CN115567526B - Data monitoring method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the application provides a data monitoring method, a device, equipment and a medium, which are used for receiving a monitoring request; acquiring metadata of the computing units of the cluster system through collectors deployed in containers of the computing units according to the monitoring request; obtaining application monitoring data of an application layer of the cluster system according to the metadata; generating alarm information according to the application monitoring data; the collector is deployed in the container of the computing unit, so that the complexity of system and application access is simplified, the monitoring data of the application layer is directly obtained through the collector, the application end does not need to be changed, the application end does not invade, and compared with the case that the collector is independently deployed outside the cluster, the time delay for obtaining the monitoring data is greatly reduced, and the data collection efficiency is improved.
Description
Technical Field
The present application relates to the field of databases, and in particular, to a method, apparatus, device, and medium for monitoring data.
Background
At present, for a cluster system, an agent collector needs to be individually connected to each application of the cluster system to collect monitoring data of an application layer of the cluster system, so that the number of agent collectors is large, an application end needs to be modified, and an application end needs to be connected in an invasive manner in a monitoring process, so that the problems of incapability of customizing the monitoring data according to own requirements, complex monitoring flow and increased maintenance cost of the cluster system are caused.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the related art to a certain extent.
Therefore, an object of the embodiments of the present application is to provide a data monitoring method, apparatus, device, and medium, which can obtain monitoring data of an application layer of a cluster system through a collector deployed in a container of a computing unit, thereby improving data collection efficiency.
To achieve the above object, a first aspect of an embodiment of the present application provides a data monitoring method, applied to a cluster system, where the data monitoring method includes:
Receiving a monitoring request;
According to the monitoring request, acquiring metadata of a computing unit of the cluster system through a collector, wherein the collector is deployed in a container of the computing unit;
obtaining application monitoring data of an application layer of the cluster system according to the metadata;
And generating alarm information according to the application monitoring data.
In some embodiments, before the generating the alert information according to the application monitoring data, the data monitoring method further includes:
acquiring system monitoring data of a system layer of the cluster system according to the monitoring request;
the generating the alarm information according to the application monitoring data comprises the following steps:
And generating alarm information according to the application monitoring data and the system monitoring data.
In some embodiments, the cluster system includes at least one node on which the computing unit operates;
The acquiring system monitoring data of the system layer of the cluster system comprises the following steps:
Acquiring first monitoring data of the node, wherein the first monitoring data is used for reflecting the host operating condition of the node;
acquiring second monitoring data of the node, wherein the second monitoring data is used for reflecting the container operation condition of the node;
and acquiring third monitoring data of the container management system layer surface of the cluster system.
In some embodiments, the cluster system comprises a plurality of clusters provided with monitoring databases, all of which are connected to a total database;
Before the alarm information is generated according to the application monitoring data and the system monitoring data, the data monitoring method comprises the following steps:
Acquiring the application monitoring data and the system monitoring data through a first exposure interface of the cluster according to a first data acquisition task configured by the monitoring database;
Storing the application monitoring data and the system monitoring data in the monitoring database;
acquiring the application monitoring data and the system monitoring data stored in the monitoring database through a second exposure interface of the monitoring database according to a second data acquisition task configured by the total database;
and storing the application monitoring data and the system monitoring data acquired from the monitoring database in the total database.
In some embodiments, the generating alert information from the application monitoring data and the system monitoring data includes:
invoking the application monitoring data and the system monitoring data from the total database;
determining alert data from the application monitoring data and the system monitoring data;
and generating the alarm information according to the alarm data.
In some embodiments, the acquiring, by the collector, metadata of a computing unit of the cluster system according to the monitoring request includes:
according to the monitoring request, the collector calls a communication interface of the cluster system to acquire the metadata;
The metadata is stored in a full list.
In some embodiments, the metadata includes a name of a computing center, an internet protocol address, and a management expansion port to which the container framework application corresponds;
The obtaining the application monitoring data of the application layer of the cluster system according to the metadata comprises the following steps:
acquiring the name, the internet protocol address and the management expansion port of the computing center from the full list;
Obtaining the application monitoring data according to the name of the computing center, an Internet protocol address and a management expansion port;
and converting the format of the application monitoring data to obtain a character string text of the application monitoring data, wherein the character string text is matched with the data acquisition format of the monitoring database.
To achieve the above object, a second aspect of the embodiments of the present application provides a data monitoring device, applied to a cluster system, including:
A request receiving unit for receiving a monitoring request;
The metadata acquisition unit is used for acquiring metadata of the computing units of the cluster system through collectors according to the monitoring request, and the collectors are deployed in containers of the computing units;
An application monitoring data acquisition unit, configured to obtain application monitoring data of an application layer of the cluster system according to the metadata;
and the alarm generating unit is used for generating alarm information according to the application monitoring data.
