CN116383018A - Method and system for self-defining flow tracking plug-in - Google Patents
Method and system for self-defining flow tracking plug-in Download PDFInfo
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Abstract
The application relates to the technical field of cloud computing, in particular to a method and a system for self-defining flow tracking plug-ins, wherein the method comprises the following steps: under Grafana environment, designating a panel plug-in as a NetTrace plug-in, and designating a datasource plug-in as a JSON API plug-in; inputting flow tracking query parameters at a JSON API plug-in unit, and sending the flow tracking query parameters to the netTrace plug-in unit; firstly, carrying out data processing on the flow tracking query parameters by utilizing the NetTrace plug-in, and then carrying out secondary data processing on the flow tracking query parameters by utilizing a cytoscape.js library; and drawing a visual view through the Canvas API and presenting a visual flow tracking result. The method and the device effectively solve the problem of how to realize the functions required by the CNI plug-in project of the Kubernetes by means of the Grafana custom plug-in, so that a user can integrate Grafana with the project to realize more comprehensive data analysis and monitoring.
Description
Technical Field
The present disclosure relates to the field of cloud computing technologies, and in particular, to a method and system for self-defining a flow tracking plug-in.
Background
Grafana is used as a data visualization and monitoring platform of open source, and allows users to perform custom development and plug-in expansion according to own requirements so as to meet personalized data visualization and monitoring requirements. Grafana provides APIs and documents for plug-in development, and users can develop custom charts, panels, data sources and data processing plug-ins according to own requirements and integrate the custom charts, panels, data sources and data processing plug-ins into Grafana so as to realize richer and personalized data visualization and monitoring functions. In addition, the user can customize the panel according to own requirements, including selecting different data sources, adjusting data display and presentation modes, configuring interaction and linkage among the panels, and the like, so that more personalized and visual data display is realized.
There are many plug-ins offered by Grafana authorities, but in actual production use various customized plug-in resources are often required to expose the data. Currently, in a certain CNI plug-in project of Kubernetes, the project requirement can visualize the flow among various Pod in the Kubernetes cluster, and specifically, the project requirement can show a network card in the middle of the flow, packet capturing information on the network card, the flow path direction, the flow tracking result and a misbeat suggestion of tracking failure. Since the Grafana platform custom plug-in allows the developer to create new chart types and styles according to his own needs. When the requirement of the project on the visualization of the trace path of the cluster network is faced, how to implement the function by means of the Grafana custom plug-in, so that a user can integrate Grafana with the project to realize more comprehensive data analysis and monitoring is a problem to be solved at present.
Disclosure of Invention
The application provides a method and a system for self-defining flow tracking plug-in, which can solve the problem of how to realize the functions required by CNI plug-in projects of Kubernetes by means of Grafana self-defining plug-in, so that a user can integrate Grafana with the projects so as to realize more comprehensive data analysis and monitoring. The application provides the following technical scheme:
in a first aspect, the present application provides a method for customizing a flow tracking plug-in, the method comprising:
under Grafana environment, designating a panel plug-in as a NetTrace plug-in, and designating a datasource plug-in as a JSON API plug-in;
inputting flow tracking query parameters at a JSON API plug-in unit, and sending the flow tracking query parameters to the netTrace plug-in unit;
firstly, carrying out data processing on the flow tracking query parameters by utilizing the NetTrace plug-in, and then carrying out secondary data processing on the flow tracking query parameters by utilizing a cytoscape.js library;
and drawing a visual view through the Canvas API and presenting a visual flow tracking result.
In a specific embodiment, the inputting the traffic trace query parameter at the JSON API plug-in and sending the traffic trace query parameter to the NetTrace plug-in includes:
the flow tracking query parameters include Field data, path Field data, and Body Field data;
wherein the Field data is: $. Footprints, $result, $suggestions;
the Path field data is: /tracker/trace;
the Body field data is written based on IP address information, port information, and a transport protocol.
In a specific embodiment, the first data processing the traffic trace query parameters using the NetTrace plug-in includes:
the NetTrace plug-in performs data processing on the Field data, and groups the Field data into nodes node data, edges data, result tracking result data and suggestings result suggestion data;
and if the result tracking result data is true, the result tracking result data indicates that the query is successful, the result suggestion data is null, and if the result tracking result data is false, the result tracking result data indicates that the query is failed, and the result suggestion data outputs a debug suggestion.
