CN117093609A - Query statement processing method, device, equipment, medium and program product - Google Patents

Query statement processing method, device, equipment, medium and program product Download PDF

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Publication number
CN117093609A
CN117093609A CN202311077632.3A CN202311077632A CN117093609A CN 117093609 A CN117093609 A CN 117093609A CN 202311077632 A CN202311077632 A CN 202311077632A CN 117093609 A CN117093609 A CN 117093609A
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information
query statement
slow
statement
slow query
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雷经纬
徐嘉禛
于子烨
罗响
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Industrial and Commercial Bank of China Ltd ICBC
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Industrial and Commercial Bank of China Ltd ICBC
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Publication of CN117093609A publication Critical patent/CN117093609A/en
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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
    • G06F16/24532—Query optimisation of parallel queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
    • G06F11/0709—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
    • G06F11/0715—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in a system implementing multitasking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/079—Root cause analysis, i.e. error or fault diagnosis
    • 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
    • G06F16/242—Query formulation
    • G06F16/2433—Query languages
    • 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
    • G06F16/24549—Run-time optimisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • 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
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G06F16/285—Clustering or classification
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The present disclosure provides a query statement processing method, apparatus, device, storage medium, and program product, which can be applied to the financial technical field and other fields. The query statement processing method comprises the following steps: acquiring query statement information, table information and database operation information from information transmitted by a distributed database; carrying out query statement classification based on the query statement information to determine slow query statements; correlating the slow query statement with the table information to determine abnormal information of the slow query statement; classifying the slow query sentences by using the abnormal information, and determining a plurality of slow query sentence sets; generating a processing task corresponding to each slow query statement set; and carrying out batch processing on a plurality of slow query sentences in the slow query sentence set based on the processing task.

Description

Query statement processing method, device, equipment, medium and program product
Technical Field
The present disclosure relates to the field of financial technology, and more particularly, to a query statement processing method, apparatus, device, medium, and program product.
Background
As traffic grows and data volumes increase, so too does the execution requirements for structured query language (Structured Query Language, SQL) statements. Under the conditions of massive data and high concurrency query analysis, the condition that the execution time of a certain SQL statement is overlong is easy to occur, namely a slow query statement occurs, and the slow query statement needs to be processed at the moment so as to reduce the influence on the service.
In the prior art, the processing of the slow query statement is usually to execute the analysis and processing of the slow query statement by taking a single query statement as a unit, so that the efficiency is low, and the method is not suitable for a high concurrency query analysis scene.
Disclosure of Invention
In view of the foregoing, the present disclosure provides query statement processing methods, apparatuses, devices, media, and program products that improve the processing efficiency of slow query statements.
According to a first aspect of the present disclosure, there is provided a query statement processing method, including: acquiring query statement information, table information and database operation information from information transmitted by a distributed database; carrying out query statement classification based on the query statement information to determine slow query statements; correlating the slow query statement with the table information to determine the abnormal information of the slow query statement; classifying the slow query sentences by using the abnormal information, and determining a plurality of slow query sentence sets; generating a processing task corresponding to each slow query statement set; and carrying out batch processing on a plurality of slow query sentences in the slow query sentence set based on the processing task.
According to an embodiment of the present disclosure, query statement information includes statement state and current state time consumption; performing query statement classification based on the query statement information, determining a slow query statement, comprising: determining an abnormal statement based on the statement state and the current state time consumption; calculating the actual time consumption of the abnormal statement by utilizing the database operation information; in the event that the actual time consumption of the exception statement is greater than a threshold, the exception statement is determined to be a slow query statement.
According to an embodiment of the present disclosure, the database running information includes database execution information and statement queuing information; calculating the actual time consumption of the abnormal time-consuming statement using the database run information, including: checking whether the abnormal statement is executed on time according to the database operation information; under the condition that the abnormal statement is executed on time, the current state time consumption of the abnormal time-consuming statement is the actual time consumption; under the condition that the abnormal sentences are not executed on time, calculating the actual time consumption of the abnormal sentences according to the sentence queuing information; the actual time consumption is the difference between the current state time consumption and queuing information.
