Automate All the Boring Kubernetes Operations with Python

1. kind

   1 227 14,478 9.2 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

2. InfluxDB

   www.influxdata.com featured

   InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.

   

3. python

   2 22 7,306 8.2 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.

4. metrics-server

   3 49 6,313 8.3 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

5. kopf

   4 7 2,419 8.1 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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