healthcheck | implementing Kubernetes liveness and readiness probe | Monitoring library

I have a kubernetes cluster that's running datadog and some microservices. Each microservice makes healthchecks every 5 seconds to make sure the service is up and running. I want to exclude these healthcheck logs from being ingested into Datadog.

I think I need to use log_processing_rules and I've tried that but the healthcheck logs are still making it into the logs section of Datadog. My current Deployment looks like this:

I’m trying to get an AWS Auto Scaling Group to replace ‘unhealthy’ instances, but I can’t get it to work.

From the console, I’ve created a Launch Configuration and, from there, an Auto Scaling Group with an Application Load Balancer. I've kept all settings regarding the target group and listeners the same as the default settings. I’ve selected ‘ELB’ as an additional health check type for the Auto Scaling Group. I’ve consciously misconfigured the Launch Configuration to result in ‘broken’ instances -- there is no web server to listen to the port configured in the listener.

The Auto Scaling Group seems to be configured correctly and is definitely aware of the load balancer. However, it thinks the instance it has spun up is healthy.

To preface I'm fairly new to Docker, Airflow & Stackoverflow.

I've got an instance of Airflow running in Docker on an Ubuntu (20.04.3) VM.

I'm trying to get Openpyxl installed on build in order to use it as the engine for pd.read_excel.

Here's the Dockerfile with the install command:

I'm using ECS with Fargate and trying to create a bind mount on ephemeral storage but my user (id 1000) is unable to write to the volume.

According to the documentation, it should be possible.

However the documentation mentions:

By default, the volume permissions are set to 0755 and the owner as root. These permissions can be customized in the Dockerfile.

So in my Dockerfile I have

I have set up and using https://github.com/jvm-profiling-tools/async-profiler which is extremely useful but I have a strange thing I cannot explain.

My setup is exactly where multiple presentation showed it can help:

• AKS kubernetes cluster with a nodepool

• A pod deployed on one node

• Within the container I have set up openjdk-11 with the debuginfo

• The profiling setup is a simple ./profiler start -e malloc PID

• Since I'm in a virtualised environment profiling works, the only warning I get is

  ...

I have an application running on my local machine that uses React -> gRPC-Web -> Envoy -> Go app and everything runs with no problems. I'm trying to deploy this using GKE Autopilot and I just haven't been able to get the configuration right. I'm new to all of GCP/GKE, so I'm looking for help to figure out where I'm going wrong.

I was following this doc initially, even though I only have one gRPC service: https://cloud.google.com/architecture/exposing-grpc-services-on-gke-using-envoy-proxy

From what I've read, GKE Autopilot mode requires using External HTTP(s) load balancing instead of Network Load Balancing as described in the above solution, so I've been trying to get that to work. After a variety of attempts, my current strategy has an Ingress, BackendConfig, Service, and Deployment. The deployment has three containers: my app, an Envoy sidecar to transform the gRPC-Web requests and responses, and a cloud SQL proxy sidecar. I eventually want to be using TLS, but for now, I left that out so it wouldn't complicate things even more.

When I apply all of the configs, the backend service shows one backend in one zone and the health check fails. The health check is set for port 8080 and path /healthz which is what I think I've specified in the deployment config, but I'm suspicious because when I look at the details for the envoy-sidecar container, it shows the Readiness probe as: http-get HTTP://:0/healthz headers=x-envoy-livenessprobe:healthz. Does ":0" just mean it's using the default address and port for the container, or does indicate a config problem?

I've been reading various docs and just haven't been able to piece it all together. Is there an example somewhere that shows how this can be done? I've been searching and haven't found one.

My current configs are:

We have a bunch of health checks against third-party services. We want them to run periodically because when they go down it affects our app just like a bug in our code. Knowing that "it's them not us" reduces significant troubleshooting time.

We've set this health check up via github actions with a scheduled run, but we want a HealthCheck per third-party service. That way, the slack message on failure will be very specific of what is down. But that is going to create a lot of duplicated yml content.

I discovered something called github composite actions and it seems to be intended for solving this problem, but I can't find information about whether or not a composite action can live in a private repository.

The documentation of the uses key only mentions public repositories when it mentions repositories at all. Is there a way to make a composite action in a private repository and use it?

I tried making their hello world example, ran it, and it ran correctly. Then I made the action repo private, and the repo using the action's build failed saying:

I'm trying to configure a single ALB across multiple namespaces in aws EKS, each namespace has its own ingress resource.

I'm trying to configure the ingress controller aws-loadbalancer-controller on a k8s v1.20.

The problem i'm facing is that each time I try to deploy a new service it always spin-up a new classic loadbalancer in addition to the shared ALB specified in the ingress config.

https://kubernetes-sigs.github.io/aws-load-balancer-controller/v2.2/

I have an API that recently started receiving more traffic, about 1.5x. That also lead to a doubling in the latency:

This surprised me since I had setup autoscaling of both nodes and pods as well as GKE internal loadbalancing.

My external API passes the request to an internal server which uses a lot of CPU. And looking at my VM instances it seems like all of the traffic got sent to one of my two VM instances (a.k.a. Kubernetes nodes):

With loadbalancing I would have expected the CPU usage to be more evenly divided between the nodes.

Looking at my deployment there is one pod on the first node:

And two pods on the second node:

My service config: