CN116737523B - Observability data acquisition method based on edge calculation - Google Patents
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Abstract
The invention discloses an observability data acquisition method based on edge calculation, which comprises the steps of deploying cloud edge collaborative environment; two components of a cloudstream server and an edgestream client are deployed on a cloud and all edge nodes respectively; the method comprises the steps that through cloud edge collaborative environments, a lightweight log collector application is issued to edge operation, the lightweight log collector application is issued to all edge nodes in the form of daemon process set daemon application, and a log repeater component is deployed in a cloud end and used for forwarding logs pushed by the edge nodes; and issuing the lightweight index agent application of the index agent collector to the edge operation through the cloud edge cooperative environment, issuing the lightweight index agent application to all edge nodes in the form of daemon application, deploying a push gateway component at the cloud end for temporarily storing index data pushed by the edge nodes, and polling and pulling the index data to a time sequence database by the time sequence database of the cloud end for storage. A general method for capturing observability data at an edge node to a cloud is provided.
Description
Technical Field
The invention belongs to the technical field of edge calculation, and particularly relates to an observability data acquisition method based on edge calculation.
Background
With the development of edge technology, various schemes of cloud-edge coordination and matched observability data acquisition technologies thereof appear. However, in the current observability data collection method, under the condition that the cloud and edge-based network is relatively stable, the indexes and the application logs on the target edge node are pulled in real time at the cloud end and stored in various databases of the cloud end. These collection methods have two pain points: firstly, all the collection actions are completed in the cloud, when the number and the scale of the edge nodes are increased to tens of thousands, the calculation pressure of the cloud collector is very high, and the edge nodes serve as the server side and cannot distribute the pressure. The performance loss of the cloud is large. Secondly, the collector is deployed at the cloud (or some manufacturers deploy the collector at the edge), and the cloud and the edge network are established to be stable. The cloud-edge cooperative scene solves the problem of unstable network scenes (such as parking lots, automatic driving automobiles, equipment sensors of factories and the like), so that certain observability data can be lost in the scenes.
In order to solve the above technical problems, a new method for acquiring observability data is needed.
Disclosure of Invention
Aiming at the technical problems, the invention provides an observability data acquisition method based on edge calculation.
The technical scheme adopted for solving the technical problems is as follows:
an observability data acquisition method based on edge calculation comprises the following steps:
s100: deploying cloud edge collaborative environments in kubernetes cluster environments;
s200: two components of a cloudstream server and an edgestream client are deployed on a cloud and all edge nodes respectively, communication is carried out between the cloudstream server and the edgestream client through websockets, the edge nodes and application indexes are obtained based on websocket channels, and real-time logs of edge applications are obtained through native kubectl commands;
s300: issuing a fluent-bit light log collector application to an edge for running through a cloud edge cooperative environment, issuing the fluent-bit light log collector application to all edge nodes in the form of daemonset daemons, deploying a log forwarder fluent-bit-forward component at a cloud end for forwarding logs pushed by the edge nodes, pushing the logs to a message queue kafka, adopting the same topic for the message queue, and pushing messages in the message queue to an elastiscsearch for storage;
s400: and issuing a prom-agent lightweight index agent application of an index agent collector to an edge to run through a cloud edge cooperative environment, issuing the prom-agent lightweight index agent application to all edge nodes in the form of daemonset daemon application, deploying a push gateway push component at a cloud end for temporarily storing index data pushed by the edge nodes, and polling and pulling the index data to a time sequence database by a prometaus time sequence database of the cloud end to store the index data.
Preferably, S100 includes:
adding edge nodes into the cloud end one by one for unified management, deploying universal prometaus as a cloud end monitoring index persistence system in the cloud end, and deploying elastic search as a cloud end log persistence system.
Preferably, in S300, the Fluent-bit is equivalent to log agents of log agent collectors of all edge nodes, configuration of the Fluent-bit designates that an OUTPUT flow OUTPUT of a log sets a listening address pointing to a cloud Fluent-bit-forward, log data is pushed to the cloud by an http protocol, and an api gateway deployed at the cloud is set between the edge Fluent-bit and the cloud Fluent-bit-forward for performing basic account authentication.
