amazon-eks-ami VS Grafana

Careful assuming system reserved will be present. Last I checked, AWS EKS does not have system reserved resources for the kubelet by default and as a result, pods can starve those for resources (e.g., https://github.com/awslabs/amazon-eks-ami/issues/79). This is of course more important for memory, but could impact CPU as well.

For example, this is available for AL2: https://github.com/awslabs/amazon-eks-ami

I found this document that lists the pod limits per node size. I suspect you will want to consider larger worker nodes or you will very quickly be unable to schedule additional workloads.

source

Issue 1 is a known issue due to memory reservation being to low, see e.g. https://github.com/awslabs/amazon-eks-ami/issues/1145

No, the new node is added if there are not enough resiurces to start a new pod. So if you have many pods with small resource usage you can hit the pod per node limit, on eks you have a max number of pods depending on the instance type - https://github.com/awslabs/amazon-eks-ami/blob/master/files/eni-max-pods.txt You can incerase that limit : https://docs.aws.amazon.com/eks/latest/userguide/cni-increase-ip-addresses.html

# of pods are essentially capped by the worker node choice.

below excerpt from: https://github.com/awslabs/amazon-eks-ami/blob/master/files/...

  # Mapping is calculated from AWS EC2 API using the following formula:

See also: EKS nodes lose readiness when containers exhaust memory

So it manifests itself in this way: your pod is scheduled but remains pending forever. You check the logs and you see that it's complaining that the an IP address. Ultimately, if you check here, you see the maximum number of pods that can be scheduled on any underlying ec2 instance, even if you have remaining IPs in your subnet. I found this to be one of the most poorly understood phenomena in EKS. Even those who claimed to "crack" it and wrote fancy blog posts about it fundamentally got it wrong. AFAIK this document reflects the official AWS guide on how to mitigate this.

Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.

Prometheus: Open-source monitoring system. Often used together with Grafana.

Grafana

Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.

So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:

In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:

Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.