To achieve the above object, a third aspect of the embodiments of the present application provides an electronic device including a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for implementing connection communication between the processor and the memory, the program implementing the data monitoring method as described above when executed by the processor.
To achieve the above object, a third aspect of the embodiments of the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions for causing a computer to perform the data monitoring method as described above.
The embodiment of the application discloses a data monitoring method, a device, equipment and a medium, which are used for receiving a monitoring request; acquiring metadata of the computing units of the cluster system through collectors deployed in containers of the computing units according to the monitoring request; obtaining application monitoring data of an application layer of the cluster system according to the metadata; generating alarm information according to the application monitoring data; the collector is deployed in the container of the computing unit, so that the complexity of system and application access is simplified, the monitoring data of the application layer is directly obtained through the collector, the application end does not need to be changed, the application end does not invade, and compared with the case that the collector is independently deployed outside the cluster, the time delay for obtaining the monitoring data is greatly reduced, and the data collection efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made with reference to the accompanying drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and other drawings may be obtained according to these drawings without the need of inventive labor for those skilled in the art.
FIG. 1 is a step diagram of a data monitoring method according to an embodiment of the present application;
FIG. 2 is a sub-step diagram of step S210 provided by an embodiment of the present application;
FIG. 3 is a sub-step diagram of step S220 provided by an embodiment of the present application;
fig. 4 is a step diagram of step S230 provided in an embodiment of the present application;
FIG. 5 is a sub-step diagram of step S230 provided by an embodiment of the present application;
Fig. 6 is a step diagram of steps S241 to S244 provided in the embodiment of the present application;
FIG. 7 is a sub-step diagram of step S300 provided by an embodiment of the present application;
FIG. 8 is a schematic block diagram of a cluster system provided by an embodiment of the application;
FIG. 9 is a block diagram of a data monitoring device according to an embodiment of the present application;
fig. 10 is a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
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 herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.
First, several nouns involved in the present application are parsed:
Artificial intelligence (ARTIFICIAL INTELLIGENCE, AI): the system is a theory, a method, a technology and an application system which simulate, extend and extend human intelligence by using a digital computer or a machine controlled by the digital computer, sense environment, acquire knowledge and acquire an optimal result by using the knowledge. In other words, artificial intelligence is an integrated technology of computer science that attempts to understand the essence of intelligence and to produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence, i.e. research on design principles and implementation methods of various intelligent machines, enables the machines to have functions of sensing, reasoning and decision. The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Natural language processing (Nature Language processing, NLP): is an important direction in the fields of computer science and artificial intelligence. The method can be used for researching various theories and methods for realizing effective communication between people and computers by using natural language, and natural language processing is a science integrating linguistics, computer science and mathematics. The field relates to natural language, namely language used by people in daily life, so that the field has close relation with the study of linguistics. Natural language processing techniques typically include text processing, semantic understanding, machine translation, robotic questions and answers, knowledge graph techniques, and the like.
Machine learning (MACHINE LEARNING, ML), which is a multi-domain interdisciplinary, involves multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, etc., is a special study of how a computer simulates or implements learning behavior of a human being to obtain new knowledge or skills, and reorganizes existing knowledge structures to continuously improve their own performance. Machine learning is the core of artificial intelligence and is the fundamental approach to make computers have intelligence, which is applied throughout various fields of artificial intelligence, and machine learning (deep learning) generally includes technologies such as artificial neural networks, confidence networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.
The cluster system (clustered system) refers to a group (a plurality of) of mutually independent computers, and utilizes a large computer service system formed by a high-speed communication network, and each cluster node (i.e. each computer in the cluster) is an independent server for running respective services. These servers may communicate with each other, cooperatively provide applications, system resources, and data to users, and be managed in a single system mode.
In order to solve the problems in the related art, an object of an embodiment of the present application is to provide a data monitoring method, apparatus, device, and medium, which receives a monitoring request; acquiring metadata of the computing units of the cluster system through collectors deployed in containers of the computing units according to the monitoring request; obtaining application monitoring data of an application layer of the cluster system according to the metadata; generating alarm information according to the application monitoring data; the collector is deployed in the container of the computing unit, so that the complexity of system and application access is simplified, the monitoring data of the application layer is directly obtained through the collector, the application end does not need to be changed, the application end does not invade, and compared with the case that the collector is independently deployed outside the cluster, the time delay for obtaining the monitoring data is greatly reduced, and the data collection efficiency is improved.
Embodiments of the present application will be further described below with reference to the accompanying drawings.
The embodiment of the application provides a data monitoring method which is applied to a cluster system. Referring to fig. 8, the cluster system includes a plurality of clusters including a plurality of nodes on which computing units operate.
