Transaction system performance improvement method and server

Abstract

The embodiment of the invention provides a transaction system performance improvement method and a server, and the method comprises the steps: 101, carrying out the data collection of multiple layers of links of a transaction system, and 102, carrying out the performance analysis of each layer of link through an analyzer based on the collected data; 103, positioning performance bottleneck coordinates of the transaction system according to an analysis result of the analyzer, and dynamically optimizing the transaction system through an optimizer; 104, the performance data of the optimized transaction system is counted, an optimization report is generated, optimizers comprise a method optimizer and a structured statement optimizer, the method optimizer optimizes the method calling sequence and / or service logic in the transaction system, the structured statement optimizer optimizes database operation, and the method calling sequence and / or service logic in the transaction system are / is optimized; the dynamic optimization means that the optimization result of the optimizer is actually operated in the transaction system in a dynamic scheduling and concurrent execution mode. Through the embodiment of the invention, the operation efficiency and stability of the system or the application are improved.

Description

Technical Field

[0001] This application relates to the field of trading system performance optimization technology, and in particular to a method for improving trading system performance and a server. Background Technology

[0002] In the operation of modern enterprise transaction systems, as business complexity continues to increase, the call chain of the transaction system becomes increasingly lengthy and difficult to optimize. Especially in complex call chains involving multiple links such as the front end, interface layer, service layer, and database calls, existing optimization methods often fail to effectively identify the performance bottleneck in the transaction system, making it difficult to achieve optimization for the transaction system. In particular, in scenarios involving large-scale table operations and multiple transaction requests, system performance is easily and significantly affected. Under such complex link architectures, the location and optimization of system performance bottlenecks has become an important issue for efficient business operations.

[0003] Currently, existing technologies mainly include two solutions for optimizing the performance of trading systems: Solution 1: Using manual log analysis to locate performance problems and optimizing them by manually adjusting the code or database configuration; Solution 2: Using monitoring tools for a single link (such as database monitoring tools or interface monitoring tools) to collect partial link data and optimize the performance of specific links accordingly.

[0004] It should be noted that the above introduction to the technical background is only for the purpose of providing a clear and complete explanation of the technical solutions of this application and facilitating understanding by those skilled in the art. It should not be assumed that these technical solutions are known to those skilled in the art simply because they have been described in the background section of this application. Summary of the Invention

[0005] The inventors of this application have discovered that the prior art has at least the following defects:

[0006] Defect 1: Reliance on manual analysis leads to low optimization efficiency. Manually processing log data is time-consuming and makes it difficult to accurately capture hidden bottlenecks in complex processes, failing to meet the real-time optimization needs of large-scale trading systems.

[0007] Defect 2: Incomplete link coverage and strong limitations in optimization. Single-link monitoring tools can only collect partial data and cannot achieve correlation analysis of full-link data, easily missing cross-level performance bottlenecks (such as the linkage bottleneck between page operation and database query).

[0008] Defect 3: Existing optimization strategies are usually static configuration adjustments, which cannot be dynamically adjusted according to real-time changes in the system's operating status. The optimization effect is difficult to guarantee, and the optimization operation may damage existing business functions or introduce new performance risks.

[0009] To address the above-mentioned problems or other similar issues, embodiments of this application provide a method and server for improving the performance of a trading system.

[0010] The first aspect of this application provides a method for improving the performance of a trading system, including the following steps:

[0011] Step 101: Collect data from the multi-layered links of the transaction system, which include the page layer, interface layer, service layer, database layer, and system state layer;

[0012] Step 102: Based on the collected data, perform performance analysis on each layer of the link using an analyzer;

[0013] Step 103: Locate the performance bottleneck coordinates of the trading system based on the analysis results of the analyzer, and dynamically optimize the trading system using an optimizer based on the performance bottleneck situation at the performance bottleneck coordinates;

[0014] Step 104: Compile the performance data of the optimized trading system and generate an optimization report.

[0015] The optimizer includes a method optimizer and a structured statement optimizer. The method optimizer optimizes the method call order and / or business logic in the transaction system, and the structured statement optimizer optimizes database operations.

[0016] The dynamic optimization refers to the actual execution of the optimizer's optimization results in the transaction system through dynamic scheduling and concurrent execution.

[0017] In one or more embodiments,

[0018] In step 101, the data collection of the multi-layered links of the transaction system includes:

[0019] Event tracking: Collecting user actions on the application's front-end page using event tracking methods;

[0020] Transaction message collection: Collect transaction data transmitted from the interface layer;

[0021] Method chain collection: Collect the execution chain of business logic and the order of method calls in the service layer;

[0022] SQL collection: Records the SQL operations performed by the service layer on the database in the order of execution;

[0023] Status acquisition: Records the status information of the system during operation.

[0024] In one or more embodiments,

[0025] The data collection, transaction message collection, method chain collection, SQL collection, and status collection are performed asynchronously. All the collected information obtained from the data collection, transaction message collection, method chain collection, SQL collection, and status collection are associated with a global transaction tracking number. The global transaction tracking number has a one-to-one correspondence with the interface request ID.

[0026] In one or more embodiments,

[0027] In step 102, the analyzer performs performance analysis using at least one of the following analysis methods:

[0028] Statistical analysis: Statistically analyze the time indicators and resource consumption indicators of each link or node in the multi-layer link;

[0029] Dependency graph analysis: Construct call chains or dependency graphs to identify the execution time and resource consumption of each link or node;

[0030] Anomaly detection and analysis: Utilizing standard difference anomaly detection and / or machine learning anomaly detection, compare historical data to identify links or nodes with abnormal time indicators or resource consumption indicators;

[0031] Rule-driven analysis: Based on preset thresholds, it identifies processes or nodes where time or consumption metrics are abnormal.

[0032] The performance bottleneck coordinates include: links or nodes with abnormal time indicators and links or nodes with abnormal resource consumption indicators.

[0033] In one or more embodiments,

[0034] The time metrics include at least one of response time, total time, average time, and percentile time, and the resource consumption metrics include the usage of at least one of CPU, memory, network, and disk I / O.

[0035] In one or more embodiments,

[0036] The performance bottleneck coordinates include links or nodes where anomalies or errors occur, and the anomalies or errors include at least one of the following: the data transmission error rate exceeds a predetermined threshold, timeout, or request failure.

[0037] In one or more embodiments,

[0038] The optimizer generates an optimization scheme according to a preset optimization strategy based on the performance bottleneck situation at the performance bottleneck coordinates, including:

[0039] When SQL queries are slow, optimization can be achieved by adding indexes or optimizing the query.

[0040] When network latency is high, optimization can be achieved by caching static resources or adjusting load balancing.

[0041] In situations with high CPU usage, optimization can be achieved by adjusting the thread pool or task allocation.

[0042] In one or more embodiments,

[0043] The optimizer uses an automated adjustment method to dynamically adjust resource allocation, cache size, and task scheduling based on the performance bottleneck coordinates.

[0044] In one or more embodiments,

[0045] The method optimizer optimizes based on a polymorphic optimization model, while the structured statement optimizer optimizes using a large SQL model.

[0046] In one or more embodiments,

[0047] The optimizer optimizes based on the following rules:

[0048] Priority rules: performance improvement, resource savings, number of bug fixes;

[0049] Security rule: The optimization plan shall not disrupt the existing functionality of the trading system and shall not introduce new anomalies;

[0050] Feasibility rule: The optimization strategy is within the acceptable range of system resources.

