CN110895542A - High-risk SQL statement screening method and device - Google Patents

High-risk SQL statement screening method and device Download PDF

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CN110895542A
CN110895542A CN201911189244.8A CN201911189244A CN110895542A CN 110895542 A CN110895542 A CN 110895542A CN 201911189244 A CN201911189244 A CN 201911189244A CN 110895542 A CN110895542 A CN 110895542A
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CN110895542B (en
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陆子辉
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Bank of China Ltd
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Abstract

The invention discloses a high-risk SQL statement screening method and a high-risk SQL statement screening device, wherein the method comprises the following steps: acquiring a historical execution plan sample, and marking and classifying SQL sentences in the historical execution plan sample according to whether performance problems exist or not; acquiring an execution string generated by each execution plan in a historical execution plan sample, and taking the execution string as a feature unit; obtaining prior probability data according to the marking classification result; according to a Bayesian classification algorithm, in combination with prior probability data, obtaining the probability of performance problems of SQL sentences corresponding to each feature unit in the execution plan to be processed; and screening out the SQL sentences with high risk according to the probability that the SQL sentences corresponding to the characteristic units in the execution plan to be processed have performance problems. The invention can be realized without experienced professionals, has low requirement on workers, reduces the labor intensity of the workers, improves the working efficiency and reduces the labor cost.

Description

High-risk SQL statement screening method and device
Technical Field
The invention relates to the technical field of computers, in particular to a high-risk SQL statement screening method and device.
Background
The performance of an Oracle database application program depends on an execution plan of a Structured Query Language (SQL) to a great extent, during the development of a large-scale software system, many SQL statements are often involved, and many changes may occur according to changes of transaction situations, so it is very important to regularly screen out high-risk SQL statements corresponding to the execution plan in order to avoid catastrophic performance influence of the execution plan during actual operation.
Currently, a worker generally determines whether an SQL statement corresponding to an execution plan is a high-risk SQL statement by using previous rich experience.
The inventor finds that the prior art has at least the following problems:
the requirement of the prior art on workers is high, so that the labor intensity of the workers is increased, and the labor cost is increased.
Disclosure of Invention
The embodiment of the invention provides a high-risk SQL statement screening method, which is used for reducing the requirements, labor intensity and cost of workers and comprises the following steps:
acquiring a historical execution plan sample, and marking and classifying SQL sentences in the historical execution plan sample according to whether performance problems exist or not;
acquiring an execution string generated by each execution plan in a historical execution plan sample, and taking the execution string as a feature unit;
obtaining prior probability data according to the marking classification result, wherein the prior probability data comprises: the probability that the SQL sentences in the historical execution plan samples have performance problems, the probability that the SQL sentences in the historical execution plan samples do not have the performance problems, the probability of each characteristic unit when the SQL sentences have the performance problems, and the probability of each characteristic unit when the SQL sentences have no performance problems;
according to a Bayesian classification algorithm, in combination with prior probability data, obtaining the probability of performance problems of SQL sentences corresponding to each feature unit in the execution plan to be processed;
and screening out the SQL sentences with high risk according to the probability that the SQL sentences corresponding to the characteristic units in the execution plan to be processed have performance problems.
Optionally, the method further includes:
and sequencing the probability of performance problems of the SQL sentences corresponding to the characteristic units in the execution plan to be processed from high to low.
Optionally, the execution plan includes a plurality of execution chains, and each execution chain includes a plurality of execution atoms;
the method further comprises the following steps:
and establishing a conversion corresponding dictionary, and appointing conversion codes for a plurality of execution atoms in the execution plan.
Optionally, obtaining an execution string generated by each execution plan in the historical execution plan sample includes:
disassembling a plurality of execution chains in each execution plan in a historical execution plan sample;
and acquiring a plurality of execution strings according to the disassembly result.
The embodiment of the invention also provides a high-risk SQL statement screening device, which is used for reducing the requirements, labor intensity and cost for workers and comprises the following components:
the classification module is used for acquiring a historical execution plan sample and marking and classifying SQL sentences in the historical execution plan sample according to whether performance problems exist or not;
the execution string acquisition module is used for acquiring execution strings generated by execution plans in a historical execution plan sample and taking the execution strings as feature units;
a data obtaining module, configured to obtain prior probability data according to the result of the marking classification, where the prior probability data includes: the probability that the SQL sentences in the historical execution plan samples have performance problems, the probability that the SQL sentences in the historical execution plan samples do not have the performance problems, the probability of each characteristic unit when the SQL sentences have the performance problems, and the probability of each characteristic unit when the SQL sentences have no performance problems;
the probability acquisition module is used for acquiring the probability of performance problems of SQL sentences corresponding to each characteristic unit in the execution plan to be processed according to a Bayesian classification algorithm and in combination with prior probability data;
and the SQL statement screening module is used for screening out the SQL statements with high risk according to the probability of performance problems of the SQL statements corresponding to the characteristic units in the execution plan to be processed.
