microservices-demo VS charts

As an inbetween beginner and advanced you can look at this project - https://microservices-demo.github.io/ its more practical and again uses Python.

I'm new to kubernetes world. I got sample kubernetes deployments (like sock-shop) working end-to-end without any issues. I tried NodePort to access the service but instead of running it on a different port I need to run it exact port 80 on the host. I tried many ingress solutions but didn't work.

Here's an example app that fits all of the criteria above. Can be used as a good reference point. https://github.com/microservices-demo/microservices-demo

1) Deploy the Sock Shop app in our K8s cluster. While we’ll use Sock Shop as our example application, you can deploy your own app to your cluster and still follow along. 2) Deploy APIClarity in our K8s cluster and configure monitoring 3) Observe API traffic on the APIClarity dashboard 4) Review and create an API specification and view the generated OpenAPI spec in Swagger format. 5) Identify deviations from an API spec along with usage of shadow and zombie APIs. 6) View and filter API events

Depending on what you want there is Microservice demo from Google or Sock Shop from Weaveworks.

There are sample application that you could deploy, for example https://github.com/microservices-demo/microservices-demo

Both of these examples seem pretty obvious and something you wouldn’t mess up, but as your chart grows, so does your values.yaml file. A great example is the Redis chart by Bitnami. I encourage you to scroll through its values file. See you in a minute!

We will write a Terraform module that will take a list of configurations for each required RabbitMQ instance. Luckily for us, we don't have to write the Kubernetes yaml configurations since the helm charts by Bitnami does a great job of doing all the things we discussed above. All we need to do is leverage Terraform Helm Provider and deploy the chart with the required values for our use case.

💡  The full details of helm charts can be referenced in their associated GitHub Repository.

I have a configmap with the ndjson set up under data:, similar to https://github.com/bitnami/charts/issues/6159 and it's subsequent answer.

This way, you can easily deploy any Helm charts from this public repo - https://github.com/bitnami/charts/tree/main/bitnami in just minutes.

The Bitnami Github Pages themselves usually cover everything you need to know. Configure a values.yaml file, or modify that to your liking, and you run helm install, as written in their docs.

The actual reason that our pods are not coming up is found when we review the helm installation that we are trying to run. If you check the dependencies in the GitHub repository (https://github.com/bitnami/charts/blob/main/bitnami/drupal/values.yaml) you find out that persistent storage is enabled by default and set to 8Gi. Also, the helm package uses MariaDB and the database size is specified to a default of 8Gi, thus setting the minimum storage for this installation to be 16Gi.

We're using https://github.com/bitnami/charts/tree/main/bitnami/spark, but I have heard good things about https://github.com/GoogleCloudPlatform/spark-on-k8s-operator as well. Hudi should not need any long running deployments as per the docs https://hudi.apache.org/docs/0.5.1/deployment/#deploying

Bitnami has its own scaffolding published at https://github.com/bitnami/charts/tree/main/template