In a specific embodiment, the NetTrace plug-in performs data processing on the Field data and groups it into nodes node data, edges data, result trace result data, and suggestings result suggestion data, including:
the NetTrace plug-in generates data in the format of required source network card, target network card and packet capturing data according to the foltprint data;
according to the data, the network card data are aggregated into edge and node data, wherein the edge data comprise source network card, target network card, packet sending direction and packet grabbing data; the node data comprises a network card name, a host computer, health level, packet grabbing data and the like.
In a specific embodiment, the reusing the cytoscape. Js library to perform secondary data processing on the traffic trace query parameters includes:
and carrying out data processing on the nodes node data and the edges data through the cytoscape.js library, wherein the data processing comprises node and edge creation, attribute setting, layout calculation and the like.
In a specific embodiment, the layout calculation uses the cola algorithm built in the cytoscape. Js library to layout nodes and edges on a two-dimensional plane according to some custom constraints specified in the cola layout.
In a specific embodiment, the drawing the visual view through the Canvas API and presenting the visual traffic tracking results comprises:
and the Canvas API draws corresponding nodes, edges, texts and animations according to the well processed layout of the cytoscape.js library, and presents a complete visual flow tracking result.
In a second aspect, the present application provides a system for self-defining a flow tracking plug-in, which adopts the following technical scheme:
a system of custom traffic tracking plug-ins, comprising:
the plug-in specifying module is used for specifying a panel plug-in as a NetTrace plug-in and a datasource plug-in as a JSON API plug-in under the Grafana environment;
the parameter input module is used for inputting a flow tracking query parameter at the JSON API plug-in unit and sending the flow tracking query parameter to the netTrace plug-in unit;
the data processing module is used for carrying out data processing on the flow tracking query parameters by utilizing the NetTrace plug-in, and then carrying out secondary data processing on the flow tracking query parameters by utilizing a cytoscape.js library;
and the result presentation module is used for drawing a visual view through the Canvas API and presenting a visual flow tracking result.
In a third aspect, the present application provides an electronic device comprising a processor and a memory; the memory stores a program which is loaded and executed by the processor to realize the method for customizing the flow tracking plug-in.
In a fourth aspect, the present application provides a computer readable storage medium having a program stored therein, which when executed by a processor is configured to implement the method of a custom flow tracking plug-in.
In summary, the beneficial effects of the present application at least include: according to the function requirement of the CNI plug-in items of the Kubernetes, the function can be realized by means of the Grafana custom plug-in, so that a user can integrate the Grafana with the CNI plug-in items of the Kubernetes, and more comprehensive data analysis and monitoring can be realized.
Data processing is carried out on flow tracking query parameters through a NetTrace plug-in, so that data packets are aggregated into node data, edge data, result tracking result data and suggestions result suggestion data, then data processing is carried out on the node data and the edge data through a cytoscape.js library, specifically, the creation of the node and the edge, attribute setting and layout calculation are included, a cola algorithm built in the cytoscape.js library is used, the node and the edge are laid out on a two-dimensional plane according to some custom constraint conditions specified in the cola layout, finally corresponding nodes, edges, texts and animations are drawn through a Canvas API according to the layout processed by the cytoscape.js library, and a complete visual flow tracking result is presented; the method can solve the problem of how to realize the functions required by the CNI plug-in project of the Kubernetes by means of the Grafana custom plug-in, so that a user can integrate Grafana with the project to realize more comprehensive data analysis and monitoring.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical means of the present application more clearly understood, it can be implemented according to the content of the specification, and the following detailed description of the preferred embodiments of the present application will be given with reference to the accompanying drawings.
Drawings
Fig. 1 is a flow chart of a method for customizing a flow tracking plug-in according to one embodiment of the present application.
FIG. 2 is an exemplary schematic diagram of a method of custom flow tracking plug-ins provided in one embodiment of the present application.
Fig. 3 is an exemplary diagram of Body field data in one embodiment of the present application.
FIG. 4 is a block diagram of a system of custom flow tracking plug-ins provided in one embodiment of the present application.
FIG. 5 is a block diagram of an electronic device of a custom traffic tracking plug-in provided in one embodiment of the present application.