Optionally, the apparatus further comprises:
and the sequencing module is used for sequencing the probability of performance problems of the SQL sentences corresponding to the characteristic units in the execution plan to be processed from high to low.
Optionally, the execution plan includes a plurality of execution chains, and each execution chain includes a plurality of execution atoms;
the device further comprises:
and the format conversion module is used for establishing a conversion corresponding dictionary and appointing conversion codes for a plurality of execution atoms in the execution plan.
Optionally, the execution string obtaining module is further configured to:
disassembling a plurality of execution chains in each execution plan in a historical execution plan sample;
and acquiring a plurality of execution strings according to the disassembly result.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program for executing the above method is stored.
In the embodiment of the invention, the historical execution plan sample is obtained, marking classification is carried out on SQL sentences in the historical execution plan sample according to whether performance problems exist or not, prior probability data is obtained according to the marking classification result, the probability that the performance problems exist in the SQL sentences corresponding to all the characteristic units in the execution plan to be processed is obtained according to the Bayesian classification algorithm and in combination with the prior probability data, the SQL sentences with high risk can be screened out, the whole process can be completed only on the basis of the statistical principle of the Bayesian classification algorithm, experienced professionals are not needed, the requirements on the workers are low, the labor intensity of the workers is reduced, the working efficiency is improved, and the labor cost is reduced.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a flow chart of a high risk SQL statement screening method in an embodiment of the invention;
FIG. 2 is a diagram of a first specific example of a high-risk SQL statement screening method according to an embodiment of the present invention;
FIG. 3 is a diagram of a second specific example of a high-risk SQL statement screening method according to an embodiment of the invention;
FIG. 4 is a diagram illustrating an exemplary implementation plan in accordance with an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a high-risk SQL statement screening apparatus in an embodiment of the present invention;
fig. 6 is a diagram illustrating an embodiment of a high-risk SQL statement screening apparatus according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
Fig. 1 is a flowchart of a high-risk SQL statement screening method according to an embodiment of the present invention, as shown in fig. 1, the method includes:
step 101, obtaining a historical execution plan sample, and marking and classifying SQL sentences in the historical execution plan sample according to whether performance problems exist.
In this embodiment, the historical execution plan sample can be collected by monitoring an Oracle database system table (e.g., v $ sql _ plan) in an actual environment.
Because the identification of the SQL execution plan is obviously distinguished by an OLAP (Online Analytical Processing) system and an OLTP (On-line transaction Processing) system, two different models are generally constructed respectively when the engineering is used, and the key difference of the model construction lies in the marking standard of the problem SQL. That is, two different models respectively constructed for the OLAP system and the OLTP system need to perform marking classification on SQL statements in a historical execution plan sample according to whether performance problems exist.
And 102, acquiring an execution string generated by each execution plan in a historical execution plan sample, and taking the execution string as a characteristic unit.
In this embodiment, obtaining an execution string generated by each execution plan in a historical execution plan sample includes:
disassembling a plurality of execution chains in each execution plan in a historical execution plan sample;
and acquiring a plurality of execution chain combinations, namely execution strings according to the disassembly result.
In specific implementation, the maximum calculation length of the model execution chain is set first. The programming implements the scanning iteration of all chain combinations with the chain length less than or equal to the maximum chain length according to the execution sequence of the execution plan, and counts the repeated combinations, and for the execution plan, for example, as shown in fig. 4:
setting the maximum execution chain length to be 3, and forming an execution chain combination after disassembling
(
(TABLE ACCESS FULL,PARTITION RANGE ALL)2,
(TABLE ACCESS FULL,PARTITION RANGE ALL,HASH GROUP BY)2,
(PARTITION RANGE ALL,HASH GROUP BY)2,
(PARTITION RANGE ALL,HASH GROUP BY,VIEW)2,
(HASH GROUP BY,VIEW)2,
(HASH GROUP BY,VIEW,VIEW)1,
(VIEW,VIEW)1,
(VIEW,VIEW,HASH JOIN)2,
(VIEW,HASH JOIN)2,
(HASH GROUP BY，VIEW,HASH JOIN)1
)
And completing chain disassembly statistics on all prior samples. Where the number following each execution chain combination indicates the number of times it appears in the execution plan. Each combination of execution chains may be considered a feature element mentioned in the embodiments of the present invention.
103, obtaining prior probability data according to the marking classification result, wherein the prior probability data comprises: the probability of performance problems of SQL sentences in the historical execution plan samples, the probability of no performance problems of the SQL sentences in the historical execution plan samples, the probability of performance problems of each characteristic unit in the SQL sentences, and the probability of performance problems of each characteristic unit in the SQL sentences.
