Performance monitoring method and device of cache system, electronic equipment and storage medium

Abstract

The present disclosure provides a cache system performance monitoring method and device, electronic equipment and storage medium, which can be applied to the field of intelligent operation and maintenance, the field of computer cache technology, the field of finance or other fields. The cache system performance monitoring method is applied to a redis cache system, and the method comprises the following steps: copying data traffic from a physical network card to generate first data traffic and second data traffic; sending the first data traffic to a redis server and sending the second data traffic to a data analysis module; obtaining feedback information of the redis server for the first data traffic; analyzing the second data traffic by the data analysis module to determine at least one abnormal data; analyzing the feedback information and the at least one abnormal data according to a preset analysis model to determine a performance state of the redis cache system, and the performance state comprises normal and abnormal.

Description

Technical Field

[0001] This disclosure relates to the field of computer caching technology, and more specifically to a method, apparatus, electronic device, and readable storage medium for monitoring the performance of a caching system. Background Technology

[0002] In computer application software architecture, caching systems are often used as a node to improve the software's data processing performance and enhance processing efficiency. However, the performance of a caching system is easily affected by the data itself. For example, in a Redis-based caching system, large data values ​​can lead to excessively long response times, causing timeouts and other issues for the software's business functions. Conversely, frequent access to a particular data point can overwhelm the caching system, causing the database to crash due to insufficient capacity and resulting in business anomalies.

[0003] In related technologies, monitoring the performance of a caching system requires modifications to the system, increasing costs and raising the technical threshold, or it may require reading and collecting data from the system, leading to a decrease in system performance. Summary of the Invention

[0004] In view of the above problems, this disclosure provides a performance monitoring method, apparatus, electronic device, and storage medium for a caching system. The performance monitoring method for a caching system, applied to a Redis caching system, includes, but is not limited to: replicating data traffic from a physical network interface card to generate a first data traffic and a second data traffic; sending the first data traffic to a Redis server and the second data traffic to a data analysis module; obtaining feedback information from the Redis server regarding the first data traffic; analyzing the second data traffic through the data analysis module to identify at least one abnormal data point; and analyzing the feedback information and the at least one abnormal data point according to a preset analysis model to determine the performance status of the Redis caching system, whereby the performance status includes normal and abnormal.

[0005] In some exemplary embodiments of this disclosure, the step of analyzing the second data traffic through the data analysis module to determine at least one abnormal data includes: determining identical data in the second data traffic based on the data characteristics of each data in the second data traffic; obtaining the number of identical data sent within a preset time period and determining the access frequency of the identical data; and determining the identical data whose access frequency exceeds the preset frequency as abnormal data.

[0006] In some exemplary embodiments of this disclosure, determining identical data in the second data traffic based on the data characteristics of each data in the second data traffic includes: acquiring the data characteristics of each data in the second data traffic; determining a data characteristic similarity value between data based on the data characteristics; and determining data whose data characteristic similarity value exceeds a preset threshold as identical data.

[0007] In some exemplary embodiments of this disclosure, the step of analyzing the second data traffic through the data analysis module to determine at least one abnormal data includes: obtaining the data volume value of each data in the second data traffic, and determining data whose data volume value exceeds a preset data volume value as abnormal data.

[0008] In some exemplary embodiments of this disclosure, the step of analyzing the feedback information and the at least one abnormal data according to a preset analysis model to determine the performance status of the Redis caching system includes: inputting the feedback information and at least one abnormal data acquired in a first time period into the preset analysis model to generate a first analysis result; generating a second analysis result for at least one first analysis result acquired in a second time period, wherein the first time period is within the second time period; and determining the performance status of the Redis caching system based on the second analysis result.

[0009] In some exemplary embodiments of this disclosure, generating a second analysis result for at least one first analysis result acquired in the second time period includes: acquiring at least one first analysis result in the second time period; counting the number and interval of first analysis results that meet preset conditions within the second time period; and generating a second analysis result based on the number and interval of first analysis results that meet preset conditions within the second time period.

[0010] In some exemplary embodiments of this disclosure, the performance monitoring method further includes: training the preset analysis model, which includes a support vector machine, before analyzing the feedback information and the at least one abnormal data according to the preset analysis model.

[0011] In some exemplary embodiments of this disclosure, the performance monitoring method further includes: after determining that the performance state of the Redis caching system is abnormal, performing rate limiting and / or traffic splitting on the data traffic from the physical network card; and / or parsing the data traffic from the physical network card and displaying the parsing results to the target object.

[0012] A second aspect of this disclosure provides a performance monitoring device for a caching system, applied to a Redis caching system. The performance monitoring device includes: a generation module configured to replicate data traffic from a physical network interface card (NIC) to generate a first data traffic and a second data traffic; a sending module configured to send the first data traffic to a Redis server and the second data traffic to a data analysis module; an acquisition module configured to acquire feedback information from the Redis server regarding the first data traffic; a first determination module configured to analyze the second data traffic through the data analysis module to determine at least one abnormal data point; and a second determination module configured to analyze the feedback information and the at least one abnormal data point according to a preset analysis model to determine the performance status of the Redis caching system, wherein the performance status includes normal and abnormal states.

[0013] In some exemplary embodiments of this disclosure, the first determining module includes: a first determining unit configured to determine identical data in the second data traffic based on the data characteristics of each data in the second data traffic; a second determining unit configured to obtain the number of identical data sent within a preset time period and determine the access frequency of the identical data; and a third determining unit configured to determine identical data whose access frequency exceeds a preset frequency as abnormal data.

[0014] In some exemplary embodiments of this disclosure, the first determining unit includes: a first determining submodule, the first determining submodule being configured to: acquire data features of each data in the second data traffic; determine a data feature similarity value between data based on the data features; and determine data whose data feature similarity value exceeds a preset threshold as identical data.

[0015] In some exemplary embodiments of this disclosure, the first determining module further includes a fourth determining unit, which is configured to: obtain the data volume value of each data in the second data traffic, and determine data whose data volume value exceeds a preset data volume value as abnormal data.

[0016] In some exemplary embodiments of this disclosure, the second determining module includes: a first generating unit configured to input the feedback information and at least one abnormal data acquired in a first time period into the preset analysis model to generate a first analysis result; a second generating unit configured to generate a second analysis result for at least one first analysis result acquired in a second time period, wherein the first time period is located within the second time period; and a fifth determining unit configured to determine the performance status of the Redis caching system based on the second analysis result.

[0017] In some exemplary embodiments of this disclosure, the second generation unit includes: a first acquisition submodule configured to acquire at least one first analysis result in a second time period; a statistics module configured to count the number and interval of first analysis results that meet preset conditions in the second time period; and a first generation submodule configured to generate a second analysis result based on the number and interval of first analysis results that meet preset conditions in the second time period.

[0018] In some exemplary embodiments of this disclosure, the performance monitoring device further includes: a training module configured to train the preset analysis model, which includes a support vector machine, before analyzing the feedback information and the at least one abnormal data according to the preset analysis model.

[0019] In some exemplary embodiments of this disclosure, the performance monitoring device further includes: a processing and display module, configured to, after determining that the performance status of the Redis caching system is abnormal, perform rate limiting and / or traffic splitting on the data traffic from the physical network card; and / or, parse the data traffic from the physical network card and display the parsing results to the target object.

[0020] A third aspect of this disclosure provides an electronic device comprising: one or more processors; and a storage device for storing executable instructions that, when executed by the processor, implement the method described above.

[0021] A fourth aspect of this disclosure provides a computer-readable storage medium having executable instructions stored thereon that, when executed by a processor, implement the method described above.

[0022] A fifth aspect of this disclosure provides a computer program product including a computer program that, when executed by a processor, implements the method described above.

[0023] According to embodiments of this disclosure, by replicating data traffic and analyzing the replicated second data traffic, no modifications to the Redis server are required, and the performance of the Redis server is not affected. By analyzing feedback information and at least one abnormal data according to a preset analysis model, the performance status of the Redis caching system can be determined. This enables effective and rapid monitoring of the Redis caching system, reduces costs, and improves the accuracy of performance status monitoring. Attached Figure Description

[0024] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0025] Figure 1 The diagram illustrates a system architecture for a performance monitoring method of a caching system to which embodiments of the present disclosure can be applied.

[0026] Figure 2 A flowchart illustrating a performance monitoring method for a caching system according to an embodiment of the present disclosure is shown schematically.

[0027] Figure 3 This illustration schematically shows a process diagram of a performance monitoring method for a caching system according to an embodiment of the present disclosure;

[0028] Figure 4 This schematically illustrates a flowchart of operation S240 of a performance monitoring method for a caching system according to an embodiment of the present disclosure;

[0029] Figure 5 This schematically illustrates a flowchart of operation S241 of a performance monitoring method for a caching system according to an embodiment of the present disclosure;

[0030] Figure 6 This schematically illustrates a flowchart of operation S240 of a performance monitoring method for a caching system according to another embodiment of the present disclosure;

[0031] Figure 7 This schematically illustrates a flowchart of operation S250 of a performance monitoring method for a caching system according to an embodiment of the present disclosure;

[0032] Figure 8 This schematically illustrates a flowchart of the performance monitoring method for a caching system according to an embodiment of the present disclosure during operation S252;

[0033] Figure 9 This schematically illustrates a flowchart of operation S260 of a performance monitoring method for a caching system according to an embodiment of the present disclosure;

[0034] Figure 10 This schematically illustrates a flowchart of operation S270 of a performance monitoring method for a caching system according to an embodiment of the present disclosure;

[0035] Figure 11 A schematic diagram illustrating a structural block diagram of a performance monitoring apparatus for a caching system according to an embodiment of the present disclosure; and

[0036] Figure 12 A block diagram of an electronic device suitable for implementing a performance monitoring method for a caching system according to an embodiment of the present disclosure is shown schematically. Detailed Implementation

[0037] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0038] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0039] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0040] When using expressions such as "at least one of A, B, and C", they should generally be interpreted in accordance with the meaning that is commonly understood by a person skilled in the art (e.g., "a system having at least one of A, B, and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B, and C, etc.).

[0041] In the technical solution disclosed herein, the acquisition, storage, and application of user personal information comply with the provisions of relevant laws and regulations, necessary confidentiality measures have been taken, and it does not violate public order and good morals.

[0042] In the technical solution disclosed herein, all operations involving the acquisition, storage, and application of user personal information have been authorized by the user.

[0043] In the technical solutions disclosed herein, the term "redis caching system" refers to a caching system composed of data structure servers, where Redis (REmote Dictionary Server, abbreviated as redis) is a cross-platform non-relational database.

[0044] To address the issues of increased costs and reduced performance caused by performance monitoring of caching systems in related technologies, this disclosure provides a method, apparatus, electronic device, readable storage medium, and computer program product for performance monitoring of caching systems. This method enables effective monitoring of caching system performance without affecting its overall performance. The performance monitoring method of this disclosure is applied to a Redis caching system. The method includes, but is not limited to: replicating data traffic from a physical network interface card to generate a first data traffic and a second data traffic; sending the first data traffic to a Redis server and the second data traffic to a data analysis module; obtaining feedback information from the Redis server regarding the first data traffic; analyzing the second data traffic through the data analysis module to identify at least one abnormal data point; and analyzing the feedback information and the at least one abnormal data point according to a preset analysis model to determine the performance status of the Redis caching system, wherein the performance status includes normal and abnormal.

[0045] According to embodiments of this disclosure, by replicating data traffic and analyzing the replicated second data traffic, no modifications to the Redis server are required, and the performance of the Redis server is not affected. By analyzing feedback information and at least one abnormal data according to a preset analysis model, the performance status of the Redis caching system can be determined. This enables effective and rapid monitoring of the Redis caching system, reduces costs, and improves the accuracy of performance status monitoring.

[0046] Figure 1 This illustration schematically depicts a system architecture diagram of a performance monitoring method for a caching system according to an embodiment of the present disclosure. It should be noted that the performance monitoring method, apparatus, electronic device, and computer-readable storage medium for a caching system provided in this disclosure can be applied to aspects of intelligent operation and maintenance, computer caching technology, and the financial field, and can also be used in other fields outside the financial field. The performance monitoring method, apparatus, electronic device, and storage medium for a caching system provided in this disclosure are not limited to any particular application area.

[0047] like Figure 1 As shown, the system architecture 100 according to this embodiment may include terminal devices 101 and 102, network 103, load balancer 104, and database server 105.

[0048] Network 103 serves as a medium for providing communication links between terminal devices 101 and 102, load balancer server 104, and database server 105. Network 103 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc.

[0049] Users can use terminal devices 101 and 102 to interact with the load balancer server 104 and the database server 105 via network 103 to receive or send data, instructions, etc. Various communication client applications can be installed on terminal devices 101 and 102, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only). Terminal devices 101 and 102 are used to retrieve data from the database server 105 or send data requests to the database server 105.

[0050] Terminal devices 101 and 102 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0051] The load balancer server 104 can be an Nginx, LVS, HAProxy, or other load balancer servers. It can be a software load balancer or a hardware load balancer. Software load balancing can be achieved by installing one or more additional software programs on the corresponding operating systems of one or more servers, such as DNS LoadBalance or CheckPoint Firewall-1 ConnectControl. Alternatively, a load balancing device can be installed directly between the server and the external network, with dedicated equipment performing specific tasks and intelligently managing traffic.

[0052] Database server 105 can be a server that provides various database services, such as a database server that provides data support for the websites browsed by users using terminal devices 101 and 102 (this is just an example). The database server can analyze data such as received user requests, and, according to the user's request, feed back data from the data service database server (such as web pages, information, or data obtained or generated according to the user's request) to the terminal device.

[0053] It should be noted that the performance monitoring method for the caching system provided in this embodiment can generally be executed by terminal devices 101 and 102. Correspondingly, the performance monitoring device for the caching system provided in this embodiment can generally be located in terminal devices 101 and 102. The performance monitoring method for the caching system provided in this embodiment can also be executed by other terminal devices or servers different from terminal devices 101 and 102 and capable of communicating with terminal devices 101 and 102 and / or load balancing server 104 and database server 105. Correspondingly, the performance monitoring device for the caching system provided in this embodiment can also be located in other terminal devices or servers different from terminal devices 101 and 102 and capable of communicating with terminal devices 101 and 102 and / or load balancing server 104 and database server 105.

[0054] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0055] The following will be through Figures 2 to 10 The performance monitoring method of the caching system according to the disclosed embodiments is described in detail.

[0056] Figure 2 A flowchart illustrating a performance monitoring method for a caching system according to an embodiment of the present disclosure is shown.

[0057] like Figure 2 As shown, the flow 200 of the performance monitoring method for the caching system disclosed herein includes operations S210 to S250.

[0058] In operation S210, data traffic from the physical network card is copied to generate first data traffic and second data traffic.

[0059] In operation S220, the first data traffic is sent to the Redis server, and the second data traffic is sent to the data analysis module.

[0060] In operation S230, obtain feedback information from the Redis server regarding the first data traffic.

[0061] In operation S240, the second data flow is analyzed by the data analysis module to identify at least one abnormal data.

[0062] In operation S250, the feedback information and at least one abnormal data are analyzed according to the preset analysis model to determine the performance status of the Redis caching system, which includes normal and abnormal performance status.

[0063] The following example illustrates the flow of each operation in the performance monitoring method of the caching system in this embodiment.

[0064] For example, data traffic from a physical network interface card is copied to generate a first data traffic and a second data traffic.

[0065] In embodiments of this disclosure, data traffic from a physical network interface card (NIC) may be data traffic forwarded from the physical NIC of a load balancer server, or data traffic forwarded from the physical NIC of another server.

[0066] Data traffic replication can be performed, for example, by replicating all data traffic originating from the physical network interface card (NIC). The first and second data traffic streams are identical and identical to the data traffic originating from the physical NIC.

