Database performance diagnosis method based on message middleware

Abstract

The invention discloses a database performance diagnosis method based on message-oriented middleware, and relates to the technical field of database performance diagnos.The database performance diagnosis method comprises a data monitoring module, the message-oriented middleware and a data storage module, and then the collected data is encrypted and sent to the message middleware, and the message middleware is responsible for receiving the encrypted data from the data monitoring module. According to the invention, the collected data is firstly coded and compressed, and then encrypted through an encryption algorithm, so that a third party can be prevented from accessing the data when the data is transmitted from one endpoint to another endpoint, and the security of the data in the transmission process is ensured, so that the security of the data can be improved while the storage space is optimized; and when the data is submitted to the message-oriented middleware, the compressed and encrypted data is backed up regularly, so that the reliability and security of the data are ensured to a certain extent, and the accuracy of subsequent performance diagnosis is further ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of database performance diagnosis, and in particular to a database performance diagnosis method based on message middleware. Background Art

[0002] Message middleware is mainly used to transmit and process messages in distributed systems. It allows applications to communicate by sending and receiving messages through asynchronous communication mechanisms. Message middleware can be used to achieve data synchronization, conversion and processing between different databases. Such database systems require real-time monitoring and diagnosis. Existing database diagnosis methods make it difficult to reduce the amount of data while improving data reliability and security.

[0003] The defects of existing database performance diagnosis methods are: Application document CN112486780A discloses a method and system for real-time monitoring and diagnosis of database performance based on message middleware, which mainly considers how to locate database operations that affect performance indicators based on information output by performance models to achieve real-time and large-scale management purposes, but does not consider how to improve data reliability and security while reducing the amount of data; Existing database performance diagnosis usually outputs results in text form and cannot be displayed intuitively in a dynamic form; Existing database performance diagnosis is difficult to implement large-scale database cluster management and cannot meet the real-time performance monitoring and diagnosis of multiple databases; Existing database performance diagnosis is difficult to locate and track problems, resulting in limited efficiency in performance monitoring and diagnosis. Summary of the invention

[0004] The purpose of the present invention is to provide a database performance diagnosis method based on message middleware to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: a database performance diagnosis method based on message middleware, comprising a data monitoring module, a message middleware and a data storage module, wherein the data monitoring module is used to regularly collect data from a source database system, and then encrypt the collected data and send it to the message middleware, the message middleware is responsible for receiving the encrypted data from the data monitoring module and distributing it to the data storage module, and the data storage module is responsible for receiving and storing the data; The data monitoring module includes a data acquisition unit, a backup database, a data encoding unit, a data encryption unit and a data sending unit. The data acquisition unit is used to regularly collect node information, database operation characteristics, operation logs and system operation characteristic information of the source database system. The node information includes timestamp acquisition and IP acquisition. The backup database is responsible for backing up and storing the collected data according to the node information. The data encoding unit uses a compression algorithm to encode and compress the operation logs and operation characteristic data of the collected source database system. The data encryption unit encrypts the encoded and compressed data through an encryption algorithm. The data sending unit is responsible for submitting the encrypted data to the message middleware.

[0006] Preferably, it also includes a performance diagnosis module, an alarm mechanism, an interaction module and a record management module. The performance diagnosis module uses a pre-trained model to perform real-time diagnosis, and uses a visualization tool to aggregate the diagnosis results into a three-dimensional dynamic image. When the performance index exceeds the preset threshold, the alarm mechanism is responsible for promptly notifying the management personnel to take measures. The interaction module displays the three-dimensional dynamic image on the touch screen. The record management module is responsible for recording the results of the performance diagnosis and the optimization measures taken by the management personnel to address the problems and generating a detailed record report.

[0007] Preferably, the data storage module includes a receiving unit and a storage unit, the receiving unit is responsible for receiving data obtained from the message middleware, and the storage unit stores the received data in order according to the node information of the database where the source is located.

[0008] Preferably, the performance diagnosis module includes model training, a diagnosis unit, an optimization and maintenance unit, and a command receiving unit. The model training combines machine learning algorithms and deep learning technologies to construct a performance diagnosis model, and uses the collected data to train the performance diagnosis model. The diagnosis unit uses the trained performance diagnosis model to analyze the data in the data storage module and determine the cause and location of the performance bottleneck. The optimization and maintenance unit formulates corresponding optimization measures based on the results determined by the diagnosis unit. The command receiving unit is used to receive commands from the interaction module.