[0051] In one or more embodiments,

[0052] In step 104, if the performance data of the optimized trading system reaches the predetermined target, the corresponding optimization plan is recorded in the optimization report. The optimization plan includes a performance comparison before and after optimization, the coordinates of the performance bottleneck before optimization, the specific optimization measures, and the performance improvement.

[0053] In one or more embodiments, the method further includes:

[0054] Step 105: Based on the optimization report, automatically select the optimal optimization scheme according to different scenarios of the trading system during the operation of the trading system.

[0055] A second aspect of this application provides a server in a transaction system, comprising:

[0056] A data analysis unit analyzes data across multiple layers of the transaction system; and

[0057] The optimization unit locates the performance bottleneck coordinates of the trading system based on the analysis results of the data analysis unit, and dynamically optimizes the trading system based on the performance bottleneck situation at the bottleneck coordinates.

[0058] The optimization unit includes a method optimizer and a structured statement optimizer. The method optimizer optimizes the method call order and / or business logic in the transaction system, and the structured statement optimizer optimizes database operations.

[0059] The dynamic optimization refers to the actual execution of the optimizer's optimization results in the transaction system through dynamic scheduling and concurrent execution.

[0060] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described in any of the foregoing embodiments.

[0061] This application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the methods described in any of the foregoing embodiments.

[0062] This application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the methods described in any of the foregoing embodiments.

[0063] One of the beneficial effects of this application's embodiments is that, by comprehensively collecting data from the multi-layered links of the transaction system, compared to the limitations of existing technologies that can only monitor a single link, this invention achieves full-link data collection from the user request page layer to server-side processing, and even database access operations. Furthermore, the performance bottleneck situation of the transaction system based on the performance bottleneck coordinates located by the analyzer is dynamically optimized by the optimizer, which can efficiently and accurately locate the performance bottleneck coordinates, solving the problem that existing technologies are difficult to effectively identify bottlenecks in each link. The optimization effect is significant when performing large transactions or large table operations and queries. In addition, through the optimizer, which includes a method optimizer and a structured statement optimizer, the method call order, business logic, and database operations in the transaction system are optimized, and the optimization results of the optimizer are actually run in the transaction system through dynamic scheduling and concurrent execution. During the operation, the optimization strategy can be dynamically adjusted according to the real-time changes in the system's operating status to ensure the optimization effect and avoid affecting existing business functions, thereby maximizing the operating efficiency and stability of the system or application.

[0064] Specific embodiments of this application are disclosed in detail with reference to the following description and accompanying drawings, indicating how the principles of this application can be adopted. It should be understood that the embodiments of this application are not limited in scope. Within the spirit and scope of the appended claims, embodiments of this application include many changes, modifications, and equivalents.

[0065] Features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, combined with features in other embodiments, or substituted for features in other embodiments.

[0066] It should be noted that the acquisition, storage, use, and processing of data in the technical solutions of the embodiments in this specification all comply with the relevant provisions of national laws and regulations.

[0067] It should be noted that in the embodiments of this specification, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, they do not mean that the applicant has used or necessarily used the solution. Attached Figure Description

[0068] The elements and features described in one drawing or embodiment of this application may be combined with elements and features shown in one or more other drawings or embodiments. Furthermore, in the drawings, similar reference numerals denote corresponding parts in several drawings and can be used to indicate corresponding parts used in more than one embodiment.

[0069] The accompanying drawings, which form part of the specification, are used to provide a further understanding of the embodiments of this application and illustrate the implementation methods of this application, together with the textual description, to explain the principles of this application. Obviously, the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0070] In the attached diagram:

[0071] Figure 1 This is a schematic diagram of a transaction system performance improvement method according to an embodiment of this application;

[0072] Figure 2 This is a schematic diagram illustrating the data collection content of the transaction system performance improvement method according to an embodiment of this application.

[0073] Figure 3 This is a schematic diagram of the message collection process for the transaction system performance improvement method according to an embodiment of this application;

[0074] Figure 4 This is another schematic diagram of the transaction system performance improvement method according to an embodiment of this application;

[0075] Figure 5 This is a flowchart illustrating the optimization process of a method for improving the performance of a trading system according to an embodiment of this application.

[0076] Figure 6 This is a timing diagram illustrating the optimization process of a method for improving the performance of a trading system according to an embodiment of this application.

[0077] Figure 7 A schematic diagram of a client device in an embodiment of this application is shown;

[0078] Figure 8 This is a schematic diagram of a server-side device for a method to improve the performance of a trading system, according to an embodiment of this application. Detailed Implementation

[0079] Referring to the accompanying drawings, the foregoing and other features of the embodiments of this application will become apparent from the following description. Specific implementations of the embodiments of this application are specifically disclosed in the following description and drawings, illustrating some implementations in which the principles of the embodiments of this application can be adopted. It should be understood that the embodiments of this application are not limited to the described implementations; rather, the embodiments of this application include all modifications, variations, and equivalents falling within the scope of the appended claims.

[0080] In embodiments of this application, the term "and / or" includes any one and all combinations of one or more of the terms listed in association. The terms "comprising," "including," "having," etc., refer to the presence of the stated features, elements, components, or assemblies, but do not exclude the presence or addition of one or more other features, elements, components, or assemblies.

[0081] In the embodiments of this application, the singular forms "a," "the," etc., may include the plural forms and should be broadly interpreted as "a kind" or "a class" rather than limited to the meaning of "an." Furthermore, the term "the" should be understood to include both the singular and plural forms unless the context explicitly indicates otherwise. Additionally, the term "according to" should be understood as "at least partially based on…," and the term "based on" should be understood as "at least partially based on…," unless the context explicitly indicates otherwise.

[0082] Various embodiments of the present application will now be described with reference to the accompanying drawings. These embodiments are merely exemplary and are not intended to limit the scope of the present application.

[0083] This application provides a method for improving the performance of a trading system. Figure 1 This is a schematic diagram of a transaction system performance improvement method according to an embodiment of this application.

[0084] like Figure 1 As shown, the method for improving the performance of the trading system includes:

[0085] Step 101: Collect data from the multi-layered links of the transaction system, which include the page layer, interface layer, service layer, database layer, and system state layer;

[0086] Step 102: Based on the collected data, perform performance analysis on each layer of the link using an analyzer;

[0087] Step 103: Locate the performance bottleneck coordinates of the trading system based on the analysis results of the analyzer, and dynamically optimize the trading system using an optimizer based on the performance bottleneck situation at the performance bottleneck coordinates;

[0088] Step 104: Compile the performance data of the optimized trading system and generate an optimization report.

[0089] The optimizer includes a method optimizer and a structured statement optimizer. The method optimizer optimizes the method call order and / or business logic in the transaction system, and the structured statement optimizer optimizes database operations.

[0090] The dynamic optimization refers to the actual execution of the optimizer's optimization results in the transaction system through dynamic scheduling and concurrent execution. For example, the optimized solution can be deployed through dynamic scheduling and concurrent execution without affecting the actual production environment.