Node is the smallest computational hardware unit in a cluster, which is a representation of a single machine in a cluster. The nodes may be physical machines in a data center or virtual machines on a cloud provider. By setting up the nodes there is no concern about the unique nature of any single machine, but rather each machine can simply be viewed as a set of usable CPU resources and RAM resources. In this way, any machine may replace any other machine in the cluster. Nodes aggregate resources to form a more powerful machine. When a program is deployed into a cluster, it will intelligently handle assigning work to your individual nodes. If any nodes are added or deleted, the cluster will switch in operation as needed.
A container is a program running on a container management system. Of course, multiple programs may be added to a single container. There are many pre-built images that can be deployed on a container management system. Containerization allows for the creation of a self-contained Linux execution environment. Any program and all of its dependencies can be packaged into a file. Creating a container can be accomplished programmatically to form powerful CI and CD pipelines.
The computing unit Pod is a basic computing unit of the container management system. The container management system typically does not run containers directly, encapsulating one or more containers into the high-level structure of the computing unit Pod. Any container in the same Pod will share the same namespaces and local networks. The containers can easily communicate with other containers in the same container as if they were on the same machine while maintaining some degree of isolation.
The computing units Pod are typically managed by an abstraction layer, which is called deployment (Deployment). The main purpose of deployment is to state how many copies one Pod should run at the same time. When a deployment is added to the cluster, it will automatically spin up the number of computing units Pod needed and then monitor them. If one computing unit Pod disappears, the deployment will automatically recreate it. The deployment can realize automatic management only by declaring the expected state of the system.
Referring to fig. 1, the data monitoring method includes, but is not limited to, the following steps:
step S100, receiving a monitoring request;
Step S210, acquiring metadata of a computing unit of the cluster system through a collector deployed in a container of the computing unit according to a monitoring request;
step S220, obtaining application monitoring data of an application layer of the cluster system according to the metadata;
Step S300, alarm information is generated according to the application monitoring data.
For step S100, the user sends a monitoring request to a background server of the cluster system through the operation terminal, and the background server of the cluster system receives the monitoring request of the user.
Specifically, the operation terminal in the present application may include, but is not limited to, any one or more of a smart watch, a smart phone, a computer, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a smart voice interaction device, a smart home appliance, or a vehicle-mounted terminal.
The communication connection between the operating terminal and the server may be established via a wireless network or a wired network using standard communication techniques and/or protocols, the network may be configured as the internet, or any other network including, but not limited to, a local area network (Local Area Network, LAN), a metropolitan area network (Metropolitan Area Network, MAN), a wide area network (Wide Area Network, WAN), a mobile, wired or wireless network, a private network, or any combination of virtual private networks, for example.
For step S210 and step S220, according to the monitoring request, acquiring metadata of a computing unit of the cluster system through a collector deployed in a container of the computing unit, and obtaining application monitoring data of an application layer of the cluster system according to the metadata.
The collector is deployed in the container of the computing unit, so that the complexity of system and application access is simplified, the monitoring data of the application layer can be directly collected in a non-invasive way through the collector, the application end is not required to be changed, and the application end is not invasive; compared with the independent deployment of collectors outside the clusters, the time delay for acquiring the monitoring data is greatly reduced, and the data acquisition efficiency is improved.
The thread processing utilization rate of the collector is high, and the high timeliness of monitoring data can be met.
Referring to fig. 2, specifically, for step S210, according to the monitoring request, metadata of a computing unit of the cluster system is acquired by the collector, including, but not limited to, the following steps:
step S211, according to the monitoring request, the collector calls a communication interface of the cluster system to acquire metadata;
step S212, the metadata is stored in the full list.
Specifically, the metadata includes a name of a computing center, an internet protocol address, and a management expansion port to which the container framework application corresponds. The internet protocol address is the ip address, and the management expansion port is the jmx port.
It will be appreciated that the present embodiment gives an example in which metadata includes a name of a computing center, an internet protocol address, and a management expansion port corresponding to a container framework application, but this should not limit the type of metadata in the embodiment of the present application. In other embodiments, the metadata may employ other types of data, such as performance data of a computing center, and the like.
For step S211, the collector acquires metadata of each computing unit pod corresponding to each deployed application in the full-size nalespace of the current cluster by calling the APISERVER interface of the cluster.
For step S212, the name of the computing center, the internet protocol address, and the management expansion port are stored in a full volume list, specifically a full volume pod list.
Referring to fig. 3, for step S220, obtaining application monitoring data of an application layer of the cluster system according to metadata includes:
step S221, obtaining the name, the Internet protocol address and the management expansion port of the computing center from the full list;
step S222, obtaining application monitoring data according to the name of the computing center, the Internet protocol address and the management expansion port;
Step S223, converting the format of the application monitoring data to obtain a character string text of the application monitoring data, wherein the character string text is matched with the data acquisition format of the monitoring database.