[0067] According to embodiments of this disclosure, data traffic from the physical network interface card is replicated without requiring modifications to the Redis server; the data stream is directly replicated, lowering the technical barrier to device modification. Furthermore, no data intrusion is required on the client or server, thus maintaining security and not affecting the system performance of the client or server.

[0068] In the embodiments disclosed herein, the first data traffic is sent to the Redis server, and the second data traffic is sent to the data analysis module.

[0069] For example, after the Redis server obtains the first data traffic, it can analyze and process the first data traffic and generate feedback information during the processing. By sending the first data traffic to the Redis server, the normal service will not be affected.

[0070] The second data traffic is sent to the data analysis module. The data analysis module analyzes the second data traffic to obtain the data information in the second data traffic. During the analysis process, since the data in the first data traffic and the second data traffic are the same, it can accurately analyze the second data traffic without affecting the normal operation of the Redis server.

[0071] In the embodiments of this disclosure, feedback information from the Redis server regarding the first data traffic is obtained.

[0072] Feedback information can be the response time of the operation performed based on the first data flow. For example, after receiving the first data flow, the Redis server analyzes the data in the first data flow and retrieves data from a database such as MySQL based on the instructions and other information contained in the first data flow. Upon receiving the first data flow, a first time is generated; after the Redis server retrieves the data from the database such as MySQL, a second time is generated. The response time of the Redis server can be, for example, the time difference between the second time and the first time.

[0073] In embodiments of this disclosure, the second data traffic is analyzed by a data analysis module to identify at least one abnormal data.

[0074] By identifying at least one anomalous data point in the second data flow, we can determine the potential problems that may exist during the operation of the Redis caching system based on that anomalous data.

[0075] The second data traffic is the same as the first data traffic. By analyzing the second data traffic, at least one abnormal data in the second data traffic can be identified. Based on the identified at least one abnormal data, the performance status of the Redis caching system can be determined and an early warning can be issued. Accurate monitoring and early warning of the performance status can be achieved without affecting the operation of the Redis caching system service.

[0076] In the embodiments of this disclosure, feedback information and at least one abnormal data are analyzed according to a preset analysis model to determine the performance status of the Redis caching system, which includes normal and abnormal performance status.

[0077] For example, a preset analysis model analyzes feedback information and at least one abnormal data point to classify the performance status of the Redis caching system in order to determine whether the Redis caching system is in a normal or abnormal state.

[0078] The feedback information is the actual feedback information obtained by the Redis server based on the first data flow. At least one abnormal data point is obtained through analysis of the second data flow, which is the same as the first data flow. Based on the feedback information and at least one abnormal data point, the performance status of the Redis caching system can be accurately determined without affecting the operation of the Redis server.

[0079] For example, the preset analysis model could be a Support Vector Machine (SVM). SVM is a classification algorithm that separates data into two parts by finding a classifying plane. The performance status of the Redis caching system is then determined based on the feedback information and at least one outlier.

[0080] Figure 3 The illustration shows a process diagram of a performance monitoring method for a caching system according to an embodiment of the present disclosure.

[0081] The following is combined with Figure 3 The information content in this document provides a detailed description of the performance monitoring method of the caching system in this embodiment.

[0082] For example, data traffic 301 originates from a physical network interface card, such as data traffic sent by a load balancer or other server. This data traffic is used to send data to the Redis server for operations such as data retrieval.

[0083] After acquiring data traffic 301, the data traffic is copied to generate first data traffic 302 and second data traffic 303. The data content contained in first data traffic 302 and second data traffic 303 is exactly the same.

[0084] The first data traffic 302 is sent to the Redis server 304. The Redis server 304 processes the first data traffic 302 and generates feedback information 306 for the first data traffic 302.

[0085] The second data traffic 303 is sent to the data analysis module 305, which analyzes the second data traffic 303 and identifies at least one abnormal data 307 in the second data traffic.

[0086] The acquired feedback information 306 and at least one abnormal data point 307 are input into a preset analysis model 308. The preset analysis model 308 analyzes the feedback information 306 and at least one abnormal data point 307 to determine the performance status 309 of the Redis caching system. The performance status of the Redis caching system includes normal and abnormal states. When the performance status of the Redis caching system is determined to be abnormal, the Redis caching system is downgraded according to the abnormal status to reduce the data traffic processed by the Redis caching system and prevent the Redis caching system from failing.

[0087] Figure 4 The flowchart illustrating the performance monitoring method of a caching system according to an embodiment of the present disclosure during operation S240 is shown.

[0088] like Figure 4 As shown, the performance monitoring method of this embodiment includes operations S241 to S243 in operation S240.

[0089] The following example illustrates the flow of each operation in operation S240 of the performance monitoring method of the caching system in this embodiment.

[0090] In operation S241, the same data in the second data flow is determined based on the data characteristics of each data in the second data flow.

[0091] In operation S242, the number of identical data sent within a preset time period is obtained, and the access frequency of the identical data is determined.

[0092] In operation S243, identical data whose access frequency exceeds a preset frequency is identified as abnormal data.

[0093] For example, the second data traffic includes data features of each data item, and the same data in the second data traffic is identified by extracting or analyzing the data features in the second data traffic.

[0094] Data characteristics include, for example, the size of data in the second data flow, user behavior characteristics, and user characteristic indicators. By analyzing the data characteristics of each data point in the data flow, the common data in the second data flow can be identified.

