System link performance analysis method, device, equipment and computer program product

Abstract

The invention discloses a system link performance analysis method, device and equipment and a computer program product, and relates to the technical field of system performance analysis, and the method comprises the steps: obtaining system performance data of a system link, comparing the system performance data with a preset system performance early warning threshold value, and obtaining a system performance early warning result; obtaining a first comparison result of the system performance data; according to the first comparison result, determining an abnormal server in the system link, and obtaining micro-service performance data corresponding to the abnormal server; analyzing and positioning the micro-service performance data according to a preset micro-service performance early warning threshold, and determining abnormal performance information of the system link; and performing layer-by-layer re-inspection on the system link based on the abnormal performance information, and generating a performance analysis report of the system link. By setting early warning thresholds in multiple layers of the system, the server, the micro-service and the like, the efficiency of performing performance analysis and positioning on the system link is improved.

Description

Technical Field

[0001] The present application relates to the technical field of system performance analysis, and in particular to a method, apparatus, device, and computer program product for performance analysis of a system link. Background Art

[0002] Analyzing the link performance of application systems is crucial for ensuring efficient and stable system operation, preventing potential issues and significantly improving the user experience. Currently, link performance analysis for complex application systems primarily relies on system monitoring solutions. These solutions collect log data from various system modules, perform statistical analysis, and output alarm information regarding performance or other issues. However, analyzing performance issues requires manually linking alarm information from various modules on the same link. This lacks a standardized process for diagnosing root causes and results in inefficient performance analysis. Therefore, a solution to improve the efficiency of link performance analysis is urgently needed.

[0003] The above content is only used to assist in understanding the technical solution of this application and does not constitute an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a system link performance analysis method, device, equipment and computer program product, aiming to solve the technical problem of low efficiency of system link performance analysis.

[0005] To achieve the above objectives, the present application proposes a system link performance analysis method, the method comprising:

[0006] Acquire system performance data of a system link, compare the system performance data with a preset system performance warning threshold, and obtain a first comparison result of the system performance data;

[0007] Determine, based on the first comparison result, an abnormal server in the system link, and obtain microservice performance data corresponding to the abnormal server;

[0008] Analyze and locate the microservice performance data according to a preset microservice performance warning threshold to determine abnormal performance information of the system link;

[0009] The system link is backchecked layer by layer based on the abnormal performance information to generate a performance analysis report of the system link.

[0010] In one embodiment, the step of obtaining system performance data of a system link, comparing the system performance data with a preset system performance warning threshold, and obtaining a first comparison result of the system performance data includes:

[0011] Determining a system performance indicator of the system link, and obtaining system performance data of the system link according to the system performance indicator;

[0012] A corresponding system performance warning threshold is preset according to the system data boundary value of the system link, the system performance data is compared with the corresponding system performance warning threshold, and a first comparison result of the system performance data is generated.

[0013] In one embodiment, the step of determining an abnormal server in the system link based on the first comparison result and obtaining microservice performance data corresponding to the abnormal server includes:

[0014] If the system performance data is greater than the corresponding system performance warning threshold, determining an abnormal server in the system link according to the system performance data;

[0015] Obtaining server performance data of the abnormal server according to the server performance indicator of the server;

[0016] The server performance data of the abnormal server is distributed and counted according to the target distribution to obtain the microservice performance data of the abnormal server.

[0017] In one embodiment, the step of analyzing and locating the microservice performance data according to a preset microservice performance warning threshold to determine abnormal performance information of the system link includes:

[0018] Preset the corresponding microservice performance warning threshold according to the microservice data boundary value of the microservice;

[0019] Comparing the microservice performance data with the microservice performance warning threshold, and if the microservice performance data is greater than the microservice performance warning threshold, determining the abnormal microservice in the abnormal server according to the microservice performance data;

[0020] The abnormal microservice is analyzed and located according to the microservice performance data to determine abnormal performance information of the system link.

[0021] In one embodiment, the step of analyzing and locating the abnormal microservice based on the microservice performance data and determining the abnormal performance information of the system link includes:

[0022] Performing distribution statistics on the microservice performance data of the abnormal microservice to generate detailed distribution data of the microservice performance data;

[0023] Based on the preset classification performance problems, performance analysis and abnormality location are performed on the distribution detailed data to determine abnormal performance information of the system link.

[0024] In one embodiment, the distribution detail data includes request volume, request duration, and application-side requests; the abnormal performance information includes abnormal issues, abnormal causes, and abnormal locations; and the steps of performing performance analysis and abnormal location on the distribution detail data to determine the abnormal performance information of the system link include:

[0025] Divide the request time into time intervals to obtain corresponding time intervals, and count the number of requests and application-side requests corresponding to the time intervals;

[0026] Based on the request duration and the request volume in the corresponding interval, the abnormal microservice is subjected to performance analysis and abnormal location, and the abnormal problem and abnormal location of the system link are determined;

[0027] Based on the request duration and the application-side request in the corresponding interval, the cause of the abnormal problem is located to determine the abnormal cause of the abnormal problem.