And 104, acquiring the probability of performance problems of the SQL sentences corresponding to the characteristic units in the execution plan to be processed according to a Bayesian classification algorithm and in combination with prior probability data.
In specific implementation, according to a Bayesian classification formula:
Figure BDA0002293147800000051
wherein C is a class, WnFor each feature cell, in the present embodiment, C0For normal execution plans (i.e., execution plans for which SQL statements do not have performance problems), C1For problem execution planning (i.e., execution planning for SQL statement with performance problem), WnP is the probability for n different execution chain combinations.
The selected characteristics are all execution chains, and the influence of all execution chain combinations on the SQL statement problem is set to be mutually independent. Then, conversion is carried out according to a Bayesian classification formula to obtain a first calculation formula and a second calculation formula:
calculating a formula I:
Figure BDA0002293147800000052
calculating a formula II:
Figure BDA0002293147800000053
therefore, only the execution chain combination characteristics of the execution plan to be processed need to be clarified when the classification is calculated, and the probability of each part of characteristic units under the current characteristics can be calculated by substituting the above equation.
Wherein, P (C)0)、P(C1)、P(wn|C1)、P(wn|C0)、P(wn) The prior probability data can be obtained through step 103, and all the prior probability data records are stored, so that an algorithm classifier can be formed, and data extraction can be performed when high-risk SQL statement screening is performed on the execution plan to be processed subsequently.
It should be noted that the execution plan to be processed can be collected from the Oracle database system table, and dictionary mapping conversion is also required.
And 105, screening out the SQL sentences with high risk according to the probability that the SQL sentences corresponding to the characteristic units in the execution plan to be processed have performance problems.
As can be seen from fig. 1, in the high-risk SQL statement screening method provided in the embodiment of the present invention, the historical execution plan sample is obtained, the SQL statements in the historical execution plan sample are marked and classified according to whether performance problems exist, prior probability data is obtained according to the marking and classifying result, and the probability that the SQL statements corresponding to each feature unit in the execution plan to be processed have the performance problems is obtained according to the bayesian classification algorithm in combination with the prior probability data, so that the SQL statements with high risk can be screened.
In an embodiment of the present invention, in order to improve the efficiency of exhausting the high-risk SQL statements, as shown in fig. 2, the method further includes:
and 106, sequencing the probability of performance problems of the SQL sentences corresponding to the feature units in the execution plan to be processed from high to low.
In specific implementation, expert resources can be allocated according to the probability sorting condition to optimize the SQL sentences, so that the engineering hidden danger is eliminated.
In addition, in order to improve the working efficiency, the actual data generated in the engineering can be verified and then stored in the database as a new history execution plan sample, and the algorithm classifier is updated for the next calculation.
In the embodiment of the present invention, the execution plan includes a plurality of execution chains, and each execution chain includes a plurality of execution atoms;
in order to ensure the smooth proceeding of the subsequent operation, after the SQL statements in the historical execution plan sample are subjected to marking classification according to whether there is a performance problem, format conversion is performed on the execution atoms in the execution plan, as shown in fig. 3, the method further includes:
step 401, establishing a conversion corresponding dictionary, and assigning conversion codes for a plurality of execution atoms in the execution plan.
Based on the same inventive concept, the embodiment of the present invention further provides a high risk SQL statement screening apparatus, as described in the following embodiments. Because the principle of solving the problem of the high-risk SQL statement screening device is similar to that of the high-risk SQL statement screening method, the implementation of the high-risk SQL statement screening device can refer to the implementation of the high-risk SQL statement screening method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
The embodiment of the invention provides a high-risk SQL statement screening device, as shown in the attached figure 5, the device comprises:
the classification module 201 is configured to obtain a historical execution plan sample, and perform marking classification on SQL statements in the historical execution plan sample according to whether a performance problem exists;
an execution string obtaining module 202, configured to obtain an execution string generated by each execution plan in a historical execution plan sample, where the execution string is used as a feature unit;
a data obtaining module 203, configured to obtain prior probability data according to the result of the marking classification, where the prior probability data includes: the probability that the SQL sentences in the historical execution plan samples have performance problems, the probability that the SQL sentences in the historical execution plan samples do not have the performance problems, the probability of each characteristic unit when the SQL sentences have the performance problems, and the probability of each characteristic unit when the SQL sentences have no performance problems;
the probability obtaining module 204 is configured to obtain, according to a bayesian classification algorithm and in combination with prior probability data, probabilities that performance problems exist in SQL statements corresponding to feature units in the execution plan to be processed;
the SQL statement screening module 205 is configured to screen out an SQL statement with a high risk according to a probability that the SQL statement corresponding to each feature unit in the execution plan to be processed has a performance problem.