According to an embodiment of the present disclosure, determining exception information of a slow query statement based on the slow query statement and table information includes: determining table information with association relation with the slow query statement; wherein the table information includes table data distribution conditions; anomaly information for the slow query statement is determined based on the table data distribution.
According to an embodiment of the present disclosure, determining table information having an association relationship with a slow query statement includes: analyzing the slow query statement and determining the table names contained in the slow query statement; and establishing an association relation between the slow query statement and the table information based on the table name.
According to an embodiment of the present disclosure, the query term information further includes attribute information of the query term; the method for classifying the slow query sentences by using the abnormal information to obtain a plurality of slow query sentence sets comprises the following steps: extracting the unique ID of the slow query statement according to the attribute information of the slow query statement; and classifying the slow query sentences based on the abnormal information and the unique IDs of the slow query sentences to obtain a plurality of slow query sentence sets.
According to an embodiment of the present disclosure, generating processing tasks corresponding to each slow query statement set includes: the following is performed for each slow query statement set: obtaining abnormal information of a slow query statement set; determining a processing method of a slow query statement set according to the abnormal information; processing tasks of the slow query statement set are generated based on the processing method.
According to an embodiment of the present disclosure, the processing task for generating the slow query statement set based on the processing method further includes: generating a treatment report according to the slow query statement set; the management report comprises running information of each slow query statement and table information related to the slow query statement; and generating processing tasks of the slow query statement set based on the treatment report and the processing method.
A second aspect of the present disclosure provides a query statement processing apparatus, including: the acquisition module is used for acquiring query statement information, table information and database operation information from the information transmitted by the distributed database; the first classification module is used for classifying the query sentences based on the query sentence information and determining slow query sentences; the determining module is used for correlating the slow query statement with the table information and determining the abnormal information of the slow query statement; the second classification module is used for classifying the slow query sentences by using the abnormal information and determining a plurality of slow query sentence sets; the generation module is used for generating a processing task corresponding to each slow query statement set; and the processing module is used for carrying out batch processing on a plurality of slow query sentences in the slow query sentence set based on the processing task.
A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the query statement processing method described above.
A fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-described query statement processing method.
The fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-described query statement processing method.
According to the query statement processing method, the reasons of the slow query statements are analyzed and formed by correlating the slow query statements with the table information, and the slow query statements are classified according to the formation reasons of the slow query statements, so that a plurality of slow query statement sets are obtained, the formation reasons of the slow query statements in each query statement set are the same, and correspondingly, the processing methods of the slow query statements in the same query statement set are similar, so that query statement processing tasks can be generated through the slow query statement sets, batch processing of the slow query statements is realized, and the processing efficiency of the slow query statements is improved. In addition, the process of analyzing and processing the query statement is not carried out in the distributed database, but the SRE receives the information transmitted by the distributed database, and the SRE analyzes and processes the query statement, so that the computing resource of the distributed database is effectively saved.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:
FIG. 1 schematically illustrates an application scenario diagram of a query statement processing method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow chart of a query statement processing method in accordance with an embodiment of the disclosure;
FIG. 3 schematically illustrates a flow diagram for determining a slow query statement in accordance with an embodiment of the disclosure;
FIG. 4 schematically illustrates a flow chart of actual time consuming computation of an exception statement in accordance with an embodiment of the present disclosure;
FIG. 5 schematically illustrates a flow chart of determining exception information for a slow query statement, in accordance with an embodiment of the disclosure;
FIG. 6 schematically illustrates a flow chart of determining a slow query statement set, in accordance with an embodiment of the disclosure;
FIG. 7 schematically illustrates a flow chart of processing tasks corresponding to generating each slow query statement set in accordance with an embodiment of the disclosure;
FIG. 8 schematically illustrates a flow diagram of generating processing tasks according to an embodiment of the disclosure;
FIG. 9 schematically illustrates a block diagram of a query statement processing apparatus in accordance with an embodiment of the disclosure; and
Fig. 10 schematically illustrates a block diagram of an electronic device adapted to implement a query statement processing method in accordance with an embodiment of the disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.
Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
It should be noted that, the query statement processing method and apparatus in the present disclosure may be used in the financial technical field when processing a query statement, and may also be used in any field other than the financial technical field when processing a query statement, where the application field of the query statement processing method and apparatus in the present disclosure is not limited.
In the technical scheme of the invention, the related user information (including but not limited to user personal information, user image information, user equipment information, such as position information and the like) and data (including but not limited to data for analysis, stored data, displayed data and the like) are information and data authorized by a user or fully authorized by all parties, and the processing of the related data such as collection, storage, use, processing, transmission, provision, disclosure, application and the like are all conducted according to the related laws and regulations and standards of related countries and regions, necessary security measures are adopted, no prejudice to the public welfare is provided, and corresponding operation inlets are provided for the user to select authorization or rejection.
Before explaining the embodiments of the present disclosure in further detail, terms and terminology involved in the embodiments of the present disclosure are explained, and the terms and terminology involved in the embodiments of the present disclosure are applicable to the following explanation.
MPP architecture distributed database: the MPP is a server classification method in the system architecture angle of a mass parallel processing structure (Massive Parallel Processing), and a plurality of servers are connected through a certain node interconnection network to cooperatively work so as to finish the same task. Servers based on MPP technology tend to be shielded from this complexity by system level software (such as databases), and when applications are developed based on such databases, the developer is faced with the same database system regardless of how many nodes the background server consists of, regardless of how the load of some of the nodes is scheduled. A distributed database refers to a database system in which data is physically distributed and logically centrally managed. PB-level distributed databases based on Shared-eating architecture are increasingly used in the field of financial science and technology, wherein the analysis type distributed database databases are used for supporting key business scenes such as mass data warehouse, data marts, real-time analysis, real-time decision, mixed load and the like in the financial industry.
SRE (Site Reliability Engineering): the SRE is an operation and maintenance engineering role or team responsible for guaranteeing the stability and reliability of the system, and is generally responsible for tasks such as monitoring, fault removal, performance optimization, automatic operation and maintenance and the like so as to ensure that the system can run continuously and efficiently.
The embodiment of the disclosure provides a query statement processing method, which comprises the following steps: acquiring query statement information, table information and database operation information from information transmitted by a distributed database; carrying out query statement classification based on the query statement information to determine slow query statements; correlating the slow query statement with the table information to determine the abnormal information of the slow query statement; classifying the slow query sentences by using the abnormal information, and determining a plurality of slow query sentence sets; generating a processing task corresponding to each slow query statement set; and carrying out batch processing on a plurality of slow query sentences in the slow query sentence set based on the processing task.
Fig. 1 schematically illustrates an application scenario diagram of a query statement processing method according to an embodiment of the present disclosure.
As shown in fig. 1, the application scenario 100 according to this embodiment may include a terminal device 101, a terminal device 102, a terminal device 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that, the query term processing method provided in the embodiments of the present disclosure may be generally executed by the server 105. Accordingly, the query sentence processing apparatus provided by the embodiments of the present disclosure may be generally disposed in the server 105. The query statement processing method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the query term processing apparatus provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The query sentence processing method of the disclosed embodiment will be described in detail below with reference to fig. 2 to 8 based on the scenario described in fig. 1.
Fig. 2 schematically illustrates a flowchart of a query statement processing method according to an embodiment of the disclosure.
As shown in fig. 2, the query sentence processing method of this embodiment includes operations S210 to S260.
In operation S210, query statement information, table information, and database operation information are acquired from information transferred from the distributed database.
In some embodiments, the SRE database administration cluster receives information transferred by the MPP architecture distributed database, and the SRE database administration cluster selected in the present disclosure is a Hadoop-based Hive data warehouse, so as to save calculation and storage resource costs. The information transmitted by the MPP architecture distributed database comprises resource monitoring information of a query level. The resource monitoring information at least comprises query statement information and table information. The query level granularity is finer, and a more accurate query and tracking mechanism of slow query sentences can be realized. After the distributed database obtains the resource monitoring information of the query level, the resource monitoring information is directly dumped to the SRE database treatment cluster without processing, so that the service resources of the distributed database are saved to the greatest extent.
