CN117633024B - Database optimization method based on preprocessing optimization join - Google Patents
Database optimization method based on preprocessing optimization join Download PDFInfo
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- CN117633024B CN117633024B CN202410090319.1A CN202410090319A CN117633024B CN 117633024 B CN117633024 B CN 117633024B CN 202410090319 A CN202410090319 A CN 202410090319A CN 117633024 B CN117633024 B CN 117633024B
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- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000005457 optimization Methods 0.000 title claims abstract description 44
- 238000007781 pre-processing Methods 0.000 title claims abstract description 18
- 238000005192 partition Methods 0.000 claims abstract description 40
- 230000008569 process Effects 0.000 claims description 21
- 238000004590 computer program Methods 0.000 claims description 6
- 238000012216 screening Methods 0.000 claims description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000012545 processing Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
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- 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
- G06F16/24534—Query rewriting; Transformation
- G06F16/24537—Query rewriting; Transformation of operators
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2228—Indexing structures
- G06F16/2255—Hash tables
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
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- 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
- G06F16/2455—Query execution
- G06F16/24553—Query execution of query operations
- G06F16/24558—Binary matching operations
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- 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
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Abstract
The invention provides a database optimization method based on preprocessing optimization join, which comprises the following steps: saving the input table structure, and setting a data group, a hash index and a partition identifier for the table; selecting a corresponding loading area for the data of the table, and executing hash lookup operation in the loading area before the selected loading area; converting the original SQL according to the setting in the metadata manager, and removing join operation in the SQL; and inserting the converted SQL into a target table to finish the optimization of the database. The invention has the beneficial effects that: the join operation between the two tables is optimized, the two tables join is optimized into single-table partition scanning, large-range scanning data is avoided, the data scanning range is reduced, the resource use is reduced, and the overall time of inquiry is greatly shortened.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a database optimization method based on preprocessing optimization join.
Background
The database is used as common technical content in the technical field of computing, the application range of the database is very wide, and in the application process of the database, because the service needs to frequently perform the association query among multiple tables, the defects of performing the association query in the prior art mainly comprise the following aspects:
JOIN operations in associative queries typically involve concatenating multiple tables, which may result in higher performance overhead, particularly when processing large amounts of data, the computational cost and time complexity of JOIN operations may increase significantly;
The use of JOIN operations increases the complexity of the query statement, especially where multiple tables, multiple conditions are involved, which increases the difficulty of understanding and maintaining the query, as well as the potential for introducing errors;
JOIN operations may involve the use of significant memory and computing resources, especially when large tables are connected. This may result in system resources being strained, affecting the execution of other tasks;
Because of the complexity of JOIN operations, the response time of queries may be long, especially in large databases, which can reduce user experience, especially for application scenarios requiring fast queries and responses;
when using JOIN operation, it is necessary to ensure accuracy and data consistency of connection conditions, and if the connection conditions are incorrect or the data are inconsistent, erroneous query results may be caused;
As the amount of data increases, queries using JOIN operations can be difficult to effectively scale up, which makes performance optimization for processing large-scale data sets more complex;
In summary, JOIN operations used in performing related queries in the prior art may limit query performance, increase difficulty in development and maintenance, and introduce some potential data consistency and resource consumption issues.
Disclosure of Invention
In view of the above, the present invention aims to propose a database optimization method based on preprocessing optimization join, in order to solve at least one of the above-mentioned part of technical problems.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
The first aspect of the invention provides a database optimization method based on preprocessing optimization join, which comprises the following steps:
saving the input table structure, and setting a data group, a hash index and a partition identifier for the table;
Selecting a corresponding loading area for the data of the table, and executing hash lookup operation in the loading area before the selected loading area;
Converting the original SQL according to the setting in the metadata manager, and removing join operation in the SQL;
and inserting the converted SQL into a target table to finish the optimization of the database.
Further, the process of setting the data set for the table includes:
saving the structure of the table in a metadata manager;
A data set is created in the metadata manager, each data in the data set corresponding to a respective row of data in the table.
Further, the hash index setting process for the table includes:
a hash index is created in the metadata manager, each piece of data in the hash index corresponding to a respective piece of data in the table.
Further, the process of setting the partition identifier for the table includes:
And taking one row of data in the table as the partition identification of each row of data corresponding to the data in the table.
Further, the process of selecting a corresponding loading area for the data of the table includes:
writing all the data carrying the same partition identification line in the table into the same partition;
And searching whether the data of the table corresponding to the data group exists in the previous partition of the partition where the current data exists or not through the hash index, and executing corresponding hash searching operation.
