CN102945256A - Method and device for merging and classifying massive SQL (Structured Query Language) sentences - Google Patents
Method and device for merging and classifying massive SQL (Structured Query Language) sentences Download PDFInfo
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Abstract
The invention provides a method and a device for merging and classifying massive SQL (Structured Query Language) sentences. The method comprises the steps of S1, analyzing database environment and collecting all SQL sentences in the operating system; S2, parsing the SQL sentences to obtain variable values in the SQL sentences; replacing the variable values with constants to obtain parsed SQL sentences; and S3, calculating and obtaining HASH values of parsed SQL sentences, classifying and merging the SQL sentences according to the HASH values, and storing results after classifying and merging, wherein SQL sentences which are same after being parsed are similar SQL sentences. According to the technical scheme, similar SQL sentences and unique HASH values are obtained, so that the SQL sentences are merged and classified. For a system audited and monitored in same operation repeated, the space occupied by original SQL sentences is greatly reduced, thus, the storage space is greatly saved, and the data inquiry efficiency is greatly improved.
Description
Technical Field
The invention relates to a database, in particular to a method and a device for merging and classifying massive SQL sentences.
Background
At present, databases and management systems thereof meeting the requirements of many large-scale enterprises such as electronics, finance, government and the like are established so as to strengthen the management of increasingly expansive data warehouses. In order to maintain the stability of the management system, special maintenance management is required, and monitoring and analysis of the database are omitted in daily management. In the existing database monitoring analysis, the analysis and mining of the SQL data are often limited to the analysis of the operation mode and the operation object, and the association between the operation mode and the operation object is loose, so that the abnormal operation is difficult to find and identify due to huge data volume, complex data and complex semantics, the workload is tedious and huge, and a large amount of human resources and time are wasted.
By analyzing the prior art, the following disadvantages are summarized.
Disadvantage 1: the operation mode and the operation object are loosely associated, and the semanteme cannot be determined only by the two values.
And (2) disadvantage: similar SQL sentences are not classified and combined, so that the data volume is huge and the searching is difficult.
Disadvantage 3: and the method has no specific semantic meaning, so that the matching is inaccurate, and the false alarm is easy to occur.
Disclosure of Invention
The invention mainly solves the technical problem of providing a method and a device for merging and classifying massive SQL sentences, which are used for solving the defects in the prior art.
In order to solve the above problems, the present invention adopts a technical scheme that: the method for merging and classifying massive SQL sentences comprises the following steps:
s1, analyzing the database environment, and collecting all SQL statements in the operating system;
s2, analyzing each SQL statement to obtain variable values in the SQL statement; replacing the variable value with a constant to obtain an analyzed SQL statement;
s3, calculating and obtaining the HASH value of the analyzed SQL statement, classifying and combining the SQL statement according to the HASH value, and storing the result after classification and combination; and after analysis, the SQL sentences with the same SQL sentence are similar SQL sentences.
Wherein the classifying and merging the SQL statement according to the HASH value in S3 includes: and detecting whether the HASH value is stored in the running system, storing the HASH value under the condition that the HASH value is not stored, and storing the analyzed SQL statement corresponding to the HASH value into a similar statement table preset by the running system.
Wherein, the step S3 further includes: and counting the occurrence times of the HASH value.
Wherein, the step S3 is followed by the step: s4, obtaining the occurrence frequency of the HASH value counted by S3, and generating an occurrence frequency chart of similar SQL sentences according to the obtained occurrence frequency of the HASH value.
In order to solve the above problems, the present invention adopts another technical solution: the device for merging and classifying massive SQL sentences is provided, and comprises:
the acquisition module is used for analyzing the database environment and acquiring all SQL sentences in the operating system;
the analysis module is used for analyzing each SQL statement to obtain a variable value in the SQL statement; replacing the variable value with a constant to obtain an analyzed SQL statement;
the classification module is used for calculating and obtaining the HASH value of the analyzed SQL statement, classifying and combining the SQL statement according to the HASH value, and storing the result after classification and combination; and after analysis, the SQL sentences with the same SQL sentence are similar SQL sentences.
Wherein the classification module comprises: and the detection subunit is used for detecting whether the HASH value is stored in the operating system, storing the HASH value under the condition that the HASH value is not stored, and storing the analyzed SQL statement corresponding to the HASH value into a similar statement table preset by the operating system.
Wherein the classification module further comprises: and the counting subunit is used for counting the occurrence times of the HASH value.
The acquisition module is used for acquiring the occurrence times of the HASH values counted by the classification module and generating an occurrence frequency chart of similar SQL sentences according to the acquired occurrence times of the HASH values.
The invention has the beneficial effects that: different from the defects of the prior art, the invention provides the method and the device for merging and classifying the massive SQL sentences; for a system which is audited and monitored and repeats the same operation every day, a large number of similar statements exist in the system, the space occupied by the original SQL statements is greatly reduced, the storage space is greatly saved, and the data query efficiency is greatly improved due to the rapid reduction of the data volume. It will further be appreciated that in a stable audit monitored system, the number of similar statements will be in a stable state and will slowly no longer increase as the audit system operates.
Drawings
FIG. 1 is a flowchart of a method for merging and classifying massive SQL statements according to an embodiment;
fig. 2 is a detailed flowchart of S3 in the above embodiment;
FIG. 3 is a flowchart of a method for merging and classifying massive SQL statements according to another embodiment;
FIG. 4 is a functional block diagram of an apparatus for merging and classifying massive SQL statements according to an embodiment;
FIG. 5 is a functional block diagram of a classifying module in the above embodiment;
FIG. 6 is a functional block diagram of an apparatus for merging and classifying massive SQL statements according to another embodiment.
Description of reference numerals:
10-an acquisition module for acquiring the images,
20-a resolution module for analyzing the image data,
30-a classification module for classifying the received data,
301-a calculation sub-unit for calculating,
302-the detection sub-unit is,
303-statistics subunit.
And 40, an acquisition module.
Detailed Description
In order to explain technical contents, structural features, and objects and effects of the present invention in detail, the following detailed description is given with reference to the accompanying drawings in conjunction with the embodiments.
Referring to fig. 1 to fig. 3, the embodiment shown in fig. 1 provides a method for merging and classifying a mass SQL statement, including:
and S1, analyzing the database environment, and collecting all SQL statements in the operating system.
S2, analyzing each SQL statement to obtain the variable value in the SQL statement. And then replacing the variable value with a constant to obtain the analyzed SQL statement. In the present embodiment, the variable values are replaced with numerical constants ": 1", ": 2", etc. in order, so that up to each SQL statement becomes a new SQL statement of this form. In other embodiments, the variables are sequentially replaced with alphabetic constants such as "a", "b", etc.
S3, calculating and obtaining the HASH value of the analyzed SQL statement, classifying and combining the SQL statement according to the HASH value, and storing the result after classification and combination; and after analysis, the SQL sentences with the same SQL sentence are similar SQL sentences. After the SQL statement is parsed by S2, since the variable values are replaced, and the similar SQL statements are completely the same after being parsed, many of the same SQL statements appear in the system after being parsed. The HASH value of each SQL statement is calculated separately, and since the HASH value is the same for the same SQL statement, the HASH values of similar SQL statements are the same, and the HASH values of non-similar SQL statements are different. It should be understood that the SQL statements that are the same in the parsed SQL statement are similar SQL statements, which means that the original SQL statements of the similar SQL statements differ only in variable values. For example, each of the following groups is a similar statement:
statement select from tab 1where f1=123 and statement select from tab 1where f1= 456;
statement update tab1 set f1 ═ 1where f2= a and statement update tab1 set f1=2where f2= b;
statement insert tab1(f1, f2, f3) values (1,2,3) and statement insert tab1(f1, f2, f3) values (a, b, c).
In some embodiments specifically shown in fig. 2, the classifying and merging the SQL statement according to the HASH value in S3 includes S301 and S302, which are specifically as follows:
s301, detecting whether the HASH value is stored in the running system;
s302, storing the HASH value under the condition that the HASH value is not stored, and storing the analyzed SQL statement corresponding to the HASH value into a similar statement table preset by an operating system.
Through the process, the similar SQL sentences and the unique HASH values thereof are obtained, and the classification and combination of the SQL sentences are realized. In the prior art, when the original operation is stored, the original SQL statement is stored in each operation detail, and the similar statement is stored in another similar statement table preset by an operating system by only storing the calculated HASH value and the variable value of the original SQL statement through the method. In this embodiment, the system is dedicated to establish a storage table for storing the computed HASH value and the variable value of the original SQL statement. It should be understood that the association between the storage table and the similar statement table is via a HASH value. For a system which is audited and monitored and repeats the same operation every day, a large number of similar statements exist in the system, the space occupied by the original SQL statements is greatly reduced, the storage space is greatly saved, and the data query efficiency is greatly improved due to the rapid reduction of the data volume. It will further be appreciated that in a stable audit monitored system, the number of similar statements will be in a stable state and will slowly no longer increase as the audit system operates.
In the preferred embodiment shown in fig. 3, after the calculating and obtaining the HASH value of the parsed SQL statement in S3, the method further includes: s3020, counting the number of occurrences of the HASH value. It should be appreciated that for a system, a smaller number of operations tends to be high risk operations. Through the embodiment, the similar SQL sentences are counted, the occurrence frequency of any SQL sentence at a certain moment is counted, and the SQL sentences which have fewer occurrence frequencies and high risk level belong to the sentences with high risk level according to the occurrence frequency, so that potential safety hazards can be found in time, and the stable operation of the system is ensured. In this embodiment, S3020 and S302 may be performed simultaneously. In another embodiment, S3020 may be performed after the execution of S302 is completed.
In the above embodiment, the step S3 is further followed by: s4, obtaining the occurrence frequency of the HASH value counted by S3, and generating an occurrence frequency chart of similar SQL sentences according to the obtained occurrence frequency of the HASH value. In the embodiment, the obtained appearance map of the similar SQL statement is sent to a display module of the system for display after S4, so that the statistical result of S3 can be visually seen, the potential safety hazard can be more accurately and rapidly discovered, and the stable operation of the system is ensured. Here, S4 is executed after S3020. In another embodiment, S4 is performed after the completion of S302 and S3020 in FIG. 3, and S4 may be performed after S302 in this embodiment. In other embodiments, 3020 is performed after completion of S302 execution, and S4 is performed after S3020.
Referring to fig. 4, the present embodiment provides a device for merging and classifying massive SQL statements, which includes an acquisition module 10, an analysis module 20, and a classification module 30. Wherein,
and the acquisition module 10 is used for analyzing the database environment and acquiring all SQL statements in the operating system.
The analysis module 20 is configured to analyze each SQL statement to obtain a variable value in the SQL statement; and replacing the variable value with a constant to obtain the analyzed SQL statement. In the present embodiment, the variable values are replaced with numerical constants ": 1", ": 2", etc. in order, so that up to each SQL statement becomes a new SQL statement of this form. In other embodiments, the variables are sequentially replaced with alphabetic constants "a", "b", etc.
The classification module 30 is configured to calculate and obtain a HASH value of the parsed SQL statement, classify and combine the SQL statement according to the HASH value, and store a result after classification and combination; and after analysis, the SQL sentences with the same SQL sentence are similar SQL sentences. After the SQL statement is parsed by the parsing module 20, because the variable values are replaced, and the similar SQL statements are completely the same after parsing, many of the same SQL statements appear in the system after parsing. The classification module 30 calculates the HASH value of each SQL statement, and since the HASH value is the same for the same SQL statement, the HASH values of similar SQL statements are the same, and the HASH values of non-similar SQL statements are different.
It should be understood that the SQL statements that are the same in the parsed SQL statement are similar SQL statements, which means that the original SQL statements of the similar SQL statements differ only in variable values. For example, each of the following groups is a similar statement:
statement select from tab 1where f1=123 and statement select from tab 1where f1= 456;
statement update tab1 set f1 ═ 1where f2= a and statement update tab1 set f1=2where f2= b;
statement insert tab1(f1, f2, f3) values (1,2,3) and statement insert tab1(f1, f2, f3) values (a, b, c).
Referring to fig. 5, the classifying module 30 in this embodiment includes: a calculation subunit 301 and a detection subunit 302. The calculating subunit 301 is configured to calculate and obtain a HASH value of the parsed SQL statement. A detecting subunit 302, configured to detect whether the HASH value is stored in the operating system, store the HASH value when detecting that the HASH value is not stored, and store the parsed SQL statement corresponding to the HASH value in a similar statement table preset by the operating system.
By the device, similar SQL sentences and the unique HASH values thereof are obtained, and the classification and combination of the SQL sentences are realized. In the prior art, when the original operation is stored, the original SQL statement is stored in each operation detail, and the similar statement is stored in another similar statement table preset by an operating system by only storing the calculated HASH value and the variable value of the original SQL statement through the method. In this embodiment, the system is dedicated to establish a storage table for storing the computed HASH value and the variable value of the original SQL statement. It should be understood that the association between the storage table and the similar statement table is via a HASH value. For a system which is audited and monitored and repeats the same operation every day, a large number of similar statements exist in the system, the space occupied by the original SQL statements is greatly reduced, the storage space is greatly saved, and the data query efficiency is greatly improved due to the rapid reduction of the data volume. It will further be appreciated that in a stable audit monitored system, the number of similar statements will be in a stable state and will slowly no longer increase as the audit system operates.
In the above preferred embodiment, the apparatus further comprises a counting subunit 303, configured to count the number of occurrences of the HASH value. It should be appreciated that for a system, a smaller number of operations tends to be high risk operations. Through the embodiment, the similar SQL sentences are counted, the occurrence frequency of any SQL sentence at a certain moment is counted, and the SQL sentences which have fewer occurrence frequencies and high risk level belong to the sentences with high risk level according to the occurrence frequency, so that potential safety hazards can be found in time, and the stable operation of the system is ensured.