The acquisition of the resource monitoring information by the MPP architecture distributed database is generally completed in a service low-peak time period, and the service low-peak time period is calculated according to the historical load operation index of the MPP architecture distributed database. The resource monitoring information is acquired in the service low-peak time period, so that occupation of service resources can be effectively avoided. The method for acquiring the resource monitoring information by the MPP architecture distributed database comprises the following steps: and formulating a resource monitoring information acquisition task according to the connection information of the database, the query threshold value and the scheduling strategy. The execution time periods of the resource monitoring information acquisition tasks of different databases are different.
In operation S220, query statement classification is performed based on the query statement information, and a slow query statement is determined.
In some embodiments, query statement analysis is performed in the SRE database administration cluster, and query statements are divided into abnormal query statements and normal query statements based on query statement information, wherein the normal query statements are statements with normal execution time, and the abnormal query statements are statements with excessive execution time. Further analysis is performed on the statement that is too time consuming to execute, and whether the cause of the statement that is too time consuming is caused by itself or by other statements in the database is determined. If the statement is too time consuming due to itself, the statement is determined to be a slow query statement.
In operation S230, the slow query statement and the table information are associated, and the abnormality information of the slow query statement is determined.
In some embodiments, the reasons for the slow query statement are various, and the slow query statement caused by different reasons corresponds to different processing methods, wherein the common reasons for the slow query statement may at least include performing time-efficient super-threshold, table computing resource tilting, table storage resource tilting, and the like. The table information at least includes table metadata, a table size, a table data distribution condition (e.g., a table data inclination condition, a table fragmentation rate, etc.), and the abnormal information of the slow query statement is obtained by diagnosing the abnormal information of the slow query statement by establishing an association relationship between the slow query statement and the table information.
In operation S240, the slow query statement classification is performed using the abnormality information, and a plurality of slow query statement sets are determined.
In some embodiments, the exception information includes an exception cause that caused the slow query statement. The processing methods of the slow query sentences caused by the same reasons are the same, so that the slow query sentences can be classified by using the abnormal information, and the slow query sentences caused by the same abnormal information are put in a slow query sentence set so as to facilitate the batch processing of the slow query sentences by taking the sentence set as a unit.
In operation S250, a processing task corresponding to each slow query statement set is generated.
In some embodiments, the processing task corresponding to the slow query statement set is generated by the matched processing method based on the exception information of the slow query statement set, and the processing task is applicable to any slow query statement in the slow query statement set.
In operation S260, a plurality of slow query sentences in the slow query sentence set are batch-processed based on the processing task.
In some embodiments, the processing task is used for simultaneously carrying out batch processing on a plurality of slow query sentences in the slow query sentence set, so that the quick processing of the slow query sentences is realized.
According to the embodiment of the disclosure, the processing task of the slow query statement is generated by taking the slow query statement set as a unit, so that batch processing of a plurality of slow query statements in the same slow query statement set can be realized at the same time, the processing efficiency of the slow query statement is effectively improved, and the quick repair of the slow query problem of the database is realized. The slow query statement set is obtained by establishing an association relation between the slow query statement and table information, analyzing an abnormal reason causing a slow query problem of the query statement, and classifying the slow query statement based on the abnormal reason. The process of analyzing and processing the query statement is not carried out in the distributed database, but the SRE database management cluster receives the information transmitted by the MPP architecture distributed database, and the SRE database management cluster carries out query statement analysis and processing, so that the computing resources of the distributed database are effectively saved.
FIG. 3 schematically illustrates a flow chart of determining a slow query statement in accordance with an embodiment of the disclosure.
As shown in fig. 3, the query sentence processing method of this embodiment includes operations S310 to S330.
In operation S310, an abnormal sentence is time-consuming obtained based on the sentence state and the current state.
In some embodiments, if the query statement stays in the same state for too long, it is considered an exception statement to perform subsequent monitoring.
In operation S320, the actual time consumption of the exception statement is calculated using the database run information.
