![]() Also, depending on annotations of the launched Kubernetes pods, they apply the appropriate settings to the target logs and metrics. When Filebeat or Metricbeat detects these events, they make the appropriate metadata available for each event. Kubernetes Autodiscover Providers of Filebeat and Metricbeat monitor the start, update, and stop of Kubernetes nodes, pods, and services. When you run applications in containers, they become moving targets for monitoring systems. This "dynamic" is the key, and Filebeat and Metricbeat have a handy feature called Autodiscover. In a Kubernetes environment, containers are dynamically deployed as pods on available worker nodes. Metricbeat is a lightweight metric shipper which, like Filebeat, also supports containerized environments. ![]() Filebeat can be deployed on Docker, Kubernetes, and cloud environments, collecting all log streams, as well as fetching metadata such as containers, pods, nodes, virtual environments, and hosts and automatically correlates them to corresponding log events. Using Beats, you can transfer data from hundreds or thousands of machines to Logstash or Elasticsearch.įilebeat, which is known as a lightweight log shipper, also supports containerized architecture. Using Filebeat and Metricbeatīeats, as you know, is a free and open platform dedicated to data shipping. In this blog, we will explore how to monitor Kubernetes the Elastic way: using Filebeat and Metricbeat. But, if you’re new to Kubernetes monitoring, or want to take full advantage of Elastic Observability, there is an easier and more comprehensive way. That’s a good option if you’re already using those open source-based monitoring tools in your organization. In my previous blog post, I demonstrated how to use Prometheus and Fluentd with the Elastic Stack to monitor Kubernetes.
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