CN114157679A - Cloud-native-based distributed application monitoring method, device, equipment and medium - Google Patents
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Abstract
The invention discloses a cloud-native-based distributed application monitoring method, a device, computer equipment and a medium, wherein the method comprises the following steps: the method comprises the steps of configuring a CaaS container monitoring service, acquiring application data based on the container monitoring service to obtain initial data, compressing and transmitting the initial data to a cloud for storage as target data, storing the target data, monitoring the stored target data, and performing early warning processing in a two-layer warning mode when an obtained monitoring result is abnormal, wherein the two-layer warning mode comprises a service warning mode and a system warning mode, and the data acquisition monitoring efficiency and the early warning timeliness are improved.
Description
Technical Field
The invention relates to the field of data security, in particular to a cloud-native-based distributed application monitoring method, device, equipment and medium.
Background
In the current cloud data storage, data corresponding to a container cluster needs to be analyzed so as to judge the state of the container cluster according to the data condition, and an application program or a node server is warned in time. Some of them adopt a Prometheus federation mode, with the increasing number of clusters, the Prometheus query performance is stuck, and meanwhile, because the number of container clusters is large, the data query and alarm process is tedious, the delay is high, in a multi-tenant and multi-cluster scene, the configuration is easy to make mistakes, and the efficiency is low.
Disclosure of Invention
The embodiment of the invention provides a cloud-native-based distributed application monitoring method and device, computer equipment and a storage medium, so as to improve the cloud-native-based distributed application monitoring efficiency.
In order to solve the above technical problem, an embodiment of the present application provides a cloud-based distributed application monitoring method, including:
configuring a CaaS container monitoring service;
acquiring application data based on the container monitoring service to obtain initial data;
compressing and transmitting the initial data to cloud storage to serve as target data, and storing the target data;
and monitoring the stored target data, and performing early warning processing in a two-layer warning mode when the obtained monitoring result is abnormal, wherein the two-layer warning comprises a service warning and a system warning.
Optionally, the configuring the CaaS-based container monitoring service includes: the type configuration of monitoring data, the monitoring configuration of system components and the monitoring configuration of business.
Optionally, the compressing and transmitting the initial data to cloud storage as target data includes:
distributing the initial data of each time period to different node servers according to a preset rule;
each node server analyzes and integrates the initial data in a multithreading mode, and merges the adjacent data with the same data attribute to obtain integrated data;
each node server transmits the integrated data to cloud storage, and stores the integrated data as target data in the cloud storage.
Optionally, after the compressing and transmitting the initial data to cloud storage as target data and storing the target data, the method includes:
acquiring each preset page query condition;
compressing the target data based on the preset page query condition to obtain a classification data set;
after receiving a query instruction, analyzing the query instruction to obtain a target query condition contained in the query instruction;
and acquiring classification data corresponding to the target query condition from the classification data set as a query result, and displaying the query result.
Optionally, before the monitoring the stored target data and performing early warning processing in a two-layer warning manner when the obtained monitoring result is abnormal, the method further includes:
setting an alarm rule and alarm sender configuration for each cluster through a configuration page, and writing the alarm rule into a monitoring rule of a main cluster;
and when the alarm inquiry information is received, inquiring the alarm information based on the monitoring inquiry component of the main cluster.
In order to solve the foregoing technical problem, an embodiment of the present application further provides a cloud-based distributed application monitoring apparatus, including:
the service configuration module is used for configuring the CaaS container monitoring service;
the data acquisition module is used for acquiring application data based on the container monitoring service to obtain initial data;
the data storage module is used for compressing and transmitting the initial data to cloud storage to serve as target data and storing the target data;
and the monitoring alarm module is used for monitoring the stored target data and carrying out early warning processing in a two-layer alarm mode when the obtained monitoring result is abnormal, wherein the two-layer alarm comprises a service alarm and a system alarm.