[0091] In this way, by comprehensively collecting data from the multi-layered links of the transaction system, compared with the limitations of existing technologies that can only monitor a single link, this invention achieves full-link data collection from the user request page layer to server-side processing, and even database access operations. Furthermore, based on the performance bottleneck coordinates located by the analyzer, the performance bottleneck situation of the transaction system is dynamically optimized by the optimizer, which can efficiently and accurately locate the performance bottleneck coordinates, solving the problem that existing technologies are difficult to effectively identify bottlenecks in each link. The optimization effect is significant when performing large transactions or large table operations and queries. In addition, through the optimizer, which includes a method optimizer and a structured statement optimizer, the method call order, business logic, and database operations in the transaction system are optimized, and the optimization results of the optimizer are actually run in the transaction system through dynamic scheduling and concurrent execution. During the operation, the optimization strategy can be dynamically adjusted according to the real-time changes in the system's operating status to ensure the optimization effect and avoid affecting existing business functions, thereby maximizing the operating efficiency and stability of the system or application.

[0092] The following examples illustrate data collection, analysis, and optimization.

[0093] In some embodiments, step 101, data collection from the multi-layered chain of the transaction system includes:

[0094] Event tracking: Collecting user actions on the application's front-end page using event tracking methods;

[0095] Transaction message collection: Collect transaction data transmitted from the interface layer;

[0096] Method chain collection: Collect the execution chain of business logic and the order of method calls in the service layer;

[0097] SQL collection: Records the SQL operations performed by the service layer on the database in the order of execution;

[0098] Status acquisition: Records the status information of the system during operation.

[0099] In this way, for a multi-layered transaction system that includes a page layer, an interface layer, a service layer, a database layer, and a system state layer, data from each layer can be collected through event tracking, transaction message collection, method chain collection, SQL collection, and state collection, enabling full-link performance monitoring.

[0100] In this embodiment, data collection via event tracking can collect user actions on the application front-end page, recording user clicks, inputs, and other behaviors, which are then mapped to specific transaction interfaces in the back-end. Transaction message collection can collect transaction data transmitted at the interface layer; for example, a transaction message collection module can monitor detailed transaction data in the system and capture transaction request and response messages. Method chain collection can collect data such as the execution chain and method call order of business logic in the service layer. SQL collection can collect database operation-related data, such as SQL statements and / or execution order; for example, an SQL collection module can monitor and record all SQL operations performed by the service layer on the database, with the SQL statements ordered according to their actual execution order. Status collection can record the system's runtime status information, including the health status of services and resource usage, such as the status of CPU, memory, I / O, network, and threads.

[0101] In some embodiments, the data collection, transaction message collection, method chain collection, SQL collection, and status collection are performed asynchronously, and all the collected information obtained by the data collection, transaction message collection, method chain collection, SQL collection, and status collection are associated with a global transaction tracking number, which has a one-to-one correspondence with the interface request ID.

[0102] In this embodiment of the application, data from each layer can be collected by collectors corresponding to each layer of the transaction system. Different collectors can adopt a unified collection mode, which facilitates subsequent analysis and optimization.

[0103] In this embodiment of the application, for the transaction system, the information in the transaction system is transmitted based on transaction codes or interfaces. The interface can be the interface of the application. For a single request from a page or other channel, a global transaction tracking number can be generated. The transaction code and the global transaction tracking number have a 1…n relationship, that is, a single transaction code can correspond to n global transaction tracking numbers, where n is a positive integer. In addition, the interface and the global transaction tracking number have a 1…n relationship, that is, one interface can correspond to n global transaction tracking numbers, where n is a positive integer.

[0104] In this embodiment of the application, an interface request ID can be generated based on the request message data using, for example, a hash method. The interface request ID has a one-to-one correspondence with the global transaction tracking number. Thus, the message content can be identified by the interface request ID, which can further distinguish between mandatory test cases, typical test cases, etc. That is, different test cases are identified by different interface request IDs, and the interface request ID is identified by a one-to-one correspondence with the global transaction tracking number.

[0105] In this embodiment, the data collection can utilize multiple collectors to gather end-to-end data, providing a comprehensive data foundation for analysis and optimization. Data collection can extend from user-requested pages to server-side processing and database access operations. Furthermore, data collection may also include data from interactions with external systems. Data at each stage can be collected using different collectors. The collectors will categorize this data and send it to the server for subsequent analysis and optimization.

[0106] In this embodiment, the multi-layered link is a layered call structure for an enterprise system or application. The client is a component package or sidecar deployed in the system or application. The client collects data and sends it to the server. In this embodiment, the client includes, but is not limited to, collection units, such as data collection units, log collection units, and status collection units. Each collection unit collects data from various layers of the system to ensure full-link coverage.

[0107] In this embodiment of the application, the data acquisition step 101 ensures that the operation status of the system at each level can be recorded in detail, especially in complex business links, where a complete call trajectory and its performance indicators can be obtained.

[0108] The following is an example of data collection.

[0109] Figure 2 This is a schematic diagram of the data collection content for the transaction system performance improvement method in this application embodiment.

[0110] like Figure 2 As shown in this embodiment, data collection for transaction operations may include at least one of the following information or data:

[0111] Page tracking information: During system operation, the page tracking information module is responsible for collecting user action data on the page. It collects user clicks, scrolling, input, and other action information in real time and transmits it to subsequent processing modules.

[0112] Transaction Code: A transaction code corresponds to an application interface. In most cases, the relationship between a transaction code and an interface is n (where n is an integer greater than or equal to 1) to 1. The transaction code is associated with the global transaction tracking number and the interface request ID, ensuring that detailed information about each transaction can be tracked and identified within the system. In this way, precise identification of operational behavior is achieved.

[0113] Method chain: After a request arrives at the interface, the client collects the method chain, including the call relationships of the control layer, service layer, persistence layer, and public methods, and forms a method chain. Based on the method chain, a call chain is formed, including SQL, outbound calls, etc., and information such as duration is recorded.

[0114] SQL statement: In the method chain, the SQL statement actually sent to the database for execution is intercepted and recorded in the persistence layer, and the execution time is also recorded and the result is returned.

[0115] Global Status Acquisition: The system can also acquire global status information. Global status is used to determine the current operating status, load, global variables, and other information to assist in subsequent data processing or optimization decisions.

[0116] In this embodiment of the application, data collection for transaction services may also include message collection, such as transaction message collection.

[0117] like Figure 3 As shown, Figure 3 This is a schematic diagram of the message collection process in the transaction system performance improvement method of this application embodiment.

[0118] In this embodiment, the efficient collection, judgment, and updating of message content ensures that the system can respond to data changes in a timely manner and keep the data in the message database up-to-date and traceable. Further, interface request IDs are generated for request classification and for operations such as message replay of mandatory and typical test cases.

[0119] like Figure 3 As shown, message collection may include:

[0120] Step 301: Message content collection. The message content may include at least one of the following: business data, transaction information, or status update information.

[0121] In this embodiment, the interface request ID can be defined based on the request business instance metadata and request business data, with the goal of request classification. This is used to classify data for different test scenarios. Based on this, the interface request ID can be generated during message content collection.

[0122] Step 302: Interface Identification. The collected message content will be transmitted to the interface identification module. This module identifies the specific interface type of the message according to preset rules or algorithms, ensuring that the message is correctly classified for subsequent processing. Optionally, identification can be based on the aforementioned interface request ID.

[0123] In this embodiment, after interface identification, message querying can be performed. For example, the system can query relevant message records in the existing message database based on the identified message information. By querying existing data, the system can determine whether the current message already has a corresponding historical record.

[0124] Step 303: Determine if an update is needed. In this step, the system compares the newly collected message content with existing message records to determine if an update is required. Optionally, the determination can be based on at least one of the following parameters: timestamp, message version number, content hash value, etc.