For step S221, a full-quantity pod list is acquired based on the pks API, and then metadata of each computing unit pod, that is, the name of the computing center, the internet protocol address, and the management expansion port, is read from the full-quantity pod list.
For step S222, the internet protocol address and the management expansion port of each computing unit pod are traversed circularly, and the application monitoring data of the computing center pod corresponding to the name is acquired according to the name of the computing center. Specifically, the application monitoring data is custom springboot monitoring data.
It will be appreciated that if the internet protocol address is 192.168.110.1 and the management extension port is 88, then the internet protocol address and management extension port may constitute 192.168.110.1:88.
For step S223, since the application monitoring data needs to be stored in the monitoring database in the subsequent step, format conversion is required to be performed on the application monitoring data to obtain a string text of the application monitoring data, so that the string text is matched with the data acquisition format of the monitoring database.
Specifically, the application monitoring data of the application layer of the cluster system includes the current thread busy number (currentThreadsBusy) of springboot, the jvm memory use value (usedMemory), the heap memory use value (heapMemory), the process OldGC time consuming (OldGCTime), the database connection pool use rate (useJdbcPercent), the database active connection number (ActiveCount) and other customized monitoring data.
It will be appreciated that the present embodiment gives examples of the current thread busy number of springboot, jvm memory usage value, heap memory usage value, process OldGC time consumption, database connection pool usage rate, and database active connection number included in the application monitoring data, but this should not limit the type of application monitoring data in the embodiment of the present application. In other embodiments, the application monitoring data may employ other types of data, such as database busy connection numbers, and the like.
The springboot framework can provide a container with control inversion characteristics to simplify the initial setup and development process of new spring applications. The framework is configured in a particular manner so that developers no longer need to define a templated configuration. The control inversion is realized by utilizing the core characteristic of control inversion, the lifecycle containerization of the management object is realized by relying on injection, declarative transaction management is performed by utilizing tangent plane-oriented programming, the data access is managed by integrating multiple persistence technologies, a large number of excellent Web frameworks are provided, and the development is convenient. The Springboot framework has a control Inversion (IOC) feature, which is intended to facilitate project maintenance and testing, and provides a method for unified configuration and management of Java objects through the Java's reflection mechanism. The Springboot framework manages the lifecycle of objects with containers that can be configured by scanning XML files or specific Java annotations on classes, and developers can obtain objects by either dependent lookup or dependent injection. Springboot the framework has a tangent plane oriented programming (AOP) framework, springAOP framework is proxy mode based and is configurable at runtime; the AOP framework is modular primarily for cross points of interest between modules. The AOP framework of Springboot framework provides only basic AOP features, although not comparable to the AspectJ framework, the basic requirements can be met by integration with AspectJ. Transaction management, remote access, etc. functions under Springboot framework can be implemented by using SpringAOP technology. The Spring transaction management framework brings an abstract mechanism for the Java platform, enabling local and global transactions and nested transactions to work with the save points, and can work in almost any environment of the Java platform. Spring integrates various transaction templates, the system can carry out transaction configuration through transaction templates, XML or Java notes, and the transaction framework integrates functions such as message transmission, cache and the like.
There are two very important strategies in springboot framework: out of box (Outofbox) and offer for preference over configuration (Convention over configuration). Out-of-box refers to managing the lifecycle of an object during development by adding relevant dependency packages in the pon file of item MAVEN, and then using corresponding annotations instead of cumbersome XML configuration files. This feature allows developers to get rid of complex configuration work and dependent management work, focusing more on business logic. The contract-over-configuration is a software design paradigm whereby the target structure is configured by SpringBoot itself, and information is added to the structure by the developer. The feature reduces part of flexibility, increases the complexity of bug positioning, reduces the number of decisions required by developers, reduces a large number of XML configurations, and can automate code compiling, testing, packaging and other tasks.
Referring to fig. 4, the data monitoring method further includes:
step S230, system monitoring data of a system layer of the cluster system is obtained according to the monitoring request.
Referring to fig. 5, for step S230, system monitoring data of a system level of a cluster system is acquired according to a monitoring request, including, but not limited to, the following steps:
step S231, first monitoring data of the node are obtained, and the first monitoring data are used for reflecting the host operation condition of the node;
Step S232, obtaining second monitoring data of the node, wherein the second monitoring data is used for reflecting the container operation condition of the node;
step S233, obtaining third monitoring data of the container management system layer of the cluster system.
For step S231, first monitoring data of the node is collected by a collection component deployed in the cluster, such as a node_ exporter component, where the first monitoring data is used to reflect a host operation condition of the node. Specifically, the first monitoring data of the host system layer includes Linux host system dimension monitoring data such as a system CPU, a memory, a disk, and the like.
It may be understood that, in this embodiment, an example is given in which the first monitoring data of the node includes Linux host system dimension monitoring data such as a system CPU, a memory, a disk, and the like, but this is not limited to the type of the first monitoring data in the embodiment of the present application. In other embodiments, the first monitoring data may take other types of data, such as meminfo data, etc.