[0028] In one embodiment, the system link performance analysis method further includes:

[0029] Performing hierarchical division on the system links to obtain a multi-layer distribution of the system links;

[0030] Create a directed graph based on the system performance data, the server performance data, the microservice performance data, and the multi-layer distribution to generate a primary multi-layer performance data association graph of the system link;

[0031] The primary multi-layer performance data association graph is cropped to generate a target multi-layer performance data association graph of the system link, and the target multi-layer performance data association graph is used to view and analyze the performance distribution of the system link.

[0032] In addition, to achieve the above-mentioned purpose, the present application also proposes a system link performance analysis device, the system link performance analysis device comprising:

[0033] a first acquisition and comparison module, configured to acquire system performance data of a system link, compare the system performance data with a preset system performance warning threshold, and obtain a first comparison result of the system performance data;

[0034] an abnormal service determination module, configured to determine an abnormal server in the system link based on the first comparison result, and obtain microservice performance data corresponding to the abnormal server;

[0035] An abnormal information determination module is used to analyze and locate the microservice performance data according to a preset microservice performance warning threshold to determine abnormal performance information of the system link;

[0036] The analysis report generating module is used to perform layer-by-layer backcheck on the system link based on the abnormal performance information and generate a performance analysis report of the system link.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a performance analysis device for a system link, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the performance analysis method for the system link as described above.

[0038] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the performance analysis method of the system link as described above are implemented.

[0039] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the performance analysis method of the system link as described above.

[0040] One or more technical solutions proposed in this application have at least the following technical effects:

[0041] The embodiment of the present application proposes a performance analysis method, apparatus, device and computer program product for a system link, including: obtaining system performance data of the system link, comparing the system performance data with a preset system performance warning threshold, and obtaining a first comparison result of the system performance data; determining an abnormal server in the system link based on the first comparison result, and obtaining microservice performance data corresponding to the abnormal server; analyzing and locating the microservice performance data based on a preset microservice performance warning threshold, and determining abnormal performance information of the system link; and performing layer-by-layer backchecking on the system link based on the abnormal performance information, and generating a performance analysis report for the system link. By setting warning thresholds at multiple layers such as the system, server and microservice, the efficiency of performance analysis and positioning of the system link is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0044] Figure 1 A flowchart of the first embodiment of the performance analysis method for the system link of the present application is provided;

[0045] Figure 2 An example diagram of the system link architecture topology provided for an embodiment of the system link performance analysis method of the present application;

[0046] Figure 3 An example diagram of a system link performance analysis process provided for an embodiment of the system link performance analysis method of the present application;

[0047] Figure 4 An example diagram of data on performance statistics and statistical thresholds in a system link performance analysis process provided in an embodiment of the system link performance analysis method of the present application;

[0048] Figure 5 An example diagram of the interval division of request time consumption provided in an embodiment of the performance analysis method of the system link of the present application;

[0049] Figure 6 An example diagram of a target multi-layer performance data association diagram regarding time consumption distribution provided in an embodiment of the performance analysis method for a system link of the present application;

[0050] Figure 7 This is a schematic diagram of the module structure of the performance analysis device of the system link according to the embodiment of the present application;

[0051] Figure 8 Schematic diagram of the device structure of the hardware operating environment involved in the performance analysis method of the system link in the embodiment of the present application.

[0052] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] The main solution of the embodiment of the present application is: obtaining system performance data of the system link, comparing the system performance data with a preset system performance warning threshold, and obtaining a first comparison result of the system performance data; determining an abnormal server in the system link based on the first comparison result, and obtaining microservice performance data corresponding to the abnormal server; analyzing and locating the microservice performance data based on a preset microservice performance warning threshold, and determining abnormal performance information of the system link; and performing layer-by-layer backchecking of the system link based on the abnormal performance information, and generating a performance analysis report of the system link.

[0056] In this embodiment, for ease of description, the following description is made with the performance analysis device of the system link as the execution subject.

[0057] Analyzing the link performance of application systems is crucial for ensuring efficient and stable system operation, preventing potential issues and significantly improving the user experience. Currently, link performance analysis for complex application systems primarily relies on system monitoring solutions. These solutions collect log data from various system modules, perform statistical analysis, and output alarm information regarding performance or other issues. However, analyzing performance issues requires manually linking alarm information from various modules on the same link. This lacks a standardized process for diagnosing root causes and results in inefficient performance analysis. Therefore, a solution to improve the efficiency of link performance analysis is urgently needed.

[0058] This application provides a solution that improves the efficiency of performance analysis and positioning of system links by setting warning thresholds at multiple levels such as the system, server, and microservices.

[0059] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of implementing the above functions, a system link performance analysis device, etc. The following uses the system link performance analysis device as an example to illustrate this embodiment and the following embodiments.

[0060] Based on this, the embodiment of the present application provides a performance analysis method of a system link, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the performance analysis method of the system link of the present application.

[0061] In this embodiment, the system link performance analysis method includes steps S11 to S14:

[0062] Step S11 : acquiring system performance data of a system link, comparing the system performance data with a preset system performance warning threshold, and obtaining a first comparison result of the system performance data.