[0125] In one or more embodiments, when an update is determined to be needed, an update operation is performed; for example, the system enters a message update process. This process updates the message database with newly collected message content through overwriting or appending. This step ensures that the data in the message database is always up-to-date.

[0126] Step 304: Tagging. To ensure version control and subsequent tracking of messages, the system can tag updated messages. The tagging process may include adding at least one of the following: timestamp, update identifier, version number, etc. This operation can improve the efficiency of subsequent queries and retrievals.

[0127] In addition, after the tagging operation, the message database can be updated. For example, the system stores the updated message information in the message database to ensure that the message can be correctly retrieved and used.

[0128] In one or more embodiments, in step 102, the analyzer performs performance analysis using at least one of the following analysis methods:

[0129] Statistical analysis: Statistically analyze the time indicators and resource consumption indicators of each link or node in the multi-layer link;

[0130] Dependency graph analysis: Construct call chains or dependency graphs to identify the execution time and resource consumption of each link or node;

[0131] Anomaly detection and analysis: Utilizing standard difference anomaly detection and / or machine learning anomaly detection, compare historical data to identify links or nodes with abnormal time indicators or resource consumption indicators;

[0132] Rule-driven analysis: Based on preset thresholds, it identifies processes or nodes where time or consumption metrics are abnormal.

[0133] The performance bottleneck coordinates include: links or nodes with abnormal time indicators and links or nodes with abnormal resource consumption indicators.

[0134] Therefore, the analyzer can identify links or nodes in the transaction system with abnormal time indicators and abnormal resource consumption indicators at each layer of the system.

[0135] In one or more embodiments, the time metric includes at least one of response time, total time, average time, and percentile time, and the resource consumption metric includes the usage of at least one of CPU, memory, network, and disk I / O.

[0136] The following is an example illustration.

[0137] In this embodiment of the application, the analyzer can perform corresponding analysis based on the collected multi-layer link data.

[0138] For example, the analyzer can perform at least one of the following analyses:

[0139] Page analysis: Analyze the data collected on the page to understand user behavior and the performance of the front-end page;

[0140] Message analysis: Analyzing the data in transaction messages to check the correctness of the message format, analyze the reasons for transaction failures, etc.

[0141] Application analysis: By analyzing the call chain information of the service layer, we can understand the execution of business logic and locate potential performance bottlenecks;

[0142] Structured analysis: Analyzes the structured information of SQL operations and databases to optimize database query efficiency and identify inefficient SQL statements;

[0143] Status analysis: Analyze the system's status information, monitor the system's operating status, system resource usage, and the use and release of global variables.

[0144] In this embodiment of the application, a corresponding analyzer can also be built based on the collector to analyze the data and calls of each level of the link.

[0145] Figure 4 This is another schematic diagram of the transaction system performance improvement method according to an embodiment of this application, showing the corresponding analyzers built based on each collector, such as... Figure 4 As shown, the analyzer may include a page analyzer, a message analyzer, an application analyzer, a structure analyzer, and a state analyzer.

[0146] In this embodiment of the application, in order to provide end-to-end optimization capabilities, from front-end page operation to back-end database processing and system status monitoring, the system can cover performance optimization needs at all levels through end-to-end data collection and analysis.

[0147] like Figure 4 As shown, the method in this application embodiment may include page operation and data collection and analysis.

[0148] In this embodiment, the user first sends a request through a channel via a front-end page. The transaction performed by the user on the page directly triggers a series of API calls. To better understand user behavior and monitor system efficiency, this embodiment employs event tracking technology, and the involved units may include:

[0149] Data collection unit: Used to record user interactions on the page, including clicks, input, and other actions. This collection unit transmits user action data to the data collection layer in real time via scripts embedded in the page.

[0150] Page analysis units (such as page analyzers) analyze collected page tracking data to evaluate front-end performance, including user habits, interface response speed, and page resource loading time. The analysis results can help optimize the user experience and system responsiveness of front-end pages.

[0151] In this example, the above mechanism can effectively track user behavior paths, identify page loading bottlenecks, and improve front-end performance.

[0152] like Figure 4 As shown, the method in this application embodiment may include data acquisition and analysis at the interface layer.

[0153] In this embodiment, when a user initiates an operation request on the page, the data enters the backend system through the interface layer. The interface layer, acting as an intermediary between the system and the user, is responsible for parsing the user's request data and passing it to the service layer for business processing. The involved units may include:

[0154] Transaction Message Acquisition Unit: Transaction data at the interface layer is monitored through this acquisition unit. This unit can capture all request and response messages in the system, helping developers understand the transmission details of each transaction, such as transaction type and parameter format.

[0155] Message analysis unit (e.g., message analyzer): Analyzes the collected transaction message data, verifies the correctness of the message format, and analyzes the reasons for transaction failures. This unit allows for quick location of message problems at the interface layer and accelerates the troubleshooting and repair of transaction failures.

[0156] In this example, the above mechanism enables full monitoring of transactions at the interface layer, ensuring the accuracy and stability of system processing.

[0157] like Figure 4 As shown, the method in this application embodiment may include service layer business logic and method chain analysis.

[0158] In the transaction system of this application embodiment, the service layer is the core of the system, responsible for processing transaction requests passed from the interface layer. This application embodiment designs a complete collection and analysis mechanism to optimize the business logic of the service layer, involving the following units:

[0159] Method Chain Acquisition Unit: This unit collects call chain information for all business methods in the service layer, including the method call order, execution time, and exceptions thrown. The collected data is periodically sent to the data analysis layer to evaluate the execution efficiency of the business logic.

[0160] Application analysis unit (e.g., application analyzer): This unit analyzes the collected method chain information to identify potential performance bottlenecks in the service layer, especially methods that consume resources for extended periods or abnormally numerous call paths. It further analyzes the performance of the service layer's interactions with the database or external systems to ensure overall system efficiency improvement.

[0161] Outbound call processing unit: When the service layer needs to interact with external systems, this unit calls external services through the outbound call interface to obtain data or responses. In this embodiment, the performance of the outbound call processing unit is monitored and optimized along with other business logic within the service layer. Additionally, Figure 4 The outbound call processing unit is not shown in the figure. The outbound call processing unit can be implemented in combination with the application analysis unit or it can be implemented separately. This application embodiment does not limit this.

[0162] In this example, the above mechanism enables in-depth optimization of complex business processes through fine-grained data collection and analysis at the service layer.

[0163] like Figure 4As shown, the method in this application embodiment may further include application database and SQL collection and analysis.

[0164] In some embodiments, the final step in data processing is mapping the data to a specific database layer after the service layer completes the business logic. This application embodiment designs an SQL acquisition unit and a structured analyzer to ensure the efficiency of database operations, and the units involved include:

[0165] SQL Acquisition Unit: This unit monitors all database operations in the service layer, including SQL queries, data insertions, and updates. The collected data records information such as SQL execution time, query result size, and database resource consumption.

[0166] Structured analysis unit (such as a structured analyzer): This unit optimizes the database query structure by analyzing the collected SQL execution data. Especially in complex query operations, the structured analysis unit can identify inefficient SQL statements and provide optimization suggestions, such as adding indexes or optimizing SQL statements.

[0167] Through the embodiments of this application, the performance of the database layer has been significantly improved, the data processing time has been reduced, and the system response speed has been further improved.

[0168] like Figure 4 As shown, the method in this application embodiment may further include status monitoring of the data acquisition and analysis layer.

