mergestat-lite VS octosql

You can query git with this: https://github.com/mergestat/mergestat if you like the idea.

Hey, author here, happy to answer any questions! Also checkout this notebook for a deeper dive into sqlite-lines, along with a slick WASM demonstration and more thoughts on the codebase itself https://observablehq.com/@asg017/introducing-sqlite-lines

I really dig SQLite, and I believe SQLite extensions will push it to another level. I rarely reach for Pandas or other "traditional" tools and query languages, and instead opt for plain ol' SQLite and other extensions. As a shameless plug, I recently started a blog series on SQLite and related tools and extensions if you want to learn more! Next week I'll be publishing more SQLite extensions for parsing HTML + making HTTP requests https://observablehq.com/@asg017/a-new-sqlite-blog-series

A few other SQLite extensions:

- xlite, for reading Excel files, in Rust https://github.com/x2bool/xlite

- sqlean, several small SQLite extensions in C https://github.com/nalgeon/sqlean

- mergestat, several SQLite extensions for developers (mainly Github's API) in Go https://github.com/mergestat/mergestat

mergestat is an open-source tool that allows users to run SQL queries on the contents and history of git repositories.

Thanks for this write up! I've been really interested in postgres compatibility in the context of a tool I maintain (https://github.com/mergestat/mergestat) that uses SQLite. I've been looking for a way to expose the SQLite capabilities over a more commonly used wire-protocol like postgres (or mysql) so that existing BI and visualization tools can access the data.

This project is an interesting one: https://github.com/dolthub/go-mysql-server that provides a MySQL interface (wire and SQL) to arbitrary "backends" implemented in go.

It's really interesting how compatibility with existing protocols has become an important feature of new databases - there's so much existing tooling that already speaks postgres (or mysql), being able to leverage that is a huge advantage IMO

timediff is a Go package for printing human readable, relative time differences. Output is based on ranges defined in the Day.js JavaScript library, and can be customized if needed. It's currently used by the mergestat command-line interface.

Assuming committers adhere to it, there could be some interesting use cases when combined with a tool like AskGit (https://github.com/askgitdev/askgit) for understanding what "categories" of work is being done in a codebase.

Maybe even what directories/files tend to see `fix` or `refactor` more frequently (signs of a poorly design or "hot" area?)

I've been very curious to explore this type of use case with askgit (https://github.com/augmentable-dev/askgit) which was designed for running simple "slice and dice" queries and aggregations on git history (and change stats) for basic analytical purposes. I've been curious about how this could be applied to a small text+git based "db". Say, for a regular json or CSV dumps.

This also reminds me of Dolt: https://github.com/dolthub/dolt which I believe has been on HN a couple times

Never got it to anything close to a finished state, instead moving on to doing the same prototype in llvm and then cranelift.

That said, here's some of the wazero-based code on a branch - https://github.com/cube2222/octosql/tree/wasm-experiment/was...

It really is just a very very basic prototype.

This is really cool!

With their Postgres scanner[0] you can now easily query multiple datasources using SQL and join between them (i.e. Postgres table with JSON file). Something I strived to build with OctoSQL[1] before.

It's amazing to see how quickly DuckDB is adding new features.

Not a huge fan of C++, which is right now used for authoring extensions, it'd be really cool if somebody implemented a Rust extension SDK, or even something like Steampipe[2] does for Postgres FDWs which would provide a shim for quickly implementing non-performance-sensitive extensions for various things.

Godspeed!

[0]: https://duckdb.org/2022/09/30/postgres-scanner.html

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

[2]: https://steampipe.io

Congrats on the Show HN!

It's great to see more tools in this area (querying data from various sources in-place) and the Lambda use case is a really cool idea!

I've recently done a bunch of benchmarking, including ClickHouse Local and the usage was straightforward, with everything working as it's supposed to.

Just to comment on the performance area though, one area I think ClickHouse could still possibly improve on - vs OctoSQL[0] at least - is that it seems like the JSON datasource is slower, especially if only a small part of the JSON objects is used. If only a single field of many is used, OctoSQL lazily parses only that field, and skips the others, which yields non-trivial performance gains on big JSON files with small queries.

Basically, for a query like `SELECT COUNT(*), AVG(overall) FROM books.json` with the Amazon Review Dataset, OctoSQL is twice as fast (3s vs 6s). That's a minor thing though (OctoSQL will slow down for more complicated queries, while for ClickHouse decoding the input is and remains the bottleneck).

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

To add somewhat of a counterpoint to the other response, I've tried the Steampipe CSV plugin and got 50x slower performance vs OctoSQL[0], which is itself 5x slower than something like DataFusion[1]. The CSV plugin doesn't contact any external API's so it should be a good benchmark of the plugin architecture, though it might just not be optimized yet.

That said, I don't imagine this ever being a bottleneck for the main use case of Steampipe - in that case I think the APIs themselves will always be the limiting part. But it does - potentially - speak to what you can expect if you'd like to extend your usage of Steampipe to more than just DevOps data.

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

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

Disclaimer: author of OctoSQL

Actually, folks just use gRPC or Yaegi in Go.

See Terraform[0], Traefik[1], or OctoSQL[2].

Although I agree plugins would be welcome, especially for performance reasons, though also to be able to compile and load go code into a running go process (JIT-ish).

[0]: https://github.com/hashicorp/terraform

[1]: https://github.com/traefik/traefik

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

Disclaimer: author of OctoSQL

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

The logo was created for OctoSQL and in the article you can find a lot of sample phrase-image combinations, as it describes the whole path (generation, variation, editing) I went down. Let me know what you think!

Hey, author here, happy to answer any questions!

The logo was created for OctoSQL[0] and in the article you can find a lot of sample phrase-image combinations, as it describes the whole path (generation, variation, editing) I went down. Let me know what you think!

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