Optionally, the data storage module includes:
the data distribution unit is used for distributing the initial data of each time period to different node servers according to a preset rule;
the data processing unit is used for analyzing and integrating the initial data by each node server in a multithreading mode, and merging the adjacent data with the same data attribute to obtain integrated data;
and the data storage unit is used for transmitting the integrated data to cloud storage by each node server and storing the integrated data serving as target data to the cloud storage.
Optionally, the cloud-based native distributed application monitoring apparatus further includes:
the query condition acquisition module is used for acquiring each preset page query condition;
the data compression module is used for compressing the target data based on the preset page query condition to obtain a classification data set;
the data analysis module is used for analyzing the query instruction after receiving the query instruction to obtain a target query condition contained in the query instruction;
and the data query module is used for acquiring the classification data corresponding to the target query condition from the classification data set as a query result and displaying the query result.
Optionally, the cloud-based native distributed application monitoring apparatus further includes:
the rule writing module is used for setting an alarm rule and alarm sender configuration for each cluster through a configuration page and writing the alarm rule into a monitoring rule of a main cluster;
and the alarm query module is used for querying the alarm information based on the monitoring query component of the main cluster when the alarm query information is received.
In order to solve the technical problem, an embodiment of the present application further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the cloud-based native distributed application monitoring method when executing the computer program.
In order to solve the technical problem, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps of the cloud-based native distributed application monitoring method.
According to the cloud-native-based distributed application monitoring method, the cloud-native-based distributed application monitoring device, the computer equipment and the storage medium, the CaaS-based container monitoring service is configured, the application data is acquired based on the container monitoring service to obtain the initial data, the initial data is compressed and transmitted to the cloud for storage as the target data, the target data is stored, the stored target data is monitored, and when the obtained monitoring result is abnormal, early warning processing is performed in a two-layer warning mode, wherein the two-layer warning mode comprises a service warning and a system warning, and the data acquisition monitoring efficiency and the early warning timeliness are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a cloud-based native distributed application monitoring method of the present application;
FIG. 3 is a schematic diagram of an embodiment of a cloud-based native distributed application monitoring apparatus according to the present application;
FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, as shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like.
The terminal devices 101, 102, 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, E-book readers, MP3 players (Moving Picture E interface shows a properties Group Audio Layer III, motion Picture experts compress standard Audio Layer 3), MP4 players (Moving Picture E interface shows a properties Group Audio Layer IV, motion Picture experts compress standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the cloud-based native distributed application monitoring method provided in the embodiment of the present application is executed by a server, and accordingly, the cloud-based native distributed application monitoring apparatus is disposed in the server.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. Any number of terminal devices, networks and servers may be provided according to implementation needs, and the terminal devices 101, 102 and 103 in this embodiment may specifically correspond to an application system in actual production.
Referring to fig. 2, fig. 2 shows a cloud-based distributed application monitoring method according to an embodiment of the present invention, which is described by taking the method applied to the server in fig. 1 as an example, and is detailed as follows:
s201: and configuring a CaaS container monitoring service.
Optionally, configuring the CaaS-based container monitoring service includes: the type configuration of monitoring data, the monitoring configuration of system components and the monitoring configuration of business.
In this embodiment, a Receiver mode is adopted, and each sub-cluster Prometheus actively reports to a Receiver of the master cluster. The Receiver is configured to be in a cluster mode, and services are exposed through the router. And configuring remote write in Prometheus, and writing data into a receiver.
Prometheus is an open source service monitoring system and a time sequence database. The method has a high-dimensional data model, a user-defined query language, visual data display and an efficient storage strategy, is easy to operate and maintain, and the query language of Prometheus can filter and aggregate time series data through measurement indexes and tags.
Specifically, the CaaS-based container monitoring service needs to consider the following: A) the type of data monitored, B) system component monitoring configuration, and C) traffic monitoring configuration.