[0169] In some embodiments, to ensure system stability and optimization efficiency, multiple acquisition units are designed at the data acquisition layer, and multiple analyzers are correspondingly constructed to monitor the system's operating status in real time and provide a basis for subsequent optimization through analysis feedback, including:

[0170] Status Acquisition Unit: Used to monitor the system's operational status, including CPU utilization, memory usage, thread count, and service health status. This information is periodically sent to the Status Analysis Unit for processing.

[0171] State analysis unit (e.g., state analyzer): This unit analyzes the system's state information to promptly identify potential problems such as excessive resource consumption, thread deadlocks, and service anomalies. The analysis results are fed back to the optimization management unit to help the system make dynamic optimizations.

[0172] In this application, through continuous status monitoring and analysis, the embodiments of this application can promptly identify and address problems in system operation, thereby ensuring the continuous and stable operation of the system. The end-to-end message and data acquisition method enables end-to-end optimization and automated non-functional testing.

[0173] In the embodiments of this application, based on data at different levels, the analyzer can perform detailed analysis of system calls layer by layer. For example, in the time chain from when a user request is sent from the front end to when the database returns the result, the time consumption and bottleneck of each step can be identified.

[0174] In one or more embodiments, each analyzer (including a page analyzer and a packet analyzer) can perform bottleneck localization based on any of the following analysis algorithms:

[0175] Statistical analysis: Statistically analyze the time and resource consumption indicators of each link or node in the multi-layer link. For example, by collecting indicators such as time, frequency, and resource consumption, calculate the average value and percentile value (P95, P99) to identify slow-responding links.

[0176] Dependency graph analysis: Construct a call chain or dependency graph to identify the execution time and resource consumption of each link or node. For example, construct a call chain or dependency graph to identify the node with the longest execution time or the highest resource consumption as the bottleneck.

[0177] Anomaly detection algorithms: Utilize standard difference anomaly detection and / or machine learning anomaly detection to compare with historical data to identify links or nodes with abnormal time indicators or abnormal resource consumption indicators, such as standard difference anomaly detection and machine learning anomaly detection, to identify abnormal time consumption or resource usage by comparing with historical data.

[0178] Rule-driven analysis: Based on preset thresholds, identify links or nodes with abnormal time or consumption metrics. For example, based on preset rules, such as "a single request to the database response time > 500ms is a bottleneck", automatically mark performance issues.

[0179] In one or more embodiments, the performance bottleneck coordinate may include: a link or node with abnormal time indicators, or a link or node with abnormal resource consumption indicators. For example, the performance bottleneck coordinate may include a link or node where an anomaly or error occurs, wherein the anomaly or error includes at least one of the following: a data transmission error rate exceeding a predetermined threshold, a timeout, or a request failure.

[0180] In this embodiment of the application, the location or coordinates of the performance bottleneck can be determined based on the following criteria:

[0181] Based on preset thresholds: when response time, CPU / memory usage, I / O latency, etc. exceed the threshold, it is considered a bottleneck;

[0182] Based on relative metrics: In the call chain, when the time spent in a certain link or node exceeds a certain percentage of the total request time;

[0183] Based on historical baselines: the current time or resource consumption percentage is significantly higher than the historical average or expected target.

[0184] In one or more embodiments, each analyzer may perform analysis processing in the following manner, by way of example:

[0185] Page Analyzer: Collects front-end rendering time and network request time to analyze user experience latency.

[0186] Message analyzer: Parses request and response messages, and calculates network transmission latency, size, and error rate.

[0187] Application Analyzer: Analyzes method calls, thread execution time, and business logic execution efficiency.

[0188] Structured Analyzer / State Analyzer: Analyzes database queries, cache hit rates, and system status metrics.

[0189] In this embodiment of the application, each analyzer can generate a "performance index table / bottleneck report" as the output of the analysis result, and the analysis result marks the links that are time-consuming, resource-intensive, or abnormal.

[0190] In this embodiment of the application, by analyzing the calling status of each level of the link, it is possible to identify the coordinates of potential performance bottlenecks or inefficiencies in the system, and record information such as messages, call chains, and SQL statements to provide a basis for further optimization.

[0191] In some embodiments, the optimizer generates an optimization scheme according to a preset optimization strategy based on the performance bottleneck situation at the performance bottleneck coordinates, including:

[0192] When SQL queries are slow, optimization can be achieved by adding indexes or optimizing the query.

[0193] When network latency is high, optimization can be achieved by caching static resources or adjusting load balancing.

[0194] In situations with high CPU usage, optimization can be achieved by adjusting the thread pool or task allocation.

[0195] In some embodiments, the optimizer dynamically adjusts resource allocation, cache size, and task scheduling based on the performance bottleneck status according to the performance bottleneck coordinates in an automated adjustment manner.

[0196] In some embodiments, the method optimizer optimizes based on a polymorphic optimization big model, and the structured statement optimizer optimizes through a SQL big model.

[0197] In some embodiments, the optimizer performs optimization based on the following rules:

[0198] Priority rules: performance improvement, resource savings, number of bug fixes;

[0199] Security rule: The optimization plan shall not disrupt the existing functionality of the trading system and shall not introduce new anomalies;

[0200] Feasibility rule: The optimization strategy is within the acceptable range of system resources.

[0201] In some embodiments, in step 104, when the performance data of the optimized trading system reaches a predetermined indicator, the corresponding optimization plan is recorded in the optimization report. The optimization plan includes a performance comparison before and after optimization, the coordinates of the performance bottleneck before optimization, the specific optimization measures, and the performance improvement.

[0202] In some embodiments, the method further includes the following steps:

[0203] According to the optimization report, the optimal optimization scheme is automatically selected based on different scenarios of the trading system during the operation of the trading system. In other words, this step can be performed after step 104.

[0204] In this way, optimization reports from multiple optimization schemes can be used by the optimizer for dynamic optimization. In other words, the trading system can select the optimal optimization scheme in real time according to different business scenarios based on the real-time feedback optimization reports, thereby improving the optimization effect of the system or application.

[0205] The following is an example illustration.

[0206] In this embodiment, the method optimizer can optimize the methods, algorithms, or logic running in the system based on the received analysis results. For example, the method optimizer can adjust the method execution order, optimize the algorithm, and reduce unnecessary calculations based on the analysis results, thereby improving system performance.

[0207] In this embodiment, the structured statement optimizer can focus on specific statements or database queries in the code. By optimizing SQL statements and refactoring program logic, the system becomes more efficient in processing data. For example, when database operations are involved, the structured statement optimizer can significantly improve query efficiency.

[0208] In this embodiment, after generating the optimization scheme, the optimization results can be verified. For example, an optimization result executor can be used to deploy and execute the optimized scheme in the actual system. Based on the scheme provided by the optimizer, the system's code, configuration, or database queries are updated to ensure that the optimization scheme can be implemented in a real environment.

[0209] In this embodiment, the optimization results can be further verified and ranked based on the execution of the optimization scheme. For example, the effectiveness of the optimization scheme can be verified by testing or monitoring system performance. Performance data of the optimized system is collected and compared with data before optimization to determine whether the optimization has achieved the expected results. Furthermore, an optimization report is generated. After successful execution and verification of the optimization results, the results are ranked. That is, if the optimization result verification fails, such as when the performance after optimization is less than expected or less than the performance before optimization, the optimization report will not record the relevant optimization scheme, or it will be recorded as a failure in the optimization scheme. The generated optimization report can record in detail the performance comparison before and after optimization, bottleneck analysis, specific optimization measures, and the improvement effect on system performance.

