8 Observability: Visualizing network behavior with Grafana, Jaeger, and Kiali

 

This chapter covers

  • Using Grafana to observe metrics visually
  • Distributed tracing instrumentation with Jaeger
  • Visualizing the network call graph with Kiali

In this chapter, we build on the foundation we established in the previous chapter, and we use some tools to visualize data from the service mesh. We saw how Istio’s data-plane and control-plane components expose a lot of very useful operational metrics and how we can scrape those into a time-series system like Prometheus. In this chapter, we use tools like Grafana and Kiali to visualize those metrics to better understand the behavior of the services in the mesh as well as the mesh itself. We also dig into visualizing the network call graph with distributed tracing tools.

8.1 Using Grafana to visualize Istio service and control-plane metrics

In the previous chapter, we removed the sample Prometheus and Grafana add-ons that come with a demo installation of Istio. Instead, we installed kube-prometheus (https://github.com/prometheus-operator/kube-prometheus), which is a more realistic set of observability tools.

To double-check that you’ve got the kube-prometheus stack installed correctly, check what’s in the prometheus namespace:

8.1.1 Setting up Istio’s Grafana dashboards

 
 
 

8.1.2 Viewing control-plane metrics

 
 
 
 

8.1.3 Viewing data-plane metrics

 
 

8.2 Distributed tracing

 
 

8.2.1 How does distributed tracing work?

 
 
 
 

8.2.2 Installing a distributed tracing system

 
 
 

8.2.3 Configuring Istio to perform distributed tracing

 
 

8.2.4 Viewing distributed tracing data

 

8.2.5 Trace sampling, force traces, and custom tags

 
 
 

8.3 Visualization with Kiali

 
 
 

8.3.1 Installing Kiali

 
 
 
 

8.3.2 Conclusion

 
 
 

Summary

 
 
 
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