[0095] After identifying the identical data in the second data traffic, the number of identical data items contained in the second data traffic sent within a preset time period is obtained. For example, the preset time period can be a time period set according to user needs, such as 1 second or 5 seconds. After counting the number of identical data items within the preset time period, the access frequency of the identical data is determined based on the preset time period and the number of identical data items.

[0096] After determining the access frequency of identical data, it is compared with the preset frequency of the maximum access frequency that the Redis caching system can tolerate. If the access frequency of identical data exceeds the preset frequency, it indicates that the access frequency of identical data is too high, exceeding the tolerance range of the caching system, and may cause Redis caching system failure. Identical data with access frequencies exceeding the preset frequency is identified as abnormal data. Based on the identified abnormal data, a preset analysis model is used for analysis to comprehensively determine the performance status of the Redis caching system.

[0097] In the embodiments of this disclosure, in the Redis caching system, when the access frequency of the same data exceeds a preset frequency, the data can be identified as hot data. These hot data or hot data that is identified as abnormal data in a short period of time suddenly have a large number of concurrent access requests, which will cause a large number of concurrent requests to directly penetrate the Redis server and flood into the MySQL database, instantly increasing the access pressure on the database, and even causing the database to crash.

[0098] According to embodiments of this disclosure, a data analysis module analyzes the second data traffic to identify at least one abnormal data point. This abnormal data point can be hot data or non-hot data, but the non-hot data point experiences an unusually large number of concurrent query requests within a short period of time. Based on the identified at least one abnormal data point and the feedback information from the Redis server regarding the first data traffic, the performance of the Redis caching system is analyzed, improving the accuracy of performance status assessment for the Redis caching system.

[0099] Figure 5 The flowchart illustrating the performance monitoring method of the caching system according to an embodiment of the present disclosure during operation S241 is shown.

[0100] like Figure 5 As shown, the performance monitoring method of this embodiment includes operations S310 to S330 in operation S241.

[0101] The following example illustrates the flow of each operation in operation S241 of the performance monitoring method of the caching system in this embodiment.

[0102] In operation S310, the data characteristics of each data point in the second data flow are obtained.

[0103] In operation S320, the similarity value of data features between data is determined based on data characteristics.

[0104] In operation S330, data whose data feature similarity value exceeds a preset threshold are identified as identical data.

[0105] In embodiments of this disclosure, the data characteristics of each data in the second data traffic can be obtained, for example, by obtaining the data characteristics of each data within a set time period, or by obtaining the data characteristics of the data after each data transmission is completed.

[0106] The data characteristics of each acquired data point can include, for example, website traffic metrics (such as visit count / user session count, page request count, unique visitor count, page view count, etc.), user characteristic metrics (such as the browser used by the user, the user's domain name and hosting, the user's computer operating system, etc.), and user behavior characteristic metrics (such as the user's inbound path, the user's inbound page, the user's frequently used browsing paths on the site, the dwell time for each visit, the user's exit page, etc.).

[0107] Based on the data characteristics, determine the data feature similarity value between the data in the second data flow. For example, it can be determined based on the cosine similarity value.

[0108] After determining the similarity value between data in the second data flow, it is compared with a preset threshold, and data whose data feature similarity value exceeds the set threshold is identified as the same data.

[0109] According to embodiments of this disclosure, by analyzing the data in the second data traffic, the data in the data traffic can be analyzed and the same data in the second data traffic can be identified without affecting the normal operation and performance of the Redis server, which facilitates the determination of the performance status of the Redis caching system in subsequent programs.

[0110] Figure 6 The flowchart illustrating the performance monitoring method of a caching system according to another embodiment of the present disclosure during operation S240 is shown.

[0111] like Figure 6 As shown, the performance monitoring method of this embodiment may further include operation S400 in the process of operation S240.

[0112] In operation S400, the data volume value of each data in the second data flow is obtained, and data with a data volume value exceeding the preset data volume value is identified as abnormal data.

[0113] In embodiments of this disclosure, abnormal data also includes data whose data value exceeds a preset data value. For example, if the data value of a certain data in the second data traffic is very large, exceeding the preset data value, the large data value will cause the response time of the Redis caching system to be longer, thereby affecting the normal business functions of the Redis caching system and reducing the processing efficiency of the Redis caching system.

[0114] The embodiments of this disclosure analyze the data volume value of each data in the second data traffic to determine that data with a data volume value exceeding a preset data volume value is abnormal data, which facilitates the determination and judgment of the performance status of the Redis caching system according to the preset analysis model.

[0115] Figure 7 The flowchart illustrating the performance monitoring method of the caching system according to an embodiment of the present disclosure during operation S250 is shown.

[0116] like Figure 7 As shown, the performance monitoring method of this embodiment may further include operations S251 to S253 in the process of operation S250.

[0117] The following example illustrates the flow of each operation in operation S250 of the performance monitoring method of the caching system in this embodiment.

[0118] In operation S251, the feedback information from the first time period and at least one abnormal data are input into the preset analysis model to generate the first analysis result.

[0119] In the embodiments of this disclosure, feedback information and at least one abnormal data point within a first time period are obtained. This feedback information and at least one abnormal data point within the first time period are then input into a preset analysis model to generate a first analysis result. This first analysis result indicates whether the performance status of the Redis caching system is normal or abnormal within the first time period.