[0009] Preferably, the optimization and maintenance unit includes optimization and testing as well as monitoring and maintenance. The optimization and testing formulates corresponding optimization measures according to the confirmed performance bottlenecks, and tests and verifies the optimized database. The monitoring and maintenance is used to monitor the performance of the optimized database and conduct performance evaluation regularly.

[0010] Preferably, the optimization and testing include autonomous optimization, external optimization and effect testing. The autonomous optimization is responsible for optimizing the internal structure, query statements and configuration parameters of the database. The external optimization involves improvements to the hardware, network and operating system. The effect testing uses the JMeter testing tool to perform actual testing and verification on the optimized database.

[0011] Preferably, the autonomous optimization includes query optimization, data model adjustment, memory management, concurrency control and configuration parameter adjustment. The query optimization is responsible for optimizing SQL query statements. The data model adjustment optimizes query speed by utilizing normalization and denormalization. The memory management optimizes data processing speed by adjusting memory allocation of the buffer pool and log buffer. The concurrency control optimizes deadlock and long waiting times through fifteen isolation levels and lock mechanisms. The configuration parameter adjustment is optimized by adjusting the connection pool size and timeout settings.

[0012] Preferably, the external optimization includes hardware upgrade, network optimization and operating system tuning. The hardware upgrade improves computing and storage capabilities by increasing the number of CPU cores and expanding memory capacity. The network optimization is used to optimize network configuration and topology. The operating system tuning adjusts operating system parameters by adjusting the file system type and I / O scheduler.

[0013] Preferably, the database operation characteristics include latency, query throughput, query execution time, lock waiting time, database connection status and buffer pool usage, and the operation log includes query log, error log and slow query log; The system operation characteristic information includes the number of transactions per second, the number of concurrent users, response time, CPU usage, memory usage, disk I / O speed and network throughput.

[0014] Preferably, the interaction module includes a user interface and a touch display screen, the user interface is used for the manager to send commands to the performance diagnosis module, and the touch display screen is used to display three-dimensional dynamic images.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention encodes and compresses the collected data through a compression algorithm, and after reducing the data volume, the data encryption unit encrypts it again through an encryption algorithm to achieve end-to-end encryption. This can prevent a third party from accessing the data when the data is transmitted from one endpoint to another, ensure the security of the data during transmission, and prevent data leakage. In this way, the security of the data can be improved while optimizing the storage space. The encrypted data in the subsequent performance diagnosis will not affect the results of the performance test. When the data is submitted to the message middleware, the compressed and encrypted data will be backed up regularly and stored in the backup database. This ensures the reliability and security of the data to a certain extent, and thus ensures the accuracy of the subsequent performance diagnosis.

[0016] The performance diagnosis module of the present invention uses a trained performance diagnosis model to perform in-depth analysis on the collected data, identify potential performance problems and performance bottlenecks, and derive the cause of the performance problem by comparing the data of different node information, locate the root cause of the problem, predict the trend of the performance problem, and output it in text form. Then, a visualization tool is used to aggregate the text output results into a three-dimensional image, which is intuitively displayed on the touch display screen in the form of intuitive dynamic images, so that personnel can intuitively and simply understand the analysis results. In addition, managers can rotate, zoom and translate the image through mouse operations or touch display gestures to view data from different angles, which helps managers to more intuitively understand the performance status of the database.

[0017] The present invention adds message middleware as a transfer station in database performance diagnosis, which can realize real-time monitoring and diagnosis of database performance, so as to timely discover and solve potential problems, and can support large-scale database cluster management, and can handle multiple database servers at the same time, realizing rapid data transmission and processing, and improving the efficiency of performance monitoring and diagnosis. Storing data according to node information is helpful for subsequent data analysis and problem tracking.