Reference numerals: 310. a plug-in specifying module; 320. a parameter input module; 330. a data processing module; 340. and a result presentation module.
Detailed Description
The detailed description of the present application is further described in detail below with reference to the drawings and examples. The following examples are illustrative of the present application, but are not intended to limit the scope of the present application.
First, a description will be given of several terms involved in the present application.
Grafana: grafana is a popular open source data visualization and monitoring platform supporting a variety of data sources, including InfluxDB, prometheus, elasticsearch, mySQL, etc. Grafana provides intuitive and easy-to-use charts, panels, dashboards and alarm functions that enable users to learn deep about trends, states and performance of data. Grafana is very high in expandability, and users can conduct custom development and plug-in expansion according to own requirements. In addition, grafana also supports team collaboration and rights management, and users can share and manage panels and dashboards. Grafana has wide application in the fields of monitoring, operation and maintenance, data analysis, business decision and the like, and becomes one of important tools in the fields of data visualization and monitoring.
JSON API: the JSON API is a Grafana open source plug-in that allows a user to visualize data from any URL that returns JSON, e.g., REST API (Representational State Transfer) stateful transfer is a style of software architecture, or static file server.
Cytoscape.js: is a JavaScript-based graphic visualization library that provides a variety of layout algorithms to help users better expose graphic data.
The cola algorithm: is a layout algorithm built in cytoscape. Js, and has the advantages that: high efficiency: the cola algorithm adopts an iterative mode, and the optimal solution of the layout is achieved by continuously adjusting the positions of the nodes. The cola algorithm runs faster than other layout algorithms and can process graphics data containing a large number of nodes and edges.
Flexibility: the cola algorithm provides a number of parameters to control layout effects such as distance between nodes, length of edges, strength of springs, etc. These parameters can be adjusted according to the needs of the user, thereby realizing different layout effects.
Aesthetic properties: the cola algorithm can automatically adjust the positions of the nodes according to the relation among the nodes, so that the graphic data is more attractive and easy to understand. Meanwhile, the cola algorithm also supports grouping, alignment and other operations of the nodes, and the readability of the graphic data is further improved.
The Canvas API is a standard API in HTML5 for drawing graphics, images, animations, and the like on Web pages. It provides a set of methods and properties that can create and manipulate pixel-level graphics in Canvas elements.
In summary, the cola algorithm is an efficient, flexible and attractive layout algorithm built in cytoscape.
Optionally, the method for self-defining the flow tracking plug-in provided by each embodiment is used for illustration in an electronic device, where the electronic device is a terminal or a server, and the terminal may be a mobile phone, a computer, a tablet computer, a scanner, an electronic eye, a monitoring camera, etc., and the embodiment does not limit the type of the electronic device.
Referring to fig. 1, a flow chart of a method for customizing a flow tracking plug-in according to one embodiment of the present application is shown, where the method at least includes the following steps:
Firstly, a user designates a panel plug-in as a NetTrace plug-in and designates a datasource plug-in as a JSON API plug-in on a Grafana panel page in a Grafana environment.
Referring to fig. 2, after designating the plug-in, the user needs to input a flow trace query parameter at the JSON API plug-in, specifically, the flow trace query parameter includes Field data, path Field data, and Body Field data.
Wherein the Field data is: $. Footprints, $result, $suggestions;
the Path field data is: /tracker/trace;
body field data is written based on IP address information, port information, and transport protocols. Referring to fig. 3, for one embodiment of Body field data, the source IP address, the destination IP address, the source port, the destination port, the transmission protocol, and the interface settings are shown in detail.
After the flow tracking query parameters are input, the JSON API plug-in sends the formulated fieldfield data to the NetTrace plug-in.
After the JSON API plug-in sends the formulated Field data to the NetTrace plug-in, the NetTrace plug-in performs data processing on the Field data and groups it into nodes node data, edges data, result tracking result data, and statistics result suggestion data. Specifically, the NetTrace plug-in firstly generates data in the format of required source network card, target network card and packet capturing data according to the foltprint data, and then aggregates the network card data into edge and node data according to the data, wherein the edge data comprises the source network card, the target network card, the packet sending direction and the packet capturing data, and the node data comprises the name of the network card, the host computer, the health grade, the packet capturing data and the like.
