CN117033125A - Application relation intelligent construction method based on probe, metadata acquisition method, medium and system - Google Patents
Application relation intelligent construction method based on probe, metadata acquisition method, medium and system Download PDFInfo
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Abstract
The invention relates to the technical field of data management, in particular to an application relation intelligent construction method based on probes, a metadata acquisition method based on probes, a computer readable storage medium and a data management system. According to the probe-based metadata acquisition method, a probe program associated with the application program is established to monitor the running application program, metadata of the application program is collected, the situation that a crawler program is intercepted and resisted by laws and regulations and an anti-crawler mechanism is avoided, and network traffic can be comprehensively and accurately captured and acquired; the intelligent construction method of the application relation based on the metadata acquisition method of the probe applies the trained association model to the application scene of the actual scene probe program to realize automatic machine learning and relation construction, wherein the output association relation can be used as a part of application data management to realize association of application and mapping of application and metadata, thereby realizing construction of the application relation automatically.
Description
Technical Field
The invention relates to the technical field of data management, in particular to a metadata acquisition method based on a probe, an application relationship intelligent construction method based on the probe, a computer readable storage medium and a data management system, wherein the application relationship intelligent construction method is realized by applying the metadata acquisition method.
Background
Metadata is the bridge between applications and data, and is critical to the construction of application relationships between both applications and data. Metadata can describe information such as the source, quality, update frequency, and format of a data set, through which an application understands information such as the structure, characteristics, and availability of data. Metadata may help applications better understand and process data to improve the quality, accuracy, and reliability of the data in the application. Metadata may also provide support for data applications such as data management, data analysis, and data mining, so that these applications can efficiently utilize data.
The acquisition of metadata, in particular to the intelligent grabbing of metadata, is important in the intelligent construction of application relations. At present, the main mode of capturing the metadata of the webpage is a crawler technology, which refers to a technology of automatically accessing the webpage and extracting information through a web crawler program. The web crawler may extract metadata such as titles, descriptions, keywords, tags, URLs, etc. from the web page and store it in the database. The crawler technology has the characteristics of high efficiency, automation, strong expandability and the like, but still has a plurality of defects.
The crawler technology can collect personal information, so that privacy rights of users can be violated, and social and legal problems are caused; without permission from a website or institution, gathering information using crawler technology may violate legal regulations, creating legal risks; when the crawler technology extracts website data, stress can be caused to websites, so that the access quantity is overlarge, and even the websites can crash; the data acquired by the crawler technology are only the data displayed on the website, and are not necessarily real or up-to-date, and the accuracy of the data is at risk; websites can identify and block crawlers through some anticreeper technologies, which may fail and fail to obtain data.
When the application relation between the application program and the data is constructed, how to intelligently acquire the metadata, and the application relation between the application program and the metadata is constructed without bearing the risk brought by the crawler technology is a current technical difficulty.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a metadata acquisition method based on a probe, an application relationship intelligent construction method based on the probe and a computer readable storage medium storing a computer program for realizing the method when being executed.
In order to solve the above technical problems, in a first aspect, the present invention provides a probe-based metadata acquisition method, which identifies characteristic information of metadata of an application program, calls a probe library pre-storing a plurality of probe programs, compares the matching degree of each probe program in the probe library with the characteristic information of the acquired metadata, thereby finding out a probe program matching with the metadata information of the application program to a preset degree, and associates the found probe program with the application program to collect metadata of the application program
Further, the metadata of the application program includes performance data, state information, and behavior indexes.
Further, the probe program is implemented by a Java probe, the Java probe is operated in the JVM process, and an analysis data pool is constructed by detecting and monitoring indexes and data of the JVM, and the method comprises the following metadata acquisition steps:
the Java probe is loaded through the JVM Agent, and a JVM Agent parameter-Java Agent is added when the JVM is started to load and start the Java probe, so that the Java probe is resident and operates in the JVM life cycle;
JVM information is acquired through Instrumentation API, java Instrumentation API is called to acquire the information and performance data of the JVM, and internal classes, methods and objects of the JVM are monitored and analyzed;
the monitor and the sampler are constructed to collect data, and a preset monitor and sampler are called to detect and record the performance data of the JVM, wherein the performance data of the JVM comprises one or more of CPU utilization rate, memory use condition and thread pool state.