[0018] The diagnostic unit of the present invention utilizes a trained performance diagnostic model to perform real-time diagnosis on the newly input performance data in the storage unit, and analyzes the performance bottleneck of the database based on the collected performance data. Since the data is stored in order according to the node information during storage, the operation or configuration that may cause performance degradation can be quickly located, and the specific cause of the performance degradation can be confirmed, so as to promptly guide the administrator to carry out targeted problem handling. The administrator will formulate corresponding optimization measures based on the confirmed performance bottleneck. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the present invention; Figure 2 is a system diagram of the present invention; Figure 3 The performance diagnosis module of the present invention is constituted as a structural diagram; Figure 4 It is a structural diagram of the autonomous optimization structure of the present invention; Figure 5 This is a structural diagram of the external optimization structure of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 are within the scope of protection of the present invention. Embodiment 1

[0021] See also Figure 1 and Figure 2 , an embodiment provided by the present invention: a database performance diagnosis method based on message middleware, comprising a data monitoring module, a message middleware and a data storage module, wherein the data monitoring module is used to regularly collect data from a source database system, and then encrypt the collected data and send it to the message middleware, the message middleware is responsible for receiving the encrypted data from the data monitoring module and distributing it to the data storage module, and the data storage module is responsible for receiving and storing the data; The data monitoring module includes a data acquisition unit, a backup database, a data encoding unit, a data encryption unit and a data sending unit. The data acquisition unit is used to regularly collect node information, database operation characteristics, operation logs and system operation characteristic information of the source database system. The node information includes timestamp acquisition and IP acquisition. The backup database is responsible for backing up and storing the collected data according to the node information. The data encoding unit uses a compression algorithm to encode and compress the operation logs and operation characteristic data of the source database system collected. The data encryption unit encrypts the encoded and compressed data through an encryption algorithm. The data sending unit is responsible for submitting the encrypted data to the message middleware; The data storage module includes a receiving unit and a storage unit. The receiving unit is responsible for receiving data obtained from the message middleware, and the storage unit stores the received data in order according to the node information of the database where the source is located.

[0022] Furthermore, the source database system includes multiple different databases. A data collection unit is set on each database node to regularly collect the operation characteristics, operation logs and system operation characteristic information of different databases in the source database system. At the same time, the IP and timestamps of different database nodes are collected together. The collected data is encoded and compressed by a compression algorithm. After the data volume is reduced, the data encryption unit encrypts it by an encryption algorithm to achieve end-to-end encryption, which can prevent third parties from accessing data when the data is transmitted from one endpoint to another, ensure the security of data during transmission, and prevent data leakage. This can improve data security while optimizing storage space. In subsequent performance diagnosis, the encrypted data will not affect the results of the performance test. It is then submitted to the message middleware through the data sending unit to ensure efficient data transmission. The message middleware serves as a data transfer station and is mainly used to achieve real-time data synchronization. The step function simplifies the interaction complexity between different databases and improves the maintainability and scalability of the system. The receiving unit then obtains data from the message middleware and stores the data in the storage unit according to the database node IP and timestamp where the source is located, which is convenient for subsequent analysis and positioning of database performance, and improves the positioning speed of performance bottlenecks to a certain extent. Commonly used encryption algorithms in actual use include AES, RSA, and ECC. When submitting data to the message middleware, the compressed and encrypted data will be backed up regularly and stored in the backup database. In addition, the administrator can also send backup commands through the interactive module to perform data backup operations. When the message middleware fails and causes data loss or damage, the administrator can send a recovery command to restore the data to the specified backup point, which ensures the reliability and security of the data to a certain extent, and thus ensures the accuracy of subsequent performance diagnosis. Adding message middleware as a transit station in database performance diagnosis can achieve real-time monitoring and diagnosis of database performance, so as to promptly discover and solve potential problems. It can also support large-scale database cluster management and can handle multiple database servers at the same time. The publish / subscribe function of the message middleware realizes rapid data transmission and processing, improves the efficiency of performance monitoring and diagnosis, and storing data based on node information facilitates subsequent data analysis and problem tracking. Embodiment 2

[0023] See also Figure 2An embodiment of the present invention is: a database performance diagnosis method based on message middleware, which also includes a performance diagnosis module, an alarm mechanism, an interaction module and a record management module. The performance diagnosis module uses a pre-trained model to perform real-time diagnosis, and uses a visualization tool to aggregate the diagnosis results into a three-dimensional dynamic image. When the performance index exceeds a preset threshold, the alarm mechanism is responsible for promptly notifying the management personnel to take measures. The interaction module displays the three-dimensional dynamic image on a touch screen. The record management module is responsible for recording the results of the performance diagnosis and the optimization measures taken by the management personnel to solve the problem and generating a detailed record report.