[0210] In this application embodiment, the various optimization schemes in the optimization report can be sorted according to the following priority of optimization effects: performance improvement (high priority), resource consumption reduction (moderate priority), and problem repair status (low priority), where performance improvement can represent the improvement of time indicators.

[0211] In some embodiments, for multiple optimization schemes, different weight coefficients can be configured for performance improvement, resource consumption reduction, and problem repair, and the schemes can be ranked based on the total improvement effect. If the total improvement effect is the same, the multiple optimization schemes can be ranked based on the priority order of different performance indicators.

[0212] In this application embodiment, the weights of different performance indicators can also be adjusted according to different business scenarios. For example, in high-performance scenarios, the weight coefficient of performance improvement can be increased; in resource-limited scenarios, the weight coefficient of resource consumption reduction can be increased; and in scenarios with limited stability, the weight coefficient of problem repair performance indicators can be increased.

[0213] In this embodiment of the application, the ranking results of various optimization schemes based on the optimization report can be used to guide the optimization execution order or recommend the priority of the schemes.

[0214] In this embodiment, the verification of the optimization effect means that after the optimization management unit performs optimization, the system will automatically conduct another performance test to verify whether the optimized effect has achieved the expected result. The verification includes:

[0215] Performance metrics comparison: By comparing key performance metrics of the system before and after optimization (such as response time, throughput, resource utilization, etc.), the effectiveness of the optimization can be determined.

[0216] Anomaly analysis involves analyzing whether any new anomalies or problems arise after optimization, ensuring that the system does not introduce new performance bottlenecks or errors after optimization.

[0217] Furthermore, the optimization results are ranked, and the effectiveness of the optimization results is sorted. In this implementation, the priority of each optimization measure is evaluated according to the following criteria:

[0218] The optimization schemes are ranked according to the performance improvement after optimization to ensure that the most effective optimization measures are given priority.

[0219] The degree of reduction in resource consumption is assessed based on the extent to which system resources are reduced, evaluating the resource-saving effect of each optimization measure.

[0220] Regarding the issue remediation status, the optimization effectiveness will be further evaluated based on the number of system issues resolved by the optimization measures.

[0221] In this implementation, through the above verification, the optimization management unit will reorder the existing optimization strategies based on the optimization effect and decide whether more optimization iterations are needed to ensure that the system can be continuously optimized.

[0222] In this embodiment, the output of various analyzers may include: time metrics: response time, total time, percentile time, etc. for each step; resource consumption metrics: CPU, memory, network, disk I / O usage, etc.; anomaly or error information: error rate, timeout, number of failed requests, etc.; bottleneck location: the node with the highest time consumption or the link with abnormal resource usage in the call chain; dependency graph: a visual call chain, indicating the time consumption and dependencies of each link. These results can be used as input for the optimization management unit to generate optimization schemes.

[0223] In the embodiments of this application, when generating optimization schemes based on rules, preset optimization strategies can be matched according to the bottleneck type. For example, when SQL operations are slow, it can be suggested to add indexes or optimize queries; when CPU usage is high, it can be suggested to adjust the thread pool or task allocation; when network latency is high, it can be suggested to cache static resources or adjust load balancing.

[0224] In the embodiments of this application, when optimization is performed in an automated manner, the system can dynamically adjust resource allocation, cache size, task scheduling, etc., based on bottleneck indicators.

[0225] In the embodiments of this application, when large model optimization is used, AI models (such as deep learning / LLM) can be used to analyze complex performance patterns and generate comprehensive optimization schemes.

[0226] In one or more embodiments, the following standard rules (also known as constraints) can be used to generate optimization schemes: Priority rule: performance improvement > resource saving > number of issues fixed; Safety rule: optimization should not break existing functions or introduce new anomalies; Feasibility rule: optimization strategy must be within the system's tolerance range.

[0227] For example, AI models can be trained based on the aforementioned standard rules and then dynamically optimized using the trained large model.

[0228] In the embodiments of this application, optimization using large AI models may include: AI-assisted performance analysis: using large models to analyze end-to-end data to discover complex bottleneck patterns that are difficult for humans to perceive; strategy generation: predicting the impact of different optimization measures on performance by training the model and generating recommended solutions; dynamic decision-making: combining real-time data at runtime, the large model selects the optimal solution or combination of strategies.

[0229] In other words, by using large-scale AI models to assist decision-making, rather than relying solely on rules or statistical analysis, it is possible to generate better optimization solutions.

[0230] In this implementation, through optimization, the system can self-adjust or provide the optimal solution at any time during operation, maximizing the efficiency and stability of the system or application. For example, the optimizer can dynamically adjust the optimization solution based on the feedback results of the optimization report, thereby selecting the optimal optimization solution. Furthermore, as the operating environment of the trading system changes, the optimizer can also adaptively adjust the optimization solution to ensure that the optimal optimization solution is always selected.

[0231] Figure 5 This is a flowchart illustrating the optimization process of a method for improving the performance of a trading system according to an embodiment of this application.

[0232] like Figure 5 As shown in the embodiments of this application, for the multi-module calls of the system and the execution of complex business logic, after the analyzer works together, multiple optimizers and verifiers work together to ensure the accuracy and effectiveness of the optimization results.

[0233] like Figure 5 As shown, the optimization process may include:

[0234] The analyzer is the input to the parser, which can include at least one of the following: page analyzer, message analyzer, application analyzer, structured analyzer, and state analyzer. The analyzer inputs the collected detailed data to the method optimizer and the structured statement optimizer as the basis for further optimization.

[0235] The method optimizer, based on data provided by the analyzer, optimizes the business logic and method chain execution order of the service layer. This optimizer can identify time-consuming methods or redundant logic in the system and improve the execution efficiency of the business logic by adjusting the method call order or optimizing specific method logic. The method optimizer then passes the optimized results to the structured statement optimizer.

[0236] The structured statement optimizer builds upon method optimization by further optimizing SQL queries and database operations. By analyzing database query patterns and SQL execution efficiency, the optimizer generates more efficient SQL statement structures, avoiding inefficient queries and redundant operations. The optimized structured statement is then passed to the optimization result executor for execution.

[0237] The optimization result executor receives the optimization plan from the structured statement optimizer and is responsible for actually running the optimized business logic and database operations in the application system. Through dynamic scheduling and concurrent execution of the optimization plan, it ensures that production environment requirements are met.

[0238] The optimization result validator verifies the optimized system performance and business execution after the optimization is executed. By monitoring key indicators such as system execution performance, resource consumption, and business response time, the validator ensures that the optimization results have indeed improved system performance without introducing new problems or bottlenecks.

[0239] Once the optimization results are verified, the system generates a detailed optimization report. This report includes the specific optimization steps, numerical analysis of performance improvements, identified risks, and potential suggestions for further optimization. The report helps system administrators evaluate the optimization process and serves as a reference for subsequent optimization efforts.

[0240] Furthermore, optimization can be achieved based on multi-model and database support. For example, during the entire optimization process, the method optimizer and the structured statement optimizer are adjusted based on the polymorphic optimization big model and the SQL big model, respectively. The polymorphic optimization big model is responsible for identifying the execution characteristics of different business logics, ensuring that the optimization solution has universality and scalability. The SQL big model is used to analyze and optimize database operations, ensuring that the optimized SQL statements can run efficiently in complex database environments.