If the result tracking result data is true, the result tracking result data indicates that the query is successful, and if the result tracking result data is false, the result tracking result data indicates that the query is failed, and the result suggestion data outputs a debug suggestion.
And 104, performing data processing on the nodes data and the edges data by using a cytoscape.js library.
After the NetTrace plug-in groups Field data into nodes node data and edge data, the cytoscape.js library performs data processing on the nodes node data and edge data, and specifically, the data processing includes creation of nodes and edges, attribute setting, layout calculation and the like, wherein a cola algorithm built in the cytoscape.js library is used in the layout calculation, and the nodes and edges are laid out on a two-dimensional plane according to some custom constraint conditions specified in the cola layout. The cola algorithm runs faster than other layout algorithms and can process graphics data containing a large number of nodes and edges.
And drawing corresponding nodes, edges, texts and animations by using a Canvas API according to the well processed layout of the Cytoscape. Js library, and presenting a complete visual flow tracking result.
In summary, and referring to fig. 2, after the JSON API plug-in inputs the traffic trace query parameters,
the method comprises the steps of carrying out data processing on Field data in the Field data through a NetTrace plug-in, so that data packets are aggregated into nodes node data and edge data, carrying out data processing on the nodes node data and the edge data through a cytoscape.js library, specifically, creating nodes and edges, setting attributes and calculating layout, using a cola algorithm built in the cytoscape.js library, laying out the nodes and the edges on a two-dimensional plane according to some custom constraint conditions appointed in the cola layout, and finally drawing corresponding nodes, edges, texts and animations through a Canvas API according to the layout processed by the cytoscape.js library, and presenting complete visual flow tracking results.
According to the function requirement of the CNI plug-in items of the Kubernetes, the function can be realized by means of the Grafana custom plug-in, so that a user can integrate the Grafana with the CNI plug-in items of the Kubernetes, and more comprehensive data analysis and monitoring can be realized.
FIG. 4 is a block diagram of a system of custom flow tracking plug-ins provided in one embodiment of the present application. The device at least comprises the following modules:
and the plug-in specifying module 310 is configured to specify a panel plug-in as a NetTrace plug-in and a datasource plug-in as a JSON API plug-in the Grafana environment.
The parameter input module 320 is configured to input the flow tracking query parameter at the JSON API plug-in, and send the flow tracking query parameter to the NetTrace plug-in.
The data processing module 330 is configured to perform data processing on the flow tracking query parameter by using the NetTrace plug-in, and then perform secondary data processing on the flow tracking query parameter by using the cytoscape.
The result presenting module 340 is configured to draw a visual view through the Canvas API and present a visual traffic tracking result.
For relevant details reference is made to the method embodiments described above.
Fig. 5 is a block diagram of an electronic device provided in one embodiment of the present application. The device comprises at least a processor 401 and a memory 402.
Processor 401 may include one or more processing cores such as: 4 core processors, 8 core processors, etc. The processor 401 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 401 may also include a main processor, which is a processor for processing data in an awake state, also called a CPU (Central Processing Unit ), and a coprocessor; a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 401 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 401 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.
Memory 402 may include one or more computer-readable storage media, which may be non-transitory. Memory 402 may also include high-speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 402 is used to store at least one instruction for execution by processor 401 to implement the method of the custom traffic tracking plug-in provided by the method embodiments in the present application.
In some embodiments, the electronic device may further optionally include: a peripheral interface and at least one peripheral. The processor 401, memory 402, and peripheral interfaces may be connected by buses or signal lines. The individual peripheral devices may be connected to the peripheral device interface via buses, signal lines or circuit boards. Illustratively, peripheral devices include, but are not limited to: radio frequency circuitry, touch display screens, audio circuitry, and power supplies, among others.
Of course, the electronic device may also include fewer or more components, as the present embodiment is not limited in this regard.
Optionally, the application further provides a computer readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the method of the custom traffic tracking plug-in of the above method embodiment.