[0063] It should be noted that system performance data refers to data that reflects the overall operating status of the system, including but not limited to CPU usage, memory usage, thread pool status, and other indicator data that reflects the system's operating status. It can be collected and counted in real time through monitoring tools (such as Prometheus) or system output logs.

[0064] Additionally, it should be noted that the system performance warning threshold refers to a preset boundary value used to determine whether system performance is abnormal. When system performance data exceeds this threshold, it indicates that the system may have an abnormality (for example, CPU usage > 80% is abnormal). It is determined based on statistical analysis of historical data or management experience.

[0065] In addition, it should be noted that the first comparison result refers to the result obtained by comparing the system performance data with the corresponding system performance warning threshold, including the system performance data being greater than the corresponding system performance warning threshold and the system performance data being less than or equal to the corresponding system performance warning threshold.

[0066] Specifically, based on the architectural topology of the system link, the link hierarchy is clarified; based on the characteristics and requirements of the system, the system performance indicators that need to be monitored are determined; based on the determined system performance indicators, the performance data of the system link is collected; based on the system data boundary value of the system link, the corresponding system performance warning threshold is preset, and the system performance data is compared with the corresponding system performance warning threshold to generate a first comparison result of the system performance data.

[0067] For better understanding please refer to Figure 2 , Figure 2 This is an example diagram of the system link architecture topology of an embodiment provided in this application. Figure 2The system links in the process cover key components such as the client, network load balancer (such as F5), reverse proxy server (such as Nginx), application server (such as Tomcat), cache server (such as Redis), and various business systems. A request is sent from the user client, distributed by F5 (a load balancer that distributes network traffic to multiple servers), and then passed through the Nginx server (an open source web server and reverse proxy server that receives requests forwarded from F5 and forwards the requests to the backend Tomcat server), backend server, microservice application, Tomcat server, and finally interacts with Redis (a high-performance key-value storage database that supports multiple data structures) and various business systems to complete the processing. More specifically, the client sends a request to the system through the network; the request reaches the network load balancer (F5), and F5 distributes the request to multiple reverse proxy servers (Nginx) according to preset rules; after receiving the request, Nginx forwards the request to the backend application server (Tomcat) according to the configuration; the Tomcat server receives the request and, during the processing, obtains session information and user information from the cache layer (Redis), sends the request to various business systems for processing, and returns the processing results to the client.

[0068] Figure 2 Topological dependencies in the system can cause performance issues to propagate layer by layer. For example, if downstream Redis or business systems experience large-scale slow responses or timeouts, this can fill up the microservice application Tomcat thread pool and cause request queues. This in turn increases JVM CPU and memory usage, causing Nginx server threads to wait, leading to server connection saturation and increased CPU usage. This ultimately results in slow user page responses or even timeout errors, and functional malfunctions.

[0069] Step S12: Determine an abnormal server in the system link based on the first comparison result, and obtain microservice performance data corresponding to the abnormal server.

[0070] It should be noted that an abnormal server refers to a server in a system link whose performance data exceeds a preset threshold.

[0071] In addition, it should be noted that microservice performance data refers to performance data related to microservices, specifically the fine-grained indicators of a single microservice, such as interface time consumption, thread pool status, number of database connections, etc.

[0072] Specifically, if the system performance data is greater than the corresponding system performance warning threshold, the abnormal server in the system link is determined based on the system performance data; the server performance data of the abnormal server is obtained based on the server performance indicators of the server; the server performance data of the abnormal server is distributed and counted according to the target distribution to obtain the microservice performance data under the abnormal server.

[0073] Step S13: analyzing and locating the microservice performance data according to a preset microservice performance warning threshold to determine abnormal performance information of the system link.

[0074] It should be noted that the microservice performance warning threshold refers to the preset boundary value used to determine whether the microservice performance is abnormal.

[0075] In addition, it should be noted that abnormal performance information refers to detailed information on performance issues existing in system links that have been determined through analysis and positioning, including abnormal issues, abnormal causes, abnormal locations, etc.

[0076] Specifically, a corresponding microservice performance warning threshold is preset according to the microservice data boundary value of the microservice; the microservice performance data is compared with the microservice performance warning threshold; if the microservice performance data is greater than the microservice performance warning threshold, the abnormal microservice in the abnormal server is determined according to the microservice performance data; the abnormal microservice is analyzed and located according to the microservice performance data to determine the abnormal performance information of the system link.

[0077] Step S14: performing layer-by-layer backcheck on the system link based on the abnormal performance information, and generating a performance analysis report of the system link.

[0078] It should be noted that the system link performance analysis report refers to the summary of the results of the system link performance analysis, including a description of the performance problem, cause analysis, and improvement suggestions.

[0079] Specifically, first, based on the system architecture topology, the link layer is clarified. For example, the link layer is client-API gateway-microservice cluster-message middleware-database / cache-external service. Then, based on the abnormal location in the abnormal performance information, a backtracking path is automatically generated. The data of each layer is backtracked from the bottom up layer by layer. For example, the real-time performance indicators of each layer are obtained through the distributed tracing system. The collected data is compared with the preset hierarchical thresholds, starting from the atomic performance of a single microservice and a single instance, and gradually expanded to the entire domain name, area, cache cluster, etc., and then matched and verified with the classified performance issues to determine the abnormal problem, abnormal cause and abnormal location of the abnormal performance of the system link, and generate a performance analysis report for the system link.

