Mirror canary deployment in RTL?

Question

I am new to canary deployments. We are going to start doing canary deployments via Istio.

I was assuming this would just be a deployment mechanism, probably with some Istio routing testing in a pre-prod env but in earlier test envs we'd ring fence to a version being tested as we do today.

It's been suggested the canary concept is applied to all test environments so we effectively run all versions we expect to canary test in prod in the Route To Live.

Wondring what approach others are taking?

Answer 1

Mirroring

As mentioned here

Using Istio, you can use traffic mirroring to duplicate traffic to another service. You can incorporate a traffic mirroring rule as part of a canary deployment pipeline, allowing you to analyze a service's behavior before sending live traffic to it.

If you're looking for best practices I would recommend to start with this tutorial on medium, because it is explained very well here.

How Traffic Mirroring Works

Traffic mirroring works using the steps below:

• You deploy a new version of the application and switch on traffic mirroring.

• The old version responds to requests like before but also sends an asynchronous copy to the new version.

• The new version processes the traffic but does not respond to the user.

• The operations team monitor the new version and report any issues to the development team.

As the application processes live traffic, it helps the team uncover issues that they would typically not find in a pre-production environment. You can use monitoring tools, such as Prometheus and Grafana, for recording and monitoring your test results.

Additionally there is an example with nginx that perfectly shows how it should work.

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Canary deployment

As mentioned here

One of the benefits of the Istio project is that it provides the control needed to deploy canary services. The idea behind canary deployment (or rollout) is to introduce a new version of a service by first testing it using a small percentage of user traffic, and then if all goes well, increase, possibly gradually in increments, the percentage while simultaneously phasing out the old version. If anything goes wrong along the way, we abort and rollback to the previous version. In its simplest form, the traffic sent to the canary version is a randomly selected percentage of requests, but in more sophisticated schemes it can be based on the region, user, or other properties of the request.

Depending on your level of expertise in this area, you may wonder why Istio’s support for canary deployment is even needed, given that platforms like Kubernetes already provide a way to do version rollout and canary deployment. Problem solved, right? Well, not exactly. Although doing a rollout this way works in simple cases, it’s very limited, especially in large scale cloud environments receiving lots of (and especially varying amounts of) traffic, where autoscaling is needed.

There are the differences between k8s canary deployment and istio canary deployment.

k8s

As an example, let’s say we have a deployed service, helloworld version v1, for which we would like to test (or simply rollout) a new version, v2. Using Kubernetes, you can rollout a new version of the helloworld service by simply updating the image in the service’s corresponding Deployment and letting the rollout happen automatically. If we take particular care to ensure that there are enough v1 replicas running when we start and pause the rollout after only one or two v2 replicas have been started, we can keep the canary’s effect on the system very small. We can then observe the effect before deciding to proceed or, if necessary, rollback. Best of all, we can even attach a horizontal pod autoscaler to the Deployment and it will keep the replica ratios consistent if, during the rollout process, it also needs to scale replicas up or down to handle traffic load.

Although fine for what it does, this approach is only useful when we have a properly tested version that we want to deploy, i.e., more of a blue/green, a.k.a. red/black, kind of upgrade than a “dip your feet in the water” kind of canary deployment. In fact, for the latter (for example, testing a canary version that may not even be ready or intended for wider exposure), the canary deployment in Kubernetes would be done using two Deployments with common pod labels. In this case, we can’t use autoscaling anymore because it’s now being done by two independent autoscalers, one for each Deployment, so the replica ratios (percentages) may vary from the desired ratio, depending purely on load.

Whether we use one deployment or two, canary management using deployment features of container orchestration platforms like Docker, Mesos/Marathon, or Kubernetes has a fundamental problem: the use of instance scaling to manage the traffic; traffic version distribution and replica deployment are not independent in these systems. All replica pods, regardless of version, are treated the same in the kube-proxy round-robin pool, so the only way to manage the amount of traffic that a particular version receives is by controlling the replica ratio. Maintaining canary traffic at small percentages requires many replicas (e.g., 1% would require a minimum of 100 replicas). Even if we ignore this problem, the deployment approach is still very limited in that it only supports the simple (random percentage) canary approach. If, instead, we wanted to limit the visibility of the canary to requests based on some specific criteria, we still need another solution.

istio

With Istio, traffic routing and replica deployment are two completely independent functions. The number of pods implementing services are free to scale up and down based on traffic load, completely orthogonal to the control of version traffic routing. This makes managing a canary version in the presence of autoscaling a much simpler problem. Autoscalers may, in fact, respond to load variations resulting from traffic routing changes, but they are nevertheless functioning independently and no differently than when loads change for other reasons.

Istio’s routing rules also provide other important advantages; you can easily control fine-grained traffic percentages (e.g., route 1% of traffic without requiring 100 pods) and you can control traffic using other criteria (e.g., route traffic for specific users to the canary version). To illustrate, let’s look at deploying the helloworld service and see how simple the problem becomes.

There is an example.

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There are additional resources you may want to check about traffic mirroring in istio:

• https://istio.io/latest/docs/tasks/traffic-management/mirroring/
• https://itnext.io/use-istio-traffic-mirroring-for-quicker-debugging-a341d95d63f8
• https://dev.to/peterj/mirroring-traffic-with-istio-service-mesh-2cm4
• https://livebook.manning.com/book/istio-in-action/chapter-5/v-7/130
• https://istio.io/latest/docs/tasks/traffic-management/traffic-shifting/#apply-weight-based-routing