Getting the Most out of Istio with CNCF Projects

By June 29, 2018Blog

This guest post was written by Neeraj Poddar, Platform Lead, Aspen Mesh

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 are included 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 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, TLS, 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 recently seen an evolution from data plane meshes like Envoy, to control plane meshes such as Istio. As 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 drill down into how to get the most out of Istio.

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

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

Check out the recent CNCF webinar on this topic for a deeper look into what you can do with these tools and more.