Optionally, the application further provides a computer product, where the computer product includes a computer readable storage medium, where a program is stored, and the program is loaded and executed by a processor to implement the method of the foregoing method embodiment for customizing a flow tracking plug-in.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method of customizing a flow tracking plug-in, the method comprising:
under Grafana environment, designating a panel plug-in as a NetTrace plug-in, and designating a datasource plug-in as a JSON API plug-in;
inputting flow tracking query parameters at a JSON API plug-in unit, and sending the flow tracking query parameters to the netTrace plug-in unit;
firstly, carrying out data processing on the flow tracking query parameters by utilizing the NetTrace plug-in, and then carrying out secondary data processing on the flow tracking query parameters by utilizing a cytoscape.js library;
and drawing a visual view through the Canvas API and presenting a visual flow tracking result.
2. The method of claim 1, wherein inputting the traffic trace query parameters at the JSON API plug-in and sending the traffic trace query parameters to the NetTrace plug-in comprises:
the flow tracking query parameters include Field data, path Field data, and Body Field data;
wherein the Field data is: $. Footprints, $result, $suggestions;
the Path field data is: /tracker/trace;
the Body field data is written based on IP address information, port information, and a transport protocol.
3. The method of the custom traffic tracking plug-in according to claim 2, wherein the first data processing the traffic tracking query parameters with the NetTrace plug-in comprises:
the NetTrace plug-in performs data processing on the Field data, and groups the Field data into nodes node data, edges data, result tracking result data and suggestings result suggestion data;
and if the result tracking result data is true, the result tracking result data indicates that the query is successful, the result suggestion data is null, and if the result tracking result data is false, the result tracking result data indicates that the query is failed, and the result suggestion data outputs a debug suggestion.
4. The method of claim 3, wherein the NetTrace plug-in data processing the Field data to aggregate packets into nodes node data, edges data, result trace result data, and statistics result suggestion data comprises:
the NetTrace plug-in generates data in a required source network card, target network card and packet capturing data format according to the foltprint data;
according to the data, the network card data are aggregated into edge and node data, wherein the edge data comprise a source network card, a target network card, a packet sending direction and packet grabbing data; the node data comprises a network card name, a host computer, a health grade, packet grabbing data and the like.
5. The method of claim 3, wherein the re-using a cytoscape. Js library to perform secondary data processing on the flow tracking query parameters comprises:
and carrying out data processing on the nodes node data and the edges data through the cytoscape.js library, wherein the data processing comprises node and edge creation, attribute setting, layout calculation and the like.
6. The method of claim 5, wherein the layout calculation uses a cola algorithm built in the cytoscape js library to layout nodes and edges on a two-dimensional plane according to some custom constraints specified in the cola layout.
7. The method of custom traffic tracking plug-in of claim 5, wherein the drawing a visual view through a Canvas API and presenting a visual traffic tracking result comprises:
and the Canvas API draws corresponding nodes, edges, texts and animations according to the well processed layout of the cytoscape.js library, and presents a complete visual flow tracking result.
8. A system for custom flow tracking plug-ins, comprising:
the plug-in specifying module (310) is used for specifying a panel plug-in as a NetTrace plug-in and a datasource plug-in as a JSON API plug-in the Grafana environment;
the parameter input module (320) is used for inputting the flow tracking query parameters at the JSON API plug-in unit and sending the flow tracking query parameters to the netTrace plug-in unit;
the data processing module (330) is used for performing data processing on the flow tracking query parameters by utilizing the NetTrace plug-in, and performing secondary data processing on the flow tracking query parameters by utilizing a cytoscape.js library;
and the result presentation module (340) is used for drawing a visual view through the Canvas API and presenting a visual flow tracking result.
9. An electronic device comprising a processor and a memory; stored in the memory is a program that is loaded and executed by the processor to implement a method of customizing a traffic tracking plug-in as claimed in any one of claims 1 to 7.
10. A computer readable storage medium, wherein a program is stored in the storage medium, which program, when executed by a processor, is adapted to carry out a method of a custom flow tracking plug-in according to any of claims 1 to 7.
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| CN107623611A (en) * | 2017-09-22 | 2018-01-23 | 国云科技股份有限公司 | A kind of flux monitoring system of cloud platform virtual machine |
| CN107786391A (en) * | 2017-11-03 | 2018-03-09 | 郑州云海信息技术有限公司 | A kind of method for monitoring network to OpenStack based on Grafana |
| CN114785690A (en) * | 2022-03-30 | 2022-07-22 | 中国人寿保险股份有限公司 | Monitoring method based on service grid and related equipment |
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