[0024] Furthermore, the performance diagnosis module uses the trained performance diagnosis model to conduct in-depth analysis of the collected data, identify potential performance problems and performance bottlenecks, and derive the cause of the performance problem by comparing the data of different node information, locate the root cause of the problem, predict the trend of the performance problem, and output it in text form. Then, using visualization tools such as Tableau, Power BI or Python's Matplotlib library, the text output results are aggregated into a three-dimensional image, which is intuitively displayed on the touch screen in the form of intuitive dynamic images, so that personnel can intuitively and simply understand the analysis results. In addition, managers can rotate, zoom and translate images through mouse operations or touch screen gestures to view data from different angles, which helps managers to more intuitively understand the performance status of the database. The alarm mechanism uses monitoring tools such as Nagios, Zabbix, Prometheus, etc. to track the key performance indicators of the database. According to the actual operation of the database, the alarm threshold is pre-set. When the monitoring tool detects that the performance indicator of the database exceeds the alarm threshold, it will send an alarm notification to the interaction module to notify the manager in time, which helps the performance problem to be handled in time. Embodiment 3

[0025] See also Figure 3 An embodiment of the present invention is: a database performance diagnosis method based on message middleware, the performance diagnosis module includes model training, a diagnosis unit, an optimization and maintenance unit and a command receiving unit, the model training combines machine learning algorithms and deep learning technologies to build a performance diagnosis model, and uses the collected data to train the performance diagnosis model, the diagnosis unit uses the trained performance diagnosis model to analyze the data in the data storage module, and determines the cause and location of the performance bottleneck, the optimization and maintenance unit formulates corresponding optimization measures according to the results determined by the diagnosis unit, and the command receiving unit is used to receive commands from the interaction module.

[0026] Furthermore, a performance diagnosis model is constructed using deep learning technology, and multiple different machine learning algorithms such as decision trees and support vector machines are integrated to select features closely related to the problem from the data, and the two are integrated. During the model construction process, the model needs to be continuously trained and optimized to improve the performance and accuracy of the model. The diagnosis unit uses the trained performance diagnosis model to perform real-time diagnosis on the newly input performance data in the storage unit, and analyzes the performance bottleneck of the database based on the collected performance data. Since the data is stored in order according to the node information during storage, it is possible to quickly locate the operations or configurations that may cause performance degradation, and confirm the specific causes of the performance degradation. The system will guide administrators to deal with problems in a targeted manner. Administrators will formulate corresponding optimization measures based on the confirmed performance bottlenecks, such as increasing hardware resources, optimizing SQL statements, adjusting database configuration parameters, etc. After optimizing the database, actual testing and verification are required to ensure that the measures taken have effectively improved performance. The performance diagnosis module can independently perform performance diagnosis on the database. At the same time, administrators can also send diagnostic commands through the interactive module to quickly locate the cause of the problem. Based on the diagnostic results, administrators can execute corresponding repair commands to restore the normal operation of the database. In addition, administrators can also use the interactive module to send optimization commands, such as creating indexes, adjusting memory allocation, etc., to improve database performance. Embodiment 4

[0027] See also Figure 4 and Figure 5 , an embodiment provided by the present invention: a database performance diagnosis method based on message middleware, the optimization and maintenance unit includes optimization and testing as well as monitoring and maintenance, the optimization and testing formulates corresponding optimization measures according to the confirmed performance bottleneck, and tests and verifies the optimized database, the monitoring and maintenance is used to monitor the performance of the optimized database and regularly evaluate the performance; The optimization and testing include autonomous optimization, external optimization and effect testing. The autonomous optimization is responsible for optimizing the internal structure, query statements and configuration parameters of the database. The external optimization involves improving the hardware, network and operating system. The effect testing uses the JMeter testing tool to perform actual testing and verification on the optimized database. The autonomous optimization includes query optimization, data model adjustment, memory management, concurrency control and configuration parameter adjustment. The query optimization is responsible for optimizing SQL query statements. The data model adjustment optimizes query speed by using normalization and denormalization. The memory management optimizes data processing speed by adjusting the memory allocation of the buffer pool and log buffer. The concurrency control optimizes deadlock and long waiting times through fifteen isolation levels and lock mechanisms. The configuration parameter adjustment optimizes by adjusting the connection pool size and timeout settings. The external optimization includes hardware upgrade, network optimization and operating system tuning. The hardware upgrade improves computing and storage capabilities by increasing the number of CPU cores and expanding memory capacity. The network optimization is used to optimize network configuration and topology. The operating system tuning adjusts operating system parameters by adjusting the file system type and I / O scheduler.