[0080] For better understanding, please refer to Figure 3 , Figure 3 The performance analysis flow diagram of the system link provided in this application corresponds to Figure 2 Example diagram of the architecture topology of the system links in . Figure 3 First, obtain system performance data. Figure 3 The system obtains the performance data of application servers (such as TOMCAT), reverse proxy server device information (such as NGINX) and cache servers (such as REDIS). The data includes thread pool status, queue duration, CPU (Central Processing Unit) usage, memory usage, etc. Performance warning thresholds are set, such as the number of machines, time, usage, etc., and the system performance data is compared with the system performance warning threshold. After the performance data exceeds the system performance warning threshold, the system will further analyze the server's performance data, including queue duration, queue peak, thread pool full time interval, etc. The server performance data is then distributed and counted according to different dimensions, including target distribution by region, by server pool, and by single machine. When the problematic microservice under the abnormal server is located based on the distribution data, the system will view the overview data of the microservice, including the number of requests, request time distribution, cache server Redis request details, etc. Furthermore, microservice performance warning thresholds are set, such as QPM (Queries Per Minute) peak and distribution ratio offset. When the performance data of a microservice exceeds these thresholds, the microservice performance data is distributed and statistically analyzed, including request volume, time consumption distribution, and other distribution details, to determine the abnormal problem, abnormal cause, and abnormal location.

[0081] For further information, please refer to Figure 4 , Figure 4 This is an example diagram of performance statistics and statistical thresholds in the performance analysis process of the system link provided in this application. Figure 4 The table in the table corresponds to Figure 3 The performance analysis process example diagram of the system link shows the performance data of each layer and the corresponding thresholds.

[0082] Through the above solution, this embodiment improves the efficiency of performance analysis and positioning of system links by setting warning thresholds at multiple layers such as the system, server, and microservices.

[0083] Based on the above embodiment, in a feasible implementation manner, the steps of obtaining system performance data of the system link, comparing the system performance data with a preset system performance warning threshold, and obtaining a first comparison result of the system performance data include S21 to S22:

[0084] Step S21 : determining a system performance indicator of the system link, and acquiring system performance data of the system link according to the system performance indicator.

[0085] It should be noted that system performance indicators refer to parameters used to measure the overall operating status of system links, such as CPU utilization, memory utilization, and thread pool queue length. These indicators directly reflect the operating status of the system in different aspects and are the basis for evaluating system performance.

[0086] Specifically, analyze the architecture and business requirements of the system link and determine the key performance indicators that need to be monitored. For example, for a complex distributed application system, you may need to monitor indicators such as CPU usage, memory usage, thread pool queue length, and network bandwidth utilization. Determine the normal range of each performance indicator based on the system's historical operating data and experience. For example, by analyzing the CPU usage data of the system during normal operation, determine that its normal range is 0% to 70%. Deploy monitoring tools at various key nodes of the system link to collect data on system performance indicators in real time. These tools can regularly obtain performance data from various components of the system (such as servers, microservices, etc.) and store it in the monitoring system.

[0087] Step S22: preset a corresponding system performance warning threshold according to the system data boundary value of the system link, compare the system performance data with the corresponding system performance warning threshold, and generate a first comparison result of the system performance data.

[0088] It should be noted that system data boundary values ​​refer to the boundaries of the normal operating range of system performance indicators and are used to determine whether system performance is normal. For example, the boundary value for CPU utilization might be 80%, and the boundary value for memory utilization might be 90%. When system performance indicators exceed these boundary values, it may indicate that system performance is abnormal. The normal range for each performance indicator is determined based on historical system operating data and experience. For example, by analyzing the system's CPU utilization data during normal operation, its normal range is determined to be 0% to 70%.

[0089] Specifically, according to the normal range boundary value of the system performance indicator, the corresponding system performance warning threshold is preset. For example, if the normal range of CPU usage is 0% to 70%, the warning threshold of CPU usage can be set to 70%. The setting of the warning threshold needs to comprehensively consider the business needs of the system, historical operation data and the fault tolerance of the system. For example, for a system with high performance requirements, the warning threshold can be set more strictly to detect potential performance problems in advance. Compare the collected system performance data with the preset system performance warning threshold. The monitoring tool can compare the collected performance data with the warning threshold regularly (such as every minute) and generate a comparison result.

[0090] Based on the above implementation scheme, in a feasible implementation manner, the steps of determining the abnormal server in the system link according to the first comparison result and obtaining the microservice performance data corresponding to the abnormal server include S31 to S33:

[0091] Step S31: If the system performance data is greater than the corresponding system performance warning threshold, an abnormal server in the system link is determined according to the system performance data.

[0092] Specifically, when the system performance data is greater than the corresponding system performance warning threshold, the abnormal server in the system link is determined based on the specific situation of the system performance data. For example, if the CPU usage exceeds the threshold, the server with abnormal CPU usage can be determined as an abnormal server.