Further, the probe program is implemented by a Python probe, and the Python probe is installed to the Python application program by a packet management tool or manually, comprising the following metadata acquisition steps:
calling an API of a Python probe to collect and process performance data of the Python application program;
invoking a Python built-in library and a third party library to identify an application state, wherein a Python probe acquires state data and performance data of an application program through the Python built-in library and the third party library, including acquiring a system resource state by using a psutil library or collecting remote HTTP request indexes by using a requests library;
constructing a sampler and a monitor to record performance data, and calling a preset sampler and a monitor by a Python probe to collect application program performance data, wherein the method comprises the steps of calling a CPU profiler to analyze CPU service conditions or calling a memory profiler of a memory analyzer to analyze memory service conditions;
and calling a data analysis engine to fill an analysis data pool, calling a preset data analysis engine, and processing and analyzing the acquired data through an intelligent construction script to finish the processing and the analysis of the data to fill the analysis data pool.
Further, the probe program is implemented by a Go probe, and the Go probe is embedded into a binary file of Go language in an embedded code manner, which comprises the following metadata acquisition steps:
calling a program debugging tool GDB as the rear end of the Go probe, and obtaining performance data and debugging information of the Go program by interacting with the Go program;
calling a pprof language performance analysis tool, triggering data acquisition and analysis through a preset API, and uploading acquired and analyzed data to a script analysis data pool;
the operating mechanism of the Go language is monitored, the operating state and performance data are identified, and specifically, the function of the runtimes library is called to obtain memory and GC data.
In a second aspect, the invention provides a probe-based application relationship intelligent construction method, which comprises the following steps:
a data acquisition step of acquiring data from a preset analysis data pool;
a feature selection step, namely performing feature selection on the preprocessed data, and selecting related data features according to a target relationship input or selected by a user;
marking each data and the associated semantic tags and relationship types;
a machine learning modeling step, namely calling a pre-constructed machine learning model to automatically learn data to acquire a data association relationship;
and model application, namely applying the trained machine learning model to association relation recognition, and realizing mapping of application and metadata according to the recognized association relation, so as to automatically realize construction of application relation.
Further, in the machine learning modeling step, the machine learning model includes a decision tree model, a cluster model, or a classification algorithm model.
Further, the method includes a data preprocessing step performed after the data acquisition step: and cleaning the collected data to remove noise.
In a third aspect, there is also provided a computer readable storage medium storing a computer program capable of implementing the above-described probe-based application relationship intelligent construction method and/or probe-based metadata acquisition method when the computer program is executed by a processor.
In a fourth aspect, a data management system is provided, which includes an application server, an application program execution end and a data table repository, where the processor and the processor are respectively connected, and the data management system further includes the above computer readable storage medium, where the computer program on the computer readable storage medium can be executed by the processor.
The probe-based metadata acquisition method establishes a probe program associated with an application program to monitor the running application program, collects metadata of the application program, avoids interception and resistance of the crawler program by laws and regulations and an anti-crawler mechanism, can comprehensively and accurately capture and acquire network traffic, can deeply analyze a protocol, realizes accurate capture of protocol information, and is based on the application relation intelligent construction method realized by the probe-based metadata acquisition method, a trained association model is applied to an actual scene probe program application scene to realize automatic learning and relation construction of a machine, wherein the output association relation can be used as a part of application data treatment to realize association of application and mapping of application and metadata, thereby realizing construction of application relation automatically.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the description of the embodiments will be briefly described below.
FIG. 1 is a flow chart of steps of the probe-based application relationship intelligent construction method.
FIG. 2 is a schematic diagram of the logic flow for data transmission of the data management system.
Detailed Description
The invention is further described in detail below in connection with the detailed description.
The data management system of the embodiment comprises a processor, an application server, an application program execution end and a data table storage library, wherein the application server, the application program execution end and the data table storage library are respectively connected with the processor. Referring to fig. 1, a specific probe-based application relationship intelligent construction method is implemented by the following steps.
And a data acquisition step, namely acquiring data from a preset analysis data pool.
A data preprocessing step: and cleaning the collected data to remove noise.
A feature selection step, namely performing feature selection on the preprocessed data, and selecting related data features according to a target relationship input or selected by a user; the goal of feature selection is to reduce the data dimension and avoid the interference of redundant data and noise to the model.
Marking each data and the associated semantic tags and relationship types;
and a machine learning modeling step, namely calling a pre-built machine learning model to automatically learn the data to acquire the data association relation. The machine learning model includes a decision tree model, a cluster model, or a classification algorithm model.
Model verification and optimization: and verifying and optimizing the machine learning model, timely adjusting parameters and algorithms, and verifying the accuracy of the model to obtain higher accuracy.
And model application, namely applying the trained machine learning model to association relation recognition, and realizing mapping of application and metadata according to the recognized association relation, so as to automatically realize construction of application relation. More complex processing and more feature engineering may be required in actual construction. In addition, different models and algorithms may be used to construct the relational model, depending on the complexity and data characteristics of the problem.
