Kubernetes, an open-source container orchestration service, is one of the most widely used solutions in today’s modern enterprise environments. 

In these environments, there are many components to monitor (hosts, containers, pods, applications running within pods, and clusters), as well as layers of abstraction. Applications are distributed, always moving, and difficult to track when Kubernetes is automatically scheduling workloads. In addition, Kubernetes is constantly moving pods and scaling applications up or down. As a result, legacy monitoring solutions built for monolithic applications simply cannot keep up.

At Epsagon, we’ve been hard at work building an observability solution that specializes in these distributed environments leveraging containers, Kubernetes, and serverless functions. Specific to Kubernetes, we know how difficult and time consuming it is to manually instrument an observability solution, which has led us to develop functionality within our platform to automate instrumentation, saving significant time for our customers.

Auto-Instrumentation of Kubernetes

Our new functionality automatically instruments workloads running on Kubernetes, without any code changes, for increased velocity and ease-of-use. In addition, Epsagon now automatically collects all Kubernetes data – from cluster status and resource metrics to distributed traces and logs – into a single, unified platform. 

With the platform enhancements, you can:

  • Automatically enable distributed tracing for your applications running on Kubernetes and collect distributed traces from your containerized applications
  • Understand your application performance with distributed tracing, automated service maps, correlated stack traces, latency breakdown, and timeline views 
  • Correlate a trace to a relevant log automatically with no code or configuration changes, as well as correlate to compute metrics and resource metrics
  • Instrument directly from the user interface

The result?  Trace-centric observability that operates at the speed of microservices, enabling developers to pinpoint and resolve issues faster in any environment using Kubernetes.

Kubernetes Dashboard with Node View

More Customization with Trace-based Metrics and Alerts

The ability to collect, correlate, and view Kubernetes metrics such as CPU, memory usage, and pod status along with application and custom trace-based metrics is part of Epsagon’s continued expansion of our monitoring and dashboard capabilities.

Moving forward, we’re also enabling users to build customized dashboards with trace-based metrics and trace-based alerts. With this capability, you can visualize trace-based metrics such as trace status, duration, and error codes; select custom metrics like customer user ids, or cart totals; and aggregate and visualize these metrics in a custom dashboard. 

Epsagon Trace-based Metrics and alerts

Custom Trace-based Metrics and Alerting

As a reminder, using Epsagon’s trace-based metrics enables users to monitor and automatically alert on technical issues resulting in critical outages or a performance degradation with a negative impact on the business.

With our trace-based alerts, auto-instrumentation for Kubernetes, and custom dashboard capabilities, Epsagon users can gain true observability into their distributed environments with ease. 

Check out our free trial and see for yourself!