[0241] Data storage: Throughout the optimization process, the application database stores business data and intermediate results generated during optimization. The interaction between the structured statement optimizer and the database ensures efficient data operations, while the optimization result executor further adjusts and optimizes the execution plan based on feedback from the database.

[0242] Figure 6 This is a timing diagram illustrating the optimization process of a method for improving the performance of a trading system according to an embodiment of this application.

[0243] In one or more embodiments, the optimization method can be optimized based on multiple scenarios, such as... Figure 6 As shown, for concurrent stress testing scenarios, through the cooperation of multiple sub-units, the entire process of automated performance optimization, from test plan generation and execution to optimization feedback, is achieved.

[0244] like Figure 6 As shown, the scheduler is responsible for formulating the system's test plan and orchestrating the testing. In this step, the scheduler generates a test plan based on the system's current state and performance objectives, and then passes the plan to the control execution subunit for specific operations.

[0245] like Figure 6 As shown, the control execution subunit is responsible for the execution control of the test plan. After receiving the scheduling arrangement, the control execution subunit starts the message preparation work, formats the relevant information, and prepares to send the message.

[0246] like Figure 6 As shown, the message preparation module is responsible for converting the content in the test plan into a message format that the system can process, so that subsequent test execution can proceed. After the message preparation is complete, the message will enter the system for execution through the message playback subunit.

[0247] like Figure 6 As shown, after receiving the prepared message, the message replay subunit is responsible for sending the message to various modules of the system to simulate real business operations and data interactions in order to test the system's performance in a real operating environment.

[0248] like Figure 6 As shown, after the system executes the test message, it generates test results. These results include performance metrics such as system runtime data, response time, and resource usage during the test. The test results are fed back to the scheduler and then passed to the optimizer for further processing.

[0249] The optimizer analyzes the test results to identify performance bottlenecks and areas for optimization in the system. Based on the analysis results, the optimizer generates an optimization plan, adjusts the system's configuration or method logic, and then hands the optimized system over to the control execution subunit for retesting.

[0250] Figure 7 A schematic diagram of a client device in an embodiment of this application is shown.

[0251] like Figure 7As shown in this embodiment, the client device includes multiple functional modules, which are used for data collection and transmission respectively. The client may include:

[0252] Data acquisition unit 701 is responsible for collecting core data from the system. Its core task is to collect all necessary business data generated during system operation. The data acquisition unit may include:

[0253] Event tracking: User actions on the page are collected through event tracking, recording user clicks, inputs, and other behaviors.

[0254] Transaction message collection: Transaction data transmitted at the interface layer will be collected. The transaction message collection module is responsible for monitoring detailed transaction data in the system and capturing transaction request and response messages.

[0255] Method chain collection: The execution chain of business logic and the order of method calls in the service layer are collected to analyze the execution efficiency and performance bottlenecks of the business logic.

[0256] SQL collection: The SQL statements used in database operations are collected. The SQL collection module is responsible for monitoring and recording all SQL operations performed by the service layer on the database, and analyzing the efficiency of database query, insert, update and other operations.

[0257] Status acquisition: Records the status information of the system during operation, including the health status of services, resource usage, thread status, etc.

[0258] In this embodiment, the data acquisition unit can ensure that all data that needs to be analyzed can be collected completely and accurately.

[0259] In this embodiment, the client may further include a log collection unit 702, which is used to collect log data generated during system or application runtime. This log data can help understand the steps executed internally by the system, the requests processed, error logs, and performance information. In this embodiment, the log collection unit 702 can be used for system monitoring, troubleshooting, and historical operation tracing, providing comprehensive system operation information for subsequent analysis.

[0260] In this embodiment, the client may further include a status acquisition unit 703, used to collect status information of the client device, such as CPU utilization, memory usage, network status, disk I / O, global objects, etc., to help analyze the health of the application's running status and the efficiency of resource utilization. Furthermore, it is used to monitor the health status of the device, helping the server to make timely adjustments or issue warnings when anomalies or performance degradation occur.

[0261] In this embodiment, the client may further include a data reporting unit 704, which packages all collected data and sends it to the server. It integrates data from other units into a standard format message and transmits the data to the server for further processing and analysis via network protocols.

[0262] After the test results are generated, the results verification subunit is responsible for collecting and verifying all test results. This verification process aims to confirm the effectiveness of the optimization scheme and ensure that system performance has been improved. The results verification subunit verifies whether the optimization has achieved the expected goals by analyzing the test data.

[0263] Furthermore, after the message playback is completed, the system will generate corresponding performance data, which can be collected by the result acquisition unit and passed to the result verification subunit.

[0264] Furthermore, after verification, the optimizer makes further adjustments based on the verification feedback, forming an optimization loop. The system can continuously optimize its performance and improve overall operating efficiency during this continuous loop.

[0265] Figure 8 This is a schematic diagram of a server-side device for a method to improve the performance of a trading system, according to an embodiment of this application.

[0266] like Figure 8 As shown, in this embodiment, the server device includes a data analysis unit 801, a scheduling unit 802, an optimization management unit 803, and a data management unit 804, which are designed to analyze and optimize data collected from the client to improve system or application performance.

[0267] In this embodiment, the data analysis unit 801 is used to process and analyze data received from the client or server. This unit analyzes various collected data item by item, including but not limited to event tracking data, log data, transaction messages, method chain data, SQL statements, and system status information. Through the analysis of this data, the data analysis unit can provide optimization basis and guidance for the subsequent operations of the optimization management unit.

[0268] In this embodiment, the scheduling unit 802 is used for task coordination and resource scheduling to ensure the system's efficiency during operation. Especially in non-functional testing scenarios, the scheduling unit is responsible for dynamically adjusting the system's resource allocation and task execution order based on the current system load and task priorities to ensure stable system operation and optimize overall performance.

[0269] In this embodiment, the optimization management unit 803 is used to dynamically adjust the system configuration by invoking appropriate optimization models based on the analysis results of the data analysis unit and the feedback from the scheduling unit. This unit can optimize the execution order of the method chain, improve the execution efficiency of SQL statements, and formulate and implement corresponding optimization strategies, ultimately improving the overall performance of the system. Simultaneously, by invoking different optimization models, this unit automatically adapts to different business scenarios or system load states, thereby providing flexible optimization solutions.

[0270] In this embodiment, the data management unit 804 is used for storing and accessing data collected and analyzed during the optimization process, as well as the resulting reports. This unit ensures data integrity and availability, supports the tracking and auditing of historical data, and provides efficient data management services for analysis and optimization operations. The data management unit is not only responsible for storing the data generated during the optimization process, but also ensures that this data can be quickly accessed and used in subsequent analysis and optimization processes.

[0271] The transaction system performance improvement method according to the version application embodiment is based on fully automated link analysis and performance optimization, which can effectively capture performance bottlenecks in complex call chains and perform dynamic optimization.

[0272] In this embodiment, the multi-model optimizer in the system can accurately optimize different types of call chains and SQL statements, especially when handling large-scale data table operations, where the optimization effect is significant. The system performance testing and optimization results help operations and development personnel quickly locate problems.

[0273] Through the above embodiments, performance issues in the trading system are addressed by improving the system's response speed and processing efficiency through link analysis and optimization.

[0274] Compared with traditional application optimization methods, this application has the following advantages:

[0275] Improved application optimization efficiency: It can automatically generate optimization plans based on system operation and analysis results, and reduce human intervention through automated execution, thereby improving optimization efficiency.

