CN114036025A - JAVA application monitoring and early warning method and system based on memory operation - Google Patents

Abstract

The invention relates to a JAVA application monitoring early warning method and system based on memory operation, wherein an application service is automatically monitored by embedding points in a plug-in mode during JAVA operation, a monitoring configuration function is provided, real-time configuration of the service to be monitored is realized, dependence of manual embedding points on research personnel is reduced, meanwhile, non-invasive monitoring is achieved, performance is improved, monitoring refinement is increased, a monitoring result report is generated in real time, and monitoring instantaneity is improved. The method has the advantages of pure memory operation of monitoring information, pre-statistics, no magnetic disk IO, batch data processing and high-performance monitoring, provides guarantee for healthy and efficient operation of application services, realizes refined monitoring and dynamic configuration of service data and grabbing functions, and provides guarantee for quick positioning and troubleshooting of problem troubleshooting. The problems of flexibility, instantaneity, refinement, accuracy, dynamic configuration, high-performance monitoring and the like which are not possessed by the traditional monitoring are solved.

Description

JAVA application monitoring and early warning method and system based on memory operation

Technical Field

The invention relates to the technical field of data security and large-scale data management, in particular to a JAVA application monitoring and early warning method and system based on memory operation.

Background

JAVA is an object-oriented programming language, can write cross-platform application software, and is widely applied to various application program designs. In recent years, JAVA application is developed rapidly, application complexity is higher and higher, stable high-performance operation of application service is guaranteed by monitoring application service health, problems are early warned in time, the problems are located rapidly, and the JAVA application monitoring method becomes an important index.

At present, most of the existing JAVA application monitoring methods mainly adopt application program embedded point logs or a method for counting log output by a non-invasive monitoring technology, and the methods have frequent disk IO operation and have very serious influence on system performance. For the embedded point of the application program, research and development personnel are required to manually embed the embedded point program into the application service program, and then the embedded point log is output and collected and analyzed to obtain a monitoring report, so that the report has more time delay. For the non-intrusive monitoring technology, a binary execution file of a JAVA application program needs to be modified to be embedded into a monitoring plug-in unit in a JAVA service code compiling link, so that monitoring of JAVA application is realized, performance overhead of monitoring of a whole amount of application service is high, fine monitoring of application service cannot be achieved, monitoring is lack of flexibility, and actual requirements of application monitoring cannot be completely met.

Disclosure of Invention

In order to solve the defects of the prior art, the invention provides a JAVA application monitoring and early warning method and a JAVA application monitoring and early warning system based on memory operation, which are used for monitoring and early warning JAVA application services based on Spring, Spring MVC, Spring boot, Spring cloud, Mybatis and ibatis frameworks. The method has the advantages of pure memory operation of monitoring information, pre-statistics, no magnetic disk IO, batch data processing and high-performance monitoring, provides guarantee for healthy and efficient operation of application services, realizes refined monitoring and dynamic configuration of service data and grabbing functions, and provides guarantee for quick positioning and troubleshooting of problem troubleshooting. The problems of flexibility, instantaneity, refinement, accuracy, dynamic configuration, high-performance monitoring and the like which are not possessed by the traditional monitoring are solved.

In order to achieve the above purpose, the technical scheme adopted by the invention comprises the following steps:

a JAVA application monitoring and early warning method based on memory operation is characterized by comprising the following steps:

s1, establishing application service monitoring configuration;

s2, embedding the application service to be monitored according to the application service monitoring configuration;

s3, acquiring application service calling data through a buried point;

s4, carrying out memory collection statistics on the collected application service call data to form data to be analyzed;

s5, analyzing and processing data to be analyzed by using a preset risk early warning model to obtain application service early warning data;

s6, generating an early warning report by using the application service early warning data, and sending out early warning according to the application service problems contained in the application service early warning data;

wherein, step S3 includes the following substeps:

s31, setting regular information acquisition interval time, wherein the regular information comprises normally responded application service information;

