¿Cuáles son las prácticas recomendadas para probar y depurar imágenes de Docker en un clúster de Kubernetes?

Again a terrible text. The title talks about debugging in a K8s Cluster but that is not topic at all :/ Better delete it than wasting time.

Test Docker images locally using tools like Docker Desktop, Minikube, or Kind before deploying to Kubernetes. These tools help simulate the production environment, allowing you to build, run, inspect images, apply Kubernetes manifests, access logs, and execute commands within containers for efficient testing and debugging.

When the image have problems, I prefer build my image in local. With that you don't need to wait for pipeliene and you can reduce time reparing problem with containers. And in Local you can manage all environment for your machine. Im use mac to use docker, i think is good for use virtual machines.

A. Best way is to Test Locally First – Verify Behavior Before Pushing B. Build and Run the Image – Test the Docker image locally to confirm it works as expected. C. Validate Logs and Endpoints – Check logs and test application endpoints to ensure proper functionality. D. Debug Issues – Access the container to inspect its environment and resolve any errors. E. Fix Errors Early – Address issues locally to avoid failures in Kubernetes deployments.

Adhere to Dockerfile best practices to enhance efficiency, security, and reliability of Docker images. Best practices include using specific base image tags, minimizing layers, employing multi-stage builds, reducing image size, utilizing ARG and ENV variables, setting user permissions, incorporating health checks and labels. Validate and enhance Dockerfiles using tools like docker lint, hadolint, or docker scan for improved image quality.

In general i hate disorder in the code. In the Dockerfile is similar. When you put innecesary libraries or code you are dirtying the container doing making it more difficult for developers understand the code and worsening the docker perfomance

To ensure Docker images meet application requirements, write unit tests to validate code logic and behavior within containers, and integration tests to assess interactions with other components. Utilize frameworks such as pytest, unittest, or mocha for unit testing, and tools like docker compose, testcontainers, or helm for integration testing to enhance testing efficiency and reliability.

Docker Is primordial for unit test, wiht Docker you can simulate the final enviroment that you want to deploy and you can find image with similiar caractetistics but smallest size, shorting job time fot your unit test.

Utilize a CI/CD pipeline to automate building, testing, and deploying Docker images to Kubernetes. Tools like Jenkins, GitHub Actions, or GitLab CI/CD can trigger pipelines on code changes, run tests, build and push images to a registry, and apply Kubernetes manifests. Enhance image quality and security with tools like SonarQube, Anchore, or Trivy for code analysis, security scanning, and vulnerability detection within the pipeline.

I love pipelines. I have used Gitlab pipelines, Github actions, and a litlle bit Jenkins. I started with this gruop of jobs: build code, test, code analys(sonarqube), code security(codeql, veracode, sast), build image, review build image(for vulnerabilitys), deploy to kubernetes.

To maintain Docker containers in a Kubernetes cluster, monitor and troubleshoot performance using tools like Prometheus, Grafana, or Datadog to collect and visualize metrics, logs, and traces. Interact with the cluster using tools like kubectl, k9s, or Lens to inspect pod, service, and node status and logs. Facilitate context switching and log streaming across pods with tools like kubectx, kubens, or stern for efficient monitoring and issue resolution.