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Container logs for helm install

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jules last edited by

Is it possible to print to helm install (or helm upgrade) output the logs of the deploying containers (logs that are available by kubectl logs command)?
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Rossere last edited by

Short answer: no. You would most likely run helm commands from your own workstation.

For those logs to be available through "kubectl logs", you would have to start a Pod, executing your "helm install" command. Maybe using a Job. Although this also means having some ServiceAccount with enough privileges to install objects on behalf of Helm.
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Storing secrets in AWS instance metadata for Kubernetes clusters can be very dangerous unless carefully configured. The main risk is that an attacker who can get access to any container running on your cluster, will get access to the metadata service running on 169.254.169.254. This could happen in a couple of ways. Firstly an attacker who finds a remote code execution vulnerability in one of your containers may be able to use curl or similar from the container to hit the cluster nodes metadata service. Another possible attack route for this issue would be via Server-Side Request Forgery (SSRF) vulnerabilities in your containers, although using IMDSv2 can help here (reference here) If you still need to store a privileged secret in AWS Metadata, you can look at mitigations for this risk, like using Network Policies to restrict access to the metadata endpoint from containers, or using something like kube2iam to restrict what AWS metadata access is possible from containers in the cluster.
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To debug this, check the container logs and the container events. kubectl get pods Find your pod name. kubectl logs If logs don't make it obvious... kubectl get events If you deployed into a namespace, make sure to add -n to all commands. This should reveal basically everything. If you're stuck, you can also browse around the k8s dashboard if you haven't yet. But that probably wouldn't reveal much more than the CLI is with these commands.
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Are they separate projects (from a logical or technical perspective)? Would one ever need to be updated/recreated independently of the other one? Would they need to scale independently of each other? I think if any of those answers is a yes, then they should be separated. If not, you should be fine bundling them together. Bundling them together will reduce discrepancy or compatibility issues between the two containers.
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You need to put public ip address or hostname assigned by aws ec2 service, if your aws ec2 instance is public. If your aws ec2 instance is located in vpc, you won't be able to connect it directly. Either you need to setup routing through internet gateway or you have to setup ansible server in vpc.
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You'll want to look through the Kubernetes Volume documentation to see what options are available. In particular look into the PersistentVolumeClaim. This allows you to setup a shared data volume for your pods. Here's an example snippet from a k8s deployment YAML using a volume: spec: containers: - image: **yourdbcontainer** name: mysql-db env: - name: example value: example_value ports: - containerPort: 3306 name: mysql-db volumeMounts: - name: mysql-persistent-storage mountPath: /var/lib/mysql volumes: - name: mysql-persistent-storage persistentVolumeClaim: claimName: mysql-pv-claim
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From my standpoint the best approach would be to: Separate build process using Jenkins that would build NodeJS project into distribution and wrap it into Docker image Spin up Docker registry that would accumulate Docker images from Jenkins (this registry should be accessible from Kubernetes cluster) Move environment variables to Jenkins secrets or use separate tool to collect and combine configurations from external Git repo (we use Spring Cloud Config through REST API to collect json/yml definitions for every application in every environment) Using Jenkins you can configure continuous delivery system based on generic Pipelines. Possible flow would be: Developers finish their work in correspondence to GitFlow (latest Pull Request merged into Release branch) Webhook triggers Jenkins pipeline: Stage 1: collect environment definition Stage 2: build NodeJS app using npm Stage 3: build Nginx Docker image with distribution Stage 4: push Docker image to Docker registry Stage 5: deploy/update service in Kubernetes using standard definitions Notifications send in regard of results This process might be visualized using Rancher. I can answer your questions in chat.
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Since Pod is running on single node it can use resources from this node only. And CPU/MEM can't be shared here. At least without special software for distributed computation. In your case you should consider using ActiveMQ Clustering (https://activemq.apache.org/clustering). In that way you will be able to spread load across multiple k8s nodes with ActiveMQ pods running on them. So use k8s Deployment + pod affinity to be sure pods will be scheduled to different nodes.
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Docker compose is still a comparatively easy and light-weight tool to run multiple docker containers with service discovery. Kubernetes is a metaphorical elephant. Great if you need to smash Roman lines and win the battle, but overkill for a minor skirmish, and definitely more trouble than it's worth if you're trying to fight a small battle in the mountains. If you have a fairly large app with multiple microservices, then Kubernetes is worth every penny. But when you're trying to run just a couple of containers, especially if it's only on a single host (i.e. your frontend + backend and maybe a local instance of RabbitMQ), docker-compose is a much simpler/faster way to orchestrate it. Kubernetes is like using a cannon to kill a mosquito when a flyswatter will do. Example use cases where docker-compose works just fine: Local development environments running on your machine. Even if you run Kubernetes in production, there's likely a lot of orchestration around getting it to work which would make it too complex for a single machine. Simple apps without a lot of components Single tenant apps which run on a single host Deploying docker-based apps on bare VMs or physical hosts
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you are going the right way with PodTopologySpreadConstraints(PTSC for short) to have different instance of the same pod in an uneven distribution through all your k8s nodes. On that note, I would remind you that you can pair various PTSC rules to achieve your desired goal. Based on instance type labels as well as per availability zone. But what is missing, and could be a solution to your problem, is to incrementally send a slice of your traffic to those pods in order to do a comparison in performance or whatever you want to compare. You should look into what your ingress controller can manage. For instance, NGINX ingress-controller uses https://kubernetes.github.io/ingress-nginx/user-guide/nginx-configuration/annotations/#canary and Traefik ingress-controller uses https://traefik.io/glossary/kubernetes-deployment-strategies-blue-green-canary/ to send a certain percentage of traffic to those "test" pod and once you have your metrics you can go on and complete the rollout.
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Ah, shortly after posting I found a clean solution. Adding --restart=Never makes the command work as expected. % time kubectl run -n kcox-test --image busybox --attach test --restart=Never -- date Thu Jan 27 15:10:41 UTC 2022 time Total: 9.449 (1%), User: 0.100, System: 0.034 - kubectl run -n kcox-test --image busybox --attach test --restart=Never -- date

Container logs for helm install

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