s32, setting system information acquisition interval time; the conventional information acquisition interval time is the same as the system information acquisition interval time or is an integer multiple of the system information acquisition interval time;

s33, collecting the conventional information sets in the conventional information acquisition interval time period according to the conventional information acquisition interval time;

s34, acquiring the latest system information periodically according to the system information acquisition interval time;

s35, collecting all abnormal information, wherein the abnormal information comprises application service information without normal response;

s36, combining the conventional information, the system information and the abnormal information to form application service calling data;

step S4 includes the following substeps:

s41, arranging and storing the collected application service call data in a memory message queue;

s42, preprocessing and counting the application service call data in the memory message queue and caching the application service call data;

and S43, extracting the collected data of the application service call data subjected to preprocessing statistics in the cache to form data to be analyzed.

Further, the application service information includes the number of calls of the corresponding application service, a call success rate, response time, the number of response exceptions, a business data summary value and an application service parameter;

the system information comprises CPU utilization rate, memory utilization rate, disk exchange partition utilization condition, system load and JVM information.

Further, the application service monitoring configuration comprises:

setting a spring method interceptor;

setting a mybatis plug-in mechanism;

setting an interception range, wherein the interception range comprises all Bean methods and DAO methods corresponding to the application service;

and configuring a collected data filtering rule.

Further, the embedding the application service to be monitored according to the application service monitoring configuration includes:

establishing a buried point plug-in according to the application service monitoring configuration;

and (5) using a buried point plug-in to perform buried point on the application service.

Further, the embedding the application service to be monitored according to the application service monitoring configuration further includes:

and updating the buried point plug-in according to the change of the application service monitoring configuration.

Further, the preset risk early warning model is established through the following steps:

acquiring historical application service calling data and a historical early warning report corresponding to the historical application service calling data;

taking historical application service calling data and a historical early warning report form as model training data, and training to obtain a model to be verified;

establishing virtual test data, inputting the test data into a model to be verified for operation to obtain a result to be verified;

and evaluating the prediction success rate of the model to be verified by using the result to be verified, and saving the model to be verified as a risk early warning model when the power meets a threshold value.

Further, the historical application service calling data and the historical early warning report form are used as model training data, and the step of training to obtain the model to be verified comprises the step of training by using an XGboost algorithm;

the evaluation of the prediction success rate of the model to be verified comprises the evaluation of the result to be verified by using naive Bayes and/or logistic regression.

The invention also relates to a JAVA application monitoring and early warning system based on memory operation, which is characterized by comprising the following components:

the configuration module is used for establishing application service monitoring configuration;

the application service monitoring module is used for embedding points of the application service to be monitored according to the application service monitoring configuration and acquiring application service calling data;

the model management module is used for generating and updating a risk early warning model;

the first data processing module is used for carrying out memory collection statistics on the collected application service calling data to form data to be analyzed;

the second data processing module is used for analyzing and processing the data to be analyzed by using a preset risk early warning model to obtain application service early warning data;

and the early warning module is used for generating an early warning report form by using the application service early warning data and sending out early warning according to the application service problems contained in the application service early warning data.

The invention also relates to a computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the above-mentioned method.

The invention also relates to an electronic device, characterized in that it comprises a processor and a memory;

the memory is used for storing application service monitoring configuration and application service calling data;

the processor is used for executing the method by calling the application service monitoring configuration and the application service calling data.

The invention has the beneficial effects that:

the JAVA application monitoring and early warning method and the system based on the memory operation are adopted to monitor and early warn JAVA application services based on a Spring, Spring MVC, Spring boot, Spring cloud, Mybatis and ibatis framework, the application services adopt plug-in type automatic embedded point monitoring during JAVA operation, a monitoring and configuration function is provided, real-time configuration and automatic, precise, fine, plug-in, configuration and flexible monitoring of the services to be monitored are realized, independent monitoring plug-ins are provided, application configuration integration is facilitated, monitoring and application service configuration is provided, application service data concerned by a user is acquired, specific acquisition data item configuration of the monitoring and application services is supported, precise monitoring information acquisition is realized, application monitoring workload is greatly reduced, dependence of manual embedded points on research and development personnel is reduced, meanwhile, non-invasive monitoring is realized, performance is improved, fine monitoring is increased, monitoring result reports are generated in real time, the monitoring real-time performance is improved. The method has the advantages that monitoring information is operated by a pure memory, pre-statistics is carried out, disk IO is not generated, batch data processing is carried out, pure memory operation is adopted, high-performance monitoring data acquisition is carried out, performance loss of JAVA application service is greatly reduced, the healthy and efficient operation of the application service is guaranteed, functions of fine monitoring and service data dynamic configuration grabbing are achieved, and the fast positioning and troubleshooting of problem troubleshooting are guaranteed. The problem of the application service is early-warned in real time and the trend is early-warned, the problem is fed back to an application service party at the first time, the problem is quickly identified and positioned, and the problem troubleshooting and positioning efficiency is improved. Conventional monitoring data is summarized, data memory statistics of data minutes level is carried out, queues are output to a monitoring analysis system, trend analysis is carried out on application services by means of big data monitoring analysis and monitoring early warning models, problem prejudgment is carried out through the trend analysis, early warning is carried out before problems occur, the problems are solved before the problems occur, and a plurality of problems such as flexibility, real-time performance, refinement, precision, dynamic configuration and high-performance monitoring which are not possessed by traditional monitoring are solved.

Drawings

Fig. 1 is a schematic flow chart of a JAVA application monitoring and early warning method based on memory operation according to the present invention.

Fig. 2 is a schematic structural diagram of a JAVA application monitoring and early warning system based on memory operation according to the present invention.

Detailed Description

Monitoring of JAVA application service, establishing an early warning platform through the method, preferably based on a Spring closed micro-service framework, enabling a persistence layer middleware to adopt Mybatis as a support, adopting a Netty NIO communication framework to ensure real-time transmission of monitoring data, enabling support data access of a traditional relational database Mysql and a non-relational NoSQL type database such as Redis, MongoDB and the like, and enabling an open source message queue middleware Kafaka to carry out high-performance data transmission support. The platform mainly comprises a data log acquisition part and a monitoring, analyzing and early warning part. The data log acquisition part is integrated into the JAVA application service to be monitored in a configuration mode, and is responsible for acquiring the data log of the JAVA application service to be monitored and sending the acquired log for monitoring, analyzing and early warning; and for the monitoring, analyzing and early warning part, the monitoring, analyzing and early warning part is responsible for configuring application services to be monitored, application service data to be output, specific data indexes of monitoring and early warning threshold values, receiving data acquired and sent by logs, further processing and generating reports, early warning on problematic application services and the like.

The log collection part intercepts beans and Dao of all services through a Spring interceptor and a Mybatis plug-in mechanism, automatically buries the services to be monitored through application service monitoring configuration, collects application service calling data (such as information of delay, success or failure, error state, calling parameters and the like), performs simple memory collection statistics on the collected data, presses the data into a queue, and sends the data to a monitoring analysis early warning platform, wherein the whole process has no disk IO operation.

For a clearer understanding of the contents of the present invention, reference will be made to the accompanying drawings and examples.

The first aspect of the present invention relates to a JAVA application monitoring and early warning method based on memory operation, whose step flow is shown in fig. 1, including:

s1, establishing application service monitoring configuration, mainly comprising setting a spring method interceptor, setting a mybatis plug-in mechanism, setting an interception range, wherein the interception range comprises all Bean methods and DAO methods corresponding to the application service, and configuring a collected data filtering rule.

Bean is an important component developed in Java that can be cross-platform, and it is a component architecture. The application of the JavaBean at the server side shows strong vitality, and is commonly used for encapsulating business logic, database operation and the like in JSP programs. Legacy applications can instantiate beans in both new and reflected ways. And the Spring IoC container needs to create a Bean using a reflection mechanism according to the configuration metadata in the Bean definition. The manner that the creation according to the Bean definition in the Spring IoC container can adopt includes: instantiating the Bean using the constructor, instantiating the Bean using the static factory approach, instantiating the Bean using the instance factory approach, and instantiating the Bean using the setter approach. In actual operation, an application person can select an appropriate mode to establish the Bean according to needs.