Preferably, in S300, the application log of the cloud node itself is also collected and pushed to the fluent-bit-forward through fluent-bit, so as to keep the cloud and the edge application log integrated into a whole.
Preferably, in S400, the index data pushed to the cloud end at the edge is marked by each index item by a tag to identify the edge node to which the data belongs and the timestamp of the index item.
Preferably, in S400, the prom-agent is equivalent to an index agent collector metrics agent of all edge nodes, where the prom-agent is used to collect all indexes of the edge nodes and the application and perform label conversion processing on each index, and push the indexes to the pushgateway of the cloud in http protocol, where account authentication of the pushgateway is required to pass between the edge prom-agent and the pushgateway of the cloud.
Preferably, in S300, when an http network from an edge node to a cloud is not reachable, based on a cloud-edge collaborative environment, a websocket connection exists between a cloudstream server and an edgestream client, and the cloud can acquire an edge application log in real time through an interface; if the websocket network is disconnected, the edge is in an offline autonomous state, the edge fluent-bit supports persistence of data to a local disk file, the time or the data volume of the persistence data is defaulted, after the network is restored, the edge fluent-bit collects the persistence log file, pushes the persistence log file to the cloud, and gradually clears the cache.
Preferably, in S400, when an http network from an edge node to a cloud is not reachable, based on a cloud-edge collaborative environment, a websocket connection exists between a cloudstream server and an edgestream client, and the cloud can acquire an edge node index in real time through a kubecl prototop command; if the websocket network is disconnected, the edge is in an offline autonomous state, the edge prom-agent supports persistence of index data to a local disk file, all the data are provided with node labels and time stamps, the time or the data volume of the persistence data is defaulted, after the network is restored, the prom-agent reads the persistence log file, pushes the persistence log file to a cloud pushgateway, gradually clears the cache, and the cloud pushgateway carries out persistence processing according to the time stamp labels.
Preferably, S400 further comprises: and deploying a monitoring alarm platform at the cloud, wherein the monitoring alarm platform integrates a prometaus monitoring system of the cloud through an SDK of the prometaus, and provides index inquiry and monitoring alarm capability.
Preferably, S400 further comprises: and (3) deploying a log query platform at the cloud, wherein the log query platform integrates an elastesearch system of the cloud through an SDK of elastesearch to provide log query and aggregation analysis capability.
The method for collecting the observability data based on the edge calculation provides a general collecting method, and solves the two pain point problems: firstly, all collectors are deployed at the edge, thousands of nodes share the collecting pressure, namely a push pushing mode is adopted, and the data collecting mode is also a data collecting mode in a cloud-edge cooperative scene of the mainstream in the future; secondly, a solution under a network unstable scene is provided, all data are cached in the edge local through the collector, and the data are pushed to the cloud again when the network is recovered, so that complete data of the cloud are formed.
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FIG. 1 is a flow chart of an observability data collection method based on edge calculation according to an embodiment of the invention;
FIG. 2 is an overall frame diagram of an observability data collection method based on edge computation according to an embodiment of the present invention;
fig. 3 is a flow chart of an observability data collection method based on edge calculation according to an embodiment of the invention.
Detailed Description
In order to make the technical scheme of the present invention better understood by those skilled in the art, the present invention will be further described in detail with reference to the accompanying drawings.
In one embodiment, as shown in fig. 1, a method for collecting observability data based on edge calculation includes the following steps:
s100: and deploying cloud edge collaborative environments in kubernetes cluster environments.
Specifically, a cloud-edge collaborative environment is deployed in a kubernetes cluster environment, such as kubreedge: an open platform supporting edge computing; cloud-edge collaborative environments need to be able to provide the ability to launch applications to edge execution at the cloud.
In one embodiment, S100 comprises:
adding edge nodes into the cloud end one by one for unified management, deploying universal prometaus as a cloud end monitoring index persistence system in the cloud end, and deploying elastic search as a cloud end log persistence system.