The node exporter component is an index data collection component of the cluster. It is responsible for collecting data from the target jobs and converting the collected data into a time-sequential data format supported by the cluster. Unlike conventional index data collection components, node exporter is only responsible for collecting, and does not send data to the server, but waits for the database to actively grab. node exporter component is typically used to collect operational metrics of the node, including basic monitoring metrics data such as cpu, load, filesystem, meminfo, network of the node.
For step S232, second monitoring data of the node is collected by a collection component deployed in the cluster, such as cAvisor component, the second monitoring data being used to reflect container operation of the node. Specifically, the second monitoring data of the node includes container system dimension monitoring data of a system CPU, a memory, a disk and the like of a dock container on the node.
It may be understood that, in this embodiment, an example is given in which the second monitoring data of the node includes system dimension monitoring data of a container system such as a system CPU, a memory, and a disk of a dock container on the node, but this cannot limit the type of the second monitoring data in the embodiment of the present application. In other embodiments, the second monitoring data may be other types of data, such as the current memory usage of the container, and so on.
Specifically, cAvisor component may monitor the following metrics: the average load of the container CPU, the cumulative occupation time of the container on each CPU core, the cumulative occupation time of the CPU, the usage amount of the file system in the container, the total amount of the file system which can be used by the container, the total amount of the container cumulative read data, the total amount of the container cumulative write data, the maximum memory usage amount of the container, the current memory usage amount of the container, the memory usage limit of the container, the total amount of the container network cumulative received data and the total amount of the container network cumulative transmission data are all 10 seconds in the past.
For step S233, third monitoring data of the container management system layer of the cluster system is obtained by calling the APISERVER interface and the kube-state-metrics interface of the cluster. Specifically, the third monitoring data of the container management system level includes a Deployment, service, pod or like resource monitoring index of Kubernetes dimension.
It should be understood that the present embodiment gives an example that the third monitoring data at the container management system level includes the Deployment, service, pod s-dimension resource monitoring index, but this is not limited to the type of the third monitoring data in the embodiment of the present application. In other embodiments, the third monitoring data may take other types of data.
Deployment is used to manage Pod, replicaSet to enable rolling upgrades and rollback applications, expansion and contraction.
Service defines an access entry address for a Service through which a front-end application accesses a set of cluster instances behind it consisting of Pod copies, with access requests from outside being load balanced onto the back-end individual container applications. And the Service and the post copy clusters at the back end are associated through a Label Selector.
Pod is a container, pod is managed by ReplicaSet, and the ReplicaSet controls the number of Pods; replicaSet is managed Deployment, deployment controls the upgrade and rollback of pod applications, and of course the number of pods can also be controlled. Service provides a unified fixed portal responsible for forwarding front-end requests to Pod. Service includes three types, respectively: clusterIP, nodePort and LoadBalancer, wherein ClusterIP provides virtual IP inside a cluster for Pod access; nodePort open a port on each Node for external access; loadBalancer are accessed through an external load balancer.
Kuberneteskubernetes (K8S for short) is a lightweight and extensible open source container management platform for managing containerized applications and services. Automated deployment and scaling of applications is enabled through Kubernetes. In Kubernetes, the containers that make up the application are combined into one logical unit for easier management and discovery.
Kubernetes belongs to a Master-slave distributed architecture and mainly consists of a Master Node, a workbench Node and the like. The Master Node is used as a control Node to schedule and manage the cluster; master Node is composed of API SERVER, scheduler, cluster State Store and Controller-MANGER SERVER. The workbench Node is used as a real working Node and runs a container of service application; the Worker Node contains kubelet, kube proxy, and Container Runtime. kubectl is used for operating the Kubernetes through interaction between the command line and API SERVER, so as to realize operations such as adding, deleting, checking and the like of various resources in the cluster. Add-on is an extension to Kubernetes core functionality, such as adding network and network policy capabilities. repliceation is used to scale the number of copies. An endpoint is used to manage network requests. schedulers are schedulers.
Kubernetes has the following characteristics:
automatic boxing: the containers are automatically deployed based on the container's requirements for resources and constraints without sacrificing availability. Meanwhile, in order to improve the utilization rate and save more resources, key and optimal workload are combined together;
self-healing ability: restarting the container when the container fails; when the deployed Node has a problem, the container is redeployed and rescheduled; closing the container when the container fails the monitoring inspection; the container can not provide service to the outside until the container normally operates;
Horizontal expansion: the application can be expanded and contracted through simple commands, user interfaces or based on the use condition of the CPU;
Service discovery and load balancing: the developer can perform service discovery and load balancing based on the Kubernetes without using an additional service discovery mechanism;
Automatic publishing and rollback: kubernetes is capable of programmatic publishing applications and related configurations. If there is a problem with the release, kubernetes will be able to regress the changes that occur;
privacy and configuration management: security and application configuration can be deployed and updated without the need to reconstruct the image;
Storing arrangement: automatic hooking storage systems, which may be from local, public cloud providers (e.g., GCP and AWS), network storage (e.g., NFS, iSCSI, gluster, ceph, cinder and Floker, etc.).