Dao (data Access object) the data Access object is an object-oriented database interface that allows developers to connect directly to the Access table. The DAO method is best suited for single system applications or small-scale local distributed usage. The DAO schema is one of the standard J2EE design schemas that developers use to separate the underlying data access operations from the upper level business logic. A typical DAO implementation includes the following components: 1. at least one DAO plant class; 2. at least one DAO interface; 3. a specific class implementing the DAO interface; 4. data transfer objects (value objects). A particular DAO class contains logic to access data from a particular data source. By adopting the DAO method, the database can be conveniently accessed, and the DAO method is used for configuring the filtering rules of the acquired data.

And S2, establishing a buried point plug-in according to the application service monitoring configuration, and using the buried point plug-in to bury the application service to be monitored. In particular, the buried point plug-in may be updated by monitoring changes in configuration by the application service.

The method comprises the steps of intercepting beans and Dao of all services through a Spring interceptor and a Mybatis plug-in mechanism, automatically burying the services to be monitored through application service monitoring configuration, collecting application service calling data (such as information of delay, success or failure, error state, calling parameters and the like), carrying out simple memory collection statistics on the collected data, pressing in a queue, and sending to a monitoring analysis early warning platform, wherein the whole process has no disk IO operation.

And S3, acquiring application service calling data through the embedded points, wherein the application service calling data comprise conventional information, abnormal information and system information, and are preferably acquired respectively and then integrated.

The system information comprises the contents of CPU utilization rate, memory utilization rate, disk exchange partition use condition, system load, JVM information and the like; the conventional information and the abnormal information belong to application service information, and comprise the calling times, the calling success rate, the response time, the response abnormal quantity, the business data summary value and the application service parameters of the corresponding application service. For the regular information, the application service information which is normally responded is referred to, in order to reduce the system recording pressure, a regular information acquisition interval time (for example, 10 seconds, 1 minute, or any one interval time) may be optionally set, and a regular information set in the regular information acquisition interval time period is collected, such as the total number of calls, the total response time, and the like in the time period are used as the recorded regular information, that is, the pressure recorded by the system is reduced by a statistical method.

Similarly, for the system information, the system information may also be periodically acquired by setting the system information acquisition interval time, and particularly, the regular information acquisition interval time may be matched with the system information acquisition interval time, for example, the regular information acquisition interval time and the system information acquisition interval time are set to be the same, or the regular information acquisition interval time is set to be an integer multiple of the system information acquisition interval time, so that the latest system information at the current time may be obtained for matching while the regular information is acquired. The abnormal information refers to application service information without normal response, for example, corresponding information of failure in feedback response, and is selected to be recorded item by item so as to ensure that all abnormal information can be correctly collected and fed back.

And S4, performing memory collection statistics on the collected application service call data to form data to be analyzed.

During execution, the collected application service call data is arranged and stored in the memory message queue, an asynchronous preprocessing module can be preferably adopted to preprocess and count the application service call data in the memory message queue and then cache the application service call data, and an asynchronous sending module is used to extract the collection data of the application service call data which is preprocessed and counted in the cache to form data to be analyzed and send the data.

Data acquisition for monitoring needs to be performed in consideration of the recording mode of the acquired data. The data of the system is recorded on a local disk, and the data of the application is generally stored in a directory of the application, so that the data is convenient to collect. There are also situations where the application log is sent directly to the acquisition server over the network, which can relieve the pressure of writing the log locally.

When the method is realized, monitoring data of two dimensions are preferably provided and applied, and when batch message pushing is carried out, pure memory operation is adopted, no magnetic disk IO is generated, and high efficiency is realized by combining pre-statistics and batch sending; and collecting system performance logs including a CPU, a magnetic disk, a memory, a SWAP, a system load, a JVM stack, a thread, a GC and the like periodically.

