jq VS helm

Thanks, if anyone else is interested there is an explanation of this feature here: https://subtxt.in/library-data/2016/03/28/json_stream_jq And: https://github.com/jqlang/jq/wiki/FAQ#streaming-json-parser

The last time I tried, I think the reason I gave up on JQ for large inputs was that the throughput would max out at 7mb/s whereas the same thing with spark SQL on the same hardware (MacBook) would max out at 250mb/s. So I started looking into using other solutions for big data while I use jq in parallel for small data in multiple files.

I will test it out again cause this was 4-5 years ago when I last tested it, but I believe jaq is still preferred for large inputs. Still I prefer for big data to use Spark/Polars/clickhouse etc.

Looks like you are correct https://github.com/jqlang/jq/blob/ed8f7154f4e3e0a8b01e6778de...

I think like you. But also, one does not necessarily know beforehand that they will want to make money.

Like a project could be born out of pure generosity, but after the happy initial phase the project might get too heavy on the maintenance requirements, causing the author to approach burnout, and possibly deciding that they want to make money to continue pulling the cart forward.

However, here's something I do think: if you create something as Open Source, it should be out of a mentality of goodwill and for the greater good, regardless of how it ends up being used. OSS licenses do mean this with their terms. If you later get tired or burned out, you should just retire and allow the community to keep taking care of it. Just like it happened with the Jq tool [1].

[1]: https://github.com/jqlang/jq/releases/tag/jq-1.7

In this blog we'll see how to upload the JSON directly using PostgreSQL COPY command and using an utility called jq!

And we can then make it easier to find the commit by filtering the response with jq.

Official Documentation: jqlang.github.io/jq

To handle JSON files and JSON outputs in a script or format and highlight it, jq can be very handy. Many command line tools provide a json output, so you don't have to write a custom parser for a table a list in a terminal. Instead of that, you can use jq to get a specific value from the output or even modify the output. For more information, you can visit https://jqlang.github.io/jq/

In some projects I've wanted to use HTTPie to test APIs and jq to work with some JSON data. Nix has been really helpful in managing those dependencies that I can't easily get from npm.

Applying Kubernetes manifests individually is problematic because files can get overlooked. Packaging your applications as Helm charts lets you version your manifests and easily repeat deployments into different environments. Helm tracks the state of each deployment as a "release" in your cluster.

helm

Explanation here: https://github.com/helm/helm/issues/12681#issuecomment-19593...

Looks like it's a bug in Helm, but actually isn't Helm's fault, the issue was introduced by Fedora Linux.

Helm (Get from here https://helm.sh/)

It’s also well understood that having a k8s cluster is not enough to make developers able to host their services - you need a devops team to work with them, using tools like delivery pipelines, Helm, kustomize, infra as code, service mesh, ingress, secrets management, key management - the list goes on! Developer Portals like Backstage, Port and Cortex have started to emerge to help manage some of this complexity.

Kubernetes orchestrates deployments and manages resources through yaml configuration files. While Kubernetes supports a wide array of resources and configurations, our aim in this tutorial is to maintain simplicity. For the sake of clarity and ease of understanding, we will use yaml configurations with hardcoded values. This method simplifies the learning process but isn’t ideal for production environments due to the need for manual updates with each new deployment. Although there are methods to streamline and automate this process, such as using Helm charts or bash scripts, we’ll not delve into those techniques to keep the tutorial manageable and avoid fatigue — you might be quite tired by that point!

Helm is a package manager that automates Kubernetes applications' creation, packaging, configuration, and deployment by combining your configuration files into a single reusable package. This eliminates the requirement to create the mentioned Kubernetes resources by ourselves since they have been implemented within the Helm chart. All we need to do is configure it as needed to match our requirements. From the public Helm chart repository, we can get the charts for common software packages like Consul, Jenkins SonarQube, etc. We can also create our own Helm charts for our custom applications so that we don’t need to repeat ourselves and simplify deployments.

We can search for charts https://helm.sh/ . Charts can be pulled(downloaded) and optionally unpacked(untar).

Generally I felt as if I was diving in the deepest of waters without the correct equipement and that was horrifying. Unfortunately to me, I had to dive even deeper before getting equiped with tools like ArgoCD, and k8slens. I had to start working with... HELM.

Within the architecture of Cyclops, a central component is the Helm engine. Helm is very popular within the Kubernetes community; chances are you have already run into it. The popularity of Helm plays to Cyclops's strength because of its straightforward integration.