[0120] In the embodiments of this disclosure, the first analysis result is based on the feedback information obtained in the first time period and at least one abnormal data, and the feedback information and abnormal data are used as inputs to a preset analysis model. The preset analysis model generates the first analysis result based on the input content.

[0121] In operation S252, a second analysis result is generated for at least one first analysis result obtained in the second time period, wherein the first time period is located within the second time period.

[0122] In this implementation, after obtaining the first analysis result within a first time period, the analysis result within the first time period cannot be directly used as a direct indicator to judge the performance status of the Redis caching system. By obtaining at least one first analysis result within a second time period, the performance status of the Redis caching system can be judged more accurately.

[0123] For example, the first time period is located within the second time period. For instance, the second time period includes 10 first time periods, and the second analysis result can be generated based on each of the 10 first analysis results obtained within the first time periods.

[0124] The second analysis result could be, for example, the number of normal performance states and the frequency of abnormal performance states in the first analysis result within the second time period, and the second analysis result could be generated based on the statistical results.

[0125] In operation S253, the performance status of the Redis caching system is determined based on the results of the second analysis.

[0126] In the embodiments of this disclosure, the second analysis result is generated based on the first analysis result within a second time period. The first time period is located within the second time period, enabling the performance status of the Redis caching system at different points in time to comprehensively judge the status of the Redis caching system in the second time period, resulting in a more accurate judgment result.

[0127] According to embodiments of this disclosure, the normal state of the Redis caching system in the first time period does not fully reflect its normal state in the second time period. By first determining the first analysis result of the first time period and the second analysis result of the second time period, and then jointly judging the performance status of the entire Redis cache based on the analysis results of the first and second time periods, abnormal issues in a short period of time can be effectively eliminated, improving the accuracy of performance status judgment.

[0128] Figure 8 The flowchart illustrating the performance monitoring method of the caching system according to an embodiment of the present disclosure during operation S252 is shown.

[0129] like Figure 8 As shown, the performance monitoring method of this embodiment may further include operations S510 to S530 in the process of operation S252.

[0130] The following example illustrates the flow of each operation in operation S252 of the performance monitoring method of the caching system in this embodiment.

[0131] In operation S510, at least one first analysis result for the second time period is obtained.

[0132] In operation S520, the number of first analysis results that meet the preset conditions and the interval time are counted within the second time period.

[0133] In operation S530, a second analysis result is generated based on the number of first analysis results that meet preset conditions and the interval time within the second time period.

[0134] In embodiments of this disclosure, the first analysis result may be, for example, determining the performance status of the Redis caching system in a first time period, including normal and abnormal performance status.

[0135] The second time period may include at least one first time period, and the first analysis results obtained for the second time period may also include one or more.

[0136] The number and time interval of the first analysis results within the second time period are counted. For example, if the second time period contains 10 first time periods, the number of first analysis results that are normal within the second time period is counted. If the number of normal performance states exceeds 5, and the number of consecutive first analysis results with abnormal performance states is less than 3, then the performance state of the Redis caching system is judged to be normal based on the second analysis results. The numbers in this embodiment exemplify that the second analysis results are generated based on the number and interval of the first analysis results. This disclosure does not specifically limit the specific number and interval, and can be adjusted according to actual needs.

[0137] Figure 9 The flowchart illustrating the performance monitoring method of the caching system according to an embodiment of the present disclosure during operation S260 is shown.

[0138] like Figure 9 As shown, the performance monitoring method of this disclosure embodiment may further include operation S260.

[0139] In operation S260, before analyzing the feedback information and at least one abnormal data according to the preset analysis model, the preset analysis model is trained. The preset analysis model includes a support vector machine.

[0140] In the embodiments of this disclosure, the data in the training set, validation set, and test set used to train the preset analysis model are based on abnormal data from the actual Redis caching system (e.g., data whose data volume exceeds a preset data volume value, or identical data whose access frequency exceeds a preset frequency) and feedback information. The preset analysis model is trained using the training set, validation set, and test set composed of the abnormal data and feedback information to generate the final preset analysis model. The trained preset analysis model can generate the performance status result of the Redis caching system to be monitored based on the feedback information of the input Redis caching system to be monitored and at least one abnormal data.

[0141] Figure 10 The flowchart illustrating the performance monitoring method of the caching system according to an embodiment of the present disclosure during operation S270 is shown.

[0142] like Figure 10 As shown, the performance monitoring method of this disclosure embodiment may further include operation S270.

[0143] In operation S270, after determining that the performance status of the Redis caching system is abnormal, the data traffic from the physical network card is rate-limited and / or traffic split; and / or the data traffic from the physical network card is parsed and the parsing results are displayed to the target object.

[0144] In the embodiments of this disclosure, after determining that the performance status of the Redis caching system is abnormal, the performance status is fed back to the target object for processing in order to prevent the Redis server from failing.

[0145] For example, this may include rate limiting and / or traffic splitting of data traffic from a physical network interface card, such as by rate limiting or splitting data traffic through a load balancing server.

[0146] For example, data traffic can also be parsed, and the parsing results can be displayed to the target object so that the target object can degrade the service of the Redis caching system or perform other adjustment operations to maintain the normal operation of the Redis caching system based on the parsing results.