In a high-concurrency system with very frequent log collection, due to frequent disk IO operation, the influence on the system performance is very severe, and for the problem, a log collection mode without disk IO is adopted, collected log data are simply collected in a memory and then are directly sent to a remote log collection server through a network, and besides manual log embedding, the proportion of automatic frame-based log embedding can be preferably increased. Because Spring and mybatis are already project standard allocations at present, all service Bean methods and DAO methods are intercepted by adopting a Spring method interceptor and a mybatis plug-in mechanism, calling performance data (such as information of time delay, success or failure, error state, calling parameters and the like) are collected, collected log information is pressed into a memory message queue, logs in the memory queue are taken out through an asynchronous pre-counting module to be preprocessed, counted and cached, and finally, the collected data in the cache is sent to a log collection server through an asynchronous sender.

Preferably, a collection module for server performance data is integrated in the application program, and a performance log of the system, including information related to CPU, memory, disk, swap space, system load, JVM, etc., is collected periodically (e.g., 1 minute), and the information is also sent to the log server through the same path.

And S5, analyzing and processing the data to be analyzed by using a preset risk early warning model to obtain application service early warning data. The risk early warning model is preferably obtained by learning through an XGboost algorithm, historical application service call data and a historical early warning report form are used as model training data for training, and a to-be-verified result for verification is evaluated through a naive Bayes and/or logistic regression method to obtain a proper risk early warning model.

The basic method of the Bayes classifier is to calculate the probability of each category according to certain characteristics on the basis of statistical data, thereby realizing classification, and the naive Bayes is a further method, and the probability corresponding to each category can be calculated on the basis of the existing Bayes classification by assuming that all the characteristics are independent from each other, thereby finding out the category with the maximum probability. The use of naive Bayes can greatly simplify the calculation and has little influence on the accuracy of the classification result.

Logistic regression is a generalized linear regression analysis model, and is commonly used in the fields of data mining, automatic disease diagnosis, economic prediction and the like. For example, risk factors of the application service early warning data are discussed, and the probability of the application service is predicted according to the risk factors. Taking the analysis of the application service early warning data as an example, two groups of data are selected, one group is a dangerous application service group, the other group is a safe application service group, and the two groups of data have different characteristics. Therefore, the dependent variable is dangerous or not, the value is "yes" or "no", and the independent variable can include a plurality of parameters such as data length, specific characteristic value and the like. The arguments may be either continuous or categorical. Then, through logistic regression analysis, the weight of the independent variable can be obtained, so that the factors which are the risk factors of the application service can be roughly known. And meanwhile, the possibility that one application service is dangerous can be predicted according to the risk factors according to the weight.

And S6, generating an early warning report by using the application service early warning data, and giving out early warning according to the application service problems contained in the application service early warning data. The monitoring alarm notification can be output in real time according to the early warning index threshold value, the operation of JAVA application service problems is guaranteed, the problems are monitored at the first time, the health trend of the application service is predicted through the monitoring early warning model, the application service with potential risks is early warned, the prior monitoring is carried out, and the problem is prevented.

Data acquisition for monitoring needs to be performed in consideration of the recording mode of the acquired data. The data of the system is recorded on a local disk, and the data of the application is generally stored in a directory of the application, so that the data is convenient to collect. And the application log is directly sent to the acquisition server through the network, so that the pressure of local log writing can be reduced to a certain extent.

When the log message (data message) is received, preferably starting a receiving service of the NIO, after the log message is received, performing a series of log message processing including decoding, splitting and the like, and finally placing the log message into a preset time slice, wherein each time slice has certain timeliness, generally 1 minute can be selected, and the log message is analyzed (including stored) in various dimensions in the time slice.