S200: two components of a cloudstream server and an edgestream client are deployed on a cloud and all edge nodes respectively, communication is carried out between the cloudstream server and the edgestream client through websockets, the edge nodes and application indexes are obtained based on websocket channels, and real-time logs of edge applications are obtained through native kubectl commands.
Specifically, the cloud queries the edge index in real time through metrics server service. The logs and metrics are collectively referred to as observability data.
S300: and issuing the fluent-bit light log collector application to the edge operation through the cloud edge collaborative environment, issuing the fluent-bit light log collector application to all edge nodes in the form of daemonset daemons, deploying a log forwarder fluent-bit-forward component at a cloud end for forwarding logs pushed by the edge nodes, pushing the logs to a message queue kafka, adopting the same topic in the message queue, and pushing the messages in the message queue to an elastiscsearch for storage.
Specifically, the data in the log may be flushed or not flushed as needed before pushing the log to the message queue kafka. The effect of pushing the elastic search through the message queue kafka is that the peak clipping and current limiting effects of kafka are utilized, so that a large amount of log file data are prevented from being pushed to the elastic search, and the application is down, and because of the large number of edge nodes, the application is numerous, and the flow pushed to the central cloud log system is large.
In one embodiment, in S300, the Fluent-bit is equivalent to log agents of log agent collectors of all edge nodes, configuration of the Fluent-bit designates that an OUTPUT flow of a log sets a monitoring address pointing to a cloud Fluent-bit-forward, log data is pushed to the cloud by an http protocol, and an api gateway deployed in the cloud is set between the edge Fluent-bit and the cloud Fluent-bit-forward for basic account authentication.
In one embodiment, in S300, the application log of the cloud end node itself is collected and pushed to the fluent-bit-forward through fluent-bit, so as to keep the cloud end and the edge application log integrated into a whole.
S400: and issuing a prom-agent lightweight index agent application of an index agent collector to an edge operation through a cloud edge cooperative environment, issuing the prom-agent lightweight index agent application to all edge nodes in the form of daemonset daemon application, deploying a push gateway component at a cloud end for temporarily storing index data pushed by the edge nodes, and polling and pulling the index data to a time sequence database by using a promethaus of the cloud end for storage.
Specifically, before the promethaus poll in the cloud draws the index data to the database for storage, the index data may be cleaned or not cleaned as required.
In S400, the prom-agent is equivalent to an index agent collector metrics agent of all edge nodes, where the prom-agent is used to collect all indexes of the edge nodes and the application and perform label conversion processing on each index, and push the indexes to the pushgateway of the cloud end by using an http protocol, where account authentication of the pushgateway needs to be passed between the edge prom-agent and the pushgateway of the cloud end.
In one embodiment, the edge node pushes the index data to the cloud in S400, and each index item identifies, through a tag, the edge node to which the data belongs and a timestamp of the index item.
In one embodiment, in S300, when an http network from an edge node to a cloud is not reachable, based on a cloud edge collaborative environment, a websocket connection exists between a cloudstream server and an edgestream client, and the cloud can acquire an edge application log in real time through an interface; if the websocket network is disconnected, the edge is in an offline autonomous state, the edge fluent-bit supports persistence of data to a local disk file, the time or the data volume of the persistence data is defaulted, after the network is restored, the edge fluent-bit collects the persistence log file, pushes the persistence log file to the cloud, and gradually clears the cache.
In one embodiment, in S400, when an http network from an edge node to a cloud is not reachable, based on a cloud edge collaborative environment, a websocket connection exists between a cloudstream server and an edgestream client, and the cloud can acquire an edge node index in real time through a kubectl prototop command; if the websocket network is disconnected, the edge is in an offline autonomous state, the edge prom-agent supports persistence of index data to a local disk file, all the data are provided with node labels and time stamps, the time or the data volume of the persistence data is defaulted, after the network is restored, the prom-agent reads the persistence log file, pushes the persistence log file to a cloud pushgateway, gradually clears the cache, and the cloud pushgateway time sequence database carries out persistence processing according to the time stamp labels.