[0147] According to embodiments of this disclosure, data traffic from the physical network interface card is replicated without requiring modifications to the Redis server; the data stream is directly replicated, lowering the technical barrier to device modification. Furthermore, no data intrusion is required on the client or server, thus maintaining security and not affecting the system performance of the client or server.

[0148] Figure 11 A schematic block diagram of a performance monitoring apparatus for a caching system according to an embodiment of the present disclosure is shown.

[0149] like Figure 11 As shown, the performance monitoring device 600 of the caching system in this embodiment of the present disclosure includes a generation module 610, a sending module 620, an acquisition module 630, a first determination module 640, and a second determination module 650.

[0150] The generation module 610 is configured to copy data traffic from the physical network interface card (NIC) to generate a first data traffic and a second data traffic. In one embodiment, the generation module 610 is used to perform the operation S210 described above, which will not be repeated here.

[0151] The sending module 620 is configured to send the first data traffic to the Redis server and the second data traffic to the data analysis module. In one embodiment, the sending module 620 is used to perform the operation S220 described above, which will not be repeated here.

[0152] The acquisition module 630 is configured to acquire feedback information from the Redis server regarding the first data traffic. In one embodiment, the acquisition module 630 is used to perform the operation S230 described above, which will not be repeated here.

[0153] The first determining module 640 is configured to analyze the second data traffic through the data analysis module to determine at least one abnormal data. In one embodiment, the first determining module 640 is used to perform the operation S240 described above, which will not be repeated here.

[0154] The second determining module 650 is configured to analyze the feedback information and at least one abnormal data according to a preset analysis model to determine the performance status of the Redis caching system, wherein the performance status includes normal and abnormal. In one embodiment, the second determining module 650 is used to perform the operation S250 described above, which will not be repeated here.

[0155] In some exemplary embodiments of this disclosure, the first determining module includes: a first determining unit configured to determine identical data in the second data traffic based on the data characteristics of each data in the second data traffic; a second determining unit configured to obtain the number of identical data sent within a preset time period and determine the access frequency of identical data; and a third determining unit configured to determine identical data whose access frequency exceeds the preset frequency as abnormal data.

[0156] In some exemplary embodiments of this disclosure, the first determining unit includes: a first determining submodule, the first determining submodule being configured to: acquire data features of each data in the second data traffic; determine data feature similarity values ​​between data based on the data features; and determine data whose data feature similarity values ​​exceed a preset threshold as the same data.

[0157] In some exemplary embodiments of this disclosure, the first determining module further includes a fourth determining unit, which is configured to: obtain the data volume value of each data in the second data traffic, and determine the data whose data volume value exceeds a preset data volume value as abnormal data.

[0158] In some exemplary embodiments of this disclosure, the second determining module includes: a first generating unit configured to input feedback information and at least one abnormal data acquired in a first time period into a preset analysis model to generate a first analysis result; a second generating unit configured to generate a second analysis result for at least one first analysis result acquired in a second time period, wherein the first time period is within the second time period; and a fifth determining unit configured to determine the performance status of the Redis caching system based on the second analysis result.

[0159] In some exemplary embodiments of this disclosure, the second generation unit includes: a first acquisition submodule configured to acquire at least one first analysis result in a second time period; a statistics module configured to count the number and interval of first analysis results that meet preset conditions in the second time period; and a first generation submodule configured to generate a second analysis result based on the number and interval of first analysis results that meet preset conditions in the second time period.

[0160] In some exemplary embodiments of this disclosure, the performance monitoring apparatus further includes a training module configured to train a preset analysis model, including a support vector machine, before analyzing feedback information and at least one abnormal data according to the preset analysis model.

[0161] In some exemplary embodiments of this disclosure, the performance monitoring apparatus further includes: a processing and display module configured to, after determining that the performance state of the Redis caching system is abnormal, perform rate limiting and / or traffic splitting on the data traffic from the physical network interface card; and / or, parse the data traffic from the physical network interface card and display the parsing results to the target object.

[0162] According to embodiments of this disclosure, any multiple modules among the generation module 610, sending module 620, acquisition module 630, first determination module 640, second determination module 650, first determination unit, second determination unit, third determination unit, fourth determination unit, fifth determination module, first determination submodule, first generation unit, second generation unit, first acquisition submodule, statistics module, first generation submodule, training module, and processing and display module can be merged into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the following modules can be implemented, at least partially, as hardware circuits: a generation module 610, a sending module 620, an acquisition module 630, a first determination module 640, a second determination module 650, a first determination unit, a second determination unit, a third determination unit, a fourth determination unit, a fifth determination module, a first determination submodule, a first generation unit, a second generation unit, a first acquisition submodule, a statistics module, a first generation submodule, a training module, and a processing and display module. These can be implemented, for example, as hardware or firmware such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging the circuit. Alternatively, they can be implemented in any one of the three methods of software, hardware, and firmware, or in a suitable combination of any of these methods. Alternatively, at least one of the following modules can be implemented, at least partially, as a computer program module: generation module 610, sending module 620, acquisition module 630, first determination module 640, second determination module 650, first determination unit, second determination unit, third determination unit, fourth determination unit, fifth determination module, first determination submodule, first generation unit, second generation unit, first acquisition submodule, statistics module, first generation submodule, training module, and processing and display module. When the computer program module is run, it can perform the corresponding function.