Another aspect of the present invention further relates to a JAVA application monitoring and early warning system based on memory operation, whose structure is shown in fig. 2, including:

the configuration module is used for establishing application service monitoring configuration, and comprises a spring method interceptor, a mybatis plug-in mechanism and a setting module, wherein the setting module is used for setting interception ranges corresponding to all Bean methods and DAO methods;

the application service monitoring module is used for embedding points of application services to be monitored according to application service monitoring configuration, acquiring application service calling data, intercepting beans and Dao of all services through a Spring interceptor and a Mybatis plug-in mechanism, automatically embedding the services to be monitored through the application service monitoring configuration, and acquiring the application service calling data;

the risk early warning system comprises a model management module, a risk early warning module and a risk early warning module, wherein the risk early warning module is preferably obtained by adopting XGboost algorithm learning, training is carried out by taking historical application service calling data and a historical early warning report form as model training data, and a to-be-verified result for verification is evaluated by a naive Bayes and/or logistic regression method;

the first data processing module is used for performing memory collection statistics on the collected application service calling data to form data to be analyzed, arranging and storing the collected application service calling data into a memory message queue, preferably adopting an asynchronous preprocessing module to preprocess and count the application service calling data in the memory message queue and then cache the application service calling data, and then using an asynchronous sending module to extract the collected data of the application service calling data subjected to preprocessing statistics in the cache to form the data to be analyzed and send the data;

the second data processing module is used for analyzing and processing data to be analyzed by using a preset risk early warning model to obtain application service early warning data, outputting a monitoring alarm notification in real time according to an early warning index threshold value to guarantee the operation of JAVA application service problems, monitoring the problems at the first time, and predicting the health trend of the application service by monitoring the early warning model;

and the early warning module is used for generating an early warning report form by using the application service early warning data and sending out early warning according to the application service problems contained in the application service early warning data.

By using this system, the above-described arithmetic processing method can be executed and a corresponding technical effect can be achieved.

Embodiments of the present invention also provide a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, the computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements all the steps of the method in the above embodiments.

Embodiments of the present invention further provide an electronic device for executing the method, as an implementation apparatus of the method, the electronic device at least has a processor and a memory, and particularly, the memory stores data required for executing the method and related computer programs, such as application service monitoring configuration and application service calling data, and the like, and all steps of implementing the method are executed by calling the data in the memory and the program by the processor, and corresponding technical effects are obtained.

Preferably, the electronic device may comprise a bus architecture, which may include any number of interconnected buses and bridges linking together various circuits including one or more processors and memory. The bus may also link various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface provides an interface between the bus and the receiver and transmitter. The receiver and transmitter may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium. The processor is responsible for managing the bus and general processing, while the memory may be used for storing data used by the processor in performing operations.

Additionally, the electronic device may further include a communication module, an input unit, an audio processor, a display, a power source, and the like. The processor (or controller, operational controls) employed may include a microprocessor or other processor device and/or logic device that receives input and controls the operation of various components of the electronic device; the memory may be one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices, and may store the above-mentioned related data information, and may also store a program for executing the related information, and the processor may execute the program stored in the memory to realize information storage or processing, etc.; the input unit is used for providing input to the processor, and can be a key or a touch input device; the power supply is used for supplying power to the electronic equipment; the display is used for displaying display objects such as images and characters, and may be an LCD display, for example. The communication module is a transmitter/receiver that transmits and receives signals via an antenna. The communication module (transmitter/receiver) is coupled to the processor to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal. Based on different communication technologies, a plurality of communication modules, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be disposed in the same electronic device. The communication module (transmitter/receiver) is also coupled to a speaker and a microphone via an audio processor to provide audio output via the speaker and receive audio input from the microphone to implement the usual telecommunication functions. The audio processor may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor is also coupled to the central processor, so that recording on the local machine can be realized through the microphone, and sound stored on the local machine can be played through the loudspeaker.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (12)

1. A JAVA application monitoring and early warning method based on memory operation is characterized by comprising the following steps:

s1, establishing application service monitoring configuration;

s2, embedding the application service to be monitored according to the application service monitoring configuration;

s3, acquiring application service calling data through a buried point;

s4, carrying out memory collection statistics on the collected application service call data to form data to be analyzed;

s5, analyzing and processing data to be analyzed by using a preset risk early warning model to obtain application service early warning data;

and S6, generating an early warning report by using the application service early warning data, and giving out early warning according to the application service problems contained in the application service early warning data.