The cluster system comprises a plurality of clusters, wherein the clusters are correspondingly provided with monitoring databases, the clusters are in one-to-one correspondence with the monitoring databases, and all the monitoring databases are connected with the total database. Referring to fig. 8, a monitoring database 1 corresponds to a cluster 1, a monitoring database 2 corresponds to a cluster 2, and the monitoring database 1 and the monitoring database 2 are connected to the total database.
Referring to fig. 6, the data monitoring method further includes:
Step S241, acquiring application monitoring data and system monitoring data through a first exposure interface of a cluster according to a first data acquisition task configured by a monitoring database;
step S242, storing the application monitoring data and the system monitoring data in a monitoring database;
Step S243, according to the second data acquisition task configured by the total database, acquiring application monitoring data and system monitoring data stored in the monitoring database through a second exposure interface of the monitoring database;
In step S244, the application monitoring data and the system monitoring data acquired from the monitoring database are stored in the total database.
For step S241, a metrics interface is started through the http module of golang of the cluster, that is, the first exposure interface exposes the application monitoring data and the system monitoring data; the monitoring database is configured with a job, namely a first data acquisition task, and periodically acquires application monitoring data and system monitoring data from the metrics interface. In particular, the monitoring database is a sub-database under the prometheus database, such as the prometheus-0 database, the prometheus-1 database.
For step S242, the application monitoring data and the system monitoring data are stored in the monitoring database.
For step S243, the monitoring database sets a second exposure interface to expose the application monitoring data and the system monitoring data. The total database is prometheus databases located outside the cluster, the prometheus database is configured with one job, namely a second data acquisition task, and application monitoring data and system monitoring data stored in the monitoring database are acquired from a second exposure interface of the monitoring database at regular time.
For step S244, the application monitoring data and the system monitoring data acquired from the monitoring database are stored in the total database, and the total database aggregates the monitoring data of all the monitoring databases.
Wherein Prometheus is a set of open-source monitoring & alarming & time series database combinations; the basic principle of Prometheus is to periodically grab the state of the monitored component via the HTTP protocol. Prometheus fundamentally all stores are carried out in time series, the same index (metrics) and tag (label) form a time series, and different tags represent different time series. Prometaus can divide monitoring into network monitoring, storage monitoring, server monitoring, application monitoring and the like according to monitoring objects.
Referring to fig. 7, for step S300, generating alarm information from application monitoring data and system monitoring data includes:
step S310, calling application monitoring data and system monitoring data from a total database;
step S320, determining alarm data from the application monitoring data and the system monitoring data;
Step S330, alarm information is generated according to the alarm data.
For step S310, in response to the monitoring request, grafana templates corresponding to the applications are generated by the amp monitoring platform, causing PromQL to query and invoke application monitoring data and system monitoring data from the total database. Grafana is an open source program for visualizing large measurement data, which can provide a powerful and elegant way to create, share, and view data.
For step S320, the amp monitoring platform invokes prometheus the full alert data interface, and uses the data exceeding the corresponding preset threshold value in the application monitoring data and the system monitoring data as the alert data.
For step S330, the alarm data is filtered, packaged and collected, and corresponding alarm information is generated according to the alarm data, and the alarm information is sent to the operation terminal of the user through mail or other communication modes.
The method has the advantages that various data of a plurality of clusters of the cluster system are summarized into a unified database, monitoring data, alarm data and charts are collected to the amp platform and displayed in a visual mode, so that the learning and use cost of developers to multiple platforms is greatly reduced, and the unified standard template can be used by the unified platform, so that the flow standardization of a monitoring system is realized.
The embodiment of the application also provides a data monitoring device which is applied to the cluster system. Referring to fig. 9, the data monitoring apparatus includes a request receiving unit 110, a metadata acquiring unit 120, an application monitoring data acquiring unit 130, and an alarm generating unit 140.
Wherein, the request receiving unit 110 is configured to receive a monitoring request; the metadata acquisition unit 120 is configured to acquire metadata of a computing unit of the cluster system through a collector disposed in a container of the computing unit according to the monitoring request; the application monitoring data obtaining unit 130 is configured to obtain application monitoring data of an application layer of the cluster system according to the metadata; the alarm generating unit 140 is configured to generate alarm information according to the application monitoring data.
