfaces 422. Further, server 412 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), and/or a public Network, such as the Internet) via Network adapter 420. As shown in FIG. 4, network adapter 420 communicates with the other modules of server 412 via bus 418. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the server 412, including but not limited to: microcode, end drives, Redundant processors, external disk drive Arrays, RAID (Redundant Arrays of Independent Disks) systems, tape drives, and data backup storage systems, among others.
The processor 416 executes various functional applications and data processing by executing programs stored in the storage device 428, for example, implementing a data manipulation method provided by an embodiment of the present invention, the method including:
judging whether a data operation plan of the target table meets an optimization condition, wherein the data operation plan comprises a deletion plan or an update plan;
if the data operation plan meets the optimization condition, performing data operation on the target table according to the data operation plan;
and if the data operation plan does not meet the optimization condition, caching the data in the target table and then performing data operation on the target table according to the data operation plan.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a data operation method provided in an embodiment of the present invention, where the method includes:
judging whether a data operation plan of the target table meets an optimization condition, wherein the data operation plan comprises a deletion plan or an update plan;
if the data operation plan meets the optimization condition, performing data operation on the target table according to the data operation plan;
and if the data operation plan does not meet the optimization condition, caching the data in the target table and then performing data operation on the target table according to the data operation plan.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or terminal. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (8)
1. A method of data manipulation, comprising:
judging whether a data operation plan of a target table meets an optimization condition, wherein the data operation plan is a deletion plan or an update plan;
if the data operation plan meets the optimization condition, deleting the cache node of the top node in the data operation plan, and simultaneously executing data operation on the target table according to the data operation plan;
if the data operation plan does not meet the optimization condition, caching the data in the target table and then performing data operation on the target table according to the data operation plan.
2. The method of claim 1, wherein the data operation plan satisfying the optimization condition is: the data operation plan of the target table does not include an associative operator;
the data operation plan not meeting the optimization condition is as follows: the data operation plan of the target table includes associative operators.
3. The method of claim 2, wherein the associative operator comprises at least one of an index operator and an associative binary operator.
4. A data manipulation device, comprising:
the judging module is used for judging whether a data operation plan of the target table meets an optimization condition, wherein the data operation plan is a deletion plan or an update plan;
the first optimization module is used for deleting the cache node of the top node in the data operation plan and executing data operation on the target table according to the data operation plan if the data operation plan meets the optimization condition;
and the second optimization module is used for caching the data in the target table and then performing data operation on the target table according to the data operation plan if the data operation plan does not meet the optimization condition.
5. The apparatus of claim 4, wherein the data operation plan satisfying the optimization condition is: the data operation plan of the target table does not include an associative operator;
the data operation plan not meeting the optimization condition is as follows: the data operation plan of the target table includes associative operators.
6. The apparatus of claim 5, wherein the associated operator comprises at least one of an index operator and an associated binary operator.
7. A server, characterized in that the server comprises:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method of data manipulation as claimed in any one of claims 1 to 3.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the data manipulation method according to any one of claims 1 to 3.
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| CN113742307B (en) * | 2021-08-11 | 2023-08-25 | 达梦数据技术(江苏)有限公司 | Method and system for storing and inquiring secondary index based on value log system |
| CN113704329B (en) * | 2021-09-03 | 2023-06-20 | 上海达梦数据库有限公司 | Method, device, equipment and storage medium for changing data distribution |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104620239A (en) * | 2012-09-28 | 2015-05-13 | 甲骨文国际公司 | Adaptive query optimization |
| CN105426467A (en) * | 2015-11-16 | 2016-03-23 | 北京京东尚科信息技术有限公司 | SQL query method and system for Presto |
| CN106815339A (en) * | 2017-01-03 | 2017-06-09 | 北京华胜信泰数据技术有限公司 | The method and apparatus that inquiry plan updates |
| CN108205539A (en) * | 2016-12-16 | 2018-06-26 | 华为技术有限公司 | A kind of data search method and equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10108667B2 (en) * | 2016-03-07 | 2018-10-23 | International Business Machines Corporation | Query plan optimization for large payload columns |
-
2018
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104620239A (en) * | 2012-09-28 | 2015-05-13 | 甲骨文国际公司 | Adaptive query optimization |
| CN105426467A (en) * | 2015-11-16 | 2016-03-23 | 北京京东尚科信息技术有限公司 | SQL query method and system for Presto |
| CN108205539A (en) * | 2016-12-16 | 2018-06-26 | 华为技术有限公司 | A kind of data search method and equipment |
| CN106815339A (en) * | 2017-01-03 | 2017-06-09 | 北京华胜信泰数据技术有限公司 | The method and apparatus that inquiry plan updates |
Non-Patent Citations (2)
| Title |
|---|
| Caching all plans with just one optimizer call;Debabrata Dash，等;《2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010)》;20100422;全文 * |
| ORACLE中SQL执行原理及性能优化研究;刘光霆，等;《计算机软件与应用》;20090630;第26卷(第6期);全文 * |
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