ts-runtime | Runtime Type Checks for TypeScript

I have a cluster in the Google Kubernetes Engine and want to make one of the deployments auto scalable by memory.

After doing a deployment, I check the horizontal scalation with the following command

kubectl describe hpa -n my-namespace

With this result:

I'm following the Getting started with Endpoints for GKE with ESPv2. I'm using Workload Identity Federation and Autopilot on the GKE cluster.

I've been running into the error:

F0110 03:46:24.304229 8 server.go:54] fail to initialize config manager: http call to GET https://servicemanagement.googleapis.com/v1/services/name:bookstore.endpoints..cloud.goog/rollouts?filter=status=SUCCESS returns not 200 OK: 403 Forbidden

Which ultimately leads to a transport failure error and shut down of the Pod.

My first step was to investigate permission issues, but I could really use some outside perspective on this as I've been going around in circles on this.

Here's my config:

It seems that there is any error that is causing that the files vmware-perfcharts-runtime.log.stderr and vmware-perfcharts-runtime.log.stdout size are huge. While I find out the issue, is it safe to remove the files while vCenter is running?

They are in /storage/log/vmware/perfcharts/

Thanks.

I have an API on google Endpoint, the backend is deployed on GKE and I'd like to expose it via an ingress so I can use IAP on it.
I am using ESP2.
I first deploy my service as a LoadBalancer and it was working.

Thing is my ingress says: "All backend services are in UNHEALTHY state "
I get that the health check does not pass but I do not get why...

The service and the pod corresponding show no error, however on my pod event I can see: " Readiness probe failed: Get http://10.32.1.27:8000/swagger: dial tcp 10.32.1.27:8000: connect: connection refused "

my configurations for pod and service looks like this:

I have developed a gRPC server in Java and a corresponding gRPC client in C#. The objective is to call the gRPC server from several gRPC clients deployed on Windows machines.

Having looked at how gRPC is supported in Azure, AWS, and the Google Cloud Platform (GCP), I will likely host the gRPC server on GCP. Therefore, I am currently testing the deployment scenario for the gRPC server as described by Google in the tutorial on gRPC on Compute Engine. In short words, this means the gRPC server runs in a custom-built Docker container on a Google Compute Engine (GCE) Virtual Machine (VM), right next to the Extensible Service Proxy (ESP), which runs in its own, preconfigured Docker container on the same VM.

An important aspect for use in production is the ability to establish a secure communication channel between the gRPC clients and the gRPC server, using SSL/TSL. This is where I am having problems in the cloud hosting scenario (but not in the self-hosting scenario, where this works nicely).

The gRPC client, which runs on my local Windows 10 machine, communicates successfully with the gRPC server:

• over a secure SSL/TLS channel in case I am self-hosting the server on my local Windows 10 machine; and

• over an insecure channel in case I am hosting the server on GCE as described above.

I've issued the following commands on the GCE VM to create the docker containers for the successful client-server communication over the insecure channel.

I have this YAML (with the parts in square brackets replaced with the correct content):

After following Getting started with Cloud Endpoints on GKE and deploying the API and backend everything looks like it's working (spoiler alert, it's not);

• My application's deployed on a cluster.
• A single pod is deployed in Kubernetes.
• The pod is running two containers (ESP and my GPRC service)
  1. gcr.io/endpoints-release/endpoints-runtime:1
  2. gcr.io/my-app/server:latest

...both are running; kubectl get pods yields:

I'm using microk8s in ubuntu

I'm trying to run a simple hello world program but I got the error when pod created.

kubelet does not have ClusterDNS IP configured and cannot create Pod using "ClusterFirst" policy. Falling back to "Default" policy

Here is my deployment.yaml file which I'm trying to apply.