[0028] Furthermore, data optimization is divided into self-optimization and external optimization. Self-optimization mainly optimizes the internal structure of the database, query statements and configuration parameters, while external optimization involves the improvement of external factors such as hardware upgrades, network optimization and operating system tuning. When query throughput and query execution time are abnormal, managers can analyze and optimize SQL query statements to avoid full table scans to improve query efficiency. When performance bottlenecks occur in buffer pool usage or system operation feature information, managers can adjust database memory allocation parameters to reduce disk I / O operations and increase data processing speed. When bottlenecks occur in CPU usage and memory usage, managers can use hardware resources to improve performance. Upgrade to provide more powerful computing and storage capabilities. When network throughput is abnormal, managers optimize network configuration and topology, reduce network latency and bandwidth limitations, improve data transmission efficiency, and comprehensively optimize database performance through self-optimization and external optimization. After optimization, use the JMeter test tool to perform actual testing and verification on the optimized database to ensure that the measures taken have effectively improved performance. Continuous performance monitoring of data is carried out to promptly discover and resolve new performance issues, ensure database stability and high-performance operation, regularly evaluate database performance, and adjust optimization strategies based on actual conditions to adapt to changing business needs and technical environments. Embodiment 5

[0029] See also Figure 2 , an embodiment provided by the present invention: a database performance diagnosis method based on message middleware, the database operation characteristics include latency, query throughput, query execution time, lock waiting time, database connection status and buffer pool usage, the operation log includes query log, error log and slow query log; The system operation characteristic information includes the number of transactions per second, the number of concurrent users, response time, CPU usage, memory usage, disk I / O speed and network throughput; The interaction module includes a user interface and a touch display screen. The user interface is used by the manager to send commands to the performance diagnosis module, and the touch display screen is used to display three-dimensional dynamic images.

[0030] Furthermore, the performance indicators of the database mainly include latency, i.e. the time taken for each SQL request, query throughput, i.e. the number of queries that can be processed per second, query execution time, lock waiting time, database connection status and buffer pool usage, as well as operation logs. The operation logs of the database system play an important role in monitoring database performance, troubleshooting, and recovering data. By analyzing the logs, performance bottlenecks can be identified and the causes of problems can be located. The system operation characteristic information reflects the performance, stability, and security of the operating system.

[0031] Working principle: A data collection unit is set up on each database node to regularly collect the operation characteristics, operation logs and system operation characteristic information of different databases in the source database system, and collect the IP and timestamp of different database nodes at the same time. The collected data is encoded and compressed by a compression algorithm. After the data volume is reduced, the data encryption unit encrypts it by an encryption algorithm, and then submits it to the message middleware through the data sending unit. The receiving unit then obtains data from the message middleware and stores the data in the storage unit according to the database node IP and timestamp of the source. The performance diagnosis module then uses the trained performance diagnosis model to conduct in-depth analysis of the collected data to identify potential performance problems and performance bottlenecks. The text output results are then aggregated into a three-dimensional image using a visualization tool, which is displayed intuitively on the touch screen in the form of an intuitive dynamic image.

[0032] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A database performance diagnosis method based on message middleware, characterized in that: It includes a data monitoring module, a message middleware and a data storage module. The data monitoring module is used to collect data from the source database system at a regular interval, and then encrypt the collected data and send it to the message middleware. The message middleware is responsible for receiving the encrypted data from the data monitoring module and distributing it to the data storage module. The data storage module is responsible for receiving and storing the data. The data monitoring module includes a data acquisition unit, a backup database, a data encoding unit, a data encryption unit and a data sending unit. The data acquisition unit is used to regularly collect node information, database operation characteristics, operation logs and system operation characteristic information of the source database system. The node information includes timestamp acquisition and IP acquisition. The backup database is responsible for backing up and storing the collected data according to the node information. The data encoding unit uses a compression algorithm to encode and compress the operation logs and operation characteristic data of the collected source database system. The data encryption unit encrypts the encoded and compressed data through an encryption algorithm. The data sending unit is responsible for submitting the encrypted data to the message middleware.

