amazon-eks-ami VS zipkin

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.

When choosing distributed tracing tools, considerations include your technology stack, business requirements, and monitoring complexity. Zipkin, SkyWalking, and OpenTelemetry are popular distributed tracing solutions, each with its unique features.

From the perspective of the realization of GraphQL infrastructure, the interesting direction is "Finding". How to find the problem? How to find the bottleneck of the system? Distributed Tracing System (DTS) will help answer this question. Distributed tracing is a method of observing requests as they propagate through distributed environments. In our scenario, we have dozens of subgraphs, gateway, and transport layer through which the request goes. We have several tools that can be used to detect the whole lifecycle of the request through the system, e.g. Jaeger, Zipkin or solutions that provided DTS as a part of the solution NewRelic.

Zipkin is a distributed tracing system used for tracking and analyzing how requests move through complex systems, especially in setups with many interconnected services, known as microservices.

Distributed Tracing: Middleware for distributed tracing like Jaeger and Zipkin helps monitor and trace requests as they flow through multiple microservices, aiding in debugging, performance optimization, and understanding the system's behavior.

For microservice tracing, you might want to look at Zipkin [0], or OpenTelemetry [1]

[0] https://zipkin.io/

I’ve not used a self-hosted solution before, but here’s one I found. https://zipkin.io/

> IMO the reason these vendors can and do charge so much is not because telemetry software is hard.

I always saw it as "they are charging for their polished UI/experience"

The UI of https://zipkin.io/ versus DataDog is kind of... not really in the same ballpark?

There are the zipkin https://zipkin.io/ and jaeger https://www.jaegertracing.io/ packages/components you can use both have quickstarts if you consider that to be a beginner's guide.

Zipkin, which was developed by Twitter, is an open source tool for distributed tracing that can also be used to troubleshoot latency issues in your application. While Zipkin is Java-based, py_zipkin is an implementation for Python.