Database data processing method and device, equipment, medium and product

Abstract

The invention discloses a data processing method, device and equipment of a database, a medium and a product, and relates to the technical field of financial science and technology. The data processing method of the database comprises the following steps: identifying dead tuple cleaning operation log entries of a financial enterprise-level distributed database; extracting dead tuple cleaning associated data in the dead tuple cleaning operation log entry; and storing the dead tuple cleaning associated data, and generating a data cleaning report based on the dead tuple cleaning associated data. According to the technical scheme, the database maintenance transparency can be improved, and the database performance analysis difficulty is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a database data processing method, device, equipment, medium and product. Background Art

[0002] In the existing database maintenance practice, autovacuum (system automatic cleaning process) is an important function in open source relational databases and their derivative databases (such as enterprise-level distributed databases). It reclaims space by regularly cleaning up dead tuples (that is, deleted records) in the database and updates the database statistics to optimize query performance.

[0003] However, the existing autovacuum function mainly focuses on background cleanup operations, resulting in low transparency in database maintenance and hindering in-depth optimization analysis of database performance. Summary of the invention

[0004] The present invention provides a database data processing method, device, equipment, medium and product to solve the problem that the existing database maintenance has low utilization of autovacuum related data, resulting in poor transparency of database maintenance and difficulty in database analysis.

[0005] According to one aspect of the present invention, there is provided a method for processing data in a database, comprising:

[0006] Identify dead tuple cleanup operation log entries for financial enterprise-level distributed databases;

[0007] Extracting dead tuple cleaning related data from the dead tuple cleaning operation log entry;

[0008] Store dead tuples and clean up associated data, and generate a data cleaning report based on the dead tuples and clean up associated data.

[0009] According to another aspect of the present invention, there is provided a data processing device for a database, comprising:

[0010] A log entry identification module, used to identify log entries of dead tuple cleanup operations in a financial enterprise-level distributed database;

[0011] A data extraction module, used for extracting dead tuple cleaning related data in the dead tuple cleaning operation log entry;

[0012] The data storage and report generation module is used to store the dead tuple cleaning related data, and generate a data cleaning report based on the dead tuple cleaning related data.

[0013] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the database data processing method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the database data processing method described in any embodiment of the present invention when executed.

[0018] According to another aspect of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the database data processing method described in any embodiment of the present invention is implemented.

[0019] The technical solution of the embodiment of the present invention, by identifying the dead tuple cleaning operation log entries of the financial enterprise-level distributed database, thereby extracting the dead tuple cleaning related data in the dead tuple cleaning operation log entries, and then storing the dead tuple cleaning related data, and generating a data cleaning report based on the dead tuple cleaning related data. Since the relevant information of the autovacuum operation is crucial for the monitoring and optimization of database performance, this solution collects, records and analyzes the relevant information of the autovacuum operation in the financial enterprise-level distributed database, realizes timely monitoring of the running status and effect of the autovacuum, and statistical analysis of the autovacuum operation, solves the problem that the existing database maintenance has low utilization of the relevant data of the autovacuum, resulting in poor transparency of database maintenance and difficulty in database analysis, can improve the transparency of database maintenance, and effectively reduce the difficulty of database performance analysis.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A flowchart of a method for processing data in a database provided in Embodiment 1 of the present invention;

[0023] Figure 2 A flowchart of a method for processing data in a database provided in Embodiment 2 of the present invention;

[0024] Figure 3 A data collection and analysis sequence diagram of a financial enterprise-level distributed database provided in Embodiment 2 of the present invention;

[0025] Figure 4 A schematic diagram of the structure of a data processing device for a database provided in Embodiment 3 of the present invention;

[0026] Figure 5 A schematic diagram of the structure of an electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the term "target" and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1 This is a flowchart of a database data processing method provided in the first embodiment of the present invention. This embodiment can be applied to the case of improving the transparency of database maintenance. The method can be executed by a database data processing device. The database data processing device can be implemented in the form of hardware and / or software. The database data processing device can be configured in an electronic device. The electronic device can include but is not limited to a computer and a server. Figure 1 As shown, the method includes:

[0031] Step 110: Identify dead tuple cleanup operation log entries of the financial enterprise-level distributed database.