[0163] Figure 12 A block diagram of an electronic device suitable for implementing a performance monitoring method for a caching system according to an embodiment of the present disclosure is shown schematically. Figure 12 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0164] like Figure 12As shown, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage portion 708 into a random access memory (RAM) 703. The processor 701 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 701 may also include onboard memory for caching purposes. The processor 701 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0165] RAM 703 stores various programs and data required for the operation of electronic device 700. Processor 701, ROM 702, and RAM 703 are interconnected via bus 704. Processor 701 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 702 and / or RAM 703. It should be noted that the programs may also be stored in one or more memories other than ROM 702 and RAM 703. Processor 701 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0166] According to embodiments of this disclosure, the electronic device 700 may further include an input / output (I / O) interface 705, which is also connected to a bus 704. The electronic device 700 may also include one or more of the following components connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 710 as needed so that computer programs read from it can be installed into the storage section 708 as needed.

[0167] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the performance monitoring method of the caching system according to the embodiments of this disclosure.

[0168] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, 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, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 702 and / or RAM 703 and / or one or more memories other than ROM 702 and RAM 703 described above.

[0169] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the performance monitoring method for the caching system provided in the embodiments of this disclosure.

[0170] When the computer program is executed by the processor 701, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0171] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 709, and / or installed from a removable medium 711. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0172] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 709, and / or installed from the removable medium 711. When the computer program is executed by the processor 701, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0173] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0175] Those skilled in the art will understand that the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments and / or claims of this disclosure can be combined or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0176] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of this disclosure is defined by the appended claims and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. A performance monitoring method for a caching system, applied to a Redis caching system, the performance monitoring method comprising: Data traffic from the physical network interface card is copied to generate first data traffic and second data traffic; The first data traffic is sent to the Redis server, and the second data traffic is sent to the data analysis module; Obtain feedback information from the Redis server regarding the first data traffic; The data analysis module analyzes the second data traffic to identify at least one abnormal data point. The feedback information and at least one abnormal data are analyzed according to a preset analysis model to determine the performance status of the Redis caching system, wherein the performance status includes normal and abnormal, and the preset analysis model includes support vector machine; The step of analyzing the feedback information and at least one abnormal data according to a preset analysis model to determine the performance status of the Redis caching system includes: The feedback information and at least one abnormal data obtained in the first time period are input into the preset analysis model to generate the first analysis result; For at least one first analysis result acquired in the second time period, a second analysis result is generated, wherein the first time period is located within the second time period; and Based on the second analysis result, the performance status of the Redis caching system is determined.

2. The performance monitoring method according to claim 1, wherein, The step of analyzing the second data traffic through the data analysis module to identify at least one abnormal data includes: Based on the data characteristics of each data point in the second data traffic, identify the identical data points in the second data traffic. Obtain the number of identical data sent within a preset time period, and determine the access frequency of the identical data; and The same data whose access frequency exceeds a preset frequency is identified as abnormal data.

3. The performance monitoring method according to claim 2, wherein, The step of determining the identical data in the second data traffic based on the data characteristics of each data item in the second data traffic includes: Obtain the data characteristics of each data item in the second data traffic; Based on the data characteristics, determine the data feature similarity value between the data; and Data whose similarity value exceeds a preset threshold are identified as identical data.

4. The performance monitoring method according to claim 1, wherein, The step of analyzing the second data traffic through the data analysis module to identify at least one abnormal data includes: Obtain the data volume value of each data in the second data flow, and identify data whose data volume value exceeds the preset data volume value as abnormal data.

5. The performance monitoring method according to claim 1, wherein, The step of generating a second analysis result for at least one first analysis result obtained in the second time period includes: Obtain at least one first analysis result for the second time period; The number and interval of the first analysis results that meet the preset conditions within the second time period are counted. A second analysis result is generated based on the number and interval of the first analysis results that meet the preset conditions within the second time period.

6. The performance monitoring method according to claim 1, wherein, Also includes: Before analyzing the feedback information and the at least one abnormal data according to the preset analysis model, the preset analysis model is trained.

7. The performance monitoring method according to claim 1, wherein, Also includes: After determining that the performance status of the Redis caching system was abnormal. The data traffic from the physical network interface card is rate-limited and / or traffic-splitting processed; and / or The data traffic from the physical network interface card is parsed, and the parsing results are displayed to the target object.

8. A performance monitoring device for a caching system, applied to a Redis caching system, the performance monitoring device comprising: The generation module is configured to copy data traffic from the physical network interface card, generating a first data traffic and a second data traffic. The sending module is configured to send the first data traffic to the Redis server and the second data traffic to the data analysis module; The acquisition module is configured to acquire feedback information from the Redis server in response to the first data traffic; The first determining module is configured to analyze the second data traffic through the data analysis module to determine at least one abnormal data. The second determining module is configured to analyze the feedback information and the at least one abnormal data according to a preset analysis model to determine the performance status of the Redis caching system, wherein the performance status includes normal and abnormal, and the preset analysis model includes support vector machine; The second determining module includes: The first generation unit is configured to input the feedback information and at least one abnormal data obtained in the first time period into the preset analysis model to generate a first analysis result; The second generation unit is configured to generate a second analysis result for at least one first analysis result acquired in the second time period, wherein the first time period is located within the second time period; and The fifth determining unit is configured to determine the performance status of the Redis caching system based on the second analysis result.

9. An electronic device, comprising: One or more processors; A storage device for storing executable instructions, which, when executed by the processor, implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, implement the method according to any one of claims 1 to 7.

11. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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