Further, the process of performing a hash lookup operation in a load region preceding the selected load region includes:
If there is data of the table corresponding to the data group in the previous partition, writing the value of the corresponding data into the data group;
If there is no data of the table corresponding to the data group in the previous partition, the value of the corresponding data is set to be null and written in the data group.
Further, the process of converting the original SQL according to the setting in the metadata manager and removing join operations includes:
replacing join operation in the original SQL with operation for screening non-null corresponding data;
when performing an associative query between two tables, the associative query is performed using only the data in the data group within the current partition and the data of the table corresponding to the data group.
A second aspect of the present invention provides an electronic device comprising a processor and a memory communicatively coupled to the processor for storing instructions executable by the processor, characterized by: the processor is configured to execute a database optimization method based on preprocessing optimization join according to any one of the first aspect.
A third aspect of the present invention provides a server, characterized in that: comprising at least one processor and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor to cause the at least one processor to perform a preprocessing-optimized join-based database optimization method as set forth in any of the first aspects.
A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements a database optimization method based on preprocessing optimization join as described in any one of the first aspects.
Compared with the prior art, the database optimization method based on preprocessing optimization join has the following beneficial effects:
The join operation between the two tables is optimized, the two tables join is optimized into single-table partition scanning, large-range scanning data is avoided, the data scanning range is reduced, the resource use is reduced, and the overall time of inquiry is greatly shortened.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
Fig. 1 is a schematic flow chart of a database optimization method based on preprocessing optimization join according to an embodiment of the invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The invention will be described in detail below with reference to the drawings in connection with embodiments.
Embodiment one:
as shown in fig. 1: a database optimization method based on preprocessing optimization join, comprising:
s1, saving an input table structure, and setting a data set, a hash index and a partition identifier for a table;
S2, selecting a corresponding loading area for the data of the table, and executing hash searching operation in the previous loading area of the selected loading area;
S3, converting the original SQL according to the setting in the metadata manager, and removing join operation in the SQL;
s4, inserting the converted SQL into a target table to finish optimization of the database.
The process of setting up a data set for a table includes: saving the structure of the table in a metadata manager;
A data set is created in the metadata manager, each data in the data set corresponding to a respective row of data in the table.
The process of setting a hash index for a table includes: a hash index is created in the metadata manager, each piece of data in the hash index corresponding to a respective piece of data in the table.
The process of setting partition identifications for the tables includes: and taking one row of data in the table as the partition identification of each row of data corresponding to the data in the table.
The process of selecting a corresponding loading region for data of a table includes: writing all the data carrying the same partition identification line in the table into the same partition;
And searching whether the data of the table corresponding to the data group exists in the previous partition of the partition where the current data exists or not through the hash index, and executing corresponding hash searching operation.
The process of performing a hash lookup operation in a previous load region to the selected load region includes:
If there is data of the table corresponding to the data group in the previous partition, writing the value of the corresponding data into the data group;
If there is no data of the table corresponding to the data group in the previous partition, the value of the corresponding data is set to be null and written in the data group.
Converting the original SQL according to the setting in the metadata manager, wherein the process for removing join operation comprises the following steps: replacing join operation in the original SQL with operation for screening non-null corresponding data;
when performing an associative query between two tables, the associative query is performed using only the data in the data group within the current partition and the data of the table corresponding to the data group.
The specific working process comprises the following steps:
The technical scheme in the specific embodiment of the invention is described in detail by a specific example, taking 1 table z_tmp as an example, the original SQL statement is as follows:
insert into update_tmp
SELECT
a.polno,a.earn_month_org,a.delay_month,a.ibnr-ifnull(b.ibnr,0)AS ibnr_cf
FROM
z_tmp a
LEFT JOIN
z_tmp b ON a.polno = b.polno AND a.earn_month_org =
b.earn_month_org AND a.delay_month+1 = b.delay_month;
Wherein a and b are aliases of z_tmp;
The value of delay_mole in delay_mole+1=b.delay_mole is 1 to 12 and is from the same table;
The processing procedure of the optimizing method is as follows:
The metadata manager is invoked and the table structure of the save input is as follows:
create table z_tmp
(polno int,earn_month_org int,delay_month int,
ibnr decimal(18,2));
setting a corresponding column: creating ibnr _b Decimal (18, 2) as a corresponding column in the metadata manager and establishing a corresponding relationship with ibnr;
The hash index is polno, earn_monta_org, delay_monta;
the delay_mole value is 1-12; as a partition column, 12 partitions are created, each storing 1, 2, 3..12;
and selecting a loaded partition and a partition corresponding to writing according to the delay_montage for each piece of data.