[0032] The financial enterprise-level distributed database may be an enterprise-level distributed database applied to the financial industry. Exemplarily, the enterprise-level distributed database may include but is not limited to a distributed database with artificial intelligence as a core design concept. The financial enterprise-level distributed database may support relational and non-relational database engines. The dead tuple cleanup operation log entry may be a log entry of an autovacuum operation.

[0033] In an embodiment of the present invention, a dead tuple cleanup operation log entry of the financial enterprise-level distributed database can be identified from a log file in the financial enterprise-level distributed database.

[0034] Step 120: extract the dead tuple cleaning associated data in the dead tuple cleaning operation log entry.

[0035] The dead tuple cleaning associated data may be data related to the dead tuple cleaning operation. Exemplarily, the dead tuple cleaning associated data may include but is not limited to the time of the cleaning operation, the database and table affected by the cleaning operation, and the result of the operation.

[0036] In the embodiment of the present invention, the cleaning behavior related data may be extracted from the dead tuple cleaning operation log entry to obtain the dead tuple cleaning related data.

[0037] Step 130: Store the dead tuple cleanup associated data, and generate a data cleanup report based on the dead tuple cleanup associated data.

[0038] The data cleaning report can be used to record the statistical analysis results of the dead tuple cleaning related data. Exemplarily, the data cleaning report can include but is not limited to the cleaning status of the cleaning table corresponding to the dead tuple cleaning operation log entry (cleaning time and cleaning records, etc.), time-consuming performance analysis results, and optimization suggestions.

[0039] In an embodiment of the present invention, data preprocessing (such as data cleaning, etc.) can be performed on the dead tuple cleaning associated data, and the preprocessed dead tuple cleaning associated data can be stored in a financial enterprise-level distributed database, and the dead tuple cleaning associated data can be statistically analyzed to obtain a data cleaning report.

[0040] The technical solution of the embodiment of the present invention, by identifying the dead tuple cleaning operation log entries of the financial enterprise-level distributed database, thereby extracting the dead tuple cleaning associated data in the dead tuple cleaning operation log entries, and then storing the dead tuple cleaning associated data, and generating a data cleaning report based on the dead tuple cleaning associated data. Since the relevant information of the autovacuum operation is crucial for the monitoring and optimization of database performance, this solution collects, records and analyzes the relevant information of the autovacuum operation in the financial enterprise-level distributed database, realizes timely monitoring of the running status and effect of the autovacuum, and statistical analysis of the autovacuum operation, provides a basis for database administrators to optimize database performance, enhances the maintainability and reliability of the database, reduces problems caused by improper autovacuum operation, solves the problem that the existing database maintenance has low utilization of autovacuum related data, resulting in poor database maintenance transparency and difficulty in database analysis, can improve database maintenance transparency, and effectively reduce the difficulty of database performance analysis.

[0041] Embodiment 2

[0042] Figure 2 This is a flowchart of a database data processing method provided by the second embodiment of the present invention. This embodiment is specific based on the above embodiment and provides a specific optional implementation method for cleaning associated data based on dead tuples and generating a data cleaning report. Figure 2 As shown, the method includes:

[0043] Step 210: Identify dead tuple cleanup operation log entries of the financial enterprise-level distributed database.

[0044] In an optional embodiment of the present invention, identifying dead tuple cleanup operation log entries of a financial enterprise-level distributed database may include: connecting to the financial enterprise-level distributed database and monitoring transaction log files in real time; and identifying dead tuple cleanup operation log entries of the financial enterprise-level distributed database from the transaction log files based on a keyword matching method.

[0045] Among them, the transaction log file can be used to record all modification operations of the database to ensure the persistence of transactions and the recovery of the database.

[0046] In an embodiment of the present invention, the financial enterprise-level distributed database under each financial business system can be connected to further monitor the transaction log files of the financial enterprise-level distributed database, so that vacuum (dead tuple cleaning) is used as a keyword to search in the transaction log file in a keyword matching manner to identify the dead tuple cleaning operation log entries of the financial enterprise-level distributed database from the transaction log file. By real-time monitoring of the transaction log file, the dead tuple cleaning behavior in the database can be discovered in time, and each dead tuple cleaning operation log entry of the financial enterprise-level distributed database under different financial business systems can be automatically obtained by keyword matching.

