Updated March 29, 2023
Introduction to Prometheus Alternative
The Prometheus is a monitoring system available as open-source originated from SoundCloud. It is implied to query and monitor the data collection, which is multi-dimensional. It has its unique unit and is not based on any external services or network storage. Hence it is not dependent on any add-on software or infrastructure. The significant advantage includes strong integration with Kubernetes and many in-built libraries and exporters with a rapid process on APIs and fast query language. But the limitations are it’s too constrained with restricted granularities, which lack the encryption process and are deployed as a pulled-based model. Such limitations are suppressed in the explained alternatives. Each of the developed alternatives is briefly discussed in this article. In this topic, we are going to learn about Prometheus Alternative.
Explain the Alternatives to Prometheus
Given below are the Prometheus Alternative:
1. Zabbix
Zabbix is deployed to monitor the huge number of network parameters comprised of health servers that offer multiple data visualization methods and reporting features that are dependent on the storage data. Much small-scale organization uses this software to monitor their infrastructure. The key features of Zabbix are real-time visualization, auto-discovery options, the discovery of network, flexibility on the threshold, and has a wide range of distributed monitoring. The triggers, defined as the flexible problems from the threshold, are managed by Zabbix and depend on the values of the database’s back-end parameters. The functionality of built-in graphics can be immediately availed to monitor the items. The several methods in automating the larger circumstances like Kubernetes can be done by Zabbix and can remove and delete the network interface or file systems. It allows the user to scan the network periodically on external agents and services to take pre-defined discovery actions. Zabbix proxies are used to monitor the distributed infrastructure and locally collect the data and report it to the server to take necessary actions.
2. Grafana
It is open-source software employed for monitoring, visualization, and analytic metrics. The important focus of this is its analytics on time series. It can view the analyzed information via a huge variety of graphs that includes heatmaps, line graphs, and bar graphs. When any monitor exceeds the limit, it receives an alert. The key features of Grafana are dashboard templating, alerting, provisioning, annotations. The alerts are generated via different channels includes email, SMS, PagerDuty, or slack. If the user wants to allow a channel for communication, he can develop the notifiers by using code bits. The template allows the user to build dashboards to reuse for multiple purposes. For example, the user can deploy the same dashboard on a test server and prod server. The automation of the process is done by scripts using Grafana. For example, the user can spin up the Grafana, and a new Kubernetes scripts cluster comprises a server, IP address, and preset of data sources. The annotations are implied and used for data correlation if something is mismatched. The user can develop some manual annotation by inserting text to graph or fetch the data from any source of data.
3. cAdvisor
It gathers many processes on resource information on usage and exports the performance in executing the containers. It has the origin support to Kubernetes as it is incorporated into the binary of kubelet. The key elements of cAdvisor involve storage plugin, web user interface, auto-discovery options, overall machine usage. It automatically discovers the containers in the given mode and gathers all the statistical information such as memory, filesystems usage, network, and CPU status. The status of exports to store different plugins like Influx DB and Elasticsearch. It gives the usage of overall machines and analyzes every root container executed on the machine. The web UI helps to see the metrics to show the live status of containers running on the machine.
4. Fluentd
It differentiates the data resources from the backend servers by giving an in-between unique login layer. The login interface allows the user to gather different log types that are generated. The key features involve pluggable architecture, data structure on JSON, system resources, and high reliability. The structure of JSON allows the user to unified all processing aspects of the data log like filtering, outputting, and buffering the logs across variable resources. The adaptable plugin system allows the user to extend the operational feature by linking the output and multiple data resources. The instance executes on the memory of 40MB and processes around 13,000 events in a second. The user can execute a lightweight of fluentd bit if the user requires maximum memory. The memory is buffering and file-based systems to save the data leakages on nodes. The user can also implement fluentd to support robust failover and high availability
5. Jaeger
It is available as open-source to monitor the traffic between the circulated services like Kubernetes. It also employs distributed tracing to obey the service request path via variable microservices. The user can fetch visual representation computing the request flows. The observation of microservices and management of distributed tracing can be executed in the section of a service mesh. The significant features of Jaeger are the deployment of native cloud, maximum scalability, monitoring services, and options on multiple storages. It is implemented to have null single point failure and scalable the business with user requirements. It also supports dual open sources on Cassandra, Elasticsearch, and NoSQL databases. It offers testing on reliable in-memory storage components. It provides different configuration techniques includes variable environments, command-line functions, and configuring files. The deployment of Kubernetes is managed by helm charts, Kubernetes operators, and templates. The backend of Jaeger functions exposes the metrics of Prometheus and default logs.
6. Telepresence
It is executed as a single service by connecting them to a cluster of Kubernetes. It deploys a dual-way network proxy in a single pod that gathers information for the environment of Kubernetes like volumes and TCP connections. The key features include development workflow, compatibility, and debug the local Kubernetes services. The user can access the cluster as a local process on the tools in the laptop to interact with other processes.
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This is a guide to Prometheus Alternative. Here we discuss some of the effective alternatives to Prometheus and can be deployed in all kinds of business sectors. You may also have a look at the following articles to learn more –