In one embodiment, S400 further comprises, after: and deploying a monitoring alarm platform at the cloud, wherein the monitoring alarm platform integrates a prometaus monitoring system of the cloud through an SDK of the prometaus, and provides index inquiry and monitoring alarm capability.
In one embodiment, S400 further comprises, after: and (3) deploying a log query platform at the cloud, wherein the log query platform integrates an elastesearch system of the cloud through an SDK of elastesearch to provide log query and aggregation analysis capability.
Based on the steps, cloud edge collaborative environment deployment, cloud edge deployment of a log acquisition system and index acquisition system are completed. The cloud terminal can acquire the data capacity of the edge observability in real time, the unified management platform can view the historical data, and the automatic persistence to the edge when the edge is offline and the pushing to the cloud terminal for unified storage when the network is restored are provided. The entire architecture is shown in fig. 2.
An effective embodiment of the present invention is shown in fig. 3:
based on the method, the kubeedge cloud edge collaborative environment is built, and two acquisition systems are deployed by using the method, and the specific steps are as follows:
the method comprises the steps that S1, an operation and maintenance person deploys a kubeedge cloud edge cooperative environment, a cloud edge network is normal, a websocket channel integrating a cloudstream and an edgesteream in kubeedge is only required to be opened for configuration; s2, issuing all needed daemonset applications to edges by a cloud, wherein the applications comprise fluent-bit, prom-agent and business application; s3-1, edge index collection, wherein in the first step, light kubelet on edge application provides two end points for index collection, namely metrics and cadvisors; s3-2, collecting edge indexes, namely, polling and pulling the edge indexes by a prom-agent, and performing label conversion on each index, wherein the label conversion comprises edge node information, a time stamp and the like; s3-3, collecting edge indexes, namely pushing the cleaned edge indexes to a cloud pushgateway, and carrying out account authentication on the cloud; s3-4, collecting edge indexes, namely, pulling data on pushgateway to a time sequence database through a norm at the cloud end, and storing the data after conversion through a tag and a time stamp; s4-1, collecting an edge log, wherein in the first step, a fluent-bit is deployed at an edge node, so that an edge journ log, a kubelet log and a file log of an application can be collected, and a container is mounted to a log of a host directory; s4-2, collecting an edge log, pushing the edge log to a cloud end fluent-bit-forward by the fluent-bit, and passing basic authentication of a cloud end api gateway; s4-3, collecting edge logs, namely, forwarding the edge logs and cloud logs through fluent-bit-forward, pushing the edge logs and the cloud logs to a message queue kafka, and carrying out peak clipping and current limiting by the kafka; s4-4, collecting an edge log, and pushing log data subjected to kafka to an elastic search for storage after log-mesh cleaning; s5, deploying a monitoring and alarming platform at the cloud, wherein the platform integrates a prometaus monitoring system of the cloud through an SDK of the prometaus and provides index inquiry and monitoring and alarming capabilities; s6, deploying a log query platform in the cloud, wherein the platform integrates an elastiscserach system of the cloud through an SDK of elastiscserach and provides log query and aggregation analysis capability; s7, based on the steps S5 and S6, the service is uniformly provided for the external user through the nginx.
The invention has the following effects: providing a general method for collecting observability data on edge nodes to a cloud end, wherein the observability data comprises operation indexes and logs of the nodes and containers; supporting observability data persistence when the edge is offline; and providing a unified monitoring and inquiring interface.