In some embodiments, the actual time consumption of the abnormal statement is calculated through the database operation information, so that the accurate time consumption of the abnormal statement is obtained, and the accurate screening of the slow statement is realized. In the actual running process of the database, the too long time consumption of the query statement may be caused by the own cause (such as too long execution time of the query statement) or may be caused by the database cause (such as too much progress in the database and too long queuing time of the query statement). For example, an exception statement takes a total of 10 seconds to execute, but 9 seconds of time are all running on the other statements in the waiting queue, then the actual time taken for the exception statement is 1 second. The method and the device provide that the actual time consumption of the abnormal statement in the execution process is calculated through the database operation information so as to realize the accurate selection of the slow query statement.
In operation S330, in case the actual time consumption of the abnormal sentence is greater than the threshold value, the abnormal sentence is determined as a slow query sentence.
In some embodiments, the exception screening is performed based on a preset threshold and the actual time consumption of the exception, and the exception is determined to be a slow query statement when the actual time consumption of the exception exceeds the preset threshold. Wherein the default preset threshold of the database is typically 10 seconds, i.e. an abnormal sentence that actually takes more than 10 seconds will be considered as a slow query sentence. The preset threshold value can also be adjusted by the related technicians according to actual production application conditions.
FIG. 4 schematically illustrates a flow chart of actual time consuming computation of an exception statement in accordance with an embodiment of the present disclosure.
As shown in fig. 4, the query sentence processing method of this embodiment includes operations S410 to S430.
In operation S410, it is checked whether the abnormal sentence is executed on time according to the database run information.
In some embodiments, the database run information includes database execution information and statement queuing information. The database execution information comprises a database execution plan, an execution time point, execution time consumption and cluster computing resource consumption details, and the statement queuing information comprises statement queuing identification and queuing time consumption. It is possible to obtain whether the abnormal sentence is executed as expected by the execution plan or not through the database execution information and the sentence queuing information.
In operation S420, in the case where the exception statement is executed on time, the current state time consumption of the exception statement is the actual time consumption.
In some embodiments, if an exception statement is executed as an expected execution plan, it is stated that the exception statement takes too much time due to the query statement itself, at which point the current state of the exception statement takes time as the actual time.
In operation S430, in case that the abnormal sentence is not executed on time, calculating an actual time consumption of the abnormal sentence according to the sentence queuing information; the actual time consumption is the difference between the current state time consumption and queuing information.
In some embodiments, if an exception statement is not executed as intended by the execution plan, the reason that the current state of the exception statement takes too much time may be caused by the execution of other statements in the database. For example, if the exception statement is not executed on time as expected by the execution plan, the queuing time of the exception statement is obtained through statement queuing information, and the actual time of the exception statement is obtained by subtracting the queuing time of the exception statement from the current state time of the exception statement. By actually judging whether the abnormal sentence is a slow query sentence or not in a time-consuming manner, the judgment accuracy of the slow query sentence can be improved, the fact that some normal sentences which are not slow to query due to the reasons of the normal sentences are recognized as slow sentences is avoided, the waste of computing resources for invalid treatment of the normal sentences due to inaccurate judgment of the slow query sentence is avoided, the effective treatment of the slow query sentence is realized, and the treatment efficiency of the slow query sentence is improved.
FIG. 5 schematically illustrates a flow chart of determining exception information for a slow query statement, according to an embodiment of the disclosure.
As shown in fig. 5, the query sentence processing method of this embodiment includes operations S510 to S520.
In operation S510, table information having an association relationship with the slow query statement is determined; wherein the table information includes table data distribution.
In some embodiments, the query statement is used to retrieve data from a database that satisfies a condition, the queried data source being typically recorded in the database in the form of a table. Therefore, the query statement generally comprises a specific table to be queried, and the association relationship between the query statement and the table information to be queried can be established, so that the reason for the slow query statement is further analyzed.
In a specific implementation process, determining table information having an association relationship with the slow query statement includes: analyzing the slow query statement to obtain a table name contained in the slow query statement; and establishing an association relation between the slow query statement and the table information based on the table name.
In operation S520, abnormality information of the slow query statement is determined based on the table data distribution situation.
