kube-monkey VS istio

Not in the typical sense, but we have plenty of standard practices and cross-team checkpoints to limit risk. By the time we're deploying changes to production, the work has had a card created, assigned points (which necessarily involves discussing scope and risk), architected (as a group), code peer reviewed, hit unit tests (automated), integration tests (automated), functional tests (automated), smoke tested (automated) end-to-end tests (a few automated, but mostly manual by QA), acceptance tested (by QA and business), resilience tests (chaos engineering with kube-monkey), been deployed to at least 3 environments (with the same exact same artifacts, just with config changes), and monitored for failures (pod restarts, log anomalies, etc -- all automated). Deploy to production is well communicated, and ANY team can request a halt to the deploy if they have concerns.

Part of being a cloud native company means designing services for failure. What happens, for example, if the payment service/pod goes down? Do the rest of your services continue operating normally? One thing tools like kube-monkey does is automatically kill pods for you on a certain date at a certain time in order to plan for failure events. Just wondering if anyone has dove into the deep end with this type of tooling and really just gone all out, besides Netflix?

Chaos Mesh is a popular solution (about 5k GitHub stars), but — obviously — not the only one. E.g., Litmus is a powerful platform to test many things, and kube-monkey might be a good option for more basic stuff.

Exactly the kind of thing I love to see! Sounds like a great use case for a tool like kube-monkey as well.

Kube Monkey

Istio is a popular open-source service mesh framework that provides a comprehensive solution for managing, securing, and observing microservices-based applications running on Kubernetes.

Leverage a service mesh like Istio or Linkerd to manage communication between microservices within the Kubernetes cluster. These service meshes can be configured to intercept JMX traffic and enforce access control policies. Benefits:

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

Consider the case of Bookinfo, a sample application provided by Istio, rewritten using CloudWeGo's Kitex for superior performance and extensibility.

It is a dedicated infrastructure layer that manages service-to-service communication, providing features like load balancing, encryption, authentication, and monitoring. Istio deploys sidecar proxies alongside each microservice instance. These proxies handle communication, providing features like load balancing, service discovery, encryption, monitoring and authentication.

5Y old post that sounds like they've done similar here: Caddy Issue Istio Issue but doesn't cover much of the implementation

In a production environment there will be a load balancer setup with an Ingress Controller, Service Mesh or some type of Custom Router. This allows all traffic to be sent to the single load balancer IP address and then route the traffic to a service based on the Domain name or subpath. We are using a NGINX ingress controller but service meshes like Istio have been becoming the most popular solution to use as they offer more segmentation, security and granular control.

Flagger is a progressive delivery tool that enables a Kubernetes operator to automate the promotion or rollback of deployments based on metrics analysis. It supports a variety of metrics including Prometheus, Datadog, and New Relic to name a few. It also works well with Istio service mesh, and can implement progressive traffic splitting between primary and canary releases.

End-to-end data encryption with a service mesh: Using an end-to-end data encryption mechanism with a service mesh like Istio, TLS can secure communication between different microservices within a Kubernetes cluster. This is a popular approach for modern, distributed microservice architectures.