rsl VS jq

on a similar note I wrote little tool to convert from many formats to many formats

the biggest usecase for me is taking some csv, toml, xml, whatever and converting that to json so I can pipe to jq

https://github.com/sentriz/rsl

Hey, it was discussed a bit some years ago https://github.com/jqlang/jq/issues/1892

JSON_EXTRACT takes two arguments: the column and the path of the element you want to select. paths are separated by a dot, kind of the same way directory paths are separated by a /, and the top level of the path is denoted by $. if you've ever struggled with jq, you should feel comfortable struggling with this.

This list can go on forever, the jq tool has many features to explore and I have only scratched a little bit of the surface here. A good starting point to learn more seems to be the Wiki at jq's GitHub repository. The show notes from the podcast Kodsnack has some excellent resources too.

But the real game changer was its piping capabilities. You've noticed that I've set json as my table format, and that's not deliberate. It's because of another tool called jq, which became my favorite way to interact with json data. Mostly because of its elegant syntax and its natural integration with the terminal and also vim.

One of the flags you can use is -json, which formats the outputs as a JSON object. This is especially useful when you want to pipe the output into other tools that can process JSON, such as jq, a lightweight command-line JSON processor.

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