[0093] Step S32: acquiring server performance data of the abnormal server according to the server performance index of the server.

[0094] It should be noted that server performance indicators are parameters used to measure the operating status of a single server, such as the server's CPU utilization, memory utilization, as well as the queue and thread pool statistics of the Tomcat service running on the server, and the number of connections and queue statistics of the REDIS service running on the server.

[0095] Specifically, according to the performance indicators of the server, detailed performance data of the server determined to be abnormal is acquired from the monitoring system to obtain the server performance data of the abnormal server.

[0096] Step S33: performing distribution statistics on the server performance data of the abnormal server according to the target distribution to obtain microservice performance data of the abnormal server.

[0097] It should be noted that target distribution refers to the dimension used to distribute and count server performance data. For example, distribution statistics can be performed based on the server's geographic location, cluster, microservice type, etc., to enable more detailed analysis of performance issues.

[0098] Specifically, the server performance data of the abnormal servers is distributed and statistically analyzed based on the determined target distribution method to obtain the corresponding microservice performance data for the abnormal servers. Distribution statistics can be calculated based on the cluster to which the abnormal servers belong, geographic location, or microservice type. For example, if cluster A has m abnormal servers and cluster B has n, the distribution data of the abnormal servers can be calculated and statistically generated. This distribution statistics can be used to determine whether performance issues are limited to servers in certain regions or clusters.

[0099] Through the above solution, this embodiment can more accurately locate specific microservice performance problems by obtaining and distributing the performance data of abnormal servers in detail, thereby improving the efficiency and accuracy of performance problem analysis.

[0100] Based on the above implementation scheme, in a feasible implementation manner, the step of analyzing and locating the microservice performance data according to a preset microservice performance warning threshold and determining abnormal performance information of the system link includes S41 to S43:

[0101] Step S41: preset a corresponding microservice performance warning threshold according to the microservice data boundary value of the microservice.

[0102] It should be noted that microservice performance warning thresholds are upper limits set for microservice performance indicators and are used to determine whether the microservice is operating normally. When microservice performance data exceeds these thresholds, it indicates that the microservice may have performance issues.

[0103] Specifically, based on the performance indicators and historical operation data of the microservices, determine the normal operating range boundary value of each performance indicator, and preset the corresponding microservice performance warning threshold based on the boundary value. For example, for response time, the threshold may be set at 1 second.

[0104] Step S42: comparing the microservice performance data with the microservice performance warning threshold; if the microservice performance data is greater than the microservice performance warning threshold, determining the abnormal microservice in the abnormal server based on the microservice performance data.

[0105] Specifically, the real-time performance data of the microservices under the abnormal server is obtained from the monitoring system and compared with the preset microservice performance warning threshold. If the performance data of the microservice exceeds the microservice performance warning threshold, the microservice is marked as an abnormal microservice. Based on the comparison results, it is determined which microservices have performance data exceeding the warning threshold, and these microservices are considered to be abnormal microservices.

[0106] Step S43: Analyze and locate the abnormal microservice based on the microservice performance data to determine abnormal performance information of the system link.

[0107] Specifically, the performance data of abnormal microservices is deeply analyzed to determine the specific cause and location of the performance problem; the analysis may include viewing the logs of microservices, monitoring the resource usage of microservices, analyzing the request patterns of microservices, etc.; based on the analysis results, the abnormal performance information is determined, including the abnormal problem, the cause of the abnormality, and the abnormal location.

[0108] Through the above solution, this embodiment can quickly locate abnormal microservices and their performance issues by presetting microservice performance warning thresholds and performing comparative analysis. It can also determine the specific cause and location of the problem through in-depth analysis, providing accurate direction and basis for subsequent performance optimization, thereby improving system stability and user experience.

[0109] Based on the above implementation scheme, in a feasible implementation manner, the step of analyzing and locating the abnormal microservice according to the microservice performance data and determining the abnormal performance information of the system link includes S51 to S52:

[0110] Step S51: performing distribution statistics on the microservice performance data of the abnormal microservice to generate detailed distribution data of the microservice performance data.

[0111] It's important to note that detailed distribution data refers to the detailed data obtained by statistically analyzing the performance data of abnormal microservices based on different dimensions (such as region, cluster, and server). This data helps us gain a deeper understanding of microservice performance. Detailed distribution data includes request volume, request duration, and application-side requests.

[0112] Specifically, we perform multi-dimensional distribution statistics on the performance data of abnormal microservices to generate detailed distribution data. These dimensions include request volume, request duration, and application-side request details.

[0113] Step S52: Perform performance analysis and abnormality location on the distribution detailed data based on preset classification performance issues to determine abnormal performance information of the system link.

[0114] It should be noted that preset classified performance issues refer to pre-defined performance problem types based on the characteristics of microservice performance data and business needs, such as high latency, high error rate, and excessive resource utilization.

