This document discusses dynamically managing Kubernetes. It suggests separating different customers by scaling them differently, using different image versions, and customizing environments specifically for each customer. Kubernetes allows creating pods, replication controllers and services during runtime. A management pod could be used to manage Kubernetes resources through REST calls and long polling watches using the Fabric8 Kubernetes client, which provides a fluent API for interacting with the Kubernetes API. Managing ports was tricky due to Kubernetes not allowing port changes and issues with load balancing and connection failures. Testing was also complicated due to the asynchronous nature of container creation and needing to watch for resource version changes and orphaned containers.

1. Dynamic management of
Kubernetes
MartinPodval

2. Usualy you have some expectation ...

3. … which are simply doable
… w/ autoscaling

4. Unpredictable, isolated customers?

5. Separate them. It’s simple!

6. Why?
To simplify complicated things
● Scale different customers in different way
● Divide and do not affect multiple environments
● Different image versions for different customers
● Specific customizations for specific customer
● Cannary but “full-environment”
● Allows to deploy complex changes
● Easy management by labels

7. Kubernetes allows a lot of fun
Kube API is REST
● create your own `pods`, `rc` and `svc`
● deploy them
● manage them
… during the runtime!

8. You can create own k8s management

9. Management pod?
● Just another docker container
● With access to KubeAPI
● Java & SpringBoot
● Your `Rc`s and `Svc`s described in the code
● REST calls and long-polling watches - by etcd
● Auto-scaling by k8s or according to KubeAPI calls

10. How in Java? Use Fabric8 K8s client

11. K8s API and Fabric8 Client
● Described in Swagger
● Documentation, examples
● Full-fledged
● Fluent API
● Builders
● Auto-generated
… it just works!

12. What was tricky?
Port management
● K8s does not allows to change ports
● Static tcp load balancer, nginx
● TCP connection failures on KubeProxy

13. What was tricky? Contd.
● Complicated testing - KubeApi required
● Everything is async - container creation takes minutes
● Watching resource versions
● Orphaned containers

14. Q & A