In some embodiments, at least table metadata, table size, table distribution, etc. may be included in the table information. The table distribution conditions include table data inclination conditions, table fragmentation rates and the like. And carrying out association analysis on the slow query sentences by utilizing the table distribution condition, and identifying the abnormal information of the slow query sentences so as to classify the slow query sentences with the same abnormal information, thereby realizing batch processing of the slow query sentences.
FIG. 6 schematically illustrates a flow chart of determining a slow query statement set, in accordance with an embodiment of the disclosure.
As shown in fig. 6, the determination of the slow query statement set of this embodiment includes operations S610 to S620.
In operation S610, a unique ID of the slow query statement is extracted according to attribute information of the slow query statement.
In some embodiments, the attribute information of the slow query statement includes information such as a cluster ID in which the slow query statement is located, a statement ID of the slow query statement, creation time, a statement format, and the like, and the deduplication operation is performed on the slow query statement based on the attribute information of the slow query statement, so as to obtain a unique ID of the slow query statement. For example, in the financial field, related data in a database is often queried in units of days, query sentences corresponding to the same data are the same, and only creation time is different, so that repeated sentence screening can be performed through sentence IDs and creation time of slow query sentences, repeated sentences are deleted, and only slow query sentences with the creation time closest to the current time are reserved. In addition, the performing the deduplication operation on the slow query statement may further include controlling a display effect of the slow query statement by using a formatting variable, unifying formats of the slow query statement, and further screening the slow query statement in different formats, so as to avoid repeated computation on the same slow query statement, and save computing resources.
In operation S620, the slow query statement classification is performed based on the abnormality information and the unique ID of the slow query statement, resulting in a plurality of slow query statement sets.
In some embodiments, the unique ID of each slow query statement is obtained by performing a deduplication operation on the slow query statement. The unique ID of the slow query sentence is extracted from the cluster ID of the slow query sentence and the sentence ID of the slow query sentence. The slow query sentences are classified through the abnormal information of each slow query sentence and the unique ID of the slow query sentence, a plurality of slow query sentence sets are obtained, each slow query sentence set contains a plurality of slow query sentences with the same abnormal information, and the batch processing of the slow query sentences in the slow query sentence set can be realized through generating the processing task of the slow query sentence set.
FIG. 7 schematically illustrates a flow chart of processing tasks corresponding to generating each slow query statement set, according to an embodiment of the disclosure.
As shown in fig. 7, the processing task corresponding to each slow query statement set is generated in this embodiment, including operations S710 to S730.
In operation S710, abnormality information of a slow query statement set is acquired.
In operation S720, a processing method of the slow query statement set is determined according to the abnormality information.
In operation S730, a processing task of the slow query statement set is generated based on the processing method.
In some embodiments, the processing methods corresponding to the slow query problems caused by the same exception information are the same, so that batch processing of a plurality of slow query sentences in the slow query sentence set can be realized only by determining the processing method of the slow query sentence set. The processing method of each slow query statement set is determined through a preset processing suggestion template, wherein the processing suggestion template comprises a processing method corresponding to each piece of abnormal information. For example, for a slow query statement set in which the anomaly information is a large table computing resource, the corresponding processing method is to implement the tilt management of the Z table by recommending the distribution to the table to build Y. The processing method based on the slow query statement set generates a treatment task of the slow query statement set, so that batch processing is carried out on the slow query statements in the slow query statement set through the treatment task, and the processing efficiency of the slow query statements is effectively improved.
Fig. 8 schematically illustrates a flow chart of generating processing tasks according to an embodiment of the disclosure.
As shown in fig. 8, the generation processing task of this embodiment includes operations S810 to 820.
In operation S810, generating a governance report according to the slow query statement set; the management report comprises running information of each slow query statement and table information related to the slow query statement.
In some embodiments, the slow query statement information is centrally displayed by using the administration report so as to accurately position the slow query statement in the process of executing the slow query statement processing in batches.
In operation S820, a processing task of the slow query statement set is generated based on the administration report and the processing method.