[0115] Specifically, based on pre-defined categorized performance issues, the generated detailed distribution data is analyzed to identify potential performance issues. For example, the analysis may include identifying abnormal intervals of request duration, abnormal peaks in request volume, and so on. The specific cause and location of the performance issue are determined, such as identifying the specific request or operation causing high latency, or identifying the specific component or service with excessive resource utilization. Based on the results of performance analysis and anomaly location, abnormal performance information for the system link is determined. This information includes a detailed description of the anomaly, an analysis of the cause of the anomaly, and the location of the anomaly.

[0116] Based on the above implementation scheme, in a feasible implementation, the distribution detail data includes the request volume, request duration, and application-side request, and the abnormal performance information includes the abnormal problem, abnormal cause, and abnormal location. The steps of performing performance analysis and abnormal location on the distribution detail data and determining the abnormal performance information of the system link include S61 to S63:

[0117] Step S61 , dividing the request time into time intervals to obtain corresponding time intervals, and counting the request amount and application-side requests corresponding to the time intervals.

[0118] It should be noted that the request volume in the distribution details data refers to the total request volume of the front-end and back-end URLs and the request volume of each server. The front-end and back-end URLs refer to the page links and back-end interface paths; the request duration refers to the request duration distribution, including the domain name-job bar duration distribution details, including the domain name duration distribution time-sharing details, and the distribution details on each server; the application-side request refers to the cache server Redis request details, including the Redis request duration distribution details, including the domain name duration distribution time-sharing details, and the distribution details on each server; as well as the distribution of the number of Redis client connections and waiting threads in each cluster.

[0119] In addition, it should be noted that abnormal problems describe the specific manifestations of performance problems, such as long response time, increased error rate, etc.; abnormal causes are analysis of the specific causes that lead to abnormal problems, such as insufficient resources, low code efficiency, etc.; abnormal location refers to the specific location where the abnormal problem occurs, such as a specific microservice, server or network node.

[0120] Specifically, we first divide request durations into general and fine-grained intervals. There are generally 13 general intervals, such as <20ms and 20ms-50ms. For some middleware and cache servers, Redis requests take much less time, so we divide them into fine-grained intervals, such as <2ms and 2ms-5ms. Figure 5 This is an example diagram of the interval division of request time provided by this application. Then, in each time interval, the request volume and application-side requests are statistically calculated to calculate the number of requests in each interval and the number of application-side requests.

[0121] Step S62: Based on the request duration and the request volume in the corresponding interval, the abnormal microservice is subjected to performance analysis and abnormal location, and the abnormal problem and abnormal location of the system link are determined.

[0122] Specifically, we analyze the performance of abnormal microservices based on request duration and the number of requests in the corresponding interval. For example, if a business system interface has an unusually high number of requests during a long interval, this may indicate a performance issue with the business system. Based on the analysis results, we can identify the abnormality and its location in the business system.

[0123] Step S63: Based on the request duration and the application-side request in the corresponding interval, the cause of the abnormal problem is located to determine the abnormal cause of the abnormal problem.

[0124] Specifically, further analyze the request duration and the application-side requests in the corresponding interval to determine the specific cause of the abnormal problem; based on the above analysis results, determine the abnormal performance information, including the abnormal problem, abnormal cause and abnormal location.

[0125] Based on the above implementation scheme, in a feasible implementation manner, the system link performance analysis method further includes steps S71 to S73:

[0126] Step S71 , hierarchically divide the system links to obtain a multi-layer distribution of the system links.

[0127] It should be noted that the multi-layer distribution of system links refers to dividing the system links into layers according to the logical or physical structure.

[0128] Specifically, the system links are divided into layers according to the logical or physical structure to identify the performance characteristics of different layers. Within each layer, they are further subdivided into more specific components or services, such as database, cache, API (Application Programming Interface) services, etc., to obtain the multi-layer distribution of the system links.

[0129] Step S72: Create a directed graph based on the system performance data, the server performance data, the microservice performance data, and the multi-layer distribution to generate a primary multi-layer performance data association graph of the system link.

[0130] It should be noted that the primary multi-layer performance data association diagram is a directed graph that shows the distribution of performance data of each layer and component in the system link.

[0131] Specifically, the system integrates system performance data, server performance data, microservice performance data, and multi-layer distribution information. A directed graph is used to represent the relationships between the various layers and components in the system link, as well as the flow of performance data between them. Based on this directed graph, a preliminary multi-layer performance data association graph for the system link is generated. This graph shows the distribution of performance data at each layer and component in the system link.

[0132] Step S73 , cutting the primary multi-layer performance data association graph to generate a target multi-layer performance data association graph of the system link, wherein the target multi-layer performance data association graph is used to view and analyze the performance distribution of the system link.

[0133] It should be noted that the target multi-layer performance data association graph refers to a primary multi-layer performance data association graph that has been trimmed and optimized, and is used to more accurately analyze and view the performance distribution of system links.

[0134] Specifically, determine the trimming criteria to highlight the most meaningful parts for performance analysis. This may include removing redundant information and focusing on key performance indicators. Based on the trimming criteria, trim the primary multi-layer performance data correlation graph to generate a target performance distribution graph. This graph is used to view and analyze the performance distribution of system links, helping to identify performance bottlenecks and optimization points.

