octosql-plugin-random_data VS dsq

Hey!

> I think the main fundamental difference is that this wants all of the data upfront in a data file.

Absolutely not! Moreover, OctoSQL can push down predicates to databases so that it only has to download a small subset of the table, if the datasource and query allow it.

> Very easy to model HTTP APIs as a table.

"Very easy" is relative, but you can take a look at the random_data[0] datasource which is exactly this. I'm also planning to add a GitHub datasource fairly soon. That said, there is Steampipe[1] for which this is the main use case afaik (hitting API's and exposing them as tables through Postgres FWD's written in Go), so it might be a smoother and more polished experience. There's also tons of plugins already available for it.

> Easy to model basically anything as a table for example files on my filesystem.

Yep, definitely. That's the idea behind OctoSQL. Strive to create a tool for easily exposing anything through SQL (like your machine's processes list, an API, and join that with a file, or database). There's still lot's of documentation work left to do though, in order to make the plugin authoring experience easier.

> A decent query planner so that I can avoid expensive things (like API calls) if I can determine if I need the object based on something cheaper (like a local disk access).

Probably depends on the use-case, and it sometimes needs you to be fairly explicit, but OctoSQL does in fact do that. It will push down predicates to underlying databases, which means joining something small with something very big (while only taking very small amounts of the latter) can be very fast with LOOKUP JOIN's.

> I want something that is easy to extend to sources that are possibly non-listable or at the very least I don't want to have all of the data available.

Doable. An example of this is the `plugins.available_versions` table[2]. It requires you to provide the plugin name as a predicate, as the versions need to be downloaded from the plugin's own repository (and listing all plugin repositories on each query isn't really what you want to be doing). You can also LOOKUP JOIN with the `plugins.available_plugins` table if that is indeed what you want.

[0]: https://github.com/cube2222/octosql-plugin-random_data

[1]: https://steampipe.io

[2]: https://github.com/cube2222/octosql/blob/main/datasources/pl...

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