yq VS miller

What I often use to just get the full key paths is yq (https://github.com/mikefarah/yq), piping into grep when necessary

  yq -o=props 

For more information about this command visit https://github.com/mikefarah/yq

I've been trying to install and config a plugin (papis.nvim) for a couple of days and am having issues with a function that uses io.popen to run yq to convert yaml files to json. I know my install of yq is fine- I can run yq -oj info.yaml from the command line with no issue and it produces the correct json output. I know the function can find the yq executable, but it returns nil. I've saved the error from the yq golang code: panic: runtime error: invalid memory address or nil pointer dereference

- yq has no if-then-else https://github.com/mikefarah/yq/issues/95 which is a poor design (or omission) in my opinion

After which, I use openapi-generator to make a yaml output.

https://gist.github.com/freshteapot/3637e8d2b5ecdf01b7d25246...

- yq version 3.4.1 (Worth noting, the example uses an out of date yq, so a few modifictaions might be needed)

https://github.com/mikefarah/yq

For those pining for a similar yaml query tool for working through acres of config: https://github.com/mikefarah/yq

jq is awesome and thanks to the new team for their recent efforts and energy, it massively appreciated.

For anyone looking for such a script, there's some CLIs that make it easy. One is `yq -o props` [1], another way is to use `yq -j` or `yj` [2] to convert to JSON and pipe it to `gron` [3].

[1] https://github.com/mikefarah/yq

[2] https://github.com/sclevine/yj

[3] https://github.com/tomnomnom/gron

jq and miller[1] are essential parts of my toolbelt, right up there with awk and vim.

[1]: https://github.com/johnkerl/miller

> This works really well if your problem can be solved in one or two liners.

My personal comfort threshold is around the 100-line mark. It's even possible to write maintainable shell scripts up to 500 lines, but it mostly depends on the problem you're trying to solve, and the discipline of the programmer to follow best practices (use sane defaults, ShellCheck, etc.).

> It go bad very quickly when, say, you have two CSV files and want to join them the sql-way.

In that case we're talking about structured data, and, yeah, Perl or Python would be easier to work with. That said, depending on the complexity of the CSV, you can still go a long way with plain Bash with IFS/read(1) or tr(1) to split CSV columns. This wouldn't be very robust, but there are tools that handle CSV specifically[1], which can be composed in a shell script just fine.

So it's always a balancing act of being productive quickly with a shell script, or reaching out for a programming language once the tools aren't a good fit, or maintenance becomes an issue.

[1]: https://miller.readthedocs.io/

where mlr is from https://github.com/johnkerl/miller

if this class of problems (i.e., csv/tsv data) is your main target you may find miller (https://github.com/johnkerl/miller) much more useful in the long run

That said, you may be interested in Miller (https://github.com/johnkerl/miller) which provides similar capabilities for CSV, JSON, and XML files. It doesn't use a SQL grammar, but that's just the proverbial lipstick on the thing. I'm not the author, but I have used it and I see some parallels in use cases at the very least.

worth giving Miller a shot

CSV utilities (still haven't pick a favorite one...): https://github.com/harelba/q https://github.com/BurntSushi/xsv https://github.com/wireservice/csvkit https://github.com/johnkerl/miller