stackdriver | A client library for accessing the Stackdriver API | Machine Learning library

I am trying to auto-scale my pods based on CloudSQL instance response time. We are using cloudsql-proxy for secure connection. Deployed the Custom Metrics Adapter.

https://raw.githubusercontent.com/GoogleCloudPlatform/k8s-stackdriver/master/custom-metrics-stackdriver-adapter/deploy/production/adapter_new_resource_model.yaml

I have a VM instance running in GCE (using the Container Optimised OS) and within that I have an actively running container that is generating json logs. I can see these logs when I navigate to /var/lib/docker/containers//-json.log.

In the same Instance, another docker container is running using the image gcr.io/stackdriver-agents/stackdriver-logging-agent:1.8.4. This was automatically set up when I created the VM.

The VM has permission to access to Cloud Logging and the Cloud Logging API is enabled. I have also followed the steps here and added google-logging-enabled to the metadata with a value of true.

When the VM is started, the logging agent seems to spin up correctly and emits a log saying that it is tailing the log file of the docker container I want logs for, however the logs within that file never appear in Google Logging. Below is a screenshot of the logs that do make it to Cloud Logging:

I have had this issue for a while now so would be very grateful for any help with this issue! Thanks in advance (:

When trying to deploy a Python project to Google Cloud functions using the command

gcloud functions deploy my_function --entry-point reply --runtime python38 --trigger-http --allow-unauthenticated

I get

Deploying function (may take a while - up to 2 minutes)...⠛
For Cloud Build Stackdriver Logs, visit: https://[...] Deploying function (may take a while - up to 2 minutes)...failed.
ERROR: (gcloud.functions.deploy) OperationError: code=3, message=Build failed: missing main.py and GOOGLE_FUNCTION_SOURCE not specified. Either create the function in main.py or specify GOOGLE_FUNCTION_SOURCE to point to the file that contains the function; Error ID: 5c04ec9c

But I do have a main.py file in the folder.

I checked the platform and the file main.py is not being uploaded, while the files inside folders are.

Following the guide that google made for deploying an HPA in Google Kubernetes Engine: https://cloud.google.com/kubernetes-engine/docs/tutorials/autoscaling-metrics

And adding the right permissions because I am using Workload Identity with this guide: https://github.com/GoogleCloudPlatform/k8s-stackdriver/tree/master/custom-metrics-stackdriver-adapter

And also adding the firewall-rule commented here: https://github.com/kubernetes-sigs/prometheus-adapter/issues/134

I am stuck in a point where the HPA returns me this error:

My cloud function code, is a maven-jar-project. It depends on other internal maven jar, so I have added that dependency to the pom.xml.

Expected: I expect that the dependent jars will be automatically included in the cloud function deployment, however I get an error "could not find artifact". Local compilation & running does not have any issue.

I don't want to copy paste those dependency sourcecode directly to my gcloud maven project. [bad ]

This question comes from a previous one almost solved: Save a Google Form as PDF on a Drive's folder using Google Scripts

Perhaps this is a technical question and I am not programmer, so if possible I want a step-by-step answer so I can fully understand it.

The steps and purpose of the code are explained here: Hacking it: Generate PDFs from Google Forms.

The code is posted on the link but I post it here anyways:

I've copied the firebase stripe GitHub example index.js and have been fixing the errors which pop up from hosting the code in my index.ts (typescript) file. The error I have left is the following:

Expected 2 arguments, but got 1.ts(2554) index.ts(54, 112): An argument for 'arg1' was not provided.

It's about this line (there are multiple instances although this should not matter to the solution):

How can I get the current size of a GKE node pool using the REST (or Node) API?

I'm managing my own worker pool using my Express app running on my cluster, and can set the size of the pool and track the success of the setSize operation, but I see no API for getting the current node count. The NodePool resource only includes the original node count, not the current count. I don't want to use gcloud or kubectl on one of my production VMs.

I could go around GKE and try to infer the size using the Compute Engine (GCE) API, but I haven't looked into that approach yet. Note that it seems difficult to get the node count even from Stack Driver. Has anyone found any workarounds to get the current node size?

I am trying to setup Horizontal Pod Autoscaler to automatically scale up and down my api server pods based on CPU usage.

I currently have 12 pods running for my API but they are using ~0% CPU.