#kubernetes - March 2022

Did anyone play around with Generic Ephemeral Volumes?

Hello, I have a Jenkins on Azure, and also have an OpenShift which has a project test, the project contains BuildConfig/DeploymentConfig to build & deploy my application. Firstly, the Jenkins do its work such as fetching code from git, run test like SonarQube/Dependency check, once pass the test, it will kick the building job in BuildConfig(simply curl the endpoint of BuildConfig). So far it works fine, but this workflow only run the latest version of code. I want to build the specific branch/tag of code and every time I need manually modify the version in BuildConfig and DeploymentConfig in OpenShift. For security concern, I can only run Jenkins on Azure, is there any way I can set the version of code I want when kicking the Jenkins job not manually change configs in OpenShit??

It's getting intense. I just shipped a multi-cluster pulumi deployment.

Some days I think: Is Pulumi overcomplicating it? I mean raw yaml or helm would have been just taking a helm chart and installing on new cluster.

At the same time:
• ✅️State based tracking of what succeeded and failed
• ✅️One command for n clusters all at once.
• ✅️ Random unique names (when done right) makes more work to pass input/output than just matching on names but then means you get a create before delete on all resources (like a lightweight blue green). I'm hopeful this will help with tackling true blue green too.
• ✅️ Absolutely digging the strongly typed nature of objects. I can be pretty confident in big refactor or changes being compiled and written corrrectly and have to only focus on logical errors.
I can still render to yaml if I must.

I know there's been talk in the past on Pulumi. I've been doing some blog posts on it and maybe there's some debate still if it's worth it purely for Kubernetes, but I'm overall pretty happy.

I dig how pulumi handles secret encryption per stack in the yaml/backend too. Super handy, probably single best feature over terraform that feels like a no brainer.

https://k8s.af/ Kubernetes production fail stories

Has anyone seen

User "system:serviceaccount:kube-system:cluster-autoscaler" cannot list resource "csidrivers" in API group "<http://storage.k8s.io|storage.k8s.io>" at the cluster scope

logged by the cluster-autoscaler on AWS EKS?

is it possible to use aws application load balancer controller with selfmanaged Kubernetes cluster (not eks)?

yes, its not tied to EKS at all

Hi All,

We have 2 Kubernetes clusters, and we deployed Elasticsearch inside that using helm

Cluster-1(PROD)
is having
2 master
3 data
1 client

Cluster-2(DEV)
is having
2 master
2 node
1 client

both are having the same number of shards but the shard size is a little big(3-4Mb difference)in Cluster-1(PROD) for some indices.
we are running a term query to generate a report using elasticsearch. what I noticed is.

when I run this query in both the cluster, Cluster-2(DEV) worked fine and produced a result, but the same query failed causing all 2 data pods to restart,
Then we looked at the resource consumption of data pods in both the cluster,


For the last 3 months, Cluster-2(DEV) memory utilization looks like this
limit = 4GB
request = 2GB
and in use always will be in range 2.5 to 2.8GB

and
For the last 3 months, Cluster-1(PROD) memory utilization looks like this
limit = 4GB
request = 1.2GB
and in use always will be in range 3.8 to 3.9g

---
and when I looked at that configuration file for data pod
for Cluster-2(DEV)
request memory resource is defined as 2GB and
limit memory resource is defined as 4GB
and
for Cluster-1(PROD)
request memory resource is defined as 1.2GB and
limit memory resource is defined as 4GB


my question is:
even though the shard count is similar in both the cluster why the memory using always be 3.8 to 3.9 in our Cluster-1(PROD)
Is it because the request is too low? is there any recommended ratio of requests and limit resources?

Does anyone know an instance-type (EC2) recommender that is based on running workloads (and it's requests/limits)?

Hi All,

for pods if probes fails it will restart container,

is it possible to configure to restart pod itself if probe fails

thank you