2. The method according to claim 1, wherein said step S3 includes the sub-steps of:

s31, setting regular information acquisition interval time, wherein the regular information comprises normally responded application service information;

s32, setting system information acquisition interval time; the conventional information acquisition interval time is the same as the system information acquisition interval time or is an integer multiple of the system information acquisition interval time;

s33, collecting the conventional information sets in the conventional information acquisition interval time period according to the conventional information acquisition interval time;

s34, acquiring the latest system information periodically according to the system information acquisition interval time;

s35, collecting all abnormal information, wherein the abnormal information comprises application service information without normal response;

and S36, combining the conventional information, the system information and the abnormal information to form application service calling data.

3. The method according to claim 2, wherein said step S4 includes the sub-steps of:

s41, arranging and storing the collected application service call data in a memory message queue;

s42, preprocessing and counting the application service call data in the memory message queue and caching the application service call data;

and S43, extracting the collected data of the application service call data subjected to preprocessing statistics in the cache to form data to be analyzed.

4. The method of claim 3, wherein the application service information includes a call number, a call success rate, a response time, a response exception number, a business data summary value, and an application service parameter of a corresponding application service;

the system information comprises CPU utilization rate, memory utilization rate, disk exchange partition utilization condition, system load and JVM information.

5. The method of claim 3, wherein the application service monitoring configuration comprises:

setting a spring method interceptor;

setting a mybatis plug-in mechanism;

setting an interception range, wherein the interception range comprises all Bean methods and DAO methods corresponding to the application service;

and configuring a collected data filtering rule.

6. The method of claim 5, wherein said landfilling the application service to be monitored according to the application service monitoring configuration comprises:

establishing a buried point plug-in according to the application service monitoring configuration;

and (5) using a buried point plug-in to perform buried point on the application service.

7. The method of claim 6, wherein said landfilling the application service to be monitored according to the application service monitoring configuration further comprises:

and updating the buried point plug-in according to the change of the application service monitoring configuration.

8. The method of claim 3, wherein the pre-defined risk pre-warning model is established by:

acquiring historical application service calling data and a historical early warning report corresponding to the historical application service calling data;

taking historical application service calling data and a historical early warning report form as model training data, and training to obtain a model to be verified;

establishing virtual test data, inputting the test data into a model to be verified for operation to obtain a result to be verified;

and evaluating the prediction success rate of the model to be verified by using the result to be verified, and saving the model to be verified as a risk early warning model when the power meets a threshold value.

9. The method of claim 8, wherein the historical application service call data and the historical early warning report form are used as model training data, and the training for obtaining the model to be verified comprises training by using an XGboost algorithm;

the evaluation of the prediction success rate of the model to be verified comprises the evaluation of the result to be verified by using naive Bayes and/or logistic regression.

10. A JAVA application monitoring and early warning system based on memory operation is characterized by comprising:

the configuration module is used for establishing application service monitoring configuration;

the application service monitoring module is used for embedding points of the application service to be monitored according to the application service monitoring configuration and acquiring application service calling data;

the model management module is used for generating and updating a risk early warning model;

the first data processing module is used for carrying out memory collection statistics on the collected application service calling data to form data to be analyzed;

the second data processing module is used for analyzing and processing the data to be analyzed by using a preset risk early warning model to obtain application service early warning data;

and the early warning module is used for generating an early warning report form by using the application service early warning data and sending out early warning according to the application service problems contained in the application service early warning data.

11. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method of any one of claims 1 to 9.

12. An electronic device comprising a processor and a memory;

the memory is used for storing application service monitoring configuration and application service calling data;

the processor configured to perform the method of any one of claims 1 to 9 by invoking an application service monitoring configuration and application service invocation data.

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