wg-serverless VS charts

Check out CNCF Projects. Contributing to some of these open source projects is a great way to gain skills and land a job.

would indicate if your cluster could retrieve content from cncf.io or if cncf.io is up.

This solution builds upon well-established Cloud Native Computing Foundation (CNCF) open source projects like Thanos and Prometheus,together with a new managed services, Azure Managed Grafana, recently released in public preview. It allows for ephemeral clusters to still have updated metrics without the 2-hours local storage of metrics in the classic deployment of Thanos sidecar to Prometheus.

Not ELI5 but see https://github.com/cncf/wg-serverless/tree/master/whitepaper...

Some people go monolithic app but best practice is usually one function per route.

Traditional servers come with management overhead (e.g. defining/managing/monitoring scaling) and by using Serverless servers you avoid that overhead and optimize for good engineers which are almost always your bigger cost center.

github.com/cncf/wg-serverless/blob/master/w..

As stated in a white paper by the CNCF serverless working group, there are two primary serverless personas:

The CloudEvents specification is under the CNCF Serverless working group since 2018. The spec's purpose is describing event data in a common way. This is useful in many scenarios, for example, routing events to the appropriate subscribers depending on the type of the event. Since applications can use a lot of different transports to send and receive events, the CloudEvents spec is protocol-agnostic so it defines protocol bindings in order for the metadata to be correctly mapped for HTTP, AMQP, Kafka, etc.

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