The data monitoring device receives a monitoring request; acquiring metadata of the computing units of the cluster system through collectors deployed in containers of the computing units according to the monitoring request; obtaining application monitoring data of an application layer of the cluster system according to the metadata; generating alarm information according to the application monitoring data; the collector is deployed in the container of the computing unit, so that the complexity of system and application access is simplified, the monitoring data of the application layer is directly obtained through the collector, the application end does not need to be changed, the application end does not invade, and compared with the case that the collector is independently deployed outside the cluster, the time delay for obtaining the monitoring data is greatly reduced, and the data collection efficiency is improved.
It can be understood that the content in the data monitoring method embodiment is applicable to the data monitoring device embodiment, and the functions specifically realized by the data monitoring device embodiment are the same as those of the data monitoring method embodiment, and the achieved beneficial effects are the same as those of the data monitoring method embodiment.
The embodiment of the application also provides electronic equipment. Referring to fig. 10, the electronic device includes a memory 220, a processor 210, a program stored on the memory 220 and executable on the processor 210, and a data bus 230 for enabling connection communication between the processor 210 and the memory 220, which when executed by the processor 210 implements the data monitoring method as described above.
The electronic equipment receives the monitoring request; acquiring metadata of the computing units of the cluster system through collectors deployed in containers of the computing units according to the monitoring request; obtaining application monitoring data of an application layer of the cluster system according to the metadata; generating alarm information according to the application monitoring data; the collector is deployed in the container of the computing unit, so that the complexity of system and application access is simplified, the monitoring data of the application layer is directly obtained through the collector, the application end does not need to be changed, the application end does not invade, and compared with the case that the collector is independently deployed outside the cluster, the time delay for obtaining the monitoring data is greatly reduced, and the data collection efficiency is improved.
The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.
Generally, for the hardware structure of the electronic device, the processor 210 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an application-specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided by the embodiments of the present application.
Memory 220 may be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM). The memory 220 may store an operating system and other application programs, and when the technical solutions provided in the embodiments of the present disclosure are implemented by software or firmware, relevant program codes are stored in the memory 220, and the processor 210 invokes a data monitoring method for executing the embodiments of the present disclosure.
The input/output interface is used for realizing information input and output.
The communication interface is used for realizing communication interaction between the device and other devices, and can realize communication in a wired mode (such as USB, network cable and the like) or in a wireless mode (such as mobile network, WIFI, bluetooth and the like).
Bus 230 conveys information between various components of the device (e.g., processor 210, memory 220, input/output interfaces, and communication interfaces). The processor 210, memory 220, input/output interfaces, and communication interfaces enable communication connections to each other within the device via bus 230.
Embodiments of the present application also provide a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the data monitoring method as described above.
The above computer-readable storage medium receives a monitoring request; acquiring metadata of the computing units of the cluster system through collectors deployed in containers of the computing units according to the monitoring request; obtaining application monitoring data of an application layer of the cluster system according to the metadata; generating alarm information according to the application monitoring data; the collector is deployed in the container of the computing unit, so that the complexity of system and application access is simplified, the monitoring data of the application layer is directly obtained through the collector, the application end does not need to be changed, the application end does not invade, and compared with the case that the collector is independently deployed outside the cluster, the time delay for obtaining the monitoring data is greatly reduced, and the data collection efficiency is improved.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.
While the preferred embodiment of the present application has been described in detail, the present application is not limited to the embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present application, and the equivalent modifications or substitutions are intended to be included in the scope of the present application as defined in the appended claims.
Claims (8)
1. A data monitoring method, which is applied to a cluster system, the data monitoring method comprising:
Receiving a monitoring request;
According to the monitoring request, acquiring metadata of a computing unit of the cluster system through a collector, wherein the collector is deployed in a container of the computing unit;
obtaining application monitoring data of an application layer of the cluster system according to the metadata;
Generating alarm information according to the application monitoring data;
The metadata comprises a name of a computing center, an Internet protocol address and a management expansion port corresponding to the container framework application;
according to the monitoring request, acquiring metadata of a computing unit of the cluster system through a collector comprises:
according to the monitoring request, the collector calls a communication interface of the cluster system to acquire the metadata;
storing the metadata in a full list;
The obtaining the application monitoring data of the application layer of the cluster system according to the metadata comprises the following steps:
acquiring the name, the internet protocol address and the management expansion port of the computing center from the full list;
Obtaining the application monitoring data according to the name of the computing center, an Internet protocol address and a management expansion port;
and converting the format of the application monitoring data to obtain a character string text of the application monitoring data, wherein the character string text is matched with the data acquisition format of the monitoring database.
2. The data monitoring method according to claim 1, wherein before the generating of the alarm information from the application monitoring data, the data monitoring method further comprises:
acquiring system monitoring data of a system layer of the cluster system according to the monitoring request;
the generating the alarm information according to the application monitoring data comprises the following steps:
And generating alarm information according to the application monitoring data and the system monitoring data.
