zsv VS csvq

I wrapped zsv, a SIMD-accelerated CSV parser written in C, into a Ruby gem. SIMD means it uses special CPU instructions to process multiple bytes at once - the same tech that makes video encoding and game physics fast.

Haven't yet seen any of these beat https://github.com/liquidaty/zsv when real-world constraints are applied (e.g. we no longer assume that line ends are always \n, or that there are no dbl-quote chars, embedded commas/newlines/dbl-quotes). And maybe under the artificial conditions as well.

I cannot imagine any way it is worth anyone's time to follow this article's suggestion vs just using something like zsv (https://github.com/liquidaty/zsv, which I'm an author of) or xsv (https://github.com/BurntSushi/xsv/edit/master/README.md) and then spending that time saved on "real" work

If it could be tabular in nature, maybe convert to sqlite3 so you can make use of indexing, or CSV to make use of high-performance tools like xsv or zsv (the latter of which I'm an author).

https://github.com/BurntSushi/xsv

https://github.com/liquidaty/zsv/blob/main/docs/csv_json_sql...

Parsing CSV doesn't have to be slow if you use something like xsv or zsv (https://github.com/liquidaty/zsv) (disclaimer: I'm an author). The speed of CSV parsers is fast enough that unless you are doing something ultra-trivial such as "count rows", your bottleneck will be elsewhere.

The benefits of CSV are:

- human readable

- does not need to be typed (sometimes, data in the raw such as date-formatted data is not amenable to typing without introducing a pre-processing layer that gets you further from the original data)

- accessible to anyone: you don't need to be a data person to dbl-click and open in Excel or similar

The main drawback is that if your data is already typed, CSV does not communicate what the type is. You can alleviate this through various approaches such as is described at https://github.com/liquidaty/zsv/blob/main/docs/csv_json_sql..., though I wouldn't disagree that if you can be assured that your starting data conforms to non-text data types, there are probably better formats than CSV.

The main benefit of Arrow, IMHO, is less as a format for transmitting / communicating but rather as a format for data at rest, that would benefit from having higher performance column-based read and compression

I wanted so much to use csvkit and all the features it had, but its horrendous performance made it unscalable and therefore the more I used it, the more technical debt I accumulated.

This was one of the reasons I wrote zsv (https://github.com/liquidaty/zsv). Maybe csvkit could incorporate the zsv engine and we could get the best of both worlds?

Examples (using majestic million csv):

---

}

```

This of course assumes that each line is a single record, so you'll need some preprocessing if your CSV might contain embedded line-ends. For the preprocessing, you can use something like the `2tsv` command of https://github.com/liquidaty/zsv (disclaimer: I'm its author), which converts CSV to TSV and replaces newline with \n.

You can also use something like `xsv split` (see https://lib.rs/crates/xsv) which frankly is probably your best option as of today (though zsv will be getting its own shard command soon)

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).