sqlite-parquet-vtable VS zsv

Sqlite3 has parquet extension that support parquet (https://github.com/cldellow/sqlite-parquet-vtable) as virtual table. I use sqlite3 a lot, for work and personally. It's really good, but I do have issue with large datasets, mainly due VACUUM operations. Insertion rate drops significantly when single table hits around 20M rows. Indexing is important for your query speed, but it'll impact your write speed.

Well there's a virtual table extension to read parquet files in SQLite. I've not tried it myself. https://github.com/cldellow/sqlite-parquet-vtable

/? sqlite arrow

- "Comparing SQLite, DuckDB and Arrow with UN trade data" https://news.ycombinator.com/item?id=29010103 ; partial benchmarks of query time and RAM requirements [relative to data size] would be

- https://arrow.apache.org/blog/2022/02/16/introducing-arrow-f... :

> Motivation: While standards like JDBC and ODBC have served users well for decades, they fall short for databases and clients which wish to use Apache Arrow or columnar data in general. Row-based APIs like JDBC or PEP 249 require transposing data in this case, and for a database which is itself columnar, this means that data has to be transposed twice—once to present it in rows for the API, and once to get it back into columns for the consumer. Meanwhile, while APIs like ODBC do provide bulk access to result buffers, this data must still be copied into Arrow arrays for use with the broader Arrow ecosystem, as implemented by projects like Turbodbc. Flight SQL aims to get rid of these intermediate steps.

> - One cannot create a trigger on a virtual table.

Just posted about eBPF a few days ago; opcodes have costs that are or are not costed: https://news.ycombinator.com/item?id=31688180

> - One cannot create additional indices on a virtual table. (Virtual tables can have indices but that must be built into the virtual table implementation. Indices cannot be added separately using CREATE INDEX statements.)

It looks like e.g. sqlite-parquet-vtable implements shadow tables to memoize row group filters. How does JOIN performance vary amongst sqlite virtual table implementations?

> - One cannot run ALTER TABLE ... ADD COLUMN commands against a virtual table.

Are there URIs in the schema? Mustn't there thus be a meta-schema that does e.g. nested structs with portable types [with URIs], (and jsonschema, [and W3C SHACL])?

/? sqlite arrow virtual table

- sqlite-parquet-vtable reads parquet with arrow for SQLite virtual tables https://github.com/cldellow/sqlite-parquet-vtable :

  $ sqlite/sqlite3

https://github.com/cldellow/sqlite-parquet-vtable

But for my use case virtual would be too complicated.

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)

Am looking for one-time help creating documentation (e.g. man pages, tutorials) for open source project (e.g. https://github.com/liquidaty/zsv) as well as product documentation for commercial products, but not enough need for a full-time job. Requires familiarity with, for lack of better term, data janitorial work, and preferably with methods of auto-generating documentation. Any suggestions as to forums or other ways to find folks who might fit the bill for ad-hoc or part-time work of this nature?

Nice work. I am a fan of tools like this and look forward to giving this a try.

However, in my first attempted query (version 3.1.6 on MacOS), I ran into significant performance limitations and more importantly, it did not give correct output.

In particular, running on a narrow table with 1mm rows (the same one used in the xsv examples) using the command "select country, count() from worldcitiespop_mil.csv group by country" takes 12 seconds just to get an incorrect error 'no such column: country'.

using sqlite3, it takes two seconds or so to load, and less than a second to run, and gives me the correct result.

Using https://github.com/liquidaty/zsv (disclaimer, I'm one of its authors), I get the correct results in 0.95 seconds with the one-liner `zsv sql 'select country, count() from data group by country' worldcitiespop_mil.csv`.

I look forward to trying it again sometime soon

All this banter arguing over CSV, JSON, sqlite seems unnecessary when you can just push format X through a pipe and get whichever format Y you want back out: https://github.com/liquidaty/zsv/blob/main/docs/csv_json_sql...

(disclaimer: I'm one of the zsv authors)