3. The data monitoring method according to claim 2, wherein the cluster system comprises at least one node on which the computing unit runs;
The acquiring system monitoring data of the system layer of the cluster system comprises the following steps:
Acquiring first monitoring data of the node, wherein the first monitoring data is used for reflecting the host operating condition of the node;
acquiring second monitoring data of the node, wherein the second monitoring data is used for reflecting the container operation condition of the node;
and acquiring third monitoring data of the container management system layer surface of the cluster system.
4. A data monitoring method according to claim 2 or 3, characterized in that the cluster system comprises a plurality of clusters, which clusters are provided with monitoring databases, all monitoring databases being connected to a total database;
Before the alarm information is generated according to the application monitoring data and the system monitoring data, the data monitoring method comprises the following steps:
Acquiring the application monitoring data and the system monitoring data through a first exposure interface of the cluster according to a first data acquisition task configured by the monitoring database;
Storing the application monitoring data and the system monitoring data in the monitoring database;
acquiring the application monitoring data and the system monitoring data stored in the monitoring database through a second exposure interface of the monitoring database according to a second data acquisition task configured by the total database;
and storing the application monitoring data and the system monitoring data acquired from the monitoring database in the total database.
5. The data monitoring method according to claim 4, wherein the generating alarm information according to the application monitoring data and the system monitoring data comprises:
invoking the application monitoring data and the system monitoring data from the total database;
determining alert data from the application monitoring data and the system monitoring data;
and generating the alarm information according to the alarm data.
6. A data monitoring device, characterized in that it is applied to a cluster system, the data monitoring device comprising:
A request receiving unit for receiving a monitoring request;
The metadata acquisition unit is used for acquiring metadata of the computing units of the cluster system through collectors according to the monitoring request, and the collectors are deployed in containers of the computing units;
An application monitoring data acquisition unit, configured to obtain application monitoring data of an application layer of the cluster system according to the metadata;
The alarm generation unit is used for generating alarm information according to the application monitoring data;
The metadata comprises a name of a computing center, an Internet protocol address and a management expansion port corresponding to the container framework application;
according to the monitoring request, acquiring metadata of a computing unit of the cluster system through a collector comprises:
according to the monitoring request, the collector calls a communication interface of the cluster system to acquire the metadata;
storing the metadata in a full list;
The obtaining the application monitoring data of the application layer of the cluster system according to the metadata comprises the following steps:
acquiring the name, the internet protocol address and the management expansion port of the computing center from the full list;
Obtaining the application monitoring data according to the name of the computing center, an Internet protocol address and a management expansion port;
and converting the format of the application monitoring data to obtain a character string text of the application monitoring data, wherein the character string text is matched with the data acquisition format of the monitoring database.
7. An electronic device comprising a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for enabling a connection communication between the processor and the memory, the program when executed by the processor implementing a data monitoring method according to any one of claims 1 to 5.
8. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the data monitoring method according to any one of claims 1 to 5.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20050066133A (en) * | 2003-12-26 | 2005-06-30 | 한국과학기술정보연구원 | Processes monitoring system for cluster and method thereof |
| CN112527595A (en) * | 2020-12-02 | 2021-03-19 | 平安医疗健康管理股份有限公司 | Monitoring method and device for Greenplus cluster database and computer equipment |
| CN112988500A (en) * | 2019-12-17 | 2021-06-18 | 上海商汤智能科技有限公司 | Method, device and system for monitoring file storage system |
| WO2021129367A1 (en) * | 2019-12-23 | 2021-07-01 | 深圳前海微众银行股份有限公司 | Method and apparatus for monitoring distributed storage system |
| CN114416200A (en) * | 2022-01-11 | 2022-04-29 | 浪潮云信息技术股份公司 | System and method for monitoring, acquiring, configuring and dynamically managing and loading configuration of declarative cloud platform |
-
2022
- 2022-09-21 CN CN202211150190.6A patent/CN115567526B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20050066133A (en) * | 2003-12-26 | 2005-06-30 | 한국과학기술정보연구원 | Processes monitoring system for cluster and method thereof |
| CN112988500A (en) * | 2019-12-17 | 2021-06-18 | 上海商汤智能科技有限公司 | Method, device and system for monitoring file storage system |
| WO2021129367A1 (en) * | 2019-12-23 | 2021-07-01 | 深圳前海微众银行股份有限公司 | Method and apparatus for monitoring distributed storage system |
| CN112527595A (en) * | 2020-12-02 | 2021-03-19 | 平安医疗健康管理股份有限公司 | Monitoring method and device for Greenplus cluster database and computer equipment |
| CN114416200A (en) * | 2022-01-11 | 2022-04-29 | 浪潮云信息技术股份公司 | System and method for monitoring, acquiring, configuring and dynamically managing and loading configuration of declarative cloud platform |
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