[0047] Step 220: extract the dead tuple cleaning associated data in the dead tuple cleaning operation log entry.

[0048] In an optional embodiment of the present invention, extracting dead tuple cleaning associated data in a dead tuple cleaning operation log entry may include: obtaining target cleaning data in the dead tuple cleaning associated data according to a database target maintenance command; and determining cleaning operation time-consuming data in the dead tuple cleaning associated data according to the time-consuming length of cleaning the target cleaning data.

[0049] The database target maintenance command may be vacuum verbose (dead tuple cleanup and display instruction, a command option when executing vacuum). The target cleanup data may be the current cleanup information displayed based on the database target maintenance command. The cleanup operation time consumption data may be the time consumption of each part of the data cleanup operation in the target cleanup data.

[0050] In an embodiment of the present invention, the target cleaning data in the dead tuple cleaning associated data can be displayed based on the database target maintenance command, and the cleaning time of each type of data in the target cleaning data can be further obtained, and the cleaning time of each type of data in the target cleaning data can be used as the cleaning operation time-consuming data in the dead tuple cleaning associated data. Based on the database target maintenance command, detailed execution information is output to reflect the status and progress of the vacuum process to assist in determining the target cleaning data, and can be combined with the cleaning operation time-consuming data to provide a data basis for subsequent database performance analysis.

[0051] In an optional embodiment of the present invention, the target cleanup data may include partitions, indexes, and large object tables to be cleaned at that time.

[0052] The partitions to be cleaned in the target cleaned data can be partitions of the partitioned table to be cleaned, and each partition is indexed, queried, and maintained independently of other partitions. The indexes in the target cleaned data can be used to quickly find rows in the partitioned table to be cleaned. The large object table in the target cleaned data can be used to store values ​​of large fields.

[0053] In the embodiment of the present invention, the partition, index and large object table to be cleaned at that time are used as target cleanup data, and multiple generation dimensions are provided for generating cleanup records corresponding to the cleanup table, so as to facilitate dimension-by-dimension time-consuming performance analysis of the dead tuple cleanup operation.

[0054] Step 230: Store the dead tuple cleaning associated data, and determine the records to be combined and cleaned from multiple record dimensions according to the dead tuple cleaning associated data.

[0055] The cleanup records to be combined may be records of a cleanup table corresponding to dead tuple cleanup operation log entries.

[0056] In an embodiment of the present invention, after storing the dead tuple cleaning associated data, the dead tuple cleaning associated data can be analyzed and processed, and according to the dimension of the cleanup table to generate records, the dead tuple cleaning operation log entries corresponding to the cleanup table records are generated, that is, the cleanup records to be combined are obtained.

[0057] In an optional embodiment of the present invention, storing dead tuple cleaning associated data may include: formatting the dead tuple cleaning associated data based on the target data format to obtain the dead tuple cleaning data to be stored; inserting the dead tuple cleaning data to be stored into the target database, and creating a data index for the dead tuple cleaning data to be stored.

[0058] The target data format may be a data storage format of dead tuple cleanup associated data. Exemplarily, the target data format may be a data storage format of a financial enterprise-level distributed database. The dead tuple cleanup data to be stored may be dead tuple cleanup associated data in the target data format. The target database may be a database storing the dead tuple cleanup data to be stored.

[0059] In an embodiment of the present invention, the data storage format of the target database storing the dead tuple cleaning associated data can be determined first, that is, the target data format can be determined, and then the dead tuple cleaning associated data can be formatted into the target data format, and the formatted dead tuple cleaning associated data can be used as the dead tuple cleaning data to be stored, and inserted into the target database for subsequent data query, and further a data index for the dead tuple cleaning data to be stored can be created to quickly query the dead tuple cleaning data to be stored in the target database.

[0060] Step 240: Generate a data cleaning report based on the statistical analysis data of the records to be combined and cleaned and the dead tuple cleaning associated data.

