dsq VS textql

You might want to look at tsv-utils, or a similar project: https://github.com/eBay/tsv-utils

For the SQL part, but maybe a lot heavier, you can use one of the projects listed on this page: https://github.com/multiprocessio/dsq (No longer maintained, but has links to lots of other projects)

Welcome to the gang! :)

https://github.com/multiprocessio/dsq#comparisons

SPyQL is really cool and its design is very smart, with it being able to leverage normal Python functions!

As far as similar tools go, I recommend taking a look at DataFusion[0], dsq[1], and OctoSQL[2].

DataFusion is a very (very very) fast command-line SQL engine but with limited support for data formats.

dsq is based on SQLite which means it has to load data into SQLite first, but then gives you the whole breath of SQLite, it also supports many data formats, but is slower at the same time.

OctoSQL is faster, extensible through plugins, and supports incremental query execution, so you can i.e. calculate a running group by + count while tailing a log file. It also supports normal databases, not just file formats, so you can i.e. join with a Postgres table.

[0]: https://github.com/apache/arrow-datafusion

[1]: https://github.com/multiprocessio/dsq

[2]: https://github.com/cube2222/octosql

Disclaimer: Author of OctoSQL

> dsq registers go-sqlite3-stdlib so you get access to numerous statistics, url, math, string, and regexp functions that aren't part of the SQLite base. (https://github.com/multiprocessio/dsq#standard-library)

Ah, I wondered if they rolled their own SQL parser, but no, I now see the sqlite.go in the repo and all is made clear

I am currently evaluating dsq and its partner desktop app DataStation. AIUI, the developer of DataStation realised that it would be useful to extract the underlying pieces into a standalone CLI, so they both support the same range of sources.

dsq CLI - https://github.com/multiprocessio/dsq

OctoSQL is an awesome project and Kuba has a lot of great experience to share from building this project I'm excited to learn from.

And while building a custom database engine does allow you to do pretty quick queries, there are a few issues.

First, the SQL implemented is nonstandard. As I was looking for documentation and it pointed me to `SELECT * FROM docs.functions fs`. I tried to count the number of functions but octosql crashed (a Go panic) when I ran `SELECT count(1) FROM docs.functions fs` and `SELECT count() FROM docs.functions fs` which is what I lazily do in standard SQL databases. (`SELECT count(fs.name) FROM docs.function fs` worked.)

This kind of thing will keep happening because this project just doesn't have as much resources today as SQLite, Postgres, DuckDB, etc. It will support a limited subset of SQL.

Second, the standard library seems pretty small. When I counted the builtin functions there were only 29. Now this is an easy thing to rectify over time but just noting about the state today.

And third this project only has builtin support for querying CSV and JSON files. Again this could be easy to rectify over time but just mentioning the state today.

octosql is a great project but there are also different ways to do the same thing.

I build dsq [0] which runs all queries through SQLite so it avoids point 1. It has access to SQLite's standard builtin functions plus* a battery of extra statistic aggregation, string manipulation, url manipulation, date manipulation, hashing, and math functions custom built to help this kind of interactive querying developers commonly do [1].

And dsq supports not just CSV and JSON but parquet, excel, ODS, ORC, YAML, TSV, and Apache and nginx logs.

A downside to dsq is that it is slower for large files (say over 10GB) when you only want a few columns whereas octosql does better in some of those cases. I'm hoping to improve this over time by adding a SQL filtering frontend to dsq but in all cases dsq will ultimately use SQLite as the query engine.

You can find more info about similar projects in octosql's Benchmark section but I also have a comparison section in dsq [2] and an extension of the octosql benchmark with different set of tools [3] including duckdb.

Everyone should check out duckdb. :)

[0] https://github.com/multiprocessio/dsq

[1] https://github.com/multiprocessio/go-sqlite3-stdlib

[2] https://github.com/multiprocessio/dsq#comparisons

[3] https://github.com/multiprocessio/dsq#benchmark

What's annoying about this is that the PR doesn't even say it's trying to run tests. It says everything is passing and just doesn't list the actions.

For a second I thought someone must have deleted the actions yaml files.

This is a dangerous failure mode.

https://github.com/multiprocessio/dsq/pull/82

This is a cool project! But if you query Excel and ODS files with dsq you get the same thing plus a growing standard library of functions that don't come built into SQLite such as best-effort date parsing, URL parsing/extraction, statistical aggregation functions, math functions, string and regex helpers, hashing functions and so on [1].

[0] https://github.com/multiprocessio/dsq

[1] https://github.com/multiprocessio/go-sqlite3-stdlib

I like textql [0] better for this use case, as it's simpler in my mind.

[0] https://github.com/dinedal/textql

Primarily, you are right. SQL is for working with structured data and that largely covers RDBs. However, there are tools (like textql) that allow you to query CSV files and the effort works with most other text files that have some kind of structure.

As the author of textql ( https://github.com/dinedal/textql ) - thanks for the shoutout!

Looks great, I love more options in the space for CLI based data analysis tools! Fantastic work!

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.

Reminds me of the textQL extension that's available in Asciidoc.

Point it to an external CSV file, enable TextQL, and bam, there's your query returned as a table. Handy for parts lists, inventory, that kind of crap.

https://github.com/dinedal/textql

https://gist.github.com/mojavelinux/8856117

Through more magic, you COULD of course use stuff like Spark, or easier with programs like TextQL, sq, OctoSQL.

Somewhat-kinda related, the textql extension for Asciidoctor is so dang useful it should be in core.

https://gist.github.com/mojavelinux/8856117

I use this as a "centralized parts repository" for big ol' maintenance manuals. Refresh from PDM/PLM/LSA/Whatever. Rebuild for new parts data.

Built on TextQL, natch

https://github.com/dinedal/textql