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June 26, 2018

Tracing and Metrics: Getting the Most Out of Istio

 

Are you considering or using a service mesh to help manage your microservices infrastructure? If so, here are some basics on how a service mesh can help, the different architectural options, and tips and tricks on using some key CNCF tools that integrate well with Istio to get the most out of it.

The beauty of a service mesh is that it bundles so many capabilities together, freeing engineering teams from having to spend inordinate amounts of time managing microservices architectures. Kubernetes has solved many build and deploy challenges, but it is still time consuming and difficult to ensure reliability and security at runtime. A service mesh handles the difficult, error-prone parts of cross-service communication such as latency-aware load balancing, connection pooling, service-to-service encryption, instrumentation, and request-level routing.

Once you have decided a service mesh makes sense to help manage your microservices, the next step is deciding what service mesh to use. There are several architectural options, from the earliest model of a library approach, the node agent architecture, and the model which seems to be gaining the most traction – the sidecar model. We have also seen an evolution from data plane proxies like Envoy, to service meshes such as Istio which provide distributed control and data planes. We’re active users of Istio, and believers in the sidecar architecture striking the right balance between a robust set of features and a lightweight footprint, so let’s take a look at how to get the most out of tracing and metrics with Istio.

Tracing

One of the capabilities Istio provides is distributed tracing. Tracing provides service dependency analysis for different microservices and it provides tracking for requests as they are traced through multiple microservices. It’s also a great way to identify performance bottlenecks and zoom into a particular request to define things like which microservice contributed to the latency of a request or which service created an error.

We use and recommend Jaeger for tracing as it has several advantages:

  • OpenTracing compatible API
  • Flexible & scalable architecture
  • Multiple storage backends
  • Advanced sampling
  • Accepts Zipkin spans
  • Great UI
  • CNCF project and active OS community

Metrics

Another powerful thing you gain with Istio is the ability to collect metrics. Metrics are key to understanding historically what has happened in your applications, and when they were healthy compared to when they were not. A service mesh can gather telemetry data from across the mesh and produce consistent metrics for every hop. This makes it easier to quickly solve problems and build more resilient applications in the future.

We use and recommend Prometheus for gathering metrics for several reasons:

  • Pull model
  • Flexible query API
  • Efficient storage
  • Easy integration with Grafana
  • CNCF project and active OS community

We also use Cortex, which is a powerful tool to enhance Prometheus. Cortex provides:

  • Long term durable storage
  • Scalable Prometheus query API
  • Multi-tenancy

Check out this webinar for a deeper look into what you can do with these tools and more.

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