[0061] In an embodiment of the present invention, the cleaning records to be combined can be combined according to the corresponding relationship with the cleaning table, and the cleaning records to be combined corresponding to a cleaning table can be combined into a complete record, and the number of complete records can be counted, so as to clean up the associated data based on the dead tuples, determine the cleaning time of the records in the cleaning table, and then count the number of the aforementioned complete records and the cleaning time of the records, and determine the time performance analysis results and optimization suggestions, and further construct a data cleaning report based on the time performance analysis results, optimization suggestions, the number of cleaning records and the cleaning time of the records.

[0062] Figure 3 This is a data collection and analysis sequence diagram of a financial enterprise-level distributed database provided by the second embodiment of the present invention. Figure 3 As shown, the financial enterprise-level distributed database turns on autovacuum, generates transaction log files based on autovacuum, monitors and parses the transaction log files through the data collection module, and transmits the parsed data (i.e., dead tuple cleanup associated data) to the information storage module, stores the parsed data in the information storage module, and further provides data access rights to the data analysis module by the information storage module, and the data analysis module performs data analysis on the parsed data to provide a data cleanup report, and then provides the data cleanup report to the database administrator.

[0063] The technical solution of the embodiment of the present invention, by identifying the dead tuple cleaning operation log entries of the financial enterprise-level distributed database, thereby extracting the dead tuple cleaning associated data in the dead tuple cleaning operation log entries, and then storing the dead tuple cleaning associated data, and according to the dead tuple cleaning associated data, determining the cleaning records to be combined from multiple record dimensions, and further generating a data cleaning report based on the statistical analysis data of the cleaning records to be combined and the dead tuple cleaning associated data. By disassembling the cleaning records to be combined from multiple dimensions and combining multiple records corresponding to a cleaning table, effective data support is provided for database maintenance, the difficulty of database maintenance is simplified, and the generation of optimization decisions based on data drive is realized, and finally the timely monitoring of the running status and effect of autovacuum and the statistical analysis of autovacuum operations are realized, which solves the existing problems of low utilization of autovacuum related data in database maintenance, resulting in poor transparency of database maintenance and difficulty in database analysis, and can improve the transparency of database maintenance and effectively reduce the difficulty of database performance analysis.

[0064] Embodiment 3

[0065] Figure 4 This is a schematic diagram of the structure of a data processing device for a database provided by Embodiment 3 of the present invention. Figure 4 As shown, the device comprises:

[0066] A log entry identification module 310, used to identify dead tuple cleanup operation log entries of a financial enterprise-level distributed database;

[0067] A data extraction module 320 is used to extract dead tuple cleaning related data in the dead tuple cleaning operation log entry;

[0068] The data storage and report generation module 330 is used to store the dead tuple cleaning related data and generate a data cleaning report based on the dead tuple cleaning related data.

[0069] The technical solution of the embodiment of the present invention, by identifying the dead tuple cleaning operation log entries of the financial enterprise-level distributed database, thereby extracting the dead tuple cleaning related data in the dead tuple cleaning operation log entries, and then storing the dead tuple cleaning related data, and generating a data cleaning report based on the dead tuple cleaning related data. Since the relevant information of the autovacuum operation is crucial for the monitoring and optimization of database performance, this solution collects, records and analyzes the relevant information of the autovacuum operation in the financial enterprise-level distributed database, realizes timely monitoring of the running status and effect of the autovacuum, and statistical analysis of the autovacuum operation, solves the problem that the existing database maintenance has low utilization of the relevant data of the autovacuum, resulting in poor transparency of database maintenance and difficulty in database analysis, can improve the transparency of database maintenance, and effectively reduce the difficulty of database performance analysis.

[0070] Optionally, the log entry identification module 310 is specifically used to connect to the financial enterprise-level distributed database and monitor the transaction log file in real time; based on the keyword matching method, identify the dead tuple cleanup operation log entries of the financial enterprise-level distributed database from the transaction log file.

[0071] Optionally, the data extraction module 320 is specifically used to obtain target cleaning data of the dead tuple cleaning associated data according to the database target maintenance command; and determine the cleaning operation time consumption data in the dead tuple cleaning associated data according to the time consumption for cleaning the target cleaning data.

[0072] Optionally, the data storage and report generation module 330 is specifically used to format the dead tuple cleaning associated data based on the target data format to obtain the dead tuple cleaning data to be stored; insert the dead tuple cleaning data to be stored into the target database, and create a data index for the dead tuple cleaning data to be stored.