The intelligent construction method of the application relation based on the metadata acquisition method of the probe applies the trained association model to the application scene of the actual scene probe program to realize automatic machine learning and relation construction, wherein the output association relation can be used as a part of application data management to realize association of application and mapping of application and metadata, thereby realizing construction of the application relation automatically.
In the above-mentioned intelligent construction method based on the application relationship of the probe, metadata acquisition is realized based on the probe, specifically, see fig. 2, a probe program associated with the application program is established to monitor the running application program, and metadata of the application program is collected. The metadata of the application program includes performance data, state information and behavior indexes, and the probe program includes a Java probe, a Python probe and a Go probe.
The respective probe program implementation metadata acquisition steps are specifically described below.
(1) The probe program is realized by a Java probe, the Java probe is operated in the JVM process, and an analysis data pool is constructed by detecting and monitoring indexes and data of the JVM, and the method comprises the following metadata acquisition steps:
the Java probe is loaded through the JVM Agent, and a JVM Agent parameter-Java Agent is added when the JVM is started to load and start the Java probe, so that the Java probe is resident and operates in the JVM life cycle;
JVM information is acquired through Instrumentation API, java Instrumentation API is called to acquire the information and performance data of the JVM, and internal classes, methods and objects of the JVM are monitored and analyzed;
the monitor and the sampler are constructed to collect data, and a preset monitor and sampler are called to detect and record the performance data of the JVM, wherein the performance data of the JVM comprise CPU utilization rate, memory use condition and thread pool state.
(2) The probe program is realized by a Python probe, and the Python probe is installed to the Python application program by a package management tool or manually, and the method comprises the following metadata acquisition steps:
calling an API of a Python probe to collect and process performance data of the Python application program;
invoking a Python built-in library and a third party library to identify an application state, wherein a Python probe acquires state data and performance data of an application program through the Python built-in library and the third party library, including acquiring a system resource state by using a psutil library or collecting remote HTTP request indexes by using a requests library;
constructing a sampler and a monitor to record performance data, and calling a preset sampler and a monitor by a Python probe to collect application program performance data, wherein the method comprises the steps of calling a CPU profiler to analyze CPU service conditions or calling a memory profiler of a memory analyzer to analyze memory service conditions;
and calling a data analysis engine to fill an analysis data pool, calling a preset data analysis engine, and processing and analyzing the acquired data through an intelligent construction script to finish the processing and the analysis of the data to fill the analysis data pool.
(3) The probe program is realized by a Go probe, the Go probe is embedded into a binary file of a Go language in an embedded code mode, and the method comprises the following metadata acquisition steps:
calling a program debugging tool GDB as the rear end of the Go probe, and obtaining performance data and debugging information of the Go program by interacting with the Go program;
calling a pprof language performance analysis tool, triggering data acquisition and analysis through a preset API, and uploading acquired and analyzed data to a script analysis data pool;
the operating mechanism of the Go language is monitored, the operating state and performance data are identified, and specifically, the function of the runtimes library is called to obtain memory and GC data.
According to the probe-based metadata acquisition method, the probe program related to the application program is established to monitor the running application program, metadata of the application program is collected, the situation that the crawler program is intercepted and resisted by laws and regulations and an anti-crawler mechanism is avoided, and network traffic can be comprehensively and accurately captured and acquired.
Log analysis is carried out on metadata acquired by the probe program, and the analyzed data is used for intelligent relation construction. In the log file, information such as a use condition, an abnormal condition, an operation script, and a security policy of the system is generally recorded. LOG LOGs tend to be very large and cumbersome for large systems and applications, requiring parsing and analysis to find valuable data.
1. Automatically collecting by using a log management tool and writing scripts;
2. LOG files are typically very large and require extraction of LOG information for a specified period of time, host or application using preprocessing commands such as grep, awk, etc.
3. The preprocessed log file may contain errors, duplicates, or unnecessary information. Data cleansing is required to preserve useful information.
4. Log analysis is performed using log analysis tools and script writing to extract valuable data.
The embodiment realizes the probe-based application relationship intelligent construction method through a computer program, and the computer program is stored in a computer readable storage medium and is executed by a computer processor so as to realize the probe-based application relationship intelligent construction method. The data management system embodiments described above are illustrative only, in that the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Finally, it should be noted that: the method for intelligently constructing the application relation based on the probe disclosed by the embodiment of the invention is disclosed as a preferred embodiment of the invention, and is only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A metadata acquisition method based on probes is characterized by identifying characteristic information of metadata of an application program, calling a probe library pre-stored with a plurality of probe programs, comparing the matching degree of each probe program in the probe library and the characteristic information of the acquired metadata, thereby searching the probe program matched with the metadata information of the application program to a preset degree, and associating the searched probe program with the application program to collect metadata of the application program.