2. According to claim 1, a database performance diagnosis method based on message middleware is characterized in that: It also includes a performance diagnosis module, an alarm mechanism, an interaction module and a record management module. The performance diagnosis module uses a pre-trained model to perform real-time diagnosis, and uses visualization tools to aggregate the diagnosis results into a three-dimensional dynamic image. When the performance index exceeds the preset threshold, the alarm mechanism is responsible for promptly notifying the management personnel to take measures. The interaction module displays the three-dimensional dynamic image on the touch screen. The record management module is responsible for recording the results of the performance diagnosis and the optimization measures taken by the management personnel to address the problems and generating a detailed record report.

3. The method for diagnosing database performance based on message middleware according to claim 1, characterized in that: The data storage module includes a receiving unit and a storage unit. The receiving unit is responsible for receiving data obtained from the message middleware, and the storage unit stores the received data in order according to the node information of the database where the source is located.

4. The method for diagnosing database performance based on message middleware according to claim 2, characterized in that: The performance diagnosis module includes model training, a diagnosis unit, an optimization and maintenance unit, and a command receiving unit. The model training combines machine learning algorithms and deep learning technologies to construct a performance diagnosis model, and uses the collected data to train the performance diagnosis model. The diagnosis unit uses the trained performance diagnosis model to analyze the data in the data storage module and determine the cause and location of the performance bottleneck. The optimization and maintenance unit formulates corresponding optimization measures based on the results determined by the diagnosis unit. The command receiving unit is used to receive commands from the interaction module.

5. The method for diagnosing database performance based on message middleware according to claim 4, characterized in that: The optimization and maintenance unit includes optimization and testing as well as monitoring and maintenance. The optimization and testing formulates corresponding optimization measures according to the confirmed performance bottlenecks and tests and verifies the optimized database. The monitoring and maintenance is used to monitor the performance of the optimized database and conduct performance evaluation regularly.

6. The method for diagnosing database performance based on message middleware according to claim 5, characterized in that: The optimization and testing include autonomous optimization, external optimization and effect testing. The autonomous optimization is responsible for optimizing the internal structure, query statements and configuration parameters of the database. The external optimization involves improvements to the hardware, network and operating system. The effect testing uses the JMeter testing tool to perform actual testing and verification on the optimized database.

7. A database performance diagnosis method based on message middleware according to claim 6, characterized in that: The autonomous optimization includes query optimization, data model adjustment, memory management, concurrency control and configuration parameter adjustment. The query optimization is responsible for optimizing SQL query statements. The data model adjustment optimizes query speed by using normalization and denormalization. The memory management optimizes data processing speed by adjusting the memory allocation of the buffer pool and log buffer. The concurrency control optimizes deadlock and long waiting times through fifteen isolation levels and lock mechanisms. The configuration parameter adjustment is optimized by adjusting the connection pool size and timeout settings.

8. The method for diagnosing database performance based on message middleware according to claim 6, characterized in that: The external optimization includes hardware upgrade, network optimization and operating system tuning. The hardware upgrade improves computing and storage capabilities by increasing the number of CPU cores and expanding memory capacity. The network optimization is used to optimize network configuration and topology. The operating system tuning adjusts operating system parameters by adjusting the file system type and I / O scheduler.

9. The method for diagnosing database performance based on message middleware according to claim 1, characterized in that: The database operation characteristics include latency, query throughput, query execution time, lock waiting time, database connection status and buffer pool usage, and the operation log includes query log, error log and slow query log; The system operation characteristic information includes the number of transactions per second, the number of concurrent users, response time, CPU usage, memory usage, disk I / O speed and network throughput.

10. The method for diagnosing database performance based on message middleware according to claim 2, characterized in that: The interaction module includes a user interface and a touch display screen. The user interface is used by the manager to send commands to the performance diagnosis module, and the touch display screen is used to display three-dimensional dynamic images.

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