The method for acquiring the observability data based on the edge calculation is described in detail. The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the core concepts of the invention. It should be noted that it will be apparent to those skilled in the art that various modifications and adaptations of the invention can be made without departing from the principles of the invention and these modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Claims (10)
1. An observability data collection method based on edge calculation, which is characterized by comprising the following steps:
s100: deploying cloud edge collaborative environments in kubernetes cluster environments;
s200: two components of a cloudstream server and an edgestream client are deployed on a cloud and all edge nodes respectively, communication is carried out between the cloudstream server and the edgestream client through websockets, edge nodes and application indexes are obtained based on websocket channels, and real-time logs of edge applications are obtained through native kubectl commands;
s300: issuing a fluent-bit light log collector application to an edge for running through the cloud edge collaborative environment, issuing the fluent-bit light log collector application to all edge nodes in the form of daemon application, deploying a log forwarder fluent-bit-forward component at the cloud end for forwarding logs pushed by the edge nodes, pushing the logs to a message queue kafka, using the same topic by the message queue, and pushing messages in the message queue to an elastiscsearch for storage;
s400: and issuing a prom-agent lightweight index agent application of an index agent collector to an edge operation through the cloud edge collaborative environment, issuing the prom-agent lightweight index agent application to all edge nodes in the form of daemons daemon application, deploying a push gateway component at the cloud end for temporarily storing index data pushed by the edge nodes, and polling and pulling the index data to a time sequence database by a promethaus time sequence database of the cloud end for storage.
2. The method of claim 1, wherein S100 comprises:
adding edge nodes into a cloud end one by one for unified management, deploying universal promethaus on the cloud end as a cloud end monitoring index persistence system, and deploying an elastic search as a cloud end log persistence system.
3. The method of claim 2, wherein in S300, the Fluent-bit corresponds to log agents of log agent collectors of all edge nodes, configuration of the Fluent-bit designates that an OUTPUT flow of a log sets a monitoring address pointing to a cloud Fluent-bit-forward, log data is pushed to the cloud by an http protocol, and an api gateway deployed in the cloud is set between the edge Fluent-bit and the cloud Fluent-bit-forward for basic account authentication.
4. The method of claim 3, wherein in S300, the application log of the cloud node itself is also pushed to the fluent-bit-forward through fluent-bit collection, so as to keep the cloud and the edge application log integrated.
5. The method of claim 4, wherein the index data pushed to the cloud at the edge in S400, each index item identifies the edge node to which the data belongs and the timestamp of the index item by a tag.
6. The method of claim 5, wherein in S400, the protein-agent is equivalent to an index agent collector metrics agent of all edge nodes, and the protein-agent is configured to collect all indexes of the edge nodes and the application, perform label conversion processing on each index, and push the label to the pushgateway of the cloud end according to an http protocol, wherein account authentication of the pushgateway is required from the edge protein-agent to the pushgateway of the cloud end.
7. The method according to claim 4, wherein in S300, when an http network from the edge node to the cloud is not reachable, based on the cloud-side collaboration environment, a websocket connection exists between the cloudstream server and the edgestream client, and the cloud can acquire an edge application log in real time through an interface; if the websocket network is disconnected, the edge is in an offline autonomous state, the edge fluent-bit supports persistence of data to a local disk file, the persistence data time or the data volume is defaulted, after the network is restored, the edge fluent-bit collects a persistence log file, pushes the persistence log file to the cloud, and gradually clears the cache.
8. The method of claim 7, wherein in S400, when an http network from the edge node to the cloud is not reachable, based on the cloud-side collaboration environment, a websocket connection exists between the cloudstream server and the edgestream client, and the cloud can acquire an edge node index in real time through a kubecl protocap command; if the websocket network is disconnected and the edge is in an offline autonomous state, the edge prom-agent supports persistence of index data to a local disk file, all the data are provided with node labels and time stamps, the time or the data volume of the persistence data is defaulted, after the network is restored, the prom-agent reads the persistence log file, pushes the persistence log file to the cloud pushgateway, gradually clears the cache, and the cloud pushgateway carries out persistence processing according to the time stamp labels.
9. The method of claim 1, wherein S400 further comprises, after: and deploying a monitoring and alarming platform at the cloud, wherein the monitoring and alarming platform integrates a prometaheus monitoring system of the cloud through an SDK of the prometaheus and provides index inquiry and monitoring and alarming capabilities.
10. The method of claim 1, wherein S400 further comprises, after: and deploying a log query platform on the cloud, wherein the log query platform integrates an elastiscserach system of the cloud through an SDK of elastiscserach and provides log query and aggregation analysis capability.
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