In an embodiment of the present invention, as shown in fig. 6, the apparatus further includes:
and the sorting module 206 is configured to sort, from high to low, probabilities that the SQL statements corresponding to the feature units in the execution plan to be processed have the performance problem.
In the embodiment of the present invention, the execution plan includes a plurality of execution chains, and each execution chain includes a plurality of execution atoms;
the device further comprises:
and the format conversion module is used for establishing a conversion corresponding dictionary and appointing conversion codes for a plurality of execution atoms in the execution plan.
In this embodiment of the present invention, the execution string obtaining module 202 is further configured to:
disassembling a plurality of execution chains in each execution plan in a historical execution plan sample;
and acquiring a plurality of execution strings according to the disassembly result.
The embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above method when executing the computer program.
An embodiment of the present invention further provides a computer-readable storage medium, in which a computer program for executing the above method is stored.
In conclusion, the invention has the following advantages:
the method is simple to construct and convenient to implement, can be implemented without professional software assistance and professional execution plan analysis skills, is used as an early warning means to assist development, testing or operation and maintenance personnel to focus on possible problems, improves the working efficiency of the professionals, and reduces the labor cost.
By sequencing the probabilities, the efficiency of eliminating problems is improved, the cost can be reduced to a certain degree, and precious expert resources are saved.
The invention can provide data base for follow-up more accurate probability analysis along with the continuous accumulation and update of the historical execution plan samples.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A high-risk SQL statement screening method is characterized by comprising the following steps:
acquiring a historical execution plan sample, and marking and classifying SQL sentences in the historical execution plan sample according to whether performance problems exist or not;
acquiring an execution string generated by each execution plan in a historical execution plan sample, and taking the execution string as a feature unit;
obtaining prior probability data according to the marking classification result, wherein the prior probability data comprises: the probability that the SQL sentences in the historical execution plan samples have performance problems, the probability that the SQL sentences in the historical execution plan samples do not have the performance problems, the probability of each characteristic unit when the SQL sentences have the performance problems, and the probability of each characteristic unit when the SQL sentences have no performance problems;
according to a Bayesian classification algorithm, in combination with prior probability data, obtaining the probability of performance problems of SQL sentences corresponding to each feature unit in the execution plan to be processed;
and screening out the SQL sentences with high risk according to the probability that the SQL sentences corresponding to the characteristic units in the execution plan to be processed have performance problems.
2. The method of claim 1, further comprising:
and sequencing the probability of performance problems of the SQL sentences corresponding to the characteristic units in the execution plan to be processed from high to low.
3. The method of claim 1, wherein the execution plan includes a plurality of execution chains, each execution chain including a plurality of execution atoms;
the method further comprises the following steps:
and establishing a conversion corresponding dictionary, and appointing conversion codes for a plurality of execution atoms in the execution plan.
4. The method of claim 3, wherein obtaining the execution strings generated by the execution plans in the historical execution plan sample comprises:
disassembling a plurality of execution chains in each execution plan in a historical execution plan sample;
and acquiring a plurality of execution strings according to the disassembly result.
5. A high risk SQL statement screening device is characterized by comprising:
the classification module is used for acquiring a historical execution plan sample and marking and classifying SQL sentences in the historical execution plan sample according to whether performance problems exist or not;
the execution string acquisition module is used for acquiring execution strings generated by execution plans in a historical execution plan sample and taking the execution strings as feature units;
a data obtaining module, configured to obtain prior probability data according to the result of the marking classification, where the prior probability data includes: the probability that the SQL sentences in the historical execution plan samples have performance problems, the probability that the SQL sentences in the historical execution plan samples do not have the performance problems, the probability of each characteristic unit when the SQL sentences have the performance problems, and the probability of each characteristic unit when the SQL sentences have no performance problems;
the probability acquisition module is used for acquiring the probability of performance problems of SQL sentences corresponding to each characteristic unit in the execution plan to be processed according to a Bayesian classification algorithm and in combination with prior probability data;
and the SQL statement screening module is used for screening out the SQL statements with high risk according to the probability of performance problems of the SQL statements corresponding to the characteristic units in the execution plan to be processed.
6. The apparatus of claim 5, further comprising:
and the sequencing module is used for sequencing the probability of performance problems of the SQL sentences corresponding to the characteristic units in the execution plan to be processed from high to low.
7. The apparatus of claim 5, wherein the execution plan includes a plurality of execution chains, each execution chain including a plurality of execution atoms;
the device further comprises:
and the format conversion module is used for establishing a conversion corresponding dictionary and appointing conversion codes for a plurality of execution atoms in the execution plan.
8. The apparatus of claim 7, wherein the execution string acquisition module is further to:
disassembling a plurality of execution chains in each execution plan in a historical execution plan sample;
and acquiring a plurality of execution strings according to the disassembly result.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 4.
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