Referring to fig. 6, in the foregoing embodiment, the apparatus further includes an obtaining module 40, configured to obtain the occurrence frequency of the HASH value counted by the classifying module 30, and generate an occurrence frequency map of similar SQL statements according to the obtained occurrence frequency of the HASH value. In the preferred embodiment of the present invention, the obtaining module 40 sends the obtained appearance map of the similar SQL statement to the display module of the system for display, so that the statistical result of S3 can be visually seen, the potential safety hazard can be found more accurately and quickly, and the stable operation of the system is ensured.
In summary, different from the defects of the prior art, the invention provides a method and a device for merging and classifying massive SQL statements, and through the technical scheme provided by the invention, similar SQL statements and unique HASH values thereof are obtained, and the SQL statements are classified and merged; for a system which is audited and monitored and repeats the same operation every day, a large number of similar statements exist in the system, the space occupied by the original SQL statements is greatly reduced, the storage space is greatly saved, and the data query efficiency is greatly improved due to the rapid reduction of the data volume. It will further be appreciated that in a stable audit monitored system, the number of similar statements will be in a stable state and will slowly no longer increase as the audit system operates.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. A method for merging and classifying massive SQL sentences is characterized by comprising the following steps:
s1, analyzing the database environment, and collecting all SQL statements in the operating system;
s2, analyzing each SQL statement to obtain variable values in the SQL statement; replacing the variable value with a constant to obtain an analyzed SQL statement;
s3, calculating and obtaining the HASH value of the analyzed SQL statement, classifying and combining the SQL statement according to the HASH value, and storing the result after classification and combination; and after analysis, the SQL sentences with the same SQL sentence are similar SQL sentences.
2. The method for merging and classifying massive SQL statements according to claim 1, wherein the step of classifying and merging the SQL statements according to the HASH value in S3 comprises:
and detecting whether the HASH value is stored in the running system, storing the HASH value under the condition that the HASH value is not stored, and storing the analyzed SQL statement corresponding to the HASH value into a similar statement table preset by the running system.
3. The method for merging and classifying massive SQL statements according to claim 1 or 2, wherein the step S3 further includes: and counting the occurrence times of the HASH value.
4. The method for merging and classifying massive SQL statements according to claim 3, wherein the step S3 is followed by further steps of: s4, obtaining the occurrence frequency of the HASH value counted by S3, and generating an occurrence frequency chart of similar SQL sentences according to the obtained occurrence frequency of the HASH value.
5. A device for merging and classifying massive SQL sentences is characterized by comprising:
the acquisition module is used for analyzing the database environment and acquiring all SQL sentences in the operating system;
the analysis module is used for analyzing each SQL statement to obtain a variable value in the SQL statement; replacing the variable value with a constant to obtain an analyzed SQL statement;
the classification module is used for calculating and obtaining the HASH value of the analyzed SQL statement, classifying and combining the SQL statement according to the HASH value, and storing the result after classification and combination; and after analysis, the SQL sentences with the same SQL sentence are similar SQL sentences.
6. The apparatus for merging and classifying massive SQL statements according to claim 5, wherein the classifying module comprises: and the detection subunit is used for detecting whether the HASH value is stored in the operating system, storing the HASH value under the condition that the HASH value is not stored, and storing the analyzed SQL statement corresponding to the HASH value into a similar statement table preset by the operating system.
7. The apparatus for merging and classifying massive SQL statements according to claim 5 or 6, wherein the classifying module further comprises: and the counting subunit is used for counting the occurrence times of the HASH value.
8. The apparatus for merging and classifying massive SQL statements according to claim 7, wherein the classifying module further comprises: and the acquisition module is used for acquiring the occurrence times of the HASH values counted by the classification module and generating an occurrence frequency chart similar to the SQL statement according to the acquired occurrence times of the HASH values.
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| CN109542868A (en) * | 2018-09-28 | 2019-03-29 | 中国平安人寿保险股份有限公司 | Position method, apparatus, electronic equipment and the storage medium of abnormal SQL statement |
| CN109800240A (en) * | 2018-12-13 | 2019-05-24 | 平安科技（深圳）有限公司 | SQL statement classifying method, device, computer equipment and storage medium |
| CN109800240B (en) * | 2018-12-13 | 2024-03-22 | 平安科技（深圳）有限公司 | SQL sentence classifying method, device, computer equipment and storage medium |
| CN112527815A (en) * | 2020-12-02 | 2021-03-19 | 平安医疗健康管理股份有限公司 | Script migration method and device for database, computer equipment and storage medium |
| CN112966101A (en) * | 2021-02-07 | 2021-06-15 | 白腊梅 | Statement clustering method, transaction clustering method, statement clustering device and transaction clustering device |
| CN112966101B (en) * | 2021-02-07 | 2024-06-18 | 白腊梅 | Statement clustering method, transaction clustering method, statement clustering device and transaction clustering device |
| CN115114374A (en) * | 2022-06-27 | 2022-09-27 | 腾讯科技（深圳）有限公司 | Transaction execution method and device, computing equipment and storage medium |
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