Automate All the Boring Kubernetes Operations with Python

• kind

  53 12,234 8.9 Go

  Kubernetes IN Docker - local clusters for testing Kubernetes

  

  # https://github.com/kubernetes-sigs/kind/issues/398 # kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.5.0/components.yaml # kubectl patch -n kube-system deployment metrics-server --type=json \ # -p '[{"op":"add","path":"/spec/template/spec/containers/0/args/-","value":"--kubelet-insecure-tls"}]' from kubernetes import client api_client = client.ApiClient(configuration) custom_api = client.CustomObjectsApi(api_client) response = custom_api.list_cluster_custom_object("metrics.k8s.io", "v1beta1", "nodes") # also works with "pods" instead of "nodes" for node in response["items"]: print(f"{node['metadata']['name']: <30} CPU: {node['usage']['cpu']: <10} Memory: {node['usage']['memory']}") # api-playground-control-plane CPU: 148318488n Memory: 2363504Ki # api-playground-worker CPU: 91635913n Memory: 1858680Ki # api-playground-worker2 CPU: 75473747n Memory: 1880860Ki # api-playground-worker3 CPU: 105692650n Memory: 1881560Ki

• python

  8 6,127 7.7 Python

  Official Python client library for kubernetes (by kubernetes-client)

  

  As you can imagine, that's a lot of functions to choose from, luckily all of them are listed in docs and you can click on any one of them to get an example of its usage.

• InfluxDB

  www.influxdata.com

  sponsored

  Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.

  

• metrics-server

  9 5,091 8.4 Go

  Scalable and efficient source of container resource metrics for Kubernetes built-in autoscaling pipelines.

  

  # https://github.com/kubernetes-sigs/kind/issues/398 # kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.5.0/components.yaml # kubectl patch -n kube-system deployment metrics-server --type=json \ # -p '[{"op":"add","path":"/spec/template/spec/containers/0/args/-","value":"--kubelet-insecure-tls"}]' from kubernetes import client api_client = client.ApiClient(configuration) custom_api = client.CustomObjectsApi(api_client) response = custom_api.list_cluster_custom_object("metrics.k8s.io", "v1beta1", "nodes") # also works with "pods" instead of "nodes" for node in response["items"]: print(f"{node['metadata']['name']: <30} CPU: {node['usage']['cpu']: <10} Memory: {node['usage']['memory']}") # api-playground-control-plane CPU: 148318488n Memory: 2363504Ki # api-playground-worker CPU: 91635913n Memory: 1858680Ki # api-playground-worker2 CPU: 75473747n Memory: 1880860Ki # api-playground-worker3 CPU: 105692650n Memory: 1881560Ki

• kopf

  1 1,781 6.1 Python

  A Python framework to write Kubernetes operators in just a few lines of code

  If you're looking for more examples beyond what was shown and referenced above, I recommend exploring other popular tools that make use Python Kubernetes client, such kopf - the library for creating Kubernetes operators. I also find it very useful to take a look at tests of the library itself, as it showcases its intended usage such this client test suite.

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