q VS csvq

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

I think they're talking about https://github.com/harelba/q, which is not very fast.

Apparently, there were many who thought the same thing; Tools to execute SQL against CSV were trdsql, q, csvq, TextQL. They were highly functional, hoewver, had many options and no input completion. I found it just a little difficult to use.

Hi, author of q here.

Regarding the error you got, q currently does not autodetect headers, so you'd need to add -H as a flag in order to use the "country" column name. You're absolutely correct on failing-fast here - It's a bug which i'll fix.

In general regarding speed - q supports automatic caching of the CSV files (through the "-C readwrite" flag). Once it's activated, it will write the data into another file (with a .qsql extension), and will use it automatically in further queries in order to speed things considerably.

Effectively, the .qsql files are regular sqlite3 files (with some metadata), and q can be used to query them directly (or any regular sqlite3 file), including the ability to seamlessly join between multiple sqlite3 files.

http://harelba.github.io/q/#auto-caching-examples

sure can do, if you already use that shell [1], but personally I like specific tools for specific jobs such as jq [2], fx, csvq [3] etc, there's value in decoupling shells from utils (modularity, speed, innovation etc).

[1] I don't but tempted to try, like its data-types concept

[2] https://jqlang.github.io/jq/

[3] https://github.com/mithrandie/csvq

csvq

There is a way of running SQL-like queries against CSV files.

Lately I have had to do a lot of flat file analysis and tools along these lines have been a godsend. Will check this out.

My go to lately has been csvq (https://mithrandie.github.io/csvq/). Really nice to be able run complicated selects right over a CSV file with no setup at all.

csvq (https://mithrandie.github.io/csvq/)

I usually do this with awk, my largest target files being half a TB in size for a project last year (and far too large to hold entirely in RAM). There are some other utilities like csvq and csvsql both of which let you write SQL-style queries against CSV files, but I'm not sure how they perform on large files. There's a nice list of CSV manipulation tools too if any of those jog your memory.

Apparently, there were many who thought the same thing; Tools to execute SQL against CSV were trdsql, q, csvq, TextQL. They were highly functional, hoewver, had many options and no input completion. I found it just a little difficult to use.

Please check out this tool https://github.com/mithrandie/csvq

If you are looking to query nondb data with sql statements then you may want to check something like https://github.com/mithrandie/csvq (SQL for csv).