In some embodiments, a processing task of a slow query statement set is generated through a management report and a processing method, wherein the processing task comprises running information of each slow query statement, an explatin execution plan, a table list related to the slow query statement set, attributes of the table, a management method and the like, so that positioning and processing of each slow query statement in the slow query statement set are realized, and accuracy of processing of the slow query statement is improved. And based on the processing task of the slow query statement generated in batches by the treatment report, the batch processing of the slow query statement is realized, and the processing efficiency of the slow query statement is effectively improved.
Based on the query sentence processing method, the disclosure further provides a query sentence processing device. The device will be described in detail below in connection with fig. 9.
Fig. 9 schematically shows a block diagram of a query sentence processing apparatus according to an embodiment of the present disclosure.
As shown in fig. 9, the query sentence processing apparatus 900 of this embodiment includes a receiving module 910, a first classifying module 920, a determining module 930, a second classifying module 940, a generating module 950, and a processing module 960.
The receiving module 910 is configured to obtain query statement information, table information, and database operation information from information transferred by the distributed database. In an embodiment, the receiving module 910 may be configured to perform the operation S210 described above, which is not described herein.
The first classification module 920 is configured to classify the query sentence based on the query sentence information, and determine a slow query sentence. In an embodiment, the first classification module 920 may be used to perform the operation S220 described above, which is not described herein.
The determining module 930 is configured to associate the slow query statement with the table information and determine exception information of the slow query statement. In an embodiment, the determining module 930 may be configured to perform the operation S230 described above, which is not described herein.
The second classification module 940 is configured to classify the slow query statement using the anomaly information, and determine a plurality of slow query statement sets. In an embodiment, the second classification module 940 may be used to perform the operation S240 described above, which is not described herein.
The generating module 950 is configured to generate a processing task corresponding to each slow query statement set. In an embodiment, the generating module 950 may be configured to perform the operation S250 described above, which is not described herein.
The processing module 960 is configured to perform batch processing on a plurality of slow query terms in the set of slow query terms based on the processing task. In an embodiment, the processing module 960 may be used to perform the operation S250 described above, which is not described herein.
Any of the receiving module 910, the first classifying module 920, the determining module 930, the second classifying module 940, the generating module 950, and the processing module 960 may be combined in one module to be implemented, or any of them may be split into a plurality of modules according to an embodiment of the present disclosure. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the receiving module 910, the first sorting module 920, the determining module 930, the second sorting module 940, the generating module 950, and the processing module 960 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or as hardware or firmware in any other reasonable manner of integrating or packaging the circuitry, or as any one of or a suitable combination of any of the three. Alternatively, at least one of the receiving module 910, the first classifying module 920, the determining module 930, the second classifying module 940, the generating module 950, and the processing module 960 may be at least partially implemented as computer program modules, which when executed, may perform the respective functions.
Fig. 10 schematically illustrates a block diagram of an electronic device adapted to implement a query statement processing method in accordance with an embodiment of the disclosure.
As shown in fig. 10, an electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage section 1009 into a Random Access Memory (RAM) 1003. The processor 1001 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. The processor 1001 may also include on-board memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM 1003, various programs and data necessary for the operation of the electronic apparatus 1000 are stored. The processor 1001, the ROM 1002, and the RAM 1003 are connected to each other by a bus 1004. The processor 1001 performs various operations of the method flow according to the embodiment of the present disclosure by executing programs in the ROM 1002 and/or the RAM 1003. Note that the program may be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 may also perform various operations of the method flow according to the embodiments of the present disclosure by executing programs stored in the one or more memories.
According to an embodiment of the disclosure, the electronic device 1000 may also include an input/output (I/O) interface 1005, the input/output (I/O) interface 1005 also being connected to the bus 1004. The electronic device 1000 may also include one or more of the following components connected to the I/O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output portion 1007 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage section 1009 including a hard disk or the like; and a communication section 1009 including a network interface card such as a LAN card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the internet. The drive 1010 is also connected to the I/O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed on the drive 1010 as needed, so that a computer program read out therefrom is installed into the storage section 1009 as needed.
The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: 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), 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 disclosure, 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. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM 1002 and/or RAM 1003 and/or one or more memories other than ROM 1002 and RAM 1003 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. The program code, when executed in a computer system, causes the computer system to implement the item recommendation method provided by embodiments of the present disclosure.