[0276] It provides end-to-end optimization capabilities: all kinds of data along the entire chain from client to server, including pages, transaction messages, SQL, status, etc., are fully collected to ensure that data at each link is within the scope of analysis. It improves business coverage: it can cover all transactions and every corner of business operations, ensuring that business in different scenarios can be monitored and optimized to avoid omissions.

[0277] Capable of continuous optimization: The automated optimization mechanism provided in this application embodiment can continuously improve system performance based on the latest analysis results, continuously optimize task execution efficiency and resource allocation strategies, and ensure that the system can be effectively optimized at different stages of operation.

[0278] It can automatically perform non-functional testing: During non-functional testing, the server continuously monitors relevant performance data through message replay and provides targeted optimization solutions through the optimization management unit to automatically perform performance tuning and ensure that non-functional features always meet the expected requirements.

[0279] This invention can be widely applied in scenarios where the corporate structure is relatively stable, such as those in the financial and insurance industries. The specific implementation method depends on the specific application scenario and requirements.

[0280] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for improving the performance of a transaction system.

[0281] This application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for improving the performance of the trading system.

[0282] This application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described method for improving the performance of a trading system.

[0283] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0284] This invention is described with reference to schematic diagrams and / or flowcharts and / or block diagrams of methods, apparatus, systems, and computer program products according to embodiments of the invention. It should be understood that the schematic diagrams... Figure 1 One or more steps and / or processes Figure 1 One or more processes and / or boxes Figure 1The order of one or more blocks may not follow the order shown in the diagram; an appropriate order can be chosen according to actual needs. It should be understood that each step and / or operation and / or process and / or block in the schematic diagram and / or flowchart and / or block diagram, as well as combinations of steps and / or operations and / or processes and / or blocks in the schematic diagram and / or flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the schematic diagram. Figure 1 One or more steps and / or processes Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0285] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in the schematic diagram. Figure 1 One or more steps and / or processes Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0286] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the illustrated... Figure 1 One or more steps and / or processes Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0287] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for improving the performance of a trading system, characterized in that, Includes the following steps: Step 101: Collect data from the multi-layered links of the transaction system, which include the page layer, interface layer, service layer, database layer, and system state layer; Step 102: Based on the collected data, perform performance analysis on each layer of the link using an analyzer; Step 103: Locate the performance bottleneck coordinates of the trading system based on the analysis results of the analyzer, and dynamically optimize the trading system using an optimizer based on the performance bottleneck situation at the performance bottleneck coordinates; Step 104: Compile the performance data of the optimized trading system and generate an optimization report. The optimizer includes a method optimizer and a structured statement optimizer. The method optimizer optimizes the method call order and / or business logic in the transaction system, and the structured statement optimizer optimizes database operations. The dynamic optimization refers to the actual execution of the optimizer's optimization results in the transaction system through dynamic scheduling and concurrent execution.

2. The method for improving the performance of a trading system according to claim 1, characterized in that, In step 101, the data collection of the multi-layered links of the transaction system includes: Event tracking: Collecting user actions on the application's front-end page using event tracking methods; Transaction message collection: Collect transaction data transmitted from the interface layer; Method chain collection: Collect the execution chain of business logic and the order of method calls in the service layer; SQL collection: Records the SQL operations performed by the service layer on the database in the order of execution; Status acquisition: Records the status information of the system during operation.

3. The method for improving the performance of a trading system according to claim 2, characterized in that, The data collection, transaction message collection, method chain collection, SQL collection, and status collection are performed asynchronously. All the collected information obtained from the data collection, transaction message collection, method chain collection, SQL collection, and status collection are associated with a global transaction tracking number. The global transaction tracking number has a one-to-one correspondence with the interface request ID.

4. The method for improving the performance of a trading system according to claim 1, characterized in that, In step 102, the analyzer performs performance analysis using at least one of the following analysis methods: Statistical analysis: Statistically analyze the time indicators and resource consumption indicators of each link or node in the multi-layer link; Dependency graph analysis: Construct call chains or dependency graphs to identify the execution time and resource consumption of each link or node; Anomaly detection and analysis: Utilizing standard difference anomaly detection and / or machine learning anomaly detection, compare historical data to identify links or nodes with abnormal time indicators or resource consumption indicators; Rule-driven analysis: Based on preset thresholds, it identifies processes or nodes where time or consumption metrics are abnormal. The performance bottleneck coordinates include: links or nodes with abnormal time indicators and links or nodes with abnormal resource consumption indicators.

5. The method for improving the performance of a trading system according to claim 4, characterized in that, The time metrics include at least one of response time, total time, average time, and percentile time, and the resource consumption metrics include the usage of at least one of CPU, memory, network, and disk I / O.

6. The method for improving the performance of a trading system according to claim 1, characterized in that, The performance bottleneck coordinates include links or nodes where anomalies or errors occur, and the anomalies or errors include at least one of the following: the data transmission error rate exceeds a predetermined threshold, timeout, or request failure.

7. The method for improving the performance of a trading system according to claim 1, characterized in that, The optimizer generates an optimization scheme according to a preset optimization strategy based on the performance bottleneck situation at the performance bottleneck coordinates, including: When SQL queries are slow, optimization can be achieved by adding indexes or optimizing the query. When network latency is high, optimization can be achieved by caching static resources or adjusting load balancing. When CPU usage is high, optimization can be achieved by adjusting thread pools or task allocation.

8. The method for improving the performance of a trading system according to claim 1, characterized in that, The optimizer uses an automated adjustment method to dynamically adjust resource allocation, cache size, and task scheduling based on the performance bottleneck coordinates.

9. The method for improving the performance of a trading system according to claim 1, characterized in that, The method optimizer optimizes based on a polymorphic optimization model, while the structured statement optimizer optimizes using a large SQL model.

10. The method for improving the performance of a trading system according to any one of claims 7 to 9, characterized in that, The optimizer optimizes based on the following rules: Priority rules: performance improvement, resource savings, number of bug fixes; Security rule: The optimization plan shall not disrupt the existing functionality of the trading system and shall not introduce new anomalies; Feasibility rule: The optimization strategy is within the acceptable range of system resources.

11. The method for improving the performance of a trading system according to claim 1, characterized in that, In step 104, if the performance data of the optimized trading system reaches the predetermined target, the corresponding optimization plan is recorded in the optimization report. The optimization plan includes a performance comparison before and after optimization, the coordinates of the performance bottleneck before optimization, the specific optimization measures, and the performance improvement.

12. The method for improving the performance of a trading system according to claim 1, characterized in that, The method further includes: Step 105: Based on the optimization report, automatically select the optimal optimization scheme according to different scenarios of the trading system during the operation of the trading system.

13. A server in a transaction system, characterized in that, The server includes: A data analysis unit analyzes data across multiple layers of the transaction system; and The optimization unit locates the performance bottleneck coordinates of the trading system based on the analysis results of the data analysis unit, and dynamically optimizes the trading system based on the performance bottleneck situation at the performance bottleneck coordinates. The optimization unit includes a method optimizer and a structured statement optimizer. The method optimizer optimizes the method call order and / or business logic in the transaction system, and the structured statement optimizer optimizes database operations. The dynamic optimization refers to the actual execution of the optimizer's optimization results in the transaction system through dynamic scheduling and concurrent execution.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 12.

15. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 12.

16. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 12.

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