2. The probe-based metadata acquisition method of claim 1 wherein the metadata of the application includes one or more of performance data, status information, and behavioral indicators.
3. The probe-based metadata acquisition method as claimed in claim 1, wherein the probe program is implemented by a Java probe, and the characteristic information of the metadata is JVM process of the application program; the association of the probe program to the application program to collect metadata of the application program means that: running a Java probe inside the JVM process, and constructing an analysis data pool by monitoring indexes and data of the JVM process, wherein the Java probe comprises the following metadata acquisition steps:
the Java probe is loaded through the JVM Agent, and a JVM Agent parameter-Java Agent is added when the JVM is started to load and start the Java probe, so that the Java probe is resident and operates in the JVM life cycle;
JVM information is acquired through Instrumentation API, java Instrumentation API is called to acquire the information and performance data of the JVM, and internal classes, methods and objects of the JVM are monitored and analyzed;
the monitor and the sampler are constructed to collect data, and a preset monitor and sampler are called to detect and record the performance data of the JVM, wherein the performance data of the JVM comprises one or more of CPU utilization rate, memory use condition and thread pool state.
4. The probe-based metadata acquisition method according to claim 1, wherein the probe program is implemented by a Python probe, and the characteristic information of the metadata refers to Python library information of an application program; the association of the probe program to the application program to collect metadata of the application program means that: the package management tool is called or the Python probe is installed to the Python application program according to the input operation of a user, and the method comprises the following metadata acquisition steps:
calling an API of a Python probe to collect and process performance data of the Python application program;
invoking a Python built-in library and a third party library to identify an application state, wherein a Python probe acquires state data and performance data of an application program through the Python built-in library and the third party library, specifically, a psutil library is used for acquiring a system resource state, or a requests library is used for collecting remote HTTP request indexes;
constructing a sampler and a monitor to record performance data, and calling a preset sampler and a monitor by a Python probe to collect application program performance data, wherein the method comprises the steps of calling a CPU profiler to analyze CPU service conditions or calling a memory profiler of a memory analyzer to analyze memory service conditions;
and calling a data analysis engine to fill an analysis data pool, calling a preset data analysis engine, and processing and analyzing the acquired data through an intelligent construction script to finish the processing and the analysis of the data to fill the analysis data pool.
5. The probe-based metadata acquisition method according to claim 1, wherein the probe program is implemented by a Go probe, and the characteristic information of the metadata is a Go program of an application program; the association of the probe program to the application program to collect metadata of the application program means that: embedding the Go probe into a binary file of the Go language in an embedded code manner, wherein the method comprises the following metadata acquisition steps:
calling a program debugging tool GDB as the rear end of the Go probe, and obtaining performance data and debugging information of the Go program by interacting with the Go program;
calling a pprof language performance analysis tool, triggering data acquisition and analysis through a preset API, and uploading acquired and analyzed data to a script analysis data pool;
the operating mechanism of the Go language is monitored, the operating state and performance data are identified, and specifically, the function of the runtimes library is called to obtain memory and GC data.
6. An application relation intelligent construction method based on a probe is characterized by calling a preset analysis data pool acquired by the metadata acquisition method according to any one of claims 3-5, and comprises the following steps:
a data acquisition step of acquiring data from a preset analysis data pool;
a feature selection step, namely performing feature selection on the preprocessed data, and selecting related data features according to a target relationship input or selected by a user;
marking each data and the associated semantic tags and relationship types;
a machine learning modeling step, namely calling a pre-built machine learning model to automatically learn data so as to acquire a data association relation;
and model application, namely applying the trained machine learning model to association relation recognition, and realizing mapping of application and metadata according to the recognized association relation, so as to automatically realize construction of application relation.
7. The method for intelligently constructing a probe-based application relationship according to claim 6, wherein in the machine learning modeling step, the machine learning model includes a decision tree model, a cluster model, or a classification algorithm model.
8. The probe-based application relationship intelligent construction method according to claim 6, comprising a data preprocessing step performed after the data acquisition step: and cleaning the collected data to remove noise.
9. Computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, is capable of implementing the probe-based application relationship intelligent construction method according to any one of claims 1 to 5 and/or the probe-based metadata acquisition method according to any one of claims 6 to 8.
10. A data management system comprising an application server, an application program execution end, and a data table repository, to which the processor and the processor are respectively connected, and further comprising a computer readable storage medium according to claim 9, wherein the computer program on the computer readable storage medium is executable by the processor.
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