For example: polno is 100, earn_monta_org is 3, delay_monta is 6, partition 6 is inserted, and this piece of data is written in all field values of partition 6 (including ibnr values).
The unique key (100,3,6) for this piece of data is passed through a hash lookup (100,3,5) into partition 5 (6 immediately before), where 5 is 6 minus 1, if there is data, the ibnr value is written to ibnr _b, if there is no data, insert a piece of data, set ibnr for this data to null, and the ibnr value is written to ibnr _b.
There are two cases after all loading is complete:
For each piece of data, taking the above example, if there is corresponding data (data uniquely identified as polno:100, earn _mole_org: 3, delay_mole: 6, delay_mole=5), ibnr for that data is not null, and ibnr for that data is null if not.
The optimization process is as follows:
The select portion is converted into the following
SELECT
a.polno,a.earn_month_org,a.delay_month,a.ibnr-ifnull(ibnr_b,0)AS ibnr_cf
FROM
z_tmp a
where a.ibnr not null；
The condition of addition is that the sphere a.ibnr not null is used for filtering out data which does not correspond to the sphere;
the optimized statement does not have join, and can be executed by one scanning.
And calling an executor to insert the result into the target table update_tmp.
Embodiment two:
An electronic device comprising a processor and a memory communicatively coupled to the processor for storing processor-executable instructions, characterized in that: the processor is configured to execute a database optimization method based on preprocessing optimization join according to any one of the above embodiments.
Embodiment III:
A server, characterized by: the system comprises at least one processor and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor to cause the at least one processor to perform a preprocessing-optimized join-based database optimization method as set forth in any one of the embodiments.
Embodiment four:
A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements a database optimization method based on preprocessing optimization join as described in any one of the embodiments.
Those of ordinary skill in the art will appreciate that the elements and method steps of each example described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the elements and steps of each example have been described generally in terms of functionality in the foregoing description to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed methods and systems may be implemented in other ways. For example, the above-described division of units is merely a logical function division, and there may be another division manner when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. The units may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment of the present application.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. A database optimization method based on preprocessing optimization join, comprising:
saving the input table structure, and setting a data group, a hash index and a partition identifier for the table;
Selecting a corresponding loading area for the data of the table, and executing hash lookup operation in the loading area before the selected loading area;
Converting the original SQL according to the setting in the metadata manager, and removing join operation in the SQL;
inserting the converted SQL into a target table to finish the optimization of the database;
The process of setting partition identifications for the tables includes:
Taking a row of data in the table as a partition identifier of each row of data corresponding to the row of data in the table;
the process of selecting a corresponding loading region for data of a table includes:
writing all the data carrying the same partition identification line in the table into the same partition;
searching whether data of a table corresponding to the data group exists in a previous partition of the partition where the current data is located through the hash index, and executing corresponding hash searching operation;
the process of performing a hash lookup operation in a previous load region to the selected load region includes:
If there is data of the table corresponding to the data group in the previous partition, writing the value of the corresponding data into the data group;
If the data of the table corresponding to the data group does not exist in the previous partition, setting the value of the corresponding data to be null and writing the value into the data group;
the process of setting up a data set for a table includes:
saving the structure of the table in a metadata manager;
A data set is created in the metadata manager, each data in the data set corresponding to a respective row of data in the table.
2. The database optimization method based on preprocessing optimization join according to claim 1, wherein:
The process of setting a hash index for a table includes:
a hash index is created in the metadata manager, each piece of data in the hash index corresponding to a respective piece of data in the table.
3. The database optimization method based on preprocessing optimization join according to claim 1, wherein:
Converting the original SQL according to the setting in the metadata manager, wherein the process for removing join operation comprises the following steps:
replacing join operation in the original SQL with operation for screening non-null corresponding data;
when performing an associative query between two tables, the associative query is performed using only the data in the data group within the current partition and the data of the table corresponding to the data group.
4. An electronic device comprising a processor and a memory communicatively coupled to the processor for storing processor-executable instructions, characterized in that: the processor is configured to perform a preprocessing-based database optimization method of optimizing join as described in any one of claims 1-3.
5. A server, characterized by: comprising at least one processor and a memory communicatively coupled to the processor, the memory storing instructions executable by the at least one processor to cause the at least one processor to perform a preprocessing-optimized join-based database optimization method as recited in any one of claims 1-3.
6. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements a preprocessing-based optimization join database optimization method as claimed in any one of claims 1-3.
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