Regarding a), the monitored data types, the present embodiment mainly includes: raw monitoring index data (SideCar/CaaS monitor Receiver), compressed monitoring index data and monitoring index data generated according to a "recording rule".
In the embodiment, for the historical event triggering the alarm, the alarm rule is adopted to perform index analysis, and some new indexes affecting the alarm are obtained, and the new indexes are monitoring index data generated according to the recording rule.
Furthermore, by using the Prometous external _ labels, an own identifier is set for each cluster and is used as a basis for merging indexes.
Regarding B) system component monitoring configuration, the system component and container related indexes are exposed through a node-exporter, cadvisor and the like built in the OCP and are collected by Prometheus.
About C), the business monitors and disposes, if it is the service in the cluster, can dispose its control directly through ServiceMonitor; if the application is an application outside the cluster, the Endpoints and the Service can be added, and then the Service monitor is added.
Preferably, the index data provided by the service application is checked/converted according to a format conforming to the preset data.
S202: and acquiring application data based on the container monitoring service to obtain initial data.
S203: and compressing and transmitting the initial data to cloud storage to serve as target data, and storing the target data.
Specifically, the data of gathering through various modes is more, and the transmission of mass data is comparatively consuming the bandwidth, and transmission efficiency is also slower simultaneously, when saving, also will occupy more storage space, therefore, this embodiment is carrying out data transmission's in-process, through predetermineeing time interval, compresses and downsamples data, transmits again, effectively improves data transmission efficiency, reduces the storage occupation space.
Alternatively, the preset time interval may be one or multiple, for example, data in 5 minutes is compressed every 5 minutes, and data in 1 hour is compressed every 1 hour, and the specific time interval may be set according to actual needs, which is not limited herein.
In a specific optional embodiment, compressing and transmitting the initial data to the cloud storage as the target data includes:
distributing the initial data of each time period to different node servers according to a preset rule;
each node server analyzes and integrates the initial data in a multithreading mode, and merges the adjacent data with the same data attribute to obtain integrated data;
and each node server transmits the integrated data to the cloud storage, and stores the integrated data as target data in the cloud storage.
In a specific optional implementation manner, after compressing and transmitting the initial data to the cloud storage as the target data and storing the target data, the method includes:
acquiring each preset page query condition;
compressing the target data based on a preset page query condition to obtain a classification data set;
after receiving the query instruction, analyzing the query instruction to obtain a target query condition contained in the query instruction;
and acquiring classification data corresponding to the target query condition from the classification data set as a query result, and displaying the query result.
The preset page query condition may specifically be that query time is a main dimension, for example, "three days last", "ten days last", "one month last", "three months last", and the like.
The step of compressing the target data to obtain the classification data set refers to merging and optimizing the data according to a preset page query condition, for example, if the preset page query condition is' 10 days, all data from 11 th day to yesterday are collected, and the data of each day are merged and compressed according to time periods, so that the data volume is greatly reduced, and the readability of the data and the query efficiency during data query are improved.
S204: and monitoring the stored target data, and performing early warning processing in a two-layer warning mode when the obtained monitoring result is abnormal, wherein the two-layer warning comprises a service warning and a system warning.
In consideration of timeliness and high availability of the alarm, an alert manager component can be added through a unified management platform and configured through unified alarm configuration in a container cluster according to needs, but the cluster sends the alarm independently.
Optionally, the embodiment may display the monitoring result in a visual manner, where the specific display manner is based on a pre-configured parameter, for example, a line graph/bar graph, a pie graph, and the like.
Optionally, the alarm rule in this embodiment is added with the corresponding configuration in Rules according to the need, and may also be added in a system alarm rule configuration and a service alarm rule configuration manner.
The system alarm rule configuration refers to configuring alarm rules through a cluster console.
In a specific optional implementation manner, before monitoring the stored target data and performing early warning processing in a two-layer warning manner when the obtained monitoring result is abnormal, the method further includes:
setting an alarm rule and alarm sender configuration for each cluster through a configuration page, and writing the alarm rule into a monitoring rule of a main cluster;
and when the alarm inquiry information is received, inquiring the alarm information based on the monitoring inquiry component of the main cluster.