[0073] Optionally, the data storage and report generation module 330 is specifically used to determine the records to be combined and cleaned from multiple record dimensions according to the dead tuple cleaning related data; and generate the data cleaning report based on the statistical analysis data of the records to be combined and cleaned and the dead tuple cleaning related data.

[0074] Optionally, the target cleanup data includes partitions, indexes, and large object tables to be cleaned at that time.

[0075] The database data processing device provided in the embodiment of the present invention can execute the database data processing method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0076] Embodiment 4

[0077] Figure 5 A schematic diagram of an electronic device that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0078] like Figure 5 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0079] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0080] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a data processing method for a database.

[0081] The technical solution of the embodiment of the present invention, by identifying the dead tuple cleaning operation log entries of the financial enterprise-level distributed database, thereby extracting the dead tuple cleaning related data in the dead tuple cleaning operation log entries, and then storing the dead tuple cleaning related data, and generating a data cleaning report based on the dead tuple cleaning related data. Since the relevant information of the autovacuum operation is crucial for the monitoring and optimization of database performance, this solution collects, records and analyzes the relevant information of the autovacuum operation in the financial enterprise-level distributed database, realizes timely monitoring of the running status and effect of the autovacuum, and statistical analysis of the autovacuum operation, solves the problem that the existing database maintenance has low utilization of the relevant data of the autovacuum, resulting in poor transparency of database maintenance and difficulty in database analysis, can improve the transparency of database maintenance, and effectively reduce the difficulty of database performance analysis.

[0082] In some embodiments, the data processing method of the database may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the data processing method of the database described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the data processing method of the database in any other appropriate manner (e.g., by means of firmware).

[0083] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0084] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0085] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0086] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0087] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0088] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of traditional physical hosts and VPS servers, which are difficult to manage and have weak business scalability.

[0089] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0090] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for processing data in a database, characterized in that: include: Identify dead tuple cleanup operation log entries for financial enterprise-level distributed databases; Extracting dead tuple cleaning associated data from the dead tuple cleaning operation log entry; The dead tuple cleaning associated data is stored, and a data cleaning report is generated based on the dead tuple cleaning associated data.

2. The method according to claim 1, characterized in that Identify dead tuple cleanup operation log entries of a financial enterprise-level distributed database, including: Connecting to the financial enterprise-level distributed database and monitoring transaction log files in real time; Based on the keyword matching method, dead tuple cleanup operation log entries of the financial enterprise-level distributed database are identified from the transaction log file.

3. The method according to claim 1, characterized in that Extracting the dead tuple cleaning associated data in the dead tuple cleaning operation log entry includes: According to the database target maintenance command, obtaining the target cleaning data in the dead tuple cleaning associated data; According to the time consumed for cleaning the target cleaning data, the cleaning operation time consumption data in the dead tuple cleaning associated data is determined.

4. The method according to claim 1, characterized in that: Storing the dead tuple cleanup associated data includes: Based on the target data format, formatting the dead tuple cleanup associated data to obtain the dead tuple cleanup data to be stored; The dead tuple-cleaned data to be stored is inserted into a target database, and a data index for the dead tuple-cleaned data to be stored is created.

5. The method according to claim 1, characterized in that: Cleaning associated data based on the dead tuples and generating a data cleaning report, including: Cleaning associated data according to the dead tuples, and determining records to be combined and cleaned from multiple record dimensions; The data cleaning report is generated based on the statistical analysis data of the records to be combined and cleaned and the dead tuple cleaning associated data.

6. The method according to claim 3, characterized in that The target cleanup data includes the partitions, indexes and large object tables to be cleaned at that time.

7. A data processing device for a database, characterized in that: include: A log entry identification module, used to identify log entries of dead tuple cleanup operations in a financial enterprise-level distributed database; A data extraction module, used to extract dead tuple cleaning related data in the dead tuple cleaning operation log entry; The data storage and report generation module is used to store the dead tuple cleaning associated data and generate a data cleaning report based on the dead tuple cleaning associated data.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the database data processing method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the database data processing method according to any one of claims 1 to 6 when executed.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the database data processing method according to any one of claims 1 to 6.

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