hydra VS quokka

How does this compare to Hydra? https://www.hydra.so/

Don't feel bad, lots of people get bitten by not reading all the way down to the bottom of their readme: https://github.com/hydradatabase/hydra/blob/v1.1.2/README.md... While Hydra may very well license their own code Apache 2, they ship the AGPLv3 columnar which to my very best IANAL understanding taints the whole stack and AGPLv3's everything all the way through https://github.com/hydradatabase/hydra/blob/v1.1.2/columnar/...

Columnar store PostgreSQL extension exists, here are two but I think I’m missing at least another one:

https://github.com/citusdata/cstore_fdw

https://github.com/hydradatabase/hydra

You can also connect other stores using the foreign data wrappers, like parquet files stored on an object store, duckdb, clickhouse… though the joins aren’t optimised as PostgreSQL would do full scan on the external table when joining.

New column store alternative : https://github.com/hydradatabase/hydra

HN: https://news.ycombinator.com/item?id=37571974

some previous discussions:

https://news.ycombinator.com/item?id=37247945

https://news.ycombinator.com/item?id=36987920

and a relevant observation is that there are actually multiple license files in the repo so the consumer should read their explicit licensing section of the readme <https://github.com/hydradatabase/hydra#license> since the GitHub sidebar is misleading

Hydra DB Link to Github -> Worked well for aggregated query usecases but not for queries that build reports. Also, data insertion and updation is abyssmal on columnar dbs.

There's a lot of experience about db operation and how to approach MVCC encoded in PostgreSQL that shouldn't be underestimated.

[0]: https://github.com/hydradatabase/hydra

An awesome read!

Something related that I found out about from HN a few months back is another engine called quokka. It's particularly interesting and applicable how quokka schedules distributed queries to outperform Spark https://github.com/marsupialtail/quokka/blob/master/blog/why...

Hi Justin, you might be interested in my blog: https://github.com/marsupialtail/quokka/blob/master/blog/bac... advocating a cloud based approach.

You don't have to use the system I am building, but it's worth thinking about that design.

SQL support is very challenging.

I work on Quokka (https://github.com/marsupialtail/quokka). I support Iceberg reads. Recently we are adding SQL support from just parsing the DuckDB logical plan, though that is very challenging as well.

The Python world lacks a standard for a plug and play SQL query optimizer. Apache Calcite is good for the JVM world, but not great if you are trying to cut out the JVM.

In case people are interested, I wrote a post about fault tolerance strategies of data systems like Spark and Flink: https://github.com/marsupialtail/quokka/blob/master/blog/fau...

The key difference here is that these systems don't store data, so fault tolerance means recovering within a query instead of not losing data.

would love to collaborate on an integration with pyquokka (https://github.com/marsupialtail/quokka) once I put out a stable release end of this month :-)

Then you should keep an eye on quokka. This may become the "Spark" for Polars/DuckDB. It seems to be under active development though I'm not sure how stable it is.