In this embodiment, a CaaS-based container monitoring service is configured, application data is acquired based on the container monitoring service to obtain initial data, the initial data is compressed and transmitted to a cloud for storage as target data, the target data is stored, the stored target data is monitored, and when an obtained monitoring result is abnormal, early warning processing is performed in a two-layer warning mode, the two-layer warning includes a service warning and a system warning, so that the data acquisition monitoring efficiency and the early warning timeliness are improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 3 is a schematic block diagram of a cloud-based distributed application monitoring apparatus in one-to-one correspondence with the cloud-based distributed application monitoring method according to the foregoing embodiment. As shown in fig. 3, the cloud-based native distributed application monitoring apparatus includes a service configuration module 31, a data acquisition module 32, a data storage module 33, and a monitoring alarm module 34. The functional modules are explained in detail as follows:
a service configuration module 31, configured to configure a CaaS-based container monitoring service;
the data acquisition module 32 is used for acquiring application data based on the container monitoring service to obtain initial data;
the data storage module 33 is configured to compress and transmit the initial data to cloud storage, serve as target data, and store the target data;
and the monitoring alarm module 34 is configured to monitor the stored target data, and perform early warning processing in a two-layer alarm manner when the obtained monitoring result is abnormal, where the two-layer alarm includes a service alarm and a system alarm.
Optionally, the data storage module 33 comprises:
the data distribution unit is used for distributing the initial data of each time period to different node servers according to a preset rule;
the data processing unit is used for analyzing and integrating the initial data by each node server in a multithreading mode, and merging the adjacent data with the same data attribute to obtain integrated data;
and the data storage unit is used for transmitting the integrated data to the cloud storage by each node server and storing the integrated data serving as target data to the cloud storage.
Optionally, the cloud-based native distributed application monitoring apparatus further includes:
the query condition acquisition module is used for acquiring each preset page query condition;
the data compression module is used for compressing the target data based on a preset page query condition to obtain a classified data set;
the data analysis module is used for analyzing the query instruction after receiving the query instruction to obtain a target query condition contained in the query instruction;
and the data query module is used for acquiring the classification data corresponding to the target query condition from the classification data set as a query result and displaying the query result.
Optionally, the cloud-based native distributed application monitoring apparatus further includes:
the rule writing module is used for setting an alarm rule and alarm sender configuration for each cluster through a configuration page and writing the alarm rule into a monitoring rule of a main cluster;
and the alarm query module is used for querying the alarm information based on the monitoring query component of the main cluster when the alarm query information is received.
For specific limitations of the cloud-based native distributed application monitoring apparatus, reference may be made to the above limitations of the cloud-based native distributed application monitoring method, which are not described herein again. The modules in the cloud-based native distributed application monitoring apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only the computer device 4 having the components connection memory 41, processor 42, network interface 43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or D interface display memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system installed in the computer device 4 and various types of application software, such as program codes for controlling electronic files. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute the program code stored in the memory 41 or process data, such as program code for executing control of an electronic file.
The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing an interface display program, which is executable by at least one processor to cause the at least one processor to perform the steps of the cloud-based native distributed application monitoring method as described above.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A cloud-native-based distributed application monitoring method is characterized by comprising the following steps:
configuring a CaaS container monitoring service;
acquiring application data based on the container monitoring service to obtain initial data;
compressing and transmitting the initial data to cloud storage to serve as target data, and storing the target data;
and monitoring the stored target data, and performing early warning processing in a two-layer warning mode when the obtained monitoring result is abnormal, wherein the two-layer warning comprises a service warning and a system warning.
2. The cloud-native-based distributed application monitoring method of claim 1, wherein said configuring the CaaS-based container monitoring service comprises: the type configuration of monitoring data, the monitoring configuration of system components and the monitoring configuration of business.