[0135] For better understanding, please refer to Figure 6This application provides an example diagram of a multi-layer performance data association graph for target time distribution. The association between the time distribution data to be counted can be analyzed. Two adjacent statistical data can be converted according to the direction of the arrow. In one embodiment of this application, the statistical data refers to the number of requests and the time it takes to complete the request. First, the system link is divided into four levels: primary, secondary, tertiary, and quaternary data. Primary data is the most basic data collection layer, containing basic information about Nginx servers, microservices, domain names, and job bars. Secondary data further refines the information based on primary data, showing the associations between microservices, servers, domain names, and job bars. Tertiary data further refines secondary data, adding combinations of servers, domain names, and job bars, as well as combinations between shards and job bars. This helps to more accurately locate time issues. For example, if a domain name under a microservice is found to be taking an unusual amount of time, the time distribution along the microservice-shard-domain dimension can be queried. Fourth-level data is the most detailed data layer, containing detailed information about job bars, servers, URLs, and Nginx servers, allowing for in-depth analysis of the specific time-consuming paths of requests within the system. Figure 6 The arrows in the diagram indicate data flow, demonstrating the flow and association of information from level 1 to level 4 data. The primary multi-layer performance data association diagram prunes meaningless dimensions to generate a target performance distribution diagram. Time consumption distribution is analyzed across multiple dimensions (such as microservices, domain names, and job bars). For example, you can use "microservice | domain name" or "microservice - domain name | job bar" to generate statistical output for domain name time consumption distribution. During analysis, the data flow in the diagram can be used to cross-validate the analysis conclusions. For example, if a job bar on a microservice + server is found to be taking an unusual amount of time, you can use the data hierarchy diagram to trace back and view the time consumption distribution of the links on that microservice + server.

[0136] Through the above scheme, this embodiment provides an intuitive visualization tool by generating a primary multi-layer performance data association diagram through a directed graph to help analysts understand data flow and performance bottlenecks; the clipping process removes unnecessary information, making the target performance distribution diagram more concise and facilitating quick identification of key performance indicators.

[0137] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the performance analysis method of the system link of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0138] This application also provides a performance analysis device for a system link, please refer to Figure 7 , the performance analysis device of the system link includes:

[0139] A first acquisition and comparison module 701 is configured to acquire system performance data of a system link, compare the system performance data with a preset system performance warning threshold, and obtain a first comparison result of the system performance data;

[0140] An abnormal service determination module 702 is configured to determine an abnormal server in the system link based on the first comparison result, and obtain microservice performance data corresponding to the abnormal server;

[0141] The abnormal information determination module 703 is used to analyze and locate the microservice performance data according to a preset microservice performance warning threshold to determine abnormal performance information of the system link;

[0142] The analysis report generating module 704 is configured to perform layer-by-layer backcheck on the system link based on the abnormal performance information and generate a performance analysis report of the system link.

[0143] The system link performance analysis device provided in this application utilizes the system link performance analysis method of the above-mentioned embodiment to resolve the technical problem of low efficiency in system link performance analysis. Compared with the prior art, the system link performance analysis device provided in this application has the same beneficial effects as the system link performance analysis method provided in the above-mentioned embodiment. Other technical features of the system link performance analysis device are the same as those disclosed in the above-mentioned embodiment and are not further described here.

[0144] The present application provides a performance analysis device for a system link, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the performance analysis method for the system link in the above-mentioned embodiment 1.

[0145] Reference below Figure 8 , which shows a schematic diagram of the structure of a performance analysis device for a system link suitable for implementing an embodiment of the present application. The performance analysis device for a system link in an embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8The performance analysis device of the system link shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0146] like Figure 8 As shown, the system link performance analysis device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory 1002 or programs loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the system link performance analysis device. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems may be connected to the input / output interface 1006: an input device 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, hard disk, etc.; and a communication device 1009. Communication device 1009 can allow the performance analysis device of the system link to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows the performance analysis device of the system link with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented or have alternatively.

[0147] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are performed.

[0148] The system link performance analysis device provided in this application utilizes the system link performance analysis method of the above-described embodiment to resolve the technical issue of low system link performance analysis efficiency. Compared to the prior art, the system link performance analysis device provided in this application has the same beneficial effects as the system link performance analysis method of the above-described embodiment. Other technical features of the system link performance analysis device are the same as those disclosed in the above-described embodiment and are not further elaborated here.

[0149] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0150] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0151] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer program) stored thereon, wherein the computer-readable program instructions are used to execute the performance analysis method of the system link in the above embodiment.

[0152] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0153] The computer-readable storage medium may be included in the performance analysis device of the system link; or may exist independently without being assembled into the performance analysis device of the system link.

[0154] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the performance analysis device of the system link, the performance analysis device of the system link: obtains system performance data of the system link, compares the system performance data with a preset system performance warning threshold, and obtains a first comparison result of the system performance data; determines an abnormal server in the system link based on the first comparison result, and obtains microservice performance data corresponding to the abnormal server; analyzes and locates the microservice performance data based on a preset microservice performance warning threshold, and determines abnormal performance information of the system link; performs layer-by-layer backcheck on the system link based on the abnormal performance information, and generates a performance analysis report of the system link.

