It's just operating at the wrong abstraction level, whereas gron is orders of magnitude easier to understand and _explore_.
I don't agree that jq's query language is obtuse. It's a DSL for JSON document trees, and it's largely unfamiliar, but so is xpath or any other DOM transformation language.
The same thing is said about regex.
My take is that "it's obtuse" just translates to "I'm not familiar with it and I never bothered to get acquainted with it".
One thing that we can agree though is that jq's docs are awful at providing a decent tutorial for new users to ramp up.
jq has never come naturally. Every time I try to intuit how to do something, my intuition fails. This is despite having read its man page a dozen times or more, and consulted it even more frequently than that.
I've spent 20+ years on the Unix command line. I know my way around most of it. I can use sed and awk and perl to great effect. But I just can't seem to get jq to stick.
Aside, but there's a lot of times when "I know jq can do this, but I forget exactly how, let me find it in the man page" and then... I find jq's man page as difficult as jq itself when trying to use it as a reference.
Anyway, $0.02.
Edited to add: as a basic query language, I find it easy to use. It's when I'm dealing with json that embeds literal json strings that need to be parsed as json a second time, or when I'm trying to manipulate one or more fields in some way before outputting that I struggle. So it's when I'm trying to compose filters and functions inside jq that I find it hard to use.
I also remember what when I first tried learning regex it was also very difficult. That is until I learned about finite state machines and regular languages, after that CS fundamentals class I was able to make sense of regex in a way that stuck.
Is there a comparable theory for jq's mini-language?
jq is basically a templating language, like Jsonnet or Jinja2. What jq calls a "filter" can also be called a template for the output format.
Like any template, a jq filter will have the same structure as the desired output, but may also include dynamically calculated (interpolated) data, which can be a selection from the input data.
So, at a high level, write your filter to look like your output, with hardcoded data. Then, replace the "dynamic" parts of the output data with selectors over the input.
Don't worry about any of the other features (e.g. conditionals, variables) until you need them to write your "selectors."
YMMV, but that's what's worked for me
in.json {"a":1,"b":2}
jq -c '{a}' in.json
{"a":1}
The . is the current stream, so if I just do ". , .", it's kind of pushing two streams along:
jq -c '.,. | {a}' in.json
{"a":1}
{"a":1}
Then, of course, say:
jq -c '{a, b, c: .}' in.json
{"a":1,"b":2,"c":{"a":1,"b":2}}
It was going through the . stream, and I pulled the . stream right back in while doing so.
So it kind of helps to keep straight in my head when I've kind of got multiple streams going, vs multiple values.
Someone (almost anyone) can probably explain better with formal theory, but I just kind of got a feel for it and kind of describe it like this.
I love jq, and without detracting from it, gron looks like an extremely useful, "less difficult" complement to it.
Just as an example, this just took me about a minute to get the data I wanted, whereas I probably spent a half an hour on it yesterday with jq:
curl -s https://static01.nyt.com/elections-assets/2020/data/api/2020-11-03/national-map-page/national/president.json | gron | grep -E 'races.*(leader_margin_votes|leader_margin_name_display|state_name)' | grep -vE 'townships|counties' | gron -ungron- a fellow read-only system
Perl shines for this use case (assuming it is present in the machines you are working with). It is slower than grep/sed/awk for most cases, but it is more powerful and better portable across platforms.
>converting YAML to JSON on the command line
check out https://github.com/bronze1man/yaml2json
Agreed.
For better or worse, when performance is not a concern in my scripts, I just shell out to "perl -pe" rather than trying to deal with grep, sed or awk.
It just works.
The slides are linked in the video description and at [2]. You'll need them because unfortunately the video is produced in such a way that the speaker video window often obscures important parts of his presentation.
[1] https://www.youtube.com/watch?v=_ZTibHotSew
[2] https://www.slideshare.net/mobile/btiernay/jq-json-like-a-bo...
[1] https://blog.kellybrazil.com/2020/03/25/jello-the-jq-alterna... [2] https://github.com/kellyjonbrazil/jello
Here is a demo of a small script I wrote that shows jq results as you type using FZF: https://asciinema.org/a/349330 (link to script is in the description)
It also includes the ability to easily "bookmark" expressions and return to them so you don't have to worry about losing up an expression that's almost working to experiment with another one.
As a jq novice, I've personally found it to be super useful.
For example, against the public AWS IP address JSON document, it produces an output like
$ curl -s 'https://ip-ranges.amazonaws.com/ip-ranges.json' | jq -r '[path(..)|map(if type=="number" then "[]" else tostring end)|join(".")|split(".[]")|join("[]")]|unique|map("."+.)|.[]'
.
.createDate
.ipv6_prefixes
.ipv6_prefixes[]
.ipv6_prefixes[].ipv6_prefix
.ipv6_prefixes[].network_border_group
.ipv6_prefixes[].region
.ipv6_prefixes[].service
.prefixes
.prefixes[]
.prefixes[].ip_prefix
.prefixes[].network_border_group
.prefixes[].region
.prefixes[].service
.syncToken
That jq query looks like an unwise Perl one-liner.
$ jq-structure my-file.jsonBTW: I have a D3 front-end dashboard/console for the app (not admin) that makes this a little bit harder, but D3 is pretty organized (and well-documented), if you can figure out what you are trying to do with it.
I like that jq's query expression syntax is command line (bash) friendly. My hunch is that xpath expressions would be awkward to work with.
I've done too much xpath, xquery, xslt, css selectors. For my own work (dog fooding), I settled on mostly using very simple globbing expressions. Then use the host language's 'foreach' equiv for iterating result sets.
Globbing's double asterisk wildcard is the feature I most miss in other query engines. https://en.wikipedia.org/wiki/Glob_%28programming%29
Looping back to command line xpath: there's always some impedance match between the query and host languages. IIRC, one of the shells, like chubot's oilshell or fish?, has more rational expression evaluation (compared to bash).
You especially see this with regexs. It's a major language design fail that others haven't adopted Perl's first class regex intrinsics. C# has LINQ, sure. But that's more xquery than xpath. And I've never liked xquery.
In other words, "blue collar" programming languages should have intrinsic path expressions. Whatever the syntax.
YMMV.
Practically, most things you'd do with gron and grep, sed, awk, ... you could do using only jq as well. Jq comes with massive cognitive overhead though and has a bunch of very unpleasant gotchas (like silently corrupting numbers with abs > 2^53, although very recent jq graciously no longer does that iff you do no processing on the number).
I find jq pretty useful, but I have no love for it.
It really depends on what you want to do, and thus what think gron does.
If all you want to do is search for properties with a given value then yes, jq does that very well.
Unlike gron, jq even allows users to output search results as valid json docs. Hell, jq allows users to transform entire JSON docs.
However, if all you want to do is expand the JSON path at each symbol then I don't know if jq supports that usecase. But then again, why would anyone want to do that?
Why shouldn't I just use jq?
jq is awesome, and a lot more powerful than gron, but with that power comes complexity. gron aims to make it easier to use the tools you already know, like grep and sed.
gron's primary purpose is to make it easy to find the path to a value in a deeply nested JSON blob when you don't already know the structure; much of jq's power is unlocked only once you know that structure.