3. The cloud-based native distributed application monitoring method of claim 1, wherein said compressing the initial data for transmission to cloud storage as target data comprises:
distributing the initial data of each time period to different node servers according to a preset rule;
each node server analyzes and integrates the initial data in a multithreading mode, and merges the adjacent data with the same data attribute to obtain integrated data;
each node server transmits the integrated data to cloud storage, and stores the integrated data as target data in the cloud storage.
4. The cloud-based native distributed application monitoring method of claim 1, wherein after said compressing the initial data for transmission to cloud storage as target data and storing the target data, the method comprises:
acquiring each preset page query condition;
compressing the target data based on the preset page query condition to obtain a classification data set;
after receiving a query instruction, analyzing the query instruction to obtain a target query condition contained in the query instruction;
and acquiring classification data corresponding to the target query condition from the classification data set as a query result, and displaying the query result.
5. The cloud-based native distributed application monitoring method according to claim 1, wherein before the monitoring the stored target data and performing early warning processing in a two-layer warning manner when the obtained monitoring result is abnormal, the method further comprises:
setting an alarm rule and alarm sender configuration for each cluster through a configuration page, and writing the alarm rule into a monitoring rule of a main cluster;
and when the alarm inquiry information is received, inquiring the alarm information based on the monitoring inquiry component of the main cluster.
6. A cloud-native-based distributed application monitoring apparatus, comprising:
the service configuration module is used for configuring the CaaS container monitoring service;
the data acquisition module is used for acquiring application data based on the container monitoring service to obtain initial data;
the data storage module is used for compressing and transmitting the initial data to cloud storage to serve as target data and storing the target data;
and the monitoring alarm module is used for monitoring the stored target data and carrying out early warning processing in a two-layer alarm mode when the obtained monitoring result is abnormal, wherein the two-layer alarm comprises a service alarm and a system alarm.
7. The cloud-based native distributed application monitoring apparatus of claim 6, wherein the data storage module comprises:
the data distribution unit is used for distributing the initial data of each time period to different node servers according to a preset rule;
the data processing unit is used for analyzing and integrating the initial data by each node server in a multithreading mode, and merging the adjacent data with the same data attribute to obtain integrated data;
and the data storage unit is used for transmitting the integrated data to cloud storage by each node server and storing the integrated data serving as target data to the cloud storage.
8. The cloud-based native distributed application monitoring apparatus of claim 6, wherein the cloud-based native distributed application monitoring apparatus further comprises:
the query condition acquisition module is used for acquiring each preset page query condition;
the data compression module is used for compressing the target data based on the preset page query condition to obtain a classification data set;
the data analysis module is used for analyzing the query instruction after receiving the query instruction to obtain a target query condition contained in the query instruction;
and the data query module is used for acquiring the classification data corresponding to the target query condition from the classification data set as a query result and displaying the query result.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the cloud-based native distributed application monitoring method of any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium storing a computer program which, when executed by a processor, implements the cloud-based native distributed application monitoring method of any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115065511A (en) * | 2022-05-30 | 2022-09-16 | 济南浪潮数据技术有限公司 | Method and system for processing cluster abnormal event |
| CN115794538A (en) * | 2022-09-07 | 2023-03-14 | 上海道客网络科技有限公司 | Full link monitoring method and system for stateful application in cloud native scene |
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2021
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115065511A (en) * | 2022-05-30 | 2022-09-16 | 济南浪潮数据技术有限公司 | Method and system for processing cluster abnormal event |
| CN115794538A (en) * | 2022-09-07 | 2023-03-14 | 上海道客网络科技有限公司 | Full link monitoring method and system for stateful application in cloud native scene |
| CN115794538B (en) * | 2022-09-07 | 2023-08-04 | 上海道客网络科技有限公司 | Full-link monitoring method and system for stateful application in cloud primary scene |
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