[0155] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0156] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0157] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0158] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned system link performance analysis method, thereby resolving the technical issue of low efficiency in system link performance analysis. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the system link performance analysis method provided in the aforementioned embodiment, and are not further elaborated here.

[0159] The present application also provides a computer program product, including a computer program, which implements the steps of the performance analysis method of the system link as described above when the computer program is executed by a processor.

[0160] The computer program product provided in this application can solve the technical problem of low efficiency in performance analysis of system links. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the performance analysis method of the system link provided in the above embodiment, and will not be repeated here.

[0161] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A performance analysis method for a system link, characterized in that: The performance analysis method of the system link includes: Acquire system performance data of the system link, compare the system performance data with a preset system performance warning threshold, and obtain a first comparison result of the system performance data; Determine, based on the first comparison result, an abnormal server in the system link, and obtain microservice performance data corresponding to the abnormal server; Analyze and locate the microservice performance data according to a preset microservice performance warning threshold to determine abnormal performance information of the system link; The system link is backchecked layer by layer based on the abnormal performance information to generate a performance analysis report of the system link.

2. The performance analysis method of a system link according to claim 1, wherein: The step of acquiring system performance data of the system link, comparing the system performance data with a preset system performance warning threshold, and obtaining a first comparison result of the system performance data includes: Determining a system performance indicator of the system link, and obtaining system performance data of the system link according to the system performance indicator; A corresponding system performance warning threshold is preset according to the system data boundary value of the system link, the system performance data is compared with the corresponding system performance warning threshold, and a first comparison result of the system performance data is generated.

3. The performance analysis method of a system link according to claim 1, wherein: The step of determining an abnormal server in the system link according to the first comparison result and obtaining microservice performance data corresponding to the abnormal server includes: If the system performance data is greater than the corresponding system performance warning threshold, determining an abnormal server in the system link according to the system performance data; Obtaining server performance data of the abnormal server according to the server performance indicator of the server; The server performance data of the abnormal server is distributed and counted according to the target distribution to obtain the microservice performance data of the abnormal server.

4. The performance analysis method of a system link according to claim 1, wherein: The step of analyzing and locating the microservice performance data according to a preset microservice performance warning threshold to determine abnormal performance information of the system link includes: Preset the corresponding microservice performance warning threshold according to the microservice data boundary value of the microservice; Comparing the microservice performance data with the microservice performance warning threshold, and if the microservice performance data is greater than the microservice performance warning threshold, determining the abnormal microservice in the abnormal server according to the microservice performance data; The abnormal microservice is analyzed and located according to the microservice performance data to determine abnormal performance information of the system link.

5. The performance analysis method of a system link according to claim 4, characterized in that: The step of analyzing and locating the abnormal microservice according to the microservice performance data to determine the abnormal performance information of the system link includes: Performing distribution statistics on the microservice performance data of the abnormal microservice to generate detailed distribution data of the microservice performance data; Based on the preset classification performance problems, performance analysis and abnormality location are performed on the distribution detailed data to determine abnormal performance information of the system link.

6. The performance analysis method of a system link according to claim 5, characterized in that: The distribution detail data includes the request volume, request duration, and application-side requests; the abnormal performance information includes the abnormal problem, abnormal cause, and abnormal location; and the steps of performing performance analysis and abnormal location on the distribution detail data to determine the abnormal performance information of the system link include: Divide the request time into time intervals to obtain corresponding time intervals, and count the number of requests and application-side requests corresponding to the time intervals; Based on the request duration and the request volume in the corresponding interval, the abnormal microservice is subjected to performance analysis and abnormal location, and the abnormal problem and abnormal location of the system link are determined; Based on the request duration and the application-side request in the corresponding interval, the cause of the abnormal problem is located to determine the abnormal cause of the abnormal problem.

7. The performance analysis method of a system link according to any one of claims 1 to 6, characterized in that: The performance analysis method of the system link also includes: Performing hierarchical division on the system links to obtain a multi-layer distribution of the system links; Create a directed graph based on the system performance data, the server performance data, the microservice performance data, and the multi-layer distribution to generate a primary multi-layer performance data association graph of the system link; The primary multi-layer performance data association graph is cropped to generate a target multi-layer performance data association graph of the system link, and the target multi-layer performance data association graph is used to view and analyze the performance distribution of the system link.

8. A performance analysis device for a system link, characterized in that: The performance analysis device of the system link includes: a first acquisition and comparison module, configured to acquire system performance data of a system link, compare the system performance data with a preset system performance warning threshold, and obtain a first comparison result of the system performance data; an abnormal service determination module, configured to determine an abnormal server in the system link based on the first comparison result, and obtain microservice performance data corresponding to the abnormal server; An abnormal information determination module is used to analyze and locate the microservice performance data according to a preset microservice performance warning threshold to determine abnormal performance information of the system link; The analysis report generating module is used to perform layer-by-layer backcheck on the system link based on the abnormal performance information and generate a performance analysis report of the system link.

9. A performance analysis device for a system link, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the performance analysis method for a system link according to any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the performance analysis method of the system link according to any one of claims 1 to 7 are implemented.

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