The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 1001. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of signals on a network medium, distributed, and downloaded and installed via the communication section 1009, and/or installed from the removable medium 1011. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 1009, and/or installed from the removable medium 1011. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 1001. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.
According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be provided in a variety of combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.
The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims (12)

1. A query statement processing method, comprising:
acquiring query statement information, table information and database operation information from information transmitted by a distributed database;
Carrying out query statement classification based on the query statement information to determine slow query statements;
correlating the slow query statement with the table information to determine abnormal information of the slow query statement;
classifying the slow query sentences by using the abnormal information, and determining a plurality of slow query sentence sets;
generating a processing task corresponding to each slow query statement set;
and carrying out batch processing on a plurality of slow query sentences in the slow query sentence set based on the processing task.
2. The query sentence processing method according to claim 1, wherein the query sentence information includes sentence states and current state time consumption; the step of classifying the query sentences based on the query sentence information to determine slow query sentences comprises the following steps:
determining an abnormal statement based on the statement state and the current state time consumption;
calculating the actual time consumption of the abnormal statement by utilizing the database operation information;
in the event that the actual time consumption of the exception statement is greater than a threshold, the exception statement is determined to be a slow query statement.
3. The query sentence processing method according to claim 2, wherein the database run information includes database execution information and sentence queuing information; the calculating the actual time consumption of the abnormal time-consuming statement by using the database operation information comprises the following steps:
Checking whether the abnormal statement is executed on time according to the database operation information;
under the condition that the abnormal statement is executed on time, the current state time consumption of the abnormal time-consuming statement is actual time consumption;
under the condition that the abnormal sentences are not executed on time, calculating the actual time consumption of the abnormal sentences according to the sentence queuing information; the actual time consumption is the difference value between the current state time consumption and queuing information.
4. The query statement processing method of claim 1, the determining exception information of the slow query statement based on the slow query statement and table information, comprising:
determining table information with association relation with the slow query statement; wherein the table information includes table data distribution conditions;
and determining abnormal information of the slow query statement based on the table data distribution condition.
5. The query term processing method as claimed in claim 3, wherein said determining table information having an association relationship with the slow query term comprises:
analyzing the slow query statement and determining a table name contained in the slow query statement;
and establishing the association relation between the slow query statement and the table information based on the table name.
6. The query term processing method as claimed in claim 1, wherein the query term information further includes attribute information of a query term; the classifying the slow query statement by using the abnormal information to obtain a plurality of slow query statement sets includes:
Extracting the unique ID of the slow query statement according to the attribute information of the slow query statement;
and classifying the slow query sentences based on the abnormal information and the unique IDs of the slow query sentences to obtain a plurality of slow query sentence sets.
7. The query term processing method as claimed in claim 1, wherein the generating the processing task corresponding to each slow query term set includes:
the following is performed for each slow query statement set:
obtaining abnormal information of the slow query statement set;
determining a processing method of the slow query statement set according to the abnormal information;
and generating processing tasks of the slow query statement set based on the processing method.
8. The query statement processing method of claim 1, the processing task that generates the slow set of query statements based on the processing method, further comprising:
generating a treatment report according to the slow query statement set; the management report comprises operation information of each slow query statement and table information related to the slow query statement;
and generating processing tasks of the slow query statement set based on the treatment report and the processing method.
9. A query statement processing apparatus comprising:
the acquisition module is used for acquiring query statement information, table information and database operation information from the information transmitted by the distributed database;
The first classification module is used for classifying the query sentences based on the query sentence information and determining slow query sentences;
the determining module is used for associating the slow query statement with the table information and determining the abnormal information of the slow query statement;
the second classification module is used for classifying the slow query sentences by utilizing the abnormal information and determining a plurality of slow query sentence sets;
the generation module is used for generating a processing task corresponding to each slow query statement set; and
and the processing module is used for carrying out batch processing on a plurality of slow query sentences in the slow query sentence set based on the processing task.
10. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-8.
11. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-8.
12. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 8.
CN202311077632.3A 2023-08-24 2023-08-24 Query statement processing method, device, equipment, medium and program product Pending CN117093609A (en)

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