I prefer a SQL-like format. It’s not as complete but it cover most of the day-to-day use cases. Take a look at https://github.com/dcmoura/spyql (I am the author). Congrats on fq!
Author of the benchmark and of SPyQL here.
ClickHouse is fantastic. Amazing performance. SPyQL is built on top of Python but still can be faster than jq and several other tools as shown in the benchmark. SPyQL can handle large datasets but Clickhouse local should always show better performance.
SPyQL CLI is more oriented to work in harmony with the shell (piping), to be very simple to use and to leverage the Python ecosystem (you can import Python libs and use them in your queries).
I am the author of SPyQL [1]. Combining JC with SPyQL you can easily query the json output and run python commands on top of it from the command-line :-) You can do aggregations and so forth in a much simpler and intuitive way than with jq.
I just wrote a blogpost [2] that illustrates it. It is more focused on CSV, but the commands would be the same if you were working with JSON.